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Wireless Body Sensors for medical purposes offer valuable contributions to improve patients’ healthcare, including diagnosis and/or therapeutics monitoring. Body temperature is a crucial parameter in healthcare diagnosis. In gynecology and obstetrics it is measured at the skin’s surface, which is very influenced by the environment. This paper proposes a new intra-body sensor for long-term intra-vaginal temperature collection. The embedded IEEE 802.15.4 communication module allows the integration of this sensor in a Wireless Sensor Network (WSN) for remote data access and monitoring. We present the sensor architecture, the construction of the corresponding testbed, and its performance evaluation. This sensor may be used in different medical applications, including preterm labor prevention and fertility and ovulation period detection. The features of the constructed testbed were validated in laboratory tests verifying its accuracy and performance.
Over the years some medical studies have tried to better understand the internal
behavior of human beings. Many researchers in this domain have been striving to find
relationships between intra-vaginal temperature and certain female health conditions, such
as ovulation and fertile period since woman’s intra-vaginal temperature is one of the body
parameters most preferred in such studies. However, due to lack of a appropriate
technology, medical research devoted to studying correlations of such body parameters
with certain womans’ body phenomena could not obtain better results. This article presents
the design and implementation of a novel intra-body sensor for acquisition and monitoring
of intra-vaginal temperatures. This novel intra-body sensor provides data collection that is
used for studying the relation between temperature variations and female health conditions,
such as anticipation and monitoring of the ovulation period, detection of pregnancy
contractions, preterm labor prevention, etc.. The motivation for this work focuses on the
development of this new intra-body sensor that will represent a major step in medical
technology. The novel sensor was tested and validated on hospitalized women as well as
normal healthy women. Finally our medical team has attested to the accuracy, usability and
performance of this novel intra-body sensor.
The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008), fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression) and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines) were tested. Classification and regression trees (CART) was the most accurate model for the prediction of pulp ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64–0.75) by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and
pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.
In this paper, the morphological properties of fiber length (weighted in length) and of fiber width of unbleached Kraft pulp of Acacia melanoxylon were determined using TECHPAP Morfi® equipment (Techpap SAS, Grenoble, France), and were used in the calibration development of Near Infrared (NIR) partial least squares regression (PLS-R) models based on the spectral data obtained for the wood. It is the first time that fiber length and width of pulp were predicted with NIR spectral data of the initial woodmeal, with high accuracy and precision, and with ratios of performance to deviation (RPD) fulfilling the requirements for screening in breeding programs. The selected models for fiber length and fiber width used the second derivative and first derivative + multiplicative scatter correction (2ndDer and 1stDer + MSC) pre-processed spectra, respectively, in the wavenumber ranges from 7506 to 5440 cm−1. The statistical parameters of cross-validation (RMSECV (root mean square error of cross-validation) of 0.009 mm and 0.39 μm) and validation (RMSEP (root mean square error of prediction) of 0.007 mm and 0.36 μm) with RPDTS (ratios of performance to deviation of test set) values of 3.9 and 3.3, respectively, confirmed that the models are robust and well qualified for prediction. This modeling approach shows a high potential to be used for tree breeding and improvement programs, providing a rapid screening for desired fiber morphological properties of pulp prediction.
The paper presents and discusses a framework to promote older adults cognitive and emotional stimulation via Robotic Cognitive Games. The work is based on classic games for older adults, e.g., to place objects in pre-defined positions in an arena, where the authors introduce a robot in the games. The paper not only presents the robotic games, but also the methodology developed to properly introduce them to older adults in a nursing home. As such, the paper proposes three cognitive robotic games, a methodology to assess the success of its introduction to older adults, keeping in mind cognitive and emotional aspects. To validate the proposed robotic solution, experimental tests were performed in a nursing home. A prior cognitive and emotional test was done with older adults to have a ground truth to compare with after a batch of games was completed by each older adult. The results and their discussion validate the robotic games approach, and also the methodology used for its introduction in the nursing home.
The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.
Research Highlights: This study bridges a gap of knowledge about the maximum size–density trajectory for juvenile stands of maritime pine. The continuity of the trajectory along the development stages to maturity is assured with a straightforward approach providing support to determine optimum density along all the revolution periods for the species. Background and Objectives: Forest fire is a significant threat to forests in the Mediterranean regions, but also a natural disturbance that plays a vital role in the perpetuation of forest stands. In recent decades, there has been an increase of burnt area in maritime forests in Portugal, followed by an increased interest in managing the natural and usually abundant regeneration occurring after the fires. The gap in the knowledge of growth dynamics for juvenile stages, for these forest systems, currently constrains their correct management, for forest planning, particularly in determining the optimal densities. The study aims to identify the maximum attainable density trajectory at the early stages of development of the species that could support a non-empirical definition of silvicultural prescriptions and thinning decisions, along the revolution. Materials and Methods: A representative data set collected in stands regenerated after fire supports the analysis of the maximum size–density trajectory for the species. Results: The maximum size–density trajectory for the juvenile stands deviates from the expected trajectory defined in the self-thinning line published for the species. Significant deviation occurs at the lower end of the line, indicating the need for a reevaluation of the existing self-thinning line. We propose a new self-thinning model for the species that explicitly considers the behavior of size–density for juvenile stands. The new model assures a logical continuity for the trajectory from
the young stages of development to maturity. Conclusions: The proposed model based on the maximum attainable size–density trajectory provides ecological-based support to define silvicultural guidelines for management of the species.
Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.
Due to the increasing age of the European population, there is a growing interest in performing research that will aid in the timely and unobtrusive detection of emerging diseases. For such tasks, mobile devices have several sensors, facilitating the acquisition of diverse data. This study focuses on the analysis of the data collected from the mobile devices sensors and a pressure sensor connected to a Bitalino device for the measurement of the Timed-Up and Go test. The data acquisition was performed within different environments from multiple individuals with distinct types of diseases. Then this data was analyzed to estimate the various parameters of the Timed-Up and Go test. Firstly, the pressure sensor is used to extract the reaction and total test time. Secondly, the magnetometer sensors are used to identify the total test time and different parameters related to turning around. Finally, the accelerometer sensor is used to extract the reaction time, total test time, duration of turning around, going time, return time, and many other derived metrics. Our experiments showed that these parameters could be automatically and reliably detected with a mobile device. Moreover, we identified that the time to perform the Timed-Up and Go test increases with age and the presence of diseases related to locomotion.
The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.
This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.
Background: Sports athletes, namely high-intensity practitioners, suffer from vascular remodeling overtime. The purpose of this study was to analyze the systolic and diastolic velocities' variation between non-athletes and futsal athletes by means of arterial lower limb doppler ultrasound. Additionally, we intended to verify if the velocity variations occur primarily at the systolic or the diastolic level and in which arteries. Methods: Seventy-six young males (mean ± SD: 24.9 ± 2.8 years old) volunteered to participate in this cross-sectional study and were divided into two groups: a futsal athletes group (n = 38; 24 ± 2.78 years) in the central region of Portugal playing on the 2nd national league with the same level of practice (16 ± 2.4 years of practice) and a non-athletes group (n = 38: 26 ± 1.8 years) who did not practice sports regularly and were not federated in any sport. All the subjects agreed to participate in the study with the aim of assessing the arterial lower limb through doppler ultrasound (Philips HD7 echograph with linear transducer 7-12 MHz). Results: Differences between groups (p ≤ 0.05) in the systolic velocity of the left deep femoral artery (p = 0.022; d = 0.546, small) and in the right superficial femoral artery (p = 0.028; d = -0.515, small) were found. We also found differences in the diastolic velocity: in the left common femoral artery (p = 0.002; d = -0.748, moderate), in the right deep femoral artery (p = 0.028; d = -0.521, small), in the right superficial femoral artery (p = 0.026; d = -0.522, small), in the right popliteal artery (p = 0.002; d = -0.763, moderate), and in the left popliteal artery (p = 0.007; d = -0.655, moderate). Moreover, the athletes' group presented the highest mean values, with the exception of the systolic velocity of the left deep femoral artery. In intragroup analysis of variance referring to systolic and diastolic velocities in arterial levels in the right and left arteries, differences were found in all analyses (p ≤ 0.05). Conclusions: We conclude that futsal athletes of our sample go through a process of changes such as increased blood flow velocity in systolic and diastolic cardiac phase in all studied lower limb arteries, showing that the remodeling occurs regardless of vessel radius. Our results reinforce the existence of vascular remodeling that may vary with the sport and its intensity.
The agro-industrial sector consumes a significant amount of natural resources for farming and meat production. By 2050, population growth is expected, generating more demand and, consequently, more consumption of scarce resources. This challenging scenario is a concern of the European Commission, revealed in the Green Deal commitment and by the United Nations’ 12th goal of sustainable development. Thus, organizations must increase productivity and be more sustainable as soon as possible. Internet of Things (IoT) is introduced as a solution to facilitate agro-food companies to be more eco-efficient, mainly facing difficulties on farms, such as food loss and waste, best efficiency in management of resources, and production. The deployment of this technology depends on the stage of maturity and potential of implementation. To assess and characterize companies, with respect of IoT implementation, a survey was applied in 21 micro, small and medium agro-food companies, belonging to milk, honey, olive oil, jams, fruticulture, bakery and pastry, meat, coffee, and wine sectors, in the central region of Portugal. As results, this paper reveals the stage of maturity, level of sophistication, potential, opportunities, solutions, and barriers for implementation of IoT. Additionally, suggestions and recommendations to improve practices are discussed.
