Search Results (9,879)

Human activity recognition (HAR) in smart environments plays a critical role in applications such as healthcare monitoring, intelligent transportation systems, and ambient assisted living; however, existing approaches are limited by their inability to effectively handle heterogeneous multimodal sensor data, capture long-range temporal dependencies, and generalize across diverse real-world environments under domain shifts. In this work, we present DMART-HAR, a Dynamic Multimodal Activity Recognition Transformer framework that unifies structured multimodal representation learning, transformer-based temporal modeling, cross-modal interaction, and adversarial domain adaptation within a single architecture. Specifically, the developed method introduces a sensor tokenization mechanism to encode heterogeneous IoT data into a unified representation space, followed by a transformer encoder to capture global contextual dependencies, while a cross-modal attention module enables deep interaction among sensor modalities and an adversarial domain adaptation strategy enhances robustness to unseen environments. Extensive experiments on benchmark datasets, including CASAS, PAMAP2, and Opportunity, demonstrate that DMART-HAR consistently outperforms both conventional baselines and recent state-of-the-art methods, achieving accuracy/F1-scores of 94.3%/92.8%, 96.2%/94.7%, and 89.8%/88.1%, respectively, and consistently outperforms the strongest competing approaches under cross-domain evaluation settings. These findings demonstrate the effectiveness of modeling temporal dynamics, multimodal relationships, and domain invariance simultaneously, establishing DMART-HAR as a scalable and robust solution for real-world HAR applications. Full article

Background/Objective: The H-HT represents a critical vulnerability for patient–family caregiver dyads. This study developed and content-validated a middle-range nursing theory, the Adaptarte Model, designed to guide dyadic adaptation during the H-HT within the Latin American healthcare context. Methods: A sequential exploratory multimethod design was executed in two phases. Phase 1 integrated three evidence streams: clinical practice insights, a JBI-guided scoping review, and two focus groups with transitional care professionals. Qualitative content analysis and iterative consensus refined the model’s core concepts, assumptions, and propositions. Phase 2 evaluated the model’s content, structure, functionality, and projection using an international panel of eleven Latin American experts meeting strict eligibility criteria. Data were analyzed using Lawshe’s Content Validity Ratio (CVR) modified by Tristán (cutoff = 0.58) and the overall Content Validity Index (CVI). Reporting followed PRISMA-ScR and GRAMMS guidelines. Results: Expert consensus confirmed the essential model components. Item-level CVR values ranged from 0.90 to 0.99, yielding an overall CVI of 0.96, while external functionality and conceptual projection achieved an average rating of 0.88. Conclusions: The Adaptarte Model demonstrates high content validity and structural clarity, establishing a rigorous theoretical foundation for subsequent empirical research. Rather than being ready for immediate clinical implementation, it provides a structured blueprint for prospective protocol development. Systematic empirical testing and longitudinal studies are now imperative to evaluate its clinical utility and drive future healthcare transformations. The scoping review protocol was prospectively registered on the Open Science Framework (OSF) URL (accessed on 23 September 2024). Full article

The transition toward Industry 5.0 requires safety management systems that are not only intelligent and data-driven but also human-centric, resilient, and aligned with sustainable operations. However, conventional safety risk assessment in steel manufacturing remains heavily dependent on expert judgment and often lacks the adaptability required to address complex and dynamic production environments. This study proposes a text-driven intelligent framework for sustainable safety risk governance by integrating natural language processing, topic modeling, objective indicator weighting, and Bayesian decision fusion. The framework establishes a closed-loop process encompassing risk identification, quantitative assessment, risk classification, and hierarchical control. It automatically extracts risk-related information from unstructured safety records, maps the identified hazards onto a human–machine–environment–management structure, quantifies multidimensional risk indicators, and translates assessment outcomes into differentiated control measures. The framework was evaluated using field safety records collected from Tianjin Iron and Steel Group. The topic modeling results identified four major dimensions of operational risk, while the CRITIC–Bayesian weighting mechanism combined data-driven indicator differentiation with context-sensitive probabilistic reasoning. Following its integration into the company’s intelligent safety management platform and one year of operational use, the framework reduced the time required to formulate safety inspection plans by 70%, supported dynamic four-level risk classification, and achieved a 97% task completion rate. The number of recorded safety accidents also decreased by 35% compared with the pre-deployment baseline. These findings demonstrate that unstructured safety text can be transformed into actionable risk intelligence, enhancing the proactive, systematic, and adaptive governance of safety risks. The proposed framework provides a practical pathway for advancing human-centric safety management, operational resilience, and sustainable production in the steel industry. Full article

