Search Results (1,615)

A well-structured urban park system (UPS) is crucial for optimizing urban spatial layout and improving the quality of the human living environment. In response to the tendency of current planning to prioritize quantitative indicators while overlooking the relational structure arising from the collective spatial configuration of parks, this study introduces Social Network Analysis (SNA) to evaluate the spatial structure of Shanghai’s park system by constructing a service-coverage overlap network. The findings reveal the following: (1) Parks with high degree centrality are concentrated in high-density urban core areas due to service overlap, whereas large suburban parks with high betweenness centrality function as critical bridging hubs, reflecting a polycentric structure. (2) There is a discernible discrepancy between these emergent network tiers and the statutory park hierarchy, highlighting a tension between bottom-up spatial patterns and top-down planning frameworks. (3) Stability simulations indicate a dual character of the system, where the network topology is vulnerable to attacks yet functionally resilient to failures due to spatial redundancy, suggesting that a decline in service quality may precede the loss of basic accessibility. This study demonstrates the value of SNA in diagnosing park system structure, identifying key nodes, and assessing system resilience. The insights advocate for planning approaches that transcend rigid hierarchical frameworks, integrate the actual functional roles of parks, and protect structural hubs, thereby enhancing systemic resilience and promoting equitable service provision. Full article

Within the framework of a discrete-time chronon model, we consider a dual description of physical time. In this description, macroscopic time is a continuous parameter, while a microscopic integer chronon index labels elementary updates of the system. On this basis, a hierarchy of temporal layers ${\mathrm{Ch}}_N$ (Chronon) is introduced. The simple layers $\mathrm{Ch}2$, $\mathrm{Ch}3$ and $\mathrm{Ch}4$ are analysed, and it is shown that they naturally support $U\left(1\right)$ (Unitary group), $SU\left(3\right)$ (Special Unitary group) and a pair-locked $SU\left(2\right)$ (Special Unitary group) symmetry, respectively. Special attention is paid to the twelve-slot layer $\mathrm{Ch}12$. This layer is the minimal one which simultaneously separates partitions into four triads and three quartets. For $\mathrm{Ch}12$, we demonstrate that the intersection of the corresponding commutants in ${\mathbb{C}}^3\otimes {\mathbb{C}}^4$ reproduces the Standard Model gauge algebra $SU{\left(3\right)}_C\times SU{\left(2\right)}_L\times U{\left(1\right)}_Y$ and the pattern of hypercharges and anomaly cancellation. The appearance of three fermion generations is interpreted in terms of three inequivalent embeddings of a triad into the dodecad which preserve the quartet structure. Possible connections of the chronon dynamics with the hierarchy of masses (via Floquet-type quasi-energies), with dark sectors associated with misaligned layers, and with a temporal interpretation of entanglement are briefly discussed on a qualitative level. These questions are formulated as open problems for further study. Full article

High-speed jet-in-crossflow (JICF) configurations are central to several aerospace applications, including turbine-blade film cooling, thrust vectoring, and fuel or hydrogen injection in combusting or reacting flows. This study employs high-fidelity direct numerical simulations (DNS) to investigate the dynamics of a supersonic jet (Mach 3.73) interacting with a subsonic crossflow (Mach 0.8) at low Reynolds numbers. Three momentum-flux ratios (J = 2.8, 5.6, and 10.2) are considered, capturing a broad range of jet–crossflow interaction regimes. Turbulent inflow conditions are generated using the Dynamic Multiscale Approach (DMA), ensuring physically consistent boundary-layer turbulence and accurate representation of jet–crossflow interactions. Modal decomposition via proper orthogonal decomposition (POD) and spectral POD (SPOD) is used to identify the dominant spatial and spectral features of the flow. Across the three configurations, near-wall mean shear enhances small-scale turbulence, while increasing J intensifies jet penetration and vortex dynamics, producing broadband spectral gains. Downstream of the jet injection, the spectra broadly preserve the expected standard pressure and velocity scaling across the frequency range, except at high frequencies. POD reveals coherent vortical structures associated with shear-layer roll-up, jet flapping, and counter-rotating vortex pair (CVP) formation, with increasing spatial organization at higher momentum ratios. Further, POD reveals a shift in dominant structures: shear-layer roll-up governs the leading mode at high J, whereas CVP and jet–wall interactions dominate at lower J. Spectral POD identifies global plume oscillations whose Strouhal number rises with J, reflecting a transition from slow, wall-controlled flapping to faster, jet-dominated dynamics. Overall, the results demonstrate that the momentum-flux ratio (J) regulates not only jet penetration and mixing but also the hierarchy and characteristic frequencies of coherent vortical, thermal, and pressure and acoustic structures. The predominance of shear-layer roll-up over counter-rotating vortex pair (CVP) dynamics at high J, the systematic upward shift of plume-oscillation frequencies, and the strong analogy with low-frequency shock–boundary-layer interaction (SBLI) dynamics collectively provide new mechanistic insight into the unsteady behavior of supersonic jet-in-crossflow flows. Full article

