Search Results (5,374)

This study investigates the influence of surface-modified carbon fibers (CFs) on the structural and mechanical properties of acrylonitrile-butadiene-styrene (ABS)-based composites. A comprehensive approach employing Fourier Transform Infrared Spectroscopy (FTIR), contact angle measurement, and thermogravimetric analysis (TGA) characterized the CF surface chemistry, wettability, and thermal stability. Specimens were prepared via injection molding and 3D printing processes, enabling systematic evaluation of tensile, flexural, and impact properties. Combined with Scanning Electron Microscopy observations of composite fracture surfaces, the study elucidates how modification treatments influence fiber–matrix interface bonding and mechanical enhancement mechanisms. The results indicate that after resizing treatment with silane coupling agents, the surface activity of CF and its interfacial compatibility with ABS were significantly improved, leading to a marked enhancement in the composite material’s overall performance. At a CF content of 9.62 wt%, the ABS-S-CF2 system exhibited optimal mechanical properties: The tensile strength and flexural strength of the injection-molded specimens reached 58.41 MPa and 81.51 MPa, respectively, representing increases of approximately 41.6% and 29.1% compared to neat ABS. The tensile strength and flexural strength of the printed specimens also reached 49.37 MPa and 80.19 MPa, respectively. Microstructural analysis indicates that the sizing treatment improves the interfacial bonding between CF and neat ABS. Full article

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

In response to problems inherent in conventional hydraulic engineering education including compartmentalized courses, fragmented knowledge delivery, overlapping and omitted content, and insufficient development of students’ integrated practical competencies this study develops an instructional model for a coordinated curriculum group based on the OBE-BOPPPS teaching theory. The curriculum cluster model aims to integrate interdisciplinary course content, restructure curriculum structure hierarchy, eliminate disciplinary barriers, and establish clear stratified and interrelated knowledge relationships. The model centers on competency development, constructing a three-dimensional “agent–objective” system that connects “teacher–student–curriculum” with “knowledge–competency–literacy.” It further establishes a multi-indicator evaluation system encompassing teachers, students, and courses. The comprehensive evaluation employing Principal Component Analysis, Entropy Weight Method, and CRITIC method demonstrates that the curriculum group teaching model significantly outperforms traditional course-based instruction in transcending disciplinary boundaries, enhancing knowledge systematicity, improving teaching precision, and strengthening knowledge acquisition as well as students’ comprehensive competencies. This approach achieves dynamic optimization and precision feedback in the teaching process, effectively facilitating the systematic transfer of knowledge and the holistic development of students’ innovative practical abilities. It thereby provides a scientific pathway and empirical support for the reform of hydraulic engineering education and the cultivation of high-quality talent. Full article

Phosphonyl tyrosine is one of the main biomarkers to confirm exposure to highly lethal organophosphorus nerve agents (OPNAs) in vivo. However, a critical challenge remains unresolved: ionization suppression occurs during the analysis of phosphonyl tyrosine by high-resolution mass spectrometry (HRMS) or tandem mass spectrometry (MS/MS), which is induced by the high concentrations of free amino acids present in the digestion solution. In this study, based on the broad-spectrum immunomagnetic beads with high affinity antibodies, a non-targeted early warning and verification strategy was developed. Compared with the recommended operating procedures for analysis in the verification of chemical disarmament, the total analysis time was reduced from several hours to about 30 min. Moreover, the detection sensitivity was increased by nearly one order of magnitude, and the detection limit (LOD) was 0.01 ng/mL. Furthermore, the screening strategy can cover all OPNAs listed as 1A.01, 1A.02 and 1A.03 in Schedule 1 of the CWC. Therefore, we have developed a rapid, sensitive, and broad-spectrum approach to accurately screen for OPNAs exposure, while also offering a novel strategy and technical support for chemical defense and occupational health assessment. Full article

