Search Results (12,980)

In real-world operation, battery electric buses often encounter conditions with significant and rapid load variations. To improve regenerative braking energy recovery efficiency under such dynamic load conditions, this paper proposes a braking control strategy based on dynamic load estimation. First, a load estimation method based on a time-varying interactive multiple-model unscented Kalman filter (TVIMM-UKF) is developed by leveraging the vehicle longitudinal dynamics model and IMU sensor data, achieving high-accuracy online load estimation. Second, a multi-objective constrained optimization model is established, and an improved artificial bee colony algorithm is introduced to realize optimal brake force distribution under time-varying loads. Based on this, a regenerative braking control strategy is designed by incorporating motor characteristics and system-level operational constraints, enabling precise adjustment of braking torque across the full load range. Finally, simulation studies are conducted under two typical driving cycles, CHTC-B and C-WTVC, to verify the effectiveness of the proposed strategy. The results show that under dynamic load conditions, the proposed strategy can effectively improve braking energy recovery efficiency in both driving cycles. Full article

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice. Full article

Accurate prediction of user mobility patterns is essential for location-based services and intelligent transportation systems. In this study, we propose a sequence modeling framework that utilizes Gated Recurrent Units (GRUs) to predict future geographic coordinates (latitude and longitude) from user trajectory data stored in CSV format. By constructing input sequences of past GPS positions and training the GRU network to estimate the next position, we achieve robust trajectory forecasting performance. Experimental evaluation demonstrates that the GRU-based approach consistently yields higher prediction accuracy than the conventional Long Short-Term Memory (LSTM) model under the same conditions. The results highlight the effectiveness of GRUs in handling sequential spatial data with reduced computational complexity, suggesting their suitability for real-time and resource-constrained location prediction tasks. The models are evaluated on real-world GPS trajectory data consisting of over 800 sequential location samples, using distance-based metrics including MAE, RMSE, Average Displacement Error (ADE), and Final Displacement Error (FDE) to assess prediction accuracy in meters. This study proposes an enhanced GRU model, representing a key innovation and the main contribution of our work. The primary contribution of this study lies not merely in comparing GRU and LSTM models, but in proposing an enhanced GRU architecture that integrates motion features and an attention mechanism for improved GPS trajectory prediction. Unlike prior studies focusing solely on model comparison, our approach demonstrates methodological advancements through attention-based feature weighting and validated performance in real-world autonomous vehicle experiments. Full article

The Ahmed model significantly simplifies general vehicle geometry and has been employed extensively as a reference model for drag mechanism analysis. Platooning is a driving strategy that can reduce aerodynamic drag through intervehicle aerodynamic interactions. In this study, numerical analyses were performed under two-, three-, and four-vehicle platoon-driving conditions of the Ahmed model at various speeds and intervehicle distances. A RANS-based shear stress transport k–ω turbulence model was used to predict the drag coefficient ($C_d$) changes. A comparison with previously studied experimental data demonstrated high reliability, with a relative error of 0.6–5.0%. CFD analysis results showed that the drag reduction was significantly greater at shorter intervehicle distances, and that increasing the number of vehicles in a platoon reduced the fuel consumption. Furthermore, the intervehicle fluid interactions weakened and the $C_d$ values became more similar with an increasing intervehicle distance. On this basis, a $C_d$ correlation was proposed in terms of the speed and intervehicle distance. Our quantitative evaluation of the aerodynamic interactions between multiple vehicles during platoon driving and the analysis of $C_d$ correlations that are applicable to real-world conditions can improve fuel efficiency and reduce carbon emissions in real-world transportation systems. Full article

