Search Results (2,664)

Background: Plant-based protein supplementation in supporting muscle recovery following resistance exercise remains an area of growing interest, particularly among vegan athletes, as a potential alternative to animal-based proteins. This systematic review aimed to evaluate the effectiveness of plant-based proteins on recovery from resistance exercise-induced muscle damage in healthy young adults. Methods: A systematic and comprehensive search was administered in eight databases up to 1 May 2025, identifying 1407 articles. Following deduplication and screening, 24 studies met the eligibility criteria, including 22 randomized controlled trials and 2 non-randomized studies, with the majority from high income western countries. Results: Interventions primarily involved soy, pea, rice, hemp, potato, and blended plant protein sources, with doses ranging from 15 to 50 g, typically administered post resistance exercise. Outcomes assessed included muscle protein synthesis (MPS), delayed-onset muscle soreness (DOMS), inflammatory biomarkers, muscle function, and fatigue. The review findings reaffirm that single-source plant proteins generally offer limited benefits compared to animal proteins such as whey, particularly in acute recovery settings, a limitation well-documented consistently in the literature. However, our synthesis highlights that well-formulated plant protein blends (e.g., combinations of pea, rice, and canola) can stimulate MPS at levels comparable to whey when consumed at adequate doses (≥30 g with ~2.5 g leucine). Some studies also reported improvements in subjective recovery outcomes and reductions in muscle damage biomarkers with soy or pea protein. However, overall evidence remains limited by small sample sizes, moderate to high risk of bias, and heterogeneity in intervention protocols, protein formulations, and outcome measures. Risk of bias assessments revealed concerns related to detection and reporting bias in nearly half the studies. Due to clinical and methodological variability, a meta-analysis was not conducted. Conclusion: plant-based proteins particularly in the form of protein blends and when dosed appropriately, may support muscle recovery in resistance-trained individuals and offer a viable alternative to animal-based proteins. However, further high-quality, long-term trials in vegan populations are needed to establish definitive recommendations for plant protein use in sports nutrition. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model—with conventional convolutional layers replacing the separable convolutional layers—can be adopted as a more precise alternative. Full article

The present study explores the application of RNNs for the prediction and propagation of flood waves along a section of the Bârsa River, Romania, as a fast alternative to classical hydraulic models, aiming to identify new ways to alert the population. Five neural architectures were analyzed as follows: S-RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU. The input data for the neural networks were derived from 2D hydraulic simulations conducted using HEC-RAS software, which provided the necessary training data for the models. It should be mentioned that the input data for the hydraulic model are synthetic hydrographs, derived from the statistical processing of recorded floods. Performance evaluation was based on standard metrics such as NSE, $R^2$ MSE, and RMSE. The results indicate that all studied networks performed well, with NSE and $R^2$ values close to 1, thus validating their capacity to reproduce complex hydrological dynamics. Overall, all models yielded satisfactory results, making them useful tools particularly the GRU and Bi-GRU architectures, which showed the most balanced behavior, delivering low errors and high stability in predicting peak discharge, water level, and flood wave volume. The GRU and Bi-GRU networks yielded the best performance, with RMSE values below 1.45, MAE under 0.3, and volume errors typically under 3%. On the other hand, LSTM architecture exhibited the most significant instability and errors, especially in estimating the flood wave volume, often having errors exceeding 9% in some sections. The study concludes by identifying several limitations, including the heavy reliance on synthetic data and its local applicability, while also proposing solutions for future analyses, such as the integration of real-world data and the expansion of the methodology to diverse river basins thus providing greater significance to RNN models. The final conclusions highlight that RNNs are powerful tools in flood risk management, contributing to the development of fast and efficient early warning systems for extreme hydrological and meteorological events. Full article

