Search Results (1,996)

Serbia’s nominal exchange rate has remained near 117 RSD/EUR since 2018, while domestic inflation has persistently exceeded the euro-area rate, producing real appreciation and raising concerns about agricultural competitiveness. This paper assesses dinar misalignment over 2010–2024 using three complementary but non-equivalent benchmarks: purchasing power parity (PPP), the behavioral equilibrium exchange rate (BEER, estimated through Johansen cointegration), and the fundamental equilibrium exchange rate (FEER). The PPP and FEER benchmarks indicate dinar overvaluation of approximately −23% and −13% by end-2024, whereas the fundamentals-based BEER places the dinar close to equilibrium over 2016–2024. These results are interpreted as layered diagnostics of price competitiveness, fundamentals consistency, and external sustainability, rather than as a single jointly identified equilibrium range. Sectoral simulations show strong asymmetry: depreciation raises IRR in export-oriented fruit/IQF production from 3% to 12%, while reducing livestock-fattening IRR from 9% to 6% through higher imported feed costs. Monte Carlo checks confirm these qualitative rankings across plausible pass-through and cost-share ranges. Maintaining the nominal corridor also carries a quasi-fiscal burden: NBS official interest expenses linked to sterilization amount to RSD 111.4 billion over 2021–2024, or 0.07–0.57% of GDP annually. The findings suggest that exchange-rate policy has simultaneous competitiveness, sectoral-distributional and quasi-fiscal dimensions, requiring gradual, transparent and exposure-differentiated policy responses. Full article

The construction sector accounts for 34% of global energy-related CO₂ emissions, yet existing Digital Twin (DT) applications in construction remain largely descriptive, lacking the predictive and prescriptive capabilities required for proactive execution management. This study presents and evaluates, as a simulation-based proof of concept, a risk-informed DT framework integrating a formalized DT state transition model, a dual deep learning prediction engine (Long Short-Term Memory (LSTM) and Transformer), Monte Carlo-based probabilistic carbon quantification for lifecycle modules A1–A5 (S = 10,000), and a risk-adjusted, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimizer incorporating carbon variance, within a closed weekly feedback loop. The framework is evaluated across five European case studies (residential, education, and commercial office; the Netherlands, the UK, and France) anchored to published project data, with primary calibration on a hybrid real–synthetic mid-rise residential building (CS1; 7200 m², eight stories, the Netherlands) and a fully documented synthetic layer generating the execution records that the published sources do not provide. Under common random number simulation with 50 execution realizations per case, the full framework achieved simultaneous improvements of 5.8–13.2% in expected schedule duration and 10.4–16.4% in expected embodied carbon relative to a static Critical Path Method (CPM) baseline; for CS1, mean intensity fell from 476 to 426 kgCO₂e/m². Component ablation identified the bi-objective optimizer as the dominant contributor, producing an 11.7–20.2% carbon increase when removed, and the selected Pareto solution was invariant to the risk-aversion parameter λ over [0, 1], indicating that risk adjustment functions as a conservatism margin on the reported carbon target rather than a decision-altering preference. The framework advances construction DTs from descriptive monitoring tools toward risk-informed decision support, offering a transparent and reproducible benchmark that advances data-driven sustainability in construction scheduling and embodied carbon management. Full article

Earthquake-induced soil liquefaction represents a severe geohazard causing catastrophic infrastructure failure in prone coastal zones, requiring an advanced environmental spatial assessment for their sustainable land-use planning. This study utilizes advanced computational intelligence models to predict earthquake-induced soil liquefaction in Boumerdès, Algeria, an area heavily affected by the 2003 (M_w 6.8) earthquake. Utilizing a comprehensive subsurface database of 1984 geotechnical records encompassing lithology, hydrogeological configurations, and seismic parameters, advanced deep learning frameworks are developed and optimized via automated Neural Architecture Search (NAS). The continuous Factor of Safety (F_s) is calculated to distinguish stable profiles from vulnerable strata, benchmarking conventional ANN and DNN models against NAS-optimized variants (NAS-ANN and NAS-DNN) using a stratified 5-fold cross-validation scheme. The optimized hybrid NAS-DNN framework effectively captured non-linear soil responses, achieving a training correlation coefficient ($R_{train}$) of 0.9518, a validation coefficient ($R_{validation}$) of 0.8843, and a cross-validated mean $R$ of approximately 0.82, demonstrating improved predictive reliability compared to traditional models. Ultimately, this optimal network is embedded into the ‘GeoLiquefy-AI (v1.0)’ interface. To ensure reliability for safety-critical applications, we integrated a SHAP explainable AI framework, validating the model’s geomechanical logic by mapping physical soil-liquefaction dependencies. This deployment-ready tool enables rapid, transparent hazard calculations, providing a scalable platform for seismic microzonation and proactive urban risk mitigation. Full article

