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Recent systems for position and movement monitoring are increasingly enhanced by the integration of advanced AI techniques. Besides traditional analytical and algorithmic approaches, which are still applied in areas such as signal processing, sensor fusion, and kinematic modeling, there is a growing body of research that leverages AI-based methods to improve accuracy, robustness, and real-time decision-making capabilities. Artificial neural networks and deep learning methods are more and more often used for tasks such as predicting movement trajectories, detecting position anomalies, and approximating complex motion patterns. The main aim of this work is to provide the main contributions of the recent publications to the current state of the field. Key trends, challenges, and prospects for their future development are also highlighted. Initial statistical analysis was conducted based on responses to queries formulated for searching engines of leading online databases since 2006. Next, the retrieved articles from the last 6 years were subjected to a more detailed analysis. They were divided into thematic areas, including models for human pose estimation; systems for motion detection and tracking, with special attention to human movement; and, eventually, more specialized applications such as action recognition, autonomous driving, motion analysis, and surveillance. The architectures of the created models, the methods for parameter tuning or training, the input datasets used, and the result evaluation metrics were classified. Finally, some more general conclusions were drawn. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

Thoracic malignancies are uniquely positioned for the integration of emerging technologies such as artificial intelligence (AI), which have the potential to advance precision oncology across the cancer care continuum. In cancer screening, AI has emerged as a promising strategy to enhance diagnostic accuracy, efficiency, and scalability. Deep learning applied to pathology (pathomics) and imaging (radiomics) has enabled the development of novel, noninvasive tools capable of predicting histologic and molecular features that may correlate with treatment response or toxicity. In drug discovery, computational approaches can analyze large-scale genomic, chemical, and clinical datasets to accelerate target identification and match candidate compounds to available targets; this may be particularly useful in the context of resistance to targeted therapy. AI tools may also support treatment planning for radiation and surgery, guide systemic therapy selection, and facilitate continuous monitoring for early identification of treatment resistance or toxicity. As these technologies are integrated into clinical workflows, careful attention to ethical, regulatory, and clinical governance frameworks will be essential to ensure equitable implementation and bias mitigation. Maintaining human oversight and a human-centered approach remain critical, as complex treatment decisions and sensitive patient interactions are central to the care of patients with thoracic malignancies. Full article

This study examines the relationship between AI leadership, human-centered independence, and organizational innovation processes and outcomes, challenging the prevailing assumption that leadership-driven AI adoption is inherently associated with improved performance. The research draws on a dual-structured model of AI leadership—AI-driven innovation leadership (Sun) and reflective AI governance leadership (Moon)—to examine whether these approaches are associated with human capability development and innovation performance. Data were collected from 2754 respondents across diverse organizational contexts using a structured survey. The measurement model was validated through exploratory and confirmatory factor analysis, and the hypotheses were tested using structural equation modeling (SEM). The results indicate that none of the proposed positive relationships are empirically supported. Neither leadership dimension shows a statistically significant relationship with human-centered independence or innovation performance, while the only statistically significant relationship is negative, indicating that human-centered independence, when not integrated with AI, is associated with lower levels of innovation outcomes. The absence of mediation and negligible explained variance further indicate the lack of an integrated structural relationship among the examined constructs. These findings challenge linear models of AI leadership by showing that the coexistence of AI-oriented leadership and human-centered capabilities does not ensure their integration. The study proposes the AI–Human Misalignment Framework as an interpretative lens, suggesting that innovation outcomes may depend on alignment rather than the mere presence of capabilities. Full article

The epithelium represents the first line of defense against viral infection, yet the precise mechanisms by which viruses penetrate this complex barrier remain incompletely understood. Single-virus tracking (SVT) has emerged as a powerful fluorescence microscopy approach to directly visualize viral dynamics with nanometer spatial precision and millisecond temporal resolution. In this review, we survey recent progress in SVT methodologies, from image-based approaches to active feedback techniques, and assess their capacity to resolve viral behavior in physiologically relevant epithelial models. We further evaluate advances in virus labeling strategies—including fluorescent proteins, organic dyes, and nanoparticles—that enable prolonged observation while preserving infectivity. By integrating developments in optical instrumentation and molecular labeling, SVT is increasingly capable of capturing critical processes, including extracellular diffusion, receptor engagement, internalization, and trans-epithelial transport. Finally, we discuss current challenges, including limited penetration depth, photobleaching, and the complexity of 3D epithelial tissues, and outline future opportunities to extend SVT towards in situ and tissue-level studies. Together, these advances position SVT as a transformative tool to illuminate virus–epithelium interactions and guide therapeutic strategies. Full article

