Search Results (66,211)

Reference evapotranspiration (ET₀) represents the atmospheric demand for water from a well-watered vegetated surface and is a key component of the hydrological cycle and agricultural water management. This study evaluated the performance of seven machine learning (ML) models: linear regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), and Cubist, for predicting monthly FAO-56 Penman–Monteith ET₀ in Benin. The target variable was calculated from data collected at six synoptic stations over the 2017–2021 period. Ten remote-sensing and topographic predictors were used: MODIS Land Surface Temperature (LST), six Sentinel-2 optical vegetation indices (NDVI, EVI, NDMI, NDWI, MSI, NDRE), elevation, and cyclic month encoding. Models were trained on the 2017–2019 period and evaluated on an independent temporal test set (2020–2021). All models showed positive predictive performance, with the BMA ensemble achieving the highest accuracy (RMSE = 7.0% of mean ET₀, R² = 0.802), followed by Cubist (RMSE = 7.3%, R² = 0.787) and DT (RMSE = 7.5%, R² = 0.776). The seven models were combined via Bayesian Model Averaging (BMA) with posterior weights estimated by the EM algorithm to produce 1 km monthly ET₀ maps for Benin for 2025. BMA-derived inter-model standard deviation provided spatially explicit uncertainty estimates, revealing that prediction uncertainty is greatest in the northern Sudanian zone during the dry season. The ET₀ target variable was constructed as a hybrid product combining station temperature observations with solar radiation, wind speed, and vapor pressure deficit extracted from the TerraClimate gridded reanalysis dataset; this methodological choice is discussed as a study limitation. Full article

Soft robots built from compliant materials and deformable structures are increasingly used in medical intervention, wearable assistance, delicate manipulation, and environmental exploration, where conventional rigid robots are limited by high mechanical impedance and poor morphological adaptability. However, their transition from laboratory prototypes to deployable systems remains constrained by coupled bottlenecks in materials, actuation, sensing, modeling, control, energy supply, and manufacturing. This review summarizes recent advances in soft robotics through an evaluative framework covering actuation and materials, modeling and simulation, control strategies, multimodal sensing, and representative applications. Instead of treating these topics as independent descriptions, we compare the underlying mechanisms, measurable performance indicators, strengths, limitations, and application boundaries. Three conclusions emerge. First, no single actuation strategy can simultaneously maximize output force, response speed, energy efficiency, durability, miniaturization, and untethered operation. Second, high-fidelity continuum models improve physical accuracy but remain difficult to use for real-time control, whereas reduced-order and data-driven models improve efficiency at the cost of generalization, interpretability, or contact fidelity. Third, practical soft robots will depend on system-level integration of embedded sensing, physics-informed learning, robust control, reliable materials, and scalable fabrication. Future progress should therefore prioritize standardized benchmarks, lifecycle reliability, energy-autonomous operation, and task-specific comparisons with rigid robotic systems. Full article

Accurately predicting human lane-changing (LC) decisions is critical for enhancing the safety and efficiency of autonomous driving. Most existing machine learning-based LC decision models rely on immediate neighboring vehicle interaction features, which may fail to capture drivers’ consideration of long-term traffic conditions in the target lane. Using discretionary LC trajectory data from the US101 dataset, this paper first qualitatively identifies key latent variables influencing LC decisions, then quantitatively ranks these factors using feature importance analysis, and finally constructs a prediction model based on ensemble learning. The analysis reveals that drivers consider not only neighboring vehicles but also multi-vehicle information further ahead, particularly the average speed and average spacing of multiple preceding vehicles. Feature importance ranking shows that safety-related features, especially the spacing with the following vehicle in the target lane ($d_{Lag}$, 0.187), rank significantly higher than benefit-related features such as the average speed of the target lane (${\overline{v}}_T$, 0.091), suggesting that safety considerations play a dominant role in the observed LC decisions. Among five imbalanced processing methods, SMOTE+Tomek achieves the best balance (F1 = 0.68). When the Full Feature Set is used, the KNN model achieves the best performance (F1 = 0.79, AUC = 0.97) among six baseline models. This study contributes to the understanding of LC behavior and provides insights that could inform future development of LC prediction models for autonomous vehicles. Full article

