Extreme cold snaps pose significant risks to buildings, infrastructure, energy systems, and occupants, yet standardized climatic datasets tailored for resilience-focused building performance modeling remain limited. This study presents a methodology and corresponding dataset of cold snap events for 217 U.S. cities, derived from 24 years of historical hourly temperature data obtained from the NASA POWER project. Cold snaps were detected using a percentile-based, location-specific threshold that identifies periods of "abnormal cold" with additional constraints to ensure that events reflect meaningful differences from local winter conditions. Each event was characterized using a suite of metrics, including event duration, heating degree hours, and overcooling degree. Events were further classified into four categories based on the mean outdoor air dry-bulb temperature, analogous to intensity scales used in other hazard domains. A selection procedure was applied to ensure that each city is represented by a small set of short, medium, and long-duration events, resulting in a curated dataset of 880 cold snaps suitable for building energy simulations and resilience assessments. The dataset is provided as EnergyPlus Weather (EPW) files accompanied by a summary spreadsheet containing all events and their metrics. This dataset supports the systematic evaluation of building performance under extreme cold weather conditions and provides a foundation for thermal and energy resilience modeling across the U.S. climates.

Global energy challenges establish building-integrated photovoltaics as a pivotal decarbonization frontier, where semitransparent organic photovoltaics (ST-OPVs) represent a promising technology for simultaneous power generation and daylight transmission. However, their widespread application is constrained by a fundamental efficiency and transparency trade-off governed by complex photon management. Herein, we introduce a physics-enhanced deep learning (PDL) framework that embeds optical physical priors into neural network, significantly reducing the reliance on extensive experimental datasets while enhancing predictive accuracy beyond conventional simulation and purely data driven methods. Building on a novel halogen-additive engineering strategy, that enables opaque devices with a power conversion efficiency exceeding 20%, our PDL-guided optimal optical design delivers corresponding ST-OPVs with a record light utilization efficiency of 6.09%. When scaled to large-area manufactured modules, multi-scale building energy modeling demonstrates that the nationwide deployment of such ST-OPVs could meet up to one-fifth of China's total energy demand, highlighting their transformative potential in advancing sustainable energy systems and supporting global carbon neutrality goals.

Dietary fiber and total fat intake density and self-reported cancer history in U.S. adults: NHANES 2021-2022.

ABSTRACT The study investigates the influence of friction stir welding (FSW) parameters on the hardness and impact energy of AA-7075 composites reinforced with 6 wt% nickel-coated alumina particles. The influence of tool rotation speed, welding speed, axial force and tool tilt angle on the performance of the joint was systematically examined. Hardness and impact energy were chosen as the responses owing to their sensitivity to weld integrity and damage tolerance. Experiments were designed using Taguchi’s L27 orthogonal array. Consequently, multi-response optimisation was performed using Complex Proportional Assessment (COPRAS) to achieve balanced improvement in both responses. The optimal condition (6 kN, 60 mm/min, 600 rev min−1 and 1°) resulted in enhanced hardness and impact energy. To minimise the experimental dependence and support parameter prediction, machine learning (ML) models such as Decision Tree, Random Forest, Neural Networks and Gaussian Process were implemented and assessed with statistical metrics. The predicted results converged towards the COPRAS-derived optimal parametric set. Further, experimental validation yielded hardness of 143.41 ± 2.7 HV and impact energy of 4.7 ± 0.4 J, confirming reproducibility. The proposed optimisation–prediction framework provides an effective approach for designing high-performance welded joints in advanced aluminium matrix composites.

Time series forecasting is essential across domains such as healthcare, energy, and climate modeling. While models like LSTMs, GRUs, Transformers, and State-Space Models (SSMs) have become widely used, selecting the optimal architecture remains unclear. We propose an automated framework that systematically designs hybrid architectures by combining LSTM, GRU, attention, and SSM modules. Our approach uses multi-objective optimization to explore combinations and orderings of blocks, yielding Pareto-optimal architectures that balance user-defined trade-offs among objectives. A preference function selects the most suitable model for a given application. Moreover, two sampling-based iterative procedures for Pareto-front exploration are introduced, which reduces the total training cost by nearly eightfold. Across four real-world benchmarks, our framework reveals that simple models excel in speed, while hybrid compositions dominate when balancing accuracy and complexity. Our findings challenge the notion of a universally superior neural architecture, emphasizing instead the value of data- and objective-driven design in time series forecasting.