Patents are essential carriers of technological innovation, and their efficient transfer is critical for accelerating technological iteration in the lithium battery industry and supporting sustainability in the new energy sector. However, existing patent recommendation methods lack frameworks for handling heterogeneous enterprise demands, which limits the accuracy of supply–demand matching. This study proposes a knowledge graph-based differentiated patent recommendation framework for enterprise technological demands in the lithium battery domain. A five-element content framework—material, method, efficacy, product, and application—is constructed from both the supply and demand sides. Enterprise demands are classified into complete and incomplete types based on element coverage, and patent supply knowledge graphs are built for potentially relevant patents. Two differentiated recommendation methods are then developed. For complete demands, the Precision Recommendation Method for Complete Technological Demands integrates BERT-based semantic encoding, TransE-based structural modeling, and RAG-based constraint retrieval to achieve precise matching under full element coverage. For incomplete demands, the Fuzzy Recommendation Method for Incomplete Technological Demands incorporates multi-source enterprise data to enrich demand categories and constructs augmented query contexts to generate diversified candidate patent sets. Empirical validation based on 25 demand-driven patent transfer cases shows that the PR-CTD method exactly identifies the actual transferred patents in three cases. The FR-ITD method ranks 6 out of 14 actual transferred patents within the Top-5 results, while the remaining cases are all within the Top 13. These results demonstrate the effectiveness of the proposed framework in real-world patent transfer scenarios. This study provides a novel theoretical perspective for the structured modeling of heterogeneous technological demands and supply–demand semantic matching. It also offers practical value by improving the efficiency of patent retrieval and matching, thereby supporting patent technology transfer in the lithium battery industry. Full article

The escalating scale and complexity of cybercrime necessitate standardized digital forensic protocols to ensure the integrity and admissibility of digital evidence. This study empirically assesses the use of ISO/IEC 27037 and ISO/IEC 27041 through three real-world digital forensic case studies conducted in organizational settings. A multi-case methodology was employed, encompassing a multinational corporate criminal investigation, an internal employee misbehaviour probe, and an examination into mobile- and cloud-based data leaks. The effect of synchronized standard implementation was evaluated using audit-based and quantitative indicators that measure forensic process quality as a system attribute. The findings demonstrate that the systematic implementation of ISO/IEC 27037 and ISO/IEC 27041 improves investigative traceability, documentation quality, and evidentiary robustness. In the worldwide case study, documentation completeness increased by 18%, and all digital evidence was deemed admissible in judicial proceedings, surpassing the institutional baseline admissibility rate of 82%. In other instances, evidence gathered within the same framework was acknowledged in organizational or disciplinary review processes, resulting in similar enhancements in documentation quality and procedural consistency, notwithstanding technological and organizational limitations. The paper develops and empirically substantiates an integrated procedural validation model that connects evidence-handling practices with method and instrument validation. The results indicate that the synchronized implementation of ISO/IEC forensic standards improves the transparency, dependability, and auditability of digital forensic investigations. Full article

Objectives: To describe the efficacy and safety of tocilizumab (TCZ) and rituximab (RTX) in real-world patients with systemic sclerosis (SSc), across different clinical phenotypes and lines of therapy, evaluating both global clinical outcomes and lung function. Methods: SSc patients treated with TCZ (n = 27) or RTX (n = 23) were retrospectively followed for 12–24 months. Clinical measures, including modified Rodnan Skin Score (mRSS), C-reactive protein (CRP), and revised EUSTAR activity index 2017 (RAI), as well as spirometric parameters, were recorded at baseline and 6, 12, and 24 months. Statistical methods for repeated measures were applied to investigate outcome trends. Given the baseline differences, between-group comparisons were considered exploratory. Results: RTX was used earlier in disease course, while TCZ was mainly used as a rescue therapy. In both groups, mRSS, CRP levels and RAI significantly decreased over time. RTX-treated patients showed a greater absolute mRSS improvement, in line with higher baseline skin scores. No treatment discontinuations due to adverse events occurred in either group; one death and one discontinuation due to inefficacy were observed in the TCZ group. Among SSc-ILD patients, FVC% showed a modest decline in both groups, while DLCO% remained overall stable, and only a few patients met the OMERACT criteria for functional progression. Conclusions: In this real-world, single-center cohort of SSc patients, both agents were associated with a positive impact on disease activity, with a low rate of lung progression and with favorable safety profiles. Owing to substantial baseline imbalances and confounding by indication, between-group comparisons do not allow firm conclusions on comparative effectiveness. Overall, these data support the use of both agents in different clinical scenarios. Full article

