Search Results (269)

Droplet manipulation constitutes a fundamental operation in numerous bio-microfluidic applications, including but not limited to medical diagnostics and targeted drug delivery. Among the various technologies developed for this purpose, magnetic digital microfluidics (MDMF) has emerged as a compelling approach due to its inherent advantages of contamination-free actuation, low cost, and configurational flexibility. Nevertheless, conventional MDMF remains constrained by its reliance on bulky instrumentation and substantial power consumption for generating controllable magnetic fields, which limit its in-field applications. To address these limitations, this work presents a programmable and portable electromagnetic microfluidic droplet manipulation platform that synergistically integrates static and dynamic magnetic fields to enable non-contact, high-precision droplet control under ultra-low power conditions. The proposed system comprises an electromagnetic actuation module, a permanent magnet, and a glass substrate coated with Teflon film. The entire system is secured by a PMMA support structure, within which a glass substrate is mounted and spatially separated from the permanent magnet. The PMMA support is fabricated using a milling process, offering a simple manufacturing procedure and high structural reusability and reproducibility. The control logic is implemented on a field-programmable gate array (FPGA) development board, facilitating fully autonomous operation powered by a standard battery. The platform operates at a low voltage of 3.5 V and a driving current of 180 mA, corresponding to a total power consumption of merely 0.63 W, while achieving robust manipulation of droplets in the volume range of 0.5 to 5 μL. A maximum average droplet velocity of up to 0.6 cm/s was attained under optimal conditions. The proposed platform offers a scalable and energy-efficient solution for portable droplet-based assays and holds significant promise for integration into point-of-care diagnostic tools and field-ready biochemical analysis systems. The platform demonstrates excellent operational stability and reproducibility, as validated by repeated actuation experiments with a positioning deviation of approximately 0.1 mm under optimized conditions. The fabrication process also exhibits high reliability with consistent performance across multiple experimental runs. Full article

Cellular automata-based wildfire simulation models are widely used to support fire management, risk assessment, and operational decision-making, due to their efficiency and computational advantages. However, the accuracy of these models heavily depends on the quality of input data provided by the user, including the composition and geospatial extend of forest fuels, current meteorological conditions and terrain information. This publication examines how quantitative and spatial input data uncertainties affect the estimates of the impacted areas. Using a series of simulation experiments, inaccurate data are introduced to specific input variables (such as the vegetation type and the fuel moisture content) to reflect realistic levels of uncertainty commonly observed in operational scenarios, where users with different cognitive backgrounds fail to properly identify key characteristics of a fire. Model outputs are then compared using spatial and temporal performance metrics, including the rate of spread and burned area extent. The results demonstrate that uncertainties in fuel models and meteorological inputs exert a dominant influence on simulated fire behavior. Our findings highlight the sensitivity of wildfire simulations to compounded input uncertainties and stress the need for improved in-field data acquisition strategies. Full article

The infield transport of harvested sugarcane stalks (transshipment operation) during mechanized harvesting is widely recognized as the operation with the greatest potential to induce soil compaction. Nevertheless, there is still a lack of experimental data on the effect of compaction resulting from transshipment vehicles on soil physical functionality. We assessed the effects of realistic infield traffic from different transshipment configurations on soil structural and functional properties and their effects on crop yield. Three transshipment systems under controlled traffic farming system were evaluated: a tractor pulling one four-axle trailer unit with 21 Mg carrying capacity (1T/21), a tractor pulling two axle trailer units with 10 Mg carrying capacity (2T/10), and an autonomous truck with four axles and one trailer with 20 Mg carrying capacity (1TT/20). Several analyses were conducted, including degree of compaction (DC), macroporosity (MaP), air-filled porosity (ε_a10), soil air permeability (k_a10), and saturated hydraulic conductivity (Ks). Soil samplings were performed in surface and subsurface layers of an Oxisol in southeastern Brazil at the planting row and inter-row, and at the midpoint between these positions, over two consecutive sugarcane harvests. Although machine traffic occurred at low soil water content, all transshipment configurations promoted soil compaction during the first harvest, with the greatest changes in soil physical attributes in the 0–10 and 10–20 cm layers in the inter-row center and, in some cases, at the midpoint. However, all treatments preserved soil conditions in the planting row. The 1TT/20 transshipment induced the greatest compaction, with significant effects on DC, MaP, and ε_a10 in the inter-row and midpoint positions. Despite structural alterations, no significant differences were observed among treatments for k_a10 and Ks. However, after the first harvest, k_a10 frequently reached critical thresholds of low permeability in trafficked areas, indicating functional degradation of soil aeration. Sugarcane yield was not affected by the transshipment configurations. The absence of productivity differences reflects the effectiveness of controlled traffic in confining compaction to the inter-row center and midpoint while preserving the planting row. Although short-term yield was not affected, structural degradation in trafficked areas and the persistence of high subsoil compaction indicate the potential for cumulative long-term impacts. Continuous monitoring and integrated soil management strategies remain essential to mitigate progressive compaction under mechanized sugarcane harvesting. Full article

