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Numerous policies have been proposed by international and supranational institutions, such as the European Union, to surveil Earth from space and furnish indicators of environmental conditions across diverse scenarios. In tandem with these policies, different initiatives, particularly on both sides of the Atlantic, have emerged to provide valuable data for environmental management such as the concept of essential climate variables. However, a key question arises: do the available data align with the monitoring requirements outlined in these policies? In this paper, we concentrate on Earth Observation (EO) optical data applications for environmental monitoring, with a specific emphasis on ocean colour. In a rapidly changing climate, it becomes imperative to consider data requirements for upcoming space missions. We place particular significance on the application of these data when monitoring lakes and marine protected areas (MPAs). These two use cases, albeit very different in nature, underscore the necessity for higher-spatial-resolution imagery to effectively study these vital habitats. Limnological ecosystems, sensitive to ice melting and temperature fluctuations, serve as crucial indicators of a climate in change. Simultaneously, MPAs, although generally small in size, play a crucial role in safeguarding marine biodiversity and supporting sustainable marine resource management. They are increasingly acknowledged as a critical component of global efforts to conserve and manage marine ecosystems, as exemplified by Target 3 of the Kunming–Montreal Global Biodiversity Framework (GBF), which aims to effectively conserve 30% of terrestrial, inland water, coastal, and marine areas by 2030 through protected areas and other conservation measures. In this paper, we analysed different policies concerning EO data and their application to environmental-based monitoring. We also reviewed and analysed the existing relevant literature in order to find gaps that need to be bridged to effectively monitor these habitats in an ecosystem-based approach, making data more accessible, leading to the generation of water quality indicators derived from new high- and very high-resolution satellite monitoring focusing especially on Chlorophyll-a concentrations. Such data are pivotal for comprehending, at small and local scales, how these habitats are responding to climate change and various stressors. [ABSTRACT FROM AUTHOR]

The current era witnesses a remarkable advancement in biomass utilization, guided by the principles of green chemistry and biorefinery and the comprehensive exploitation of plant-based raw materials. Predominantly, large-scale production methods have been pursued, akin to approaches in the oil industry, enabling the incorporation of novel products into energy and petrochemical markets. However, the viability of such systems on a small and medium scale is hindered by logistical challenges and the constraints of economies of scale. For small agricultural producers and food processing companies, the complete utilization of biomass transcends environmental responsibility, evolving into a strategy for survival through the diversification of by-products with enhanced value. The state of Rio de Janeiro in Brazil presents a range of population dynamics, geographical features, climate conditions, and agricultural production patterns that closely resemble those found in various tropical countries and agricultural regions worldwide. This region, sustaining a green belt supporting 17 million people, provides an apt case study for investigating chemical compounds with potential value among agro-industrial residues, which can motivate the creation of a lucrative biotechnological industry. Examples include naringenin and hesperidin from oranges and lemons, epi-gallo-catechin gallate from bananas, caffeic acids from coffee, and the bromelain enzyme from pineapples. This study addresses the challenges associated with developing biotechnological alternatives within the agroindustry, considering economic, technological, logistical, and market-related aspects. The insights from examining the Brazilian state of Rio de Janeiro will contribute to the broader discourse on sustainable biomass utilization and the creation of value-added by-products. [ABSTRACT FROM AUTHOR]

Background: Healthcare-associated infections (HAIs) pose a significant global threat, particularly in developing regions such as Southeast Asia. International bodies emphasize the role of formal undergraduate training in the prevention and control of HAIs. To address this, we aimed to explore the perceptions of Southeast Asian nursing students regarding a novel educational approach developed by a European–Southeast Asian project consortium. Methods: A pilot study was conducted in four nursing higher education institutions from Cambodia and Vietnam. First, local nursing educators conducted a 2 h classroom-based training session. Then, students were invited to participate for the first time in one of twelve evidence-based simulation scenarios developed by the research team, covering a range of nursing care situations related to the prevention and control of HAIs. After attending both components, students were asked to complete a paper-based questionnaire and rate their agreement with a set of statements on the appropriateness and meaningfulness of both components. Results: A total of 430 nursing students enrolled in the pilot study; 77.4% were female, with an average age of 19.8 years. The PrevInf educational intervention received positive feedback from participating students across settings, with strong agreement on the importance of proactiveness in competency development (M = 5.9, SD = 1.4). Notable differences between Cambodian and Vietnamese students were observed in terms of their receptiveness to the pre-selected teaching materials (p = 0.001) and strategies (p = 0.01) used by the nursing educators during their experience with the simulation scenarios. Conclusions: The PrevInf educational intervention shows promise in engaging Southeast Asian nursing students and fostering a deeper understanding of the prevention and control of HAIs. Further studies are warranted to refine the learning content and standardize the pedagogical strategies used by nursing educators across settings. This study was not registered. [ABSTRACT FROM AUTHOR]