Reliable condition monitoring of wind turbine gearboxes is critical to reducing unplanned downtime and maintenance costs in wind farms. However, this task presents significant challenges due to the non-stationary nature of vibration signals, in which fault-relevant features are sparsely and unevenly distributed across the time–frequency map. Although time–frequency analysis has been widely adopted to represent nonlinear and non-stationary vibration signals, existing deep learning methods typically process the full spectrogram directly, without distinguishing redundant or uninformative regions. This leads to high input dimensionality and exposes the model to substantial spectral noise. Consequently, it increases computational burden and potentially reduces the diagnostic reliability. To address this issue, this paper proposes a two-stage hybrid framework based on complementary selection mechanisms operating on two distinct feature spaces. In the first stage, eXtreme Gradient Boosting (XGBoost) importance scores are used to identify and permanently prune uninformative time–frequency features from the input spectrogram, reducing the input map size by 25%. In the second stage, a Squeeze-and-Excitation (SE) block, inserted after the deepest residual layer, performs soft channel-wise recalibration of the abstract feature maps produced by the residual convolutional neural network (ResCNN), thereby amplifying discriminative representations prior to classification. The proposed method was evaluated in an eight-class variable-speed fault classification task using the MCC5-THU benchmark, where data were collected from a 2.2 kW motor-driven gearbox test rig. The proposed method achieves a mean accuracy of 97.81% ± 0.33% under 5-fold stratified cross-validation (CV), while reducing classifier training time by approximately 23% compared to a baseline model trained on the full spectrogram. These results demonstrate that explicit input-level spectrogram pruning, combined with model-level channel attention, yields a robust and computationally efficient diagnostic framework for wind turbine gearbox condition monitoring. Full article

Background/Objectives: Direct compression of cohesive powders is often challenged by poor flow, blend heterogeneity, and variable tablet quality. This study investigated how mixing time, fill level, and rotational speed affect the blending behavior and tablet properties of a sodium naproxen–calcium carbonate formulation and aimed to define a robust operating space for process optimization. Methods: Powder blends were prepared in a V-type mixer according to a central composite design and analyzed using response surface methodology. The effects of the three process parameters were evaluated through powder flow descriptors (angle of repose, angle of fall, and angle of difference) and tablet quality attributes, including thickness, mass, active pharmaceutical ingredient (API) content, and abrasiveness. Statistical significance was assessed by ANOVA, and a design space was established using predefined acceptance criteria. Results: Mixing time significantly affected the angle of difference, indicating changes in blend cohesiveness and flow uniformity, whereas fill level significantly influenced API content. Tablet thickness and mass remained relatively stable across the tested conditions. Abrasiveness showed the greatest numerical variability and tended to increase at high fill levels combined with short mixing times. Response surface analysis identified two acceptable operational regions that satisfied the quality criteria for blend homogeneity, API content, and abrasiveness. Conclusions: The studied process variables exerted selective, property-specific effects rather than uniform changes across all quality attributes. The results support QbD-based process design for cohesive direct-compression systems and show that robust tablet manufacture can be achieved within more than one operating window. Full article

Older primary schools in Croatia are frequently located in densely built older settlement cores. Micro-locations surrounding school entrances are often not the result of prior urban or traffic planning; instead, they are retroactively managed through infrastructure and signalling interventions. Pupils participate in traffic as pedestrians, cyclists, e-scooter users, or passengers in cars, school buses, or public buses. The proposed integrated research approach includes: an online survey of pupils’ travel behaviour, systematic safety assessments of entrance micro-locations using the iRAP methodology, as well as field measurements and in-depth analysis of vehicle speeds, traffic flow and structure. For classes organised in two shifts, an online survey of parents (for classroom-based education) and pupils (for subject-based education) covered 56% of the pupil population. Because pupils’ travel mode is the factor most susceptible to influence through infrastructure improvements, statistical analysis was conducted using the χ²-test for the purpose of investigating relationships with the other three traffic-relevant determinants: school age group, pupils’ sex, and distance from school. Approximately three-quarters of pupils live less than 2 km from the typical school. If peak vehicle traffic does not coincide with the peak of pupil arrivals and departures during the overlap of two school shifts, part of the traffic on the school-access street may be unrelated to direct school-access activities. Vehicle-type restrictions and one-way traffic operation should be considered as measures to improve pupils’ safety. The proposed groups of measures for improving pupils’ safety include: (i) educational workshops for pupils, parents and teachers; (ii) reconstruction of school entrance micro-locations; (iii) targeted interventions in the traffic environment within a 2 km perimeter. Full article

