Search Results (4,785)

Despite pressure to scale green building (GB) adoption in Indonesia, many government building projects underperform against their initial intended design, creating a compliance gap between the design and construction phases and reducing the GB rating and its potential benefits. This study investigated the barriers and drivers affecting the Indonesian government’s GB projects’ compliance using a systems thinking (ST) approach. A causal loop diagram (CLD) was constructed from stakeholder interviews and literature scoping, followed by semi-qualitative analysis, combining systems archetype identification, eigenvector centrality (EC), and influence mapping to propose potential leverage points as a basis for policy analysis of the current regulatory scenario. Key findings show that knowledge development, sustained stakeholder integration, project documentation readiness, and government support reinforce GB compliance, but are undermined by financial constraints. CLD analysis identified that the more sustainable factors, including regulation alignment, capacity building, and enhancing collaboration, should become a focus of interventions in the system, instead of focusing solely on the provision of funding. This study presents a novel exploration of the GB adoption problem in an Indonesian governmental context through a comprehensive and systems approach. Further research might require narrowing the system boundaries, broadening the literature and stakeholder validation, and performing quantitative modelling to test intervention scenarios to support rigorous decision-making processes. Full article

This study develops a multiregional forecasting framework for road traffic accidents in Ecuador, addressing a critical limitation in existing predictive approaches that rely predominantly on point error metrics without validating the statistical assumptions underlying forecast uncertainty. Although the analysis is conducted at the provincial level, the spatial dimension is used primarily for cross-regional comparison and risk classification rather than for explicit spatial interaction modeling. Using a dataset of 27,648 monthly observations covering all 24 provinces from 2014 to 2025, the study applies the Prophet model within a Design Science Research paradigm and a CRISP-DM implementation cycle. Separate provincial models are estimated with a 24-month forecasting horizon, and methodological rigor is ensured through systematic residual diagnostics using the Shapiro–Wilk test for normality and the Ljung–Box test for temporal independence. Empirical results indicate that the Prophet-based artifact outperforms a naïve seasonal benchmark in 70.8% of the provinces, demonstrating excellent predictive accuracy in structurally stable regions such as Tungurahua (MAPE = 10.9%). At the same time, the framework enables the identification of critical emerging risks in provinces such as Santo Domingo and Cotopaxi, where projected increases exceed 49% despite acceptable point forecasts. The findings confirm that point accuracy alone does not guarantee the validity of confidence intervals and that residual validation is essential for trustworthy uncertainty quantification. Overall, the proposed approach provides a robust foundation for a predictive surveillance system capable of supporting differentiated, evidence-based road safety policies in territorially heterogeneous contexts. Full article

The reinforcement and renovation of existing buildings constitute an important component of the future development of the civil engineering industry. Such projects typically require the original construction drawings of the building. However, for older structures, the original paper-based drawings may be damaged or lost. Moreover, traditional manual surveying and mapping methods are time-consuming, labor-intensive, and limited in accuracy. To address these issues, this paper proposes a floor plan generation method for existing buildings that integrates deep learning and stereo vision based on a fusion of synthetic and real data. First, collaborative modeling and automated rendering between a large language model and Blender are implemented based on the Model Context Protocol (MCP), enabling indoor scene modeling and image acquisition to construct a synthetic dataset containing structural components such as doors, windows, and walls. Meanwhile, manually annotated real indoor images are incorporated. Synthetic and real data are mixed in different proportions to form multiple dataset configurations for model training and validation. Subsequently, the SegFormer model is employed to perform semantic segmentation of indoor components. Combined with stereo camera calibration results, disparity computation is conducted to extract the three-dimensional spatial coordinates of component corner points. On this basis, the architectural floor plan is generated according to the spatial geometric relationships among structural components. Experimental results demonstrate that the proposed method effectively reduces the need for manual annotation and on-site measurement, providing an efficient technical solution for indoor floor plan generation of existing buildings. Full article