This review article addresses the antioxidant properties of different natural products, including ascorbic acid, gallic acid, oxalic acid, L-glutathione (GSH), bacteriorhodopsin, green tea polyphenols, glucose, hydroxycinnamic acid, ethanoic acid, betanin, and L-glutathione, in the reduction of graphene oxide (rGO). rGO can cause damage to cells, including oxidative stress and inflammation, limiting its application in different sectors that use graphene, such as technologies used in medicine and dentistry. The natural substances reviewed have properties that help reduce this damage, neutralizing free radicals and maintaining cellular integrity. This survey demonstrates that the combination of these antioxidant compounds can be an effective strategy to minimize the harmful effects of rGO and promote cellular health. [ABSTRACT FROM AUTHOR]

The mechanical analysis of photovoltaics and building integrated photovoltaics is a key step for their optimal design and certification, and requires careful consideration, alongside solar power, durability and functionality issues. The solar cells are encapsulated in thin interlayers that are usually composed of a viscoelastic Ethylene–Vinyl Acetate compound, and protected by thin glass and/or plastic layers. This paper investigates the out-of-plane bending response of a full-scale commercial PV module and focuses attention on the shear bonding efficiency of the thin encapsulant for quasi-static and dynamic mechanical considerations. The parametric analytical analysis, carried out in this study for a laminated glass plate, highlights the possible consequences of the viscoelastic shear coupling on the cross-section load-bearing demand in the covers. As a direct effect of severe operational conditions (i.e., ageing, non-uniform/cyclic thermal gradients, humidity, extreme mechanical/thermal loads, etc.) the shear rigidity and adhesion of these films can suffer from repeated/progressive modification and even degradation, and thus induce major stress and deflection effects in the out-of-plane mechanical response of the PV module components. The minimum shear bond efficiency required to prevent mechanical issues is calculated for various configurations of technical interest. Accordingly, it is shown how the quasi-static and dynamic mechanical performance of the system modifies as a function of a more rigid or weak shear coupling. [ABSTRACT FROM AUTHOR]

Early and accurate disease diagnosis is pivotal for effective phytosanitary management strategies in agriculture. Hyperspectral sensing has emerged as a promising tool for early disease detection, yet challenges remain in effectively harnessing its potential. This study compares parametric spectral Vegetation Indices (VIs) and a nonparametric Gaussian Process Classification based on an Automated Spectral Band Analysis Tool (GPC-BAT) for diagnosing plant bacterial diseases using hyperspectral data. The study conducted experiments on tomato plants in controlled conditions and kiwi plants in field settings to assess the performance of VIs and GPC-BAT. In the tomato experiment, the modeling processes were applied to classify the spectral data measured on the healthy class of plants (sprayed with water only) and discriminate them from the data captured on plants inoculated with the two bacterial suspensions (108 CFU mL−1). In the kiwi experiment, the standard modeling results of the spectral data collected on nonsymptomatic plants were compared to the ones obtained using symptomatic plants' spectral data. VIs, known for their simplicity in extracting biophysical information, successfully distinguished healthy and diseased tissues in both plant species. The overall accuracy achieved was 63% and 71% for tomato and kiwi, respectively. Limitations were observed, particularly in differentiating specific disease infections accurately. On the other hand, GPC-BAT, after feature reduction, showcased enhanced accuracy in identifying healthy and diseased tissues. The overall accuracy ranged from 70% to 75% in the tomato and kiwi case studies. Despite its effectiveness, the model faced challenges in accurately predicting certain disease infections, especially in the early stages. Comparative analysis revealed commonalities and differences in the spectral bands identified by both approaches, with overlaps in critical regions across plant species. Notably, these spectral regions corresponded to the absorption regions of various photosynthetic pigments and structural components affected by bacterial infections in plant leaves. The study underscores the potential of hyperspectral sensing in disease diagnosis and highlights the strengths and limitations of VIs and GPC-BAT. The identified spectral features hold biological significance, suggesting correlations between bacterial infections and alterations in plant pigments and structural components. Future research avenues could focus on refining these approaches for improved accuracy in diagnosing diverse plant–pathogen interactions, thereby aiding disease diagnosis. Specifically, efforts could be directed towards adapting these methodologies for early detection, even before symptom manifestation, to better manage agricultural diseases. [ABSTRACT FROM AUTHOR]

Spectral point-of-care technology is reagentless with minimal sampling (<10 μL) and can be performed in real-time. White blood cells are non-dominant in blood and in spectral information, suffering significant interferences from dominant constituents such as red blood cells, hemoglobin and billirubin. White blood cells of a bigger size can account for 0.5% to 22.5% of blood spectra information. Knowledge expansion was performed using data augmentation through the hybridization of 94 real-world blood samples into 300 synthetic data samples. Synthetic data samples are representative of real-world data, expanding the detailed spectral information through sample hybridization, allowing us to unscramble the spectral white blood cell information from spectra, with correlations of 0.7975 to 0.8397 and a mean absolute error of 32.25% to 34.13%; furthermore, we achieved a diagnostic efficiency between 83% and 100% inside the reference interval (5.5 to 19.5 × 10 9 cell/L), and 85.11% for cases with extreme high white blood cell counts. At the covariance mode level, white blood cells are quantified using orthogonal information on red blood cells, maximizing sensitivity and specificity towards white blood cells, and avoiding the use of non-specific natural correlations present in the dataset; thus, the specifity of white blood cells spectral information is increased. The presented research is a step towards high-specificity, reagentless, miniaturized spectral point-of-care hematology technology for Veterinary Medicine. [ABSTRACT FROM AUTHOR]

