MMF-Gait: A Multi-Model Fusion-Enhanced Gait Recognition Framework Integrating Convolutional and Attention Networks

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance

Edge computing in big data: challenges and benefits

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the network edge, enabling improvements in response times and bandwidth utilization. It offers potential privacy benefits by facilitating local data processing, thereby reducing the need to transmit sensitive data to centralized cloud systems. This technology is particularly beneficial for big data applications. We analyze the transformative benefits of edge computing in big data systems, such as reduced latency, bandwidth optimization, and near-real-time decision making, alongside the potential for enhanced data control when processing occurs locally. Despite its potential, the integration of edge computing with big data analytics introduces significant technical challenges. We examine these challenges, including data consistency, fault tolerance, energy efficiency, and notably, the new security and privacy concerns arising from the distributed nature of edge devices, managing decentralized data access, and securing computation on potentially vulnerable edge infrastructure. While acknowledging the potential of current approaches, this paper identifies their limitations and proposes key future research directions and fully realize the potential of edge computing in big data analytics in the coming years. Edge-cloud computing, AI-driven orchestration, 6G networks, and quantum edge computing, as well as bio-inspired computing, represent key areas of technological advancement

To enlighten or to obscure: a meta-analytic inquiry into the effects of leadership behavior on employees’ knowledge hiding

In the evolving business paradigm, knowledge has become a crucial resource for organizations, drawing senior managers’ attention to employee knowledge hiding. While extensively discussed, particularly regarding leadership behavior, research conclusions on its antecedents vary. Existing studies haven’t comprehensively quantified the relationship between leadership behaviors and employee knowledge hiding. This study aims to systematically integrate the impact of different leadership behaviors on employee knowledge hiding under cross-situational and large-sample conditions. A meta-analysis of 41 empirical studies involving 14,776 samples explored how relational-oriented, change-oriented, and destructive-passive leadership behaviors influence employee knowledge hiding. The findings reveal that relational-oriented and change-oriented leadership behaviors negatively impact employee knowledge hiding, while destructive-passive leadership behavior positively influences it. The strength of these impacts, from strongest to weakest, is change-oriented, destructive-passive, and relational-oriented leadership behaviors. This research underscores the importance of implementing benign leadership within organizations to reduce knowledge hiding

Next-generation building envelopes: Smart materials, energy efficiency and environmental impact

Building envelopes play a crucial role in enhancing the energy efficiency, sustainability, and overall performance of modern buildings. This paper provides a comprehensive review of cutting-edge materials and technologies for smart and sustainable building envelopes. It highlights the transition from traditional to advanced materials, focusing on the integration of smart materials such as thermochromic and electrochromic systems, shape memory alloys, and self-healing materials. These innovations enable dynamic responses to environmental changes, enhancing comfort and energy efficiency. Additionally, the review explores sustainable materials, including natural, biodegradable insulation, recycled components, and low-carbon alternatives that contribute to the circular economy. Advanced insulation technologies such as vacuum insulation panels, phase change materials, and aerogels are discussed, emphasizing their superior thermal performance. The study also examines innovative facade solutions, such as adaptive facades, photovoltaic-integrated systems, and hybrid designs that merge sustainability with energy generation. Key challenges in adopting these materials—such as cost, regulatory compliance, and market readiness—are discussed, along with the environmental benefits, including energy savings and reduced carbon footprints. The paper concludes by identifying opportunities for future research and development in smart and sustainable building envelopes, highlighting their potential in advancing energy-efficient, climate-responsive architecture

Platformization in operations and supply chain management: A bibliometric-systematic literature review with content analyses

