Search Results (7,785)

This study investigates the dynamic artifacts (DAs) in knitted resistive strain sensors (KRSS) subjected to various deformation types, including stair-wise, trapezoidal, and triangle-type deformations. The presence of DAs, characterized by sharp peak-wise increases in resistance followed by a gradual decline, was observed across all KRSS samples. The amplitude of DA peaks increased with higher deformation velocities within the investigated range of 2.6–40 cm/s. The study also identified the temporal offset between resistance and deformation during linear deformation, suggesting a complex mechanism underlying DAs. The results demonstrate that DAs are most prominent in stepwise and trapezoidal deformations, while continuous deformations like triangle-type loading partially mask these artifacts. The resistance signals were recorded at a sampling rate of 150 Hz, with temporal desynchronization between recorded parameters not exceeding 6.7 ms, enabling the observation of dynamic effects. Manifestation of DAs in KRSS degrades the metrological characteristics of KRSS and cannot be ignored. This paper provides insights into the relationship between KRSS structure, deformation velocity, and DA behavior, and provides an experimental basis for future compensation approaches to mitigate the impact of DAs on measurement accuracy. Full article

We study the design of efficient approximation algorithms for the ℓ-center clustering and minimum-diameter ℓ-clustering problems in high-dimensional Euclidean and Hamming spaces. Our main tool is randomized dimension reduction. First, we present a general method of reducing the dependency of the running time of a hypothetical algorithm for the ℓ-center problem in a high-dimensional Euclidean space on the dimension. Utilizing this method in part, we provide $(2+ϵ)$-approximation algorithms for the ℓ-center clustering and minimum-diameter ℓ-clustering problems in Euclidean and Hamming spaces that are substantially faster than the known 2-approximation algorithms when both ℓ and the dimension are super-logarithmic. Next, we apply the general method to the recent fast approximation algorithms with higher approximation guarantees for the ℓ-center clustering problem in a high-dimensional Euclidean space. Finally, we provide a speed-up of the known $O\left(1\right)$-approximation method for the generalization of the ℓ-center clustering problem that allows z outliers (i.e., z input points may be ignored when computing the maximum distance from an input point to a center) in high-dimensional Euclidean and Hamming spaces. Full article

Research on financial risk tolerance and risk-taking increasingly incorporates personality traits into predictive and descriptive models of risk-taking behavior; however, intercorrelations among traits can obscure the unique contributions of individual traits. This is known as the suppressor effect. This study employed a two-stage analytic framework to test and adjust for suppressor effects across the Big Five personality dimensions in describing financial risk tolerance. In Stage 1, correlation and OLS regression analyses identified suppression patterns, revealing that the explanatory validity of some factors was distorted by shared variance. In Stage 2, suppression-adjusted trait estimates were used to reassess their unique association with financial risk-taking mediated through financial risk tolerance. Results indicate that Openness to Experience and Extraversion are the strongest descriptors of financial risk-taking once suppressor effects are controlled. At the same time, Agreeableness and Conscientiousness contribute modestly and context-dependently to descriptions of financial risk-taking. These findings demonstrate that ignoring suppression effects can lead to mischaracterizing the role of personality in financial decision-making. This study shows that more precise estimates of trait influences can improve theoretical models of investor behavior and enhance the delivery of financial advice and education. Full article

To meet the growing demand for autonomous decision-making and real-time optimization in industrial manufacturing, integrating Artificial Intelligence-Generated Content (AIGC) services with Industry 5.0 can enable real-time industrial intelligence. The effectiveness of a generative model is closely related to the current state of the production environment. However, existing studies often ignore the dynamic temporal relationship between generative models and production environments, especially in industrial scenarios with large model transmission delays and random AIGC task arrivals. Therefore, we define a novel metric, namely the Age of Model (AoM), to measure the freshness of generative models with respect to current industrial tasks. We then formulate an average-AoM-minimization problem that jointly considers LoRA-based fine-tuning, wireless transmission and resource allocation. To solve this problem, we propose a Hybrid-Action Multi-Agent Proximal Policy Optimization (HA-MAPPO) algorithm. The proposed algorithm follows the centralized training and decentralized execution (CTDE) paradigm and introduces a Main-Agent Priority State Strategy to support coordinated training and independent execution. In addition, a multi-head output structure is designed to handle the hybrid-action space, which includes discrete fine-tuning association decisions and continuous transmission resource allocation. Simulation results show that the proposed scheme outperforms all benchmark methods. Specifically, the cumulative rewards are improved by approximately $11.13\%$, $20.32\%$, $36.61\%$, and $38.78\%$ compared with the four benchmark algorithms, respectively. These results demonstrate that the proposed scheme can significantly reduce the average AoM while providing high-quality and timely industrial AIGC services. Full article