Managed colonies of the Western honey bee, Apis mellifera, are essential to global food security by ensuring the pollination of a wide array of crops that are crucial for human consumption. However, substantial declines in managed honey bee populations have been reported worldwide, including in Australia, the United States and Europe. These losses have been attributed to the multifaceted interplay of stressors encompassing agrochemical impact, climate fluctuations, pathogens, suboptimal forage conditions, and habitat reduction. In particular, Paenibacillus larvae, the causative agent of American foulbrood (AFB), is one of the most destructive bacterial pathogens for honey bees due to its high transmissibility, environmental persistence, and capacity to cause complete colony collapse. Recurrent and widespread AFB outbreaks impose significant economic and biosecurity burdens on apiarists, exacerbating declines in pollination services and agricultural productivity. This review synthesises the current landscape of therapeutic strategies targeting AFB, including bacteriophage-based approaches, vaccine development, probiotics, and essential oils, and evaluate their reported field applications, efficacy, and practical limitations. Bacteriophages and immune-priming approaches show the greatest potential to reduce larval mortality and pathogen load, although their application is constrained by formulation stability, delivery challenges, and limited large-scale field validation. Probiotics and essential oils produce highly variable and inconsistent effectiveness under field conditions. Overall, these alternatives currently represent promising complementary tools rather than standalone treatments, underscoring the need for further investigation. Full article

To establish a biorefinery within kraft-pulp mills, the extraction of fermentable sugars must be balanced with the preservation of fiber quality for papermaking. This study investigates this trade-off by applying partial enzymatic hydrolysis to unbleached high-kappa eucalyptus kraft pulp to co-produce bioethanol and packaging-grade materials. Although the mass-transfer limitations inherent to the high-consistency strategy (15% solids or 150 g L⁻¹) restrict extensive saccharification (keeping glucose conversion below 5% at 1.5 h), it naturally directs the process toward a low-severity regime essential for fiber conservation. Structural analysis (X-ray diffraction and microscopy) revealed that enzymes preferentially targeted amorphous regions, increasing crystallinity (from ≈74% to ≈82%) but reducing intrinsic fiber strength (tear) over time (dropping from ~5.6 to ~2.3 mN·m²·g⁻¹ within 30 min). However, a strategic window for valorization has been identified. Instead of direct papermaking, hydrolyzed residue is highly effective as a strength-enhancing additive. When blended (20% w w⁻¹) with commercial pulp, the modified fibers improved interfiber bonding, restored the tensile strength, and significantly increased the Burst Index (up to ~1.7 kPa·m²·g⁻¹). These results demonstrate a viable industrial approach using partial hydrolysis to recover hemicellulose-based sugars for biofuels, while transforming the solid fraction into a high-performance reinforcement agent for paper packaging. This approach effectively converts a potential trade-off into a synergistic dual-product stream. Full article

Lipoprotein(a) [Lp(a)] is a low-density lipoprotein-like particle that contains a unique apolipoprotein(a) [apo(a)] component covalently bound to apolipoprotein B-100. Elevated levels of Lp(a) have been identified as a well-established and genetically determined risk factor for atherosclerotic cardiovascular disease, including coronary artery disease, stroke, and calcific aortic valve stenosis. In contrast to other lipids, Lp(a) concentrations are minimally influenced by lifestyle or traditional lipid-lowering therapies, emphasizing the necessity for novel treatment approaches. This narrative review summarizes current and emerging therapeutic strategies for reducing Lp(a) levels. Such strategies include traditional agents such as niacin and PCSK9 inhibitors, as well as innovative therapies such as antisense oligonucleotides, RNA interference-based molecules, and small-molecule inhibitors. The mechanisms of action of these agents, in addition to clinical trial data and their capacity to modify cardiovascular outcomes, are explored in further detail. Furthermore, the current status of clinical guidelines and the evolving role of Lp(a)-targeted therapies in cardiovascular risk stratification are reviewed. A particular emphasis is placed on therapies that are in the advanced stages of clinical development. These include late-phase outcome trials and orally administered agents, which have the potential to significantly impact future clinical practice. The integration of mechanistic data with ongoing and completed clinical studies has been undertaken in order to provide a comprehensive framework for understanding the therapeutic potential of Lp(a) in the context of cardiovascular prevention. Full article

