Search Results (926)

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article

Osmotic dehydration (OD) is an effective pre-treatment for fruit preservation, but conventional processes often lack precision due to manual control of critical variables. This work reports the design and validation of an automated OD system integrating a programmable logic controller (PLC), human–machine interface (HMI), and IoT-enabled sensors for real-time monitoring of syrup concentration and process temperature. Mango (Mangifera indica) cubes were treated under a 2³ factorial design with sucrose concentrations of 45 and 50 °Brix, immersion times of 120 and 180 min, and temperatures of 30 and 40 °C. Validation demonstrated that the IoT hydrometer achieved strong agreement with reference devices (R² = 0.985, RMSE = 0.36 °Brix), while the PLC-integrated tank sensor also demonstrate improved performance over existing calibrated thermometer (R² = 0.992, MAE = 0.20 °C). ANOVA indicated that concentration, temperature, and time significantly affected water loss and weight reduction (p < 0.01), with temperature being the dominant factor. Water loss ranged from 18.62% to 39.15% and weight reduction from 9.48% to 34.47%, while maximum solid gain reached 9.31% at 50 °Brix and 40 °C for 180 min, with stabilization consistent with case hardening. Drying kinetics were best described by the Page model (R² > 0.97). The findings highlight the effectiveness of the system for precise monitoring and optimization of OD processes. Full article

Accurate estimation of aboveground biomass (AGB) is essential for monitoring forage availability and guiding sustainable management in high-altitude pastures, where grazing sustains livelihoods but also drives ecological degradation. Although remote sensing has advanced biomass modeling in rangelands, applications in Andean–Amazonian ecosystems remain limited, particularly using UAV-based structural and spectral data. This study evaluated the potential of UAV LiDAR and multispectral imagery to estimate fresh and dry AGB in ryegrass (Lolium multiflorum Lam.) pastures of Amazonas, Peru. Field data were collected from subplots within 13 plots across two sites (Atuen and Molinopampa) and modeled using Random Forest (RF), Support Vector Machines, and Elastic Net. AGB maps were generated at 0.2 m and 1 m resolutions. Results revealed clear site- and month-specific contrasts, with Atuen yielding higher AGB than Molinopampa, linked to differences in climate, topography, and grazing intensity. RF achieved the best accuracy, with chlorophyll-sensitive indices dominating fresh biomass estimation, while LiDAR-derived height metrics contributed more to dry biomass prediction. Predicted maps captured grazing-induced heterogeneity at fine scales, while aggregated products retained broader gradients. Overall, this study shows the feasibility of UAV-based multi-sensor integration for biomass monitoring and supports adaptive grazing strategies for sustainable management in Andean–Amazonian ecosystems. Full article

Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management. Full article

Gallium oxide-based devices are critical in various applications, including industrial safety, the gas and petroleum sectors, and research environments. However, the deep etching process has not been thoroughly explored. Key parameters such as etching rate, selectivity, uniformity, isotropic/anisotropic behavior, and surface properties all influence the effectiveness of the etching process and its reproducibility. This research was motivated by the need for efficient fabrication processes, particularly in applications where sensors must operate in harsh environments, due to their instead of owning to low leakage current density of their power devices. In this study, we studied a deep etching technique for Ga₂O₃, focusing on the chemical stability of the two planes and identifying suitable protocols that could enhance etching depth via a dry-etching process. A deep ion-etching process for Ga₂O₃ was successfully developed, achieving deep etches of 6.97 µm in the Ga₂O₃. These advancements pave the way for high-aspect-ratio Ga₂O₃ nanostructures, offering new possibilities for robust nanosensors in harsh environments. Full article

