Search Results (515)

Aiming to address the complex coupling of transient impulses and steady-state components in vibration signals of scroll compressors in air conditioning systems, this study proposes a physically driven heterogeneous dual-path adaptive weighting network (PDW-Net). The approach constructs a physics-inspired weighting module based on kurtosis and energy criteria, enabling adaptive reconstruction of transient impulses and steady-state vibration components. Feature extraction and decision-level fusion are achieved through a heterogeneous dual-branch network comprising a Fast Fourier Transform (FFT)-based one-dimensional convolutional neural network (1D-CNN) and a Short-Time Fourier Transform (STFT)-based two-dimensional convolutional neural network (2D-CNN). In experimental validation covering four typical fault conditions—condenser failure, refrigerant deficiency, refrigerant overcharge, and main shaft wear—the PDW-Net achieved an average diagnostic accuracy of 97.87% (standard deviation: 2.60%), with 100% accuracy in identifying refrigerant deficiency and normal operating states, demonstrating significant superiority over existing mainstream methods. Ablation studies reveal that the adaptive weighting mechanism contributes most substantially to performance, as its removal results in a 34.24 percentage point drop in accuracy. Replacing the heterogeneous dual-branch structure with a homogeneous counterpart reduces accuracy by 16.18 percentage points, robustly validating the efficacy of the physics-guided and heterogeneous fusion design. Full article

Island regions face dual challenges of renewable energy accommodation and freshwater scarcity, severely constraining operational economy and reliability. However, existing research regards wastewater treatment and seawater desalination as isolated subsystems, overlooking the significant synergistic potential in their water-energy nexus. This paper proposes a novel optimal operation framework for standalone island multi-energy microgrids, constructing a water-energy coupled system that integrates wastewater treatment, seawater desalination, hydrogen electrolysis, methanation, and diversified energy storage. A hierarchical collaborative dynamic weighting mechanism is proposed to facilitate system coupling coordination. At the system macro-level, a Sigmoid-based adaptive strategy responds to real-time operating conditions by dynamically adjusting the weighting ratios of four-dimensional objectives; at the water system micro-level, the load allocation between wastewater treatment and seawater desalination is optimized through a continuous regulation mechanism. This method establishes a framework to maximize the coupling coordination between wastewater treatment and seawater desalination, fully exploiting the flexible load characteristics of water treatment facilities to mitigate renewable energy fluctuations. Simulation results from a case study validate the effectiveness of the proposed strategy; the method achieves collaborative and efficient system operation alongside water-energy security assurance and significantly reduces the total system operating cost by 76,259.14 CNY compared to traditional methods. Full article

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and tourism potential circulation characteristics. Meanwhile, the industry confronts practical challenges, including an unbalanced layout between high-end and local brands, intense competition in core areas, and distinct service coverage blind spots in non-core areas. To fill these research gaps, this study takes the Macau Special Administrative Region as the research scope, integrates POI kernel density estimation, Voronoi diagram analysis, and space syntax to construct a three-dimensional analytical framework encompassing agglomeration intensity, service scope, and tourism flow matching, and systematically investigates the spatial clustering pattern of jewelry stores and its coupling mechanism with tourism potential circulation. The study reveals the following findings: (1) Jewelry stores exhibit a dual-segment, four-core clustering pattern. Among these, 38 high-end brands are concentrated in casino complexes and their surrounding areas, 34 comprehensive brands are evenly distributed across core and residential areas, and 300 local brands are mainly scattered in residential areas of the Macau Peninsula. (2) The service scope of jewelry stores is negatively correlated with agglomeration density. The Voronoi diagram area in core areas is 62% smaller than that in non-core areas, accompanied by a high degree of overlap—35% for high-end brands—and intense competition. In contrast, non-core areas have coverage blind spots accounting for 18% of Macau’s total land area. (3) Under a 300 m walking radius, high-integration paths identified by space syntax demonstrate an 85% matching degree with tourist routes, and the four core areas form differentiated coupling types. This study is the first to quantify the differentiated coupling mechanism between multi-level jewelry brands and tourism potential circulation. It further improves the GIS analysis framework for the coupling between commercial agglomeration and tourist behavior. The revealed negative correlation between service scope and agglomeration density, and the adaptive principle between brand spatial layout and regional functional attributes, provide universal references for similar business formats in tourist cities, including cultural and creative retail and characteristic catering. In practice, this research optimizes the spatial layout of Macau’s jewelry industry and increases the coverage rate of service blind spots to over 85%. It also provides scientific support for tourism route planning and the coordinated development of tourism and commerce in high-density tourist destinations. Full article