As high-dimensional behavioral datasets grow, interactive 3D visualization is increasingly limited by rendering and annotation bottlenecks rather than data availability. Existing web-based tools often degrade sharply under large node counts, making real-time exploration impractical. We propose a web-based 3D point-cloud visualization system optimized for rendering performance under condition-locked experimental settings (load × guidance). To enable reproducible evaluation without proprietary data, the system uses a synthetic surrogate dataset with clustered structure and three node types (user/attribute/action) and provides guided/free exploration workflows with interaction logging. We report a technical benchmark across two load scales (N = 500 vs. N = 5000) and two modes (guided vs. free). Under the high-load setting (N = 5000), the system maintains real-time rendering performance while supporting interactive selection (point/cluster), tooltips/inspector, and session logging. We discuss practical strategies for controlling on-screen annotations under overload conditions and outline limitations and future work for validating the approach on real-world embeddings. Full article

Accurate rice detection is essential for food security, sustainable agriculture, and environmental monitoring. Satellite time series observations provide scalable capabilities for rice detection; however, their application in tropical regions is challenged by persistent cloud contamination, asynchronous crop development cycles, and temporal misalignment among multisensor observations, which reduce classification reliability. This study introduces Multivariate Robust Time Series Boosting (MRTS-Boosting), a quality-aware framework for multivariate time series classification (TSC) designed to improve robustness under noisy and irregular observational conditions. The framework integrates quality-weighted feature construction, joint extraction of full-series and interval-based temporal features, and a flexible multivariate formulation that accommodates heterogeneous satellite inputs without strict temporal alignment. Performance was evaluated using synthetic datasets with controlled cloud contamination, 103 benchmark datasets from the University of California, Riverside (UCR) TSC Archive, and 3261 real-world rice field observations from Indonesia. Comparisons were conducted against representative whole-series, interval-based, shapelet-based, kernel-based, and ensemble classifiers. MRTS-Boosting achieved up to 87% accuracy under severe cloud contamination, an average rank of 2.7 on noise-augmented UCR datasets, and 93% accuracy with Cohen’s kappa of 0.76 for Indonesian rice detection, while maintaining moderate computational cost. These results demonstrate that MRTS-Boosting provides a robust, scalable, and computationally efficient framework for satellite-based rice detection. The framework remains competitive in univariate settings while benefiting from multisensor integration, indicating that performance gains arise from both methodological design and the effective use of heterogeneous data. MRTS-Boosting is therefore well-suited for precision agriculture applications under challenging observational conditions. Full article

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Financial time series forecasting remains a challenging task due to the high non-stationarity and concept drift inherent to market data. Existing deep learning models, such as LSTMs and transformers, typically employ static weights after training, limiting their ability to adapt to rapid market regime shifts (e.g., from trends to reversals). To bridge this gap between static parameters and dynamic environments, we propose a novel framework named Game-Theoretic HyperNetwork (GTH-Net), which introduces a context-aware meta-learning mechanism to achieve adaptive forecasting. Specifically, we first introduce an Evolutionary Game-Theoretic Correction Module (E-GTCM) to explicitly extract latent buying and selling pressure based on market microstructure priors through an iterative gated evolution process. Subsequently, we propose a HyperNetwork-based fusion mechanism that treats the extracted game state as a meta-context to dynamically generate the weights of the forecasting head. This allows the model to automatically switch its prediction rules in response to shifting market regimes. Extensive experiments on real-world stock datasets demonstrate that GTH-Net significantly outperforms baselines in terms of machine learning predictive accuracy and simulated financial profitability. Furthermore, ablation studies and parameter analysis confirm that the dynamic weight generation mechanism effectively captures market reversals caused by overcrowded trades. Full article

