Search Results (4,268)

In the Internet of Vehicles (IoV), zero-day attacks pose a significant security threat. These attacks are characterized by unknown patterns and limited sample availability. Traditional anomaly detection methods often fail because they rely on oversimplified assumptions, hindering their ability to model complex normal IoV behavior. This limitation results in low detection accuracy and high false alarm rates. To overcome these challenges, we propose a novel zero-day attack detection framework based on Feature-Enhanced Inverse Mapping (IM-ZDD). The framework introduces a two-stage process. In the first stage, a feature enhancement module mitigates data scarcity by employing an innovative multi-generator, multi-discriminator Conditional GAN (CGAN) with dynamic focusing loss to generate a large-scale, high-quality synthetic normal dataset characterized by sharply defined feature boundaries. In the second stage, a learning-based inverse mapping module is trained exclusively on this synthetic data. Through adversarial training, the module learns a precise inverse mapping function, thereby establishing a compact and expressive representation of normal behavior. During detection, samples that cannot be effectively mapped are identified as attacks. Experimental results on the F2MD platform show IM-ZDD achieves superior accuracy and a low false alarm rate, yielding an average AUC of 98.25% and F1-Score of 96.41%, surpassing state-of-the-art methods by up to 4.4 and 10.8 percentage points. Moreover, with a median detection latency of only 3 ms, the framework meets real-time requirements, providing a robust solution for zero-day attack detection in data-scarce IoV environments. Full article

Bio-based lubricants are rapidly gaining prominence as sustainable alternatives to petroleum-derived counterparts, driven by their inherent biodegradability, low ecotoxicity, and strong alignment with global environmental and regulatory imperatives. Despite their promising tribological properties, their widespread adoption continues to confront significant challenges, particularly related to oxidative and thermal instability, cold-flow behavior, and cost competitiveness in demanding high-performance applications. This comprehensive review critically synthesizes the latest advancements in bio-based lubricant technology, spanning feedstock innovations, sophisticated chemical modification strategies, and the development of advanced additive systems. Notably, recent formulations demonstrate remarkable performance enhancements, achieving friction reductions of up to 40% and contributing to substantial CO₂ emission reductions, ranging from 30 to 60%, as evidenced by comparative life-cycle assessments and energy efficiency studies. Distinguishing this review from existing literature, this study offers a unique, holistic perspective by integrally analyzing global market trends, industrial adoption dynamics, and evolving regulatory frameworks, such as the European Union Eco-Label and the U.S. EPA Vessel General Permit, alongside technological advancements. This study critically assesses emerging methodologies for tribological evaluation and benchmark performance across diverse, critical sectors including automotive, industrial, and marine applications. By connecting in-depth technical innovations with crucial socio-economic and environmental considerations, this paper not only identifies key research gaps but also outlines a pragmatic roadmap for accelerating the mainstream adoption of bio-based lubricants, positioning them as an indispensable cornerstone of sustainable tribology. Full article

Fusobacterium nucleatum (Fn) has been increasingly recognized as a crucial mediator of colorectal cancer (CRC) biology, particularly in microsatellite-stable (MSS) tumors, where immune checkpoint inhibitors (ICIs) have shown limited efficacy. Rather than representing a passive microbial passenger, Fn actively shapes tumor behavior by adhering to epithelial cells, activating oncogenic signaling, and promoting epithelial–mesenchymal transition (EMT). At the same time, it remodels the tumor microenvironment, driving immune suppression through inhibitory receptor engagement, accumulation of myeloid-derived cells, and metabolic reprogramming of tumor-associated macrophages. These mechanisms converge to impair cytotoxic immunity and contribute to both intrinsic and acquired resistance to ICIs. Beyond immune escape, Fn interferes with conventional chemotherapy by sustaining autophagy and blocking ferroptosis, thereby linking microbial colonization to multidrug resistance. Most of these mechanisms derive from preclinical in vitro and in vivo models, where causal relationships can be inferred. In contrast, human data are mainly observational and provide correlative evidence without proving causality. No interventional clinical studies directly targeting Fn have yet been conducted. Its enrichment across the adenoma–carcinoma sequence and consistent detection in both tumor and fecal samples highlight its potential as a biomarker for early detection and patient stratification. Importantly, multidimensional stool assays that integrate microbial, genetic, and epigenetic markers are emerging as promising non-invasive tools for CRC screening. Therapeutic strategies targeting Fn are also under exploration, ranging from antibiotics and bacteriophages to multifunctional nanodrugs, dietary modulation, and natural microbiota-derived products. These approaches may not only reduce microbial burden but also restore immune competence and enhance the efficacy of immunotherapy in MSS CRC. Altogether, current evidence positions Fn at the intersection of microbial dysbiosis, tumor progression, and therapy resistance. A deeper understanding of its pathogenic role may support the integration of microbial profiling into precision oncology frameworks, paving the way for innovative diagnostic and therapeutic strategies in CRC. Full article