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has become increasingly integrated into dentistry because of its high precision, efficiency, and ability to fabricate patient-specific devices. This review comprehensively discusses the historical development of 3D printing and outlines the fundamental principles of the most widely used technologies in dentistry, including stereolithography (SLA), digital light processing (DLP), and liquid crystal display (LCD). These technologies enable the accurate and efficient fabrication of dental models, crowns, bridges, dentures, surgical guides, orthodontic appliances, and tissue engineering scaffolds. Current clinical applications are systematically summarized across major dental disciplines, including prosthodontics, orthodontics, oral and maxillofacial surgery, endodontics, periodontics, and pediatric dentistry. Despite existing challenges, such as limited long-term clinical data for certain materials, high initial equipment costs, and post-processing requirements, 3D printing offers substantial advantages in terms of customization, workflow efficiency, and clinical predictability of the final product. Future developments in advanced biomaterials, artificial intelligence-assisted workflows, bioprinting, and four-dimensional (4D) printing are expected to further expand the role of additive manufacturing in personalized and regenerative dentistry. Full article

Emerging technologies are increasingly positioned as key enablers of sustainable management; however, existing research largely examines digital transformation and sustainability as parallel rather than integrated processes, particularly within national transformation contexts. Moreover, prior studies tend to focus on individual technologies or environmental outcomes, offering limited insight into how emerging technologies are embedded within ESG-oriented management systems and institutional governance frameworks. To address this gap, this study adopts a structured conceptual literature review methodology guided by a systematic PRISMA-informed selection process. Based on the qualitative synthesis of 76 peer-reviewed studies, the paper develops an integrative framework explaining how emerging technologies enable sustainable management and digital transformation within the context of Saudi Arabia’s Vision 2030. Drawing on sustainability transitions, digital transformation, and ESG management literature, emerging technologies are conceptualized as combinatorial digital capabilities operating through a recursive capability loop (Sense–Analyze–Decide–Act–Verify). These capabilities influence sustainability outcomes through four mediating mechanisms—measurement, optimization, transparency, and institutionalization—and are conditionally shaped by national institutional enablers. The proposed framework positions ESG-oriented management systems as a mediating layer between technological capabilities and multi-dimensional sustainability outcomes, while explicitly addressing the double transition paradox, which recognizes both the sustainability benefits and environmental costs of digital infrastructures. The study advances theory by integrating ESG mediation, institutional moderation, and capability-based mechanisms into a unified analytical architecture and formulates six theoretically grounded propositions to guide future empirical research. The framework also provides actionable insights for managers and policymakers seeking to align digital transformation, ESG integration, and national sustainability agendas in Saudi Arabia and comparable emerging economy contexts. Full article

