Search Results (53)

Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, purely data-driven models, though efficient, lack physical consistency and generalization capability. This review systematically examines Physics-Informed Neural Networks (PINNs), a hybrid paradigm in which physical prior knowledge is embedded directly into the neural network training process. A structured keyword search of the Web of Science Core Collection was performed, and 94 peer-reviewed journal articles were analyzed. The evolution from numerical simulations and data-driven surrogate models to PINNs is outlined. PINN methods are classified according to the stage at which physical prior information is integrated (i.e., dataset development, model construction, or loss function formulation). Current research remains heavily focused on loss function constraints, whereas systematic integration into data augmentation and model construction remains limited. Application domains span indoor environments, outdoor environments, and building systems, with each domain exhibiting unique prior integration strategies tailored to specific problems. Future PINN modeling should evolve toward multi-physics coupling, adaptive loss balancing, cross-scenario transfer learning, and unified evaluation benchmarks. PINNs in this field are promising but remain at an early stage, especially for complex urban-scale deployment. This review synthesizes existing research around the three stages of dataset development, model construction, and loss function formulation, summarizes the prior integration strategies adopted in the domain of building thermal environments, and provides a practical workflow for embedding physical prior knowledge at different stages of model development. Full article

The integration of the digital and real economies is a pivotal engine driving the development of new, quality productive forces. Tourism ecological governance is the concrete manifestation of the green dimension of new-quality productive forces in the cultural and tourism sector, as well as being a path for converting ecological value to drive regional sustainable development. The relationship and mechanisms between digital–real integration and tourism ecological governance are critical issues requiring urgent breakthroughs. However, existing research primarily explores the economic factors influencing tourism ecology and has yet to systematically reveal the intrinsic mechanisms through which digital–real integration affects tourism ecological efficiency from the perspective of typical ecological functional zones. Based on data from 78 counties (municipalities, districts) in China’s Taihang Mountains from 2011 to 2023, this study examines the impact of digital–real integration on tourism ecological efficiency and its operational pathways. The findings are as follows: Firstly, from a temporal evolution perspective, tourism ecological efficiency in the Taihang Mountains underwent a phase of dynamic adjustment and gradual improvement between 2011 and 2023, while the level of digital–real integration experienced a phase of general enhancement and phased advancement. From a spatial evolution perspective, neighboring sub-regions within the Taihang Mountains exhibit positive spatial correlations in terms of both digital–real integration and tourism ecological efficiency. From the perspective of spatiotemporal transfer characteristics, changes in tourism ecological efficiency and the level of integration of the digital and real economies in the Taihang Mountains are influenced by neighboring regions. The development processes of tourism ecology and digital–real integration exhibit a relatively stable and gradually improving pattern, driving the agglomeration of regions toward higher levels. Secondly, digital–real integration has a positive impact on improving tourism ecological efficiency by releasing ecological pressure, promoting industrial synergy agglomeration, and driving green innovation development. Heterogeneity analysis reveals that the positive effect of this integration on tourism ecological efficiency is more pronounced in national e-commerce demonstration cities. Digital–real integration has had a positive impact on improving tourism ecological efficiency in the Southern and Western Taihang Mountain regions, while its impact on the Eastern Taihang Mountain region was not statistically significant. This study incorporates digital–real integration with tourism ecological efficiency, as well as environmental, structural, and capacity factors, into a unified analytical framework, providing theoretical references and practical insights for exploring pathways of digital transformation and innovative tourism ecological governance in ecologically sensitive functional zones. Full article

Conventional photodetectors and image sensors deliver high-fidelity digital outputs but face a growing data-movement bottleneck: the energy and latency cost of transferring raw pixel streams to off-chip memory and processors increasingly dominates over both sensing and computation in modern machine-vision pipelines. An emerging response is the neuromorphic photodetector, a class of optoelectronic device that converts incident light into an electrical signal while simultaneously storing, modulating, and pre-processing that signal in a manner inspired by biological synapses and retinas. Over the past decade, demonstrations have spanned at least eight material platforms—organic semiconductors, organic–carbon-nanotube hybrids, perovskite and perovskite hybrids, metal oxides (including ultra-wide-bandgap and printable variants), wide-bandgap III-nitrides and 4H-SiC, two-dimensional materials, photo-memristors, and silicon CMOS in-sensor compute architectures—and have been realised through four distinct architectural families: phototransistor synapses, photo-memristors, heterojunction in-sensor compute, and linear photovoltaic neural networks. Here, we provide a quantitative cross-platform benchmark across forty in-scope articles, identify persistent photoconductivity as a near-universal device-physical substrate underlying synaptic functionality, characterise the responsivity–speed–energy trade-off structure observed across platforms, and present a critical assessment of energy-reporting practice in the field. We further identify three best-practice exemplars from three independent material platforms that converge on operating biases of 0.01–0.1 V and energies of 0.07–0.8 fJ per event, and we propose a unified reporting framework to enable meaningful cross-platform benchmarking of next-generation neuromorphic photodetectors. Full article