Significant advances have been achieved in multilateral negotiations regarding human development and environmental safeguarding since the 1972 UN Stockholm Conference. There is much greater global awareness and action towards sustainability. However, sustainability has persistently been sidelined, leading to the identification and definition of a transgressed "safe and just space for humanity". Here we develop a new evolutionary approach and methodology to explain the reasons why sustainability continues to be a difficult challenge for contemporary societies to adopt. We argue that these originate in six major biological, social, psychological, political, and cultural critical determinants that resulted from human biologic and cultural evolution. Although they are essential for human prosperity and wellbeing, these characteristics may also act as human sustainability boundaries. It is possible to reduce the inhibiting power of each critical determinant in the pathways to sustainability, a vital process that we term softening. Identifying, knowing, and softening these impediments is a necessary first step to achieving sustainability through greater self-knowledge and transformational processes. The application of the present methodology is restricted here to the climate change challenge. We examine the ways in which each human sustainability boundary is capable of obstructing climate action and offer possible ways to soften its hardness. [ABSTRACT FROM AUTHOR]

Urinary tract infections are among the most prevalent types of healthcare-associated infections (HAIs) in hospitals and nursing homes, and they are primarily a result of unnecessary catheter usage and inadequate care. In Portugal, epidemiological data indicate that catheter-associated urinary tract infections (CAUTIs) remain widespread in clinical settings, resulting in increased morbidity and mortality rates among vulnerable populations. This study aimed to assess urinary catheter use in an oncology ward in Portugal and to evaluate nurses' adherence to the government-endorsed standards for preventing CAUTIs. An observational study was conducted over a four-month period with daily assessments of nurses' practices during urinary catheter insertion and maintenance using a government-endorsed auditing tool. Data were collected through on-site observations and nurses' feedback. The findings revealed a urinary catheter utilization rate of 17.99%. However, there was a lack of complete adherence to government-endorsed standards among oncology nurses (0%). These results indicate that current practices lack evidence-based standardization. Therefore, there is a need to develop and implement quality improvement initiatives to enhance patient safety and experiences. [ABSTRACT FROM AUTHOR]

Pollination is a crucial reproductive process that underpins crop yield and quality as well as sustains other ecosystem services essential for our planet's life. Insects are the largest group of pollinators, particularly bees, handling the pollination of 71 of the 100 crops that contribute to 90% of the world's food supply. Nevertheless, both biotic and abiotic factors exert considerable influence on bee behaviour, which in turn affects the pollination process. Moreover, the alarming decline in bee populations and other essential insect pollinators presents a major challenge to natural pollination. This work focuses on Actinidia, a dioecious plant, i.e., with female and male flowers on separate plants, which introduces entropy into the pollination phase. In this plant, the number of pollinated seeds directly influences the size of Actinidia fruits (kiwi), so the success of the pollination phase is fundamental. However, natural pollination in Actinidia is mainly entomophilic, i.e., by insects. Hence, the exploration of alternative approaches becomes essential. To address this need, there has been a growing interest in robotic solutions for pollination, which include several tools to perform pollination. This research investigates the existing technologies for conducting artificial pollination procedures. It involves a comprehensive examination of various methods outlined in the literature, thoroughly analysing their strengths and weaknesses. The ultimate objective is to provide valuable insights and guidance to enhance the efficacy of artificial pollination processes [ABSTRACT FROM AUTHOR]

Predawn leaf water potential (Ψpd) is the main parameter to determine plant water status, and it has been broadly used to support irrigation management. However, the Scholander pressure chamber methodology is laborious, time-consuming and invasive. This study examined a low-cost hyperspectral proximal sensor to estimate the Ψpd in grapevine (Vitis vinifera L.). For this, both the Ψpd and spectral reflectance (340–850 nm) were accessed in grapevines in a commercial vineyard located in the Douro Wine Region, northeast Portugal. A machine-learning algorithm was tested and validated to assess grapevine's water status. The experiment was performed in a randomized design with 12 grapevines (cv. Touriga Nacional) per irrigation treatment: non-irrigated, 30% crop evapotranspiration (Etc), and 60% Etc. The dataset was analyzed using Principal Component Analysis (PCA), and the machine-learning regression algorithm applied was Extreme Gradient Boosting (Xgboost). Results from the validation dataset (n = 108) for the Xgboost tested exhibited a root mean square error (RMSE) of 0.23 MPa, a mean absolute error (MAPE) of 16.57% and an R² value of 0.95. These results demonstrate that the hyperspectral sensor and Xgboost algorithm show potential for predicting the Ψpd in vineyards, regardless of a plant's water status. [ABSTRACT FROM AUTHOR]