• An integrated framework combining energy modelling, sustainability, and synergy analyses enables comprehensive planning for the renewable energy transition. • Cuba can achieve 93% of its electricity from renewable sources by 2050 (13,000 GWh of solar energy and 11,000 GWh of wind energy). • Green hydrogen production can generate cross-sectoral benefits: synthesising fertilisers from ammonia reduces dependence on agricultural imports. • The Integrated SuWi Doughnut analysis confirms the achievements in social sustainability but identifies a critical gap in renewable energy. • The analytical methodology is replicable in countries that depend on fossil fuels and face challenges in sectoral integration. The global transition to renewable energy systems is imperative for climate sustainability. However, nations face significant challenges, including financial constraints, grid vulnerabilities, and dependence on fossil fuels. This study evaluates the feasibility of a 100% renewable electricity scenario for Cuba by 2050, employing a multidisciplinary framework integrating energy modelling (CUBALINDA), sustainability threshold quantification (Integrated SuWi Doughnut Approach), and cross-sectoral impact analysis (Dynamic Synergy Analysis). Using CUBALINDA—an adaptation of the LINDA framework calibrated for Cuban conditions—a backcasting scenario was constructed based on solar PV, wind energy, and Power-to-X technologies, supplemented by energy storage and green hydrogen production to address renewable intermittency. The Integrated SuWi Doughnut Approach reveals that while Cuba meets all social sustainability thresholds, it currently operates outside environmental limits regarding renewable energy share and ecological footprint. The Dynamic Synergy Analysis demonstrates that ammonia derived from green hydrogen could replace fertiliser imports, increase agricultural production, and reduce dependence on food imports. Cuba's energy transition is technically feasible but requires coherent policies, intersectoral integration, and substantial infrastructure investments. Green hydrogen yields significant collateral benefits, fostering energy sovereignty and agricultural revitalisation. By 2050, solar photovoltaic and wind will dominate the energy mix (93% renewable share), progressively replacing fossil fuels with sustainable biofuels and e-fuels. Critical challenges include grid modernisation, seasonal supply-demand imbalances, and financing for hydrogen infrastructure, all of which require coordinated policy interventions and innovative financing mechanisms. This study provides a replicable framework for integrated energy-sustainability planning, emphasising the need for decomposition and resilience analyses to optimise transition pathways.

Predictive modelling of hydrogen adsorption energy using hybrid EMT and machine learning techniques

Atomic-scale modeling of interfacial cohesive energy in finite-size low-dimensional carbon materials

Innovative microwave and hybrid drying strategies for the reduction of GHG emissions, comparative evaluation of specific energy consumption, drying kinetics, and modeling with quality traits of green jackfruit slices

A measurement-based calibration approach for highly scalable timing and energy modeling of EdgeAI multi-core systems

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

The growing complexity of modern energy systems demands intelligent, adaptable, and theoretically grounded approaches to modelling and management. Generative Artificial Intelligence (Gen-AI) and Deep Learning Models (DLMs) configured for generative tasks offer significant potential for scenario simulation and rare-event modelling; however, their adoption remains constrained by conceptual ambiguity and the absence of structured, theory-informed frameworks. To address this gap, this study proposes the Generative Models for Energy Modelling and Management (GM-EMM) framework. By establishing a functional taxonomy of generative approaches, the research addresses critical analytical challenges, including counterfactual reasoning and data scarcity in decentralised energy systems. A multi-method research design was employed, integrating a scoping review, expert elicitation via the Fuzzy Delphi method, and non-parametric statistical prioritisation using Friedman and Nemenyi tests to evaluate six core technologies (RNNs, GANs, GNNs, TNNs, VAEs, and CNNs). Based on expert consensus and statistical differentiation, technologies were positioned across four analytically defined phases: Deployment, Validation, Exploration, and Monitoring. To enhance practical relevance, an interpretive validation stage with industry experts confirmed the roadmap’s coherence and decision-support value.

In this prospective longitudinal study, the authors investigated the mechanisms of coupling between energy compensation behaviors and metabolic adaptation in the course of a 48-week training cycle in 120 elite athletes stratified by sport type (endurance versus power/strength). Using energy compensation rate, multi-omics profiling, endocrine biomarkers, and gut microbiota composition in six measurement points from baseline to recovery phases, they found that energy compensation follows a characteristic U-shaped pattern, with nadirs of 79.6% and 82.6% in endurance and power/strength athletes at peak training load, reflecting persistent energy deficits amounting to 624-840kcal/day. This energy deficit was accompanied by a coordinated suppression of leptin (effect size: - 1.9/- 1.4), an increase in cortisol (+ 1.7/+1.4), upregulation of pathways for fatty acid oxidation, and decreased Firmicutes-to-Bacteroidetes ratios. Systematic correlation analyses point to hierarchical patterns of coupling, according to which endocrine markers most closely related to energy status showed the highest association with compensation rate (leptin: r = 0.55; cortisol: r = - 0.46), whereas downstream phenotypes only express weaker associations. In subgroup analyses, greater metabolic perturbations were observed in athletes experiencing severe energy deficits (effect size: - 1.68 vs. - 0.72). These findings support an integrated "energy behavior-metabolic state" approach for personalized nutritional monitoring and intervention in high-performance sport.