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article

Objectives: The long-term effects of rheumatoid arthritis (RA) therapies on bone mineral density (BMD) remain incompletely characterized. We aimed to evaluate BMD trajectories over an 8-year follow-up in patients with RA treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) or biological DMARDs (bDMARDs) in real-world practice. Methods: Patients were selected from an observational RA cohort established at Nice University Hospital between 2001 and 2016. Participants were classified into two groups according to treatment regimen (csDMARD only or any bDMARD exposure). BMD was assessed by dual-energy X-ray absorptiometry at baseline and after 1, 2, 3, 5, and 8 years at the lumbar spine, femoral neck, and total hip. Longitudinal changes in BMD were analyzed using multivariable linear mixed-effects models adjusted for age, sex, body mass index (BMI), disease duration, seropositivity, glucocorticoid use, anti-osteoporosis treatment, and clinical response. Results: A total of 312 patients were included, of whom 181 received bDMARDs and 131 were treated exclusively with csDMARDs. BMD showed limited change during the first two years in both groups. Beyond two years, modest declines were observed at hip sites at subsequent time points, whereas lumbar spine BMD did not demonstrate significant longitudinal change in pointwise analyses. In mixed-effects models, the treatment group–time interaction was significant for lumbar spine (p = 0.004) and total hip (p = 0.04), but not for the femoral neck (p = 0.34), indicating differential BMD trajectories over time between treatment groups. In the csDMARD group, lumbar spine and total hip BMD decreased by a mean of 0.0006 and 0.0005 g/cm² per month, respectively, whereas no significant slopes were observed in the bDMARD group. Older age was associated with lower BMD, while male sex and higher BMI were associated with higher BMD across sites. Conclusions: In this long-term real-world cohort, BMD remained relatively stable during the first two years of follow-up. Longitudinal analyses suggested a less pronounced decline in lumbar spine and total hip BMD trajectories among bDMARD-treated patients compared with those receiving csDMARD alone, underscoring the need for ongoing bone health monitoring in RA. Full article

Objectives: As widely used first-line antihypertensive drugs, dihydropyridine calcium channel blockers (DHP-CCBs) have relatively few studies comparing their adverse reactions based on real-world data. This study aims to identify and compare the potential adverse drug reaction (ADR) signals of four DHP-CCBs (amlodipine, felodipine, nicardipine, and nifedipine) through the US Food and Drug Administration Adverse Event Reporting System (FAERS), providing a reference for further drug safety assessment and clinical medication risk awareness. Methods: Adverse event reports from medical professionals (Q3 2014–Q4 2024) were analyzed using signal detection methods, including reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), and the Medicines and Healthcare Products Regulatory Agency (MHRA) methods. Risk signals for the four DHP-CCBs were compared with both the full database and the DHP-CCBs background. For high-risk signals in amlodipine, multivariate logistic regression was used for validation. The analysis reveals distinct ADR profiles for the four DHP-CCBs. Results: Amlodipine is strongly linked to suicide-related risks, confirmed by logistic regression. Nicardipine and nifedipine show significant risks for pregnancy-related events, such as premature delivery and exposure during pregnancy. Nicardipine is also associated with hyponatremia, hyperkalemia, and lactic acidosis. These adverse events are not yet included in the FDA labeling for any of the DHP-CCBs. Although palpitations and angioedema are listed for felodipine, their signal strength is much higher compared to the other DHP-CCBs. Conclusions: The ADR risk profiles of the four DHP-CCBs differ significantly. This study identified several high-risk adverse events not included in current labels. Clinical use should consider each drug’s risk profile and patient-specific factors, with particular attention to serious risk signals. For pregnant and postpartum women, the benefits and risks of using nicardipine and nifedipine should be carefully evaluated. Full article

Background/Objectives: Secondary prevention after a first acute myocardial infarction (AMI) is crucial to reduce complications and improve long-term outcomes. Persistent gender inequalities in cardiovascular care suggest differences in post-AMI healthcare pathways between men and women. Understanding these trajectories could guide post-discharge clinical management, secondary prevention, and follow-up after acute myocardial infarction. This study aimed to describe healthcare pathways following a first AMI and explore gender inequalities within these trajectories. Methods: We conducted an observational, population-based study using real-world data (RWD) from the CARhES cohort. A total of 4298 individuals discharged alive after a first AMI between 2017 and 2022 were included. Healthcare trajectories during the 90 days following discharge were reconstructed across primary and specialised care, emergency services, and hospital admissions, and stratified by sex and the occurrence of clinical outcomes. Results: Post-AMI care pathways were highly heterogeneous. Although general practitioners often served as the first point of contact, most trajectories began in emergency departments. Patients with clinical outcomes showed higher healthcare utilisation. Women accessed primary care more frequently, whereas men showed greater use of specialised services and higher readmission rates, patterns that may reflect differences in follow-up strategies and clinical management during the early post-discharge period. Despite this variability, overall trajectories showed no significant sex-based differences. Conclusions: Post-AMI care pathways are complex and variable, with gender differences in patterns of service use. Women more often accessed scheduled care, while men relied more on emergency services. These findings highlight the need for gender-sensitive post-discharge follow-up to guide clinicians in secondary prevention strategies for AMI. Full article