Search Results (15,850)

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods. Full article

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

Background/Objectives: Total hip arthroplasty (THA) is a widely accepted and effective intervention for advanced degenerative hip disease. However, prosthetic dislocation remains one of the most common postoperative complications. This study aimed to evaluate the biomechanical consequences of implant positioning variations and their influence on prosthetic stability. Methods: A three-dimensional finite element model (FEM) of the pelvis and hip joint was developed using SolidWorks Professional 2025, based on CT imaging of an anatomically normal adult. Multiple implant configurations were simulated, varying acetabular cup inclination and anteversion angles, femoral stem depth, and femoral offset. Muscle force vectors replicating single-leg stance conditions were applied according to biomechanical reference data. The mechanical performance of each configuration was quantified using the safety factor (SF), defined as the ratio of allowable material stress to calculated stress in the model. Results: The configuration with 45° cup inclination, 15° anteversion, standard femoral offset, and optimal stem depth demonstrated the highest SF values (9–12), indicating a low risk of mechanical failure or dislocation. In contrast, malpositioned implants—particularly those with low or high anteversion, excessive offset, or shallow stem insertion—resulted in a marked decrease in SF values (2–5), especially in the anterosuperior and posterosuperior quadrants of the acetabular interface. Conclusions: The findings underscore the critical importance of precise implant alignment in THA. Even moderate deviations from optimal positioning can substantially compromise biomechanical stability and increase the risk of dislocation. These results support the need for individualized preoperative planning and the use of assistive technologies during surgery to enhance implant placement accuracy and improve clinical outcomes. Full article

Improving green total factor productivity (GTFP) is critical for balancing economic benefits and ecological constraints. While most existing studies emphasize the pivotal role of governments in GTFP enhancement, they predominantly treat governments as homogeneous entities, overlooking the fundamental premise of local government attention allocation. Analyzing 2010–2020 data from 285 Chinese cities, this study reveals that increased local government green attention significantly stimulates GTFP through three channels: fostering green technology collaboration among firms, deepening green involvement of public research institutions, and elevating green innovation quality. Heterogeneity analyses demonstrate amplified effects in cities characterized by intense intergovernmental competition, stringent intellectual property protection, robust fiscal capacity, and advanced technological infrastructure, but attenuated impacts in resource-dependent regions with heavy reliance on extractive industries. 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

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article

As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product design, process monitoring, quality control, and fault diagnosis. A DT creates a virtual replica of the physical system by integrating real-time data with simulation technologies, providing new possibilities to make CNC machining more intelligent. In the past decade, extensive research has been conducted on the implementation of CNC machining DTs (CNCDTs). This paper focuses specifically on multisensor data fusion-driven CNCDTs by introducing key technologies including sensors, data fusion, and CNCDT architecture. A comprehensive survey is conducted on existing studies of CNCDTs according to their application areas, followed by critical analysis on existing challenges. This review summarizes the current progress of CNCDTs and provides guidance for further development. Full article

Ovarian cancer is a highly lethal malignancy, characterised by late-stage diagnosis, marked inter- and intra-tumoural heterogeneity, and frequent development of chemoresistance. Existing preclinical models, including conventional two-dimensional cultures, three-dimensional spheroids, and organoids, only partially recapitulate the structural and functional complexity of the ovarian tumour microenvironment (TME). Tumour-on-chip (CoC) technology has emerged as a promising alternative, enabling the co-culture of tumour and stromal cells within a microengineered platform that incorporates relevant extracellular matrix components, biochemical gradients, and biomechanical cues under precisely controlled microfluidic conditions. This review provides a comprehensive overview of CoC technology relevant to ovarian cancer research, outlining fabrication strategies, device architectures, and TME-integration approaches. We systematically analyse published ovarian cancer-specific CoC models, revealing a surprisingly limited number of studies and a lack of standardisation across design parameters, materials, and outcome measures. Based on these findings, we identify critical technical and biological considerations to inform the rational design of next-generation CoC platforms, with the aim of improving their reproducibility, translational value, and potential for personalised medicine applications. Full article