This study proposes a sequential Hybrid GP-ANN architecture based on the concept of autonomous feature engineering for multi-output performance prediction of single-phase induction motors, serving as a high-fidelity surrogate model ready for digital twin integration. The required high-resolution motor dataset (88,375 samples) was generated using a parametric sweep approach with Ansys RMxprt. In the proposed architecture, Genetic Programming (GP) is not positioned as a final predictor but as an analytical filter that discovers hidden physical relationships in raw input data and converts them into super features, thereby structurally eliminating the polynomial explosion risk and scale sensitivity that arise when raw data are modelled directly. The nonlinear physical relationships discovered autonomously by GP are added to the network input matrix as mathematical vectors, breaking the black-box structure of standard artificial neural networks and transforming it into a physics-inspired grey-box model. The nonlinear terms discovered by GP are fed into the network after independent Z-score normalisation to preserve gradient stability. To comprehensively evaluate this framework, its predictive performance is benchmarked against industry-standard machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Results on variation-based unseen test data show that the hybrid model achieves competitive prediction accuracy relative to XGBoost—matching or exceeding it for Output Torque and Input Current, and remaining broadly comparable for Output Power, though XGBoost achieves substantially lower RMSE for Efficiency and Power Factor—while additionally offering transparent mathematical traceability, reducing error rates (RMSE) by 42.1% to 66.1% per parameter compared to the standard ANN. The model achieves an $R^2$ score of 0.9879 for the power factor parameter, where conventional approaches struggle. To rigorously validate the interpretability of this grey-box architecture, a global Permutation Feature Importance (PFI) analysis was conducted, showing that the GP-derived super features collectively account for 61.46% of the decision logic, outweighing the combined contribution of the raw inputs (38.54%). Furthermore, the autonomous feature engineering layer is found to reduce the learning burden on the ANN, allowing it to converge to a substantially lower error floor within the same fixed epoch budget. With an online inference time below 0.02 ms, the proposed architecture offers a robust methodological infrastructure for sustainable motor digital twins through a balanced trade-off between prediction accuracy and computational efficiency. Full article

Gigacasting is emerging as a disruptive manufacturing route for automotive body-in-white structures, especially for electric vehicles, by enabling large aluminium alloy components to replace assemblies traditionally produced from stamped and joined sheet-metal parts. This paper presents a technological assessment of aluminium gigacasting against conventional multi-material mix sheet-metal manufacturing. The comparison addresses product architecture, structural performance, manufacturability, factory organisation, cost, repairability, supply chain implications, and sustainability. Gigacasting offers benefits in part consolidation, reduced joining operations, shorter process chains, and potentially lower non-material manufacturing costs, making it attractive for high-volume, low-variant EV platforms and greenfield production. However, these advantages are counterbalanced by challenges, including high capital investment, limited die life, defect sensitivity, dimensional distortion, mechanical-property variation, and reduced repairability. Recent benchmark data also indicate that total part cost and production-phase CO₂ emissions may remain higher than conventional solutions when aluminium material cost, component mass, and aluminium carbon intensity are considered. Conventional sheet-metal architectures retain advantages in modularity, repairability, quality control, tooling flexibility, and lower-risk implementation in brownfield plants. The analysis concludes that gigacasting should not be regarded as a universal replacement for sheet-metal multi-material Body-in-White (BIW) manufacturing but as a platform-dependent technology whose success requires defect control, low-carbon aluminium supply, process-aware simulation and validation, and high and stable production volumes. Full article

