Search Results (12,234)

It is commonly assumed that information technology capabilities (ITCs) are instrumental in supply chain viability (SCV), despite negligible empirical evidence. Based on the dynamic resource-based view, this study explores how the SCV competitive advantage is influenced by the heterogeneous resource ITC through the internal operating capability postponement (POST). A quantitative survey was administered to 298 senior managers from retail manufacturing firms, to test hypotheses using hierarchical multiple regression analysis. The SPSS PROCESS Macro was used to determine the mediation and interaction effects of the dual-stage moderated-mediation model, identifying a positive correlation between ITC and POST strategies, highlighting that modern real-time data synchronization and on-demand customization IT systems are needed. POST strategies greatly improve SCV by enhancing operational flexibility, reactivity, and adaptability to dynamic market conditions. The moderators “market orientation” and “demand uncertainty” shape these relationships, emphasizing the need for firms to align their strategies with market dynamics and uncertainties. The research emphasizes the importance of valuable, rare, and inimitable resources in driving sustained competitive advantage. Practical implications suggest strategic investments in advanced IT systems and collaborative efforts in retail manufacturing firms are essential for optimizing supply chain processes. The results, discussion, implications, limitations, and suggestions for additional studies are addressed. Full article

Entity alignment in knowledge graphs plays a crucial role in ensuring the consistency and integration of data across different domains. For example, in power topology, accurate entity matching is essential for optimizing system design and control. However, traditional approaches to entity alignment often rely heavily on language models to extract general features, which can overlook important logical aspects such as temporal and event-centric relationships that are crucial for precise alignment.To address this issue, we propose EAL (Entity Alignment with Logical Capturing), a novel and lightweight RNN-based framework designed to enhance logical feature learning in entity alignment tasks. EAL introduces a logical paradigm learning module that effectively models complex-event relationships, capturing structured and context-aware logical patterns that are essential for alignment. This module encodes logical dependencies between entities to dynamically capture both local and global temporal-event interactions. Additionally, we integrate an adaptive logical attention mechanism that prioritizes influential logical features based on task-specific contexts, ensuring the extracted features are both relevant and discriminative. EAL also incorporates a key feature alignment framework that emphasizes critical event-centric logical structures. This framework employs a hierarchical feature aggregation strategy combining low-level information on temporal events with high-level semantic patterns, enabling robust entity matching while maintaining computational efficiency. By leveraging a multi-stage alignment process, EAL iteratively refines alignment predictions, optimizing both precision and recall. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of EAL, which not only achieves superior performance in entity alignment tasks but also provides a lightweight yet powerful solution that reduces reliance on large language models. Full article

Wireless Sensor Networks are formed by tiny, self-contained, battery-powered computers with radio links that can sense their surroundings for events of interest and store and process the sensed data. Sensor nodes wirelessly communicate with each other to relay information to a central base station. Energy consumption is the most critical parameter in Wireless Sensor Networks (WSNs). Network lifespan is directly influenced by the energy consumption of the sensor nodes. All sensors in the network send and receive data from the base station (BS) using different routing protocols and algorithms. These routing protocols use two main types of clustering: hierarchical clustering and flat clustering. Consequently, effective clustering within Wireless Sensor Network (WSN) protocols is essential for establishing secure connections among nodes, ensuring a stable network lifetime. This paper introduces a novel approach to improve energy efficiency, reduce the length of network connections, and increase network lifetime in heterogeneous Wireless Sensor Networks by employing the K-Nearest Neighbours (KNN) algorithm to optimise node selection and clustering mechanisms for four protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Threshold-sensitive Energy Efficient sensor Network (TEEN), and Distributed Energy-efficient Clustering (DEC). Simulation results obtained using MATLAB (R2024b) demonstrate the efficacy of the proposed K-Nearest Neighbours algorithm, revealing that the modified protocols achieve shorter distances between cluster heads and nodes, reduced energy consumption, and improved network lifetime compared to the original protocols. The proposed KNN-based approach enhances the network’s operational efficiency and security, offering a robust solution for energy management in WSNs. Full article

As an important part of power transmission and conversion, it is of great significance to establish a scientific and comprehensive evaluation system for substations. However, the current research on substation evaluation focuses on economy and energy efficiency, ignoring the carbon emission and environmental impact of the substation, which leads to an inadequate evaluation of the substation. For this reason, a comprehensive substation evaluation system model containing economic, energy-efficiency, safety, environmental, and low-carbon indicators is established by adopting the concept of a life cycle, using hierarchical analysis to determine the weights of the indicators, introducing the ideal-solution approximation ranking method to analyze the actual substations, and providing scoring and ranking of the substation performance. The results show that the AHP-TOPSIS-based comprehensive evaluation index and model of the whole life cycle of the substation can reflect the performance of the substation and provide help for the optimization of the substation. Full article

