Search Results (1,901)

This study examines how demographic and socioeconomic characteristics shape the aesthetic perception of building-integrated photovoltaics (BIPV) in apartment housing. A survey of 418 respondents was conducted using visual scenarios showing PV integrated into rooftops, facades, balconies, and windows. Data were analysed using descriptive statistics, Pearson correlations, one-way analysis of variance (ANOVA) with Scheffé post hoc tests, multiple regression, and thematic analysis of open-ended responses. The results indicate that aesthetic responses to BIPV are not uniform across user groups. Younger respondents, participants with higher educational attainment, and respondents working in energy or technical fields tended to be more receptive to certain forms of BIPV integration, while architecture and design professionals were generally more critical of visually dominant applications. Rooftop PV received the highest overall ratings, while façade- and balcony-integrated applications generated greater disagreement. The regression models explained only a limited share of the variance, indicating that demographic factors are associated with broad perception patterns but do not strongly predict individual aesthetic judgement. The study offers context-specific evidence for facade-sensitive design guidance, retrofit prioritisation, and targeted stakeholder engagement in Jordan, with only cautious relevance to comparable settings pending cross-cultural validation. Full article

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause–effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space. Full article

The implementation of the national emergency industry demonstration bases’ policies is a new way to achieve safety production governance and a key factor in improving the effectiveness of national safety production governance. This study regards China’s national emergency industry demonstration bases’ policies as a quasi-natural experiment. Based on panel data from 31 provinces in China from 2010 to 2022, a multi-period difference in differences (DID) model is conducted to systematically evaluate the impact and mechanism of this policy on China’s safety production governance. The results show that this policy significantly reduced the death rate of safety production accidents with a GDP of 100 million yuan and has a significant governance improvement effect. Further analysis of the mediating effect shows that policies mainly exert governance effects by increasing public safety financial investment and promoting innovation output. The heterogeneity analysis results indicate that policy effects are more significant in regions with weaker energy-resource industrial bases and lower levels of digital development, suggesting that the marginal governance benefits of policies are mainly concentrated in areas with relatively weak supporting conditions for safety governance. This study makes three primary contributions to the literature. Theoretically, it expands the safety governance paradigm by shifting the focus from traditional administrative “command and control” regulations to market-driven industrial agglomeration. Methodologically, by utilizing a multi-period DID model, it overcomes endogeneity issues prevalent in prior correlation-based studies to rigorously identify causal effects. Empirically, it opens the “black box” of policy transmission by validating dual pathways—fiscal resource allocation and technological innovation—while highlighting a critical “filling the gap” marginal utility effect in resource-constrained regions. This study empirically reveals the mechanism and context-dependent characteristics of industrial policies in safety governance, providing empirical evidence for understanding the inherent logic between industrial policies, public safety governance, and regional sustainable development. It offers practical insights for optimizing the precise implementation and resource allocation of emergency industrial policies to foster socially sustainable and resilient industrial growth. Full article

Triboelectric nanogenerators (TENGs) offer a promising route for self-powered microscale energy harvesting, yet their design optimisation remains empirically challenging due to the complex interplay of material surface physics, device geometry and operating mode. In this work, we present an integrated framework that combines atomic force microscopy (AFM) characterisation, COMSOL Multiphysics 6.0 finite element simulation and physics-informed conditional variational autoencoder (cVAE) to predict and optimise TENG output performance. Four polymer dielectric materials, HDPE, LDPE, TPU, and PMMA, were characterised via Kelvin Probe Force microscopy (KPFM) for work function, surface potential and surface roughness. Surface charge density was calculated from measured KPFM potential using the parallel plate capacitor model and used as a boundary condition in COMSOL Multiphysics simulations for contact-separation and lateral sliding TENG mode for dielectric film thicknesses of 50 µm and 100 µm. The simulated open circuit voltage (Voc) and short circuit charge (Qsc) across gap distances up to 150 mm formed the training dataset for a cVAE model with eight physicochemical condition features. The trained model demonstrated strong reconstruction accuracy (R² ≥ 0.94) and enables generative prediction across unseen design spaces. Results reveal that the LDPE/TPU pair at 50 µm thickness consistently achieves the highest electric outputs in both modes, and the sliding mode yields 25–30% higher voltages than the contact separation mode across all material pairs. This study provides a transferable data-efficient methodology for accelerating TENG material and geometry optimisation. 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

