Search Results (1,107)

Almost one in three firefighters develop mental health disorders at some point during their careers, a rate double that in the general population. Frequent exposures to potentially traumatic situations can contribute to symptoms of these disorders, two of the most common being depression and post-traumatic stress disorder (PTSD). While various psychological interventions have been implemented among this group, reports of their effectiveness include mixed results. To this end, the current study endeavored to test the effectiveness of a 12-week intervention combining occupationally-tailored high-intensity functional training (HIFT) and psychological resilience training (RES) in reducing depressive and post-traumatic stress symptoms (PTSSs), as well as increasing psychological resilience and mental wellbeing, in career firefighters. Thirty career firefighters completed four mental health measurements over 17 weeks while anthropometrics and physical performance (i.e., number of stations completed in 20 min during an eight-station simulated job-task circuit workout [T-CAC]) were measured pre- and post-intervention. Pre to post comparisons were made via repeated-measures t-tests. Significant mean differences were observed for T-CAC stations completed, PTSSs, and psychological resilience between pre- and post-intervention. In future interventions, researchers should actively engage firefighters, maximize integration with daily operations, and employ culturally-relevant practices to explore the links between physical and mental health. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

Mexico City frequently experiences high near-surface ozone concentrations, and exposure to elevated near-surface ozone causes harmful effects to the inhabitants and the environment of Mexico City. This necessitates developing models for Mexico City that predict near-surface ozone levels in advance. Such models are crucial for regulatory procedures and can save a great deal of near-surface ozone detrimental effects by serving as early warning systems. We utilize three machine-learning models, trained on seven-year data (2015–2021) and tested on one-year data (2022), to forecast the near-surface ozone concentrations. The trained models predict the next day’s 24-h near-surface ozone concentrations for up to one month; before forecasting the following months, the models are trained again and updated. Based on prediction results, the convolutional neural network outperforms the rest of the models on a yearly scale with an index of agreement of 0.93 for three stations, 0.92 for nine stations, and 0.91 for one station. 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

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

Optimizing passenger train line planning is a complex task that involves balancing operational costs and passenger service quality. This study investigates the adjustment and optimization of train line plans to better align with passenger demand and operational constraints, while minimizing systematic costs. These costs include train operation expenses (e.g., line usage fees and station service fees), passenger travel costs, and hidden costs such as imbalances in station stops. Line usage fees refer to charges for using railway tracks, whereas station service fees cover services provided at train stations. The optimization process employs a Simulated Annealing Algorithm to adjust train compositions, capacity configurations, and stop patterns to better match passenger demand. The results indicate a 13.89% reduction in the objective function value, reflecting improved overall efficiency. Notably, most costs are reduced, including train operating costs and passenger travel costs. However, ticketing service fees—which are calculated as a percentage of passenger fare revenue—increased slightly due to additional backtracking in passenger travel paths, which raised the total fare collected. Overall, the optimization improves the operational performance of the train network, enhancing both efficiency and service quality. Full article

This study focuses on surface wind analysis at the small regional airport in Svidnik, used primarily for pilot training under daytime VFR conditions. Due to the complex local terrain and lack of prior meteorological data, an automatic weather station was installed, collecting over 208,000 wind measurements over a two-year period at ten-minute intervals. The dataset was processed using hierarchical filtering and statistical selection, and visualized via wind rose diagrams. The results confirmed a dominant southeastern wind component, supporting the current runway orientation (01/19). However, a less frequent easterly wind direction was identified as a safety concern, causing turbulence near the runway due to terrain and vegetation. This is particularly critical for trainee pilots during final approach and landing. Statistical analysis showed that easterly winds, though less common, appear year-round with a peak in summer. Pearson correlation and linear regression confirmed a significant relationship between the number of easterly wind days and their measurement frequency. Daytime winds were stronger than nighttime, justifying the focus on daylight data. The study provides practical recommendations for training flight safety and highlights the value of localized wind monitoring at small airports. The presented methodology offers a framework for improving operational awareness and reducing risk in complex environments. Full article