Platformization, the integration of Internet-driven platforms into the fabric of an application ecosystem, has gained substantial momentum across economic, governance, and infrastructure domains. However, research topics and directions in platformization are scattered and lack a systematic framework, which hinders the progress of platformization research in an era of rapid technological innovation. This paper comprehensively reviews the platformization literature in operations and supply chain management (OSCM), identifying research gaps, addressing the fragmented state of platformization literature, and promoting platformization innovation. Using bibliometric knowledge mapping, we analyze 402 journal articles and identify 13 key research areas. An in-depth content analysis based on 168 papers is conducted to identify five research themes: platformization in collaborative manufacturing, platformization in operational decisions, platformization in sustainability in supply chains, platformization in e-commerce supply chains, and platformization in technology innovation in supply chains. Using CiteSpace, we conduct author analysis, institution analysis, keyword co-occurrence network-based cluster analysis, and trend analysis to reveal keywords at the forefront of platform research. The study reveals the temporal evolution of keywords and emerging research trends and concludes with actionable directions for future exploration. This research establishes a robust roadmap for the evolving field and provides a foundational bibliometric knowledge structure map for platformization in OSCM

Beyond Nearest-Neighbor Connections in Device-to-Device Cellular Networks

Device-to-device (D2D) communication enhances network efficiency by enabling direct, low-latency links between nearby users or devices. While most existing research assumes that D2D connections occur with the nearest neighbor, this assumption often fails in real-world scenarios-such as dense indoor environments, smart buildings, and industrial IoT deployments-due to factors like channel variability, physical obstructions, or limited user participation. In this paper, we investigate the performance implications of connecting to the n-th nearest neighbor in a cellular network supporting underlay D2D communication. Using a stochastic geometry framework, we derive and analyze key performance met-rics, including the coverage probability and average data rate, for both D2D and cellular links under proximity-aware connection strategies. Our results reveal that non-nearest-neighbor associations are not only common but sometimes necessary for maintaining reliable connectivity in highly dense or constrained spaces. These findings are directly relevant to IoT-enhanced localization systems, where fallback mechanisms and adaptive pairing are essential for communication resilience. This work contributes to the development of proximity-aware and spatially adaptive D2D frameworks for next-generation smart environments and 5G-and-beyond wireless networks

The Impact of Artificial Intelligence (AI)‐Enabled Collaborative Approach: Achieving Sustainable Supply Chain Performance

This study investigates the role of artificial intelligence (AI) in enhancing sustainability and efficiency within the fragmented supply chain of the tea industry. Small-scale tea gardens often face logistical inefficiencies, inconsistent quality control, and economic constraints, limiting their competitiveness. This research bridges the gap in literature by proposing an AI-enabled collaborative supply chain model tailored for small-scale tea gardens. Using a mixed-method research design incorporating extensive field studies and structural equation modeling (SEM), this study validates the model's effectiveness. Findings indicate that AI can significantly improve coordination, predictive analytics, and automation in supply chain processes, enhancing operational efficiency, profitability, and sustainability. Additionally, AI-driven collaboration fosters more transparent and data-driven decision-making among supply chain partners, reducing dependency on intermediaries. This study contributes to the theory of collaborative advantage by demonstrating AI's role in fostering cooperative synergies in agricultural supply chains. The proposed AI-enabled framework offers a scalable model for broader application in agribusiness, presenting significant policy and managerial implications

Ground waste glass as a supplementary cementitious material for concrete: sustainable utilization, material performance and environmental considerations

This review paper delves into the role, potential and peculiarities of ground waste glass as both a supplementary cementitious material (SCM) and a filler in concrete. Motivated by the increasing emphasis on sustainable construction practices, the paper explores the potential of ground waste glass in enhancing concrete performance while addressing environmental concerns associated with traditional materials. The comprehensive review encompasses the properties of ground waste glass as an SCM, its global availability, its influence on various concrete properties, compatibility with cementitious systems, optimisation techniques, challenges, and practical applications. Key considerations such as particle size distribution, replacement levels, and chemical activation in optimising recycled ground waste glass incorporation are also highlighted. This comprehensive review underscores the potential of ground waste glass as a sustainable additive in concrete, enhancing both environmental responsibility and structural performance