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean $R^2=0.994$, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making. Full article

Polymeric materials face serious fire-safety challenges in construction, electrical and electronic devices, and aerospace because of their high heat release, melt-dripping tendency, and severe smoke and toxic emissions during burning. This review systematically summarizes the roles of nanofillers in the fire safety of polymer nanocomposites across three interconnected levels: functional mechanisms, regulatory factors, and macroscopic fire behavior. It focuses on four main mechanisms, namely physical barriers, catalytic charring, free-radical scavenging, and rheological network reconstruction, and further discusses how filler geometry, loading level, interfacial compatibility, dispersion state, and spatial orientation regulate fire-safety performance. By linking these factors to time to ignition, thermal stability, heat release, flame spread, and smoke emission and toxicity, the review clarifies the intrinsic structure–mechanism–property relationships. Current studies indicate that the fire-safety improvements provided by nanofillers do not arise from any single effect, but from their coupled regulation of heat transfer, mass transfer, radical reactions, and high-temperature rheology throughout thermal degradation, ignition, heat release, flame spread, and smoke and toxic-gas emission. Remaining challenges include the lack of unified evaluation criteria, limited in situ mechanistic evidence, and insufficient application-oriented rational design. Future work should establish verifiable, comparable, and predictive structure–mechanism–property relationships for polymer nanocomposites. Full article

Rapid urbanization and increasing landscape fragmentation pose significant threats to ecological connectivity, creating a need for integrative decision support approaches in sustainable spatial planning. This study presents a systematic review of ecological network optimization studies published between 2005 and 2025, following the PRISMA protocol. A total of 78 peer-reviewed studies were analyzed to identify methodological trends, recurring limitations, and research gaps in the assessment of structural and functional connectivity. Based on the gaps identified through the systematic review, this study proposes a conceptual Sustainable Spatial Decision Support System (S-SDSS) framework that integrates Morphological Spatial Pattern Analysis (MSPA), Multi-Criteria Evaluation (MCE/AHP), Minimum Cumulative Resistance (MCR), Least-Cost Path (LCP), and Gravity Modeling (GM) within a unified analytical structure. The review findings reveal a clear shift from single-method applications toward integrated multi-model approaches that better represent ecological processes and improve corridor prioritization. The proposed framework synthesizes the complementary strengths of these established methods to support evidence-based ecological network planning. The framework operates as a hybrid structure that combines a sequential analytical workflow with a unified typological classification system, generating Hybrid Ecological Typologies (T1–T5) as planning-oriented outputs that cannot be produced by any individual method alone. The proposed S-SDSS offers a transferable and policy-relevant conceptual basis for ecological network optimization, supporting green infrastructure planning, biodiversity conservation, and long-term landscape resilience across multiple spatial scales. Full article