Real-time sweat pH monitoring offers a non-invasive window into metabolic status, disease progression, and wound healing. However, current wearable pH sensors struggle to balance high electrochemical sensitivity with mechanical compliance. Here we report a stretchable fiber-integrated pH electrode based on a polyaniline-phytic acid-polyvinyl alcohol (PANI-PA-PVA) hydrogel, which combines mechanical elasticity with enhanced electrochemical performance for continuous sweat sensing. Freeze–thaw crosslinking of the hydrogel forms a porous interpenetrating network, facilitating rapid proton transport and stable coupling with dry-spun elastic gold fibers. This wearable device exhibits an ultra-Nernstian sensitivity of 68.8 mV pH⁻¹, ultra-fast equilibrium (<10 s within the sweat-relevant acidic window), long-term drift of 0.0925 mV h⁻¹, and high mechanical tolerance (gel stretch recovery up to 165%). The sensor maintains consistent pH responses under bending and tensile strains, yielding sweat pH measurements at the skin surface during running that closely match commercial pH meters (sweat pH range measured in test subjects: 4.2–5.0). We further demonstrate real-time wireless readouts by integrating elastic gold and Ag/AgCl fibers into a three-electrode textile structure. This PANI-PA-PVA hydrogel strategy provides a scalable material platform for robust, high-performance wearable ion sensing and skin diagnostics. Full article

Tropical weather cities, such as Mérida in Yucatán, Mexico, are perceived as air pollution-free environments. This study aimed to evaluate the air quality in Mérida City over five years, focusing on PM_2.5 and PM₁₀ as well as spatial and temporal factors. A government-accredited monitoring station for PM_2.5 (2018–2022) and economic air sensors for PM_2.5 and PM₁₀ (2023) were used. Results showed the maximum daily (90 μg m⁻³) and annual PM_2.5 (23 μg m⁻³) averages for 2020 exceeded the Mexican regulations. Sensors indicated that the fixed pollution sources influenced PM_2.5 and PM₁₀. Spatially and temporally, the southwest of the city in the dry season of 2023 showed the highest PM_2.5 and PM₁₀. Tropical conditions (solar radiation and temperature) increased PM, while high humidity and precipitation decreased it. Air quality improved during the rainy season. The southwest zone had the highest density of diesel vehicles and fixed pollution sources, which contributed to the highest PM concentration. The monitoring showed that air quality related to PM in Mérida City is a concern. Local and external factors are affecting the air quality. It is mandatory to regulate air emissions from fixed sources and implement vehicle verification, even in tropical weather cities. Full article

To prevent or mitigate the negative impact of fires, spatial prediction maps of wildfires are created to identify susceptible locations and key factors that influence the occurrence of fires. This study uses artificial intelligence models, specifically machine learning (XGBoost) and deep learning (Kolmogorov-Arnold networks—KANs, and deep neural network—DNN), with data obtained from multi-sensor satellite imagery (MODIS, VIIRS, Sentinel-2, Landsat 8/9) for spatial modeling wildfires in Serbia (88,361 km²). Based on geographic information systems (GIS) and 199,598 wildfire samples, 16 quantitative variables (geomorphological, climatological, hydrological, vegetational, and anthropogenic) are presented, together with 3 synthesis maps and an integrated susceptibility map of the 3 applied models. The results show a varying percentage of Serbia’s very high vulnerability to wildfires (XGBoost = 11.5%; KAN = 14.8%; DNN = 15.2%; Ensemble = 12.7%). Among the applied models, the DNN achieved the highest predictive performance (Accuracy = 83.4%, ROC-AUC = 92.3%), followed by XGBoost and KANs, both of which also demonstrated strong predictive accuracy (ROC-AUC > 90%). These results confirm the robustness of deep and machine learning approaches for wildfire susceptibility mapping in Serbia. SHAP analysis determined that the most influential factors are elevation, air temperature, and humidity regime (precipitation, aridity, and series of consecutive dry/wet days). Full article

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

As the importance of sustainability and performance increases, new developments in the manufacturing of fiber-reinforced polymer composites (FRPC) are requested. Ultraviolet (UV) curing offers a faster, more economical, and eco-friendlier alternative to conventionally used thermal curing methods, e.g., autoclave curing, but according to extant research, also presents some shortcomings, such as limitations to thin FRPCs and transparent glass fibers (GFs). This study analyses the UV light transmission in different thermoset FRPCs by irradiating various fiber samples on one side, while a sensor on the opposite side measures the transmitted irradiance. The materials investigated include unidirectional (UD) carbon fibers (CF), UD flax fibers (FF), and six GF fabrics with different ply structures. The fiber samples are tested in a dry, non-impregnated state and a resin-impregnated state using a UV-curable vinyl-ester-based resin. The results show that up to 16 plies of five GF fabrics are fully cured within the 20 s irradiation time and still exhibit a relatively high light transmission, revealing the potential of curing thick FRPCs with UV light. Furthermore, up to three plies of non-transparent FFs are cured, which is promising for the UV curing of natural fibers. Full article