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Effective operation and maintenance (O&M) of deep-sea observation networks are challenged by complex environments and energy limitations. While digital twin (DT) technology offers promising solutions, existing frameworks struggle with high-fidelity, multi-platform orchestration and predictions of electrical energy state. This study proposes a DT framework for a deep-sea observation network (DSON-DT), encompassing telemetry acquisition, predictive analytics, and feedback control to realize a closed-loop workflow for monitoring and managing platform states within virtual scenes. Powered by real-time Internet of underwater things (IoUT) data, a high-fidelity virtual environment is constructed in the Unreal Engine 5 game engine, accurately mapping ambient marine environments and reconstructing platform dynamic behaviors via data-driven approaches and geometric constraints. An improved auto-regressive long short-term memory (AR-LSTM) network is proposed to forecast the battery state of charge (SoC). Experimental results show that this algorithm effectively mitigates the impacts of severe deep-sea noise and the flat open-circuit voltage plateau, suppressing state oscillations to provide reliable references for proactive endurance management. The Vue.js-based web prototype, deployed via pixel streaming, offers seamless interfaces for interactive visualization, analysis, and remote operation. This research achieves comprehensive situational awareness for deep-sea platforms, providing validated technical support for the holistic evaluation and intelligent O&M of heterogeneous marine infrastructures. Full article

The UK telecommunications sector’s 5G rollout is projected to consume 2.1% of national electricity by 2030, raising urgent sustainability concerns. This study empirically investigates, under controlled laboratory conditions, the energy performance and cost characteristics of two private 5G architectures—Vodafone’s Mobile Private Network (MPN) and an Open Radio Access Network (O-RAN) via BubbleRAN—and contextualises them against public network references and the United Nations Sustainable Development Goals (SDGs). Two complementary dimensions of energy performance are assessed: absolute power consumption (Watts), reflecting total system draw regardless of throughput; and throughput efficiency (Mbps/W), capturing useful data delivered per unit of energy. In terms of absolute power, O-RAN consumes less (460 W active, 378 W idle) than MPN (645 W active, 620 W idle). In terms of throughput efficiency, MPN delivers 1.45 Mbps/W versus O-RAN’s 0.44 Mbps/W under these specific controlled, single-cell conditions, a difference that reflects the tested hardware configurations (n77 vs. n78 band; 936 Mbps vs. 202 Mbps throughput; 2 × 2 vs. 4 × 4 MIMO) as much as any intrinsic architectural distinction. Both architectures offer substantially lower annual energy costs (£1060–£1486) compared to public micro-cells (£1991–£2666), representing 44–60% savings. Session continuity was 100% across all controlled trials; this reflects short-term laboratory conditions and should not be extrapolated to a long-term network availability guarantee without extended field validation. These results are configuration-specific preliminary indicators; the relative efficiency advantage of each architecture is expected to vary with load, band, and deployment scale. By 2030, UK 5G network operations are projected to generate 795,347–1,260,532 tonnes of CO₂ annually across low-to-high demand scenarios; private deployment, by reducing site proliferation 15–33%, could displace a meaningful share of this footprint. These findings support SDGs 4, 8, 9, 12, and 13. Hybrid O-RAN–MPN pilots are recommended to maximise sustainability gains while advancing social equity and net-zero targets. Full article

A novel open-loop thermosyphon heat engine driven by low-temperature waste heat is proposed for simultaneous power generation and freshwater production. Large quantities of low-grade thermal energy from sources such as data centres remain underutilised due to the limited efficiency and mechanical complexity of conventional heat engines at low temperatures. The proposed system employs thermosyphon-driven circulation and gravity-assisted condensate return, eliminating mechanical pumping and reducing parasitic losses. A mathematical model was developed to evaluate system performance under low-grade heat input conditions. For a baseline case with 50% turbine isentropic efficiency and 5000 W thermal input, the model predicts an overall efficiency of 3.8% and freshwater production of 143 kg/day. A parametric study was conducted to identify the dominant performance parameters and assess sensitivity to operating conditions. While the predicted power output does not exceed that of optimised Organic Rankine Cycle systems, the proposed configuration offers reduced mechanical complexity and inherent freshwater production through phase change. Unlike membrane-based desalination systems, the open-loop design can accommodate high-salinity feeds, including concentrated brine streams, enabling high recovery operation. These characteristics suggest potential application in low-temperature waste heat recovery scenarios where simplified operation, high-salinity tolerance, and combined energy–water generation are desirable. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Accurate electric vehicle (EV) charging load forecasting is essential for grid planning and resource allocation, yet existing approaches struggle with the inherent sparsity of charging data—a phenomenon characterized by excessive zeros representing periods of no charging activity. This paper addresses this challenge through a novel framework combining a Zero-Inflated Neural Network (ZINN) architecture with an Evolutionary Neural Architecture Search (ENAS) algorithm. ZINN explicitly decomposes the forecasting problem into binary classification (predicting charging occurrence) and regression (estimating energy magnitude conditioned on occurrence), enabling the model to learn distinct patterns for the absence and presence of charging events. Rather than relying on manually designed architectures, ENAS automatically discovers optimal encoder and decoder configurations from a comprehensive search space encompassing modern architectures (LSTM, GRU, Transformer, and iTransformer), layer configurations, activation functions, and hyperparameters. The evolutionary algorithm balances prediction accuracy with computational efficiency through multi-objective optimization. Extensive experiments on real-world EV charging data from 30 stations in Wuhan demonstrate that the ZINN+ENAS framework achieves the lowest prediction error compared to conventional baselines, with the discovered optimal configuration substantially outperforming hand-crafted designs. Comprehensive ablation studies reveal that the asymmetric dual-head architecture and adaptive regularization strategies are critical for handling data sparsity. These findings highlight the importance of explicit zero-inflation modeling and automated architecture discovery for specialized forecasting tasks, providing practitioners with an open-source framework for practical EV charging load prediction. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Machine condition monitoring increasingly depends on distributed sensing, edge intelligence, and cloud analytics, yet timely and trustworthy health assessment remains constrained by latency, bandwidth, privacy, and reliability requirements. Cloud-only architectures provide scalable computation and historical data integration but often fail to satisfy real-time industrial needs, whereas edge-only deployments are limited by restricted computing resources and fragmented local knowledge. Edge–cloud collaboration has, therefore, emerged as a practical architecture for distributing perception, inference, learning, and coordination across hierarchical industrial systems. This review examines 147 publications on edge–cloud collaboration for machine condition monitoring published between 2019 and February 2026. A four-dimensional taxonomy is developed to organize the literature into model-centric, data-centric, resource and task-centric, and architecture and trust-centric mechanisms, while 13 survey and review papers are considered separately for contextual comparison. On this basis, the review analyzes representative collaboration mechanisms and enabling technologies, with particular attention to federated learning, transfer learning, knowledge distillation, digital twins, and deep reinforcement learning, and surveys their deployment in manufacturing, energy, transportation, and infrastructure monitoring scenarios. The literature remains dominated by model-centric collaboration, while architecture and trust-centric studies increasingly provide the system foundations required for practical deployment. The review further identifies major open challenges, including robust generalization under changing operating conditions, efficient data transmission, real-time resource coordination, interoperability, and trustworthy large-scale deployment, and outlines future directions in foundation-model-based edge–cloud collaboration, continual learning, dual digital twins, trustworthy collaboration, and privacy-preserving industrial ecosystems. Full article

