Search Results (916)

Frequent seasonal phase transitions in cold and arid lakes require different remote sensing indices for frozen and open-water periods, complicating the use of traditional empirical indices for automated monitoring. To address this challenge, this study proposes an intelligent indexing framework integrating the heuristic reasoning of Large Language Models (LLMs) with Random Forest (RF) feature selection. Leveraging the Google Earth Engine (GEE) and Landsat 8 data from Ulansuhai Lake, five LLMs such as Gemini and ERNIE were employed to generate candidate spectral indices based on typical sample spectra. Optimal band combinations were identified via RF importance, and Land Surface Temperature (LST) was incorporated as a physical constraint for unified cross-seasonal classification and determine the optimal threshold. Results show that the LLM-derived ERNIE-WISI and Gemini-WISI exhibit high robustness. During the freezing period, ERNIE-WISI significantly outperformed other indices, achieving an Overall Accuracy (OA) of 89% and a Kappa of 0.86. Spatially, it yielded snow and ice mapping with clear textures and low commission errors. During the non-freezing period, ERNIE-WISI achieved an OA of 95% with a Kappa of 0.84. While Gemini-WISI achieved an OA of 94% with a Kappa of 0.80, performing comparably to MNDWI. Notably, ERNIE-WISI effectively suppressed background interference in complex landscapes like narrow channels and aquaculture areas, maintaining high geometric fidelity and spatial continuity. A key advantage of ERNIE-WISI is its consistent performance without seasonal threshold adjustments. Aligned with the AI for Science paradigm, this methodology bridges AI-driven heuristic discovery and physical remote sensing, offering a robust, transferable solution for long-term dynamic lake monitoring in extreme environments, thereby facilitating sustainable water management. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

As artificial intelligence (AI) becomes increasingly important in rural revitalization, building consensus among multiple stakeholders and developing participatory digital co-creation platforms has grown increasingly urgent. However, existing large language model (LLM) systems predominantly adopt a one-shot generation paradigm, making it challenging to accurately capture villagers’ cultural aspirations and frequently resulting in a significant disconnect between design outputs and community expectations. This situation reveals deficiencies in progressive deliberation mechanisms and cultural controllability. To address these issues, this study proposes a multimodal Participatory Landscape Demand Generation (PLDG) system to enhance AI-generated dialogue controllability, facilitate effective cultural translation in sensitive rural contexts, and promote sustainable development where landscape design both drives and reflects rural revitalization. The system leverages LLMs to simulate stakeholder participatory interactions in village landscape design scenarios. Using culturally distinctive Chinese villages as case studies, the research conducts multi-role simulated dialogues, multimodal semantic extraction, and iterative consensus-building, and evaluates the resultant data to generate landscape design proposals. The results indicate that the PLDG system significantly improves participation efficiency among diverse design stakeholders and enhances the sustainability of design decisions. Compared to conventional methods, metrics such as cultural compatibility, villager participation, and design innovation show substantial improvements. These findings demonstrate the considerable potential of human-AI collaboration in future rural planning. This study introduces the Culture Constraint-Driven Rural Landscape AI Collaborative Design Framework (PLDG), validating its practical efficacy in identifying culturally sensitive elements, ensuring cultural congruence, facilitating community participation, and fostering design innovation. Consequently, it provides a reusable, iterative operational tool for the digital renewal of sustainable rural landscapes. Full article

The processing of two-dimensional (2D) spectral images constitutes a critical and multifaceted discipline in contemporary astronomical data analysis. As spectroscopic instruments evolve towards higher multiplexing, resolution, and sensitivity, the raw 2D data captured by detectors present increasingly complex challenges that transcend simple one-dimensional extraction. This review provides a systematic and comprehensive examination of the methodological evolution in this field over the past two decades. It gathered relevant studies by searching mainstream academic repositories and general search engines with the core keyword ‘2D Spectral Image’, and selected qualified references according to accessibility and research relevance. We categorize the landscape into three major paradigms: (1) physics-based modeling and algorithmic correction techniques for geometric distortion, scattered light, and sky background; (2) data-driven machine learning and deep learning approaches for image correction, spectral classification, and faint signal detection; and (3) the development of open-source software pipelines that democratize advanced processing. A central contribution of this review is a detailed comparative analysis of the performance metrics, underlying assumptions, and practical limitations of prominent algorithms. We highlight the transformative impact of convolutional neural networks (CNNs) and vision transformers (ViTs) on tasks such as celestial object classification and exoplanet detection, while also acknowledging the enduring importance of robust physical models for calibration and uncertainty quantification. The discussion culminates in an assessment of persistent challenges—including computational scalability, model generalizability, and interpretability—and outlines promising future directions at the intersection of AI, statistical inference, and large-scale survey science. Full article