Ilyonectria is a common soil-inhabiting fungal genus that comprises numerous plant phytopathogenic species capable of infecting a wide array of crops, medicinal herbs, and horticultural plants. However, the lack of a reliable and efficient genetic transformation method has severely hindered the elucidation of the pathogenic mechanisms of Ilyonectria pathogens. In this study, we established an efficient protoplast-mediated genetic transformation method for two dominant Panax root rot pathogens, I. robusta and I. vredehoekensis. Key parameters governing high-quality protoplast preparation, including mycelium culture time, enzyme composition, osmotic stabilizer type, digestion speed, and digestion time, were systematically optimized. Subsequently, orthogonal experiments were conducted to optimize the PEG-CaCl₂-mediated transformation conditions and to screen regeneration media for protoplasts. The optimal enzymatic system is composed of 20 mg/mL driselase and 10 mg/mL lysing enzyme, with 0.7 M NaCl as the osmotic stabilizer. Under these conditions, high-viability and high-quality protoplasts were obtained from I. vredehoekensis after 3 h of digestion at 150 rpm, and from I. robusta after 2 h of digestion at 100 rpm, yielding 5.52 × 10⁷ CFU/mL and 5.75 × 10⁷ CFU/mL protoplasts, respectively. Efficient transformation was achieved using a mannitol-prepared STC buffer mediated by 40% PEG4000. PCR and fluorescence microscopy verified positive transformants. Additionally, pathogenicity assays showed no significant differences in virulence between the transformed and wild-type strains, suggesting that the transformation procedure did not alter virulence. To the best of our knowledge, this is the first study to successfully establish genetic transformation methods for I. robusta and I. vredehoekensis, providing an essential technical platform for functional gene analysis, pathogenicity studies, and host–pathogen interaction research. In addition, the optimized transformation strategy may serve as a valuable reference for studies on other Ilyonectria species. Full article

Conventional I/O Buffer Information Specification (IBIS) models often suffer from reduced fidelity in high-speed signaling because their static table-lookup mechanism cannot accurately reproduce complex transient I/O-buffer dynamics. To address this limitation, this study proposes a Verilog-AMS-based dynamic parameter compensation method. First, the conventional IBIS model is reformulated into a three-layer architecture comprising a data interface layer, an intermediate variable computation layer, and a port response synthesis layer. Then, based on Kirchhoff’s current law (KCL), the monotonic dependence of the output voltage on the pull-up and pull-down driving factors, k_pu and k_pd, is analytically derived to provide a directional criterion for parameter correction. Building on this criterion, a pulse-width-driven compensation algorithm is developed by constructing a pulse-width-indexed dual-factor empirical adjustment matrix and detecting the pulse width of the input bitstream in real time during transient simulation. The detected pulse width is then used to dynamically update k_pu and k_pd, enabling the IBIS response to converge toward the transistor-level SPICE reference waveform. Three representative device models were evaluated at 666 Mbps and 1.302 Gbps using pseudo-random binary sequence excitation, and the model fidelity was quantified using the normalized mean square error (NMSE). The proposed method reduced the NMSE from −6.73 to −1.03 dB before compensation to −54.79 to −44.19 dB after compensation, demonstrating a substantial improvement in high-frequency IBIS modeling fidelity and confirming the robustness and adaptability of the pulse-width-aware dynamic compensation strategy under random high-speed excitation. Full article

Honeybee (Apis mellifera) flight and vertical locomotion were investigated using high-speed imaging and schlieren flow visualization. Free-flight recordings were analyzed to extract wingbeat frequency, projected stroke amplitude, wingtip trajectories, and membrane deformation. The wingtip trajectory exhibited a pronounced asymmetry between upstroke and downstroke, suggesting a dominant role of the downstroke in thrust production. Significant membrane deformation was observed near stroke reversal, indicating strong wing flexibility and dynamic modulation of wing shape during flapping. A novel support-based framework was introduced to characterize vertical locomotion through the support polygon formed by leg contact points and the displacement of its centroid relative to the body. This movement function quantified changes in support distribution and revealed adaptive leg-contact strategies during wall climbing. Schlieren visualization provided qualitative evidence of wingtip vortex formation, although finer wake structures remained difficult to resolve. These findings provide new experimental observations of honeybee flight kinematics and introduce a quantitative framework for analyzing vertical locomotion using support redistribution metrics. Full article