Sustainable and efficient agricultural production is a growing priority in modern society. Viticulture, an important agricultural and food sector, also faces this challenge. Precision Viticulture (PV) has gained prominence as it aims to foster high-quality, efficient, and environmentally sustainable practices. The Soluble Solids Content (SSC) is essential for assessing grape ripeness and quality in the winemaking process. Conventional methods for determining SSC values (expressed in °Brix) are invasive, expensive, and labour-intensive, necessitating sample preparation, making large-scale analysis impractical. In response to these limitations, this study presents an innovative approach within the field of Precision Viticulture. It focuses on the non-invasive prediction of SSC using low-cost proximal hyperspectral optical sensors. These sensors rely on spectral reflectance measurements in the range of 340–850 nm. This study was conducted in a commercial vineyard in the Demarcated Douro Region, Cima-Corgo sub-region, Portugal, over six weeks during ripening. In total, 169 grape berries from Touriga Nacional vines were analysed under three irrigation regimes (no irrigation, 30% ETc, and 60% ETc). After organising and preprocessing the data, machine learning algorithms, namely Partial Least Squares Regression (PLS), Random Forest (RF), and the Generalised Linear Model (GLM), were applied to predict SSC values. These models' performance was thoroughly evaluated using cross-validation techniques. The performance of different models was evaluated, showing significant differences according to the metrics used (R2, RMSE, and MAPE). The RF model demonstrated effectiveness and precision. A high R² value of 0.9312, coupled with low RMSE (0.9199 °Brix) and MAPE (3.88%), signifies a strong fit to the data and accurate predictive capabilities. The results of this benchmarking study on predictive models of SSC provide valuable insights into the performance of various models, aiding winegrowers and winemakers in decision making. [ABSTRACT FROM AUTHOR]

Plant-soil sensing devices coupled with Artificial Intelligence autonomously collect and process in situ plant phenotypic data. A challenge of this approach is the limited incorporation of phenotype data into decision support systems designed to harness agricultural practices and forecast plant behavior within the intricate context of genotype, environment, and management interactions (G × E × M). To enhance the role of digital phenotyping in supporting Precision Agriculture, this paper proposes a sensing network based on the Internet of Things. The developed system comprises three modules: data collection, communication, and a cloud server. Several processes co-occur in the server, namely data visualization to confirm the correct sensors and data stream functioning. In addition, a crop growth model (CGM) runs on the server, which is powered by the collected data. The simulations generated by the model will support agricultural decisions, obtaining, in advance, insights about plant behavior considering several G × E × M scenarios. To assess the performance of the proposed network to provide reliable data to the model, a greenhouse was equipped with several sensors that collect plant, environment, and soil data (e.g., leaf numbers, air temperature, soil moisture). The proposed network can provide real-time causal support for advanced agricultural practices, evolving from a data-driven approach to an integrative framework where context (G × E × M) drives decision making. [ABSTRACT FROM AUTHOR]

With the rising impact of climate change on agriculture, insect-borne diseases are proliferating. There is a need to monitor the appearance of new vectors to take preventive actions that allow us to reduce the use of chemical pesticides and treatment costs. Thus, agriculture requires advanced monitoring tools for early pest and disease detection. This work presents a new concept design for a scalable, interoperable and cost-effective smart trap that can digitize daily images of crop-damaging insects and send them to the cloud server. However, this procedure can consume approximately twenty megabytes of data per day, which can increase the network infrastructure costs and requires a large bandwidth. Thus, a two-stage system is also proposed to locally detect and count insects. In the first stage, a lightweight approach based on the SVM model and a visual descriptor is used to classify and detect all regions of interest (ROIs) in the images that contain the insects. Instead of the full image, only the ROIs are then sent to a second stage in the pest monitoring system, where they will be classified. This approach can reduce, by almost 99%, the amount of data sent to the cloud server. Additionally, the classifier will identify unclassified insects in each ROI, which can be sent to the cloud for further training. This approach reduces the internet bandwidth usage and helps to identify unclassified insects and new threats. In addition, the classifier can be trained with supervised data on the cloud and then sent to each smart trap. The proposed approach is a promising new method for early pest and disease detection. [ABSTRACT FROM AUTHOR]

Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. Traditionally, this task is carried out through manual and destructive sampling of production components and their accurate assessment is expensive, time-consuming, and error-prone, resulting in erroneous projections. The number of inflorescences and flowers per vine is one of the main components and serves as an early predictor. The adoption of new non-invasive technologies can automate this task and drive viticulture yield forecasting to higher levels of accuracy. In this study, different Single Stage Instance Segmentation models from the state-of-the-art You Only Look Once (YOLO) family, such as YOLOv5 and YOLOv8, were benchmarked on a dataset of RGB images for grapevine inflorescence detection and segmentation, with the aim of validating and subsequently implementing the solution for counting the number of inflorescences and flowers. All models obtained promising results, with the YOLOv8s and the YOLOv5s models standing out with an F1-Score of 95.1% and 97.7% for the detection and segmentation tasks, respectively. Moreover, the low inference times obtained demonstrate the models' ability to be deployed in real-time applications, allowing for non-destructive predictions in uncontrolled environments. [ABSTRACT FROM AUTHOR]

