Search Results (137)

This study investigates how social crowding (e.g., overcrowded environments) influences individuals’ preferences for different modes of shared consumption (sharing-in vs. sharing-out). While existing research has predominantly explored shared consumption through the perspective of non-psychosocial environmental factors (e.g., time scarcity), this study shifts the focus to psychosocial environmental factors by analyzing the underlying mechanism through which social crowding shapes preferences for these modes of shared consumption. We conducted one field experiment and three scenario-based laboratory experiments to manipulate social crowding (vs. non-social crowding) across three distinct contexts: (1) restaurant-based social scenarios, (2) shopping mall consumption contexts, and (3) beach tourism settings. Across these experiments, diverse categories of shared goods were employed to examine participants’ preferences for sharing-in versus sharing-out. The findings demonstrate that social crowding increases individuals’ preference for sharing with close friends (i.e., sharing-in), whereas non-social crowding enhances their preference for sharing with strangers (i.e., sharing-out). This relationship is mediated by psychological distance. Furthermore, resource mindset (scarcity vs. abundance) moderates this effect. Specifically, under a scarcity mindset, social crowding strengthens preferences for sharing-in, whereas under an abundance mindset, it promotes preferences for sharing-out. By conceptualizing shared consumption from a psychosocial environmental perspective, this research extends current understanding of how environmental social cues shape interpersonal consumption decisions and provides practical implications for collaborative consumption platforms, hospitality services, retail environments, tourism destinations, and public service design. Specifically, platform managers and service designers can strategically optimize spatial layouts and perceived crowding levels to strengthen social connectedness among close friends or foster positive interactions between strangers. Full article

Multi-UAV cooperative path planning in time-varying environments requires balancing flight efficiency, threat avoidance, and coordination consistency while responding rapidly to environmental changes. To address slow population recovery, insufficient inter-task cooperation, and unreliable reuse of historical search information, this paper proposes a Gaussian mixture model (GMM)-based spatio-temporal knowledge transfer multi-objective evolutionary algorithm within the MOEA/D framework, termed STKTM-MOEA/D. The proposed method represents mission requirements and flight constraints using objective functions and feasibility constraints. Elite decision vectors selected through non-dominated sorting and crowding-distance ranking are used to construct GMMs that probabilistically describe promising search regions. Each GMM captures the locations, dispersions, and relative importance of multiple high-quality regions, providing a unified distribution-level knowledge representation for spatial and temporal transfer. Spatial knowledge transfer jointly considers task similarity and distribution complementarity between the main and auxiliary tasks to improve collaborative search. Temporal knowledge transfer retrieves reliable historical GMMs from a knowledge pool according to environmental similarity. After an environmental change, an adaptive reconstruction strategy combines current elites, temporally transferred individuals, spatially transferred individuals, and randomly generated exploratory individuals. The reconstructed population then continues MOEA/D evolution, environmental selection, GMM updating, and knowledge-pool updating under the new environment, improving convergence, diversity, and adaptability. Experiments on 14 DF and 5 FDA problems show that STKTM-MOEA/D achieves the best mean MIGD on 10 problems. In multi-UAV path planning, it attains a 100% success rate and outperforms KTM-DMOEA in HV, PD, and runtime, demonstrating strong effectiveness and efficiency in dynamic environments. Full article

The transient protoplast transformation system is a vital tool for studying protein subcellular localization and phase separation in wheat. However, current protocols remain underdeveloped, and the lack of systematic vector design analysis frequently leads to localization artifacts. Here, we established a simplified wheat mesophyll protoplast transformation method featuring a shortened cycle, streamlined handling, and no variety limitations, enabling stable acquisition of high-quality confocal imaging data. Using this method, we systematically examined the effects of the fluorescent tag position (N- vs. C-terminal) and promoter type (native, single CaMV35S and double CaMV35S) on protein localization and phase separation. Tag position proved decisive: improper fusion can affect the recognition of localization signals, leading to inaccurate patterns. Regarding promoters, the native promoter represents the optimal choice for physiological accuracy. Constitutive strong promoters such as CaMV35S boost gene expression and thereby enhance fluorescent signals for easier imaging, but overexpression may compromise localization fidelity and exacerbate molecular crowding effects, resulting in false-positive phase-separated aggregates. Conversely, insufficient expression will lead to false-negative outcomes. This standardized transformation system and the defined vector design principles offer a robust framework for minimizing artifacts in wheat protein localization and phase separation research. Full article

