Search Results (2,795)

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km²; equivalent to ≥10,000,000 m²) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km²; equivalent to 500,000–10,000,000 m²) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km²; equivalent to 10,000–500,000 m²) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database. Full article

Pandemic response requires not only epidemiological control but also the allocation of limited social resources across competing uses. This paper develops an integrated economic–epidemiological framework to examine how resources should be allocated among goods production, preventive intervention, and therapeutic intervention during an infectious disease outbreak. Building on the susceptible–infected–recovered (SIR) model, the analysis treats the infection rate and the recovery rate as policy-sensitive variables shaped by preventive and therapeutic resource allocation. The objective is intentionally parsimonious and focuses on output preservation and resource allocation under epidemic constraints; infections affect the economy indirectly by reducing effective labor input and output. Two epidemiological environments are considered: one with permanent immunity after recovery and another with possible reinfection. The results reveal a robust timing pattern across both environments. Preventive allocation tends to peak before the surge in infections, whereas therapeutic allocation tends to move more closely with the infection trajectory. The analysis also makes explicit the opportunity cost of intervention: allocating more resources to prevention or treatment reduces the resources available for goods production. Phase-diagram representations clarify the mechanism behind this timing distinction, and sensitivity analyses over alternative curvature parameters confirm that the qualitative ordering of the peaks is robust. These findings suggest that the effectiveness of pandemic response depends not only on the total amount of intervention resources, but also on their timing and functional allocation. By linking epidemic dynamics, resource scarcity, and policy timing within a unified optimization framework, the paper contributes to economic–epidemiological modeling and offers implications for pandemic preparedness, health-system resilience, and the design of response strategies for future infectious disease emergencies. Full article

The increasing demand for permanent post-disaster housing highlights the need for rapid and high-quality construction methods. This study investigates the feasibility of prefabricated modular façade systems in accelerating post-disaster permanent housing construction, while maintaining cost efficiency and construction quality. Unlike previous studies that primarily focus on fully modular building systems, this research examines façade-level prefabrication as an intermediate and scalable strategy that can be integrated into conventional post-disaster housing construction. A mixed-methods approach was adopted: semi-structured interviews were conducted with 15 industry stakeholders, and thematic analysis was applied to extract qualitative insights. Subsequently, a quantitative survey involving 366 construction professionals was carried out and statistically analyzed to validate the findings. Additionally, case studies from previous post-disaster reconstruction efforts were reviewed to contextualize the results. The findings reveal that prefabricated modular façade systems improve construction speed, implementation efficiency, and quality control. Evidence from semi-structured interviews, the survey of 366 construction professionals, and representative case-project comparisons consistently supported the applicability of façade-level prefabrication in post-disaster housing delivery. Most participants also noted quality control benefits inherent to factory-based production. However, the study identifies several limitations, including challenges related to cost and workforce training. The research contributes to the evolving discourse on disaster-responsive housing policies and provides strategic recommendations to enhance the adoption of modular façade technologies in construction practices. Full article

Flood risk assessment in urban areas is becoming increasingly important due to climate change and rapid urbanization. This study presents an integrated flood risk modeling framework for the city of Volos, Thessaly, Greece, coupling the Soil and Water Assessment Tool (SWAT) with HEC-RAS 2D for comprehensive hydrological–hydraulic analysis. The study area is characterized by complex geomorphology, intense urban development and the presence of torrent streams (Xirias, Krausidonas and Anavros) with a total catchment area of 166.25 km². Geospatial and hydrological datasets, including land use, soil types and a Digital Elevation Model, were integrated within SWAT to generate 19-year daily discharge time series. These outputs were linked to HEC-RAS 2D boundary conditions to simulate flood extent, depth and velocity under two scenarios: Baseline (Manning n = 0.05) and Nature-Based Solutions (Manning n = 0.15). Results show that NBS interventions reduce flooded area by 25.3%, maximum depth by 20.4%, and affected buildings by 28.7%, with a Benefit–Cost Ratio of approximately 2.2. The methodology provides valuable input for flood risk management, spatial planning and civil protection strategies in Mediterranean urban environments. Full article