The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.
Secreted phospholipase A2-IIA (sPLA2-IIA) is a pro-inflammatory protein associated with cardiovascular disorders, whose functions and underlying mechanisms in cardiac remodelling are still under investigation. We herein study the role of sPLA2-IIA in cardiac fibroblast (CFs)-to-myofibroblast differentiation and fibrosis, two major features involved in cardiac remodelling, and also explore potential mechanisms involved. In a mice model of dilated cardiomyopathy (DCM) after autoimmune myocarditis, serum and cardiac sPLA2-IIA protein expression were found to be increased, together with elevated cardiac levels of the cross-linking enzyme lysyl oxidase (LOX) and reactive oxygen species (ROS) accumulation. Exogenous sPLA2-IIA treatment induced proliferation and differentiation of adult rat CFs. Molecular studies demonstrated that sPLA2-IIA promoted Src phosphorylation, shedding of the membrane-anchored heparin-binding EGF-like growth factor (HB-EGF) ectodomain and EGFR phosphorylation, which triggered phosphorylation of ERK, P70S6K and rS6. This was also accompanied by an up-regulated expression of the bone morphogenic protein (BMP)-1, LOX and collagen I. ROS accumulation were also found to be increased in sPLA2-IIA-treated CFs. The presence of inhibitors of the Src/ADAMs-dependent HB-EGF shedding/EGFR pathway abolished the CF phenotype induced by sPLA2-IIA. In conclusion, sPLA2-IIA may promote myofibroblast differentiation through its ability to modulate EGFR transactivation and signalling as key mechanisms that underlie its biological and pro-fibrotic effects.
Fire blight is a major pome fruit trees disease that is caused by the quarantine phytopathogenic
Erwinia amylovora, leading to major losses, namely, in pear and apple productions.
Nevertheless, no effective sustainable control treatments and measures have yet been disclosed.
In that regard, antimicrobial peptides (AMPs) have been proposed as an alternative biomolecule
against pathogens but some of those AMPs have yet to be tested against E. amylovora. In this study,
the potential of five AMPs (RW-BP100, CA-M, 3.1, D4E1, and Dhvar-5) together with BP100, were
assessed to control E. amylovora. Antibiograms, minimal inhibitory, and bactericidal concentrations
(minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC), growth and
IC50 were determined and membrane permeabilization capacity was evaluated by flow cytometry
analysis and colony-forming units (CFUs) plate counting. For the tested AMPs, the higher inhibitory
and bactericidal capacity was observed for RW-BP100 and CA-M (5 and 5–8 M, respectively for
both MIC and MBC), whilst for IC50 RW-BP100 presented higher efficiency (2.8 to 3.5 M). Growth
curves for the first concentrations bellow MIC showed that these AMPs delayed E. amylovora growth.
Flow cytometry disclosed faster membrane permeabilization for CA-M. These results highlight the
potential of RW-BP100 and CA-M AMPs as sustainable control measures against E. amylovora.
A column experiment at a laboratory level was carried out to assess the effect of the
application of nanotechnology in the decontamination of soils and alluvial deposits with high levels
of potentially toxic elements (PTEs). A suspension of zero-valent iron nanoparticles (nZVI) was
injected at three different concentrations in selected samples (two sediments, one soil). For most of the
elements, the retention by nZVI was proportional to the concentration of the suspension and the trend
was similar. Metals were immobilized by adsorption on the surface layer of the nanoparticles and/or
by complexation, co-precipitation, and chemical reduction. By day 60 following injection, the nZVI
lost reactivity and the retained species were desorbed and back into the soluble phase. The definition
of spatial patterns for PTEs’ distribution allowed for the construction of contamination risk maps
using a geostatistical simulation approach. The analysis obtained from the extractable contents of five
target elements (Zn, Cu, Cd, Pb, As) was cross-checked with the estimated map network to assess
their retention efficiency. Data from the analysis of these elements, in the extractable phase and in the
porewater of the sediments/soils, indicate the nZVI injection as a suitable technique for reducing the
risk level of PTEs in contaminated Fe-rich tropical environments.
Themanufacture ofmineralNfertilisers by theHaber–Bosch process is highly energy-consuming.
The nutrient recovery technologies from wastes through low-cost processes will improve the sustainability of the agricultural systems. This work aimed to assess the suitability of the gas-permeable membrane (GPM) technology to recover N from an anaerobic digestate and test the agronomic behaviour of the ammonium sulphate solution (ASS) obtained. About 62% of the total ammonia nitrogen removed from digestate using GPM was recovered, producing an ASS with 14,889 +/- 2324 mg N L-1, which was more than six-fold higher than in digestate. The ASS agronomic behaviour was evaluated by a pot experiment with triticale as a plant test for 34 days in a growth chamber. Compared with the triticale fertilised with the Hoagland solution (Hoag), the ASS provided significantly higher biomass production (+29% dry matter), N uptake (+22%), and higher N agronomic efficiency 3.80 compared with 1.81 mg DM mg-1N in Hoag, and a nitrogen fertiliser replacement value of 133%.
These increases can be due to a biostimulant effect provided by the organic compounds of the ASS as
assessed by the FT-Raman spectroscopy. The ASS can be considered a bio-based mineral N fertiliser
with a biostimulant effect.
The use of wireless communications systems on the factory shop floor is becoming an appealing solution with many advantages compared to cable-based solutions, including low cost, easy deployment, and flexibility. This, combined with the continuous growth of low-cost mobile devices, creates opportunities to develop innovative and powerful applications that, in many cases, rely on computing and memory-intensive algorithms and low-latency requirements. However, as the density of connected wireless devices increases, the spectral noise density rises, and, consequently, the radio interference between radio devices increase. In this paper, we discuss how the density of AR/VR mobile applications with high throughput and low latency affect industrial environments where other wireless devices use the same frequency channel. We also discuss how the growing number of these applications may have an impact on the radio interference of wireless networks. We present an agnostic methodology to assess the radio interferences between wireless communication systems on the factory floor by using appropriate radio and system models. Several interference scenarios are simulated between commonly used radio systems: Bluetooth, Wi-Fi, and WirelessHART, using SEAMCAT. For a 1% probability of interference and considering a criterion of C/I = 14 dB, the simulations on an 80 m × 80 m factory shop floor show that low-bandwidth systems, such as Bluetooth and WirelessHART, can coexist with high-bandwidth and low-latency AR/VR applications running on Wi-Fi mobile terminals if the number of 11 Wi-Fi access points and 80 mobile AR/VR devices transmitting simultaneously is not exceeded.
Acacia spp. is an invasive species that is widespread throughout the Portuguese territory. Thus, it is pertinent to better understand this species in order to find different applications that will value its use. To evaluate the phenolic profile in Acacia flowers, ethanolic extracts obtained through an energized guided dispersive extraction were analysed, focusing on two species, Acacia
retinodes and Acacia mearnsii, at two flowering stages. The phytochemical profile of each extract was determined by ultra-high performance liquid chromatography coupled with quadrupole time-offlight mass spectrometry and high-performance liquid chromatography coupled with diode array detector. The FTIR-ATR technique was used to distinguish the different samples’ compositions. The results showed the presence of high concentrations of phenolic compounds (>300 mg GAE/g extract), among which are flavonoids (>136 mg QE/g extract), for all combinations of species/flowering stages. The phytochemical profile showed a complex composition with 21 compounds identified and
quantified (the predominant ones being epicatechin, rutin, vanillin, and catechol). Both species and flowering stages presented significant variations regarding the presence and quantity of phenols and flavonoids, so much so that a principal component analysis performed with FTIR-ATR spectra data of the extracts was able to discriminate between species and flowering stages.
Anaerobic digestion is a valuable process to use livestock effluents to produce green energy and a by-product called digestate with fertilising value. This work aimed at evaluating the fertilising value of the solid fraction (SF) of a digestate as an organic amendment and as a source of nitrogen to crops replacing mineral N. A field experiment was done with two consecutive vegetable crops.
The treatments were: a control without fertilisation; Ni85 mineral fertilisation with 85 kg ha-1 of mineral N; fertiliser with digestate at an increasing nitrogen application rate (kg N ha-1): DG-N85, DG-N170, DG-N170+85, DG-N170+170; fertilisation with digestate together with Ni: DG-N85+Ni60, DG-N170+Ni60, DG-N170+Ni25. The results showed a soil organic amendment effect of the SF with a beneficial effect on SOM, soil pH and exchangeable bases. The SF was able to replace part of the mineral N fertilisation. The low mineralisation of the stable organic matter together with some immobilisation of mineral N from SF caused low N availability. The fertilisation planning should consider the SF ratio between the organic N (NO) and total N (TKN). Low NO:TKN ratios (~0.65) needed lower Ni addition to maintaining the biomass production similar to the mineral fertilisation.