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is to establish a set of criteria for selecting container TOS, determine the priority weights of these criteria and investigate their interactions. To the author’s knowledge, this is the first study to address this topic in such a detailed context. The hybrid FAHP (Fuzzy Analytic Hierarchy Process) and F-DEMATEL (Fuzzy Decision-Making Trial and Evaluation Laboratory) methodology was employed for the 18 criteria that were identified through the academic literature and expert views. The findings demonstrated that container terminal operators have expressed an expectation for a TOS structure that integrates complex business processes, provides effective decision support, increases traceability, works in harmony with advanced technologies, supports smart port transformation processes, enhances digital maturity and enables rapid intervention in bottlenecks. Furthermore, the fact that TOSs should support integration with external stakeholders is also critical in terms of collaboration and transparency, which are of great importance in supply chain management. It is hoped that the present study will contribute to the relevant literature and also provide a structural framework for terminal operators to select the most suitable TOS and for providers to design the most effective product. Full article

Pulsed Electric Fields (PEFs) are widely investigated in cancer research, yet interpretation and optimisation of exposure protocols remain challenging due to the complex interplay between electrical parameters, thermal effects, and biological response. While voltage amplitude is often emphasised, the influence of pulse timing and structure under dose-constrained conditions is less systematically examined. This work presents an exploratory in vitro study on the HepG2 hepatocellular carcinoma cell line using a custom-built bipolar pulse delivery system. Seventy experimental conditions were tested, each varying a single pulse parameter (voltage, width, period, pulses per burst, burst count, or burst period), with temperature monitored during exposure. Cell metabolic viability was quantified 24 h post-treatment using an MTT assay. The results show that short-term viability suppression and thermal behaviour depend on pulse configuration and not on total electric dose alone. Based on comparative trends, a qualitative hierarchy of parameter influence is proposed, with total electric dosage and pulse width showing the strongest association with viability reduction, followed by voltage, pulse period, pulses per burst, and burst period. Elevated temperatures observed in some regimes indicate possibly combined electro–thermal effects rather than purely electrical responses. Overall, the study provides a parameter-sensitivity overview that highlights the importance of pulse timing and thermal management alongside voltage amplitude in PEF experiments. Full article

Accurate anatomical landmark localization is essential to automate chest X-ray analysis and improve diagnostic reliability. While global context recognition is essential in medical imaging, the inherently high-resolution nature of these images has long made this task particularly difficult. While the U-Net-based heatmap regression methods show strong performance, they still lack explicit modeling of the global spatial relationships among landmarks. To address this limitation, we propose an integrated structural learning framework that captures anatomical correlations across landmarks. The model generates probabilistic heatmaps with U-Net and derives continuous coordinates via soft-argmax. Subsequently, these coordinates, along with their corresponding local feature vectors, are fed into a Graph Neural Network (GNN) to refine the final positions by learning inter-landmark dependencies. Anatomical priors, such as bilateral symmetry and vertical hierarchy, are incorporated into the loss function to enhance spatial consistency. The experimental results show that our method consistently outperforms state-of-the-art models across all metrics, achieving significant improvements in MRE and SDR at 3, 6, and 9 pxl thresholds. This high precision demonstrates the framework’s strong potential to enhance the accuracy and robustness of clinical diagnostic systems. Full article

As digital technology becomes increasingly integral to modern industries, the risks posed by cyber threats, including malware, ransomware, and insider attacks, continue to rise, jeopardizing critical infrastructure including renewable energy system. The world is more vulnerable to sophisticated cyberattacks due to its reliance on smart grids and IoT-enabled renewable energy systems. Without specialized digital forensic frameworks, incident response and critical infrastructure resilience are limited. This research examines the pivotal role of digital forensics in defending renewable energy system against the growing wave of cyber threats. The study highlights the significance of digital forensics in enhancing incident response, evidence collection, and forensic analysis capabilities. Through detailed case studies, it investigates the implementation strategies of digital forensics to identify, track, and mitigate cyber risks. To address this objective, this study proposes a comprehensive and adaptive cybersecurity framework that integrates digital forensics and fuzzy multi-criteria decision-making to enhance cyber resilience in renewable energy systems. Drawing on relevant case studies, the research demonstrates how the integration of digital forensics with fuzzy logic supports dynamic threat evaluation and risk mitigation. Comparative analysis show that the proposed framework outperforms traditional methods in terms of detection accuracy, response time, and adaptability to evolving threat landscapes. Key contributions include: (1) a structured digital forensics-based cybersecurity model tailored to renewable energy systems, (2) application of fuzzy Analytical Hierarchy Process (AHP) for multi-criteria threat evaluation, and (3) policy-oriented recommendations for stakeholders to reinforce national cyber resilience in line with energy transition. The findings underscore the need for a cohesive cybersecurity strategy grounded in advanced decision-support systems to protect the future of sustainable energy. Full article