With the advancement of computer science, software has become increasingly prevalent across all facets of society, making software quality issues a focal point of industry concern. The scarcity of sufficient defect data in the early stages of projects undermines prediction accuracy, driving research into cross-project software defect prediction. The traditional manual measurement features face challenges due to the data distribution discrepancies between original and cross-project contexts, which hinder the prediction effectiveness. Furthermore, single features fail to comprehensively characterize software information. This paper proposes a domain adaptation and feature fusion-based cross-project software defect prediction method (DAFF-CPDP). The model employs the TCA+ algorithm for domain adaptation and utilizes an encoder layer for progressive feature fusion. Multiple Java projects were selected for evaluation. The comparisons with various baseline models demonstrated that the proposed model outperforms both the traditional machine learning-based feature models and the diverse deep learning-based single-feature or multi-feature models. Concurrently, this paper analyzes the impact of different source projects on target projects, confirming that class-balanced datasets and datasets with smaller distribution differences are more conducive to project prediction. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Seasonal ice is typically present in the southern Gulf of Saint Lawrence from December through March; however, climate change is predicted to reduce this season and alter local ecosystems, geomorphologies, and infrastructure. This impact assessment ascertains the influence of climate change on the ice coverage along Prince Edward Island’s coast. Ice concentration data from 50 study sites were logarithmically correlated with cumulative freezing degree days (FDDs). Correlations were generally good (mean R² = 0.63), although poorer values were observed in areas with greater exposure to wind and waves. An ensemble of the CMIP6 models’ forecasts of future temperatures showed that FDD will drop from an average of 487 °C days during the historical period (1981–2025) to less than 164 °C days in the 2090s under a low-emission scenario, SSP1-2.6. For the same study period, a high-emission scenario (SSP5-8.5) projects FDD to drop to 28 °C days by the end of the century, while a moderate-emission scenario (SSP2-4.5) forecasts 97 °C days annually. Seasonal ice indices demonstrated a similarly substantial decrease, from an average historical value of 11.1 to 3.8, 3.2, and 0.8 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The length of the ice season was also analyzed, with mean season lengths for the 2090s ranging from 3 to 24 days, depending on the emission scenario, representing a 70–96% reduction in season length from the baseline observation. Mild variations were measured in the rate of ice loss throughout the province; however, significant differences in the ice coverage’s baseline values, due to local currents and wave exposure, led to a broad range in the relative proportions of ice loss, with areas along the eastern coastline projecting zero ice winters. Over the next 80 years, projections point to a considerable decline in ice coverage around Prince Edward Island. Full article

The single mental barbel is a distinctive feature of the benthic Antarctic fishes of the «cryonotothenioid» subfamily Artedidraconinae. These barbels are unusual because their primary sensory modality is tactility, not chemosensation as in most other teleosts. They also exhibit considerable interspecific and intraspecific variation in length and in the appearance of the terminal expansion and its epidermis. Barbels range from short to long and the terminal expansion can be nonexistent, small and round, or large and oblong. In most species, the epidermal surface of the terminal expansion exhibits projections of various shapes and sizes. These range from smooth and furrowed, to ridged and furrowed, to pointed, to palmate (having lobes originating from a common point), and to fringed and leaf-like. Barbels are also subject to intraspecific variation among the species in the genera Dolloidraco, Histiodraco, Artedidraco and Pogonophryne. The various epidermal surface patterns all increase the sensory surface area exposed to the substrate and may enhance detection of their prey, primarily polychaetes. They also enhance surface roughness of the epidermis, thereby dissipating mechanical forces and providing some protection from abrasion by the substrate. The various patterns are likely an epigenetic response to different local conditions of the substrate. This variation warrants caution in their use as a defining taxonomic character. Full article

The FOCUS mission is an integrative project developed at the Universidad Nacional de San Martín (UNSAM), Argentina, featuring a constellation of small satellites equipped with X-band Synthetic Aperture Radar (SAR) sensors. Designed with autonomous orbit control, the mission enables Interferometric SAR (InSAR) applications for critical infrastructure monitoring, providing scalable and cost-effective global observation capabilities. This paper presents the modeling, design, and numerical evaluation of the Attitude and Orbit Determination and Control System (AODCS) for the FOCUS mission. The analysis incorporates realistic constraints, including actuator saturation, sensor noise, underactuation effects, and hardware limitations—specifically regarding magnetorquer magnetic moments, reaction wheel capacities, and propulsion unit impulse bounds. Utilizing the NASA 42 attitude and orbit simulator, numerical simulations were conducted to assess stability, pointing accuracy, and agile maneuver tracking through specialized guidance laws. The results confirm that the proposed AODCS architecture achieves stable, responsive performance and supports continuous orbit maintenance, ensuring adequate target acquisition per orbit. Additionally, the selection of star trackers allows achieving a secondary objective through the detection of Resident Space Objects. Full article