Background: Despite significant technological advances, many logistics organizations in emerging markets struggle to realize the transformative potential of artificial intelligence, with reported success rates below 65% and limited theoretical understanding of the organizational capabilities. This study develops and proposes an integrated theoretical framework examining how knowledge process capabilities and dynamic capabilities interact to enable successful artificial intelligence adoption in logistics organizations within emerging market contexts. Methods: Through comprehensive literature review and theoretical synthesis, we propose a hybrid capability framework that integrates knowledge-based view perspectives with dynamic capabilities theory. Results: Theoretical analysis suggests that knowledge combination capabilities may be the strongest predictor of artificial intelligence implementation success, while dynamic reconfiguring capabilities could mediate the relationship between artificial intelligence adoption and performance outcomes. The proposed framework indicates that organizations with hybrid capability architecture may achieve superior implementation success compared to traditional approaches. Environmental uncertainty is theorized to strengthen the knowledge process capabilities—artificial intelligence adoption relationship. Conclusions: The framework suggests that successful artificial intelligence integration requires simultaneous development of knowledge-based and adaptive capabilities rather than sequential capability building. The hybrid capability framework provides theoretical guidance for managers in emerging markets, while highlighting the critical role of environmental context in shaping transformation strategies. Full article

The transition to electric mobility has intensified efforts to develop battery technologies that are not only high-performing but also environmentally sustainable. A critical element in battery system design is the structural housing, which must provide effective impact protection to ensure passenger safety and prevent catastrophic failures. This study examines the impact response of an innovative sheet molding compound (SMC) composite battery housing, manufactured from an Elium resin modified with Martinal ATH matrix, reinforced with glass fibers, that combines fire resistance and recyclability, unlike conventional thermoset and metallic housings. The material was characterized through standardized mechanical tests, and its impact performance was evaluated via drop-weight experiments on plates and a full-scale housing. The impact tests were conducted at varying energy levels to induce barely visible impact damage (BVID) and visible impact damage (VID). A finite element model was developed in LS-DYNA using the experimentally derived material properties and was validated against the impact tests. Parametric simulations of ground and pole collisions revealed the critical velocity thresholds at which housing deformation begins to affect the first battery cells, while lower-energy impacts were absorbed without compromising the pack. The study provides one of the first combined experimental and numerical assessments of Elium SMC in battery enclosures, emphasizing its potential as a sustainable alternative for next-generation battery systems for transport applications. Full article

Spatial reasoning ability plays a critical role in predicting academic outcomes, particularly in STEM (science, technology, engineering, and mathematics) education. According to the Cattell–Horn–Carroll (CHC) theory of human intelligence, spatial reasoning is a general ability including various specific attributes. However, most spatial assessments focus on testing one specific spatial attribute or a limited set (e.g., visualization, rotation, etc.), rather than general spatial ability. To address this limitation, we created a mixed spatial test that includes mental rotation, object assembly, and isometric perception subtests to evaluate both general spatial ability and specific attributes. To understand the complex relationship between general spatial ability and mastery of specific attributes, we used a higher-order linear logistic model (HO-LLM), which is designed to simultaneously estimate high-order ability and sub-attributes. Additionally, this study compares four spatial ability classification frameworks using each to construct Q-matrices that define the relationships between test items and spatial reasoning attributes within the HO-LLM framework. Our findings indicate that HO-LLMs improve model fit and show distinct patterns of attribute mastery, highlighting which spatial attributes contribute most to general spatial ability. The results suggest that higher-order LLMs can offer a deeper and more interpretable assessment of spatial ability and support tailored training by identifying areas of strength and weakness in individual learners. Full article

With the continuous advancement of 3D perception technology, point cloud data has found increasingly widespread application. However, the presence of holes in point cloud data caused by device limitations and environmental interference severely restricts algorithmic performance, making point cloud completion a research topic of high interest. This study observes that most existing mainstream point cloud completion methods primarily focus on capturing global features, while often underrepresenting local structural details. Moreover, the generation process of complete point clouds lacks effective control over fine-grained features, leading to insufficient detail in the completed outputs and reduced data integrity. To address these issues, we propose a Set Combination Multi-Layer Perceptron (SCMP) module that enables the simultaneous extraction of both local and global features, thereby reducing the loss of local detail information. In addition, we introduce the Squeeze Excitation Pooling Network (SEP-Net) module, an informative channel attention mechanism capable of adaptively identifying and enhancing critical channel features, thus improving the overall feature representation capability. Based on these modules, we further design a novel Feature Fusion Point Fractal Network (FFPF-Net), which fuses multi-dimensional point cloud features to enhance representation capacity and progressively refines the missing regions to generate a more complete point cloud. Extensive experiments conducted on the ShapeNet-Part and MVP datasets compared to L-GAN and PCN showed average prediction error improvements of 1.3 and 1.4, respectively. The average completion errors on the ShapeNet-Part and MVP datasets are 0.783 and 0.824, highlighting the improved fine-detail reconstruction capability of our network. These results indicate that the proposed method effectively enhances point cloud completion performance and can further promote the practical application of point cloud data in various real-world scenarios. Full article