Over the last few decades, we witnessed a revolution in acquiring very high resolution and accurate geo-information. One of the reasons was the advances in photonics and LiDAR, which had a remarkable impact in applications requiring information with high accuracy and/or elevated completeness, such as flood modelling, forestry, construction, and mining. Also, miniaturization within electronics played an important role as it allowed smaller and lighter aerial cameras and LiDAR systems to be carried by unmanned aerial vehicles (UAV). While the use of aerial imagery acquired with UAV is becoming a standard procedure in geo-information extraction for several applications, the use of LiDAR for this purpose is still in its infancy. In several countries, companies have started to commercialize products derived from LiDAR data acquired using a UAV but not always with the necessary expertise and experience. The LIDAR-derived products’ price has become very attractive, but their quality must meet the contracted specifications. Few studies have reported on the quality of outsourced LiDAR data acquired with UAV and the problems that need to be handled during production. There can be significant differences between the planning and execution of a commercial project and a research field campaign, particularly concerning the size of the surveyed area, the volume of the acquired data, and the strip processing. This work addresses the quality control of LiDAR UAV data through outsourcing to develop a modelling-based flood forecast and alert system. The contracted company used the Phoenix Scout-16 from Phoenix LiDAR Systems, carrying a Velodyne VLP-16 and mounted on a DJI Matrice 600 PRO Hexacopter for an area of 560 ha along a flood-prone area of the Águeda River in Central Portugal.
The purpose of this work is to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), support vector machines (SVM) and long short-term memory networks (LSTM) to predict human pose and activity from image sequences, based on computer vision approaches to gather the required features. To obtain the human pose semantics (output classes), based on a set of 3D points that describe the human body model (the input variables of the predictive model), prediction models were obtained from the acquired data, for example, video images. In the same way, to predict the semantics of the atomic activities that compose an activity, based again in the human body model extracted at each video frame, prediction models were learned using LSTM networks. In both cases the best learned models were implemented in an application to test the systems. The SVM model obtained 95.97% of correct classification of the six different human poses tackled in this work, during tests in different situations from the training phase. The implemented LSTM learned model achieved an overall accuracy of 88%, during tests in different situations from the training phase. These results demonstrate the validity of both approaches to predict human pose and activity from image sequences. Moreover, the system is capable of obtaining the atomic activities and quantifying the time interval in which each activity takes place.
Different ageing technology of wine spirits (WSs) has been investigated, but little has
been published on the chemical evolution of aged WS during storage in bottle. The purpose of this study was to examine how 12 months of storage in bottle affected the evolution of antioxidantactivity (DPPH, FRAP and ABTS assays), total phenolic index (TPI) and low molecular weight (LMW)compounds content of the WSs aged through alternative technology using three micro-oxygenationlevels (MOX) and nitrogen control (N). Results revealed the ability of phenolic compounds fromaged WSs to scavenge free radicals during storage in bottle. Among the in vitro antioxidant-activity
methods, FRAP assay was the more effective to differentiate WSs according to the ageing technology.Concerning the overall influence of storage in bottle on antioxidant activity, and TPI and LMW compoundscontent, the higher results were obtained for the MOX modalities (O15, O30 and O60), whichshowed a similar evolution. In summary, this study provides innovative information, demonstratingthat the differences between the aged WSs imparted throughout the ageing process (resulting from different MOX levels) were mostly retained, and only slight modifications during storage in bottle were found.
info:eu-repo/semantics/publishedVersion
Honey bee broods (larvae and pupae) can be consumed as human food, offering a rich
nutritional value. Therefore, the objective of this work was to present an overview of the nutritional value of the honey bee brood and its gastronomic potential. The results indicated that honey bee broods are rich in protein (including essential amino acids), fat (essentially saturated and monounsaturated fatty acids), carbohydrates, vitamin C and those of the B complex, and minerals such as potassium, magnesium, calcium, and phosphorous. The results further highlight some variability
according to the stage of development, with increasing content of fat and protein and decreasing carbohydrates from the larval to the pupal stages. The production of the honey bee brood in the hive, as well as its removal, can impact the wellbeing of the hive. This limits the production potential of the brood aimed at application for gastronomic purposes. The consumption and purchase of honey bee broods as food may be accessible in specialised markets where, for example, ethnic communities consume this type of food. However, in some markets, insects or products produced from insects are not readily accepted because of neophobia and disgust. The role of culinary chefs allied
to traditional ways of preparing culinary dishes that include honey bee broods are relevant to motivate more people in western societies to consume of these types of food products.
Near-infrared spectroscopic (NIR) technique was used, for the first time, to predict volatile phenols content, namely guaiacol, 4-methyl-guaiacol, eugenol, syringol, 4-methyl-syringol and 4-allyl-syringol, of aged wine spirits (AWS). This study aimed to develop calibration models for the volatile phenol’s quantification in AWS, by NIR, faster and without sample preparation. Partial least square regression (PLS-R) models were developed with NIR spectra in the near-IR region (12,500–4000 cm1) and those obtained from GC-FID quantification after liquid-liquid extraction. In the PLS-R developed method, cross-validation with 50% of the samples along a validation test set
with 50% of the remaining samples. The final calibration was performed with 100% of the data. PLS-R models with a good accuracy were obtained for guaiacol (r2 = 96.34; RPD = 5.23), 4-methyl-guaiacol (r2 = 96.1; RPD = 5.07), eugenol (r2 = 96.06; RPD = 5.04), syringol (r2 = 97.32; RPD = 6.11), 4-methylsyringol (r2 = 95.79; RPD = 4.88) and 4-allyl-syringol (r2 = 95.97; RPD = 4.98). These results reveal that NIR is a valuable technique for the quality control of wine spirits and to predict the volatile phenols content, which contributes to the sensory quality of the spirit beverages.
The Picoto mining area is in the village of Vilar Seco (Viseu), central Portugal. Mineralization
occurs mainly in quartz veins with meta-torbernite and uranophane and some U-bearing
minerals, cutting a Variscan granite. Exploitation took place in two phases, between 1917 and 1953,
and since the closure, the area has never been remediated. Water–rock interaction processes, including
the mobility of potentially toxic elements through soil and water (surface and groundwater),
were identified with the determination in situ of physicochemical parameters and selected anions
and cations, by ICP-OES. The soils are contaminated with As (>44 mg/kg), Cu (>23 mg/kg), and U
(>40 mg/kg) and cannot be used for agricultural or domestic purposes. The waters are generally
weakly mineralized and have pH values ranging from acidic to neutral. However, some of them
are contaminated with NO2 (up to 2.3 mg/L), Fe (up to 1849 mg/L), Mn (up to 777 mg/L), Cu
(up to 5.4 g/L), As (up to 14.7 g/L), and U (up to 66.2 g/L) and cannot be used for human
consumption or agricultural activities. The soil and water contamination are mainly related to the
old mine activities and the subsequent human activities that have developed in the area.
Background: Sedentary behavior has been considered an independent risk factor to health. The aim of this systematic review and meta-analysis was to examine associations between objectively measured sedentary time and physical fitness components in healthy adults. Methods: Four electronic databases (Web of Science, Scopus, Pubmed and Sport Discus) were searched (up to 20 September 2020) to retrieve studies on healthy adults which used observational, cohort and cross-sectional designs. Studies were included if sedentary time was measured objectively and examined associations with the health- or skill-related attributes of physical fitness (e.g., muscular strength, cardiorespiratory fitness, balance). After applying additional search criteria, 21 papers (11,101 participants) were selected from an initial pool of 5192 identified papers. Results: Significant negative associations were found between total sedentary time with cardiorespiratory fitness (r = -0.164, 95%CI: -0.240, -0.086, p < 0.001), muscular strength (r = -0.147, 95%CI: -0.266, -0.024, p = 0.020) and balance (r = -0.133, 95%CI: -0.255, -0.006, p = 0.040). Conclusions: The evidence found suggests that sedentary time can be associated with poor physical fitness in adults (i.e., muscular strength, cardiorespiratory fitness and balance), so strategies should be created to encourage behavioral changes.
The aim of this study was to review the evolutionary tendencies of research regarding to the study of male Rink-Hockey players´ and game performance. A systematic search was conducted in PubMed, Web of Knowledge and Scopus databases according to PRISMA method. The initial search identified 815 titles, resulting in 19 articles being included within the review. Original papers (English language) contained relevant data regarding rink hockey players' performance or morphological/physiological demands, anthropometry/body composition characteristics were eligible. Studies were classified into categories: (1) Physiological Demands, (2) Anthropometry and Body Composition, (3) Game Characterization/Patterns, (4) Injuries. Results indicated that Rink hockey requires high intensity effort which demands both short and long duration efforts requirements from players. Body composition analysis shows to be an important monitoring tool which complements the understanding of the athlete's cardiac adaptation. Game patterns shows a combination of specific game momentums with different outcomes according to the game zone. The intense short-term movements, collision and contact between players, in addition to the weight and speed of the hard ball and the stick, can considerably increase the risk of moderate and severe injuries. Lack of literature in Rink-Hockey is remarkable, and research is mainly focused on children and adolescents' players. Furthermore, the existing research with adult elite athletes was assessed with a small sample size.
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.
