Search Results (308)

Managing complex and large-scale building facilities requires reliable, easily interpretable, and computationally efficient models. Considering the electrical-circuit analogy, lumped-parameter resistance–capacitance (RC) thermal models have emerged as both simulation surrogates and advanced tools for energy management. This review synthesizes recent uses of RC models for building energy management in large facilities and aggregates. A systematic review of the most recent international literature, based on the analysis of 70 peer-reviewed articles, led to the classification of three main areas: (i) the physics and modeling potential of RC models; (ii) the methods for automation, calibration, and scalability; and (iii) applications in model predictive control (MPC), energy flexibility, and digital twins (DTs). The results show that these models achieve an efficient balance between accuracy and simplicity, allowing for real-time deployment in embedded control systems and building-automation platforms. In complex and large-scale situations, a growing integration with machine learning (ML) techniques, semantic frameworks, and stochastic methods within virtual environments is evident. Nonetheless, challenges persist regarding the standardization of performance metrics, input data quality, and real-scale validation. This review provides essential and up-to-date guidance for developing interoperable solutions for complex building energy systems, supporting integrated management across district, urban, and community levels for the future. Full article

Chemical emergencies in transport are rare but operationally demanding. This study presents a framework that converts the timeline of a real intervention involving a cyclohexylamine leak after a tanker truck overturned into a Petri net and subsequently into an absorbing Markov model. This provides decision-oriented indicators for the intervention phases and enables what-if analysis. Application to a case study shows that the capacity of the decontamination line has a significant impact on the duration of the incident resolution, while introducing a small probability of returning from technical measures to decontamination slightly prolongs the course while leaving the certainty of successful completion unchanged. Mapping between activities, Petri net locations, and aggregated states promotes transparency and the repeatability of procedures and highlights activities with a high number of repeat visits. The outputs are useful for decision making related to personnel and material resources, post-review analyses, and exercise planning. The limitations lie in calibration to a single incident, the partial use of expertly estimated parameters, and the approximate conversion of “steps” to time. Future work will focus on multiple cases, finer-grained time handling, and explicit capacity modelling. Full article

High-quality, interoperable biodiversity information is a prerequisite for effective conservation policy, compliance with European Union (EU) reporting obligations, and efficient environmental decision-making. Greece’s LIFE EL-BIOS (LIFE20 GIE/GR/001317) developed the first National Biodiversity Information System, aiming to aggregate, standardise, and disseminate spatial and non-spatial data for species, habitats, pressures, and trends. This paper provides an economic valuation of this information system as a public, non-market good. We designed a two-stage stated-preference study: (i) a short pre-survey to calibrate initial bids and (ii) the main survey employing double-bounded dichotomous choice (DBDC) contingent valuation with a spike-logit specification. The payment vehicle was a hypothetical monthly subscription in a post-LIFE scenario. The instrument measured time savings (hours), perceived reliability (Likert 1–5), and key demographics/roles. A total of 167 valid responses were collected in September 2025. Participants reported an average of 5.2 h saved per use (median 4; max 14). Among those expressing willingness to pay (WTP), 77% rated EL-BIOS reliability as “High/Very high”. Econometric results indicate time savings as the strongest positive determinant of WTP; perceived reliability is positive and marginally significant; years of experience are negatively associated with acceptance; and cost has a strong negative effect. Mean WTP is estimated at €6.7 per month (median €3.5). Notably, 64% of those unwilling to pay declared protest motives (data should remain public and free). Accordingly, non-payment is decomposed into true zero WTP versus protest-based refusal, i.e., refusal to pay despite acknowledging value. This high protest share reflects principled opposition to paying for public biodiversity data rather than low perceived value of the system. The EL-BIOS database generates measurable productivity gains and social value both through positive WTP and principled protest responses supporting open public data. These findings inform policy on sustainable financing, governance, and long-term operation of national biodiversity information systems. Full article