Porcine circovirus type 3 (PCV3), initially identified in the United States in 2016, is associated with multisystemic inflammation, myocarditis, reproductive failure in sows, and growth retardation in piglets, posing a significant economic threat to the swine industry. In this study, prokaryotic-expressed recombinant PCV3 Cap protein was used to immunize mice and rabbits. A monoclonal antibody (mAb 4G1) was generated through hybridoma technology, targeting a novel linear epitope (³⁷DYYDKK⁴²) within the first β-sheet of the Cap structure. This epitope exhibits high conservation (99.35%, 1239/1247) based on sequence alignment analysis, and residues 39 and 42 are critical residues affecting mAb binding. Subsequently, using rabbit polyclonal antibody (pAb) as the capture antibody and mAb 4G1 as the detection antibody, a double antibody sandwich ELISA (DAS-ELISA) method was developed. The assay demonstrates a cut-off value of 0.271, a detection limit for positive pig serum is 1:800, and shows no cross-reactivity with other swine pathogens. Intra- and inter-assay coefficients of variation were <10%, with a linear detection range for Cap protein down to 3.4 ng/mL. The coincidence rate between the DAS-ELISA and qPCR was 93.33% (70/75) for PCV3 detection in serum, with a kappa value of 0.837. This study establishes a simple, sensitive, and operationally efficient DAS-ELISA and provides a reference for monitoring PCV3 infection in swine herds. Full article

Research visibility has become a critical issue for universities, yet the institutional conditions that shape it remain underexplored. While Current Research Information Systems (CRISs) provide essential infrastructure for managing publications and researcher profiles, their impact depends on broader governance and cultural factors. This study compares four universities—two in Peru, one in Chile, and one in Spain—that have adopted the Pure CRIS platform. Data were manually extracted from institutional portals and analyzed descriptively, using normalized indicators such as publications per researcher, Sustainable Development Goal (SDG) alignment, and collaboration networks. Although based on a limited sample, the analysis highlights substantial contrasts: European institutions show consolidated integration of CRIS into national evaluation systems, while Latin American universities remain at earlier stages of adoption, with fragmented policies and limited international reach. The findings suggest that technological platforms alone are insufficient; institutional commitment, coherent policies, and academic cultures that value dissemination are decisive. These insights contribute a comparative framework to guide universities, particularly in Latin America, seeking to strengthen their global research visibility. Full article

Microplastics (MPs), as an emerging persistent contaminant, pose a potential threat to ecosystems and human health. The adsorptive removal of MPs from aqueous environments using magnetic nanoparticles has become a particularly promising remediation technology. Nevertheless, there remain significant knowledge gaps regarding its adsorption mechanism, especially how the key physical properties of magnetic nanoparticles regulate their adsorption behavior towards MPs. This study first investigated the relationship between the particle size of Fe₃O₄ nanoparticles and their adsorption efficacy for MPs. The results demonstrated a non-monotonic, size-dependent adsorption of MPs by Fe₃O₄ nanoparticles, with the adsorption efficiency and capacity following the order: 300 nm > 15 nm > 100 nm. This non-linear relationship suggested that factors other than specific surface area (which would favor smaller particles) are significantly influencing the adsorption process. Isotherm analysis indicated that the adsorption is not an ideal monolayer coverage process. Kinetic studies showed that the adsorption process could be better described by the pseudo-second-order model, while intra-particle diffusion played a critical role throughout the adsorption process. Furthermore, the effect of pH on adsorption efficiency was examined, revealing that the optimal performance occurs under neutral to weak acidic conditions, which is consistent with measurements of surface charges of nanoparticles. These findings suggest that the adsorption is not determined by specific surface area but is dominated by electrostatic interactions. The size-dependent adsorption of MPs by Fe₃O₄ nanoparticles provides new insights for the modification of magnetic adsorbents and offers a novel perspective for the sustainable and efficient remediation of environmental MPs pollution. Full article