Background/Objectives: Regular dialysis sessions impose a fixed schedule on the patient’s days and weeks; this can lead to negative emotions, low mood, helplessness, and a lack of control over their treatment, which significantly reduces the quality of life for these patients. The aim of this study was to assess the mood, level of illness acceptance, and quality of life among dialysis patients undergoing relaxation therapy using virtual reality (VR). Methods: Sixty hemodialysis (HD) patients were recruited for a single-arm study. A personal questionnaire as well as the AIS, PHQ-9, and KDQOL-36™ were used. After one month of the control period, 22 patients were analyzed and then continued with one month of VR relaxation therapy consisting of 360° scenarios or 2D landscape movies. Finally, 16 patients were analyzed for the outcomes during dialysis, three times a week. Results: The data analysis showed a small significant increase in AIS scores after the VR therapy. In the PHQ-9, slight significant reductions in scores were observed at the end of VR therapy. Analysis of the Physical Component Summary (PCS), but not for Mental Component Summary (MCS), results showed statistically significant increases after VR therapy. Conclusions: The study group of dialysis patients showed small but significant improvements in mood, disease acceptance, and quality of life. The VR therapy intervention may be a useful complementary tool to comprehensive treatment and rehabilitation for hemodialysis patients, but multi-center studies are needed for a larger group of patients. Full article

Artificial intelligence (AI), virtual reality (VR), and exergame technologies have been increasingly used in dance and movement activities. However, this literature remains dispersed across different areas, making it difficult to determine how the field has developed. This study mapped the research landscape and thematic development of AI-, VR-, and exergame-based dance and movement research on psychological outcomes using bibliometric analysis and latent Dirichlet allocation (LDA) topic modeling. A total of 252 records indexed in the Web of Science Core Collection from 2011 to 2025 were included. Five related thematic strands were identified: immersive dance interaction and technology-supported teaching; rehabilitation-oriented dance or rhythm training; school-based exergaming and psychophysiological assessment; behavioral program design and intervention implementation; and AI-based motion or emotion recognition. These strands indicate that the field has developed into a multi-layered research space shaped by technology functions, movement contexts, intervention formats, and psychological constructs, rather than a single dance-intervention or technology-application domain. At the same time, psychological outcomes were not represented with equal clarity across these strands. Participation-related and psychosocial constructs, including enjoyment, motivation, engagement, self-efficacy, social interaction, emotional expression, and quality of life, were more frequently represented, whereas mental-health-related outcomes such as anxiety, depression, stress, loneliness, and psychological well-being were less consistently connected to technology-supported dance or movement interventions. These findings clarify where evidence is concentrated, how major themes are organized, and where psychological outcome measurement requires clearer theoretical and methodological specification. Future studies should use comparative and longitudinal designs to examine whether VR/AI-based feedback-supported movement training offers added value over conventional dance or movement programs for psychological outcomes, participation, exercise experience, and longer-term behavior change. Full article

Arthropod natural enemies—encompassing predators and parasitoids—form the backbone of sustainable agriculture, delivering irreplaceable ecosystem services via biological pest suppression. Driven by global demand for eco-friendly alternatives to synthetic pesticides, research in this domain has grown sharply over the past decade. Here, we report a systematic bibliometric analysis of 6515 Web of Science Core Collection papers focused on arthropod natural enemies in biological control (2016–2025), with the goal of charting the field’s intellectual structure. Performance metrics confirmed an initial rapid increase from 2016 to 2019 followed by a plateau and a slight rise in 2025, with the US, China, and Brazil dominating output. Keyword co-occurrence networks pinpointed core themes, including conservation biological control, predatory mites, and integrated pest management (IPM). Temporal trends further revealed a pivot toward applied work on invasive pest systems. Co-citation analysis uncovered six foundational research clusters, while bibliographic coupling of 2021–2025 papers uncovered five active emerging subfields: landscape ecology and habitat manipulation, tri-trophic interaction mechanisms, high-impact invasive pest biocontrol, non-target risk assessment for introduced agents, and fall armyworm integrated management. We synthesize cross-cutting implications and outline future priorities—including AI-enabled rearing systems, functional biodiversity boosting, climate adaptation, and multifunctional landscape tuning. By consolidating historical progress and forward-looking directions, this framework empowers researchers, extension practitioners, and policymakers to scale sustainable pest management worldwide. Full article

Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. Full article

In a time of pandemic interruptions, work arrangements and flexible work environments are becoming more and more crucial in service firms. While this issue is central to the ethics and effectiveness of human–AI interaction, it has received limited focused attention in both research and practice. As businesses increasingly deploy AI to enhance productivity and efficiency, concerns are emerging about its potential impact on employee well-being resulting specifically in work–life conflict. This study investigates how AI implementation can simultaneously drive performance and contribute to burnout, drawing on an empirical framework. Using a quantitative research design, data will be collected from employees at a university in Kuwait actively integrating AI technologies into their workflows. Guided by the IMPACT model and grounded in the Conservation of Resources (COR) theory and the Social Cognitive Theory (SCT), this study explores how organizational investment in AI influences employees’ experiences of work–life conflict. The findings will highlight AI’s dual role as a productivity enhancer and a potential stressor within a Kuwaiti institution. The study underscores the importance of balanced digital strategies—aligning technological advancement with leadership empathy, robust support systems, and employee well-being initiatives. By contextualizing global research within Kuwait’s evolving digital landscape, this study contributes region-specific insights and practical recommendations for fostering human-centered, sustainable AI integration. Ultimately, it aims to guide organizations in designing AI policies that enhance productivity without compromising employee health, advancing the responsible and ethical management of AI in the workplace. Full article

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services. Full article

The rapid integration of generative artificial intelligence (GenAI) into education across all sectors has prompted a proliferating body of scholarship addressing the ethical, social, and environmental implications of these technologies. This narrative literature review synthesises international empirical, conceptual, and policy literature published between 2022 and 2026 to trace the evolving story of ethical concerns surrounding GenAI in education. Drawing on the moral philosophy of consequentialism, particularly the utilitarian ethics of John Stuart Mill, the review analyses six interconnected domains of ethical concern: environmental sustainability and the carbon footprint of AI infrastructure; algorithmic bias, ideological encoding, and the reproduction of misinformation; user dependency and the erosion of learner agency; the displacement of critical and creative thinking; data privacy and surveillance; and the orientation of major GenAI platforms toward profit-driven and capitalistic outcomes. Unlike systematic reviews that privilege methodological replicability, this narrative review foregrounds interpretive synthesis, tracing how the ethical discourse has shifted from early alarm and prohibition toward more nuanced frameworks for responsible integration. The review identifies a consequentialist tension at the heart of the debate: while GenAI offers measurable benefits in personalisation, accessibility, and efficiency, these gains must be weighed against distributed harms that disproportionately affect vulnerable populations, the natural environment, and the epistemic foundations of education itself. The review concludes with a set of guidelines for the ethical use of GenAI in educational contexts, grounded in the literature synthesised in the article. Full article

The recent rapid growth of deep learning has significantly reshaped the landscape of computer vision, establishing itself as the preferred paradigm for various tasks. Deep learning methods have demonstrated superior performance compared to previous state-of-the-art machine learning techniques across various fields. This review provides a concise overview of artificial neural networks (ANNs) and some of the most significant deep learning architectures, such as recurrent neural networks (RNNs), generative adversarial networks (GANs) and radial basis function networks (RBFNs). This review not only outlines the historical context and structures of these architectures but also provides a sophisticated understanding of their applications across different computer vision domains. A rigorous and comprehensive overview of these architectures is discussed throughout this review, and an essential systematic comparative analysis based on specific benchmarking criteria is provided. While individual deep learning frameworks excel in distinct domains, selecting the optimal architecture requires a balanced trade-off between algorithmic complexity, computational overhead, data dependencies, and structural interpretability. An intuitive and holistic benchmarking process synthesizes the core characteristics, technical configurations, operational constraints, and developmental pathways toward Explainable AI (XAI) and Green AI sustainability for the examined architectures (ANNs, RNNs, LSTMs, GANs, and RBFNs). Additionally, in this work the advantages and limitations of these architectures are discussed. Furthermore, an investigation of their applications in diverse computer vision tasks is carried out. Full article