Millions of people across rural and peri-urban regions worldwide remain exposed to unsafe concentrations of naturally occurring fluoride in groundwater. In West Bengal, India, community-level water purification plants (CWPPs) have been widely installed to remove excess fluoride, yet their long-term operational performance remains minimally documented. This study assessed the pre-filter and post-filter water quality of 58 such groundwater-based CWPPs across the fluoride-affected districts of Bankura and Purulia in West Bengal, to evaluate in-field fluoride removal performance and potential hydrogeochemical, operational, and management drivers. Evaluation included fluoride concentration and key physicochemical parameters such as pH, temperature, electrical conductivity (EC), oxidation-reduction potential (ORP), total dissolved solids (TDS), and other anions including bromide, chloride, bicarbonate, nitrite, nitrate, phosphate, and sulphate. Fluoride concentration ranged from 1.7 mg/L to 8.2 mg/L and 1.6 mg/L to 3.9 mg/L in the sampled source water of Bankura and Purulia respectively, with both pre- and post-filter water of all the observed treatment units exceeding the WHO guideline of 1.5 mg/L. Potential contributors to underperformance may include inappropriate filter media selection, insufficient backwashing and regeneration, limited operational oversight and/or non-tailored treatment approaches. However, details on the adsorbent media and operational details were not available, and thus findings reflect observed field performance rather than necessarily causal relationships. These operational insights will contribute to the global discussion on improving decentralized groundwater treatment systems in resource-constrained settings. Full article

Background: Research on vibrations induced by running has gained significant attention due to its implications for athletes’ performance, injury prevention, and overall well-being. Distance running exposes the body to repetitive impulsive forces, causing significant vibrations to travel through physiological systems and biomechanical structures. These vibrations increase fatigue and the risk of injury. Although it has gained importance, research on induced vibration during running and wearable equipment for monitoring is scarce. This study aims to evaluate the performance of a measurement system for monitoring the acceleration levels of induced vibrations during long-distance running, exploring the capability of non-invasive wearable devices to characterise vibration transmissibility and exposure. Moreover, a preliminary quantitative assessment of induced vibration levels for an indoor testing scenario is given. Methods: Metrological characterisation of Xsens Motion Trackers Awinda (MTw), off-the-shelf inertial magnetic motion trackers, was performed by measuring the sensors’ frequency bandwidth in a controlled environment, providing logarithmic sweep sine excitations at different levels (2 g, 5 g, 7 g, where g is meant to be the gravitational acceleration). A testing protocol for indoor testing was derived from the literature, allowing characterisation of the sensors’ behaviour in terms of vibration transmissibility and exposure detection in the intended application. Time domain and frequency domain analyses were conducted by following the ISO 2631 standard guideline for vibration exposure assessment, and measurement uncertainty was defined, either for the dynamic correction of the sensors’ frequency behaviour or for the computed time and frequency domain metrics. In this framework, a treadmill-based test was conducted. The aim was to evaluate the Xsens sensors’ performance in measuring vibration dose exposure and transmissibility. Three MTws were placed on the subject’s right tibia, back, and forehead using elastic bands. A 25-year-old female amateur runner completed a series of tests consisting of walking for 1 min at 3.5 km/h (instrumentation setup), followed by running at two speeds (8 km/h and 11 km/h) for 2–4 min per trial, with 5 min rest periods between tests. Conclusions: The tested measurement system showed promising results due to its capability to assess vibration exposure during sports activities, but dynamic correction was found to be mandatory for accurate vibration level assessment. The main outcome of this study is a method for characterising the accelerometers embedded in the proposed devices, along with an analysis strategy for future testing campaigns. Thanks to the portability of IMUs (inertial measurement units), this approach enables the evaluation of induced vibrations during in-field running measurements. Full article