Background and Objectives: Type I interferon (IFN-I) transcriptional signatures are widely utilised as readouts of innate immunity. We evaluated whether age and sex affect single interferon-stimulated genes (ISGs) and the composite IFN-I score, with implications for control selection and assay calibration. Materials and Methods: Ninety-five healthy individuals (53 males, 42 females; 18 days to 89 years) were studied. Whole-blood expressions of IFI27, IFI44L, IFIT1, ISG15, RSAD2 and SIGLEC1 was quantified by RT-qPCR, normalised to GAPDH and calibrated to a paediatric reference. Age associations used Spearman’s rho; sex differences, two-sided Mann–Whitney U tests. Results: Age effects were modest and gene-specific: IFI44L declined and IFI27 increased with age (significant overall and in females), whereas in males only IFI44L decreased; other ISGs were null (|r| ≤ 0.36). The composite IFN-I score showed no association with age or sex, indicating that aggregation mitigates small gene-level variation and that demographic influences on baseline IFN-I readouts appear minimal within this six-gene whole-blood qPCR panel in our cohort. Conclusions: Methodologically, a single primary cut-off within homogeneous pipelines is appropriate. Although best practice favours age-, sex- and matrix-matched healthy controls, our data show no significant age- or sex-related differences in the composite IFN-I score; matching therefore primarily supports comparability and clinical governance rather than correction of demographic shifts. Full article

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics. Full article

Financial time series are heterogeneous, nonstationary, and dispersed across institutions that cannot share raw data. While federated learning enables collaborative modeling under privacy constraints, fixed architectures struggle to accommodate cross-market drift and device-resource diversity; conversely, existing neural architecture search techniques presume centralized data and typically ignore communication, latency, and privacy budgets. This paper introduces FedRegNAS, a regime-aware federated NAS framework that jointly optimizes forecasting accuracy, communication cost, and on-device latency under user-level $(\epsilon ,\delta )$-differential privacy. FedRegNAS trains a shared temporal supernet composed of candidate operators (dilated temporal convolutions, gated recurrent units, and attention blocks) with regime-conditioned gating and lightweight market-aware personalization. Clients perform differentiable architecture updates locally via Gumbel-Softmax and mirror descent; the server aggregates architecture distributions through Dirichlet barycenters with participation-weighted trust, while model weights are combined by adaptive, staleness-robust federated averaging. A risk-sensitive objective emphasizes downside errors and integrates transaction-cost-aware profit terms. We further inject calibrated noise into architecture gradients to decouple privacy leakage from weight updates and schedule search-to-train phases to reduce communication. Across three real-world equity datasets, FedRegNAS improves directional accuracy by 3–7 percentage points and Sharpe ratio by 18–32%. Ablations highlight the importance of regime gating and barycentric aggregation, and analyses outline convergence of the architecture mirror-descent under standard smoothness assumptions. FedRegNAS yields adaptive, privacy-aware architectures that translate into materially better trading-relevant forecasts without centralizing data. Full article

Real-time, reliable forecasting of water quality (WQ) is a critical component of sustainable environmental management. A key challenge, however, is modeling time-varying uncertainty (heteroscedasticity), particularly during disruptive events like storms where predictability decreases dramatically. Standard probabilistic models often fail in these high-stakes scenarios, producing forecasts that are either too conservative during calm periods or dangerously overconfident during volatile events. This paper introduces the Gated Hybrid–Mixture Density Network (GH-MDN), an architecture explicitly designed for adaptive uncertainty estimation. Its core innovation is a dedicated gating network that learns to adaptively modulate the prediction interval width in response to a domain-relevant, event-precursor signal. We evaluate the GH-MDN on both synthetic and real-world WQ datasets using a rigorous cross-validation protocol. The results demonstrate that our gated model provides robust calibration and trustworthy adaptive coverage; specifically, it successfully widens prediction intervals to capture extreme events where standard benchmarks fail. We further show that common aggregate metrics such as CRPS can mask over-confident behavior during rare events, underscoring the need for evaluation approaches that prioritize calibration. This science-informed approach to modeling heteroscedasticity prioritizes reliable risk coverage over aggregate error minimization, marking a critical step towards the development of more trustworthy environmental forecasting systems. Full article