Feedback is an integral part of the learning process. However, delivering feedback effectively remains challenging, particularly within massified higher education systems that are characterised by large cohorts and increasingly diverse student populations. The emergence of generative artificial intelligence (GenAI) enables new ways of embedding feedback into educational offerings, some of which may be highly beneficial. In this paper, we introduce Abundant Feedback as a conceptual lens for examining the new capabilities that may be enabled by GenAI. We present a four-dimensional framework identifying the dimensions of GenAI feedback as abundance of Volume, of Availability, of Relevance and of Character. Through a systematic literature search, we describe how these dimensions manifest in recent empirical studies, and identify two educational domains, Computer Programming and Foreign Languages, as early adopters of AI-generated feedback. Beyond merely digitising existing scarce feedback processes, we discuss the emergence of new learner-driven feedback practices that are enabled by abundance, that both stimulate and demand student feedback literacy. Our multi-dimension abundance framework provides a lens, as well as the vocabulary and conceptual tools, to guide the implementation of GenAI feedback in ways that help realise the potential of artificial intelligence to enhance student learning. Full article

This paper develops a rigorous mathematical and computational framework for four-dimensional chess defined on the discrete hypercubic lattice $\{1,\dots , 8{\}}^4$. We formalize piece movement using displacement sets in ${\mathbb{Z}}^4$, define adjacency via the Chebyshev metric, and analyze the resulting move graphs for rooks, bishops, knights, queens, and kings. We establish exact mobility formulas, parity invariants, and connectivity properties, consolidating known product-graph results for rooks and kings while introducing a boundary-sensitive analysis of the four-dimensional knight verified by exhaustive enumeration. The mathematical framework is complemented by a fully implemented 4D chess engine and interactive visualization environment rendering all 64 (z,w)-slices of the hypercube simultaneously. The system supports full move legality, generalized special rules, multi-king checkmate detection, and reproducible state enumeration. Performance measurements and exploratory branching-factor estimates are obtained through reproducible random playouts using the publicly available implementation. We contextualize this ruleset within existing work on move graphs on ${\mathbb{Z}}_n^m$, higher-dimensional leapers, spectral properties of grid graphs, toroidal analogs, and multidimensional visualization. Exploratory qualitative feedback (N = 18) is included to examine whether the visualization design is interpretable and navigable in practice, providing feasibility-oriented observations on how slice-based 4D projection and layered board rendering are perceived by non-expert users in an exploratory context. Together, the mathematical results, implemented engine, and visualization form a coherent foundation for the study of strategy, complexity, and human interaction in four-dimensional game systems. The framework provides a basis for future investigations into spectral analysis of move graphs, symmetry-aware search, hierarchical planning, and educational applications in high-dimensional geometry. Full article

In contemporary communication systems, digital images occupy an irreplaceable role; however, the privacy-related risks attendant to their prevalent application have grown increasingly salient. This paper presents an image encryption scheme integrating a novel two-dimensional Ackley-Sine chaotic map (2D-ASM) with dynamic DNA operations. First, a two-dimensional Ackley-Sine chaotic map, constructed based on the Ackley function and sine function, is designed and validated through a series of chaotic indicators. Results demonstrate that 2D-ASM exhibits superior chaotic properties compared to several existing state-of-the-art chaotic maps, with its maximum Lyapunov exponent (LE) exceeding 23, Permutation Entropy (PE) close to 1 in the full parameter range, and correlation dimension (CD) significantly higher than comparative chaotic systems. The proposed 2D-ASM-based image encryption scheme leverages the SHA-256 hash value of the plaintext image and four external keys to jointly generate the initial conditions and parameters of the 2D-ASM chaotic system, thereby ensuring a sufficiently large key space of 2²⁵⁶. Subsequently, chaotic sequences generated by 2D-ASM are employed to permute and diffuse the plaintext image, followed by dynamic DNA coding, operations, and decoding to obtain the encrypted image. Security analyses and comparisons with several existing representative algorithms confirm that the proposed encryption scheme achieves excellent encryption performance: the Number of Pixels Change Rate (NPCR) is above 99.6%, the Unified Average Changing Intensity (UACI) approaches 33.4%, and the information entropy of ciphertext images reaches 7.999 or higher. The scheme can effectively resist various potential attacks, including statistical and differential attacks, and outperforms representative algorithms in pixel correlation reduction and anti-interference performance. Full article

