Search Results (425)

Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO₂ emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments. Full article

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO${}_2$). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO${}_2$ absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R${}^2$> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO${}_2$ loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches. Full article

The decarbonisation of urban heating systems represents a key challenge for the transition towards sustainable cities. This study investigates the field integration of a Medium-Temperature Heat Pump (MTHP) within the Osimo District Heating Network (DHN) in Italy, demonstrating how low-grade return flows (30–50 °C) can be effectively upgraded to supply temperatures of 65–75 °C, in line with 4th-generation district heating requirements. Specifically, 5256 h of MTHP operation within the DHN were analysed to validate the initial design assumptions, develop surrogate performance models, and assess the system’s techno-economic and environmental performance. The results indicate stable and reliable operation, with a weighted average Coefficient of Performance (COP) of 3.96 and a weighted average thermal output of 134.5 kW. From an economic perspective, the system achieves a payback period of approximately six years and a Levelised Cost of Heat (LCOH) of 0.0245 €/kWh. Environmentally, the MTHP enables CO₂ emission reductions of about 120 t compared with conventional gas-fired boilers. Beyond its technical performance, the study highlights the strong replicability of MTHP solutions for small- and medium-scale DHNs across Europe. The proposed approach offers urban utilities a scalable and cost-competitive pathway towards low-carbon heat supply, directly supporting municipal climate strategies and aligning with key EU policy frameworks, including the European Green Deal, REPowerEU, and the “Fit-for-55” package. Full article

Melanocytic Tumors of Uncertain Malignant Potential (MELTUMPs) remain among the most challenging entities in dermatopathology due to overlapping morphologic features and marked inter-observer variability. This comprehensive review critically assesses the diagnostic and potential prognostic significance of combining p16 and methylthioadenosine phosphorylase (MTAP) immunohistochemistry (IHC) as a practical surrogate for genomic alterations involving the 9p21 (CDKN2A/MTAP) locus. We analyzed the molecular underpinnings of the CDKN2A/MTAP axis and systematically reviewed existing literature to define an integrated IHC strategy for ambiguous melanocytic lesions. The combined use of p16, a sensitive marker of CDKN2A inactivation, and MTAP, a highly specific marker for homozygous 9p21 deletion, was assessed for its diagnostic complementarity and potential clinical utility. p16 IHC demonstrates high sensitivity but limited specificity due to heterogeneous staining in borderline lesions. In contrast, MTAP loss exhibits near-absolute specificity for CDKN2A/MTAP co-deletion, albeit with lower sensitivity. Concordant loss of both markers strongly supports melanoma or high-risk melanocytoma, while MTAP retention may predict responsiveness to adjuvant interferon therapy. Combined p16/MTAP IHC provides a synergistic, biologically grounded approach that refines diagnostic accuracy in MELTUMPs. This dual-marker algorithm promotes a shift from purely morphology-based evaluation toward a reproducible, molecularly informed classification, improving both diagnostic confidence and patient management. Full article

This work addresses the issue of estimating air pollution maps for urban areas. Spatially dense maps of air pollution can be calculated using physical models, such as ADMS-Urban; however, due to the high computational cost of such models, maps are verified with low temporal resolution (such as monthly or yearly averages). We investigate the feasibility of using machine learning models to predict air pollution maps based on historical data and current measurements from a limited number of monitoring stations. The models are trained on spatially dense pollution maps generated by physical models, along with corresponding measurements from monitoring stations and selected meteorological data. We evaluate the performance of the models using real-world data from a central district in Wrocław, Poland, considering various pollutants such as $P{M}_{2.5}$, $P{M}_{10}$, CO, VOC, and NOx, presented on spatially dense pollution maps with ca. $2\times {10}^5$ points with a 10 × 10 m grid. The results demonstrate that the proposed method can effectively predict air pollution maps with high spatial resolution and a fast inference time, making it suitable for generating pollution maps with significantly higher temporal resolution (e.g., hourly) compared to physical models. We also experimentally demonstrated that PM10, CO, and VOC pollution models can be built based on measurements from $P{M}_{2.5}$ monitoring stations only with similar, and in the case of CO, higher, accuracy than using measurements from $P{M}_{10}$, CO, and VOC monitoring stations, respectively. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time ($T_{95}$ and worst-case exposure) and decreases both event energy $E_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ and CO₂-equivalent ${{CO}_2}_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load $U_{\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}$ and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