Since coaches play an important role in the development of athletes, the process and mechanisms used by Special Olympics Portugal to develop coaches’ skills are worthy of research. In this context, the study aims to identify the training paths and profiles of the Special Olympics Portugal coach. It also aims to analyze the relationship between formal and non-formal learning in the profile and training of this type of coach. The research is descriptive and transversal regarding Special Olympics Portugal coaches, with the participation of 50 subjects. Two questionnaires were used, the Coaches’ Training Profile Questionnaire to determine the training routes, and the Coaches’ Orientation Questionnaire. The results show that the Special Olympics Portugal coaches have an academic background and a somewhat critical profile. It is imperative to build formal and non-formal learning contexts that focus on the theme of adapted sports, in order to allow the training of more qualified coaches, who are consequently more effective in their interventions with this type of athlete.
The increase of data-rates that are provided by free-space optical (FSO) communications is essential in our data-driven society. When used in satellite and interplanetary networks, these optical links can ensure fast connections, yet they are susceptible to atmospheric disruptions and long orbital delays. The Delay and Disruption Tolerant Networking (DTN) architecture ensures a reliable connection between two end nodes, without the need for a direct connection. This can be an asset when used with FSO links, providing protocols that can handle the intermittent nature of the connection. This paper provides a review on the theoretical and state-of-the-art studies on FSO and DTN. The aim of this review is to provide motivation for the research of an optical wireless satellite network, with focus on the use of the Licklider Transmission Protocol. The assessment presented establishes the viability of these networks, providing many examples to rely on, and summarizing the most recent stage of the development of the technologies addressed.
Groundwater vulnerability assessment has become a useful tool for groundwater
pollution prevention. Groundwater vulnerability maps provide useful data for protecting
groundwater resources. Identification of agricultural patterns is an important issue for optimized
land management. The watershed of the Tagus River is the backbone of this study. Naturtejo
UNESCO Global Geopark, in the central interior of Portugal, corresponds to a rural area. Intensive
agricultural practices showed an increasing trend in the last decades. The method that is most used
internationally to assess vulnerability is the DRASTIC index. In this study, the DRASTICAI index
is introduced. A new attribute—anthropogenic influence—is added here. Five levels of increasing
vulnerability, from low to high, can be recognized here. The municipality of Idanha-a-Nova is most
affected by intensive agricultural activities, showing spatial patterns of DRASTICAI with a clear
influence of anthropogenic activities, with high needs for groundwater abstraction. A robust
assessment of groundwater quality has a key role. Climate change scenarios and water scarcity are
important issues in the coming years, and particularly in the studied area. Therefore, optimized
groundwater management is essential to consider in policy-making strategies.
With the universal use of mobile computing devices, there has been a notable increasein the number of mobile applications developed for educational purposes. Gamification strategiesoffer a new set of tools to educators and, combined with the location services provided by thosedevices, allow the creation of innovative location-based mobile learning experiences. In this literaturereview, we conduct an analysis of educational mobile location-based games. The review includesarticles published from January of 2010 to October of 2020, and from 127 records screened, 26 articleswere analysed in full-text form. This analysis allowed us to answer the following six predefinedresearch questions: Who are the target audiences for location-based games? In which subjects arelocation-based games most used? Which strategies are implemented with mobile devices to improvethe student’s learning process? What are the main impacts of location-based games on students’learning? What are the main challenges to the development of location-based games for education?What future tendencies and research opportunities can be identified from the analysis of the currentstate of the art?
One of the purposes of teachers is to ensure the motivation of the students in their classes and to maintain disciplined behaviours. However, the teaching styles and methodologies used do not always have a positive effect on student’s motivation and discipline. This study analysed the relationship between student’s perceptions of the controlling behaviours of their physical education teacher, together with amotivation and discipline styles from Self-Determination Theory. The sample comprised 922 students, aged between 14 and 18 years (M = 14.95; SD = 0.98). Students’ perceptions of less controlling discipline styles (control of the use of rewards) negatively predicted the thwarting of autonomy need. Conversely, a more controlling discipline style (judging and devaluing) positively predicted the thwarting of autonomy need, and this, positively predicted amotivation, which nega- tively predicted disciplinary behaviours and positively predicted undisciplined behaviours. Teachers must avoid using controlling behaviours like judging and devaluing, as this reinforces amotivation towards physical education and undisciplined student behaviours. The importance of designing classes where the student has responsibilities to make decisions and to be part of their own learning is pointed out.
Outdoor pig production (OPP) can be considered an intensive system in many areas of the
Mediterranean region. The concentration of the rainfall in the winter season, the OPP’s topographic
and soil properties, together with the continuous input of food and pigs’ excreta, contribute to a
profound increase in the nutrients leaching and soil erosion. This work aimed to evaluate the accuracy
of the DRASTIC-LU index and the Revised Universal Soil Loss Equation (RUSLE) to provide early
information to improve the planning of this type of pig production through more adequate location
and sustainable management practices. The two models were applied to an OPP with 2.24 ha, with
a heavy animal charge (one adult per 1.120 m2
). The results showed that 85% of the OPP area has
a moderate risk to the vulnerability index to groundwater pollution and 15% high risk. The risk
of soil erosion ranged from very severe to extremely severe in 96% of the area. The DRASTIC-LU
indexes and the RUSLE model produce a multi-risk assessment that agreed with the observed field
data. These two models showed accuracy to be used for early assessment when choosing the best
location and improving management practices for OPP systems.
Insects have been reported as a possible alternative solution to help feed the growing world
population with less stress on the planet, thus contributing to the preservation of the environment and
natural ecosystems. However, the consumption of edible insects (EIs), although culturally accepted for
some communities, is not readily accepted for others. Hence this work explores the level of information
that people in a traditionally non-insect-eating country have about the sustainability issues related
with EIs, and also some possible reasons that could motivate their consumption. The study was based
on a questionnaire survey and the results were explored by descriptive statistic tools, tree classification
analysis, factor analysis and cluster analysis. The results showed that the level of information is still
low in general, with most people not manifesting an opinion. However, some aspects are relatively
familiar to the participants (88.9% know that the ecological footprint of insects is smaller than other
meats and 86.9% know that they efficiently convert organic matter into protein). Factor and cluster
analysis showed three classes: cluster 1—people not informed about the facts disclosed through the
true statements and also not able to distinguish the false information; cluster 2—people not informed
about the facts disclosed through the true statements but who were able to distinguish the false
information; and cluster 3—people well informed about the facts disclosed through the true statements
but who were marginally unable to distinguish the false information. It was also found that education,
sex and professional area are the most relevant sociodemographic factors associated with the level
of information, and the highest motivations to consume EIs are their contribution to preserve the
environment and natural resources followed by being a more sustainable option (for 64.7% and 53.4%
of participants, respectively). Hence it was concluded that, although some work still needs to be done
to better inform people about EIs, there is already some conscientiousness that they constitute a good
and more sustainable alternative to other types of mea
Obstructive sleep apnea is a respiratory problem that has serious consequences for physical and mental health, but also in monetary terms, since traffic accidents and poor work performance, among other direct consequences, are attributed to it. It is estimated that between 9% and 38% of the world’s population has this disease. This is a multifactorial disease, therefore, there are several methods of detection and treatment; however, all of them cause discomfort to the patient, or to those around them. In this article we propose a system for the detection and control of obstructive sleep apnea that promises to overcome the drawbacks of the existing therapies, therefore, potentially making it a practical and effective solution for this disease. The proof of concept presented in this paper makes use of an electromyography sensor to collect the myoelectric signal produced by the genioglossus muscle. Surface electrodes provide the electromyography signals to an ESP32 microcontroller, which has the function of analyzing and comparing the data obtained with a predefined value of the apnea threshold. After the detection of an apnea, the circuit is able to create a stimulus signal that is applied directly to the muscle, so that airway occlusion does not occur, and the user does not wake up. The data from each use are automatically sent to a database to be viewed and analyzed at a later point.
Wildfires are a major environmental issue that have an impact on land degradation. Remote
sensing spectral indices provide valuable information for short-term mitigation and rehabilitation
after wildfires. A study area in the Centre inland of Portugal occupied with Maritime pine and
Eucalypts forests and affected by wildfires in 2003, 2017 and 2020 was used. The aims of the study
were twofold: (1) to compute the Normalized Difference Vegetation Index (NDVI) and with forest
inventory data derivate a Maritime pine production model, differentiate evergreen coniferous forests
(e.g., Maritime pine), evergreen broadleaved forests (e.g., Eucalypts), and shrubland, and monitor
vegetation and its post-fire recovery; and (2) to compute the Normalized Burn Ratio (NBR) difference
between pre-fire and post-fire dates for burn severity levels assessment. The plots of a previous forest
inventory were used to follow the NDVI values in 2007 and from 2020 to 2022. An aerial coverage
in 2007 and the Sentinel-2 imagery in 2020–2022 were used. Linear models fitted maritime pine
production with the transformed NDVI by age, showing a fitting efficiency of 60%. The stratification
of cover types by stand development stage and fire occurrence was possible using the NDVI time
curve, which also showed the impact of fire and of low precipitation. Cover types were ranked by
decreasing NDVI values as follows: mature Eucalypts plantations, young Maritime pine regeneration,
mature Maritime pine, young Eucalypts plantations, Strawberry tree shrubland, Eucalypts plantations
post-fire, Maritime pine post-fire, tall shrubland, and short shrubland. Vegetation post-fire recovery
was lower in higher burn severity level areas. Maritime pine areas have lost their natural regeneration
capability due to the wildfires’ short cycles. Spectral indices were effective tools to differentiate cover
types and assist in the evaluation of forest and shrubland conditions.