Amid ongoing educational reforms and the rapid advancement of the knowledge economy, innovative teaching behaviors are not only closely related to teachers’ professional growth and students’ academic achievement but are also regarded as the key driving force for the evolution of the educational system. Consequently, identifying effective ways to foster teachers’ innovative teaching behaviors has become a central concern in educational psychology and management. Grounded in the Job Demands–Resources framework, this study developed and tested a chained mediation model using survey data from 1163 Chinese elementary and secondary school teachers. The model examines how teachers’ emotional competence fosters innovative teaching behaviors and elucidates the underlying mechanisms. The results revealed that (1) emotional competence significantly and positively predicted innovative teaching behaviors, and (2) teaching efficacy and work vitality served not only as independent mediators but also as sequential mediators in this relationship. These findings extend the understanding of the antecedents of teachers’ innovative behaviors from an emotional perspective, demonstrating that emotional competence, as a critical psychological resource, can be transformed into innovative teaching behaviors through dual “cognitive–motivational” and “energy–motivational” pathways. This study offers both theoretical insights and practical implications for advancing teaching innovation by strengthening teachers’ emotional competence, teaching efficacy, and work vitality. Full article

The fashion industry, despite its global economic importance, is a major contributor to environmental degradation and social inequality. In response, sustainable fashion has emerged as a growing movement advocating ethical, ecological, and socially responsible practices. This study presents a comprehensive bibliometric analysis of 1134 peer-reviewed journal articles on sustainable fashion indexed in Scopus from 1986 to 2025. Results show an exponential rise in research output after 2015, with interdisciplinary contributions from social sciences, business, environmental science, and engineering. By applying performance analysis and science mapping techniques, the study identifies five major research themes: “Consumer Behavior,” “Design Ethics,” “Circular Economy,” “Innovation,” and “Digital Media.” The geographic distribution reveals strong outputs from both developed and emerging economies. This study provides an integrative overview of the intellectual landscape of sustainable fashion and serves as a roadmap for researchers, policymakers, and practitioners who are interested in the development of sustainable fashion. Full article

Musculoskeletal disorders are a major cause of lameness in horses, often necessitating innovative regenerative strategies to restore joint function and improve quality of life. This study investigated the effects of platelet-rich plasma (PRP), ozonized PRP, hyaluronic acid, paracetamol, and polyacrylamide hydrogel (NOLTREX^®) on the behavior of mesenchymal stem cells (MSCs) derived from equine synovial fluid. Synovial fluid samples were collected under strict cytological criteria to ensure viability, followed by in vitro expansion and phenotypic characterization of MSCs. Cultures were supplemented with the tested preparations, and cellular proliferation and viability were evaluated at 24 h, 72 h, and 7 days. PRP significantly promoted MSC proliferation in a time- and dose-dependent manner, with maximal effect at 10%. Hyaluronic acid stimulated growth, most pronounced at 1 mg/mL, while paracetamol induced a concentration-dependent proliferative response, strongest at 100 μg/mL. NOLTREX displayed a biphasic effect, initially inhibitory at high concentrations but stimulatory at 7 days. Ozonized PRP showed concentration-dependent redox activity, with lower doses maintaining viability and higher doses producing an initial suppression followed by delayed stimulation. Collectively, these findings support the therapeutic potential of PRP and related biologic preparations as intra-articular regenerative therapies in equine medicine, while underscoring the importance of dose optimization and standardized protocols to facilitate clinical translation. Full article

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

High-quality development highlights the importance of environmental protection and green low-carbon development. The high-quality development of the manufacturing industry is not only the key content for achieving green transformation, but also an important cornerstone for building a modern national industrial system. Current research focuses on companies and governments, ignoring the important value of suppliers and consumers. As a result, existing mechanisms have failed to deliver the desired results. This paper constructs an evolutionary game model involving manufacturing enterprises, local governments, suppliers, and consumers, and systematically analyzes the strategy selection process of the four participating populations. On this basis, the impact of exogenous and endogenous factors on the evolutionarily stable strategy is studied at the microscopic level using numerical simulation methods. The results show that (1) increasing any of the endogenous factors, such as innovative capability, organization building, and industrial resources, can accelerate the evolution of manufacturing enterprises evolve to smart upgrade strategy. (2) Increasing any one of the exogenous factors, such as policy environment, industrial cooperation, and market demand, can accelerate the rate at which manufacturing enterprises choose to adopt the strategy of smart upgrade. The purpose of this paper is to provide a theoretical reference for the behavioral strategies of manufacturing enterprises, and to provide a realistic reference for local governments to build a mechanism to promote the high-quality development of the manufacturing industry. Full article