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

Background/Objectives: Frailty is a prevalent geriatric syndrome associated with impaired postural control and elevated fall risk. Although conventional exercise is a core strategy for frailty management, adherence remains limited. Virtual reality (VR)-based interventions have emerged as potentially engaging alternatives, but their effects on objective postural control and task-specific confidence in frail populations remain understudied. This study aimed to evaluate the effectiveness of a supervised VR training program using the Nintendo Ring Fit Plus™ on postural control, functional mobility, and balance confidence among frail community-dwelling older adults. Methods: Fifty-one adults aged ≥65 years classified as frail or prefrail were enrolled in a four-week trial. Participants were assigned to either a VR intervention group (n = 28) or control group (n = 23). Participants were non-randomly assigned based on availability and preference. Outcome measures were collected at baseline and post-intervention. Primary outcomes included center of pressure (CoP) metrics—sway area, mean velocity, and sway path. Secondary outcomes were the Timed Up and Go (TUG), Berg Balance Scale (BBS), Activities-specific Balance Confidence (ABC), and Falls Efficacy Scale–International (FES-I). Results: After adjusting for baseline values, age, and BMI, the intervention group showed significantly greater improvements than the control group across all postural control outcomes. Notably, reductions in sway area, mean velocity, and sway path were observed under both eyes-open and eyes-closed conditions, with effect sizes ranging from moderate to very large (Cohen’s d = 0.57 to 1.61). For secondary outcomes, significant between-group differences were found in functional mobility (TUG), balance performance (BBS), and balance confidence (ABC), with moderate-to-large effect sizes (Cohen’s d = 0.53 to 0.73). However, no significant improvement was observed in fear of falling (FES-I), despite a small-to-moderate effect size. Conclusions: A supervised VR program significantly enhanced postural control, mobility, and task-specific balance confidence in frail older adults. These findings support the feasibility and efficacy of VR-based training as a scalable strategy for mitigating frailty-related mobility impairments. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

The transgenic animals have been yielding invaluable insights into amyloid pathology by replicating the key features of Alzheimer’s disease (AD). However, there is no clear relationship between senile plaques and memory deficits. Instead, cognitive impairment and synaptic dysfunction are particularly linked to a rise in Aβ_1-42 oligomer level. Thus, injection of Aβ_1-42 oligomers into a specific brain region is considered an alternative approach to investigate the effects of increased soluble Aβ species without any plaques, offering higher controllability, credibility and validity compared to the transgenic model. The hippocampal CA1 (cornu ammonis 1) region is selectively affected in the early stage of AD and specific targeting of CA1 region directly links Aβ oligomer-related pathology with memory impairment in early AD. Next, the inhibitory avoidance (IA) task, a learning paradigm to assess the synaptic basis of CA1-dependent contextual learning, triggers training-dependent synaptic plasticity similar to in vitro HFS (high-frequency stimulation). Given its reliability in assessing contextual memory and synaptic plasticity, this task provides an effective framework for studying early stage AD-related memory deficit. Therefore, in this review, we will focus on why Aβ_1-42 oligomer injection is a valid in vivo model to investigate the early stage of AD and why dorsal CA1 region serves as a target area to understand the adverse effects of Aβ_1-42 oligomers on contextual learning through the IA task. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

Gastrointestinal (GI) malignancies are diverse and particularly challenging in terms of current immunotherapy but hold great opportunity for impact given that they constitute the highest cancer incidence and mortality rates worldwide. Traditional treatment options for solid GI malignancies include surgical intervention, chemotherapy, radiation, or a combination of these treatments. Emerging modalities within immunotherapy are anticipated to extend the results with conventional therapy by stimulating the patient’s own intrinsic potential for tumor-specific immunologic rejection. Combination regimens of chemotherapy and tumor-infiltrating lymphocyte (TIL) therapy in advanced colorectal cancer and pancreatic cancer, autologous monocyte therapy in advanced gastric cancer, and CAR-T therapy trained against GI-selective tumor antigens such as carcinoembryonic antigen are currently being studied. Clinical trials are underway to study the combination of various chemotherapeutic agents along with immunotherapy in the management of cholangiocarcinoma, hepatocellular carcinoma, and esophageal cancer. Alternative therapies are needed based on the tumor immune microenvironment, which can lead to a personalized approach to treatment. In this review, we discuss the current status of various modalities of immunotherapy in common GI malignancies, along with their mechanisms of immune activation and cancer suppression. We will also discuss the use of immunotherapy in less common solid GI malignancies and touch on recent advancements and clinical trials. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article