Local Interpretable Model-agnostic Explanations (LIME) is a widely used technique for interpreting individual predictions of complex “black-box” models by fitting a simple surrogate model to synthetic perturbations of the input. However, its standard perturbation strategy of sampling features independently from a Gaussian distribution often generates unrealistic samples and neglects inter-feature dependencies. This can lead to low local fidelity (poor approximation of the model’s behavior) and unstable explanations across different runs. This paper presents CoLIME, which is a copula-based perturbation generation framework for LIME, designed to capture the underlying data distribution and inter-feature dependencies more accurately. The framework employs bivariate (2D) copula models to jointly sample correlated features while fitting suitable marginal distributions for individual features. Furthermore, perturbation localization strategies were implemented, restricting perturbations to a defined local radius and maintaining specific property values to ensure that the synthesized samples remain representative of the actual local environment. The proposed approach was evaluated on a network intrusion detection dataset, comparing the fidelity and stability of LIME under Gaussian versus copula-based perturbations, using Ridge regression as the surrogate explainer. Empirically, for the most dependent feature pairs, CoLIME increases mean surrogate fidelity by 21.84–50.31% on the merged CIC-IDS2017/2018 dataset and by 29.28–60.24% on the UNSW-NB15 dataset. Stability is similarly improved, with mean Jaccard similarity gains of 3.78–5.45% and 1.95–2.12%, respectively. These improvements demonstrate that dependency-preserving perturbations provide a significantly more reliable foundation for explaining complex network intrusion detection models. Full article

The dual imperative of mitigating carbon emissions and maximizing hydrocarbon recovery has amplified global interest in carbon capture, utilization, and storage (CCUS) technologies. These integrated processes hold significant promise for achieving net-zero targets while extending the productive life of mature oil reservoirs. However, their effectiveness hinges on a nuanced understanding of the complex interactions between geological formations, reservoir characteristics, and injection strategies. In this study, a comprehensive machine learning-based framework is presented for estimating CO₂ storage capacity and enhanced oil recovery (EOR) performance simultaneously in subsurface reservoirs. The methodology combines simulation-driven uncertainty quantification with supervised machine learning to develop predictive surrogate models. Simulation results were used to generate a diverse dataset of reservoir and operational parameters, which served as inputs for training and testing three machine learning models: Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). The models were trained to predict three key performance indicators (KPIs): cumulative oil production (bbl), oil recovery factor (%), and CO₂ sequestration volume (SCF). All three models exhibited exceptional predictive accuracy, achieving coefficients of determination (R²) greater than 0.999 across both training and testing datasets for all KPIs. Specifically, the Random Forest and XGBoost models consistently outperformed the ANN model in terms of generalization, particularly for CO₂ sequestration volume predictions. These results underscore the robustness and reliability of machine learning models for evaluating and forecasting the performance of CO₂-EOR and sequestration strategies. To enhance model interpretability and support decision-making, SHapley Additive exPlanations (SHAP) analysis was applied. SHAP, grounded in cooperative game theory, offers a model-agnostic approach to feature attribution by assigning an importance value to each input parameter for a given prediction. The SHAP results provided transparent and quantifiable insights into how geological and operational features such as porosity, injection rate, water production rate, pressure, etc., affect key output metrics. Overall, this study demonstrates that integrating machine learning with domain-specific simulation data offers a scalable approach for optimizing CCUS operations. The insights derived from the predictive models and SHAP analysis can inform strategic planning, reduce operational uncertainty, and support more sustainable oilfield development practices. Full article

CO₂-enhanced oil recovery (CO₂-EOR) with water-alternating-gas (WAG) injection offers the dual benefit of boosted oil production and CO₂ storage, addressing both energy needs and climate goals. However, designing CO₂-WAG schemes is challenging; maximizing oil recovery, CO₂ storage, and economic returns (net present value, NPV) simultaneously under a limited simulation budget leads to conflicting trade-offs. We propose a novel closed-loop multi-objective framework that integrates high-fidelity reservoir simulation with stacking surrogate modeling and active learning for multi-objective CO₂-WAG optimization. A high-diversity stacking ensemble surrogate is constructed to approximate the reservoir simulator. It fuses six heterogeneous models (gradient boosting, Gaussian process regression, polynomial ridge regression, k-nearest neighbors, generalized additive model, and radial basis SVR) via a ridge-regression meta-learner, with original control variables included to improve robustness. This ensemble surrogate significantly reduces per-evaluation cost while maintaining accuracy across the parameter space. During optimization, an NSGA-II genetic algorithm searches for Pareto-optimal CO₂-WAG designs by varying key control parameters (water and CO₂ injection rates, slug length, and project duration). Crucially, a decision-space diversity-controlled active learning scheme (DCAF) iteratively refines the surrogate: it filters candidate designs by distance to existing samples and selects the most informative points for high-fidelity simulation. This closed-loop cycle of “surrogate prediction → high-fidelity correction → model update” improves surrogate fidelity and drives convergence toward the true Pareto front. We validate the framework of the SPE5 benchmark reservoir under CO₂-WAG conditions. Results show that the integrated “stacking + NSGA-II + DCAF” approach closely recovers the true tri-objective Pareto front (oil recovery, CO₂ storage, NPV) while greatly reducing the number of expensive simulator runs. The method’s novelty lies in combining diverse stacking ensembles, NSGA-II, and active learning into a unified CO₂-EOR optimization workflow. It provides practical guidance for economically aware, low-carbon reservoir management, demonstrating a data-efficient paradigm for coordinated production, storage, and value optimization in CO₂-WAG EOR. Full article