The prediction of drag anchor trajectory is key for the anchor design. The yield envelope method based on anchor behavior under combined loadings provided an alternative method for the prediction of anchor trajectory. The traditional yield envelope method based on deep anchor behavior ignored the shallow anchor behavior that occurred during installation from shallow depth to deep depth and may lead to unreliable trajectory prediction. The present work examines how the shallow behavior of an inclined fluke influences the predicted trajectory of drag anchors. An example case was used to investigate the effects of soil non-homogeneity and anchor–soil interface friction on the predicted trajectory of drag anchors. The trajectory of another case of practical drag anchor is also predicted and compared to the existing centrifuge results. The results indicate that the prediction results are largely influenced by the selected interface friction condition and the bearing capacity factor. Incorporating both shallow and deep behaviors of inclined flukes leads to a more accurate description of anchor trajectory. Recommendations on the new method of drag anchor trajectory prediction, considering both shallow and deep anchor behavior, are proposed for a reliable trajectory prediction. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by genetic heterogeneity. Post-transcriptional regulation—particularly alternative polyadenylation (APA)—plays a critical role in the pathogenesis of ASD. APA controls mRNA stability, translational efficiency, and subcellular localization through modulating the length of the 3′ untranslated region of mRNA. APA profiling can uncover functionally relevant post-transcriptional alterations often missed by conventional gene expression analyses. However, current ASD analyses still largely rely on differential gene expression or individual APA event detection, which ignores the collective explanatory power of ASD risk genes or co-dysregulated functional gene modules within specific cell types. In this study, we present an integrative computational framework that combines matrix factorization and machine learning to identify ASD-associated gene modules driven by APA and to predict cell-type-specific ASD-related cells. Applied to human brain single-nucleus RNA sequencing (snRNA-seq) data, our approach systematically uncovers APA regulatory patterns that are specific to cell type, brain region, and sex in ASD. The identified APA modules are significantly enriched in pathways related to synaptic function, neurodevelopment, and immune response, with the strongest signals observed in excitatory neurons of the prefrontal cortex. Using APA genes from these modules as features, we built a classification model that effectively distinguishes ASD cells from normal cells. Moreover, we found that integrating APA with gene expression—two complementary modalities—substantially improves prediction accuracy, underscoring APA as an independent and biologically informative regulatory layer. Our work delineates a high-resolution APA regulatory landscape in ASD, offering novel insights and potential therapeutic avenues beyond transcriptional abundance. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (${\mathcal{L}}_{align}$), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

This study introduces a novel risk detection and control system to enhance social stability in major construction projects. Utilizing a heterogeneous cellular automaton model, the system simulates complex interactions among project stakeholders to identify and mitigate Social Stability Risks (SSR). Integrating the Ignorant–Latent–Malcontent–Recovered (ILMR) framework, the model applies principles from epidemiology to predict and manage the spread of social stability risks. Simulation results demonstrate the model’s effectiveness in reducing the number of malcontent and ignorant individuals while increasing the recovered category, stabilizing the social environment around large projects. This approach helps manage immediate risks and improves long-term social acceptance and sustainability of engineering projects. By bridging risk management with advanced simulation techniques, this research contributes to major construction projects by providing a robust framework for managing complex social dynamics, thereby enhancing project success and stakeholder satisfaction. The findings underscore the potential of integrating innovative technological tools with traditional risk management strategies to address the socio-technical challenges of large-scale engineering projects. Full article

Bundle recommendation has gained significant attention, but it faces two key challenges: sparse interaction data and complex UB, UI, and BI relations. Recent work uses multi-view contrastive learning, yet current frameworks rely on fixed-weight fusion that ignores view-specific importance and suffers from gradient suppression on sparse data. We propose TriadCBR, a tri-view adaptive contrastive learning architecture for bundle recommendation. It uses a simplified GCN to learn view-specific representations and a Mixture-of-Experts (MoE) module to generate personalized fusion weights, addressing the limitations of fixed-weight fusion. TriadCBR further incorporates a fine-grained contrastive module integrating InfoNCE, DCL, and Barlow Twins. This combination effectively mitigates gradient vanishing from invalid negatives and minimizes cross-view feature redundancy. To handle data sparsity, we design a Difficulty-Aware BPR (DA-BPR) with curriculum augmentation to dynamically refine the ranking trajectory. Extensive experiments on Youshu, iFashion, and NetEase demonstrate that TriadCBR achieves statistically significant improvements, boosting Recall and NDCG by an average of 3.61%, with 9 of 12 metric–dataset combinations reaching statistical significance, over state-of-the-art baselines, validating the robustness of its dynamic fusion and adaptive optimization. Full article