Urban traffic congestion remains a major barrier to sustainable mobility, necessitating a transition from static signaling to Intelligent Traffic Light Systems (ITLS). This study presents a systematic review in computer vision, artificial intelligence, and adaptive control algorithms for urban intersection optimization. Following a methodological framework informed by PRISMA guidelines, the study examines state-of-the-art architectures, including deep learning-based perception models and reinforcement learning agents. The findings indicate that, although two-stage detectors provide benchmark accuracy for vehicle perception, single-stage models and Vision Transformers offer the high-speed processing required for real-time traffic management. In addition, deep reinforcement learning enables autonomous, lane-specific optimization that outperforms traditional actuated and fixed-time systems. The review also identifies a persistent research gap in the deployment of these computational frameworks in the resource-constrained and heterogeneous infrastructures of developing countries. For the urban context of Riobamba, Ecuador, a phased implementation strategy is proposed that balances computational demands with the city’s morphological and social characteristics. By bridging the gap between high-fidelity simulation and practical field deployment, this review provides a scalable framework for improving throughput, reducing emissions, and enhancing safety. Overall, these advances offer a promising pathway toward more resilient transportation systems in rapidly evolving Andean urban centers. Full article

Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design. Full article

Despite growing interest in artificial intelligence (AI) applications in the construction industry, the literature still lacks a consolidated understanding of how AI functions across the full spectrum of construction supply chain processes. Existing studies are dispersed across different technologies, project stages, and application contexts, making it difficult to identify the intellectual structure of this field, the main areas of AI application, and the barriers that continue to constrain practical implementation. To address this gap, this study conducts a systematic review of AI applications in construction supply chains by combining scientometric analysis with qualitative content synthesis. A total of 212 journal articles retrieved from Scopus were analyzed using VOSviewer-based scientometric analysis and qualitative content synthesis. The scientometric analysis maps annual publication trends, keyword co-occurrence patterns, co-cited sources, influential documents, and collaboration networks. The qualitative synthesis further examines how AI supports construction supply chain management across three broad themes: procurement and production optimization, logistics and material management, and collaborative decision-making for resilience and sustainability. The findings show that AI has been primarily applied to demand forecasting, resource optimization, logistics coordination, contract and document processing, computer vision-based monitoring, and multi-agent decision support. However, its practical diffusion remains constrained by fragmented and low-quality data, limited empirical validation, high implementation costs, algorithmic opacity, cybersecurity risks, and unresolved governance and liability issues. Based on these findings, this study proposes a data-centric and phased research agenda that emphasizes benchmark datasets, human–AI collaboration, lifecycle economic evaluation, explainable AI, and multi-stakeholder governance. The study contributes to the literature by integrating fragmented AI-related research into a structured knowledge map and by clarifying future pathways for developing intelligent, transparent, and resilient construction supply chains. Full article

Tuned viscous mass dampers (TVMDs) are effective devices for wind-induced vibration control in supertall buildings, but their performance depends on a precise resonance condition that can be disturbed by manufacturing tolerances. This study identifies an insufficiently examined asymmetric sensitivity mechanism, termed the “dangerous diagonal effect”, in which opposite-sign errors in TVMD inertance and stiffness amplify tuning-frequency drift and create a worst-case sensitivity space that conventional symmetric uncertainty models may underestimate. To tackle this challenge without requiring prior statistical distributions unavailable at the design stage, an Info-Gap Decision Theory (IGDT) robust optimization framework tailored to TVMDs under stochastic wind excitation is developed. A Kriging-metamodel-assisted Efficient Global Optimization bi-level strategy reduces the computational burden of the nested worst-case search. Applied to a 76-story, 306 m benchmark building under a dual-criterion constraint combining the ISO 10137 comfort limit and a 30% relative degradation bound, the framework certifies comfort compliance for manufacturing errors up to 23.44% along the dangerous-diagonal direction. Under the most severe coupled degradation scenario, which integrates opposite-sign manufacturing detuning, 50-year power-law aging, and Arrhenius thermal drift, the nominal H₂-optimal design collapses to 36.7% vibration reduction efficiency while the IGDT robust design sustains 51.7%, reducing the Monte Carlo failure probability from 3.8% to 1.2% across 500 random realizations. An aeroelastic wind tunnel campaign spanning 620 detuning configurations on a 1:350 scaled model provides physical validation of IGDT design reliability for a TVMD system. The experiments corroborate the dangerous-diagonal sensitivity asymmetry, support the predicted robustness plateau under severe parameter detuning, and show that the IGDT framework maintains comfort compliance where the H₂-optimal design fails. Full article