With the rapid improvement of deep learning, significant progress has been made in cloud removal for remote sensing images (RSIs). However, the practical deployment of existing methods on multi-platform devices faces several limitations, including high computational complexity preventing real-time processing, substantial hardware resource demands that are unsuitable for edge devices, and inadequate performance in complex cloud scenarios. To address these challenges, we propose PMSAF-Net, a lightweight Progressive Multi-Scale Asymmetric Fusion Network designed for efficient thin cloud removal. The proposed network employs a Dual-Branch Asymmetric Attention (DBAA) module to optimize spatial details and channel dependencies, reducing computation cost while improving feature extraction. A Multi-Scale Context Aggregation (MSCA) mechanism captures multi-level contextual information through hierarchical dilated convolutions, effectively handling clouds of varying scales and complexities. A Refined Residual Block (RRB) minimizes boundary artifacts through reflection padding and residual calibration. Additionally, an Iterative Feature Refinement (IFR) module progressively enhances feature representations via dense cross-stage connections. Extensive experimental multi-platform datasets results show that the proposed method achieves favorable performance against state-of-the-art algorithms. With only 0.32 M parameters, PMSAF-Net maintains low computational costs, demonstrating its strong potential for multi-platform deployment on resource-constrained edge devices. Full article

This paper focuses on mixed seeds of Vicia villosa and Avena sativa, with their discrete element model and contact parameters being systematically calibrated and validated to provide reliable theoretical support for the structural design and parameter optimization of the air-assisted seed delivery system. The physical properties of both seed types, including triaxial dimensions, density, moisture content, Poisson’s ratio, and shear modulus, were first measured. The Hertz–Mindlin (no slip) contact model and the multi-sphere aggregation method were employed to construct the discrete element models of Vicia villosa and Avena sativa, with preliminary calibration of the intrinsic model parameters. Poisson’s ratio, elastic modulus, collision restitution coefficient, static friction coefficient, and rolling friction coefficient between the seeds and PLA plastic plate were determined through uniaxial compression, free fall, inclined sliding, and inclined rolling tests. Each test was repeated five times, and the calibration criterion for contact parameters was based on minimizing the relative error between simulation and experimental results. Based on this, experiments on the packing angle of mixed seeds, steepest slope, and a three-factor quadratic rotational orthogonal combination were conducted. The inter-seed collision restitution coefficient, static friction coefficient, and rolling friction coefficient were set as the experimental factors. A total of 23 treatments were designed with repetitions at the center point, and a regression model was established for the relative error of the packing angle with respect to each factor. Based on the measured packing angle of 28.01° for the mixed seeds, the optimal contact parameter combination for the mixed seed pile was determined to be: inter-seed collision restitution coefficient of 0.312, static friction coefficient of 0.328, and rolling friction coefficient of 0.032. The relative error between the simulated packing angle and the measured value was 1.32%. The calibrated inter-seed contact parameters were further coupled into the EDEM–Fluent gas–solid two-phase flow model. Simulations and bench verification tests were carried out under nine treatment combinations, corresponding to three fan speeds (20, 25, and 30 m·s⁻¹) and three total transport efficiencies (12.5, 17.5, and 22.5 g·s⁻¹), with the consistency coefficient of seed distribution in each row being the main evaluation variable. The results showed that the deviation in the consistency coefficient of seed distribution between the simulation and experimental measurements ranged from 1.24% to 3.94%. This indicates that the calibrated discrete element model for mixed seeds and the EDEM–Fluent coupled simulation can effectively reproduce the air-assisted seed delivery process under the conditions of Vicia villosa and Avena sativa mixed sowing, providing reliable parameters and methodological support for the structural design of seeders and DEM-CFD coupled simulations in legume–grass mixed sowing systems. Full article

An assessment of the effectiveness of measures undertaken to make urban road transport more sustainable requires appropriate tools to evaluate the impact of transport on air quality. For this purpose, emission inventories for the road network are prepared using suitable models. In cities without a calibrated travel demand model, the main challenge is obtaining data on traffic parameters. With this in mind, this study proposes a rapid model that enables the estimation of traffic parameters such as average speed, traffic volume, and the percentage share of individual vehicle categories. The proposed method is based on traffic measurements and the aggregation of different road classes into four cumulative categories. A comparison of results obtained from the simplified model and the travel demand model indicates satisfactory accuracy of the estimated parameters, confirming the usefulness of the proposed rapid model in emission inventory calculations. The performed calculations for PM_2.5 and NOx show that using traffic parameters derived from the rapid model, instead of those from a travel demand model, may result in an error of 15% in total emission for the traffic network. Full article