Satellite-derived bathymetry (SDB) provides a cost-effective means for mapping shallow-water depths, yet its scalability and cross-regional generalizability remain challenging in optically complex coastal environments. This study systematically evaluates machine learning (ML) and deep learning (DL) approaches for transferable SDB over the 0–20 m depth range using multispectral Sentinel-2 imagery. A Random Forest model and four deep learning architectures–ResNet-50, ResNet-101, EfficientNet-B4, and ConvNeXt-Large–are developed and trained using data from Pratas Island (South China Sea) and selected reef regions of the Great Barrier Reef (GBR), and subsequently evaluated on spatially independent intra-regional and cross-regional test areas to assess generalization performance. Model sensitivity is investigated with respect to key training configurations, including loss-function design and data-splitting strategy. To enhance shallow-water learning, we introduce a Smooth Weight Function (SWF)-weighted RMSE loss that emphasizes near-surface depths and compare it with conventional RMSE and relative percentage error (RPE) objectives. In terms of training data, preserving spatial continuity during training substantially improves both numerical accuracy and structural consistency of predictions compared with random patch splitting. While the Random Forest model performs competitively in intra-regional tests, its accuracy degrades under cross-regional transfer (RMSE increasing from 1.53 m to 2.99–3.78 m). Deep learning models, although not always outperforming Random Forest in intra-regional settings, exhibit greater robustness to geographic shift. Using the spatially continuous training strategy, intra-regional RMSE ranges from 1.15 to 1.92 m over the full 0–20 m range, with shallow-water RMSE as low as 0.26 m for depths ≤ 3 m. Cross-regional transfer to geographically independent reefs yields moderate RMSE values of approximately 2.46–2.98 m (0–20 m range), indicating that geographic transfer remains challenging despite meaningful improvements over Random Forest. We further benchmark the proposed architectures against a task-specific bathymetry network using the public MagicBathyNet dataset. Under a unified 0–16 m shallow-water configuration using aerial RGB imagery, the proposed models achieve RMSE values between 0.19 and 0.22 m, outperforming both the baseline U-Net and the transformer-based bathymetry architecture while using substantially fewer parameters. In addition, we exploit multi-temporal repeat imagery for both training and inference, which increases training diversity and improves robustness to temporal variability arising from changing sun angles, atmospheric conditions, water properties, and tides. During inference, predictions from multiple repeat images are aggregated using the median to reduce noise and improve stability. Finally, we release optimized network architectures and pretrained weights to facilitate scalable application to new sites. This work demonstrates a practical pathway toward transferable, large-area SDB from multispectral satellite imagery using deep learning. Full article

The survival of complex multicellular organisms depends on continuous inter-organ communication networks that coordinate organism-wide responses across physiological conditions and stress states, including adaptation to environmental challenges, infection, and injury. Rather than operating as isolated units, organ systems are integrated through interconnected signaling networks that transmit biological information across tissues. Building on prior work examining individual physiological pathways, this review introduces a unified systems-level framework that integrates inter-organ communication into a coherent model of organism-wide regulation. This review proposes a systems-level framework in which homeostasis is maintained through eight principal communication systems: neural, endocrine, immune-inflammatory, vascular, lymphatic, metabolic, microbiome–gut, and mechanical-structural. Epithelial barriers function as dynamic signaling interfaces within multiple systems, while extracellular vesicles act as cross-system mediators of information transfer rather than as independent communication networks. These systems operate across distinct temporal scales to coordinate host defense, metabolic adaptation, vascular regulation, and tissue repair. The framework further introduces a temporal hierarchy of signaling dynamics that links communication systems to phase-specific responses during physiological stress. Within this integrated network, glucocorticoid receptor α (GRα) is proposed to function as a systems-level regulator of inter-organ communication, supported by converging mechanistic, experimental, and clinical evidence, with variability in the strength of evidence across domains. In contrast to prior reviews, which addressed GRα function within individual systems, this work conceptualizes GRα as a central rheostat coordinating cross-system signaling and temporal transitions in homeostatic correction. Evidence was identified through hypothesis-driven searches using the Consensus AI platform and verified through manual review of primary biomedical literature. GRα, a ligand-activated transcription factor expressed in most nucleated cells, enables hormonal stress signals to coordinate gene-expression programs across tissues, modulating neuroendocrine responses, endothelial function, inflammatory signaling, metabolic regulation, microbiome–host interactions, and tissue remodeling. Systemic responses to stress progress through three phases of homeostatic correction—Priming, Modulatory, and Restorative—within which GRα supports integrated organism-wide adaptation. This integrative framework provides a mechanistic basis for understanding the emergence and temporal evolution of biological responses in health and critical illness. Full article