Driven by the shipping industry’s pressing need to reduce its environmental impact, methanol has emerged as a promising marine fuel. Methanol-diesel dual-fuel (DF) engines present a viable solution, yet their optimization is challenging due to complex, nonlinear interactions among operational parameters. This study develops an integrated simulation and data-driven framework for multi-objective optimization of a large-bore two-stroke marine DF engine. We first establish a high-fidelity 1D model in GT-POWER, rigorously validated against experimental data with prediction errors within 10% for emissions (NOx, CO, CO₂) and 3% for performance indicators. To address computational constraints, we implement a Polynomial Regression (PR) surrogate model that accurately captures engine response characteristics. The innovative Triple-Adaptive Chaotic Sparrow Search Algorithm (TAC-SSA) serves as the core optimization tool, efficiently exploring the parameter space to generate Pareto-optimal solutions that simultaneously minimize fuel consumption and emissions. The Entropy-Weighted TOPSIS (E-TOPSIS) method then identifies the optimal compromise solution from the Pareto set. At 75% load, the framework determines an optimal configuration: methanol substitution ratio (MSR) = 93.4%; crank angle at the beginning of combustion (CAB) = 2.15 °CA; scavenge air pressure = 1.70 bar; scavenge air temperature = 26.9 °C, achieving concurrent reductions of 7.1% in NOx, 13.3% in CO, 6.1% in CO₂, and 4.1% in specific fuel oil consumption (SFOC) relative to baseline operation. Full article

The cement industry significantly contributes to global CO₂ emissions, making material efficiency in concrete structures a crucial sustainability goal. This study addresses the challenge of excessive cement usage in traditional concrete design by optimizing a cast-in-place concrete bench. A density-based topology optimization framework was implemented in ANSYS Mechanical and enhanced with a deep-learning surrogate model to accelerate computational performance. The optimization aimed to minimize the structural mass while satisfying serviceability and strength constraints, including limits on displacement and compressive stress under realistic public-use loading conditions. The topology optimization converged after 62 iterations, achieving a 46% reduction in mass (from 258.3 kg to 139.4 kg) while maintaining a maximum deflection below 2 mm and a maximum compressive stress of 15.5 MPa, within the allowable limit for C20/25 concrete. The deep-learning surrogate model achieved strong predictive accuracy (IoU = 0.75, Dice = 0.73) and reduced computation time by over 10⁵× compared to the full finite element optimization. The optimized geometry was reconstructed and rendered using Blender for visualization. These results highlight the potential of combining topology optimization and machine learning to reduce material use, enhance structural efficiency, and support sustainable practices in concrete construction. Full article