Resident-generated service requests are increasingly used in data-driven urban management, but complaint records may reflect uneven administrative visibility rather than objective service needs. This study develops a benchmark-based 311 visibility audit as a diagnostic tool for sustainability governance, examining whether local service conditions become sufficiently visible to support inclusive, anticipatory, and resilient urban management. Using New York City’s 2023 311 records for six complaint categories—Heat/Hot Water, Noise—Residential, Rodent, Water System, Sewer, and Air Quality—the analysis constructs a tract-by-month-by-complaint-type panel and compares tract-level complaint shares with population, housing-related exposure, and rodent inspection benchmarks. The final dataset contains 661,251 geocoded requests across 2243 positive-population census tracts. Results show substantial complaint-type differences in population-benchmark mismatch: Air Quality has the largest exact total variation distance and Noise—Residential the smallest, while under-visible coverage exceeds over-visible coverage in all six categories. Alternative housing benchmarks change selected Heat/Hot Water and Rodent interpretations, and the external rodent check identifies 46 primary blind-spot candidates, narrowed to 17 benchmark-confirmed candidates. The study concludes that 311 systems should be used as diagnostic infrastructures for identifying information blind spots, not as direct measures of urban need or sustainability performance. Full article

Smart waste systems can support sustainable recycling only when sensing, classification, and routing decisions are linked to transparent uncertainty management. This study aims to develop and evaluate a measurement-calibrated, risk-aware selective routing prototype for sustainable smart waste sorting under bench-scale conditions. The proposed prototype integrates an HC-SR04 ultrasonic sensing layer, Arduino Nano control, ESP8266 wireless synchronization, calibration-based fill level conversion, median filtering, exponential smoothing, hysteresis, and persistence-driven alert logic with a conveyor-based single-item vision-routing layer. The sorting layer routes items into glass, metal, plastic, trash, or a reject stream using a reject-aware VGG19 classifier with true logit temperature scaling and calibrated confidence gating. A quantitative prototype-level experimental evaluation was conducted using a five class benchmark of 2527 TrashNet images under a stratified 75/10/15 hold-out design, with cardboard and paper serving as outlier-exposure samples for the reject class. The final model achieved 88.13% test accuracy, 88.15% weighted F1, and 96.60% reject class F1. With a final policy tau = 0.70, the system routed 76.09% of routing class test items with 92.66% routed precision, while reject recall reached 98.66% and reject false acceptance was limited to 1.34%. Structured perturbation testing showed that severe visual degradation reduced automatic routing coverage but mostly shifted uncertain cases toward rejection. The contribution of this work is a transparent prototype-level mechanism that links calibrated sensing, calibrated visual confidence, selective rejection, and actuation-aware routing to support recycling stream purity under bounded uncertainty. Future work should validate the approach under multi-bin, multi-object, and field deployment conditions. Full article

Reliable daily runoff forecasting supports flood risk mitigation and sustainable basin water management, but the added value of external information is difficult to identify when antecedent runoff strongly constrains prediction. This study develops a diagnostic framework to examine whether multi-source remote sensing and gridded precipitation provide additional value beyond runoff memory. The framework was applied to 1-, 3-, 5-, and 7-day runoff forecasting in the Beidao and Baijiachuan catchments of the Yellow River Basin. An external-forcing reference model (M1) used meteorological–remote sensing variables, precipitation statistics, and static catchment attributes, whereas a runoff memory reference model (M2) used only antecedent runoff features. Their validation-based combination formed a fusion benchmark. A residual correction model based on an SRCNN–Transformer architecture (M3) was then used to examine whether the remaining fusion errors could be corrected using gridded precipitation fields and multi-source temporal states. Results show that runoff memory dominated short-lead forecasts but weakened with lead time, while external forcing became more useful at medium and longer leads. M3 produced positive but lead time-dependent Nash–Sutcliffe efficiency gains, with the most stable improvements at 3–5 days. These results describe the potential value of remote sensing and gridded precipitation under known forcing conditions rather than operational forecast skill. These findings provide evidence from two contrasting Yellow River catchments, while their broader applicability remains to be tested under more diverse hydrological and operational forecasting conditions. Full article