During train operation, railway traction-returning current generates a power-frequency magnetic field around the rail, offering a potential energy source for self-powered trackside monitoring nodes. The H-shaped-core magnetic field energy harvester (MFEH) is attractive because it can be installed beneath the rail without enclosing the conductor, yet its output is strongly affected by the coupled rail-core-coil system. To clarify these effects, a three-dimensional electromagnetic-circuit-coupled finite-element model of an experimentally validated laminated-silicon-steel H-shaped-core MFEH was established to examine core and coil parameters. Increasing the center-leg and side-leg lengths weakens demagnetization but intensifies eddy-current losses, causing output power to approach saturation. Under a 50 Hz, 300 A current in a 54E1 rail and series-tuned matching, output power approaches 5.1 W beyond a center-leg length of 1000 mm and 3.25 W beyond a side-leg length of 700 mm. Within the investigated ranges, center-leg and side-leg lengths of approximately 800 and 400 mm provide the best power–volume performance, respectively. Increasing side-leg height or width also improves output. A larger coil span improves output by reducing internal resistance, whereas more turns yield diminishing gains because of higher winding and eddy-current losses. These findings provide a quantitative basis for parametric design of H-shaped-core MFEHs in railway environments. Full article

The wind field in urban areas is characterized by an inherent spatial variability, which is also termed spatial uncertainty. This may be manifested as a noticeable difference between rooftop-level measurements in adjacent locations, the degree of which changes throughout the day. In meteorological and environmental contexts, such uncertainty is often described as a probability distribution. Usually, studies deal with the uncertainty of each wind vector component separately, i.e., wind speed and direction. The uncertainty is assumed to be distributed symmetrically around the mean and represented by a single characteristic value. Such representation neglects the correlation between the two wind vector components together. This, in turn, may result in wind vector component combinations that are physically inconsistent with realistic wind regimes. This study proposes a method that quantifies the spatial uncertainty of the urban rooftop wind. It is based on a covariance matrix that quantifies the relationship between the rooftop spatial wind components alongside the seasonal Mahalanobis distance functions. It draws on a representative sample of weather stations and previously calculated seasonal log-logistic Mahalanobis distance functions. Thus, an elliptic-shaped tolerance region is calculated to quantitatively estimate a given proportion of the possible values of the wind vectors at a given time. The model was demonstrated on the metropolitan area of Tel Aviv. The results show that the spatial wind distribution can be very well represented by a small sample of merely four stations. The model’s results were found to be well within the confidence interval, leading to the conclusion that the model is fully capable of providing an accurate description of the current state of the urban wind field. Full article

Human gait analysis is a key component of rehabilitation medicine, enabling objective assessment of patient recovery. In crutch-assisted locomotion, however, conventional forearm crutches operate as passive devices, providing no quantitative information on load distribution or patient adherence to partial weight-bearing (PWB) prescriptions. This work presents the design and dynamic validation of a sensorized forearm crutch system for biomechanical monitoring during assisted gait. The proposed device combines a force-sensing module based on a full Wheatstone bridge strain-gauge configuration with a 6-axis inertial measurement unit (IMU) to capture both axial load and crutch orientation. Sensor fusion was implemented through a complementary filter to estimate pitch and roll angles under dynamic conditions. The system was calibrated through static loading procedures and validated against reference instrumentation, including an optoelectronic motion capture system and an instrumented dual-belt treadmill with force platforms. Unlike previous studies relying on stationary force platforms that capture discrete steps and may alter natural gait, this validation approach enabled continuous, stride-by-stride force and orientation measurements without restricting foot placement. Experimental trials were conducted with unimpaired participants performing assisted gait using 2-point and 3-point patterns at two partial weight-bearing levels (20% and 40% body weight) and two walking speeds (0.80 m/s and 1.20 m/s). Dynamic validation showed good agreement with the treadmill reference, with force RMSE values of $9.33\pm 1.70$ N for the left crutch and $12.90\pm 2.85$ N for the right crutch, and with coefficients of determination of $R^2=0.9956$ and $R^2=0.9927$, respectively. Orientation RMSE values were $1.08\pm 0.44$° (roll, right), $2.06\pm 0.56$° (roll, left), $1.79\pm 0.55$° (pitch, right), and $1.66\pm 0.37$° (pitch, left). Beyond validation accuracy, the system enabled extraction of a set of quantitative biomechanical descriptors directly from crutch signals, axial load, cadence, crutch contact variability, load asymmetry, pitch asymmetry, and crutch stance/swing asymmetries, characterizing walking stability, bilateral coordination, and gait regularity during continuous assisted locomotion. These results demonstrate the feasibility of integrating force and inertial sensors into forearm crutches to enable quantitative monitoring of assisted gait, with potential applications in rehabilitation assessment and real-time feedback. Full article