Understanding human–nature–animal relationships is central to conservation and visitor management, yet these relationships are commonly studied through language-based surveys that may exclude participants across age groups and diverse cultural or educational backgrounds. This limitation highlights the need for more inclusive and experience-sensitive approaches capable of capturing relational meanings beyond verbal expression. This study adopts a visual participatory approach in which volunteer tourists were invited to draw circles representing themselves in relation to images of a giant panda, nature, and a pet. Extending the visual idea of Schultz’s Inclusion of Nature in Self in the Connection to Nature Index, more than 1000 tourists at the Chengdu Research Base of Giant Panda Breeding produced over 3000 drawings. These drawings were systematically coded along three dimensions—circle size, orientation, and spatial relationship—and analyzed using multinomial logistic regression and non-parametric tests. The results reveal consistent yet differentiated patterns of visual representation. Participants most frequently expressed relationships with nature and pets through enclosing circles, suggesting spatial inclusion, whereas relationships with the giant panda were more often represented through separate but proximal positioning, indicating a more mediated or observational mode of connection. Demographic factors, including age, residence, and visitation stage, significantly influenced drawing configurations, supporting the interpretation of connection as a context-sensitive and dynamic process rather than a fixed individual trait. Associations between drawing dimensions and self-reported pro-environmental orientation and momentary well-being were observed, although these relationships should be interpreted cautiously given the use of brief, context-specific indicators. Overall, the findings demonstrate that participatory drawing can function as both a research instrument and an engagement tool, enabling diverse visitor groups—including children—to express relational understandings of nature and wildlife. For conservation practice, such visual methods offer a scalable and low-barrier approach to visitor engagement, with potential applications in environmental education, interpretation design, and the assessment of human–animal relationships in situ. Full article

Traditional angle estimation for sum-and-difference monopulse radar systems is predominantly designed for non-coherent sources or relies on fixed closed-form solutions. However, in the presence of coherent sources, these methods often suffer from performance degradation due to data rank deficiency or unavoidable suppression of target power. To address these limitations, this paper presents a single-snapshot angle estimation method for coherent sources by leveraging the angular super-resolution and ranging capabilities of monopulse radar to achieve 3D imaging in the range-angle domain. The approach utilizes the monopulse ratio spatial steering vector as a search vector and projects the received data onto its orthogonal subspace. By exploiting the coupling characteristics between signal polarization and angle, a cost function is constructed to validate the feedback of the search vector. Theoretical analysis demonstrates that for dual-target scenarios, the cost function reaches its minimum precisely when the search vector aligns with a target’s steering vector, enabling the accurate estimation of both targets’ angles. Furthermore, the polarization-angle coupling constraint reduces the 2D angular search space to a 1D line, significantly lowering computational complexity. Simulation results indicate that the method effectively resolves dual targets under single-snapshot conditions and maintains robust performance even with significant energy disparities. Finally, 3D localization of multiple airborne point targets is achieved by integrating 2D angular information with range data, validating the potential of the method for advanced radar imaging and positioning. Full article

In this paper, we propose a novel approach, COMPAS (COMPose Actions and Slots), which leverages the strengths of state-of-the-art object-centric approaches for modeling the dynamics of an environment. Our method encodes the environment’s state into symbol-like, object-centric representations, known as slots, where each slot corresponds to an individual object. This approach offers a structured and interpretable way to model complex environments by combining slots with action representations for accurate next-state prediction. The primary contribution of our work is an efficient world model with a dynamics predictor capable of predicting accurate trajectories in action-dependent environments. Additionally, our slot extractor module enhances the predictive capabilities by extracting deterministic slots that remain consistent both within a single trajectory and across episodes. Unlike slots sampled from a trainable distribution, deterministic slots are generated from a single trainable parameter together with slot positional embeddings. This design improves the consistency across episodes, which in turn leads to more accurate dynamics prediction. We present a comprehensive evaluation of our approach in various environments, demonstrating that our proposed method outperforms competing models in environments with discrete and continuous action spaces. Full article