Background: The sanitary measures imposed by COVID-19 intensified challenges in the pain management of cancer patients. Methods: A descriptive study was conducted in a chronic pain unit of an oncological hospital aiming to explore the experiences of cancer patients with chronic pain and their caregivers during the pandemic period, as well as identify strategies to improve care in chronic pain management. An electronic questionnaire was developed containing sociodemographic variables, the Depression, Anxiety and Stress Scale-21, and open-ended questions exploring the experiences and circumstances of pain management. Results: A total of 30 patients and 13 caregivers filled in the questionnaire. Patients revealed a higher level of depression, anxiety, and stress than caregivers, without statistically significant differences. Both groups mentioned having experienced difficulties in self-care, particularly in relation to sleep, nutrition, and recreation. In total, 83.7% patients needed pain relief medication related to uncontrolled pain. Both mentioned that they would have benefited from a digital application to ease the communication with the healthcare professionals of the chronic pain unit, as well as non-pharmacological interventions, such as therapeutic massage. Conclusions: Recognizing that chronic pain leads to significant limitations, it is essential to implement and anticipate objective and effective responses in pain management. [ABSTRACT FROM AUTHOR]

In recent years, there has been a remarkable surge in the development and research of tethered aerial systems, thus reflecting a growing interest in their diverse applications. Long-term missions involving aerial vehicles present significant challenges due to the limitations of current battery solutions. Tethered vehicles can circumvent such restrictions by receiving their power from an element on the ground such as a ground station or a mobile terrestrial platform. Tethered Unmanned Aerial Vehicles (UAVs) can also be applied to load transportation achieved by a single or multiple UAVs. This paper presents a comprehensive systematic literature review, with a special focus on solutions published in the last five years (2017–2022). It emphasizes the key characteristics that are capable of grouping publications by application scope, propulsion method, energy transfer solution, perception sensors, and control techniques adopted. The search was performed in six different databases, thereby resulting in 1172 unique publications, from which 182 were considered for inclusion in the data extraction phase of this review. Among the various aircraft types, multirotors emerged as the most widely used category. We also identified significant variations in the application scope of tethered UAVs, thus leading to tailored approaches for each use case, such as the fixed-wing model being predominant in the wind generation application and the lighter-than-air aircraft in the meteorology field. Notably, the classical Proportional–Integral–Derivative (PID) control scheme emerged as the predominant control methodology across the surveyed publications. Regarding energy transfer techniques, most publications did not explicitly describe their approach. However, among those that did, high-voltage DC energy transfer emerged as the preferred solution. In summary, this systematic literature review provides valuable insights into the current state of tethered aerial systems, thereby showcasing their potential as a robust and sustainable alternative to address the challenges associated with long-duration aerial missions and load transportation. [ABSTRACT FROM AUTHOR]

In order to achieve a more efficient allocation of healthcare resources in the near future, it is crucial to understand the patterns and causes of excess mortality and hospitalizations. Neonatal mortality still poses a significant challenge, particularly in developed nations where the mortality rates are already low and healthcare resources are generally available to most of the population. Furthermore, the low mortality rates mean that the data available for modeling are often very limited, restricting the modeling methods that can be used. It is also important that the chosen methods allow for explainable, non-black-box models that can be interpreted by healthcare professionals. Considering these challenges, the work hereby presented thoroughly analyzed the time series of the neonatal mortality rates in Portugal between 2014 and 2019 in terms of trend and seasonal patterns. The applicability and performance of different data-based methods were also analyzed. Furthermore, the mortality rates were studied in terms of their relation to environmental variables, such as temperature and air pollution indicators, with the goal of establishing causal relations between such variables and excess mortality. The preliminary results show that ARMA, neural and fuzzzy models are able to forecast the studied mortality rates with good performance. In particular, neural models have the best predictive performance, while fuzzy models are well suited to obtain interpretable models with acceptable performance. [ABSTRACT FROM AUTHOR]

By-products from fruit industrialization retain nutritional and functional components; thus, they may find use in animal feeding. This study aimed to assess the effects of dietary fruit industrial by-products on the tilapias blood biochemical and oxidative parameters and on the composition and lipid peroxidation of their fillets. Four diets were supplied to the tilapias: a C-control diet, with no fruit meal, and three diets containing 5% of either acerola (ACM), apple (APM) or grape (GRM) meal. The phenolic compounds and the carotenoids in the meals and their antioxidant capacities were measured. Fish were weighed and measured for the calculation of the growth performance data, their blood was analyzed for health and oxidative status biomarkers and their fillets were analyzed for proximal composition and lipid peroxidation. Grape meal had the highest concentration of phenolics and carotenoids and the highest antioxidant activity, followed by acerola and apple meals. The productive performance was similar among the treatments. The fruit by-product diets either maintained or improved the biochemical biomarkers of health and improved the oxidative status of the fish. The fruit by-product diets increased the concentration of lipids in the fillets and slowed down the onset of the lipid peroxidation during frozen storage. [ABSTRACT FROM AUTHOR]

Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it's possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies. [ABSTRACT FROM AUTHOR]

Chagas disease (CD) is a neglected illness affecting approximately seven million individuals, with vector transmission occurring via triatomine bugs. The Rhodniini tribe comprises 24 species, grouped into the Rhodnius and Psammolestes genera. Given the importance of accurately identifying CD vectors, the taxonomy of Psammolestes spp. was revisited using morphological and morphometric data. Specimens of P. tertius, P. coreodes, and P. arthuri were collected, and the morphological characteristics of the head, thorax, abdomen, and eggs were analyzed. Morphometric studies of eggs were also conducted. Dichotomous keys allowing for the differentiation of Psammolestes spp. were elaborated based on adult insect and egg morphological characteristics. Through these studies, it was possible to differentiate the three Psammolestes species and confirm that this genus should not be classified under the Rhodnius genus, contributing to Rhodniini taxonomy. [ABSTRACT FROM AUTHOR]

Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command. [ABSTRACT FROM AUTHOR]

Microalgal biomass represents a very interesting biological feedstock to be converted into several high-value products in a biorefinery approach. In this study, the cyanobacterium Synechocystis sp. PCC6803 was used to obtain different classes of molecules: proteins, carotenoids and lipids by using a cascade approach. In particular, the protein extract showed a selective cytotoxicity towards cancer cells, whereas carotenoids were found to be active as antioxidants both in vitro and on a cell-based model. Finally, for the first time, lipids were recovered from Synechocystis biomass as the last class of molecules and were successfully used as an alternative substrate for the production of polyhydroxyalkanoate (PHA) by the native PHA producer Pseudomonas resinovorans. Taken together, our results lead to a significant increase in the valorization of Synechocystis sp. PCC6803 biomass, thus allowing a possible offsetting of the process costs. [ABSTRACT FROM AUTHOR]

Even though previous works showed molecular evidence of hemotropic Mycoplasma spp. (hemoplasmas) in ring-tailed coatis (Nasua nasua) from Brazil, Bartonella sp. has not been reported in these mammals so far. The present study aimed to detect the above-mentioned agents in coatis' blood and associated ectoparasites, assessing the association between these infections and red blood parameters. Between March 2018 and January 2019, coati (n = 97) blood samples, Amblyomma sp. ticks (2242 individual ticks, resulting in 265 pools), and Neotrichodectes pallidus louse (n = 59) were collected in forested urban areas from midwestern Brazil. DNA extracted from coatis' blood, and ectoparasite samples were submitted to quantitative PCR (qPCR) (16S rRNA) and conventional PCR (cPCR) (16S rRNA and 23S rRNA) for hemoplasmas and qPCR (nuoG gene) and culturing (only blood) for Bartonella spp. Two different hemoplasma genotypes were detected in blood samples: 71% coatis positive for myc1 and 17% positive for myc2. While 10% of ticks were positive for hemoplasmas (myc1), no louse was positive. The estimated bacterial load of hemoplasmas showed no association with anemia indicators. All coatis were negative for Bartonella sp. in qPCR assay and culturing, albeit two Amblyomma sp. larvae pools, and 2 A. dubitatum nymph pools were positive in the qPCR. The present work showed a high occurrence of hemoplasmas, with two distinct hemoplasma genotypes, in coatis from forested urban areas in midwestern Brazil. [ABSTRACT FROM AUTHOR]

The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%. [ABSTRACT FROM AUTHOR]

For structural design purposes, human-induced loads on pedestrian systems can be described by several simplified (i.e., deterministic equivalent-force models) or more complex computational approaches. Among others, the Spring-Mass-Damper (SMD), Single Degree of Freedom (SDOF) model has been elaborated by several researchers to describe single pedestrians (or groups) in the form of equivalent body mass m, spring stiffness k and damping coefficient c. For all these literature SMD formulations, it is proved that the biodynamic features of walking pedestrians can be realistically reproduced, with high computational efficiency for vibration serviceability assessment of those pedestrian systems mostly sensitive to human-induced loads (i.e., with vibration frequency f1 < 8 Hz). Besides, the same SMD proposals are characterized by mostly different theoretical and experimental assumptions for calibration. On the practical side, strongly different SMD input parameters can thus be obtained for a given pedestrian. This paper focuses on a selection of literature on SMD models, especially on their dynamic effects on different structural floor systems. Four different floors are explored (F#1 and F#2 made of concrete, F#3 and F#4 of glass), with high- or low-frequency, and/or high- (>1/130th) or low- (1/4th) mass ratio, compared to the occupant. Normal walking scenarios with frequency in the range fp = 1.5–2 Hz are taken into account for a total of 100 dynamic simulations. The quantitative comparison of typical structural performance indicators for vibration serviceability assessment (i.e., acceleration peak, RMS, CREST) shows significant sensitivity to input SMD assumptions. Most importantly, the sensitivity of structural behaviours is observed for low-frequency systems, as expected, but also for low-mass structures, which (as in the case of glazed floor solutions) can be characterized by the use of lightweight modular units with relatively high vibration frequency. As such, major attention can be required for their vibrational analysis and assessment. [ABSTRACT FROM AUTHOR]