Botrytis cinerea, the causal agent of gray mold disease, is a major phytopathogen responsible for substantial losses in horticultural crops. In this study, cultivation conditions for Bacillus velezensis R22 were optimized to maximize overall antifungal activity against B. cinerea. A Plackett–Burman design was used to identify medium components affecting antifungal activity in flask cultures, followed by response surface methodology based on a central composite design (CCD) to optimize sucrose concentration, temperature, and agitation speed in a stirred bioreactor. Maximum antifungal activity was obtained at 17.45 g/L initial sucrose, 31.8 °C, and 293 rpm. The biological relevance of the optimized culture was confirmed in a tomato infection model, in which gray mold severity was reduced by 85.3% relative to the untreated control and by 59.9% relative to the non-optimized R22 culture. The same CCD approach was subsequently applied to determine cultivation conditions that maximize the concentration of R22 viable cells. The optimal parameters for 24-h growth (35.46 g/L sucrose, 36.5 °C, and 454 rpm) differed markedly from those identified for maximal antifungal activity. When evaluated on uninfected tomato plants, cultures produced under conditions favoring higher cell density showed enhanced plant growth-promoting activity compared to the non-optimized culture. Mass spectrometric analysis of lipopeptide extracts revealed that the enhanced antifungal activity was accompanied by an increased abundance of long-chain homologs across all major lipopeptide families, particularly surfactins. Thus, our results indicate that maximizing overall antifungal activity may be of greater practical significance than optimization of the individual fungicidal agent. Full article

The use of bulk heat treatments to improve the resistance of the material structures to cavitation erosion remains an effective approach due to the beneficial modifications induced in the microstructure and physical-mechanical properties. Depending on the intensity of cavitation loading, various heat treatment regimes can be applied. Among these, artificial aging treatments are particularly suitable for non-ferrous alloys, especially aluminum, zinc, and magnesium-based alloys. The current study investigates the effect of artificial aging heat treatment performed at 250 °C with holding times of 12 and 24 h on the biodegradable magnesium-based AM50 alloy. Cavitation tests were carried out using the method with a stationary specimen on a standard vibratory device according to ASTM G32-2016 requirements. The analysis of cavitation-eroded surfaces through macro- and microstructural images, together with the interpretation of characteristic erosion curves and specific parameters (cumulative mass loss, erosion speed and cavitation resistance), revealed both similarities and significant differences governed primarily by surface hardness and microstructural features. Comparison with the initial (semi-finished) state and with previous studies on artificial aging treatments performed at 200 °C for 12 and 24 h confirms the similarly beneficial effect of the 250 °C aging regime on the cavitation erosion resistance of the AM50 alloy. Full article

The implementation of Raised Pedestrian Crossings (RPCs) is one of the most common strategies used to reduce vehicle speeds in urban areas, thereby enhancing road safety levels. The understanding of driver speed profile variability is critical for designing effective traffic-calming policies (such as Zone 30) that account for the full range of driver responses. This study analyzed 19,840 discrete speed measurements collected from 2480 unique drivers at eight longitudinal points approaching and departing an RPC, located in the city of Cassino, Italy. The model takes into account two distinct directional approaches due to the different slopes that characterize the case study road segments that converge at the RPC. To overcome the statistical bias of simple OLS models, a Linear Mixed-Effects Model (LMEM) for each direction has been implemented. This hierarchical approach correctly models the nested data structure (measurements within drivers) and quantifies the variance related to individual driver behavior. Within this LMEM approach, the aim is to evaluate how drivers decelerate and accelerate before and after the RPC. This step is crucial because understanding vehicle and driver behavior on the RPC provides the basis for implementing effective speed reduction strategies, such as the 30 km/h Zone. Full article