This study analyses the Environmentally Sustainable Human Development Index (ESHDI) across 42 municipalities in Puebla, Mexico, which share the same predominant biome, in 2010 and 2020. The objective was to compare four methods for constructing synthetic indices—Geometric Mean (GM), Principal Component Analysis (PCA), and two variants of Data Envelopment Analysis (DEA-CCR and DEA-SBM)—to identify the approaches most consistent with the ESHDI’s multidimensional nature. Considering the dimensions of Health, Education, Income, and per capita CO₂ emissions, the robustness of the classifications was evaluated using Kendall’s W, Spearman’s rho, and Kendall’s tau coefficients. The results reveal statistical convergence among the methods, as well as relevant structural differences in the weighting schemes and the generated rankings. While MG, ACP, and DEA-SBM assigned relatively balanced weights to the dimensions and produced similar rankings, DEA-CCR concentrated weights on the best-performing dimensions of each municipality, allowing a single dimension to exert a disproportionate influence on the aggregate index and compromise its multidimensional balance. Among the methods evaluated, DEA-SBM stood out for its ability to combine conceptual consistency with benchmarking capacity. In addition to measuring environmentally sustainable human development, the method enables the identification of reference municipalities, the establishment of realistic performance targets, and the diagnosis of specific inefficiencies in each dimension, thereby supporting the formulation of more effective and territorially contextualised public policies. The evidence reinforces the theoretical and empirical relevance of incorporating environmental sustainability into human development metrics and demonstrates that methodological choices in aggregation significantly influence the evaluation of territorial performance. Full article

Co-production plays an important role in improving community safety worldwide. However, in China’s governance system, where governmental authority remains strong, enterprises and the public have traditionally been passive participants, creating multiple challenges for the development of co-production. To address these challenges, this study examines the behavioral patterns and strategic choices of grassroots governments, property service enterprises (PSEs), and community residents in co-producing community safety. A tripartite evolutionary game model is constructed as the analytical framework, and the “Property Deliberation Council” (PDC) practice in Changsha is used to calibrate the parameters and conduct simulation analysis. The study explores the interest interactions and stability mechanisms among the three actors in community safety co-production. The results show that, under the benchmark parameter settings, the system can evolve toward an ideal stable state characterized by guided cooperation from grassroots governments, active cooperation from PSEs, and active participation from community residents. Moderate government incentives can increase the participation benefits of enterprises and residents, whereas excessive incentives may increase the cost burden on governments. Information transparency and sanction intensity generate a synergistic constraint effect. Social capital can reduce cooperation costs, suppress opportunistic behavior, and promote the transition of co-production from external mobilization to endogenous stability. This study argues that community safety co-production in the Chinese context is neither simple administrative mobilization nor citizen-led co-production. Rather, it is a governance process jointly shaped by government guidance, the embedded role of PSEs, and resident participation. The findings provide theoretical explanations and policy implications for improving community safety governance platforms, strengthening information disclosure and accountability mechanisms, cultivating community social capital, and promoting sustainable community safety co-production. Full article

This study develops an evidence-based sustainability-readiness and screening-level techno-economic framework for intelligent microgrid implementation in emerging energy markets, using Bulgaria as an evidence-based national case application. It addresses the mismatch between renewable-energy expansion and slower progress in smart metering, distribution-grid observability, data interoperability, local flexibility and institutional coordination. The methodology combines functional benchmarking, a Strengths, Weaknesses, Opportunities and Threats (SWOT)-informed readiness assessment, normalized gap (GAP) measurement, intervention prioritization and an illustrative industrial-zone scenario. Denmark and Greece provide complementary references for digital-market maturity and territorial resilience. Distribution-grid observability, smart-meter deployment, digital interoperability and local flexibility are ranked as the leading deficits, and the four highest priorities remain stable under a ±20% weight perturbation. The scenario is explicitly screening-level: local self-consumption and peak-load reduction are assumptions rather than outputs of hourly battery dispatch. Under baseline assumptions, annual photovoltaic (PV) generation is estimated at 13,500 megawatt-hours per year (MWh/year), avoided emissions at 3686 tonnes of carbon dioxide per year (tCO₂/year), and the simple payback period at 6.3 years. The framework links readiness evidence to intervention priorities and transparent screening outcomes. Full article