Surfactants are among the main chemical contaminants in greywater (GW) and can cause severe health issues in humans and aquatic organisms. We assessed the performance of a multistage constructed wetland system (EvaTAC) for GW treatment and capacity of the microbial community in linear alkyl benzene sulfonate (LAS) biodegradation. Physicochemical analyses were performed over 497 d, and biomass samples were collected for high-throughput DNA sequencing. The system was predominated by anaerobic conditions and received an average chemical oxygen demand (COD) and LAS of 374 and 32 mg·L−1, with removal rates of 66% and 43%, respectively. A positive correlation between COD and LAS suggested COD as a design parameter for LAS removal. We identified microbial genera participating in hydrolysis, fermentation, syntrophy, acetogenesis, methanogenesis, surfactant degradation, and sulphate reduction. Among the 15 surfactant-degrading genera, Pseudomonas was predominant. Community richness and diversity indices were comparable between subsystems, with a slight decrease in diversity observed towards the outlet. Among the LAS degraders, Rhodopseudomonas palustris had the highest relative abundance of operational taxonomic unit (OTU)s in all samples and the highest richness in the anaerobic chamber. The patterns in microbial community composition and environmental conditions suggest that LAS biodegradation occurred throughout the EvaTAC system. [ABSTRACT FROM AUTHOR]

The efficiency of agricultural practices depends on the timing of their execution. Environmental conditions, such as rainfall, and crop-related traits, such as plant phenology, determine the success of practices such as irrigation. Moreover, plant phenology, the seasonal timing of biological events (e.g., cotyledon emergence), is strongly influenced by genetic, environmental, and management conditions. Therefore, assessing the timing the of crops' phenological events and their spatiotemporal variability can improve decision making, allowing the thorough planning and timely execution of agricultural operations. Conventional techniques for crop phenology monitoring, such as field observations, can be prone to error, labour-intensive, and inefficient, particularly for crops with rapid growth and not very defined phenophases, such as vegetable crops. Thus, developing an accurate phenology monitoring system for vegetable crops is an important step towards sustainable practices. This paper evaluates the ability of computer vision (CV) techniques coupled with deep learning (DL) (CV_DL) as tools for the dynamic phenological classification of multiple vegetable crops at the subfield level, i.e., within the plot. Three DL models from the Single Shot Multibox Detector (SSD) architecture (SSD Inception v2, SSD MobileNet v2, and SSD ResNet 50) and one from You Only Look Once (YOLO) architecture (YOLO v4) were benchmarked through a custom dataset containing images of eight vegetable crops between emergence and harvest. The proposed benchmark includes the individual pairing of each model with the images of each crop. On average, YOLO v4 performed better than the SSD models, reaching an F1-Score of 85.5%, a mean average precision of 79.9%, and a balanced accuracy of 87.0%. In addition, YOLO v4 was tested with all available data approaching a real mixed cropping system. Hence, the same model can classify multiple vegetable crops across the growing season, allowing the accurate mapping of phenological dynamics. This study is the first to evaluate the potential of CV_DL for vegetable crops' phenological research, a pivotal step towards automating decision support systems for precision horticulture. [ABSTRACT FROM AUTHOR]

Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations. [ABSTRACT FROM AUTHOR]

Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 × 10 9 cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 × 10 9 cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer. [ABSTRACT FROM AUTHOR]

Kinetoplastids include species economically important in agriculture, livestock, and human health. We evaluated the richness of kinetoplastids that infect small mammals in patches of unflooded forests in the Pantanal biome, an area where we hypothesize that its diversity is higher than currently recognized. Hemocultures (HC) and Next Generation Sequencing (NGS) targeting the 18S rDNA gene were employed for the detection of kinetoplastids. We grouped the positive samples into pools for each small mammal species (Monodelphis domestica, Thylamys macrurus, Oecomys mamorae, Thrichomys fosteri, Clyomys laticeps, and Holochilus chacarius). Eight parasite species were identified: Leishmania amazonensis, L. infantum; Trypanosoma cascavelli (HC + NGS), T. cruzi, T. lainsoni, T. rangeli (HC + NGS), Trypanosoma sp. DID, and Neobodo sp. The use of a tool as sensitive as NGS has increased our awareness of the diversity of kinetoplastids, as well as their host range, with emphasis on the species O. mamorae (seven kinetoplastid species, excepting T. cascavelli in a pool of nine individuals) and T. macrurus (four kinetoplastid species in a single individual). Furthermore, L. infantum and L. amazonensis infections were described in small mammals from this region for the first time. These findings make it mandatory to revisit the kinetoplastids/host associations proposed so far. [ABSTRACT FROM AUTHOR]

Contrary to many international settings, there are no clinical guidelines for peripheral intravenous catheter (PIVC) insertion and maintenance in Portugal. We sought to derive an international consensus on a PIVC bundle that could guide Portuguese nurses' clinical decision-making in this scope. Methods: Two international vascular access specialist groups participated in an online Delphi panel. During the first round, specialists (n = 7) were sent a summary report from a previous observational study conducted in a surgical ward in Portugal. Based on the report findings, specialists were asked to provide five to eight PIVC insertion and maintenance interventions. Then, another set of specialists (n = 7) scored and revised the recommendations until a consensus was reached (≥70% agreement). The PIVC bundle was made available and discussed with the surgical ward's nurses. Results: After three rounds, a consensus was achieved for five evidence-informed interventions: (i) involve the person and assess the peripheral venous network; (ii) maintain an aseptic no-touch technique; (iii) ensure proper catheter dressing and fixation; (iv) perform catheter flush & lock; (v) test the peripheral venous catheter's functionality and performance at each shift. Conclusion: The final version of the PIVC bundle achieved consensus among international experts. Despite the positive feedback provided by the ward nurses, future studies are warranted to assess its effectiveness in standardizing PIVC care delivery and its potential implications for care outcomes in Portuguese clinical settings. [ABSTRACT FROM AUTHOR]

Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV–VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325–1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis. [ABSTRACT FROM AUTHOR]

Many authors suggest directional antennas to enhance the transmission performance of ZigBee networks. For line-of-sight propagation, directional antennas can extend the transmission range or reduce the transmit power. Directional antennas may also reduce interference between networks operating in the same frequency channel. However, these antennas may not perform similarly under non-line-of-sight propagation conditions. This work presents a study with ZigBee modules comparing the performance of a directional antenna with an omnidirectional one. The measurements were conducted on a university campus for different propagation outdoor environments. A deconvolution technique was applied to estimate the received signal as a function of the azimuth angle. The results demonstrated that the received power followed the gain difference between antennas only for paths with low attenuation. Considering the same Effective Isotropic Radiated Power (EIRP), the system with directional antennas started to lose packets at the same distance as the omnidirectional antennas. The directional antenna did not allow the increase in the link range compared to the omnidirectional antenna. The power consumption was also measured for different transmit power levels of the ZigBee radio. The study showed that the control circuits of directional antennas typically consume more power than omnidirectional antennas operating at a higher transmit power level. [ABSTRACT FROM AUTHOR]

Soil Moisture (SM) is one of the most critical factors for a crop's growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR's output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner. [ABSTRACT FROM AUTHOR]

Global greenhouse gas emissions have caused sea level rise (SLR) at a global and local level since the industrial revolution, mainly through thermal expansion and ice melting. Projections indicate that the acceleration of SLR will increase in the near future. This will affect coastal and deltaic populations worldwide, such as in Bangladesh, where almost half of the population resides in regions lower than 5 m above sea level. This study found three coastal tidal gauges and five deltaic gauge stations, which showed increases in SLR at greater rates than the regional and global averages. This research also used satellite altimetry data to analyze regional and global SLR averages in the recent past and the 21st century. There is a trend towards increasing sea level based on results from three tide gauge stations: Char Changa with 7.6 mm/yr, Hiron Point at 3.1 mm/yr from 1993 to 2019, and 14.5 mm/yr at Cox's Bazar from 1993 to 2011. Based on the linear trend from these time frames, it is projected that SLR in Char Changa will increase by 228 mm from 2020 to 2050, and by 608 mm by 2100, at Hiron Point by 93 mm in 2050 and 248 mm by 2100, and at Cox's Bazar by almost 435.7 mm by 2050, and more than 1162 mm by 2100. Based on an average from satellite altimeters, assuming a linear increase in SLR, the Bay of Bengal shows an increase of 0.4 mm compared to the global trend. Other river delta stations in the study area also show increasing SLR, specifically, at Kalaroa, Benarpota, Kaikhali, Tala Magura, and Elarchari. Kalaroa and Benarpota show the highest, with SLR of >40 mm/yr. It is also observed that increasing SLR trends are far higher than coastal tide gauges, indicating that physical processes in the delta region are affecting SLR, further contributing to either an increase in water volume/SLR or activating land subsidence. This is partly due to the subsidence of the delta as a result of natural and anthropomorphic effects, as well as an increase in Himalayan glacier melting due to global warming. This indicates that Bangladesh coastal areas will soon experience a far greater SLR than the rest of the Bay of Bengal or other global coastal areas. [ABSTRACT FROM AUTHOR]

Global greenhouse gas (GHG) emissions have continued to grow persistently since 1750. The United Nations Framework Convention on Climate Change (UNFCCC) entered into force in 1994 to stabilize GHG emissions. Since then, the increasingly harmful impacts of global climate change and repeated scientific warnings about future risks have not been enough to change the emissions trend and enforce policy actions. This paper synthesizes the climate change challenges and the insofar insufficient mitigation responses via an integrated literature review. The fossil industry, mainstream economic thinking, national rather than international interests, and political strive for short-term interests present key barriers to climate mitigation. A continuation of such trends is reflected in the Dice model, leading to a 3.5 °C temperature increase by 2100. Despite receiving the Nobel Prize for integrating climate change into long-run macroeconomic analysis via the Dice model, increases in global mean temperatures overshooting the 1.5 °C to 2 °C Paris targets imply an intensified disruption in the human–climate system. Past and present policy delays and climate disruption pave the way for solar radiation management (SRM) geoengineering solutions with largely unknown and potentially dangerous side effects. This paper argues against SRM geoengineering and evaluates critical mitigation solutions leading to a decrease in global temperatures without overshooting the Paris targets. The essential drivers and barriers are discussed through a unified approach to tipping points in the human–climate system. The scientific literature presents many economically and technologically viable solutions and the policy and measures required to implement them. The present paper identifies the main barriers to integrating them in a globally cooperative way, presenting an efficient, long-term, and ethical policy approach to climate change. [ABSTRACT FROM AUTHOR]

Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants' phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches' accuracy. Two applications were developed to evaluate VineInspector's consistency while a viticulturist' assistant in everyday practices. One was intended to determine the size of the very first grapevines' shoots, one of the required parameters of the well known 3–10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard's phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases. [ABSTRACT FROM AUTHOR]