High-temperature stress severely reduces the photosynthetic efficiency of radish (Raphanus sativus L.), a cool-season crop. This study evaluated five nitrogen (N) levels {0 N, 0.5 N, 1 N (234 kg urea ha⁻¹, based on RDA), 2 N, and 4 N} through an open-field experiment under high-temperature stress conditions. Analysis of OJIP transients revealed that high temperatures severely inhibited photosynthetic capacity in the 0 N, 0.5 N, and 4 N treatment groups. These groups exhibited a simultaneous increase in K and J-steps, signifying electron transport bottlenecks and structural damage to the oxygen-evolving complex (OEC). Consequently, energy absorption and trapping decreased, while heat dissipation increased. In contrast, the 2 N treatment maintained superior F_m(maximum fluorescence) and energy flux, demonstrating enhanced photosynthetic resilience. However, despite improved photosynthetic stability, the 2 N group did not show a significant increase in yield compared to the 0.5 N or 1 N treatment groups. These results suggest that photosynthetic protection under heat stress does not necessarily guarantee higher yields, highlighting the need to identify optimal fertilization points for sustainable production. Overall, the findings of this study provide fundamental data for strategic nitrogen management in open-field radish cultivation to mitigate the impacts of increasing climatic instability. Full article

Despite the growing use of tessellated and patterned façades in contemporary architecture, their thermal performance, particularly in cooling-dominated educational buildings, remains insufficiently quantified, with existing studies largely prioritizing daylighting or aesthetic outcomes over energy-driven thermal behavior. This study aims to systematically evaluate how different tessellated façade geometries and perforation ratios influence thermal performance and cooling demand in a Mediterranean climate, and to identify an optimal façade configuration that balances multiple thermal objectives. Three tessellation typologies—nature-inspired (Voronoi), Islamic geometric, and folded origami-based patterns—were parametrically generated and applied as external shading screens to an educational building. Annual thermal simulations were conducted using Climate Studio to assess four performance metrics: solar heat gain, energy use intensity, hours of overheating derived from operative temperature, and peak cooling demand. A post-simulation, data-driven, multi-objective, decision-support approach was applied using Compromise Programming to systematically evaluate and rank discrete façade alternatives based on multiple thermal performance criteria. Results indicate that all tessellated façades reduce solar heat gain and peak cooling demand relative to the unshaded baseline, with performance strongly dependent on both geometry and perforation ratio. Lower perforation ratios (20%) consistently outperform more open configurations, while Voronoi-based façades achieve the most balanced overall thermal performance across all evaluated criteria and emerging as the top-ranked solution. The study’s novelty lies in its comparative, cooling-focused evaluation of fundamentally different tessellation logics using transparent, decision-oriented optimization rather than subjective comfort indices or computationally intensive evolutionary algorithms. Beyond its specific findings, the research provides a transferable methodological framework for integrating geometry-informed façade design into early-stage decision-making, supporting climate-responsive and energy-efficient educational architecture in Mediterranean and similar climates. Full article