In this work, the structural, electronic, optical, and vibrational properties of gallium nitride (Ga_xN_x) nanostructures were systematically investigated using density functional theory (DFT) and time-dependent DFT (TD-DFT). A series of nanoparticles was constructed starting from a cubic-like Ga₄N₄ building unit, leading to one-dimensional (1D), two-dimensional (2D), three-dimensional (3D), and hexagonal configurations. Geometry optimizations and vibrational frequency calculations were performed at the B3LYP/def2-TZVP level, while optical properties were investigated using TD-DFT with the CAM-B3LYP functional. Only dynamically stable structures without imaginary vibrational frequencies were considered for spectroscopic analysis. The results reveal a strong dependence of the optical and vibrational behavior on nanoparticle size and geometry. Larger and lower-symmetry systems exhibit broader and red-shifted UV–Vis absorption spectra together with richer IR vibrational features. In contrast, elongated low-dimensional configurations such as Ga₁₂N₁₂–1D and Ga₁₆N₁₆–1D/2D were found to be dynamically unstable. The investigated nanostructures also show a clear tendency toward structural reorganization from cubic-like motifs to compact hexagonal arrangements related to the wurtzite phase of bulk GaN. Benchmark analysis demonstrates that CAM-B3LYP provides reliable excitation energies at moderate computational cost. Overall, the obtained results highlight the strong coupling between structure and optoelectronic properties in Ga_xN_x nanostructures and indicate their potential for nanoscale optoelectronic and photonic applications. Full article

Early diagnosis of Diabetic Retinopathy (DR) is critical for preventing irreversible vision loss, but precise lesion annotation by ophthalmologists is the dominant cost in building any clinical-grade DR detection model. The structural problem in real hospital settings is not labeling cost per se, but expert availability: ophthalmologists’ time is bounded by clinical duties, so the active-learning (AL) cycle can iterate only a handful of times in practice. We frame this constraint explicitly and ask which AL designs work best under a tight expert budget. We propose Virtuous Cycle, a Human-in-the-Loop (HITL) pipeline that integrates (i) a YOLOv8x-based object detector for microaneurysms, hemorrhages, and exudates, (ii) four AL sampling strategies (Average Confidence, Random, Hybrid-Diversity, Monte Carlo Dropout), and (iii) an in-hospital annotation platform (Diavision Studio) in which clinicians refine AI pre-labels rather than draw from scratch. We evaluate Virtuous Cycle on a real-world fundus dataset from the National Medical Center (NMC) across eight AL rounds, expanding the labeled pool from 81 images (R0) to 481 images (R8) within the actual expert-time budget of two ophthalmologists. Across three independent random seeds, random sampling dominates at cold start (mean mAP@50 $0.14\to 0.25$ over R0–R1), whereas Hybrid-Diversity converges to the highest mAP@50, Precision, and Recall by R7 (431 images; mAP@50 $0.40$, Precision $0.55$, Recall $0.41$), with MC Dropout close behind; by R8, the labeled pool is exhausted and all strategies converge to the same final model. A clinician crossover analysis of 36 paired clinical images, controlling for per-clinician speed bias and per-image difficulty bias, shows no statistically significant difference in overall per-image labeling time between AI-assisted and manual annotation ($p=0.52$), but a statistically significant increase in confirmed lesion detections under AI assistance ($p=0.0058$), driven predominantly (84–$100\%$ of the net increase) by microaneurysms, the lesion type most prone to being missed unaided. The results indicate that, under expert-budget constraints, AL strategy choice should be staged: random sampling for cold start, uncertainty-and-diversity sampling once the model has matured, and that AI assistance trades a modest, lesion-burden-dependent time cost for a measurable gain in the sensitivity of microaneurysm detection. Full article

Early-stage structural design requires rapid exploration of large design spaces, where the initial sizing of reinforced concrete members shapes downstream material use, cost, and the number of design iterations. Conventional predimensioning relies on experience and simplified formulae, while finite element analysis remains too slow for iterative use. This study presents a multi-task graph neural network surrogate that predicts per-element Eurocode compliance together with the required reinforcement for reinforced concrete slab-and-column buildings in one pass. A shared GraphSAGE encoder, trained on 2562 synthetic building graphs from automated finite element simulations, feeds one head for a compliance probability and another for reinforcement quantities. Because the rule-based Eurocode check is a hard pass-or-fail decision that does not vary smoothly with the design, the surrogate learns a continuous, differentiable compliance probability in its place, demonstrated for two representative criteria, one per element type, namely the $l/250$ deflection limit for slabs and the 4% reinforcement-ratio limit for columns. Across five random seeds, cost-sensitive focal-loss training that weights missed non-compliance above false alarms reached 90.9% balanced accuracy and held the share of non-compliant elements wrongly passed as compliant at 6.1% for columns and 1.6% for slabs, with a mean reinforcement error near 2% of the normalised target range. Inference averaged approximately 0.5 ms per building, between five and six orders of magnitude faster than the finite element analyses. A differentiable, multi-task graph surrogate therefore supports fast, cost-sensitive compliance screening for early-stage predimensioning, serving as a seed for gradient-based design exploration and a starting point for finite element verification. Full article