Many species of the so-called exotic plants coexist with native species in a balanced way,
but others thrive very quickly and escape human control, becoming harmful—these are called
invasive alien species. In addition to overcoming geographic barriers, these species can defeat
biotic and abiotic barriers, maintaining stable populations. Ailanthus altissima is no exception; it is
disseminated worldwide and is considered high risk due to its easy propagation and resistance to
external environmental factors. Currently, it has no particular use other than ornamental, even though
it is used to treat epilepsy, diarrhea, asthma, ophthalmic diseases, and seborrhoea in Chinese medicine.
Considering its rich composition in alkaloids, terpenoids, sterols, and flavonoids, doubtlessly, its
use in medicine or other fields can be maximised. This review will focus on the knowledge of the
chemical composition and the discovery of the biological properties of A. altissima to understand
this plant better and maximise its possible use for purposes such as medicine, pharmacy, or the food
industry. Methods for the extraction and detection to know the chemical composition will also be
discussed in detail.
The venom from Apis mellifera intermissa, the main honey bee prevailing in Morocco, has
been scarcely studied, despite its known potential for pharmacological applications. In the present
work, we investigated the composition, the anti-inflammatory activity, and the venom’s cytotoxic
properties from fifteen honey bee venom (HBV) samples collected in three regions: northeast,
central, and southern Morocco. The chemical assessment of honey bee venom was performed
using LC-DAD/ESI/MSn
, NIR spectroscopy and AAS spectroscopy. The antiproliferative effect
was evaluated using human tumor cell lines, including breast adenocarcinoma, non-small cell lung
carcinoma, cervical carcinoma, hepatocellular carcinoma, and malignant melanoma. Likewise, we
assessed the anti-inflammatory activity using the murine macrophage cell line. The study provides
information on the honey bee venom subspecies’ main components, such as melittin, apamin, and
phospholipase A2, with compositional variation depending on the region of collection. Contents
of toxic elements such as cadmium, chromium, and plumb were detected at a concentration below
5 ppm, which can be regarded as safe for pharmaceutical use. The data presented contribute to the
first study in HBV from Apis mellifera intermissa and highlight the remarkable antiproliferative and
anti-inflammatory effects of HBV, suggesting it to be a candidate natural medicine to explore.
Edible insects (EI) have been consumed as traditional foods in many parts of the globe, but in other regions, they are not readily accepted, particularly in Western countries. However, because EI are suggested to constitute a more sustainable protein food as compared with other sources of animal protein, they can be considered a future food that could help mitigate hunger and malnutrition. Additionally, new gastronomic trends are already targeting this area for exploring new potentialities. The objective of this work was to develop and validate a questionnaire to assess consumers’ perceptions and knowledge about EI in seven different domains: D1. Culture and Tradition, D2. Gastronomic Innovation and Gourmet Kitchen, D3. Environment and Sustainability, D4. Economic and Social Aspects, D5. Commercialization and Marketing, D6. Nutritional Aspects and D7. Health Effects. The 64 items were subjected to item analysis and reliability analysis for validation, and factor analysis was also conducted to identify a grouping structure. The results validated all the items of the seven subscales with high values of Cronbach’s alpha (α = 0.732 for D1, α = 0.795 for D2, α = 0.882 for D3, α = 0.742 for D4, α = 0.675 for D5, α = 0.799 for D6 and α = 0.788 for D7). However, by eliminating 17 items, the final values of the alpha increased in all subscales. Factor analysis with extraction by principal component analysis with varimax rotation extracted 14 factors that explained, in total, 65% of the variance, although the first two factors were the most important (35.7% variance explained). In conclusion, the confirmed usefulness of the questionnaire has been hereby validated for assessing consumer perceptions of and knowledge about EI.
Several approaches have been proposed to reduce the gap between software engineering education and the needs and practices of the software industry. Many of them aim to promote a more active learning attitude in students and provide them with more realistic experiences, thus recreating industry software development environments and collaborative development and, in some cases, with the involvement of companies mainly acting as potential customers. Since many degree courses typically offer separate subjects to teach requirements engineering, analysis and design, coding, or validation, the integration of all these phases normally necessitates experience in a project context and is usually carried out in a final year project. The approach described in this article benefits from the close involvement of a software house company which goes beyond the common involvement of a potential customer. Students are integrated into distributed teams comprising students, teachers and IT professionals. Teams follow the agile Scrum methodology and use the OutSystems low-code development platform providing students with the experience of an almost real scenario. The results show that this approach complements the knowledge and practice acquired in course subjects, develops the students’ technical and non-technical skills, such as commitment, teamwork, and communication, and initiates them in the methodologies and development strategies used in these companies. The feedback from the teachers involved, software companies and students was very positive.
Species ecological envelope maps were obtained for the two main Portuguese wood-production species (Eucalyptus globulus Labill. and Pinus pinaster Aiton) and projected future climate change scenarios. A machine learning approach was used to understand the most influential environmental variables that may explain current species distribution and productivity. Background and Objectives: The aims of the study were: (1) to map species potential suitability areas using ecological envelopes in the present and to project them in the future under climate change scenarios; (2) to map species current distributions; (3) to map species current productivity; and (4) to explore the most influential environmental variables on species current distribution and productivity. Materials and Methods: Climate, elevation data, and soil data sets were used to obtain present and future species ecological envelopes under two climate change scenarios. The o cial land cover maps were used to map species distributions. Forest inventory data were used to map the species productivity by
geostatistical techniques. A Bayesian machine learning approach, supported by species distributions and productivity data, was used to explore the most influential environmental variables on species distribution and productivity and to validate species ecological envelopes. Results: The species ecological envelope methodology was found to be robust. Species’ ecological envelopes showed a high potential for both species’ a orestation. In the future, a decrease in the country’s area potentiality was forecasted for both species. The distribution of maritime pine was found to be mainly determined by precipitation-related variables, but the elevation and temperature-related variables were very important to di erentiate species productivity. For eucalypts, species distribution was mainly explained by temperature-related variables, as well as the species productivity. Conclusions: These findings are key to support recommendations for future a orestation and will bring value to policy-makers and environmental authorities in policy formulation under climate change scenarios.
Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities
In recent years, research on sedentary behaviour has increased. In this regard, there is a
need for theoretical reviews that allow us to determine the past, analyse the present, and prepare the future of research in this field. The purpose of this review paper was to analyse and organise the emerging qualitative research trends (2010–2021) on the sedentary behaviour of older adults. A systematic literature search strategy was developed in various electronic scientific databases (e.g., PubMed, Web of Science, ScienceDirect, Scielo, and Scopus). The included studies were required to have different qualitative methodological approaches in terms of data collection and methods of data analysis. Studies conducted in any country and published in a peer-reviewed journal in
English, Spanish, and Portuguese were considered. A thematic analysis approach was used for data extraction and synthesis, and confidence in the results was assessed using the GRADE-CERQual approach. This study may enable accurate guidelines to be established for future primary qualitative research related to sedentary behaviour.
Abstract: Emerging economies are in an almost winless situation: they would benefit from improving
the extremely poor economic situation, making them self-reliant and economically productive.
However, the poor fight for daily survival and, therefore, cannot afford to improve themselves. This
article describes the state of the “Grande Hotel” in the city of Beira, Mozambique, built-in Portuguese
colonial times, a real vertical shanty building that is occupied by approximately 1000 inhabitants
trapped in poverty. To carry out any constructive intervention in a building or an urban complex,
it is necessary to carry out a rehabilitation project that is developed based on a diagnosis of the
building understudy, its main deteriorations, the causes, mechanisms of action, evolution, and
possible treatments to be used for its repair. Analysis methods are necessary for the conservation
of buildings since trying to stop, or correct buildings’ deterioration without a diagnosis of their
problems or a prognosis on their evolution is a risky procedure with a high percentage of failure
chances. The use of an appropriate methodology for diagnosing the damage present in the “Grande
Hotel” and its prediction of evolution and development should directly impact a better quality of
rehabilitation projects in the neighborhood where the building is located.
Abstract: Having in mind the objectives of the United Nations Development Agenda 2030, which
refers to the sustainable principles of a circular economy, it is urgent to improve the performance of the
built environment. The existing buildings must be preserved and improved in order to reduce their
environmental impact, in line with the need to revert climate change and reduce the occurrence of
natural disasters. This work had as its main goal to identify and define a methodology for promoting
the rehabilitation of buildings in the Ponte Gêa neighborhood, in the city of Beira, Mozambique,
with an emphasis on energy efficiency, water efficiency, and construction and demolition waste
management. The proposed methodology aims to create a decision support method for creating
strategic measures to be implemented by considering the three specific domains—energy, water,
and waste. This model allows for analyzing the expected improvement according to the action
to be performed, exploring both individual and community solutions. It encompasses systems of
standard supply that can reveal greater efficiency and profitability. Thus, the in-depth knowledge of
the characteristics of urban space and buildings allows for establishing guidelines for the renovation
process of the neighborhood.