Circular concrete-filled steel tube columns prepared with 100% recycled aggregate concrete (RACFST) are of interest for sustainable, carbon-neutral construction. However, recycled aggregates typically have higher water absorption and lower stiffness, raising concerns about seismic performance. This paper investigates the low-cycle cyclic behavior and displacement ductility of circular RACFST columns. Ten short columns were tested under an axial load ratio of ≈0.20, with varying diameters of 165 and 219 mm and concrete strengths of C30, C40, and C50, along with companion natural-aggregate CFST control specimens. A three-dimensional finite element model was developed and calibrated based on the test results, and parametric simulations were conducted to study the effects of geometry and material parameters. Two distinct flexural failure modes with outward bulging at the base were observed. These two distinct flexural failure modes refer to (1) local outward bulging of the steel tube accompanied by buckling near the base (e.g., specimens RACFSTC40-165-1 and RACFSTC30-219-1) and (2) flexural yielding with extensive concrete crushing around the base region (e.g., specimens RACFSTC50-219-2 and FSTC40-219-2). The first mode was characterized by early steel local deformation and shell instability, while the second showed more distributed plasticity with crushing of recycled aggregate concrete. These modes underline the influence of D/t and concrete strength on failure progression. The results indicate that RACFST columns attain a peak strength comparable to conventional CFST, while achieving significantly greater drift ductility and energy dissipation; the equivalent viscous damping ratio was found to increase with drift at ≈0.04–0.08 for low drifts and ≈0.10–0.18 for moderate drifts, suggesting that existing CFST design provisions are applicable, with only a minor ~3–5% reduction in core concrete strength recommended for stability. Full article

Smart thermostats are a key technology for reducing residential energy consumption in smart cities, but their real-world effectiveness depends on the interaction between automation, occupant behavior, and the design of behavioral interventions. This study presents a physics-informed assessment of thermostat strategies across Luxembourg’s single-family home stock, using an aggregate thermal model calibrated to eight years of hourly national heating demand and meteorological data. We simulate five categories of behavioral scenarios: dynamic thermostat adjustments, heat-wasting window-opening behavior, flexible comfort models, occupancy-based automation, and a portfolio of four probabilistic nudges (social comparison, real-time feedback, pre-commitment, and gamification). Results show that occupancy-based automation delivers the largest energy savings at 12.9%, by aligning heating with presence. In contrast, behavioral savings are highly fragile, as a stochastic window-opening behavior significantly erodes the 9.8% savings from eco-nudges, reducing the net gain to 7.6%. Among nudges, only social comparison yields significant savings, with a mean reduction of 7.6% (90% confidence interval: 5.3% to 9.8%), by durably lowering the thermal baseline. Real-time feedback and pre-commitment fail, achieving less than 0.5% savings, because they are misaligned with high-consumption periods. Thermal comfort, the psychological state of satisfaction with the thermal environment drives a large share of residential energy use. These findings demonstrate that effective smart thermostat design must prioritize robust, presence-responsive automation and interventions that reset default comfort norms, offering scalable, policy-ready pathways for residential energy reduction in urban energy systems. Full article