This study investigates the mechanical behaviour of timber–concrete composite (TCC) floor members with an innovative adhesive connection reinforced by granite chips, glass fibre yarn net in the epoxy adhesive layer, and polypropylene (PP) fibres in the concrete layer. Laboratory tests involved three groups of specimens subjected to three-point bending over a span of 500 mm with specimen lengths of 550 mm. Group A specimens exhibited crack initiation at approximately 8 kN and partial disintegration at an average load of 11.17 kN, with maximum vertical displacements ranging from 1.7 to 2.5 mm at 8 kN load, increasing rapidly to 4.3 to 5 mm post-cracking. The addition of reinforcing fibres decreased the brittleness of the adhesive connection and improved load-bearing capacity. Finite element modeling using the newly developed Verisim4D software (2025 v 0.6) and analytical micromechanics approaches demonstrated satisfactory accuracy in predicting the composite behavior. This research highlights the potential of reinforcing the adhesive layer and concrete with fibres to enhance the ductility and durability of TCC members under flexural loading. Full article

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels. Full article

As the global economy undergoes rapid digital transformation, cryptocurrencies have emerged as a prominent alternative class of financial assets. Their decentralized nature, pseudonymity, and lack of centralized oversight have attracted considerable interest among investors while simultaneously raising significant concerns among regulators and compliance professionals. While cryptocurrencies offer benefits such as enhanced accessibility and transactional privacy, they also pose notable risks, particularly their potential misuse in financial crimes, including money laundering. This study explores the perceived risks associated with cryptocurrencies in the context of money laundering, drawing on insights from a survey conducted among 50 financial sector professionals. A quantitative research design was employed, using a structured online questionnaire to assess participants’ awareness, investment behavior, and perceptions of the role of cryptocurrencies in illicit finance and financial system security. The results reveal a complex perspective: while 70% of respondents acknowledged the potential for cryptocurrencies to facilitate money laundering, 60% expressed support for their wider adoption. Notably, statistically significant correlations emerged between active investment in cryptocurrencies and the belief that they could enhance financial market security and reduce laundering risks. However, self-reported knowledge levels and general awareness did not show a significant relationship with perceived risk. The findings underscore the importance of a balanced approach to regulation, one that fosters innovation while mitigating illicit finance risks. The study recommends increased investment in user education, the development of blockchain analytics, the adoption of global regulatory standards and enhanced international cooperation to ensure the responsible evolution of the cryptocurrency ecosystem. Full article

Global efforts to achieve carbon neutrality increasingly rely on institutional green policy that reshape corporate environmental behavior. This study examines whether green policy improves corporate environmental performance (EP). Using panel data of the A-share listed firms from 2010 to 2022, we exploit the rollout of pilot cities as a quasi-natural experiment and apply a difference-in-differences (DID) framework, supplemented by double machine learning (DML) and robustness tests. The results show that the New Energy Demonstration City (NEDC) policy notably increases EP, with stronger effects for state-owned enterprises, large firms, and regulated industries. Mechanism analysis indicates that artificial intelligence innovation capacity and the stringency of regional environmental regulation amplify the policy’s effectiveness, revealing a “innovation–regulation” dual mechanism. By focusing on integrated EP rather than single outcomes, this paper extends the literature on green policy instruments. It demonstrates that structural policies combining fiscal incentives and regulatory constraints can correct market failures and foster long-term green transition. Beyond China, the findings provide insights for other developing economies where market-based instruments alone may be insufficient to trigger low-carbon transformation. Full article

The paper presents an innovative theoretical concept of a bio-inspired soft gripper system with two parallel jaws, a fixed and a mobile one. It is conceived for gripping fragile or soft objects with complex, irregular shapes that are easily deformable. This novel gripper is designed for handling small objects of masses up to 0.5 kg. The maximum gripping stroke of the mobile jaw is 13.5 mm. The driving motor is a pneumatic muscle, an actuator with inherently compliant, spring-like behavior. Compliance is the feature responsible for the soft character of the gripper system, ensuring its passive adaptability to the nature of the object to be gripped. The paper presents the structural, kinematic, static, and dynamic models of the novel gripper system and describes the compliant behavior of the entire assembly. The results of the dynamic simulation of the gripper have confirmed the attaining of the imposed motion-related performance. Full article

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation method struggles with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user-detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed with scalability and inductive capabilities in mind, avoiding the need for full-graph training. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science. Full article