Publicly funded R&D projects operate within regulatory and organisational contexts that shape how change is planned and managed, yet the system-level effects of these frameworks remain underexplored. This study examines how regulatory requirements shape change management in publicly funded R&D projects, using Spanish technology centres as a setting. It identifies the constraints affecting change management, prioritises their perceived impact among project managers, and examines how these constraints generate regulatory feedback that conditions both project design and execution-stage change decisions. A mixed-method approach combines semi-structured expert interviews with a structured survey of 38 R&D project managers. Quantitative and qualitative evidence is integrated through the Analytic Hierarchy Process to structure and rank perceived regulatory constraints. The results show that administrative procedures and regulatory requirements are perceived as the most restrictive factors, especially for scope adaptation, administrative workload, and temporal flexibility, reducing managers’ capacity to adjust projects under execution uncertainty. The empirical context reflects regulatory principles and control mechanisms consistent with the European Union framework for state aid to R&D. This study clarifies how regulation interacts with project management practice, showing that control-oriented procedures act as the dominant adaptive constraint, generating regulatory feedback that discourages change requests and systematically reduces projects’ adaptability during execution. Full article

Tunnel point cloud semantic segmentation is a critical step in achieving refined perception and intelligent management of tunnel structures. Addressing common challenges including indistinct boundaries and fine-grained category discrimination, this paper proposes MFPNet, a multi-scale feature perception network specifically designed for tunnel scenarios. This approach employs kernel convolution to effectively model local point cloud geometries within continuous spaces. Building upon this foundation, an error-feedback-based local-global feature fusion mechanism is designed. Through bidirectional information exchange, higher-level semantic information compensates for and constrains lower-level geometric features, thereby mitigating information fragmentation across semantic hierarchies. Furthermore, an adaptive feature re-calibration and cross-scale contextual correlation mechanism is introduced to dynamically modulate multi-scale feature responses. This explicitly models contextual dependencies across scales, enabling collaborative aggregation and discriminative enhancement of multi-scale semantic information. Experimental results on tunnel point cloud datasets demonstrate that the proposed MFPNet has achieved significant improvements in both overall segmentation accuracy and category balance, with mIoU reaching 87.5%, which is 5.1% to 33.0% higher than mainstream methods such as PointNet++ and RandLA-Net, and the overall classification accuracy reaching 96.3%. These results validate the method’s efficacy in achieving high-precision three-dimensional semantic understanding within complex tunnel environments, providing robust technical support for tunnel digital twin and intelligent detection applications. Full article

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (Pb) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. Full article

Identifying critical infrastructure in transportation networks requires metrics that can capture both the topological structure and how demand is redistributed during disruptions. Conventional graph-theoretic approaches fail to jointly quantify these vulnerabilities. This study presents a computational framework for edge-criticality assessment based on the Fused Unbalanced Gromov–Wasserstein (FUGW) distance, incorporating both structural similarity and demand characteristics of network nodes in an optimal transport tool. The three hyperparameters that influence FUGW accuracy—fusion weight, entropic regularization, and marginal penalties—were tuned using Bayesian optimization. This ensures the rankings remain accurate, stable, and reproducible under temporal variability and demand shifts. We apply the framework to a benchmark transportation network evaluated across four diurnal periods, capturing dynamic congestion and shifting demand patterns. Systematic variation in the fusion parameter shows seven consistently critical edges whose rankings remain stable across analytical configurations. It can be concluded from the results that monotonic scaling with increasing feature emphasis, strong cross-hyperparameter correlation, and low temporal variability confirm the robustness of the inferred criticality hierarchy. These edges represent both structural bridges and demand concentration points, offering α indicators of network vulnerability. These findings demonstrate that FUGW provides a solid and scalable method of assessing transportation vulnerabilities. It helps support clear decisions on maintenance planning, redundancy, and resilience investments. Full article