Estimating nitrogen (N) and the corresponding crude protein (CP) content in forage crops is essential for optimizing fertilization and livestock nutrition. However, standard methods such as the Dumas and Kjeldahl techniques are destructive, costly, and impractical for field use in certain regions of developing countries. This study evaluated four non-destructive approaches—morphometric measurements, Pantone^® color scales, smartphone-based RGB analysis (ColorDetector app), and SPAD chlorophyll readings—for predicting N and CP in Megathyrsus maximus (Mombasa grass). A total of 120 samples were collected under three nitrogen fertilization levels and assessed using linear mixed-effects models with cross-validation. Morphometric variables showed poor performance (R² < 0.01), indicating low correlation with nutrient content. Pantone-based RGB models provided slightly better predictions (R² ≈ 0.30) but were limited by subjectivity and discrete data. SPAD-based models demonstrated moderate predictive accuracy (R² ≈ 0.53; RMSE ≈ 0.46%). The highest accuracy was achieved with smartphone-derived RGB data, where full RGB models reached R² = 0.60 and RMSE = 0.45%. Based on these results, a practical green color scale was developed from RGB values to support real-time, in-field nitrogen and crude protein assessment. This study highlights smartphone imaging as a scalable, low-cost, and accurate tool for non-destructive estimation of nitrogen and crude protein in tropical forages, offering an accessible alternative to laboratory methods for producers and field technicians. Full article

Surface-Enhanced Raman Spectroscopy (SERS) is highly attractive as an analytical technique owing to its high sensitivity, distinctive molecular specificity, and speed of analysis. It offers the potential to match the sensitivity and molecular specificity of established techniques like Gas Chromatography–Mass Spectrometry in a more affordable, faster, and portable format, providing unique solutions for challenging analytical problems such as bedside diagnostics and in-field forensic analysis. Despite these benefits, SERS currently remains a specialized technique and has not yet successfully entered the mainstream of analytical chemistry. This transition is hindered primarily by challenges in achieving robust, reliable, and especially quantitative measurements in real-world applications. Achieving quantitative SERS requires addressing core issues arising from the heterogeneous nature of enhancing substrates and the complexity of real-life samples. This perspective summarizes the fundamental challenges associated with signal variability and matrix interference. It then details modern strategies focused on standardizing performance metrics, with particular emphasis on the newly proposed SERS Performance Factor for substrate evaluation, alongside the development of advanced quantification methods (e.g., internal standardization and digital SERS) and rapid sample pretreatment protocols. Finally, emerging prospects, including the deployment of Artificial Intelligence for enhanced analysis and advancements in deep-tissue SERS sensing, are explored as critical drivers for integrating SERS into routine analytical practice. Full article

This paper presents the rationale and design of a study of growth and development in young track and field athletes: the EXcellence and PERformance in Track and field (EXPERT) study, and details the methodologies used. Background: Longitudinal research examining individual-environment interactions in youth athletic development is scarce for track and field. Objectives: The EXPERT study investigates how individual (anthropometry, maturation, motivation) and environmental (family, coach, club) characteristics influence developmental trajectories in youth track and field athletes. Methods: A mixed-longitudinal design will follow 400 athletes (200♂, 200♀; aged 10–14 years) from 40 Portuguese clubs across five cohorts assessed biannually over three years. Guided by Bronfenbrenner’s bioecological model, assessments encompass individual, performance, health, and environmental domains. Data quality control will consist of rigorous training of all research team members, implementation of standardized protocols, a pilot study, and an in-field reliability study. Multilevel growth models will examine trajectories and predictor effects of predictors. Conclusions: EXPERT will provide evidence to optimize training and support holistic youth athlete development. Full article

Unobserved fruit crop illnesses are a major threat to agricultural productivity worldwide and frequently cause farmers to suffer large financial losses. Manual field inspection-based disease detection techniques are time-consuming, unreliable, and unsuitable for extensive monitoring. Deep learning approaches, in particular convolutional neural networks, have shown promise for automated plant disease identification, although they still face significant obstacles. These include poor generalization across complicated visual backdrops, limited resilience to different illness sizes, and high processing needs that make deployment on resource-constrained edge devices difficult. We suggest a Hybrid Multi-Scale Neural Network (HMCT-AF with GSAF) architecture for precise and effective fruit crop disease identification in order to overcome these drawbacks. In order to extract long-range dependencies, HMCT-AF with GSAF combines a Vision Transformer-based structural branch with multi-scale convolutional branches to capture both high-level contextual patterns and fine-grained local information. These disparate features are adaptively combined using a novel HMCT-AF with a GSAF module, which enhances model interpretability and classification performance. We conduct evaluations on both PlantVillage (controlled environment) and CLD (real-world in-field conditions), observing consistent performance gains that indicate strong resilience to natural lighting variations and background complexity. With an accuracy of up to 93.79%, HMCT-AF with GSAF outperforms vanilla Transformer models, EfficientNet, and traditional CNNs. These findings demonstrate how well the model captures scale-variant disease symptoms and how it may be used in real-time agricultural applications using hardware that is compatible with the edge. According to our research, HMCT-AF with GSAF presents a viable basis for intelligent, scalable plant disease monitoring systems in contemporary precision farming. Full article