Building Information Modelling (BIM) has digitized construction, yet automated cost estimation still suffers from fragmented data and deterministic forecasts that ignore uncertainty. To address this gap, this study introduces a novel framework integrating agentic artificial intelligence (AI) with large language models (LLMs) to enable probabilistic cost estimation from disparate BIM data. The system employs four specialized collaborative agents operating via a shared memory module centered on an LLM with natural language understanding, code generation, and chain-of-thought reasoning. A prototype using GPT-4 Turbo, AutoGen, and Monte Carlo simulation was tested on three real-world structures. Compared to three baselines, the framework reduced processing time (4.2 vs. 18.5–68.0 min), manual interventions (0.8 vs. 9–14), and improved entity resolution accuracy (86.5% vs. 46–62%) with well-calibrated probabilistic forecasts, achieving 86.0% empirical coverage for nominal 90% prediction intervals (Prediction Interval Coverage Probability [PICP] = 86.0%, Prediction Interval Width [PIW] = 0.28; p < 0.01). Qualitative analysis confirmed effective semantic conflict resolution and actionable risk visualization via tornado diagrams. The framework tackles long-standing BIM estimation challenges by delivering probabilistic, transparent outputs. Future work includes digital twin integration, open-source LLM deployment, and during-construction forecasting. Full article

Land-intensive high-density residential development often comes at the cost of compromised natural ventilation efficiency and reduced capacity for urban heat island mitigation, and such trade-offs are particularly pronounced in valley cities due to topographical constraints. Taking Lanzhou Yineng Huanghe Jiayuan as a case study, this research constructs a “Source-Flow-Sink” landscape ventilation efficiency measurement framework based on circuit theory and CFD numerical simulation. Combined with correlation analysis and multiple linear regression, the coupling mechanism between spatial morphology and ventilation efficiency is examined. The results indicate that: (1) The study area exhibits a ventilation pattern characterized by “Source” in the north, “Flow” in the middle, and “Sink” in the south; (2) The wind speed ratio in the residential area shows a highly significant negative correlation with vegetation coverage, and a significant negative correlation with building dispersion and average building height; (3) Based on three configuration modes of “Source-Flow-Sink”, differentiated micro-renewal strategies that do not alter the core indicators of land intensification are proposed, providing a scientific basis for climate-adaptive design of intensive residential areas in valley cities. Full article

The demand for large-scale high-performance components with tailored properties in the aerospace and automotive industries has increased interest in multi-material additive manufacturing (AM). Among AM techniques, the Wire Arc Additive Manufacturing (WAAM) process is preferred for bimetallic fabrication due to high deposition rates, low equipment costs, and efficient material utilization. However, differences in metallurgical and thermal properties between dissimilar alloys can cause heat accumulation, leading to thermal stresses, cracking, and weak interfacial bonds. To the best of the authors’ knowledge, no study has reported the fabrication and characterization of a 17-4PH SS/Inconel 625 joint using the large-scale CMT-WAAM Process. To fill this gap, this study characterizes the microstructure and elemental distribution of the joint using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray Microscopy (XRM) and energy dispersive spectroscopy (EDS). Microstructural analysis revealed a martensitic matrix with retained δ-ferrite in the 17-4PH region, a fully austenitic γ-phase in the Inconel 625 region, and a mixed BCC–FCC transition zone at the interface. EDS results demonstrated a Fe–Ni compositional gradient across the interface. Radiographic inspection confirmed a defect-free build, and XRM results showed a porosity of less than 0.003% only in the 17-4PH region. Tensile testing confirmed joint integrity, with fracture occurring in the Inconel 625 region, and average yield and ultimate tensile strengths of 391 ± 7 MPa and 676 ± 9 MPa, respectively. The simplified Johnson-Cook constitutive model successfully predicted the ultimate tensile strength (UTS), with a prediction error of 9.3% compared to the experimental result. Furthermore, a novel 3D-structured light scanner technique was developed and validated with an extensometer to provide insight into localized strain behavior. Full article

This article examines how professionals who use generative AI in their daily work reshape their temporal experience. Drawing on 21 semi-structured interviews with experienced AI users and developers in Romania, and building on Flaherty’s concept of “time work”, it introduces the notion of delegated time work: a form of temporal agency in which individuals transfer part of the time-shaping effort to an AI tool while retaining judgment over the temporal structure of activity. The results show clear support for delegated time work in three dimensions of temporal experience: duration, sequence, and allocation. Evidence for frequency, timing, and taking time is limited: delegation succeeds in the dimensions professionals control individually and fails in those governed by shared institutional rhythms. Delegation also generates its own temporal costs through learning and verification overheads, unevenly distributed between developers and users. Drawing on the “time capital” framework of Matei and Preda, the analysis traces three outcomes of the freed time: accumulation as a personal resource, conversion into professional or economic capital, and absorption by rising expectations, leaving workers faster but not freer. In Romania’s dependent market economy, market exposure shapes who keeps the time that AI frees. Full article