Background: Biomedical spectroscopy offers a non-invasive way to characterise disease, but the large number of spectral variables, redundancy, and limited interpretability can hinder the creation of compact diagnostic models. This study presents the Spectral Linear Series Decomposition Learner (Spectral-LSDL), an adaptive framework for spectral information localisation and representation that combines established spectral transformations with threshold-based decomposition and morphology-based feature extraction. The framework tests whether disease-related discriminative information can be preserved within specific spectral decompositions rather than requiring representation of the full measured spectrum. Methods: Spectral-LSDL combines complementary spectral representations, adaptive upper/lower threshold decomposition, localisation-depth optimisation, and morphology-based feature extraction to produce compact 50-feature representations of spectral structure. The framework was tested on three biomedical spectroscopy datasets: Raman spectroscopy for head and neck cancer, near-infrared (NIR) spectroscopy for skin-lesion classification, and ATR-FTIR spectroscopy for type 2 diabetes. Model training and evaluation used biological-group-aware partitioning so that spectra from the same participant or lesion did not appear in both development and hold-out sets. Spectral-LSDL was compared with Raw-LDA, PCA-LDA, PLS-DA, and a 1D-CNN. Performance was measured using balanced accuracy, macro-F1, and ROC-AUC, with paired biological-group bootstrap inference. Preferential spectral localisation was examined separately through size-matched random-region Monte Carlo analysis. The Information Localisation Index (ILI) and its dimension-normalised form (nILI) were used descriptively to assess compression efficiency and predictive retention. Results: Spectral-LSDL achieved marked dimensional reduction while maintaining competitive predictive performance across all three modalities. Group-independent hold-out ROC-AUC values were 0.739 for Raman, 0.809 for NIR, and 0.940 for ATR-FTIR, with corresponding nILI values of 1.184, 0.976, and 0.946, respectively. Paired inference showed no statistically significant differences between Spectral-LSDL and the conventional comparators for Raman or NIR after correction for multiple testing. For ATR-FTIR, Raw-LDA significantly outperformed Spectral-LSDL in balanced accuracy (ΔBA = −0.050, 95% CI −0.088 to −0.014; Holm-adjusted p = 0.016) and ROC-AUC (ΔAUC = −0.040, 95% CI −0.073 to −0.012; Holm-adjusted p = 0.020). Size-matched random-region analysis provided evidence of preferential localisation for Raman (balanced accuracy: p = 0.049; ROC-AUC: p = 0.002), whereas significant localisation enrichment was not seen for NIR or ATR-FTIR. Conclusions: Spectral-LSDL offers an adaptive framework for spectral information localisation and representation based on established analytical components, allowing compact representations of biomedical spectra while preserving predictive information. The results show that adaptive decomposition can be used to examine where predictive spectral structure is concentrated, but they do not demonstrate consistent predictive superiority, distinct biochemical regions, or clinical usefulness. These findings support further study of Spectral-LSDL as an interpretable approach to spectroscopy modelling, although external prospective validation and independent biochemical confirmation are needed to establish the reproducibility, biological specificity, and clinical relevance of the identified spectral localisations. Full article