The main objective of this study was to validate the Healthy Lifestyle Questionnaire—EVS III, using confirmatory factor analysis of the measurement model. A total of 822 Portuguese individuals of both genders, aged between 18 and 66 years old (M = 28.43 SD = 12.07), participated in this study, of which 382 were male (46.5%) and 440 were female (53.5%). The main results obtained revealed that the psychometric qualities prove the adequacy of the factor structure of the Healthy Lifestyles Questionnaire—EVS III (7 factors/32 items) and that it has acceptable validity indices: χ2 = 644.6828, p = 0.000, df = 168, χ2 = 3.84, NFI = 0.901, TLI = 0.902, CFI = 0.921, IFI = 0.922, MFI = 0.900, GFI = 0.909, AGFI = 0.901, RMR = 0.073, SRMR = 0.059 and RMSEA = 0.059, enabling the assessment of factors related to a balanced diet, respect for mealtimes, tobacco consumption, alcohol consumption, consumption of other drugs, resting habits and physical activity habits. The Portuguese version of the Healthy Lifestyles Questionnaire—EVS III can be used with reasonable confidence for the assessment of healthy lifestyles.
The continuous rise in the world’s population has increased the need for food, resulting in a rise of agricultural holdings to ensure the supply of these goods directly to the populations and indirectly to all processing industries in the food business. This situation has led agriculture to reinvent itself and introduce new technics and tools to ensure tighter control of the crops and increase yields in food production. However, the lack of labor coupled with the evolution of weeds resistant to herbicides created a crisis in agricultural food production. However, with the growing evolution in electronics, automation, and robotics, new paths are emerging to solve these problems. A robotic rover was designed to optimize the tasks of weed control and collection of fallen fruits of an orchard. In weed control, a localized spraying system is proposed, therefore reducing the amount of applied herbicides. With fruit collection, it is possible to direct fallen fruits for animal feeding and possible to reduce microbial activity on the next campaign crops, therefore avoiding damage. This study proposes the simulation of this robotic rover on robotic simulation software. It also proposes the replication of a similar environment of an orchard to generate an algorithm that controls the rover on the tasks of localized spraying and fallen fruit collection. Creating and testing these algorithms by using a robotic simulator speed up and ease the evaluation of different scenarios and hypotheses, with the added benefit of being able to test two tasks simultaneously. This method also allows greater freedom and creativity because there are no concerns about hardware damage. It should also be noted that development costs are very low.
Natural products are increasingly becoming part of our daily lives through their use in
industry, food, as therapeutic agents, etc. To evaluate their possible applications, it is essential to
characterize them chemically to explore their potential. Different techniques may be used to characterize natural products, including microextraction techniques. These techniques have been gaining popularity due to the advantages associated with their low use of organic solvents and the small
amount of sample used relative to more classical sample preparation techniques. Their application
in the extraction of compounds from natural products is still scarce. This manuscript intends to
review the most used solid-based miniaturized sample preparation techniques applied to determining compounds in natural products. The main applications of these methodologies will be discussed, with a particular focus on natural product analysis, as well as their advantages and disadvantages over traditionally used sample preparation techniques.
Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
Edible insects have been suggested as a more sustainable source of protein, but their
consumption varies according to geographical and sociocultural influences. Focusing on the different
aspects that can influence people’s attitudes towards edible insects (EI), this work aimed to carry out
the statistical validation of an instrument aimed at assessing different dimensions of this field: the
KPEI (knowledge and perceptions about EI) scale. The instrument consists of 64 questions distributed
by the following dimensions: Culture and Tradition, Gastronomic Innovation and Gourmet Kitchen,
Environment and Sustainability, Economic and Social Aspects, Commercialization and Marketing,
Nutritional Characteristics, and Health Effects. The data were collected in 13 countries (Croatia,
Greece, Latvia, Lebanon, Lithuania, Mexico, Poland, Portugal, Romania, Serbia, Slovenia, Spain,
and Turkey). The validation of the KPEI scale was made through Confirmatory Factor Analysis
(CFA) and Structural Equation Modeling (SEM). The results revealed two acceptable models, both
retaining 37 of the 64 initial items, distrusted by the seven dimensions as: Culture and Tradition
(5 items), Gastronomic Innovation and Gourmet Kitchen (5 items), Environment and Sustainability (8 items), Economic and Social Aspects (5 items), Commercialisation and Marketing (4 items),
Nutritional Aspects (6 items), Health Effects (4 items). Both multifactorial models resulting from
the CFA/SEM analyses showed approximately equal goodness of statistical fit indices with values
of Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMR), and
Standardized Root Mean Square Residual (SRMR) partially zero and values of Goodness of Fit Index
(GFI) and Comparative Fit Index (CFI) approximately one, i.e., very close to a perfect fit. For the
first-order model, the ratio between chi-square and degrees of freedom is χ
2/df = 13.734, GFI = 0.932,
CFI = 0.930, RMSEA = 0.043, RMR = 0.042, SRMR = 0.042; and for the second-order model
χ
2/df = 14.697, GFI = 0.926, CFI = 0.923, RMSEA = 0.045, RMR = 0.047, SRMR = 0.046). The values of composite reliability (CR = 0.967) and mean extracted variance (MEV = 0.448) are indicative
of a good fit. Finally, the reliability analysis indicated a very good internal consistency (Cronbach’s
α = 0.941). These results confirm the successful validation of the KPEI scale, making it a valuable
instrument for future application at the international level.
This study aimed to investigate the level of knowledge about edible insects (EIs) in a
sample of people from thirteen countries (Croatia, Greece, Latvia, Lebanon, Lithuania, Mexico,
Poland, Portugal, Romania, Serbia, Slovenia, Spain, and Turkey). Data collection was based on
a questionnaire survey applied through online tools between July and November 2021. For data
analysis, techniques such as factor analysis, cluster analysis, and chi-square tests were used, with
a significance level of 5%. A total of 27 items were used to measure knowledge on a five-point
Likert scale. Applying factor analysis with principal components and Varimax rotation, a solution
that explains about 55% of variance was obtained. This accounts for four factors that retained
22 of the 27 initial items: F1 = Sustainability (8 items), F2 = Nutrition (8 items), F3 = Production
Factors (2 items), and F4 = Health Concerns (4 items). Internal consistency was evaluated through
Cronbach’s alpha. The cluster analysis consisted of the application of hierarchical methods followed
by k-means and produced three clusters (1—‘fearful’, 2—‘farming,’ and 3—‘ecological’ individuals).
The characterisation of the clusters revealed that age did not influence cluster membership, while sex,
education, country, living environment, professional area, and income all influenced the composition
of the clusters. While participants from Mexico and Spain were fewer in the ‘fearful’ cluster, in those
from Greece, Latvia, Lebanon, and Turkey, the situation was opposed. Participants from rural areas
were mostly in cluster 2, which also included a higher percentage of participants with lower income.
Participants from professional areas linked with biology, food, and nutrition were mostly in cluster 3.
In this way, we concluded that the level of knowledge about EIs is highly variable according to the
individual characteristics, namely that the social and cultural influences of the different countries
lead to distinct levels of knowledge and interpretation of information, thus producing divergent approaches to the consumption of insects—some more reluctant and measuring possible risks. In
contrast, others consider EIs a good and sustainable protein-food alternative.
Sweet cherry is a highly appreciated seasonal fruit with a high content of bioactive compounds;
however, this highly perishable fruit has a relatively short shelf-life period. Here, we
evaluated the evolution of the physicochemical and sensory qualities of sweet cherries (Prunus avium
(L.) cv. Satin) under different storage conditions, namely at a Farmers’ Organization (FO) and in
a Research Centre (RC) under normal and four different conditions of controlled atmosphere for
49 days. Additional parameters were monitored, such as rotten fruit incidence and stem appearance.
Temperature was the factor that most influenced the fruit quality changes over the study time. In fact,
fruits stored at higher mean temperatures showed higher weight loss, higher variation in CIE-Lab
colour parameters, higher firmness loss, and browner and more dehydrated stems and were less
appealing to the consumer. Controlled atmosphere conditions showed a smaller decrease in CIE-Lab
colour parameters and lower weight loss. The incidence of rotting was very low and was always
equal or lower than 2% for all conditions. Thus, RC chamber conditions were able to sustain fruit
quality parameters over 28 days under normal atmosphere conditions and 49 days under controlled
atmosphere conditions.
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.
Acid mine drainage represents an extreme environment with high concentrations of potentially
toxic elements and low pH values. These aquatic habitats are characterised by harsh conditions
for biota, being dominated by acidophilic organisms. The study site, São Domingos mine,
located in one of the largest metallogenetic provinces in the world, the Iberian Pyrite Belt, was
closed without preventive measures. To identify the algae species and understand the relationships
with abiotic parameters of the ecosystem, water and biological material were collected and analysed.
Digital terrain models were obtained with an unmanned aerial vehicle for geomorphological
and hydrologic characterisation of the mine degraded landscape. The results show two types of
algal colours that seem to represent different degrees of photosynthetic activity. Optical and scanning
electron microscopy revealed 14 taxa at the genus level, divided into eight classes. The genus
Mougeotia is the most abundant multicellular algae. With respect to unicellular algae, diatoms are
ubiquitous and abundant. Abiotic analyses expose typical features of acid mine drainage and
support an inverse relationship between chemical contamination and biological diversity. Factorial
correspondence analysis indicates three groups of attributes and samples by their relationship with
specific toxic elements. This analysis also suggests a close association between Spirogyra and Pb,
together composing a structurally simple ecosystem. The highest contamination in the river system
is related to the hydrologic patterns obtained from photogrammetric products, such as the digital
surface model and flow map accumulation, indicating the input of leachates from the section
having the finest sulfide-rich wastes. Information about the algae community and their association
with flow patterns of toxic elements is a relevant tool from a biomonitoring perspective.
