Search Results (299)

Path planning is a pivotal technology for autonomous vehicles, directly influencing driving safety and comfort. Developing algorithms adaptable to diverse scenarios is critical for ensuring the safe operation of autonomous driving systems and advancing their engineering applications. The existing Rapidly exploring Random Tree (RRT) algorithm has limitations such as low efficiency and tortuous, lengthy paths. To address these issues, this study proposes the PAB-RRT algorithm, which integrates probabilistic goal bias, adaptive step size, and bidirectional exploration into RRT. Comparative simulations were conducted to evaluate PAB-RRT against traditional RRT, RRT*, and single-strategy improved variants (A-RRT, P-RRT, B-RRT). Results show that in static multi-obstacle scenarios, PAB-RRT completes planning with 30 iterations (6.99% of traditional RRT), 0.1255 s computation time (21.9% of traditional RRT), and a 130.83 m path length (7.2% shorter than traditional RRT). In dynamic obstacle scenarios, it requires 19 iterations (0.0434 s) at the initial stage and 37 iterations (0.0861 s) after obstacle movement, with path length stably around 130 m. Overall, PAB-RRT outperforms traditional algorithms in exploration efficiency, path performance, and robustness in complex settings, better meeting the efficiency and reliability requirements of autonomous vehicle path planning under complex scenarios and providing a feasible reference for related technology. Full article

Groundwater in Mediterranean regions is facing increasing threats from climate change and intensive agriculture, necessitating robust vulnerability assessment tools. This study evaluates nitrate pollution vulnerability of the Almyros aquifer (Thessaly, Greece) using the DRASTIC index under the high-emission scenario RCP8.5. Bias-corrected Med-CORDEX climate projections were integrated into a coupled hydrological–hydrogeological modeling framework to simulate recharge, groundwater levels, and nitrate transport. DRASTIC results for the baseline (1991–2018) showed strong agreement with observed nitrate concentrations, while future projections (2031–2060, 2071–2100) revealed shifting vulnerability patterns, particularly in low-lying agricultural areas. The findings highlight climate-driven changes in groundwater vulnerability and support targeted adaptive management strategies. Full article

This study systematically investigated the genomic alterations in Saccharomyces cerevisiae driven by Replication Factor A (RFA) dosage insufficiency using a promoter-replacement strategy combined with mutation accumulation and whole-genome sequencing. Our findings reveal that transcriptional suppression of RFA2 or RFA3 leads to severe growth inhibition. RFA deficiency induces a distinct mutational spectrum characterized by a high frequency of monosomy and terminal deletions, indicative of severe replication stress. Furthermore, loss of heterozygosity is significantly enriched at centromeres and high-GC regions, underscoring the role of RFA in stabilizing intrinsic genomic barriers. Utilizing an APOBEC3B-induced mutagenesis assay, we demonstrate that RFA insufficiency leads to the extensive accumulation of exposed ssDNA with a distinct bias towards the lagging strand template. Notably, we observed that cells spontaneously inactivate Mismatch Repair (MMR) genes, such as MSH2 and PMS1, to survive RFA-induced stress. This hypermutant phenotype grants a certain degree of growth recovery on Low Galactose (LG) medium. Overall, these findings demonstrate that RFA dosage is a key determinant of genomic integrity and elucidate how repair pathway modulation drives adaptive evolution under replication stress. Full article