Pedestrian trajectory prediction remains a challenging task, particularly in long-tail scenarios where goal distributions are sparse and inter-agent behaviors are uncertain. In this work, we propose to disentangle the trajectory prediction task into two complementary components: interaction modeling and intention modeling. For interaction modeling, we introduce an adaptive meta-strategy that proactively extracts latent and rare-yet-critical interaction patterns often overlooked by conventional trajectory-only approaches. For intention modeling, we propose Continuous Waypoint Slot-Driven Prototypical Contrastive Learning (PCL). It adapts prototype learning to the multi-modal reality where conventional PCL fails to model diverse and continuous goal distributions. Capitalizing on the complementary strengths of both components, we orchestrate a unified frequency-based fusion module that seamlessly integrates interaction and intention modeling, yielding enhanced overall prediction accuracy. In particular, our method is model-agnostic and can be seamlessly incorporated into a wide range of existing prediction frameworks. Extensive experiments on several datasets demonstrate that our approach not only achieves consistent performance gains in standard settings, but also significantly alleviates degradation on hard or long-tail trajectory samples. Full article

Background/Objectives: Drug repurposing represents a promising strategy to expand therapeutic options for inflammatory bowel disease (IBD), a chronic condition with persistent unmet clinical needs. This study aimed to identify existing drugs with potential relevance for IBD by exploring inverse associations in the FDA Adverse Event Reporting System (FAERS) as a hypothesis-generating, real-world data approach. Methods: In this retrospective observational pharmacovigilance study, drug–IBD associations were extracted from the FAERS database using OpenVigil 2.1. Inverse associations were identified based on reporting odds ratios (ROR) < 1 with adjusted p-values < 0.05. Identified drug–event pairs were further evaluated for pharmacokinetic feasibility, clinical applicability, and biological plausibility in the context of IBD, with the exclusion of drugs with implausible indications, contraindications, or mechanisms inconsistent with IBD pathophysiology. Given the immune-mediated nature of IBD and the breadth of the identified candidates, detailed evaluation focused on immunomodulatory agents. Results: Among the 3585 initial drug–IBD combinations, 73 candidates met the predefined criteria for statistical significance and feasibility. From these, nine drugs were prioritized based on inverse signal strength and mechanistic relevance to immune modulation pathways implicated in IBD. The strongest inverse association with IBD was observed for lenalidomide (ROR 0.056, 95% CI 0.043–0.073), followed by dupilumab (ROR 0.213, 95% CI 0.185–0.245), cyclophosphamide (ROR 0.215, 95% CI 0.175–0.265), fingolimod (ROR 0.216, 95% CI 0.205–0.334), dimethyl fumarate (ROR 0.332, 95% CI 0.275–0.400), apremilast (ROR 0.357, 95% CI 0.296–0.431), imatinib (ROR 0.423, 95% CI 0.339–0.527), glatiramer acetate (ROR 0.446, 95% CI 0.352–0.565), and interferon beta-1a (ROR 0.594, 95% CI 0.533–0.662). These agents possess immunomodulatory properties relevant to inflammatory pathways implicated in IBD; however, clinical evidence supporting the therapeutic efficacy of some candidates remains variable or incomplete. Conclusions: By integrating inverse signal detection with clinical and biological assessment, this study demonstrates how pharmacovigilance data can be extended from traditional safety surveillance toward systematic drug repurposing applications. The findings generate testable hypotheses and highlight candidate therapies that warrant further experimental and clinical investigation in IBD. Full article

Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. Full article

As the maritime industry transitions towards green shipping, operational sustainability and energy efficiency are increasingly crucial for long-endurance Unmanned Surface Vehicle (USV) missions. To this end, proactively adjusting driving strategies based on the prediction of other USVs’ motion is essential. This proactive approach directly minimizes carbon emissions and reduces high-energy driving behaviors resulting from passive sudden braking or sharp turns in unexpected situations. However, existing trajectory prediction methods are trained based on low-frequency automatic identification system data of large merchant vessels, which cannot be directly used on the highly dynamic USV data. To address this limitation, this study constructs a large-scale simulated USV scenario dataset grounded in nonlinear ship hydrodynamics, which contains complicated interactive scenarios with multiple USV agents. To effectively model the interaction among agents for accurate prediction, we further propose USV-Former, a hierarchical encoder-decoder architecture designed for proactive navigation. The framework integrates a symmetric encoding structure with a dual-stage pipeline: a Local Attention Module captures high-frequency dynamics, while a Global Graph Attention Module enforces COLREGs-compliant topological constraints. Experimental results demonstrate that the proposed model outperforms established baselines in prediction accuracy. Qualitative analysis further reveals that by accurately anticipating target intentions, the model minimizes unnecessary avoidance maneuvers, enabling more stable and momentum-conserving velocity profiles. Ultimately, this architecture exhibits high computational efficiency, reduces operational energy waste, and provides a robust, measurable algorithmic foundation for green autonomous shipping and marine environmental protection. Full article

This paper addresses distributed formation control for multiple unmanned aerial vehicles (UAVs) operating in obstacle-dense environments under directed switching communication topologies. A leader–follower architecture is adopted, wherein the leader performs online trajectory replanning while followers rely on delayed and intermittently available neighbor information. To simultaneously tackle collision avoidance, formation feasibility under narrow passages, and communication intermittency, we propose an integrated deformable formation navigation framework. The framework couples Safe Flight Corridor (SFC)-constrained Bézier trajectory planning with a dynamic formation scaling mechanism, allowing the swarm to adaptively shrink or expand its geometric configuration when traversing constricted spaces, thereby ensuring all agents remain within certified collision-free corridors. A nonlinear distributed consensus-based estimator is designed to propagate leader reference states under directed switching graphs with bounded delays. Using a max-min contraction analytical approach, we establish guaranteed practical convergence for both leader tracking and inter-follower agreement without requiring persistent connectivity. Extensive simulations in complex cluttered environments demonstrate that the proposed approach enables flexible and real-time formation reshaping, enhancing navigational safety and robustness while maintaining cohesive swarm behavior under challenging communication and spatial constraints. Full article

Reinforcement learning (RL) has demonstrated considerable promise in the field of autonomous driving. However, decision-making processes in real-world environments are inevitably influenced by measurement noise and perception inaccuracies, which can result in suboptimal or even unsafe actions. To mitigate these risks, it is imperative for autonomous vehicles to model and account for such perception uncertainties effectively. This study focuses on the ramp merging scenario in autonomous driving, where environmental uncertainties are modeled as adversarial perturbations to the states observed by RL agents. We propose a novel adversarial attack framework that combines RL with momentum-based projected gradient descent (MPGD), aiming to simulate worst-case perception errors by perturbing the sensory inputs of the driving agent. Experimental evaluations across varying traffic densities and multiple RL algorithms for autonomous driving demonstrate that our approach outperforms three baseline adversarial attack strategies in simulating the worst-case perception errors. Additionally, adversarial training of the driving agent with our attack model significantly enhances the robustness of the autonomous vehicle, improving its performance in the presence of such worst-case perception uncertainties. Full article

We present here a novel approach to the analysis of common knowledge based on Category Theory. We formalize knowledge hierarchies as presheaves over a category of agent sequences. The category of these presheaves constitutes a topos. We define an unfolding monad on the resulting topos, and use a Knaster–Tarski theorem to obtain common knowledge as a greatest fixed point under natural uniformity and exchangeability conditions on agent sequences. Full article