The purpose of this study was to compare measures of anthropometry characteristics and physical fitness performance between rugby union players (17.9 ± 0.5 years old) recruited (n = 39) and non-recruited (n = 145) to the Portuguese under-19 (U19) national team, controlling for their playing position (forwards or backs). Standardized anthropometric, physical, and performance assessment tests included players’ body mass and height, push up and pull-up test, squat test, sit- and-reach test, 20 m shuttle run test, flexed arm hang test, Sargent test, handgrip strength test, Illinois agility test, and 20-m and 50-m sprint test. Results showed that recruited forwards players had better agility scores (p = 0.02, ES = −0.55) than the non-recruited forwards, whereas recruited backs players had higher right (p < 0.01, ES = 0.84) and left (p = 0.01, ES = 0.74) handgrip strength scores than their counterparts. Logistic regression showed that better agility (for the forwards) and right handgrip strength scores (for the backs) were the only variables significantly associated with an increased likelihood of being recruited to the national team. In sum, these findings suggest that certain well- developed physical qualities, namely, agility for the forwards players and upper-body strength for the back players, partially explain the selection of U19 rugby players to their national team.
This paper presents a wireless mesh network testbed based on ESP8266 devices using painlessMesh library. It evaluates its feasibility and potential effectiveness as a solution to monitor perishable goods, such as fresh fruit and vegetables, which are often stored and transported inside refrigerated containers. Performance testing experiments with different numbers of nodes and traffic loads and different message payload sizes are conducted under unicast transmission. The impact on network performance is evaluated in terms of delivery ratio and delivery delay, which, consequently, affect the energy consumption and, hence, network lifetime. The results of this investigation are an important contribution to help researchers to propose mechanisms, schemes, and protocols to improve performance in such challenging networks.
Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk.
Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.
In areas contaminated by potentially toxic elements (PTEs), knowledge of processes of
metal mobilisation is the basis for the choice of appropriate remediation methodologies. The
mobilisation of metals is a function of several factors, and the response to these factors must be well
known during the planning of remediation strategies. The activity of an ore metallurgical plant in
South‐East Brazil resulted in major contamination by several heavy metals. Reversing the
contamination’s negative impact required geochemical assessment of the area, including the
physicochemical characterisation, quantification, and delimitation of PTEs, and the rating of the
solubilisation/mobilisation capacity of these elements. The definition of spatial patterns for PTEs’
distribution allowed the construction of contamination risk maps which work as a tool for the
mitigation and control of the contamination plume. The chemical analysis of interstitial water and
selective and sequential extraction methodologies showed that elements that occur in the
environment in critical concentrations (Zn, Cd, Pb, As) are mostly associated with easily mobilised
forms (soluble, exchangeable cations, associated with Mn oxides). Given the great mobility of the
contamination plume, any process of removal of contaminated material becomes unfeasible, thus
the strategy of remediation for the stream and associated alluvial deposits must be based on
methods of in situ decontamination.
Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such as packet fragmentation, low node density, short contact duration, and network disruption. These features may result in the absence of a path between the source and destination nodes, which is one of the most challenging issues faced by this type of network. To overcome some of these problems, it is necessary to provide vehicular networks with sophisticated tools or methodologies to implement monitoring and management operations. However, designing efficient solutions for this type of network is not an easy task due to its particular characteristics. This paper elaborates on a comprehensive survey focusing on promising proposals to deal with monitoring and management functionalities in vehicular networks. This work aims not only to present the state of the art on monitoring and management solutions but also to analyze their benefits and drawbacks, identify open issues, and provide guidelines for further contributions.
The food and beverage market has become broader due to globalization and consumer
claims. Under the umbrella of consumer demands, legislation, nutritional status, and sustainability,
the importance of food and beverage safety must be decisive. A significant sector of food production
is related to ensuring fruit and vegetable conservation and utilization through fermentation. In
this respect, in this review, we critically analyzed the scientific literature regarding the presence of
chemical, microbiological and physical hazards in fruit-based fermented beverages. Furthermore, the
potential formation of toxic compounds during processing is also discussed. In managing the risks,
biological, physical, and chemical techniques can reduce or eliminate any contaminant from fruitbased fermented beverages. Some of these techniques belong to the technological flow of obtaining
the beverages (i.e., mycotoxins bound by microorganisms used in fermentation) or are explicitly
applied for a specific risk reduction (i.e., mycotoxin oxidation by ozone). Providing manufacturers
with information on potential hazards that could jeopardize the safety of fermented fruit-based drinks
and strategies to lower or eliminate these hazards is of paramount importance.
Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical-chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support System (DSS) designed to predict the evolution of the quality of peaches, namely the storage time required before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and validation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple prediction of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and consumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI-based solutions for smart cities.
Globally, climate change and wildfires are disrupting natural ecosystems, thus setting
several endemic species at risk. The genus Lavandula is widely present in the Mediterranean region
and its species, namely, those included in the section Stoechas, are valuable resources of active
compounds with several biological assets. Since ancient times lavenders have been used in traditional
medicine and for domestic purposes. These species are melliferous, decorative, and essential oilproducing plants with a high economic interest in the pharmaceutical, flavor, fragrance, and food
industries. The essential oils of Lavandula section Stoechas are characterized by high amounts of
1,8-cineole, camphor, fenchone, and specifically for L. stoechas subsp. luisieri one of the major
compounds is trans-α-necrodyl acetate. On the other hand, the diversity of non-volatile components
like phenolic compounds, such as phenolic acids and flavonoids, make these species an important
source of phytochemicals with pharmacological interest. Rosmarinic, caffeic, and salvianolic B acids
are the major phenolic acids, and luteolin and eriodictyol-O-glucuronide are the main reported
flavonoids. However, the concentration of these secondary metabolites is strongly affected by
the plant’s phenological phase and varies in Lavandula sp. from different areas of origin. Indeed,
lavender extracts have shown promising antioxidant, antimicrobial, anti-inflammatory, and anticancer
properties as well as several other beneficial actions with potential for commercial applications.
Despite several studies on the bioactive potential of lavenders from the section Stoechas, a systematized
and updated review of their chemical profile is lacking. Therefore, we carried out the present review
that gathers relevant information on the different types of secondary metabolites found in these
species as well as their bioactive potential.
Weeds, pathogens, and animal pests are among the pests that pose a threat to the productivity of crops meant for human consumption. Bird-caused crop losses pose a serious and costly challenge for farmers. This work presents a survey on bird deterrent solutions for crop protection. It first introduces the related concepts. Then, it provides an extensive review and categorization of existing methods, techniques, and related studies. Further, their strengths and limitations are discussed. Based on this review, current gaps are identified, and strategies for future research are proposed.
Chemical investigations of L. stoechas subsp. luisieri and L. pedunculata essential oils were
analyzed by GC-MS, and the antimicrobial activity was performed against bacteria and fungi isolated
from food sources. The cytotoxicity of the essential oil was performed in NHDF cells using the
MTT method. According to the results, the main compounds of L. stoechas subsp. luisieri essential
oil were trans-α-necrodyl acetate (40.2%), lavandulyl acetate (11%), and trans-α-necrodol (10.4%),
while fenchone (50.5%) and camphor (30.0%) in L. pedunculata essential oil. The antifungal activity
of essential oils was confirmed with MIC values ranging from 1.2 to 18.7 µL/mL; for bacteria, it
ranged from 4.7 to 149.3 µL/mL. Both the Lavandula species tested showed low or equal MIC and
MBC/MFC values for L. stoechas subsp. luisieri essential oil, revealing greater efficacy in antimicrobial
activity. The L. stoechas subsp. luisieri essential oil revealed cytotoxic effects (30 ± 2% of cell viability)
in NHDF cells at all concentrations tested.
Digestate contains many essential nutrients for crops, including nitrogen (N) and phosphorus
(P), and it can alter the biogeochemical cycle of nutrients and soil functionality. This work
aimed to assess the fertilizing effects of digestate on chemical and biological soil properties in a
field experiment in eastern Portugal with two horticultural crops involving nine treatments: control
without fertilization; mineral N fertilization with 85 kg ha1; fertilization with digestate (DG) with
increasing N rates (85, 170, 255, or 340 kg N ha1); and fertilization with different combinations of
digestate plus mineral N (DG at 85 or 170 kg N plus 60 kg mineral N ha–1 or DG at 170 kg N plus
25 kg mineral N ha–1). In addition to N, digestate supplied significant amounts of P, Ca, K, and
Mg and significantly increased soil Olsen P, mineral N, and organic C. At high doses, it decreased
phosphatase and -glucosidase activities, as well as fungi and bacterial biomass, compared to the
control or mineral N fertilization, and it also negatively affected soil P and C cycling capacity and
microbial biomass. The organic to total N ratio and the N to P ratio in digestate are crucial properties
for evaluating its agronomic management as fertilizer.