This study addresses the limited mechanistic understanding behind medical imaging for tumor microenvironment (TME) assessment. We developed a novel framework that analyzes tumor imaging heterogeneity index (TIHI)-correlated genes to uncover underlying TME biology and therapeutic vulnerabilities. DCE-MRI and mRNA data from 987 high-risk breast cancer patients in the I-SPY2 trial, together with mRNA data from 508 patients in GSE25066, were analyzed. TIHI-associated genes were identified via Pearson correlation, clustered via weighted gene co-expression network analysis (WGCNA), and subgroups were defined via non-negative matrix factorization (NMF). The clinical relevance of the image-to-gene comprehensive (I2G-C) subtype defined by subgroups was assessed using logistic regression and Cox analysis. I2G-C comprised four clusters with distinct immune and replication/repair functions. It further stratified receptor, PAM50, and RPS5 subtypes. The “immune+/replication+” was more likely to achieve pathological complete response (pCR) (OR = 2.587, p < 0.001), while the “immune−/replication−” was the least likely to achieve pCR (OR = 0.402, p < 0.001). The “immune+/replication+” showed sensitivity to pembrolizumab (OR = 10.192, p < 0.001) and veliparib/carboplatin (OR = 5.184, p = 0.006), while “immune-/replication-” responded poorly to pembrolizumab (OR = 0.086, p < 0.001). Additionally, “immune+/replication-” had the best distant recurrence-free survival (DRFS), whereas “immune-/replication+” had the worst (log-rank p = 6 × 10⁻⁴, HR = 5.45). By linking imaging heterogeneity directly to molecular subtypes and therapeutic response, this framework provides a robust, non-invasive surrogate for genomic profiling and a strategic tool for personalized neoadjuvant therapy selection. Full article

To achieve the co-optimization of dynamic–static performance and lightweight design for the telescopic boom, we proposed an integrated approach within a multi-objective optimization framework by using hybrid surrogate model, combined weighting-TOPSIS method, and Monte Carlo simulation (MCS). First, a parametric model of the telescopic boom under extreme working conditions is established, and its dynamic and static performance is analyzed through finite element analysis. Then, using the cross-sectional parameters of the telescopic boom as input variables, design variables with significant influence on the output responses are identified via Spearman correlation analysis. Subsequently, based on sample points obtained by optimal Latin hypercube design, a high-precision RSM-RBFNN hybrid surrogate model is constructed. On this basis, the NSGA-II algorithm is applied to perform multi-objective deterministic optimization of the telescopic boom, and the combined weighting-TOPSIS method is employed to extract optimal solutions from the Pareto solution set. Finally, considering uncertainties in the design variables, 3-Sigma reliability optimization of the telescopic boom is carried out using Monte Carlo simulation. The results show that, while meeting all design requirements, the mass of the telescopic boom is reduced by 12.5%, the second-order natural frequency is improved by 9.4%, and all performance metrics achieved the 3σ level. This study provides practical guidance for the structural optimization design of the telescopic boom. Full article

Optimization design is an effective strategy for reducing carbon emissions in building structures. Various exhaustive and metaheuristic methods have been proposed to optimize the carbon emissions of structural components, which has primarily focused on sustainable design during the construction phase. This study proposes a hybrid approach for the life cycle sustainable design of reinforced concrete components, encompassing the material production, construction, carbonization, and end-of-life phases. The resistance of structural components was evaluated through time-dependent reliability indices, and surrogate models were developed using machine learning techniques. The surrogate models were subsequently integrated into a dual-objective genetic algorithm for life cycle sustainable design. Based on the proposed approach, numerical examples including a singly reinforced beam and a biaxially eccentric compressed column were analyzed. The minimum carbon emissions were optimized to 486.2 kg CO_2e and 307.8 kg CO_2e, respectively, representing a reduction of more than 10% compared to the original design. Moreover, parametric and comparative analyses were conducted to identify the key factors influencing life cycle sustainable design. The findings underlined the impact of design methods, system boundaries, and specific design variables such as material strengths and concrete cover depth. Overall, this study enhances the efficiency and applicability of sustainable design for structural components while considering life cycle impacts. Full article

As global decarbonization accelerates, the environmental and economic viability of hydrogen production largely depends on the evolving electricity supply mix. This study focused on alkaline water electrolysis (AWE) to identify the key factors affecting the competitiveness of green hydrogen. In this study, the temporal dynamics of grid transformation in Germany and the EU over a 20-year period (2025–2045) were addressed by developing a multi-objective optimization framework that integrates environmental impact analysis with machine-learning surrogate models to evaluate trade-offs between the carbon footprint and energy cost per kilogram of hydrogen. Grid-mix scenarios were generated via constrained Latin Hypercube Sampling under policy constraints, including coal phase-out and ≥80% renewables, screened for Pareto optimality, and clustered into distinct archetypes. The results indicated that cost-effective, low-carbon hydrogen production can be achieved through balanced portfolios that emphasize hydropower, biomass, and solar energy. Scenarios that minimize energy costs alone tend to breach environmental targets, whereas ultra-low-emission paths incur steep energy cost penalties. A representative scenario for 2034 (GWP = 24.57 kg CO₂-eq/kg H₂; Energy Cost = 9.47 €/kg H₂) demonstrated a realistic synergy between policy constraints, cost, and environmental impact. Full article