Solving problems associated with the efficient distribution and organization of resources has generated increasing interest in the scientific community. One of the most commonly used approaches consists of approximate solution techniques, which have been able to solve complex covering problems within acceptable computational time and cost. One of the benchmarks used to evaluate these approaches is the Set Covering Problem, which is an NP-hard combinatorial optimization problem. Among the techniques that have been investigated, metaheuristics play an important role. These methods are commonly developed for continuous search spaces and, in order to be applied to covering problems, must be modified to operate in discrete domains. This modification presents an important challenge: finding an appropriate transformation method that translates continuous solutions into binary solutions. This issue has been addressed through two main strategies: binarization using two-step schemes, and, in our proposal, the use of repair operators orchestrated according to their performance through an Adaptive Repair Selection Mechanism based on the multi-armed bandit framework. To evaluate our proposal, we selected the Binary Hunger Games Search metaheuristic because the relative quality of each individual determines its hunger level, which in turn regulates the movement of the population and the influence of the best solution found. Infeasible solutions are handled through a set of Tabu Search-based repair operators. Instead of applying a single repair rule throughout the entire execution, the proposed approach dynamically selects among these operators according to their observed contribution during the search. Each repair operator also incorporates Tabu memory to discourage repetitive decisions during feasibility restoration. The experiments were conducted using the classical Beasley benchmark instances for the Set Covering Problem. Full article

This study proposes a physics-proxy-residual-guided cross-attention ensemble neural network (PGAE-NN) for oil-immersed transformer bushing insulation-condition assessment and early warning. Eight core indicators are selected from twelve candidates via Pearson correlation and Fisher discriminant analyses, with four insulation levels defined with reference to IEEE Std C57.104-2019. LightGBM, 1D-CNN, and Transformer Encoder serve as heterogeneous base learners for statistical, local temporal, and global temporal features. A cross-attention meta-learner fuses their outputs by penalizing predictions that deviate from Arrhenius thermal-aging and Fick moisture-migration proxy residuals. A piecewise regularization strategy and a classification-precursor dual-task objective further enhance degradation-stage adaptivity and early warning. Validation uses 23,400 accelerated-aging samples from four 110 kV bushings under four typical defects. PGAE-NN achieves 96.14% test accuracy (F1 = 0.9613; AUC = 0.9835) and 96.36% ± 0.54% five-fold cross-validation accuracy, outperforming PSO-SVM and Traditional Stacking by 7.99 and 2.69 percentage points, respectively. The precursor-warning F1 reaches 0.923, and ablation studies confirm the meta-learner, dual physics constraints, and dual-task design contribute 1.82, 1.46, and 1.11 percentage points, respectively. The proxy residual under severe conditions drops by 44.9%, demonstrating that physics-guided fusion constrains predictions within physically consistent boundaries. Full article

Burial and mourning practices in Africa have long been embedded in deeply rooted cultural, spiritual and communal value systems. However, these traditions are undergoing significant transformation through modernisation, urbanisation, migration, religious change and economic pressure. This study examines the changing dynamics of burial and mourning practices in Southern Africa, with particular attention to Zimbabwe and Lesotho. Drawing on qualitative insights from existing scholarship, cultural analysis and the researchers’ contextual observations, the paper explores how mourning periods, communal participation, ritual ceremonies, ancestral relations and attachment to burial land are being reconfigured rather than simply abandoned. In both countries, prominent shifts include the professionalisation and commercialisation of funerals, the influence of Pentecostal Christianity, the growing role of funeral assurance schemes and the disruption of expected rites by COVID-19. Using functionalist, symbolic interactionist and African Indigenous Knowledge Systems (AIKS) perspectives, the paper identifies a continuing tension between cultural identity and contemporary practical realities. It argues that some indigenous practices have weakened, while others have been adapted or hybridised into new cultural forms. The comparison contributes to debates on cultural continuity, genealogy, identity and social change by showing how communities preserve the social and spiritual functions of mourning even when its outward forms change. The paper is positioned primarily within cultural studies, specifically the interdisciplinary study of religion, ritual and social change. Among the factors considered, Pentecostal reinterpretation and ritual substitution, the professionalisation and commodification of funerals, and migration, together with pressure on burial land, appear most influential. COVID-19 is retained as a historically bounded accelerator rather than the dominant continuing force. Full article