This study investigated the crucial factors for measuring the success of the information system used in the e-learning process, considering the transformations in the work environment. This study was motivated by the changes caused by COVID-19 witnessed after the shift to fully online learning environments supported by e-learning systems, i.e., learning emphasized with information systems. Empirical research was conducted on a sample comprising teaching staff from two European universities: the University of Novi Sad, Faculty of Technical Sciences in Serbia and the Polytechnic Institute of Castelo Branco in Portugal. By synthesizing knowledge from review of the prior literature, supported by the findings of this study, the authors propose an Extended Information System Success Measurement Model—EISSMM. EISSMM underlines the importance of workforce agility, which includes the factors of proactivity, adaptability, and resistance to change, in the information system performance measurement model. The results of our research provide more extensive evidence and findings for scholars and practitioners that could support measuring information system success primarily in e-learning and other various contextual settings, highlighting the importance of people’s responses to work environment changes.
Plant diseases and pests significantly influence food production and the productivity and economic profitability of agricultural crops. This has led to great interest in developing technological solutions to enable timely and accurate detection. This systematic review aimed to find studies on the automation of processes to detect, identify and classify diseases and pests in agricultural crops.
The goal is to characterize the class of algorithms, models and their characteristics and understand the efficiency of the various approaches and their applicability. The literature search was conducted in two citation databases. The initial search returned 278 studies and, after removing duplicates and applying the inclusion and exclusion criteria, 48 articles were included in the review. As a result, seven research questions were answered that allowed a characterization of the most studied crops, diseases and pests, the datasets used, the algorithms, their inputs and the levels of accuracy that have been achieved in automatic identification and classification of diseases and pests. Some trends that have been most noticed are also highlighted.
To date, a variety of species potential distribution mapping approaches have been used,
and the agreement in maps produced with different methodological approaches should be assessed.
The aims of this study were: (1) to model Maritime pine potential distributions for the present and
for the future under two climate change scenarios using the machine learning Maximum Entropy
algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental
data set and climate change scenarios; and (3) to perform an agreement analysis for the species
distribution maps produced with both methodological approaches. The species distribution
maps produced by each of the methodological approaches under study were reclassified into presence–
absence binary maps of species to perform the agreement analysis. The results showed that
the MaxEnt-predicted map for the present matched well the species’ current distribution, but the
species ecological envelope map, also for the present, was closer to the species’ empiric potential
distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt
were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the
present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in
species ecological envelopes maps were higher and with greater future losses than the latter. The
76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate
change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance
for the species occupancy in the present, decreasing around 30%–35% in the future under the climate
change scenarios. Both methodologies proved to be complementary to set species’ best suitability
areas, which are key as support decision tools for planning afforestation and forest management
to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and
productivity.
: Cistus ladanifer L. (Cistaceae) occupies extensive areas as a dominant species (shrublands) or
is associated to other major forest typologies in the Iberian Peninsula. Cistus ladanifer shrublands are
mostly present in oligotrophic lands with little valorisation and management and as they develop over
the years (up to 20-years-old) they promote the ignition and perpetuation of fire. To contribute to the
proper management and valorisation of such systems, a 5-year-old dense shrubland was evaluated for
its labdanum resin, seeds, and biomass productivity using different non-destructive harvest periodicities
(annual and biennial) and seasons (early, mid-, and late summer), in a two-year case-study. Annual harvest
modality maximized labdanum resin productivity (reaching 230 ± 50 kg·ha−1
·2 years−1 at late summer)
and photosynthetic biomass productivity. In contrast, a biennial harvest yielded significant amounts
of more diversified products. It maximized seeds productivity (reaching 75 ± 41 kg·ha−1
·2 years−1
independently of the summer season) and lignified biomass. However, it also reached a labdanum resin
productivity of 134 ± 20 kg·ha−1
·2 yearrs−1 at late summer and a photosynthetic biomass productivity
around two times lower than the annual harvest. In this study, we propose two modalities of periodic
harvest to be considered as proper long cycle management practices of rockrose lands. It intends to
minimize fire risks, break the vegetation auto-succession mechanism, and increase profit from nonproductive lands based on three direct outputs with a myriad of applications and valorisation pathways.
Fire blight is a severe bacterial plant disease that affects important chain-of-value fruit
trees such as pear and apple trees. This disease is caused by Erwinia amylovora, a quarantine
phytopathogenic bacterium, which, although highly distributed worldwide, still lacks efficient
control measures. The green revolution paradigm demands sustainable agriculture practices, for
which antimicrobial peptides (AMPs) have recently caught much attention. The goal of this work
was to disclose the bioactivity of three peptides mixtures (BP100:RW-BP100, BP100:CA-M, and RWBP100:
CA-M), against three strains of E. amylovora representing distinct genotypes and virulence
(LMG 2024, Ea 630 and Ea 680). The three AMPs’ mixtures were assayed at eight different equimolar
concentrations ranging from 0.25 to 6 M (1:1). Results showed MIC and MBC values between 2.5
and 4 M for every AMP mixture and strain. Regarding cell viability, flow cytometry and alamarBlue
reduction, showed high reduction (>25%) of viable cells after 30 min of AMP exposure, depending on
the peptide mixture and strain assayed. Hypersensitive response in tobacco plants showed that the
most efficient AMPs mixtures and concentrations caused low to no reaction of the plant. Altogether,
the AMPs mixtures studied are better treatment solutions to control fire blight disease than the same
AMPs applied individually.
A new C15-acetogenin, sagonenyne (20), exhibiting an unusual single tetrahydropyran ring was isolated from an ethyl acetate extract of Laurencia obtusa collected on the Corsican coastline. Its structure was established by detailed NMR spectroscopic analysis, mass spectrometry, and comparison with literature data. Twenty-three known compounds were identified in the same extract by means of column chromatography steps, using a (13)C-NMR computer aided method developed in our laboratory. In addition to sesquiterpenes, which represent the main chemical class of this extract, diterpenes, sterols, and C15-acetogenins were identified. The crude extract was submitted to a cytotoxicity assay and was particularly active against THP-1 cells, a human leukemia monocytic cell line.
The main objective of this study was the validation of the Healthy Lifestyle Questionnaire (EVS II), using a confirmatory factorial analysis of the measurement model, with veteran athletes. A total of 348 veteran Portuguese athletes of both genders, aged between 30 and 60 years (M = 41.64, SD = 9.83), of whom 200 were males and 148 were females, from several sports. The results of the confirmatory factor analysis demonstrate the adequacy of the adapted version of the EVS II, as the factorial structure (6 factors/24 items) has acceptable validity indexes: χ2 = 305.925, p = 0.000, df = 120.017, χ2/df = 2.549, NFI (Normed Fit Index) = 0.909, TLI (Tucker Lewis Index) = 0.918, CFI (Comparative Fit Index) = 0.944, GFI (Goodness of Fit Index) = 0.944, AGFI (Adjusted Goodness of Fit Index) = 0.909, SRMR (Standardized Root Mean Square Residual) = 0.048, RMSEA (Root Mean Square of Approximation) = 0.060, allowing evaluation of the dimensions of balanced diet, respect for mealtime, tobacco and alcohol consumption, other drugs consumption and resting habits. The adaptation to sport of the Portuguese version of EVS II can be used with reasonable confidence in the evaluation of healthy lifestyles in the context of sport
Abstract: This paper describes a real case implementation of an automatic pedestrian-detection
solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open,
publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The
presented solution makes it possible to anonymously identify pedestrians, and extract associated
information such as position, walking velocity and direction in certain areas of interest such as
pedestrian crossings or other points of interest in a smart-city context. All data computation (3D
point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier
platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time
computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations.
The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smartcity
platform). The results show that using currently affordable LiDAR sensors in a smart-city
context, despite the advertising characteristics referring to having a range of up to 100 m, presents
great challenges for the automatic detection of objects at these distances. The authors were able to
efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the
implementation challenges, the authors present usage recommendations to get the most out of the
used technologies.
The Opuntia spp., most likely few individuals, were introduced in the Iberian Peninsula in the beginning of the 16th century, after the discovery of America, spreading afterwards throughout the Mediterranean basin. We analysed, for the first time, the genetic diversity in a set of 19 Portuguese Opuntia spp. populations from the species O. ficus-indica, O. elata, O. dillenii and O. robusta using nuclear microsatellite (nuSSR) markers. The Italian cultivars ‘Bianca’, ‘Gialla’ and ‘Rossa’ were included in the study for comparison purposes. The nuSSR amplifications produced from five to 16 alleles, with an average of 9.2 alleles per primer pair, and average polymorphism information content of 0.71. The estimated Dice coefficient among populations varied from 0.26 to 1.0, indicating high interspecific genetic diversity but low genetic diversity at the intraspecific level. The hierarchical clustering analysis revealed four major groups that clearly separated the four Opuntia species. Among the O. ficus-indica populations, two sub-clusters were found, one including the white pulp fruits (with cv. Bianca) and the other with the orange pulp ones and including the cv. Gialla, the cv. Rossa, and one pale yellow pulp population. No genetic differences were found between the inermis form, O. ficus-indica f. ficus-indica, and the rewilded spiny one, O. ficus-indica f. amyclaea. The dendrogram indicated that the clustering pattern was unrelated to geographical origin. The results revealed a low level of genetic diversity among the Portuguese populations of O. ficus-indica.