The existing research on Android malware detection using graph neural networks (GNNs) has largely focused on architectural improvements, while input node feature representations have received less systematic attention. This study adopts a representation-centric approach to enhance function call graph (FCG)-based malware classification through interpretability-driven feature engineering. We propose a dual-level structural feature framework integrating local topological patterns with global graph-level properties. The initial feature set comprises 13 dimensions: five local degree profile (LDP) features and eight global structural features capturing community structure, execution flow, and connectivity patterns. To mitigate the curse of dimensionality, we apply an interpretability-driven selection using integrated gradients (IG), gradient-weighted class activation mapping (GradCAM), and Shapley additive explanations (SHAP), yielding an optimized seven-dimensional subset. Experiments on the MalNet-Tiny benchmark demonstrate that the proposed approach achieves 94.47 ± 0.25% accuracy with jumping knowledge GraphSAGE (JK-GraphSAGE), improving the LDP-only baseline by 0.32 percentage points while reducing feature dimensionality by 46%. The selected features exhibit consistent importance across four GNN architectures and multiple message-passing layers, demonstrating model-agnostic effectiveness. The results reveal that aggregation mechanisms critically influence feature utility, highlighting the necessity of interpretability-guided design for robust malware detection. This work provides a systematic methodology for feature engineering in graph-based security applications. Full article

Local governments are key actors in driving urban renewal. To implement urban renewal initiatives, in-depth research into their policy backgrounds, institutional characteristics, and governance logic is essential. Traditional policy analysis often neglects the value dimension, which undermines the effectiveness of embedding informal institutional values. To complement existing research, this study examines 50 urban renewal policy documents issued in Guangzhou between 1978 and 2025. Using content analysis and grounded theory methods, this study incorporates the value dimension into the traditional “supply–demand–environment” policy analysis framework to examine local governments’ policy preferences in urban renewal, and to interpret its governance logic from the perspective of Williams’ four-level framework. The findings are as follows: (1) Guangzhou’s urban renewal has formed a policy system centered on supply-side policies, supported by environmental policy improvements, with value embedding, demand-driven measures, and multi-dimensional guidance as supplementary components. Local governments show a distinct preference for supply-oriented policy tools. (2) Guangzhou’s urban renewal policies present a pyramid structure with resource allocation at the core and governance structure as the foundation. The policies focus on the optimal allocation of land resources, collaborative actions among government, market, and society, the deep integration of public values, the clarification of property rights rules, and the application of digital technologies. (3) The governance logic of urban renewal forms a four-tier progressive closed-loop: from value anchoring to rule linkage, then to multi-stakeholder collaboration, and finally to factor empowerment, establishing a systematic governance mechanism that balances people-centricity and efficiency. Accordingly, urban renewal should prioritize value embedding and cultural preservation, balance investment in physical assets and human capital, optimize governance structures and policy mixes, coordinate the roles of an effective market and a capable government, improve supply–demand matching and the efficiency of resource allocation, and adjust the complementarity and applicability of policy tools. Full article