With the development of technology, many fields have put forward higher requirements for the thermal conductivity of materials in high-temperature environments, for instance, in fields such as heat dissipation of electronic devices, high-temperature sensors, and thermal management. As a potential high-performance thermal management material, studying the thermal conductivity of graphene at high temperatures is of great significance for expanding its application range. In this study, high-quality suspended graphene was prepared through PDMS dry transfer, which can effectively avoid the binding and influence of the substrate. Based on the calculation model of the thermal conductivity of suspended graphene, the model was modified accordingly by measuring the attenuation coefficient of laser power. Combined with the temperature variation coefficient of suspended graphene measured experimentally and the influence of laser power on the Raman characteristic peak positions of graphene, the thermal conductance of suspended graphene with different layers under high-temperature conditions was calculated. It is conducive to a further in-depth understanding of the phonon scattering mechanism and heat conduction process of graphene at high temperatures. Full article

Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable. Full article

Background/Objectives: Recently, new methods of monitoring sublimation flow during freeze-drying operations have been proposed. They are based either on measuring the difference between the temperature of the heat transfer liquid at the inlet and outlet of the shelves (ΔT) or the difference between the chamber pressure and the condenser pressure (ΔP). In this article, we briefly explain the two methods and review their main applications. Methods: Multiple pilot or commercial-scale freeze dryers were used. The inlet and outlet shelf temperature or the capacitance pressures of the chamber and condenser were measured. Results: ΔT and ΔP methods can be implemented in most recent freeze dryers to monitor the sublimation flow. Both methods provide very consistent results and are also comparable to Tunable Diode Laser Absorption System (TDLAS) measurements. The methods can be used for different purposes: calculating the heat transfer coefficient (Kv) distribution from the mass flow curve and estimating the average product temperature and the product temperature range. Furthermore, these methods can be used as a measure of success for transferring the process from the lab to the industrial scale, or from one plant to another, or demonstrating the shelf-to-shelf homogeneity. Finally, the ΔT method is able to detect the ice nucleation during the freezing step. Conclusions: The ΔT and ΔP methods are bringing a new, easy-to-implement, cost-effective, and versatile tool to the freeze-drying study toolbox. Full article

The interaction between equine hooves and various ground surfaces is a critical factor for injury prevention and performance in modern equestrian sports. Accurate measurement of surface grip is essential for evaluating the effectiveness of different hoof protection systems. This study introduces the Vienna Grip Tester (VGT), a novel sensor-based device developed to quantify rotational resistance—an important parameter for assessing hoof–surface interaction. The VGT utilizes a torque wrench and spring-loaded mechanism to simulate lateral hoof movements under a standardized vertical load (~700 N), enabling objective grip measurements across different conditions. Twenty combinations of hoof protection (barefoot, traditional iron shoe, and two glue-on models) and surfaces (sand, sand with fiber at 25 °C and −18 °C, frozen sand, and turf) were tested, yielding 305 torque measurements. Statistical analysis (repeated-measures ANOVA with Bonferroni correction) revealed significant differences in grip among surface types and hoof protection systems. Frozen surfaces (SDAF (31 ± 8.9 Nm and SDF 33 ± 8.7 Nm, p < 0.001) exhibited the highest grip, while dry sand (SDA (18.3 ± 3.3 Nm, p < 0.001) showed the lowest. Glue-on shoes (glue-on grip, 26 ± 10 Nm; glue-on, 25 ± 10 Nm) consistently provided superior grip compared to traditional or unshod hooves (bare hoof, 21 ± 7 Nm). These results validate the VGT as a reliable and practical tool for measuring hoof–surface grip, with potential applications in injury prevention, hoof protection development, and surface optimization in equestrian sports. Full article