Purpose: This study examines how large multinational corporations translate sustained macroenvironmental volatility into deliberate strategic reconfiguration processes, and how adaptive mechanisms differ across business models and sectors under shared environmental pressures. Design/Methodology: The study employs a qualitative comparative multiple-case design with abductive logic and a longitudinal perspective covering 2019–2024. Apple, Amazon, and McDonald’s were selected through theoretical sampling for their sector heterogeneity, their shared global regulatory and operational exposure, and their contrasting adaptive architectures—vertical integration, platform diversification, and franchising, respectively—rather than a uniform majority-international-revenue criterion. A corpus of 78 primary and secondary documents was analysed through open coding, axial coding, thematic aggregation, cross-case comparison, and pattern matching. Findings: All three firms converge around digitalisation, regulatory compliance, and sustainability as environmental legitimacy requirements rather than differentiating strategic choices. Divergence emerges in execution mechanisms: Apple deploys anticipatory vertical integration; Amazon converts operational complexity into structural barriers; McDonald’s exploits franchise flexibility for local adaptation while preserving brand coherence. The analysis yields an original Adaptive Reconfiguration Cycle (ARC Framework) comprising five iterative stages, evidenced for each firm across the full cycle rather than only its dominant stage. Theoretical Contribution: The study develops an integrated macroenvironment–capability reconfiguration framework that bridges PESTEL analysis, dynamic capabilities theory, and contingency theory, addressing an under-explored integration gap in each tradition. Three theory-building propositions, generated inductively from the three comparative cases and not presented as empirically established relationships, are advanced together with their moderating conditions. Practical Implications: Firms must institutionalise environmental sensing as a permanent strategic function, treat compliance capabilities as competitive assets, and build adaptive capacity as a standing organisational competency. Originality/Value: This study is among the few comparative analyses to integrate PESTEL trigger structures with dynamic capabilities reconfiguration logic across heterogeneous sectors using longitudinal evidence. The ARC Framework is offered as an analytically transferable, theory-generating model with explicit boundary conditions and testable propositions, rather than as a broadly generalisable one. Full article

Aiming at the path tracking oscillation problem caused by sideslip of navigation agricultural machinery in complex farmland environments, this paper takes the Yanmar YR-70D transplanter as the research object and proposes an adaptive control method based on deep belief network (DBN) sideslip identification. Based on the preview tracking model, this method dynamically adjusts the preview distance by real-time identifying the sideslip status via DBN. In this research, the Optuna framework is utilized to optimize the DBN model, and the SHAP framework is introduced to quantify the feature contribution. The results show that, considering both real-time performance and accuracy, when the combined variable of “lateral deviation + heading deviation” with a data length of 20 is adopted, the prediction accuracy of the model reaches 88.04%, among which heading deviation (HD) has the highest contribution with a SHAP value of 0.957. Comparative experiments indicate that the accuracy of DBN is comparable to that of mainstream models such as LSTM and Transformer, but its forward propagation speed (0.0487 ms) is better than other models’. Upland field experiments verify that under different test factors of vehicle speed and slope angle, the line-on time (1.83–5.60 s), line-on distance (1.46–5.07 m), and maximum overshoot (0.32–15.75 cm) of the adaptive control group are all superior to those of the fixed-parameter control group. Paddy field experiments further confirm that under the working condition of 1.0 m/s vehicle speed and 15° slope, the above three indicators of the experimental group are 0.85–3.1 s, 0.75–3.14 m and 0–7.44 cm, respectively, all of which are better than those of the control group. The proposed method demonstrated superior path tracking performance under the operating conditions of the tested rice transplanting machines, indicating its potential application in lateral slip perception adaptive navigation control. However, further verification is still needed with different types of transplanting machines, field environments, and operating conditions to assess its general applicability. Full article