In cold regions, flexible pavements are vulnerable to frost-induced damage, necessitating effective insulation strategies. Foam glass aggregate (FGA) insulation layers, made from recycled glass, offer promising thermal insulation properties but are mechanically fragile and susceptible to permanent deformation under repeated loading. Manufacturers provide technical recommendations, particularly regarding load limits for installation and the dimensions of the thermal protection layer. These are considered insufficient to assist pavement designers in their work. The definition of critical criteria for permissible loads was deemed necessary to design mechanically durable structures using this alternative technology. This study investigates the critical stress conditions that FGA layers can tolerate within flexible pavement systems to ensure long-term structural integrity. Laboratory cyclic triaxial tests and full-scale accelerated pavement testing using a heavy vehicle simulator were conducted to evaluate the resilient modulus and permanent deformation behavior of FGA. The results show that FGA exhibits stress-dependent elastoplastic behavior, with resilient modulus values ranging from 70 to 200 MPa. Most samples exhibited plastic creep or incremental collapse behavior, underscoring the importance of careful stress management. A strain-hardening model was calibrated using both laboratory and full-scale data, incorporating a reliability level of 95%. This study identifies critical deviatoric stress thresholds (15–25 kPa) to maintain stable deformation behavior (Range A) under realistic confining pressures. FGA performs well as a lightweight, insulating, and draining layer, but design criteria remain to be defined for the design of multi-layer road structures adapted to local materials and traffic conditions. Establishing allowable critical stress levels would help designers mechanically validate the geometry, particularly the adequacy of the overlying layers. These findings support the development of mechanistic design criteria for FGA insulation layers, ensuring their durability and optimal performance in cold climate pavements. Full article

Background/Objectives: Muscle strain impairs mobility and quality of life, yet ultrasound diagnosis remains dependent on subjective expert interpretation, which can lead to variability in lesion detection. This study aimed to develop and evaluate MS-detector, a symmetry-aware, two-stage deep learning model that leverages bilateral B-mode ultrasound images to automatically detect muscle strain and provide clinicians with a consistent second-reader decision-support tool in routine practice. Methods: A YOLOv5-based detector proposes candidate regions, and a Siamese convolutional neural network (CNN) compares contralateral regions to filter false positives. The dataset comprised 559 bilateral pairs from 86 patients with consensus labels. All splits were enforced at the patient level. A fixed, independent hold-out test set of 32 pairs was never used for training, tuning, or threshold selection. Five-fold cross-validation (CV) on the remaining development set was used for model selection. The operating point was pre-specified at T1 = 0.01 and T2 = 0.20. Results: The detector achieved mAP = 0.4006 (five-fold CV mean). On the hold-out set at the pre-specified operating point, MS-detector attained recall = 0.826 and precision = 0.486, improving F1/F2 over the YOLOv5 baseline by increasing precision with an acceptable recall trade-off. A representative figure illustrates the reduction in low-confidence false positives after filtering; this example is illustrative rather than aggregate. Conclusions: Leveraging contralateral symmetry in a hierarchical scheme improves detection precision while maintaining clinically acceptable recall, supporting MS-detector as a decision-support tool. Future work will evaluate generalizability across scanners and centers and assess calibrated probabilistic fusion and lesion grading. Full article

This study examines how information frictions in climate policy credibility shape carbon border adjustment mechanisms when trading partners cannot fully verify each other’s commitment to green industrial policies. A dynamic signaling framework models exporters’ policy commitment capacity as private information, incorporating Bayesian belief updating subject to signal noise and reputation decay. The analysis derives a Perfect Bayesian Equilibrium characterizing optimal CBAM tariff responses conditional on importers’ evolving credibility assessments. The calibrated model achieves strong empirical validation (R² = 0.884, explaining 88% of tariff variance; rank correlation $\rho =0.950$), with Monte Carlo simulations demonstrating robust internal consistency (RMSE = 2.56 percentage points). Results identify a critical belief threshold ($p_t<0.3$) triggering hyperelastic tariff responses: below this credibility level, small perception declines generate disproportionately steep border tax increases (elasticity ≥ 2), trapping countries in high-tariff equilibria despite genuine commitment. Information frictions impose aggregate welfare losses equivalent to 30% of potential coordination gains, decomposed into four sources: information opacity (40%), cognitive biases in belief formation (27%), policy distortions induced by credibility concerns (20%), and reputation maintenance costs (13%). These quantitative patterns, while illustrative within the baseline calibration, motivate testable implications regarding elasticity asymmetries and credibility persistence. The framework identifies targeted policy interventions—third-party verification of commitment durability, rule-based tariff adjustment protocols, and institutional commitment devices—systematically prioritized by marginal welfare impact to guide beliefs away from credibility traps while maintaining environmental rigor. Full article