With the increasing complexity of optimization problems, existing methods are often inadequate for addressing these challenges, creating a pressing need for more versatile and robust approaches capable of solving a wide range of optimization problems. Meta-heuristic algorithms have become powerful tools in this regard, owing to their flexibility, ease of implementation, and suitability for high-dimensional and complex problems. This paper introduces the Higher-order Thinking Skills Optimizer (HTSO), a novel meta-heuristic algorithm inspired by Higher-order Thinking Skills (HOTS) from educational theory. HTSO simulates the four key aspects of HOTS: creativity, problem-solving, critical thinking, and decision-making. Creativity reflects the intrinsic human drive for knowledge, prompting exploration of unknown domains. When faced with difficulties, individuals focus on gathering information to solve problems. However, due to the inconsistent quality of information, critical thinking is essential for effectively assessing it. In HTSO, creativity and problem-solving serve as the exploration and exploitation mechanisms, respectively. Crucially, critical thinking functions as a metacognitive controller that evaluates the quality of solutions and dynamically guides the selection and adaptation of creativity strategies, thereby ensuring an effective balance between exploration and exploitation. Moreover, HTSO is designed as a user-friendly algorithm with minimal parameter tuning requirements, and its key parameter demonstrates strong robustness across diverse problem types and dimensions, which enhances its practical applicability. Extensive experiments were conducted across three CEC benchmark sets with multiple dimensions (CEC-2017: 30, 50, 100 dimensions; CEC-2020: 10, 15, 20 dimensions; CEC-2022: 10, 20 dimensions), comparing HTSO with 21 other algorithms, including several CEC champion algorithms. The results demonstrate that HTSO outperforms all comparative algorithms on most test functions, indicating high effectiveness and robustness. Furthermore, HTSO was compared with 14 algorithms on 12 real-world constrained engineering optimization problems. Finally, HTSO and 14 other algorithms were applied to unmanned aerial vehicle 3D path planning in seven different complex mountainous scenarios. HTSO also achieved the best performance across all tested real-world engineering problems and UAV path planning scenarios, consistently outperforming the comparative algorithms. These results demonstrate the effectiveness and potential of HTSO in addressing real-world optimization challenges. Full article

Affiliated Open Spaces (AOS) constitute vital public assets within high-density vertical cities. However, prevailing scholarship remains largely confined to two-dimensional horizontal perspectives, overlooking the quantitative impact of vertical built environment characteristics on spatial distribution and human behavior. Focusing on four high-density districts in Guangzhou typified by distinct three-dimensional morphologies, this study integrates field surveys, 3D geospatial data acquisition, and 621 valid questionnaires to empirically analyze the impact of 3D spatial features on user behavior and the mediating role of accessibility. Utilizing the ArcGIS 3D Analyst for vertical accessibility measurement and Partial Least Squares Structural Equation Modeling (PLS-SEM) for path analysis, the study tests the hypothesized relationships using multi-source data. The results indicate that (1) a user’s vertical location exerts a significant negative impact on both accessibility and human behavior; (2) building density and building functional diversity indirectly promote user engagement primarily by significantly enhancing accessibility, thereby confirming accessibility as a critical mediator; and (3) significant spatial heterogeneity exists, revealing distinct correlation patterns across varying built environments. This research elucidates the pivotal constraint of “vertical location” and validates the mediating efficacy of accessibility, offering empirical insights for human-centric vertical urban planning. Full article

This study addresses the critical need for accurate, continuous monitoring of surface water quality parameters (SWQPs) using remote sensing, overcoming limitations in existing models that often rely on pre-trained networks ill-suited for complex aquatic environments. We present a customized convolutional neural network (CNN) architecture, implemented in the MATLAB environment, designed to simultaneously predict optically active (Total Organic Carbon, TOC) and non-optically active (Dissolved Oxygen, DO) parameters from eighteen Sentinel-2 Level-2A satellite images, acquired between 2023 and 2024. Our approach integrates spatial and spectral data through a customized CNN with three convolutional layers and two dense layers, optimized via adaptive learning strategies, data augmentation, and rigorous regularization to enhance predictive performance and prevent overfitting. The models were trained and validated on fused datasets of satellite imagery and in situ measurements, organized into comprehensive four-dimensional arrays capturing spectral, spatial, and sample dimensions. The results demonstrated high accuracy, with coefficient of determination (R²) values exceeding 0.97 and low root mean square error (RMSE) across training, validation, and testing subsets. Spatial prediction maps generated at high resolution revealed realistic ecological and hydrological patterns consistent with known regional water quality dynamics in New Brunswick. Our contribution, accessible to users with MATLAB, lies in the development of a transparent, adaptable, and reproducible CNN framework tailored for multiparameter water quality estimation, which extends beyond traditional empirical, site-specific regression models by enabling non-invasive, cost-effective, and continuous monitoring from satellite platforms over a large, heterogeneous province-scale domain. Additionally, model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key spectral bands influencing predictions and provided ecological insights, offering guidance for future sensor design and data reduction strategies. This study addresses a significant research gap by providing a dual-parameter focused, end-to-end deep learning solution optimized for province-scale remote sensing data, facilitating more informed environmental management. This study can support water managers and agencies by providing province-wide DO and TOC maps derived from freely available Sentinel-2 imagery, reducing reliance on sparse field sampling alone and helping to identify areas of low oxygen or high organic carbon. Future work will extend this framework temporally and spatially and explore hybrid CNN architectures incorporating temporal dependencies for improved generalization and accuracy. Full article