The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. Agricultural production environments present uniquely challenging conditions for autonomous agricultural systems, including unstructured and dynamically changing terrain, biologically variable targets, unpredictable illumination and weather conditions, and safe human–machine coexistence. This review systematically investigates three cornerstone technologies: multi-source perception, intelligent decision-making, and precision control. Furthermore, typical agricultural operations, including soil tillage, planting, irrigation and drainage, fertilization, plant protection, harvesting, and agricultural product processing, are reviewed to illustrate their applications. Based on representative operational scenarios, the research progress and application characteristics of intelligent equipment in environmental perception, operational optimization, and control execution are summarized. Specifically, multi-source perception is evolving from isolated sensor-based acquisition toward multimodal and deep learning-enabled semantic scene understanding. Intelligent decision-making has evolved from experience-driven approaches toward physics-informed, data-driven, and knowledge-enhanced frameworks for adaptive operational optimization. Precision control has progressed from conventional PID control toward adaptive, learning-based, and digital twin-enabled control strategies, achieving robust high-precision closed-loop regulation. However, several critical challenges persist: limited cross-domain generalization and robustness of perception models under environmental distribution shift, constrained interpretability and trustworthiness of data-driven decision systems, and insufficient adaptability of control architectures under multi-disturbance coupled field conditions. To address these gaps, future research should prioritize multi-source heterogeneous data fusion and standardization, collaborative control frameworks integrating mechanistic knowledge with data-driven learning, and explainable artificial intelligence combined with agricultural domain expertise—advancing toward genuinely autonomous, trustworthy, and resilient agricultural systems. Full article

With the advancement of Earth observation technology and the improvement of multisource data acquisition capabilities, heterogeneous remote sensing image change detection technology has become increasingly important. However, existing heterogeneous remote sensing image change detection methods rely largely on fixed network architectures, which makes adapting to complex modal differences and severe noise interference difficult. To address these issues, in this paper, a decoupled differential search-based graph change detection network (DDS-Net) is proposed. First, the model designs a decoupled differential search-based dual-stream graph encoder (DDSGE). By decoupling the search space from the optimization strategy, it automatically optimizes feature extraction operators and graph topologies for different modalities, thereby significantly increasing feature adaptability while reducing computational complexity. Second, to address nonlinear geometric distortions between heterogeneous images, in this paper, a heterogeneous spatiotemporal alignment module that is based on differential localization search (HSTAM) is proposed. This module uses a local soft attention mechanism to dynamically correct registration errors in the feature space. Furthermore, to suppress erroneous graph connections caused by noise, structural consistency and smooth denoising (SCSD) loss is introduced, and deep semantic feedback and graph smoothing regularization constraints are collaboratively used to dynamically generate graphs, thereby effectively increasing the internal consistency of the transformed graph and suppressing misconnection noise. Extensive experimental results demonstrate that this method significantly improves the robustness and accuracy of the model in complex registration error scenarios while maintaining computational efficiency. Full article

Nature-Based Solutions (NBSs) are increasingly recognized as strategic tools for climate adaptation and urban regeneration, yet their implementation requires balancing environmental performance, territorial identity, and governance feasibility. This article investigates the application of the euPOLIS H2020 methodology in Palermo, Italy, focusing on the proposed transformation of Villa Turrisi—a residual peri-urban agricultural area representing one of the last remnants of the historical Conca d’Oro landscape—into a new public green infrastructure within the framework of the new Municipal Development Plan (PRG/PUG). This study examines how a structured co-design process involving municipal actors, citizens, and local associations can be coupled with ecosystem service assessments. Rather than selecting a single optimal design, the analysis evaluates two alternative vision scenarios—a multifunctional framework aligned with euPOLIS principles and an intensive urban forest model—to generate quantitative and qualitative baseline data. Environmental indicators (carbon sequestration, microclimatic regulation, and hydrological resilience) are used not as deterministic selection criteria but as theoretical decision-support evidence within a predominantly qualitative, value-driven local planning process. The core contribution of this work lies in demonstrating the operational transferability of the euPOLIS framework from front-runner to follower cities within a Mediterranean planning context. The Palermo case shows how quantitative environmental simulations can effectively inform—rather than dictate—participatory governance, heritage preservation, and regulatory feasibility. Ultimately, this article offers a transferable planning and policy framework that bridges European NBS research with municipal decision-making, providing actionable insights for integrating climate-resilient green infrastructure into local urban plans. Full article