Visual Impression in Architectural Space (VIAS) plays a central role in user response to environments, yet designer-controlled spatial variables often produce uncertain perceptual outcomes across cultural contexts. This study develops a multi-modal framework integrating VIAS theory, spatial documentation, and sentiment-aware NLP to evaluate temporary event spaces. Using a monthly market in Matsue, Japan as a case study, we introduce (1) systematic documentation of controlled spatial variables (layout, visibility, advertising strategy, (2) culturally balanced datasets comprising native Japanese and international participants across onsite, video, and virtual interviews, and (3) an adaptive sentiment-weighted keyword extraction algorithm suppressing interviewer bias and verbosity imbalance. Results demonstrate systematic modality effects: onsite participants exhibit festive atmosphere bias (+18% positive sentiment vs. video), while remote modalities elicit balanced critique of signage clarity and missing amenities. Cross-linguistic analysis reveals native participants emphasize holistic atmosphere, whereas international participants identify discrete focal points. The adaptive algorithm reduces verbosity-driven score inflation by 45%, enabling fair cross-participant comparison. By integrating spatial variable documentation with sentiment-weighted linguistic patterns, this framework provides a replicable methodology for validating architectural intent through computational analysis, offering evidence-based guidance for inclusive event space design. Full article

This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R² from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations. Full article

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35${}^{\circ }$ on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article

Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative power, they typically lack rigorous guarantees for uncertainty quantification, which are essential for safety-critical clinical decision-making. In this paper, we introduce CONFIDE (CONFormal Inference for Distribution-free Estimation), a novel framework that bridges causal inference and conformal prediction to construct valid prediction sets for cause-specific cumulative incidence functions. Unlike traditional confidence intervals for population-level parameters, CONFIDE provides individual-level prediction sets for time-to-event outcomes, which are more clinically actionable for personalized treatment decisions by directly quantifying uncertainty in future patient outcomes rather than uncertainty in population averages. By integrating semi-parametric hazard estimation with targeted bias correction strategies, CONFIDE generates calibrated prediction sets that cover the true potential outcome with a user-specified probability, irrespective of the underlying data distribution. We empirically validate our approach on four diverse medical datasets, demonstrating that CONFIDE achieves competitive discrimination (C-index up to 0.83) while providing robust finite-sample marginal coverage guarantees (e.g., 85.7% coverage on the Bone Marrow Transplant dataset). We note two key limitations: (1) coverage may degrade under heavy censoring (>40%) unless inverse probability of censoring weighted (IPCW) conformal quantiles are used, as demonstrated in our sensitivity analysis; (2) while the method guarantees marginal coverage averaged over the covariate distribution, conditional coverage for specific covariate values is theoretically impossible without structural assumptions, though practical approximations via locally-adaptive calibration can improve conditional performance. Our framework effectively enables trustworthy personalized risk assessment in complex survival settings. Full article

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Laboratory mice are the most widely used model organisms in biomedical and behavioral research, yet growing concerns regarding reproducibility and translational validity have highlighted the substantial influence of housing and husbandry conditions on experimental outcomes. Although domestication is often assumed to have rendered laboratory mice fully adapted to artificial environments, evidence from ethology indicates that many core behavioral and physiological needs remain conserved. As a result, standard laboratory housing may generate chronic stress, alter behavior, and introduce systematic bias into experimental data. This narrative review critically examines how ethological constraints persisting after domestication interact with key environmental factors, social housing, environmental enrichment, ambient temperature, and lighting regimes to shape welfare and experimental validity in laboratory mice. Rather than providing an exhaustive overview of mouse behavior, the review adopts a problem-oriented and solution-focused approach, highlighting specific welfare-related mechanisms that can distort behavioral and physiological readouts. Particular attention is given to social isolation and aggression in male mice, the role of nesting material in mitigating thermal stress, and the effects of circadian disruption under standard and reversed light–dark cycles. By integrating ethological theory with laboratory animal welfare research, this review argues that housing conditions should be regarded as integral components of experimental design rather than secondary technical variables. Addressing welfare-related bias through evidence-based refinement strategies is essential for improving reproducibility, enhancing data interpretability, and strengthening the scientific validity of mouse-based research. Full article