Requirements for robustness and performance in the frequency domain in control theory are usually formulated as constraints on the modulus of complex functions describing the open-loop system, the sensitivity function, and the complementary sensitivity function. These constraints generate circular sets that can be interpreted as admissible or forbidden regions in the complex plane, particularly in the Nyquist diagram. In engineering practice, they are often treated as method-specific constructions, without clarifying the general geometric mechanism by which they arise. This study develops a geometric interpretation in which a broad class of frequency-domain robustness constraints is represented as level sets of analytic and fractional-linear functions. The resulting circular sets in the Nyquist plane are characterized in a unified manner and mapped to admissible regions in the complex s-plane through preimage transformations. The approach is formulated entirely using complex transfer functions, remaining within the classical frequency-domain framework, without state-space representations, linear matrix inequalities, or optimization methods. Classical robustness measures, including gain margin, phase margin, and constraints on sensitivity and complementary sensitivity, are shown to be special cases of the same geometric structure. The main insight of this work is that these apparently different robustness constraints arise from the same underlying geometric mechanism. This interpretation establishes a geometric link between frequency domain robustness constraints and the location of closed-loop poles, allowing a qualitative assessment of robustness and dynamic properties of control systems without introducing new stability criteria or design procedures. The resulting admissible regions provide a geometric interpretation of frequency domain robustness specifications in terms of pole locations in the s-plane. Full article

Interface shear transfer (IST) is a critical mechanism governing composite action in reinforced concrete (RC) structures. While the IST behavior in steel-RC is well established, its application to glass fiber-reinforced polymer (GFRP)-RC remains uncertain due to the scatter of experimental data and the absence of a unified design model. This study assesses the accuracy of current IST design provisions and analytical models for GFRP-RC using a database of 107 push-off tests from the literature, including 56 specimens with an as-cast interface, 20 specimens with an intentionally roughened interface, 26 specimens with a monolithic interface, and five specimens with a smooth interface. Predictions of available models were compared with experimental peak loads. The results show that current provisions in design codes and standards either significantly underestimate or overestimate the IST capacity. The proposed analytical strain-based models in the literature improved predictions but exhibited inconsistencies across different interface conditions. Accordingly, a modified IST model is proposed based on regression analysis, incorporating a cohesion parameter as a function of the concrete strength with a GFRP strain limit of 0.003. The proposed model provides accurate, yet conservative, predictions across different interface conditions. Full article

Despite extensive research on concrete pavement performance, integrating early-age mechanical behavior with multidimensional sustainability assessment remains limited, particularly in tropical environments where rapid moisture loss increases the risk of cracking. Existing approaches often focus on long-term performance or isolated indicators, lacking a unified framework that links early-age reliability to economic, environmental, and social outcomes. This study proposes a reliability-based framework to evaluate early-age performance and sustainability across curing methods. Stress–strength ratio (SSR) relationships (R² > 0.96) were derived from HIPERPAV simulations under tropical conditions, with SSR expressed as a function of structural reliability (75–99%). Mechanical performance was linked to economic costs, environmental impacts (kg CO₂-eq), and a social index, and integrated through a multicriteria approach. The results show that the selection of the curing method strongly influences both early reliability and sustainability. Cotton blankets maintain an SSR of about 70% even at 99% reliability, whereas the no-curing condition exceeds the failure threshold (>100%) at high reliability levels (≥95%). The single-layer curing compound provides the best cost–performance balance, while plastic sheeting and no curing perform worst. The main contribution is a transferable framework that integrates early-age cracking risk with sustainability indicators, enabling consistent evaluation of curing strategies across varying reliability levels in tropical contexts. Full article

This study presents a coupled building simulation framework that evaluates thermal and daylight performance concurrently within a unified multi-objective decision space. Unlike conventional sequential workflows, where daylight metrics are assessed after energy optimization or used primarily for compliance verification, the proposed approach embeds EnergyPlus and Radiance simulations directly within the same optimization loop. This structure enables a systematic exploration of non-linear interactions between Energy Use Intensity (EUI), cooling loads, Spatial Daylight Autonomy (SDA), and Annual Sunlight Exposure (ASE) during early-stage façade design. The framework is demonstrated through a medium-rise office building in India’s National Capital Region, a composite climate characterized by strong seasonal and directional variability. Parametric variation in façade orientation, window-to-wall ratio, and external shading configurations was explored using a multi-objective genetic algorithm to identify Pareto-optimal performance regimes. The results reveal distinct orientation-dependent trade-off structures between solar exposure, cooling demand, and daylight availability that are not evident in rule-based or sequential simulation approaches. In particular, a transitional East-facing façade regime emerges in which balanced shading and glazing proportions achieve near–North-facing cooling performance while maintaining high daylight autonomy under controlled sunlight exposure. Rather than proposing a single optimal solution, the study demonstrates how tightly coupled thermal–daylight simulation can function as a knowledge-discovery tool, enabling the extraction of transferable façade response patterns from simulation outputs. The findings highlight the limitations of prescriptive orientation hierarchies in composite climates and illustrate the value of integrated simulation workflows for performance-driven early-stage design across diverse climatic contexts. Although the study references thermal performance, the optimization objectives are limited to peak cooling load and annual Energy Use Intensity (EUI). Occupant comfort indices such as Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) were not explicitly simulated. Therefore, results are interpreted as energy–daylight performance optimization rather than direct thermal comfort optimization. Full article

GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without addressing workflow-level integration overhead. We present GANSU (GPU Accelerated Numerical Simulation Utility), an open-source quantum chemistry framework written entirely in CUDA/C++ that integrates GPU-accelerated kernels for electron repulsion integrals, Fock matrix construction, and post-Hartree–Fock (post-HF) methods into a unified, GPU-resident execution pipeline. The key design principle is to eliminate host–device data transfers between computational stages by keeping all intermediate data, including density matrices, integral buffers, and Fock matrix replicas, on the GPU throughout the self-consistent field (SCF) iteration, combined with runtime-selectable integral strategies (stored ERI, resolution-of-the-identity, and Direct-SCF) that adapt to system size and available memory. On an NVIDIA H200 GPU, GANSU achieves end-to-end speedups of up to $52\times$ over PySCF for SCF, $45\times$ for MP2 on molecules with up to 470 basis functions, and $44\times$ for FCI, while outperforming GPU4PySCF by up to $34\times$ for FCI, across a range of molecular systems with up to 650 basis functions. The framework further provides analytical energy gradients and geometry optimization with nine algorithms, all operating within the same GPU-resident data flow. These results demonstrate that workflow-aware kernel integration, not just kernel-level optimization, is essential for realizing the full potential of GPU acceleration in scientific computing. GANSU is publicly available under the BSD-3-Clause license. Full article

Decentralised manufacturing is expanding as digitally controlled fabrication tools become accessible to SMEs, independent operators, and community workshops outside traditional factory settings, but the resulting heterogeneous, autonomously operated network introduces systemic uncertainty that no central authority governs. This paper proposes a systems-theoretic framework in which Free and Open-Source Software (FOSS) governance acts as the structural interoperability layer of a distributed cyber–physical manufacturing system (CPS), and node-local digital twins—each hosting a machine learning (ML) disturbance estimator—provide local adaptive compensation without centralised data aggregation. A defining property of the architecture is automatic improvement propagation: learned corrections distribute via federated learning to structurally similar nodes without operator intervention, and the open, observable FOSS ecosystem enables advances in one fabrication modality to transfer to others through shared interface standards. The framework is applied analytically to three disturbance classes: regulatory restriction, technical process variability, and supply chain disruption. Across cases, the analysis shows how open modular interfaces and local adaptation preserve functional continuity under perturbations that would more strongly affect centralised architectures. The contribution is a unified mathematical basis for robustness analysis in decentralised manufacturing CPS and a foundation for future simulation and empirical validation. Full article

This article examines the painted Torah ark as a form of material religion through which early modern rural Jewish communities shaped sacred space and articulated religious belonging. Focusing on a group of wooden synagogues in rural Franconia, Bavaria, and Württemberg, decorated between 1732 and 1740 by a school of painters led by the itinerant painter Eliezer-Zusman of Brody, the study analyzes a rare shift from carved-wood or stone arks to fully representational painted structures. Drawing on visual analysis, architectural context, and comparison with Eastern European precedents, the article argues that painted ark decoration functioned simultaneously as a practical substitute for carving, a strategy for creating unified interior environments, and a means of intensifying devotional experience. The replication of compositional schemes from the Chodorów (now Khodoriv, Ukraine) synagogue in Ruthenia to Bechhofen in Bavaria demonstrates a deliberate process of artistic and religious transfer from Eastern to Central Europe. This transfer reflects both the economic limitations of small German Jewish communities and their aspiration to appropriate the visual prestige of Eastern European synagogue art. More broadly, the case highlights how painted ornament could reshape ritual space and materialize cultural mobility, contributing to wider discussions of material religion, migration, and the circulation of sacred forms in early modern Europe. Full article