Engineering literacy research has been dominated by static, competence-based frameworks that fail to explain how attributes evolve dynamically and systematically underrepresent two critical dimensions: engineering consciousness as a cognitive precursor to decision-making, and engineering culture as a value-laden, inherited dimension sustaining professional identity across generations. These gaps are particularly consequential for STEM teacher preparation, where cultivating comprehensive engineering literacy in future educators is hypothesized to create a multiplier effect on societal STEM engagement. This study develops a theoretical model through a systematized literature retrieval and critical synthesis of engineering literacy literature (2005–2025; N = 113 publications), combined with theoretical deduction grounded in system theory and synergy theory. Two research questions guide the study: (1) How is the connotation of engineering literacy systematically reconstructed under multiple transformations? (2) What is the synergistic evolution logic among its constituent elements, and how can this logic inform curriculum design for STEM teacher preparation? The design utility of the model is illustrated through a curriculum design case. The analysis yields a “multi-driver, five-dimension synergy” dynamic model comprising five interconnected elements (knowledge, competence, consciousness, ethics, and culture) that co-evolve through a “consciousness–action–culture” spiral cycle, in which culture functions as the slow variable governing the long-term evolution of the entire system. The curriculum design case (a 64 h course for pre-service teachers centered on the Hong Kong–Zhuhai–Macao Bridge) illustrates how the model guided curricular decisions in four areas: consciousness activation before skill training, distributed cultural integration, multidimensional assessment, and operationalization of the multiplier effect through a teaching transformation module. The study advances engineering literacy theory by upgrading it from a static competence inventory to a culturally embedded, consciousness-driven adaptive ecosystem and provides a potentially transferable curriculum design framework for STEM teacher preparation. As a design case, the curriculum blueprint awaits implementation and empirical validation; claims about learning outcomes, multiplier effects, and cross-cultural applicability are theoretical hypotheses rather than empirically validated findings. Full article

The green transformation of enterprises is facing major challenges. However, how individual, organizational, and institutional factors jointly shape enterprise green development behavior remains insufficiently understood. This study constructs a three-dimensional analytical framework covering individual, organizational, and institutional levels. Using expert surveys and data from 129 listed building materials companies from 2014 to 2023, the fuzzy-DEMATEL and fuzzy-set qualitative comparative analysis (fsQCA) methods are used to identify key factors and explore multiple paths to achieve green development behavior (GDB). This study found that: (1) executives’ green cognition, technological innovation capability, government penalties, media attention, green consumer demand, and market competitiveness are key factors; (2) no single factor is a necessary condition; (3) four equivalent driving configurations are identified: public opinion and market adaptation, executive leadership and market dynamics, external pressure and innovation synergy, and policy constraints and market breakthroughs; and (4) technological innovation capability and market competitiveness exhibit potential functional substitutability across different configurations. This study identifies key factors shaping enterprise strategies, reducing uncertainty in green initiatives and improving sustainable development decisions. The findings reveal nonlinear and synergistic mechanisms of GDB, challenging traditional linear approaches and offering a new framework for complex decision-making. Full article

Cultural heritage faces escalating pressures from mass tourism, environmental degradation, and physical decay, yet existing scholarship predominantly examines Artificial Intelligence(AI) and Virtual Reality(VR) as separate tools rather than as an integrated systemic mechanism. This exploratory study investigates how their convergence can be conceptualized as a mechanism for sustainable heritage management. A qualitative multi-case design was employed, integrating system-structural analysis, comparative case analysis, and conceptual modeling across six cases: comprising four analytical AI-VR convergence cases (Digital Dunhuang, European Time Machine, Vryokastro, and Wat Sri Suphan Ubosot) and two standalone VR reference cases (Rome Reborn and Khiva). The analysis suggests a functional division: Virtual Reality constructs the immersive spatial foundation of experience, while Artificial Intelligence orchestrates adaptive narrative delivery and semantic structuring. Their convergence may generate value across educational, preservation, and economic dimensions. A conceptual model is proposed, formalising this mechanism through four transformation processes: immersive spatialisation, adaptive storytelling, temporal structuring, and interactive co-creation, with the latter two presented as testable propositions requiring further empirical investigation. The model is linked to SDG 4 (Quality Education), SDG 11 (Sustainable Cities and Communities), and SDG 12 (Responsible Consumption and Production), and offers heritage practitioners a strategic thinking framework for considering digital approaches to cultural heritage management. Full article