Based on the projected growth in passenger air traffic and the need for better utilization of existing capacities, the level of service (LOS) concept in the design and planning of airport terminal facilities is crucial. By monitoring and quickly responding to expected changes in passengers’ and airlines’ needs, better utilization of airport terminal facilities in the passenger terminal can be achieved. The factors that influence the level of service (LOS) from the passenger perspective were evaluated in order to improve the user experience. Definitions of the level of service, key indicators of customer satisfaction, and a decision-making process using the analytical hierarchy process (AHP) method are described. A survey questionnaire was developed, passengers’ preferences were collected, and an analysis of the results was conducted. A hierarchical AHP decision-making model with associated criteria and sub-criteria was developed to determine the optimal level of service for low-cost carriers. Finally, by using the AHP model, new spatial–temporal parameters for the optimal level of service (LOS) for low-cost carriers (LCCs) are proposed, developed, and presented. The main objective is to adjust the existing LOS concept considering the business characteristics of low-cost carriers, in order to improve the efficiency of airport terminal facilities. Full article

This work proposes a novel multi-camera system-based method for relative pose estimation and virtual–physical collision detection for anchor digging equipment. It is dedicated to addressing the critical challenges of achieving accurate relative pose estimation and reliable collision detection between multiple devices during collaborative operations in coal mines. The key innovation is that the multi-camera multi-target system is established to collect images, and the relative pose estimation is completed by the EPNP (Efficient Perspective N-Point) algorithm based on multiple infrared LED targets. At the same time, combined with the characteristics of a roadheader and anchor drilling machine, AABB (Axis Alignment Bounding Box) with a simple structure and convex hull with a strong wrapping are selected to create the mixed hierarchical bounding box, and the collision detection is carried out by combining SAT (Split Axis Theorem) and GJK (Gilbert–Johnson–Keerthi) algorithms. The experimental results show that the relative pose estimation error of the multi-camera system is within 20 mm, with an angular error within 1.002°. The position error in the X-axis direction is within 1.160 mm, and the maximum deviation in the Y-axis direction is within 0.957 mm in the virtual–physical space. Compared with the existing methods, our method integrates digital twin technology, and has a simple system structure, which can meet the requirements of relative attitude estimation and collision detection between equipment in the process of heading face operation, and at the same time improve the system performance. Full article

Federated learning has attracted widespread attention due to its strong capabilities of privacy protection, making it a powerful supporting technology for addressing data silos in the future. However, federated learning still lags significantly behind traditional centralized learning in terms of learning efficiency and system security. In this paper, we first construct a hierarchical federated learning architecture integrated with blockchain based on the cooperation of the cloud, edge, and terminal, which has the ability to enhance the security of federated learning while reducing the introduction costs of blockchain. Under this architecture, we propose a semi-asynchronous aggregation scheme at the edge layer and introduce a hierarchical aggregation scheme that combines it with synchronous aggregation at the cloud end to improve system efficiency. Furthermore, we present a multi-objective node selection scheme that considers various influencing factors such as security and efficiency. We formulate the node selection problem as a Markov Decision Process (MDP) and propose a solution based on deep reinforcement learning to address it more efficiently. The experimental results show that the proposed scheme can effectively improve system efficiency and enhance system security. In addition, the proposed DQN-based node selection algorithm can efficiently realize the selection of the optimal policy. Full article

To efficiently reduce the dimensionality of time series and enhance the efficiency of subsequent data-mining tasks, this study introduces cloud model theory to propose a novel information granulation method and its corresponding similarity measurement. First, we present an information granulation validity index of time series (IGV) based on the entropy and expectation of the cloud model. Taking IGV as the granulation target for time series, an adaptive information granulation algorithm for time series (CMAIG) is proposed, which can transform a time series into a granular time series consisting of several normal clouds without pre-specifying the number of information granules, achieving efficient dimensionality reduction. Then, a new similarity measurement method (CMAIG_ECM) is designed to calculate the similarity between two granular time series. Finally, the hierarchical clustering algorithm based on the proposed time series information granulation method and granular time series similarity measurement method (CMAIG_ECM_HC) is carried out on some UCR datasets and a real stock dataset, and experimental studies demonstrate that CMAIG_ECM_HC has superior performance in clustering time series with different shapes and trends. Full article