In recent years, multimodal multi-objective optimization problems (MMOPs) have become a hot research topic in the field of evolutionary computation in recent years, whose main goal is to locate all equivalent Pareto-optimal solution sets. Although existing evolutionary multimodal multi-objective algorithms (MMOAs) perform well when there is no obvious difference in the search difficulty of different Pareto-optimal solution sets, they face great challenges when such difficulty differences are prominent, as most current MMOAs fail to effectively address the imbalance of fitness landscapes, leading to an inability to stably find all Pareto-optimal modes and poor robustness in complex MMOPs. To fill this gap, the main objective of this study is to propose a novel MMOA that can adapt to imbalanced fitness landscapes, thereby improving the ability to locate all Pareto-optimal solution sets and enhancing the algorithm’s robustness. To achieve this objective, a novel multimodal multi-objective evolutionary algorithm based on a two-stage fitness learning model is proposed. First, a multi-subpopulation cooperative search strategy is designed. Based on the principle of speciation, this strategy divides the population into several subpopulations, with the formation of each subpopulation guided by individual similarity in the decision space, thereby guiding the population to perform decentralized search across different modes. Second, a two-stage fitness learning model is developed. In the early and middle stages of evolution, individual fitness is evaluated by integrating Pareto dominance strength and density estimates based on the local outlier factor; in the late stage of evolution, individual fitness is evaluated using fast non-dominated sorting and twin-mirror crowding distance. The former is used to balance the convergence and diversity of the population in the decision space, while the latter is used to improve the convergence and diversity of the population in both the decision space and the objective space. Finally, simulation experiments are conducted on 12 imbalanced multimodal multi-objective optimization problems, and the results are compared to those of seven popular evolutionary multimodal multi-objective optimization algorithms. The results demonstrate that the proposed algorithm can find all modes for different problems and exhibits better robustness. Full article

Electrically assisted processing of metallic materials has emerged as a promising paradigm for reducing deformation resistance while concurrently tailoring microstructure and service-related properties under coupled electrical, thermal, and mechanical fields. This review focuses on deformation-dominated and surface-strengthening scenarios, examining recent advances from three interconnected perspectives: fundamental mechanisms, microstructural evolution, and property responses. Available evidence suggests that Joule heating typically constitutes the dominant contribution under high-duty-cycle or near-steady-state current conditions, whereas non-thermal electroplastic effects become increasingly pronounced under short-pulse, high-current-density, and temporally decoupled loading regimes. Current assistance can accelerate recovery and recrystallization, refine grain structure, modify crystallographic texture, and alter phase transformation and precipitation kinetics. Additionally, it can relax or redistribute residual stresses while reducing flow stress and forming forces. In select hybrid surface treatments, these microstructural modifications translate into enhanced resistance to fatigue, wear, and corrosion. Nevertheless, the available evidence precludes a single universal explanation, given that current crowding, defect-selective heating, electron–dislocation interactions, and magnetic effects operate concurrently, with their relative importance varying across material systems and processing conditions. Moving forward, establishing a unified framework that links electrical parameters, defect evolution, microstructure, and performance is imperative, with focused efforts on the quantitative delineation of thermal and non-thermal contributions, predictive constitutive modeling, residual stress stability, and industrial scalability. Full article

Internet rumor refutation represents a critical issue in the current governance of the Internet information environment. Different from the mainstream research that focuses on refutation subjects, methods, and information presentation formats, this study adopts a psychological perspective at the individual level to examine how a typical environmental factor—social crowding (the subjective psychological experience arising when spatial demand exceeds supply due to high population density per unit area) affects individuals’ willingness to refute rumors, as well as the mediating mechanisms and boundary conditions of this effect. The findings provide implications for motivating individual participation in Internet rumor refutation. Considering rumor refutation as a prosocial behavior, this study integrates the moral judgment framework and focuses on the positive side of greater self-other overlap induced by social crowding. Through one questionnaire survey and two experimental studies, most of the hypotheses are supported. The results indicate that social crowding positively influences willingness to refute rumors, with impression management and social connectedness serving as parallel mediators in this relationship. Additionally, interdependent self-construal positively moderates the relationship between social crowding and social connectedness, whereas the moderating role of independent self-construal was not supported. This study expands online rumor-refutation research from the perspective of environmental antecedents, proposes an altruistic-egoistic dual-pathway model, and provides practical implications for governments and social media platforms in rumor governance. Full article

Protecting the privacy of users’ data while maintaining reliability and accuracy in crowded events remains an open issue, especially with the growing capabilities and resources of attackers. This challenge becomes more difficult in dynamic environments with moving users/devices. Unfortunately, the current privacy-preserving methods suffer from several drawbacks that include reliability and accuracy of results, the need to fully trust a third party, or the incurrence of heavy overheads. This research presents a novel approach that is enhanced by peer cooperation, which is one of the most suitable techniques for crowded environments. The proposed approach is called “Smart Monikers with Multi-Peer Cooperation (SM2Peer)”. The SM2Peer addresses all the drawbacks of the traditional peer cooperation approach through two scenarios. In addition, the SM2Peer exploits the fog computing layer to control the cooperation among peers effectively, where each fog node manages several peers with smart moniker management. Moreover, SM2Peer provides multiple caches to relax the total overhead. The simulation and comparison with other common privacy approaches show the superiority of the SM2Peer in many aspects and metrics of privacy without a significant effect on performance. Full article

Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility. Full article

Detection, tracking, and re-identification (ReID) of children wearing similar uniforms in a kindergarten environment is a very complex challenge for computer vision. Traditional surveillance systems or simple convolutional neural network (CNN) models often fail to distinguish children in crowds and occlusions. To address this challenge, this study proposes a novel hybrid framework combining YOLOv8 and Vision Transformer (ViT). Using YOLOv8 for detection and ViT for global feature extraction, we trained the model on a custom dataset of 31,521 images, achieving an overall accuracy of 93.75%, and the public benchmark MOT20 dataset of 28,630 images, achieving an overall accuracy of 96.02%. Our system showed remarkable success in tracking performance, where it achieved 86.7% MOTA and 99.7% IDF1 scores. This high IDF1 score proves that the model is highly effective in preventing identity switch. The main novelty of this study is the behavioral analysis of children beyond the boundaries of surveillance, where we measure walking distance and trajectory, and screen time. Finally, through cross-dataset comparison with the MOT20 public benchmark, we demonstrated that our proposed customized model is much more effective than current state-of-the-art methods in overcoming the domain gap in specific environments such as kindergarten. Full article

The global rise in electronic waste (e-waste), especially in semiconductor components such as circuit boards and microchips, underscores a critical need for improved recycling technology. Current industrial sorters often miss small, high-value components. This leads to the loss of precious metals and inefficient recycling processes. This paper introduces an automated detection framework for detecting semiconductor components in e-waste. It assesses ensemble learning methods that leverage the strengths of multiple YOLO (You Only Look Once) object detection models, including YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12. Three ensemble fusion strategies are systematically compared: standard Non-Maximum Suppression (NMS), voting-based strategies (Affirmative, Consensus, Unanimous), and Weighted Box Fusion (WBF) with both static and dynamic weight optimization. Our simulations demonstrate that using multiple models together is far more effective than a single model for the following reasons. 1. Higher Accuracy: The best configuration, Top-4 Consensus Voting ensemble strategy, achieved an mAP@0.5 of 59.63%, a 10.3% improvement over the best individual model (YOLOv8s, 54.04%); 2. Greater Reliability: It significantly reduced “false negatives” (missed detections), even in cluttered or crowded e-waste scenarios; 3. Enhanced Detection: While the individual YOLOv8 model is fast (taking only 62.6 ms), supporting real-time detection, the best ensemble configuration (Consensus Top-4) takes 384.9 ms, creating a trade-off between detection accuracy and speed; 4. Well-Balanced Performance: Some fusion strategies showed slight trade-offs in mAP for certain parts, but collectively achieved a 7% rise in F1-score, indicating a better balance between precision and recall. This research marks significant progress in smart recycling. Improved component identification allows for more efficient recovery of high-purity materials. This promotes a circular economy by ensuring that rare and strategic materials in electronics are reused instead of discarded. Full article

“Urban mines” are an important component of coal-resource-based cities formed by mining, forming the spatial framework of the city. Measuring the spatial vitality of “urban mines” is an effective means to enhance the vitality of urban residents, improve the quality of living, and optimize the spatial structure. Current research on urban vitality predominantly focuses on urban communities, with limited exploration of the vitality of this unique unit—the “urban mines”. Guided by the practical needs of people, this study constructs a vitality assessment system for “mine within the city” encompassing five dimensions: environmental vitality, economic vitality, facility vitality, crowd vitality, and cultural vitality. Using the Yaahp hierarchical analysis method and entropy weighting to calculate the weights of internal influencing factors, a vitality measurement system for “urban mines” in coal-resource-based cities is established. Combining Geographic Information System (GIS) spatial data and the point of interest (POI), the vitality of eight “urban mines” within Huaibei’s central district is measured. And based on the dominant factors, strategies for the spatial transformation of specific units in resource-based cities were provided, assisting in the quantitative research of urban space. This study provided scientific basis and practical paths for achieving sustainable development of coal-resource-based cities under the demand-oriented approach. Full article

Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios can be enhanced. Current research on RGB-T crowd counting primarily focuses on feature fusion strategies, multi-scale structures, and the exploration of novel network architectures such as Vision Transformer and Mamba. However, existing approaches face two key challenges: limited robustness to illumination shifts and insufficient handling of scale discrepancies. To address these challenges, this study aims to develop a robust RGB-T crowd counting framework that remains stable under illumination shifts, through introduces two key innovations beyond existing fusion and multi-scale approaches: (1) a cross-modal adaptive fusion module (CMAFM) that actively evaluates and fuses reliable cross-modal features under varying scenarios by simulating a dynamic feature selection and trust allocation mechanism; and (2) a multi-scale aggregation module (MSAM) that unifies features with different receptive fields to an intermediate scale and performs weighted fusion to enhance modeling capability for cross-modal scale variations. The proposed method achieves relative improvements of 1.57% in GAME(0) and 0.78% in RMSE on the DroneRGBT dataset compared to existing methods, and improvements of 2.48% and 1.59% on the RGBT-CC dataset, respectively. It also demonstrates higher stability and robustness under varying lighting conditions. This research provides an effective solution for building stable and reliable all-weather crowd counting systems, with significant application prospects in smart city security and management. Full article

Maize–alfalfa intercropping is practiced in Northeast China to improve land productivity and forage production. However, competition between the two crops can reduce system performance, which calls for an emphasis on optimal row ratio. Hence, the current study evaluated the effects of diverse maize–alfalfa row ratio configurations (1:1, 2:1, 2:2, 3:1, 3:2, and 3:3) on resource-use efficiency, physiological traits, and yield performance. It was noted that the mono-cropping system had higher physiological and agronomic values for both crops. With regard to the intercropping configuration, the 2:2 steadily outperformed all other intercropping row ratios. Whereas alfalfa grew tallest in 2:2, maize plant height peaked under the 3:1. Photosynthetic rate and chlorophyll content were highest under 2:2, for both crops. The yield results indicated that alfalfa achieved maximum forage and biomass, whereas maize performed best under a 3:1 configuration. Outstandingly, under the 2:2 ratio, the cumulative system yield exceeded alfalfa mono-cropping by 55% and maize mono-cropping by 56–57%. There was superior complementarity and land-use advantage under 2:2, as indicated by the highest resource-use indicators of LER (land equivalent ratio), LEC (land equivalent coefficient), SPI (system productivity index), and K (crowding index). Competitive Indices showed that competition was more balanced under 2:2, with maize dominating in systems with higher maize proportions. Overall, the 2:2 row ratio provided the best balance of reduced competition and enhanced complementarity, offering a more efficient and sustainable maize-alfalfa intercropping strategy. Full article

Introduction: The da Vinci Single-Port (SP) robotic system represents a newer minimally invasive surgical development with greater articulation and reduced surgical footprint through the use of a single incision. While originally applied in urology and otolaryngology, its application in general surgery is on the rise. This review aims to delineate the current applications, outcomes, and limitations of the SP platform in general surgical procedures. Methods: A descriptive literature search of PubMed, Scopus, and Embase databases was conducted to identify relevant peer-reviewed studies up to September 2025. The included studies reported SP robotic surgeries in various fields of general surgery. Results: A growing body of literature was found that reports the safety and feasibility of SP robotic surgery within general surgery. Advantages reported include improved cosmesis, decreased postoperative pain, and shorter recovery time. The present evidence is largely made up of small case series and initial feasibility studies. Technical drawbacks, such as crowding of instruments and a learning curve, remain issues. Conclusions: The da Vinci SP system shows promising potential for application in general surgery, particularly for certain procedures. Additional prospective studies and larger case series need to outline its long-term results, cost-effectiveness, and optimal indications. Full article

Characterized by its low melting temperature of 138 °C, the eutectic Sn-58Bi solder expands the melting temperature range of interconnect joints in electronic packaging, making it widely used in multi-level packaging processes. However, its reliability at higher current densities poses a challenge. This paper employs a hybrid process combining laser soldering and hot-air reflow to fabricate Cu/Sn-58Bi/Cu solder joints in ball grid array (BGA) structures. Through mechanical testing under current loading, the effects of increasing current density (0 A/cm², 0.85 × 10³ A/cm², 1.70 × 10³ A/cm², 2.55 × 10³ A/cm², 3.40 × 10³ A/cm², 4.25 × 10³ A/cm²) were studied systematically. Results indicate that the shear strength decreases markedly with increasing current density, exhibiting a reduction of approximately 5.63% to 95.75%. This degradation is initiated by the overall temperature increase and material softening due to Joule heating. It is further exacerbated by the loss of the non-thermal electron wind’s strengthening contribution, which weakens as the dominant thermal impact escalates with current density. Fracture mode transitions from ductile failure within the solder matrix to a ductile-brittle mixture at the solder/IMC interface, with the transition initiating at 3.40 × 10³ A/cm². Finite element simulations reveal that current crowding in Sn-rich regions and at the solder/IMC interface induces localized Joule heating and thermomechanical strain, which jointly drive the degradation in shear strength and the shift in fracture path. Full article