High-quality soybean seeds possess genetic, physical, and physiological characteristics that directly influence crop yield. The use of hyperspectral sensors combined with machine learning (ML) can streamline and accelerate seed germination testing. Therefore, the objectives of this study were: (i) to evaluate whether leaf and seed reflectance can effectively predict the physiological quality of soybean seeds using ML algorithms, and (ii) to identify which algorithm provides the highest prediction accuracy. Thirty-two soybean genotypes were evaluated in a controlled experiment. Leaves and seeds were analyzed using a hyperspectral sensor capable of measuring reflectance across the 350 to 2500 nm range. The resulting data were subjected to ML analysis with two types of input: spectral variables from leaves and seeds. The output variables predicted included germination test (GERM), electrical conductivity (EC), first germination count (FGC), vigorous tetrazolium test (VIG-TZ), and viable tetrazolium test (VIAB). Predictions were performed using stratified 10-fold cross-validation with ten repetitions (100 runs per model). All model parameters were set to the default configuration in Weka version 3.8.5. The ML models used for prediction included artificial neural networks (ANN), REPTree and M5P decision trees, random forest (RF), support vector machine (SVM), and ZeroR, with the latter serving as a control algorithm. The models showed consistent performance in predicting physiological variations in seeds, with better results when seed reflectance was used as input. For germination (GERM), the M5P, RF, and SVM algorithms obtained the highest correlations (r = 0.565–0.575). In predicting electrical conductivity (EC), M5P showed greater accuracy with leaf data (r = 0.506), while SVM performed best with seed data (r = 0.658). For first germination count (CPG), M5P was the most accurate with leaf data (r = 0.720), while M5P, RF, and SVM showed r between approximately 0.735 and 0.777 with seed data. In tetrazolium vigor (TZVG), RF showed the best performance (MAE 0.25), again highlighting seed reflection, which resulted in the lowest errors and highest correlations. Overall, the M5P, RF, and SVM algorithms achieved the most robust results, especially when used with seed spectral data. The highest germination prediction accuracy was achieved by the M5P, SVM, and RF algorithms for both input types. Seed reflectance yielded the best accuracy and the lowest MAE and RMSE values. Leaf reflectance also enabled accurate predictions, indicating that this input can serve as an early, in-field strategy for predicting soybean seed physiological quality. Full article

The deparser stage in the Protocol-Independent Switch Architecture (PISA) is often overshadowed by parser and match-action optimizations. Yet, it remains a critical performance bottleneck in P4-programmable FPGA data planes. Challenges associated with the deparser stem from dynamic header layouts, variable emission orders, and alignment constraints, which often necessitate resource-intensive designs, such as wide, dynamic crossbar routing. While compile-time specialization techniques can reduce logic usage, they sacrifice runtime adaptability: any change to the protocol graph, including adding, removing, or reordering headers, requires full hardware resynthesis and re-implementation, limiting their practicality for evolving or multi-tenant workloads. This work presents a unified FPGA-targeted deparser architecture that merges templated and overlay concepts within a hardware–software co-design framework. At design time, template parameters define upper bounds on protocol complexity, enabling resource-efficient synthesis tailored to specific workloads. Within these bounds, runtime reconfiguration is supported through overlay control tables derived from static deparser DAG analysis, which capture the per-path emission order, header alignments, and offsets. These tables drive protocol-agnostic, chunk-based emission blocks that eliminate the overhead of crossbar interconnects, thereby significantly reducing complexity and resource usage. The proposed design sustains high throughput while preserving the flexibility needed for in-field updates and long-term protocol evolution. Full article