Non-digital traditional games such as board and card formats are increasingly recognised as valuable tools for active learning in higher education. These analogue approaches promote engagement, collaboration, and conceptual understanding through embodied and social interaction. This scoping review mapped research on the use of traditional, non-digital games as active learning strategies in tertiary education and examined whether the rise in generative artificial intelligence (GenAI) since 2022 has influenced their pedagogical role. Following the PRISMA-ScR framework, a systematic search of Scopus (October 2025) identified 2480 records; after screening, 26 studies met all inclusion criteria (explicitly using card and/or board games). Whilst this was a scoping, not a systematic review, some bias due to using only one database and evidence could have missed some studies. Results analysed the use and impacts of the games and whether AI was a specific driver in its use. Studies spanned STEM, business, health, and social sciences, with board and card games most frequently employed to support engagement, understanding, and collaboration. Most reported positive learning outcomes. Post-2023 publications suggest renewed interest in analogue pedagogies as authentic, human-centred responses to AI-mediated education. While none directly investigated GenAI, its emergence appears to have acted as an indirect catalyst, highlighting the continuing importance of tactile, cooperative learning experiences. Analogue games therefore remain a resilient, adaptable form of active learning that complements technological innovation and sustains the human dimensions of higher-education practice. Full article

Background: Patient-based real-time quality control (PBRTQC) enables continuous analytical monitoring using routine patient results; however, the performance of classical statistical process control (SPC) algorithms varies across analytes, and standardized evaluation and optimization strategies remain limited. To address this gap, this study compared three SPC algorithms—moving average (MA), moving quantile (MQ), and exponentially weighted moving average (EWMA)—within a unified preprocessing framework and proposed a composite performance metric for parameter optimization. Methods: Routine patient results from six laboratory analytes were analyzed using a standardized “transform–truncate–alarm” PBRTQC workflow. Simulated systematic biases were introduced for model training, and algorithm-specific parameters were optimized using a composite metric integrating sensitivity, false-positive rate (FPR), and detection delay. Performance was subsequently evaluated on an independent validation dataset. Results: For most analytes, all three SPC algorithms demonstrated robust PBRTQC performance, achieving high sensitivity (generally ≥0.85), very low false-positive rates (<0.002), and rapid detection of systematic bias. EWMA showed more balanced performance for thyroid-stimulating hormone (TSH), with improved sensitivity and shorter detection delay compared with MA and MQ. The proposed composite metric effectively facilitated clinically meaningful parameter optimization across algorithms. Conclusions: Under a unified preprocessing framework, classical SPC algorithms provided reliable PBRTQC performance across multiple analytes, with EWMA offering advantages for more variable measurements. The proposed composite metric supports standardized, practical, and analyte-adaptive PBRTQC implementation in clinical laboratories. Full article

Rapid and accurate acquisition of landslide inventories is essential for effective disaster relief. Deep learning-based pixel-wise semantic segmentation of remote sensing imagery has greatly advanced in landslide mapping. However, the heavy dependance on extensive annotated labels and sensitivity to domain shifts severely constrain the model performance in unseen domains, leading to poor generalization. To address these limitations, we propose LandsDANet, an innovative unsupervised domain adaptation framework for cross-domain landslide identification. Firstly, adversarial learning is employed to reduce the data distribution discrepancies between the source and target domains, thereby achieving output space alignment. The improved SegFormer serves as the segmentation network, incorporating hierarchical Transformer blocks and an attention mechanism to enhance feature representation capabilities. Secondly, to alleviate inter-domain radiometric discrepancies and attain image-level alignment, a Wallis filter is utilized to perform image style transformation. Considering the class imbalance present in the landslide dataset, a Rare Class Sampling strategy is introduced to mitigate bias towards common classes and strengthen the learning of the rare landslide class. Finally, a contrastive loss is adopted to further optimize and enhance the model’s ability to delineate fine-grained class boundaries. The proposed model is validated on the Potsdam and Vaihingen benchmark datasets, followed by validation in two landslide scenarios induced by earthquakes and rainfall to evaluate its adaptability across different disaster domains. Compared to the source-only model, LandsDANet achieved improvements in IoU of 27.04% and 35.73% in two cross-domain landslide disaster recognition tasks, respectively. This performance not only showcases its outstanding capabilities but also underscores its robust potential to meet the demands for rapid response. Full article