Microplastic pollution represents a significant global environmental issue, with cigarette filters being a major contributor due to their slow biodegradation. To address this issue while creating valuable materials, we developed a novel approach to synthesize nitrogen-doped carbon nanotubes on carbonized cigarette filter powder (NCNT@cCFP) using a microwave irradiation and nickel-catalyzed process. The successful incorporation of nitrogen (~6.6 at.%) and the enhanced graphitic structure create a hierarchical conductive network with abundant active sites for electrochemical reactions. The resulting NCNT@cCFP electrode exhibits a specific capacitance of 452 F/g at 1 A/g in a three-electrode configuration. The integrated hierarchical structure facilitates efficient electron transport and ion diffusion, leading to excellent rate capability (91.6% at 10 A/g) and cycling stability (96.5% retention after 5000 cycles). Furthermore, a symmetric supercapacitor device demonstrates promising energy storage capability with a maximum energy density of 14.0 Wh/kg at 483.1 W/kg, while maintaining 10.4 Wh/kg at a high power density of 4419.1 W/kg. This synergistic waste recycling strategy combined with microwave-driven synthesis offers a sustainable pathway for developing high-performance energy storage materials. Full article

Background/Objectives: Patient safety is fundamental to primary healthcare, and medical errors impose a considerable burden on patients globally. However, the impact of medical errors on primary healthcare physicians remains understudied, especially in developing countries. This study aimed to examine the associations between self-reported medical errors and physicians’ performance and confidence in Chinese primary care practice. Methods: A cross-sectional survey was conducted from November 2021 to May 2022 with 224 primary care physicians from 38 community health centers (CHCs) across four large cities in China. The quality of clinical and preventative care, and confidence in managing commonly occurring diseases, multimorbidity, and common mental health disorders served as indicators of performance and confidence, respectively. Hierarchical linear regression and linear regression with cluster-robust standard errors were employed. Results: Clinical care quality (β = −0.159, SE = 0.075, p < 0.05), preventive care quality (β = −0.165, SE = 0.068, p < 0.05), confidence in managing multimorbidity (β = −0.175, SE = 0.074, p < 0.05), and confidence in managing common mental health disorders (β = −0.189, SE = 0.076, p < 0.05) were negatively associated with self-reported medical errors, with scores of 4.08 (SD 0.95), 3.59 (SD 0.87), 3.63 (SD 1.04), and 3.10 (SD 1.21) out of 5 (where 5 represents the best possible score), respectively. The association between self-reported medical errors and confidence in managing commonly occurring diseases (β = −0.063, SE = 0.075, p > 0.05) was not statistically significant, with a score of 3.81 (SD 1.00) out of 5 (where 5 represents the best possible score). Conclusions: This study offers new insight into the associations between self-reported medical errors and primary healthcare physicians’ performance and confidence. It is crucial for CHCs to be aware of the impact of self-reported medical errors on physicians’ performance in delivering clinic and preventative care, and confidence in managing multimorbidity and common mental health disorders. Strategies such as strengthening organizational support should be developed to maintain performance and rebuild confidence in delivering care for physicians who were involved in medical errors. Full article

The image attributes of a tourist destination are the elements that make up the perception that visitors have about a place. Segmenting by image attributes is establishing subgroups of tourists, differentiating them by the way they perceive the image of the destination. The present study in a marine protected area aimed to (i) identify image attributes, (ii) establish segments based on image attributes, (iii) determine the relationship between image segments, satisfaction, and behavioral loyalty, and (iv) ascertain the socio-demographic characteristics of image segments in insular marine protected areas. This study was conducted in the Galapagos Islands of Ecuador, a marine protected Pacific Ocean area declared a World Heritage Site. A total of 407 surveys were collected in situ. The data were interpreted using factor analysis techniques and non-hierarchical K-means cluster analysis. The results show four image attributes in marine protected areas: Staff Attention, Tourist Facilities, Nature and People, and Cultural Attractions. Likewise, three segments based on image attributes were identified: the Passive segment, with low scores overall; Nature, with high scores only in attributes related to nature; and the Want It All segment, with high scores in all image attributes. Among these groups, the Want it All segment demonstrates the highest satisfaction and loyalty levels. The results will serve as management guidelines for marine protected area administrators and contribute to academic literature. Full article

Background: The intersection of occupational burnout and digital leisure activities has garnered increasing scholarly attention in recent years. However, limited research has examined how Chinese healthcare workers engage with short-form video apps as a stress management tool. Objectives: This study employs a serial multiple mediation model to explore the impact of occupational burnout on the use of short-form video apps and its subsequent effects on a sense of community, intrinsic rewards, and life satisfaction among Chinese healthcare workers aged 18–34. Methods: Data were collected through an online survey, with 362 valid responses, and analyzed using descriptive statistics, hierarchical regression, and mediation analyses, including serial mediation via SPSS 25.0. Results: The results demonstrate a positive direct association between occupational burnout and the use of short-form video apps. Furthermore, the relationship between the use of short-form video apps and life satisfaction was mediated through two distinct pathways, namely, a sense of community and intrinsic rewards. Conclusions: These findings contribute to the expanding body of literature on the role of digital media in stress management and well-being among healthcare workers, as well as highlighting evidence-based digital interventions to support healthcare workers’ well-being in high-stress settings. Full article