Amidst European integration and the global implementation of the Carbon Border Adjustment Mechanism (CBAM), Ukrainian industry faces the necessity of a radical transformation in environmental reporting. Traditional methods of monitoring pollutant concentrations are being superseded by integrated assessments of the product carbon footprint, necessitating the implementation of high-precision standards (EMAS, ISO 14064). The issue becomes particularly acute in the context of post-war reconstruction, which must be based on the principles of low-carbon development. The aim of the article is to develop the technical and methodological foundations for integrating international environmental standards into the management systems of Ukrainian industrial enterprises to ensure their competitiveness and environmental safety. The study employs methods of systems engineering analysis, mathematical modeling of energy and material balances according to ISO 14040/44, and satellite remote sensing methods for verifying environmental data under martial law conditions. A comparative analysis of the ISO 14001 architecture and the EMAS Regulation has been conducted. It was established that for successful export activities, Ukrainian enterprises must implement mandatory key performance indicators (KPIs) and public environmental statements. The mathematical algorithm for Life Cycle Assessment (LCA) has been detailed. Using a practical example of steel rolled products manufacturing, a methodology for calculating specific embedded emissions (CO2e) was demonstrated, allowing for the identification of "hotspots" within the technological cycle. The technical aspects of applying EU Regulation 2023/956 (CBAM) are substantiated. A model for calculating direct and indirect carbon loads has been developed, accounting for the specifics of the Ukrainian power grid and embedded emissions from precursors. The role of digital tools (the "EcoZagroza" platform, Sentinel satellites) as a basis for verifying environmental damage in conditions of limited physical access to objects has been determined. Conclusions and Practical Significance. A strategic "roadmap" for the implementation of Best Available Techniques (BAT) for enterprises in the Prydniprovya region is proposed. The research results can be utilized by environmental engineers and industrial managers to prepare for cross-border carbon regulation and to implement "green recovery" projects based on the "Build Back Better" principle.

Universities worldwide are increasingly committing to carbon neutrality; however, most institutional climate strategies treat operational emissions forecasting and ecosystem-based carbon sequestration as separate analytical domains, leading to inconsistencies in accounting boundaries, temporal alignment, and verification practices. This study develops and demonstrates an integrated LEAP–InVEST framework that explicitly links energy-system modeling with spatial ecosystem carbon accounting within a unified monitoring, reporting, and verification (MRV)-aligned structure. The framework combines the Low Emissions Analysis Platform (LEAP) for scenario-based greenhouse gas emissions modeling with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for spatial carbon storage assessment. A key methodological contribution lies in reconciling emission flows and carbon stock changes by converting carbon stock variations into annualized removal flows, thereby enabling consistent estimation of gross emissions, carbon removals, and net emissions while avoiding double counting across scopes. Using a university campus in Taiwan as a case study, a baseline inventory was established following ISO 14064-1 standards, and future emissions trajectories were simulated under Business-as-Usual and mitigation pathways through 2040. In parallel, land-use and land-cover data were used to quantify historical and projected carbon stocks across forest, grassland, agricultural, and built-up areas. Results indicate that electricity consumption constitutes the dominant emissions source, and that energy efficiency improvements, photovoltaic deployment, and green power procurement provide the largest mitigation potential. Although ecosystem carbon stocks remain substantial, their annual sequestration capacity offsets only a limited portion of projected emissions, reinforcing the importance of prioritizing emissions reduction before applying nature-based removals. The proposed framework provides a transferable methodological approach for institutional carbon neutrality planning by integrating emissions reduction and carbon sequestration within a coherent analytical system. By aligning energy modeling, ecosystem dynamics, and MRV principles, the framework enhances the transparency, credibility, and robustness of net-zero pathway assessment and is applicable to universities and compact urban systems seeking data-driven and verifiable decarbonization strategies.