Cognitive diagnosis serves as a key component in personalized intelligent education, designed to accurately evaluate students’ knowledge states by analyzing their historical response data. It offers fundamental support for various educational applications such as adaptive learning and exercise recommendation. However, when leveraging student data, existing diagnostic models often incorporate sensitive attributes like family economic background and geographic location, which may lead to bias and unfairness. To address this issue, this paper introduces a Fairness-Aware Cognitive Diagnosis model (FACD) based on counterfactual graph representation learning. The approach builds student-centered causal subgraphs and integrates a graph variational autoencoder with adversarial learning to mitigate the influence of sensitive attributes on node representations. It further employs both central-node and neighbor-node perturbation strategies to generate counterfactual samples. A Siamese network is utilized to enforce representation consistency across different counterfactual scenarios, thereby deriving fair student contextual embeddings. Experimental results on the PISA 2015 dataset show that FACD outperforms conventional cognitive diagnosis models and their fairness-aware variants in terms of ACC, AUC, and RMSE. Ablation studies confirm the effectiveness and synergistic nature of each module. This work provides a viable pathway toward more reliable and equitable cognitive diagnosis systems. Full article

With the rapid development of applications such as edge computing, the Internet of Things (IoT), and embodied intelligence, massive multimodal data are continuously generated on end devices in a streaming manner. To maintain model adaptability and robustness in dynamic environments, incremental learning has gradually become the core training paradigm on edge devices. However, edge devices are constrained by limited computational, storage, and communication resources, making it infeasible to retain and process all data samples over time. This necessitates efficient data selection strategies to reduce redundancy and improve training efficiency. Existing sample selection methods primarily focus on overall sample difficulty or gradient contribution, but they overlook the heterogeneity of multimodal data in terms of information content and discriminative power. This often leads to modality imbalance, causing the model to over-rely on a single modality and suffer performance degradation. To address this issue, this paper proposes a multimodal sample selection strategy based on the Modality Balance Score (MBS). The method computes confidence scores at the modality level for each sample and further quantifies the contribution differences across modalities. In the selection process, samples with balanced modality contributions are prioritized, thereby improving training efficiency while alleviating modality bias. Experiments conducted on two benchmark datasets, CREMA-D and AVE, demonstrate that compared with existing approaches, the MBS strategy achieves the most stable performance under medium-to-high selection ratios (0.25–0.4), yielding superior results in both accuracy and robustness. These findings validate the effectiveness of the proposed strategy in resource-constrained scenarios, providing both theoretical insights and practical guidance for multimodal sample selection in learning tasks. Full article

The increasing complexity of integrating modern convolutional neural networks into software systems imposes significant computational demands on machine learning infrastructures. Existing MLOps systems lack mechanisms for dynamic model selection based on dataset complexity, leading to inefficient resource utilization and limited scalability under high-load conditions. This study employs convolutional neural network-based machine learning algorithms for image classification and ensemble methods for quantitative feature classification. The paper presents a self-optimizing machine learning pipeline that integrates a microservices-based architecture with a formal process for estimating image complexity and an optimization-based model selection strategy. The proposed methodology is based on designing an adaptive microservice-based ML pipeline that dynamically reconfigures its computation graph at runtime. The results confirm the effectiveness of the proposed approach for building resilient and high-performance distributed systems. The mechanism proposed in this work enables the adaptive use of modern deep learning algorithms, leading to improved result quality. A comparative analysis with existing approaches demonstrates superiority in model selection complexity, pipeline overhead, and scalability. The outcome of the proposed mechanism is an adaptive algorithm selection process based on bias-related parameters, enabling the selection of the most suitable module for data processing. Full article