Examining the spatial differentiation and constraining factors of rural resilience at the micro-scale is essential for navigating compounded risks and unbalanced urban–rural development. The study takes 170 villages in Qianshan City, Anhui Province, as the study sample and constructs a four-dimensional resilience evaluation system encompassing economic, social, infrastructural, and ecological dimensions. The research systematically assesses rural resilience levels and obstacle factors using the entropy weight method, spatial autocorrelation analysis, and the obstacle degree model. The results indicate that: (1) The overall rural comprehensive resilience in Qianshan City is at a moderately low level, with an average value of 0.133, ranging from 0.0604 to 0.4805. Significant inter-village disparities exist, forming a distinct “central agglomeration–peripheral dispersion” spatial pattern driven by urban proximity. (2) The resilience of each subsystem also exhibits pronounced heterogeneity: economic resilience is generally low; infrastructural resilience shows the greatest variation; social resilience is relatively stable in its spatial distribution; and ecological resilience demonstrates a “high in the northwest–low in the southeast” pattern. (3) Hotspots of comprehensive resilience, as well as economic, social, and infrastructural resilience, are concentrated around the central–southern urban areas with stronger development foundations, whereas hotspots of ecological resilience are independently distributed within ecologically advantageous zones. (4) Rural resilience is primarily constrained by deficits in public service accessibility and infrastructure conditions. Notably, the primary obstacle factors exhibit high consistency across villages with different geomorphic conditions. Finally, this study proposes coordinated enhancement strategies for economic development, infrastructure improvement, ecological conservation, and social governance in Qianshan City, providing a scientific basis for rural resilience building and governance. Full article

With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification of the governance complexity arising from multidimensional interacting pressures behind outage events. This creates a blind spot in both theoretical research and governance practice, hindering differentiated resilience decision-making. To address this gap, this study develops a four-dimensional evaluation framework of power outage governance complexity encompassing event attributes, external environment, internal system, and social impacts. Based on county-level outage data and multi-source auxiliary data in the United States from 2015 to 2024 and employing the XGBoost–SHAP interpretable machine learning approach, we construct the Power Outage Complexity Index (POCI) for all U.S. counties and systematically analyze its spatiotemporal evolution and core driving factors. The results show that outage governance complexity in the U.S. power grid exhibits a significant upward trend during 2015–2024, with an average annual growth rate of 1.84%. Spatially, significant positive autocorrelation is observed, and 146 high-complexity hotspot counties are identified, mainly clustered along the East and West Coasts, the Gulf Coast, and the Southwest. Driver analysis reveals that social impact and event attribute dimensions together account for nearly 90% of the variance in complexity, with cumulative outage exposure burden, outage frequency, and large-scale event ratio being the most critical drivers. Theoretically, this study extends power resilience research from an engineering-physical paradigm to a socio-technical governance paradigm and provides a reproducible methodological framework for assessing governance complexity in critical infrastructure systems. Practically, the POCI can serve as a governance diagnostic tool for the power industry and regulators, supporting resilience investment prioritization, emergency resource optimization, and differentiated governance strategy formulation. It also provides empirical evidence for safeguarding energy security in highly vulnerable communities and promoting energy resilience equity. Full article