The rubber industry is facing major socio-economic and environmental constraints. Rubber-based agroforestry systems represent a more sustainable solution through the diversification of income and the provision of greater ecosystem services than monoculture plantations. Participative approaches are known for their ability to co-construct solutions with stakeholders and to promote a positive impact on smallholders. This study therefore implemented a participatory research process with stakeholders in the natural rubber sector for the purpose of improving inclusion, relevance and impact. Facilitation training sessions were first organised with academic actors to prepare participatory workshops. A working group of stakeholder representatives was set up and participated in these workshops to share a common representation of the value chain and to identify problems and solutions for the sector in Indonesia. By fostering collective intelligence and systems thinking, the process is aimed at enabling the development of adaptive technical solutions and building capacity across the sector for future government replanting programmes. The resulting adaptive technical packages were then detailed and objectified by the academic consortium and are part of a participatory plant breeding approach adapted to the natural rubber industry. On-station and on-farm experimental plans have been set up to facilitate the drafting of projects for setting up field trials based on these outcomes. Research played a dual role as both knowledge provider and facilitator, guiding a co-learning process rooted in social inclusion, equity and ecological resilience. The initiative highlighted the potential of rubber cultivation to contribute to climate change mitigation and food sovereignty, provided that it can adapt through sustainable practices like agroforestry. Continued political and financial support is essential to sustain and scale these innovations. Full article

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

By utilizing daily precipitation data from 400 meteorological stations in the Yellow River Basin (YRB) of China, atmospheric and oceanic reanalysis data, this study investigates the climatological characteristics, leading modes, and relationships with atmospheric circulation and sea surface temperature (SST) of summer extreme precipitation in the YRB from 1981 to 2020 through the extreme precipitation metrics and Empirical Orthogonal Function (EOF) analysis. The results indicate that both the frequency and intensity of extreme precipitation exhibit an eastward and southward increasing pattern in terms of climate state, with regions of higher precipitation showing greater interannual variability. When precipitation in the YRB exhibits a spatially coherent enhancement pattern, high latitudes exhibits an Eurasian teleconnection wave train that facilitates the southward movement of cold air. Concurrently, the northward extension of the Western Pacific subtropical high (WPSH) enhances moisture transport from low latitudes to the YRB, against the backdrop of a transitioning SST pattern from El Niño to La Niña. When precipitation in the YRB shows a “south-increase, north-decrease” dipole pattern, the southward-shifted Ural high and westward-extended WPSH converge cold air and moist in the southern YRB region, with no dominant SST drivers identified. Full article

Precise estimation of the remaining available energy in batteries is not only key to improving energy management efficiency, but also serves as a critical safeguard for ensuring the safe operation of battery systems. To address the challenges associated with energy state estimation under dynamic operating conditions, this study proposes a method for predicting the remaining available energy of energy storage batteries based on an interpretable generalized additive neural network (IGANN). First, considering the variability in battery operating conditions, the study designs a battery working voltage threshold that accounts for safety margins and proposes an available energy state assessment metric, which enhances prediction consistency under different discharge conditions. Subsequently, 12 features are selected from both direct observation and statistical characteristics to capture the operating condition information of the battery, and a dataset is constructed using actual operational data from an energy storage station. Finally, the model is trained and validated on the feature dataset. The validation results show that the model achieves an average absolute error of 2.39%, indicating that it effectively captures the energy variation characteristics within the 0.2 C to 0.6 C dynamic current range. Furthermore, the contribution of each feature is analyzed based on the model’s interpretability, and the model is optimized by utilizing high-contribution features. This optimization improves both the accuracy and runtime efficiency of the model. Finally, a dynamic prediction is conducted for a discharge cycle, comparing the predictions of the IGANN model with those of three other machine learning methods. The IGANN model demonstrates the best performance, with the average absolute error consistently controlled within 3%, proving the model’s accuracy and robustness under complex conditions. Full article

Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by $5.5\%$, $4.8\%$, $7.8\%$, and $9.3\%$, with at least a $160\%$ improvement in the fault recall rate. Full article