This study theoretically investigates the role of plant moisture content and its spatial heterogeneity in wildfire dynamics using Cellular Automata models. The model incorporates varying moisture levels and ignition probabilities across different grid configurations, including homogeneous moisture grids and heterogeneous setups with elliptical and segmented high-moisture zones. The relationship between moisture content and ignition probability is modeled using a nonlinear formulation, reflecting threshold-like combustion dynamics observed in real ecosystems. Simulation results show that introducing high-moisture zones significantly reduces the rate of fire spread, with segmented configurations providing the most effective firebreaks. In this context, the ‘suppression effect’ denotes the reductions in forward spread and total burned area attributable to high-moisture regions acting as low-ignitability barriers. The effect is more pronounced when ignition probability depends nonlinearly on moisture, since the nonlinear mapping produces a steeper decline in ignitability above a critical moisture range, which reduces successful transmission across the barrier and increases the likelihood of fire isolation. In particular, the results highlight how modeling can be used as a decision-support tool for the strategic placement of firebreaks. By evaluating alternative spatial configurations of moisture, the approach helps identify barrier designs that maximize containment effectiveness while minimizing ecological and economic costs. This positions the methodology not only as a theoretical contribution but also as a practical framework for guiding firebreak planning and wildfire prevention policies. While the model successfully captures critical fire dynamics, its assumptions of static moisture content and simplified environmental conditions warrant further investigation. Future work will focus on integrating real-time moisture data and refining parameters with observational wildfire data to enhance the model’s predictive capabilities. This study provides valuable insights into the interplay between moisture content and wildfire spread, contributing to the development of decision-support tools for effective wildfire management. Full article

Auxetic cellular materials attract increasing attention for crashworthiness and impact protection due to their negative Poisson’s ratio (NPR). However, conventional double-arrowhead auxetic honeycombs (DAHs) with straight ligaments often exhibit limited energy absorption and unstable collapse under large deformation. In this study, a curvilinear hybrid auxetic honeycomb (CHAH) is proposed by replacing straight walls with smoothly curved ligaments and embedding a circular positive Poisson’s ratio subcell to provide symmetric support. The mechanical behavior of the CHAH is investigated through a combined experimental–numerical approach. Finite element simulations are validated by quasi-static compression experiments, and a parametric study is conducted to evaluate the influence of key geometric variables on specific energy absorption (SEA) and peak crushing force (PCF). Based on the validated simulations, a multi-objective optimization framework integrating optimal Latin hypercube sampling, radial basis function surrogate modeling, and NSGA-II is employed to optimize the structural parameters. Compared with the conventional DAH under identical material and volume conditions, the CHAH exhibits significantly improved deformation stability and energy absorption capability, with SEA increasing by up to 67.06% and a more stable plateau response. In addition, SEA and PCF can be effectively tuned by varying the geometric angles (θ₁, θ₂). Full article

This study proposes a data-driven evaluation framework to quantify the impact of artificial intelligence (AI) on industrial process performance and enterprise value creation. The framework integrates enterprise value assessment based on the Feltham–Ohlson model with a multi-level performance evaluation framework that incorporates a hybrid Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) for indicator weighting, together with Fuzzy Comprehensive Evaluation (FCE) for multi-dimensional aggregation. This integrated approach enables systematic analysis of AI-driven effects from the perspectives of intelligent investment input, operational governance environment, and process output performance. Using panel data from 3515 Chinese A-share listed firms (20,076 firm-year observations) during 2014–2022, a Process Performance Index (PI) is constructed to measure AI-enabled operational capability across resource allocation efficiency, coordination effectiveness, and production performance dimensions. Empirical results indicate that PI is positively associated with abnormal earnings and firm profitability, demonstrating that AI-enabled process capability contributes to sustained enterprise value growth. The findings further show increased digital technology investment intensity, knowledge-based human capital accumulation, and improved data governance conditions, accompanied by enhanced production and service performance. By explicitly integrating AHP–EWM weighting and FCE aggregation within the Feltham–Ohlson valuation structure, the proposed framework provides an interpretable quantitative mechanism linking AI adoption, operational capability development, and enterprise value creation. The results offer practical insights for evaluating intelligent transformation strategies in the context of Industry 5.0 and data-driven industrial development. Full article