Cost overrun remains a pervasive issue in building construction projects, yet most predictive studies operate within a single data domain, ignoring the systemic interactions across project, schedule, resource, quality, and safety dimensions. This study quantifies the incremental predictive value of integrating these five construction data domains and identifies the cross-domain interaction patterns that explain prediction accuracy. As a simulation-based methodological study, an integrated dataset of 100,000 records was synthesised with theory-grounded causal structures derived from the construction management literature; no real project data were used. Gradient Boosting (GB), Random Forest (RF), and Linear Regression were evaluated on an 80/20 hold-out test split, with robustness verified through alternative domain orderings and hyperparameter sensitivity. SHAP analysis, including exact interaction values, was used to interpret feature importance and cross-domain synergies. The full five-domain GB model achieved R² ≈ 0.97 and MAPE ≈ 6%, a 220% relative R² improvement over the Project-domain baseline (R² rising from 0.305 to 0.975), robust across three ordering schemes. Schedule and Quality contributed the largest marginal gains (ΔR² = +0.312 and +0.255), whereas Resource integration yielded approximately one-thirty-first of Schedule’s return. Because the dataset is synthetic, the results are interpreted as a methodological demonstration rather than empirical evidence from real projects; they provide a reusable framework for prioritising data-integration investment and show that, within the simulated causal structure, cross-domain interactions—particularly Schedule × Risk and Project Type × Change Cost—carry predictive information that single-domain analyses cannot recover. Validation on real, partially integrated datasets is identified as essential future work. Full article

Post-meter water leakage management is critical because leaks occurring after the subscriber meter often remain undetected for long periods, resulting in substantial water wastage and increased abstraction, treatment, and pumping requirements. This study presents a pilot application in Antalya, Türkiye, covering 29 public areas, including schools, mosques, cemeteries, parks, public toilets, health service units, and municipal buildings. Smart meters recording 15-min interval data over nine months were used to distinguish water consumption from post-meter leakage. The analysis revealed high leakage volumes, particularly in schools (7955 m³), cemeteries (3233 m³), and mosques (2721 m³), with the highest leakage ratio observed in mosques (0.77). Overall, post-meter leakages significantly increased operational costs, energy use, and associated greenhouse gas emissions. Full article

Ports combine clusters of operational buildings, shared energy infrastructure, and structurally critical assets requiring coordinated management to ensure safety and efficiency. Nevertheless, existing Digital Twin (DT) frameworks for building energy management rarely integrate Structural Health Monitoring (SHM) with energy performance assessment, while port-specific implementations remain scarce. This paper presents a pre-operational energy-aware DT architecture for port building clusters, structured in a unified five-layer framework integrating three capabilities: (i) EGMS/InSAR-based SHM screening with planned in situ sensing and computer-vision inspection workflows; (ii) smart metering and measurement and verification (M&V) protocols aligned with ISO 50001/50015 and IPMVP standards; and (iii) weighted multi-criteria prioritization considering structural condition, energy saving potential, service continuity, and cost. The framework is applied to the Port of Formia (Italy), a brownfield district comprising nine buildings (3371 m²), 16 high-mast lighting towers, shore power infrastructure, and 90 kWp of planned photovoltaics. In the absence of operational metering, energy and carbon values are reported as bounded ex-ante scenario estimates, not as verified performance outcomes. The analysis estimates photovoltaic generation of 116–137 MWh/year and lighting retrofit savings of 31.5–36.8 MWh/year; the related carbon values are treated as gross grid-displacement upper bounds pending measured self-consumption and export data. A four-phase validation roadmap with quantitative acceptance criteria supports the transition from feasibility assessment to verified performance. Full article

This study comparatively examines the effects of different active and passive energy strategies on energy performance, carbon emission reduction, economic feasibility, and thermal comfort potential in a university building in Ankara. This study uses a university building with 8760 h of recorded operational electricity consumption data as a real-world reference case and evaluates different retrofit strategies through dynamic building energy simulations. Simulation results were evaluated not only in terms of total energy consumption but also in terms of operational carbon emissions, levelized cost of energy (LCOE/LCOSE), and the potential for improving indoor temperature stability through passive design strategies. The results show that PV system integration provides the highest energy and carbon reduction performance by reducing the net grid electricity consumption by 89.76%. Among passive systems, the Trombe wall scenario provided the highest energy savings and the lowest LCOSE value. PCM application stood out in terms of indoor temperature stability potential, while the green roof system contributed to temperature control, especially during the summer. In addition, an economic sensitivity analysis based on the discount rate was carried out to reveal the strengths and weaknesses of the proposed strategies in terms of sustainable building design. The study contributes to the comparative analysis of active and passive retrofit strategies in university buildings by offering an integrated and multi-dimensional evaluation approach supported by real operational data. Full article