Characterization of energy dissipation and modeling of damage evolution in frozen soils under cyclic impact loading

Urban-scale estimation of window-to-wall ratio from street view imagery via computer vision for improved building energy modeling

• Developed new DT Strategy Schedule & Document to provide pragmatic development guidance • Developed a unified DT architecture to integrate sustainability assessments • Demonstrated through a case study of a university building • Created an immersive 3D visualization to enable actionable decision support This paper investigates the application of Digital Twin (DT) technology to support sustainability assessments in the built environment. While DTs are increasingly adopted in building contexts, three key fundamental challenges persist: (1) lack of pragmatic guidance for DT development, (2) limited integration of multiple sustainability assessments, and (3) insufficient support for decision-making through contextualized visualization. To address the scientific gaps, this study introduced the Digital Twin Strategy Schedule and Digital Twin Strategy Document, which provide guidance for defining DT objectives, analytical scope, and data requirements. These instruments were derived by interpreting and adapting general DT strategy guidelines to the specific needs of sustainability-oriented DTs for buildings and are iteratively refined through application to a real-world case study. The proposed framework integrated energy modeling, Material Flow Analysis, and Life Cycle Assessment within a unified architecture. An automated workflow was developed to link Building Information Modeling, the analytical models, and Building Automation System data, enabling consistent data exchange, validation, and traceability. The proposed approach was demonstrated through a case study of a university building equipped with smart sensors. Sustainability indicators and operational performance metrics were visualized within an immersive, interactive 3D environment, supporting anomaly detection and alert-based communication. Results highlighted the potential of DTs to enhance sustainability-informed decision-making and challenges associated with data completeness, semantic alignment, and geometric interoperability. Overall, this work formalizes and demonstrates a DT architecture to connect sustainability analytics with spatially contextualized visualization, moving beyond static dashboards toward actionable decision support for building operation and maintenance.

• New multi-criteria framework for evaluating passive retrofits in high-rise housing. • Retrofit performance is assessed under current and projected 2050 RCP 4.5 climates. • West-facing, middle, and upper-level units showed the highest heat vulnerability. • Overhang shading plus mineral-wool is the most thermally resilient scenario. • Reflective paint plus mineral-wool is the most feasible, green, and low-cost option. Residents of affordable housing apartments often experience significant thermal discomfort due to poor insulation and the absence of HVAC systems. A warming climate, energy poverty, and associated health risks further exacerbate this vulnerability. Passive thermal retrofits offer potential improvements but require evaluation across multiple technical, environmental, and economic criteria. This study conducts a comprehensive assessment of passive retrofit performance for two high-rise government-subsidized housing buildings in Sydney, Australia. Building energy modelling (BEM) was used to simulate retrofit performance under current and projected 2050 (RCP4.5) climate conditions, enabling future-oriented evaluation of thermal resilience. The analysis integrates thermal comfort by orientation, floor level, and season with construction feasibility, environmental impacts, and cost, generating both criterion-specific and overall rankings through a Weighted Sum Model (WSM). This multi-criteria framework supports transparent, flexible, and stakeholder-sensitive decision-making. Findings indicate that west-facing units, followed by east-facing units, exhibit the greatest thermal discomfort annually and across all seasons, while middle and upper levels are more prone to overheating. Among retrofit scenarios, overhang shading combined with external mineral wool insulation (OS + MW) ranked highest when thermal comfort for both hot and cold periods was prioritized. In contrast, reflective paint with mineral wool (RP + MW) offered the most favourable feasibility, environmentally friendly, and cost-efficient profile. The proposed framework provides a transferable method for improving thermal resilience in high-rise social housing

The global shift toward low-carbon and sustainable energy marks a critical step in advancing decarbonization and resilient energy systems. This study presents a process-to-system modeling of a geothermal energy integrated with direct air capture (DAC) and district heating systems. The designed geothermal-DAC-district heating configuration demonstrates a technically robust and thermodynamically synergistic pathway toward carbon-negative energy systems. By effectively integrating geothermal power generation with an all-electric DAC and heat recovery system, this designed configuration advances deep decarbonization goals and aligns directly with the United Nations Sustainable Development Goals (SGDs) on affordable clean energy, sustainable cities, and climate action. The analysis indicates that the turbine output and DAC performance are highly sensitive to geothermal operating parameters, suggesting that maximizing CO2 capture efficiency requires operation at low flashing pressures and elevated reservoir pressures to ensure stable turbine performance and uninterrupted DAC operation. Employing the operational results obtained from the sensitivity analyses and parametric studies, the designed configuration captures 666.6 tCO2 per year employing 8 DAC units and provides district heating to 124 households. The findings reveal that geothermal-DAC integration enables continuous, zero-emission CO2 removal using renewable baseload energy while supporting community-scale heating demands, positioning the system as a viable technological pathway toward carbon neutrality and a sustainable energy infrastructure.