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

As AI-powered e-commerce platforms grow more capable of predicting customer wants, a critical question remains unexplored: what makes customers perceive these experiences positively? The rapid integration of artificial intelligence (AI) into e-commerce platforms is reshaping how customers search for, evaluate, and experience digital services. However, empirical research has not kept pace with clarifying which platform-level factors most effectively shape customer experience (CX) in AI-driven environments. This study validated the Trust, Autonomy, Personalization, and Customer Engagement (TAPE) framework as a comprehensive set of CX drivers in intelligent commerce. Using survey data from 400 active e-commerce users, we employed a multi-stage approach combining exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling (SEM) with bootstrapped mediation testing. All four TAPE drivers demonstrated significant positive reflective associations with CX, with personalization and engagement emerging as the strongest contributors. CX was strongly associated with customer satisfaction, loyalty, and brand equity, and mediated the effects of all four dimensions on these strategic outcomes, with model comparison evidence supporting full mediation. The study contributes theoretically by integrating and empirically validating four established CX dimensions within the AI-enabled e-commerce context, and by demonstrating the central mediating role of CX in converting intelligent platform features into user-perceived strategic value. Managerially, the TAPE framework provides actionable guidance for designing transparent, adaptive, and engaging AI-driven customer journeys that enhance both experience quality and long-term brand outcomes. Full article

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. The kinematic chain is inverted to reflect the logic of VPP: the world reference frame is fixed to the robot’s tool (the build plate), while the tool frame is attached to the polymerization zone. A virtual degree of freedom for screen image rotation is introduced, bringing the system to eight degrees of freedom. Inverse kinematics are solved under constraints (pose tolerance, joint limits, collision avoidance, and continuity) and evaluated using multi-criteria metrics: manipulability, normalized joint-limit margin, and positional/angular sensitivity. The algorithm follows a deterministic coarse-to-fine search procedure: discrete sweeping of global part orientations, initial sampling with Halton sequences, abd feasibility filtering on a sparsified trajectory, followed by refinement and multi-criteria ranking. The pipeline successfully discarded infeasible orientations and identified feasible printing trajectories for six of the seven benchmark parts, while the remaining case highlights a limitation that may be addressed in future improvements. Full article

Botnet architectures are evolving rapidly, creating significant threats to global network security. This paper presents a homogeneous Radial Basis Function Neural Network (RBFNN) approach for botnet detection that employs a single, uniform RBFNN architecture with identical basis kernel types across all network components. Utilizing the CTU-13 dataset to extract flow-level packet length distribution features. These features are critical for identifying the distinct signatures of the 30 botnet types in the dataset, thereby enhancing the detection capabilities of our uniform RBF framework. The proposed model was designed to address the challenge of achieving high discriminative capability between Normal and Botnet activities while preserving the low latency needed for real-time deployment. Extensive experiments, including cross-validation and Operating Characteristic (ROC) analysis, show the model is effective, achieving a top classification accuracy of 98.31% and distinguishing well between Botnet and normal activities, with an Area Under the Curve )AUC( of 0.997. Furthermore, Training behavior analysis demonstrated stable convergence across different batch size configurations, highlighting trade-offs between accuracy and computational cost. A batch size of 64 provides an optimal balance between convergence speed and accuracy, with a total training time of 29.62 minutes. Crucially, the assessment of processing speed revealed a latency of 1.0118 microseconds. Such minimal delay validates the architecture’s suitability for high-speed network environments where real-time traffic analysis is imperative. Moreover, confusion matrix analysis further confirmed the reliability of the detection, with a low false-positive rate of nearly 0.018. Overall, the empirical results demonstrate that the homogeneous RBFNN offers an advanced solution for complex botnet detection. Full article