The aim of this study was to assess the association between the use of and trust in sources of information, knowledge about human papillomavirus (HPV) and vaccines against it, perceived lack of information, and the decision to receive the HPV vaccine in undergraduate students in Belgrade. The sample of this cross-sectional study included students aged 18 to 27 who received the second dose of the HPV vaccine or used other services of the general medicine department at the Institute for Students’ Health of Belgrade during the period June–July 2024. The research instrument was a questionnaire consisting of socio-demographic data, information environment (sources of information, trust in sources of information, as well as questions related to perceived lack of information), knowledge about HPV and HPV vaccines, and vaccination status. Participants filled out an online questionnaire created on the RedCap platform of the Faculty of Medicine, University of Belgrade, which they accessed via a QR code. Hierarchical logistic regression was used to assess the association between vaccine status and socio-demographic characteristics, use and trust in information sources, knowledge, and perceived lack of information. Of the 603 participants who filled out the questionnaire completely, 78.6% were vaccinated against HPV. Key factors associated with vaccine uptake were female gender (OR = 2.33, p < 0.05), use of scientific literature (OR = 1.40, p < 0.05) and family as a source of information (OR = 1.37, p < 0.01), less frequent use of regional TV channels (OR = 0.76, p < 0.05), higher level of knowledge (OR = 1.43, p < 0.01), and lower perceived lack of information (OR = 0.50, p < 0.01). These variables explained 41% of variability in vaccine uptake in the multivariate hierarchical logistic regression model. Exposure to and trust in sources of information were significantly associated with knowledge about HPV and HPV vaccination, as well as with the perceived lack of information regarding HPV vaccination, and were the most significant determinants of the decision to accept HPV vaccine in the student population. Full article

Organ-specific transcriptomes provide valuable insight into the genes involved in organ identity and developmental control. This study investigated transcriptomes of floral organs and subtending bracts in wild and domesticated Gossypium hirsutum, focusing on MADS-box genes critical for floral development. The expression profiles of A, B, C, D, and E class genes were analyzed, confirming their roles in floral organ differentiation. Hierarchical clustering revealed similar expression patterns between bracts and sepals, as well as between petals and stamens, while carpels clustered with developing cotton fibers, reflecting their shared characteristics. Beyond MADS-box genes, other transcription factors were analyzed to explore the genetic basis of floral development. While wild and domesticated cotton showed similar expression patterns for key genes, domesticated cotton exhibited significantly higher expression in carpels compared to wild cotton, which aligns with the increased number of ovules in the carpels of domesticated cotton. Functional enrichment analysis highlighted organ-specific roles: genes upregulated in bracts were enriched for photosynthesis-related GO terms, while diverse functions were enriched in floral organs, supporting their respective functions. Notably, A class genes were not significantly expressed in petals, deviating from the ABCDE model, which warrants further analysis. Lastly, the ABCDE class genes exhibited differential homoeolog expression bias toward each subgenome between two accessions, suggesting that the domestication process has influenced homoeolog utilization despite functional constraints in floral organogenesis. Full article

Self-play methods have achieved remarkable success in two-player zero-sum games, attaining superhuman performance in many complex game domains. Parallelizing learners is a feasible approach to handle complex games. However, parallelizing learners often leads to the suboptimal exploitation of computational resources, resulting in inefficiencies. This paper introduces the Mixed Hierarchical Oracle (MHO), which is designed to enhance training efficiency and performance in complex two-player zero-sum games. MHO efficiently leverages interaction data among parallelized solvers during the Parallelized Oracle (PO) process, while employing Model Soups (MS) to consolidate fragmented computational resources and Hierarchical Exploration (HE) to balance exploration and exploitation. These carefully designed enhancements for parallelized systems significantly improve the training performance of self-play. Additionally, MiniStar is introduced as an open source environment focused on small-scale combat scenarios, developed to facilitate research in self-play algorithms. The MHO is evaluated on both the AlphaStar888 matrix game and MiniStar environment, and ablation studies further demonstrates its effectiveness in improving the agent’s decision-making capabilities. This work highlight the potential of the MHO to optimize compute resource utilization and improve performance in self-play methods. Full article