The most common species of tick in Europe is the castor bean tick (Ixodes ricinus), which is found in forests, parks, and gardens and is active almost all year round. Ticks are among the most important arthropods and vectors of disease, transmitting a wide range of parasites that sometimes lead to the death of infected organisms. The peak incidence of tick-borne diseases occurs between May and September; however, due to global warming, people are increasingly exposed to tick-borne diseases throughout the year. In order to increase the possibility of preventing the transmission of diseases by ticks, it is necessary to become thoroughly familiar with the life cycle of ticks and the environment in which they live. Vaccines are available for some diseases, such as tick-borne encephalitis, while others require a highly specific diagnosis. Another major problem is the long period between the tick bite, which often goes unnoticed or is even ignored by the patient or the doctor, and the development of tick-borne diseases. Increasing attention is being paid to the prevention of tick-borne diseases through prevention of tick bites, quick tick removal, use of repellents, appropriate land management, vaccinations, and the use of plants as natural acaricides. Full article

This study reveals that a primarily ignored but crucial environmental situation—social crowding—can affect consumers’ sustainable behavior. The present research proposes a causal relationship between social crowding and consumer recycling behavior. Drawing on resource depletion theory and self-affirmation theory, three experiments were conducted across product recycling, participation in a brand-sponsored recycling program, and waste sorting activities. The results show that consumers exposed to crowded (vs. uncrowded) environments are less likely to engage in recycling. Study 1 provides initial evidence of this negative effect, demonstrating that it stems from crowd density rather than from the sheer number of people in the environment. Study 2 identifies ego depletion as the underlying mediating mechanism. Study 3 further demonstrates that self-affirmation attenuates the negative effect of social crowding on recycling behavior by mitigating ego depletion. These findings suggest that social crowding is an important situational barrier to recycling and that self-affirmation may serve as an effective intervention for promoting sustainable disposal behavior in dense consumption settings. This article concludes with a general discussion of the findings and practical implications for extending the relevant literature and benefiting consumer well-being, as well as promoting sustainable development. Full article

The aim of this study was to explore and describe how a recent public health emergency affected the management of community sport organisations (CSOs) in favelas and how the crisis management strategy shaped community resilience. We relied upon the stakeholder theory of crisis management, which posits that during crises, organisations should adopt management practices that address the needs of multiple stakeholders rather than merely focusing on organisational survival. We conducted 13 interviews with sport managers of CSOs located in favelas in four different regions of the city of Rio de Janeiro, Brazil. The findings show that managers respond to the crisis by focusing on community support and resilience. Three factors informed the relationship between management practices and community resilience: sense of leadership and responsibility, filling the gaps of the public sector, and equality, diversity, and inclusion practices. Our study extends the application of stakeholder theory of crisis management to suggest the importance of considering the inclusion of stakeholders (e.g., government, sport managers) who have been ignored in the proposition of the theory and in the sport management literature. Theoretical and practical implications are discussed. Full article

The reinforced concrete (RC) Diaojiao frame structure is a widely used building form in the Luding red bed area. A large area of damage occurred in the Luding earthquake in 2022. It is very important to carry out seismic fragility research for damage evaluation and post-earthquake emergency management. Based on the incremental dynamic analysis (IDA), this paper explores the dynamic response law of the structure: the structural damage is distributed in Floor 1 > Floor 2 > Floor 3, and the damage of the C1_1 component is the most serious. Through the quantitative analysis of the structural damage matrix, the probability of structural damage under frequent earthquakes of 7 degrees and 8 degrees can be ignored. The probability of severe damage (SD) of Floor 1, Floor 2, Floor 3 and the building under maximum considered earthquakes of 9 degrees is 58.25%, 53.03%, 2.71% and 36.79%, respectively. In this paper, PGA is used as an index to divide the damage state into four categories: elastic state, elastic-plastic state, plastic state and large deformation state. Based on the actual earthquake PGA, the structural damage can be determined quickly and accurately, which provides scientific support for the formulation of emergency measures. Full article

While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development. Full article