This study aimed to develop a deep learning-based application for the automatic detection of nutritional deficiencies in coffee plants through the analysis of in-field leaf images. Images were collected from farms in the Shipasbamba district and classified into six deficiency types: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and iron (Fe). A total of 2643 leaves were labeled and preprocessed for model training. Several YOLO architectures were evaluated, with YOLO11x achieving the best performance after 100 epochs, reaching a precision of 88.98%, recall of 88.54%, F1-Score of 88.76%, and mAP50 of 92.68%. An interactive web application was developed to allow real-time image upload and processing, providing both graphical and textual feedback on detected deficiencies. These results demonstrate the model’s effectiveness for automated diagnosis and its potential to support coffee growers in timely, data-driven decision-making, ultimately improving nutrient management and reducing production losses. Full article

Cashew apples, a byproduct of the cashew nut industry with an estimated global production of 38 million tonnes, are rich in several essential nutrients and are widely processed into juice, syrup, wine, pickles, and other value-added products. However, their morphological and physicochemical properties vary significantly across varieties, complicating in-field characterization, maturity assessment, and biochemical analysis. These challenges originate from the reliance on costly chemicals, skilled manpower, limited time, and sophisticated equipment. This study employed a user-developed computer vision-based ImageJ 1.x batch processing plugin to assess 15 physicochemical properties across six diverse cashew apple varieties from the images of slices and whole samples. Five methodologies—color grid, surface morphology, gray level co-occurrence matrix, local binary pattern, and color indices—generated image-based metrics rapidly ($2.87\pm 0.79$ s/image). The correlation of wet chemistry with image-based parameters, linear modeling, and wet chemistry parameters prediction with an independent dataset were successfully performed, and the successfully modeled properties include acidity, antioxidants, carbohydrates, carotenoids, crude fat, flavonoids, pH, phenolics, proteins, tannins, vitamin C, and total soluble solids. The results demonstrated the feasibility of predicting 11 out of 15 physicochemical properties of cashew apples ($R^2>0.5$). This methodology offers a faster, safer, and cost-effective alternative to wet chemistry and can be extended to other horticultural crops. Full article

Urban heritage, when enhanced by digital technologies, can become a living laboratory. This study explores the Art Nouveau Path, a mobile augmented reality game implemented in Aveiro, Portugal, as part of the EduCITY Digital Teaching and Learning Ecosystem. Designed as a circular path of eight georeferenced points of interest, it integrates narrative cartography, multimodal media, and sustainability competences framed by GreenComp, the European Sustainability Framework. A DBR approach guided the study, combining four interconnected datasets: the game’s structured curriculum review by 3 subject specialists (T1-R), gameplay logs from 118 student groups (4248 responses), post-game reflections from 439 students (S2-POST), and in-field observations from 24 teachers (T2-OBS). Descriptive statistics and thematic coding were triangulated to examine attention to architectural details, the mediational role of AR, spatial trajectories, and reflections about sustainability. The results present overall accuracy (85.33%), with particularly strong performance on video items (93.64%), stable outcomes on AR tasks (85.52%), and lower accuracy in denser urban contexts. Qualitative data highlight AR as a catalyst for perceiving hidden features, collaboration, and connecting heritage with sustainability. The study concludes that location-based AR games can generate semantically enriched geoinformation. They also act as cartographic interfaces that embed narrative and competence-oriented learning into urban heritage contexts. Full article

Climate change intensifies agricultural risks, requiring an integrated analysis of climatic, hydrological, and crop data to support resilient farming. Despite advances in remote sensing, in-field sensors, and artificial intelligence, fragmented data silos hinder spatiotemporal risk assessments by requiring labor-intensive data handling. We present agriclima, a federated, cloud-native, FAIR-by-design platform that unifies heterogeneous agricultural and environmental datasets under consistent identity, policy, and metadata governance. Its scalable open-source architecture, compliance with INSPIRE and RNDT standards, and privacy-preserving access enable researchers and decision-makers to perform comprehensive analyses with minimal coding, accelerating data-driven agricultural risk management. Developed and tested in a research project by a consortium of stakeholders in agricultural risk management, the platform was evaluated via: (1) FAIR assessment of 26 datasets using F-UJI, (2) system performance monitoring on Kubernetes, and (3) a demonstrative spatiotemporal aggregation use case. It achieved 80% average FAIR compliance, with perfect accessibility (7.00/7.00), while findability and reusability remain key areas for improvement. Performance showed stable operation (CPU 17.24%, memory 49.89%) with capacity headroom. The demonstrative use case validated that researchers can conduct spatiotemporal analyses with minimal coding effort through the abstracted data access components. Beyond technical evaluation, we share lessons learned to guide future platform development and metadata standardization, highlighting the platform’s effectiveness as a foundation for data-driven agricultural decision-making. Full article