Digital evidence (DE) continues to evolve alongside rapid technological innovation, including the increasing integration of AI-generated media, algorithmic tools, and digital forensic technologies used to identify, extract, and analyse digital artefacts in criminal investigations. Yet, limited work has examined how frontline police officers interpret and operationalise digital forensic outputs, particularly in the context of emerging forms of AI-mediated evidence. We investigated the perceptions of 13 police officers in England and Wales regarding the evolving role of DE and digital forensic investigations, and the implications for policing practice. Reflexive thematic analysis identified four themes: (i) AI and the evolving landscape of digital forensic investigations; (ii) digital systems and the infrastructure of investigations; (iii) human judgement and trust in the interpretation of DE; and (iv) building digital expertise in modern investigations. Participants described a marked rise in highly realistic AI-generated imagery, which complicates evidence categorisation and proportionality assessments. This also reshapes investigative decision-making while necessitating adaptations in investigative interviewing, particularly around disclosure sequencing and evidential challenge. Findings suggest that understanding of AI and algorithmic systems among some frontline officers remains underdeveloped, raising concerns about the interpretation of outputs. By foregrounding practitioner perspectives, this study contributes a human-centric understanding of digital forensic practice, while offering insights into the future development of investigative approaches in response to emerging technologies and evolving threats. Full article

Quantitative structure–activity relationship (QSAR) modeling has conventionally relied on expert-designed molecular descriptors to encode chemical structures. DeepSnap is a descriptor-free QSAR approach that converts prepared three-dimensional molecular conformers into image representations and feeds them directly into convolutional neural networks for activity prediction. This focused narrative review traces DeepSnap from its introduction in 2018 to its current state and places it within the broader landscape of descriptor-based QSAR, topology-based and 3D-aware graph neural networks, and related image-based or semi-image-based molecular representation approaches. Previous studies applied DeepSnap to Tox21 nuclear receptor and molecular initiating event endpoints, rat hepatic clearance, blood–brain barrier penetration, acute oral toxicity, and cosmetics–pharmaceutical compound classification. Across the DeepSnap series, image-based and descriptor-based predictions have provided complementary information, particularly in ensemble or consensus models. However, high or near-ceiling ROC–AUC values reported for selected endpoints should not be interpreted as indicating deterministic or universally generalizable predictions; rather, they should be considered in the context of endpoint-specific model development, image-rendering parameter optimization, possible class imbalance, split dependence, limited matched external replication, and incomplete benchmarking against modern molecular representation models. Limitations include a dependence on nonphysical rendering parameters, single- or representative-conformer input, incomplete matched benchmarking against 2D and 3D molecular representation models, and an interpretability gap addressed in part by CAM-family visualization in the AI-based Substance Hazard Integrated Prediction System (AI-SHIPS) and S-COPHY (a model developed by Shiseido for cosmetics–pharmaceutical compound classification). Future directions include standardized image-generation protocols, conformer-ensemble extensions, systematic interpretability analysis, matched benchmarking, and potential integration with graph-based and 3D-aware molecular learning approaches. Full article

The rapid advancement of AI-driven generative design brings new opportunities, but its application in landscape garden design remains limited by two gaps: (1) semantic misalignment between generated images and the designer’s intent, and (2) low-resolution outputs with insufficient details. To address these gaps, we developed GardenDiff, a domain-adapted diffusion model trained via parameter optimization and a specialized landscape garden dataset. Central to this approach is Structured Design Captioning (SDC), a hierarchical annotation system specifically designed for garden design that encodes design elements, style features, and auxiliary scene information. To develop this model, we designed a three-stage experimental framework. In Stage 1, we examined the effects of training caption systems and training resolution on generated landscape garden imagery by controlled experiments. In Stage 2, we conducted joint training across five garden styles (Chinese, Japanese, Mediterranean, Nordic, and English) based on the optimized parameter settings from Stage 1 to construct the GardenDiff model. In Stage 3, we validated the model performance through expert evaluation (N = 36) and public evaluation (N = 136) and analyzed style-specific variations in the generated outcomes. Research results showed that Structured Design Captioning (SDC) improved Spatial Rationale by 19.67–39.46% compared with generic captions, and training at 1536 × 1536 pixels improved image quality by 23.2% compared with 768 × 768-pixel training. GardenDiff trained with these optimized parameters showed notable advantages. Its overall scores (5.06) exceeded those of Stable Diffusion XL base 1.0 (SDXL 1.0) by 16.4% and DreamShaper XL by 22.4%. The model improved across four dimensions, including Design Rationale, Design Professionalism, Design Accuracy, and Design Satisfaction. Our study offers a new model to improve the perspective visualization of generative garden design and provides insights into AI-informed landscape and urban design. Full article