The increasing complexity of contemporary urban systems necessitates decision-making frameworks capable of systematically integrating multidimensional sustainability considerations into policy evaluation processes. While existing urban sustainability assessment approaches predominantly focus on isolated environmental or socio-economic indicators, they often lack methodological coherence and direct applicability to operational decision-making. This study proposes a multi-layer sustainability indicator framework explicitly designed to support evidence-based urban decision-making under conditions of uncertainty, institutional constraints, and competing policy objectives. The framework integrates environmental, economic, social, and institutional dimensions of sustainability into a structured decision-support architecture. Methodologically, the study employs a two-stage approach combining expert-based weighting techniques (Analytic Hierarchy Process and Best–Worst Method) with multi-criteria decision-making methods (TOPSIS and VIKOR) to evaluate and rank alternative urban policy scenarios. The proposed framework is empirically validated through an urban case study, demonstrating its capacity to translate abstract sustainability indicators into comparable decision outcomes and policy priorities. The results indicate that the integration of multi-layer indicator systems with formal decision-analysis tools enhances transparency, internal consistency, and strategic coherence in urban governance processes. By bridging the gap between sustainability measurement and decision implementation, the study contributes to the advancement of urban governance scholarship and provides a replicable analytical model applicable to cities facing complex sustainability trade-offs. Full article

Integrated control systems (Supervisory Control and Data Acquisition–SCADA and Manufacturing Execution Systems—MES) constitute the backbone of Industry 4.0; however, research on their implementation remains scarce. This study analyzes the opportunities and challenges of modernizing these systems within the context of the Brazilian industry. A survey of 101 experts was conducted, with results analyzed via Friedman and Holm–Sidak nonparametric tests to establish a clear hierarchy of factors. Findings reveal that while economic efficiency, productivity gains, and real-time remote access represent the most significant opportunities, they are countered by critical structural challenges: obsolete machinery and inadequate infrastructure. These challenges significantly inflate implementation costs and highlight the reality of technological obsolescence that is typical of emerging economies. By applying the Resource-Based View (RBV), this research frames digital integration as a strategic competitive capability rather than a mere technical upgrade. Practically, the study provides a roadmap for industrial leaders to balance digital agility expectations with pragmatic operational constraints. These insights offer a foundation for successful digital transformation, delivering actionable value for academics, industrial managers, and policymakers. Full article

The mechanisms driving the crown-to-root transition in tooth development remain incompletely understood, particularly the functional heterogeneity of dental epithelium. To address this gap and deconstruct this complexity, we aimed to analyze dental epithelial heterogeneity during this critical transition and to identify subpopulation-specific programs relevant to root development. We therefore established a single-cell transcriptomic atlas of the mouse molar at postnatal days 3.5 and 7.5, integrating 30,951 cells to profile the pan-tissue landscape and performing an in-depth analysis of 4323 dental epithelial cells. Our results reveal that the dental epithelium is composed of seven distinct subpopulations with a clear lineage hierarchy, originating from multipotent progenitors and bifurcating into self-renewing and differentiating trajectories. The identified particular functions of each subcluster include the following: structural maintaining progenitor that inhibits mineralization (Cluster 4), proliferation driver (Cluster 0), key signaling center (Cluster 1), terminally differentiated executing enamel formation (Cluster 3 and Cluster 6), and extracellular matrix-organizing hub (Cluster 5), communicating extensively via the Bmp, Tgf-β, and Wnt pathways. Our work defines dental epithelium as a dynamic and heterogeneous orchestrator of root morphogenesis, providing a foundational framework for understanding developmental biology and pioneering future regenerative strategies based on precise epithelial cell functions. Full article

This study investigates the demographic transformation of Spain’s settlement system from 2000 to the present, driven by intersecting forces of rural depopulation, metropolitan concentration, immigration, and welfare-state dynamics. Building on an integrated theoretical framework that combines Maslow’s hierarchy of needs, demographic accounting, territorial carrying capacity, and spatial centrality, the research aims to (1) identify the mechanisms governing population redistribution across Spanish municipalities, and (2) simulate future demographic trajectories under current policy regimes. Key findings reveal that all net population growth since 2000 stems exclusively from immigration and its demographic sequelae, while the native Spanish cohort has experienced a net decline of 5.5 million due to negative natural change. The analysis further uncovers a self-reinforcing “demographic trap,” wherein welfare eligibility tied to household size incentivizes higher fertility among economically vulnerable immigrant groups, even as native families delay childbearing due to economic precarity. These dynamics are accelerating a process of “territorial transmutation,” projected to culminate in a shift in de facto governance by 2045. The study concludes that immigration alone cannot reverse rural depopulation or ensure fiscal sustainability without structural reforms to welfare design, territorial incentives, and demographic foresight. Full article