Urban underground infrastructures are highly vulnerable to intense rainfall events, particularly access stairs, where preferential runoff paths and the most probable evacuation routes can conflict. This study presents a pluvial flood hazard assessment for underground access stairs in the Barcelona Metropolitan Area Metro network. It integrates the EU ICARIA project modeling framework and the hazard assessment criteria based on hydraulic parameters identified by the Spanish national research project FAVOUR. Both current and future climate change rainfall scenarios are considered. The results showed that out of 415 underground access points, 27 face a high risk of floods, while 35 more have potentially high-risk conditions. These figures could rise to 38 (40% increase) and 47 (74% increase) respectively by the end of the century since climate change is projected to increase rainfall intensity and frequency. By quantifying hazard levels across the network, this study allows the identification of points of the infrastructure where hazard conditions can be more critical. Furthermore, the results presented could potentially support targeted adaptation strategies such as entrance retrofitting, improved drainage design, and emergency planning to develop resilient and sustainable cities. The proposed methodology demonstrates how ICARIA’s modeling framework can effectively evaluate and anticipate flood hazards in complex urban environments at the asset level. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

In this work, a comprehensive first-principles investigation of the electronic, magnetic, and optical properties of pristine and Fe-, Co-, and Ni-doped MoS₂ monolayers is presented within the framework of density functional theory. Substitutional transition-metal doping at the Mo site is shown to induce spin-polarized impurity states within the pristine band gap, leading to significant modifications of the electronic structure, including metallic, semimetallic, or half-metallic behavior depending on the dopant species. The calculated spin-resolved band structures and projected density of states reveal a strong hybridization between the dopant $3d$ orbitals and the Mo-$4d$/S-$3p$ states, giving rise to sizable magnetic moments and dopant-dependent exchange splitting. When spin–orbit coupling is included, the combined effect of exchange interactions and relativistic effects leads to an effective valley splitting at the K and $K^{\prime }$ points, whose magnitude and sign depend sensitively on the chemical nature of the dopant. Optical properties are analyzed within a linear-response framework, showing pronounced dopant-induced modifications of the optical spectra. While the pristine monolayer exhibits well-defined excitonic features, transition-metal substitution introduces low-energy optical transitions associated with impurity-related states. Consequently, the exciton binding energies estimated from the difference between the electronic and optical gaps are interpreted as effective measures of dopant-induced perturbations to optical transitions, rather than as quantitative many-body excitonic binding energies in the strict sense. These results provide microscopic insight into the interplay between magnetism, spin–orbit coupling, and optical response in doped MoS₂ monolayers, highlighting the potential of transition-metal substitution as a route to engineer spin- and valley-dependent phenomena in two-dimensional materials. Full article

We use Reiterative Truncated Projected Least Squares (RTPLS) to estimate the correlation between real GDP per capita and income inequality for the 159 counties in Georgia, USA, from 2011 to 2021. RTPLS produces a separate slope estimate for every observation (data point), where differences in these slope estimates are due to omitted variables. Our measure of inequality is the ratio of household income at the 80th percentile divided by income at the 20th percentile. We find that the negative marginal correlation between income inequality and real per capita income has strengthened over time, and there are large differences between the effects for different counties. For example, in 2021, our estimate for d(real per capita GDP)/d(income inequality) ranged from −3.70 to −28.48. We find that this estimate becomes more negative when there are increases in the percentage of the county population with some college education, the percentage of the county population that is Black, the percentage of the county population that is Hispanic, as well as when unemployment increases. However, d(real percapita GDP)/d(income inequality) becomes less negative as the percentage of the county that is rural increases and as the percentage of the population that is less than 18 years old increases. Full article

In ground-based short-wave infrared (SWIR) astronomical observations, temperature drift in the detector readout circuit often introduces nonlinear, spatially non-uniform stripe noise together with Gaussian noise, making weak stellar targets easily submerged and difficult to detect. To address this challenge, we propose a structurally guided weighted low-rank denoising method for infrared star images. Going beyond traditional spatial filtering and standard low-rank decomposition, the proposed method integrates physical priors with mathematical optimization into a unified framework. First, the point spread function (PSF) characteristics of stellar targets are used to construct a hierarchical structural filter, which is further transformed into adaptive prior weights. This design preserves weak-target energy while suppressing noise during iterative optimization. Second, by exploiting the global spatial correlation of the image, residual stripes and the background are jointly modeled as a low-rank component for effective separation. Finally, Bilateral Random Projection (BRP) is introduced to accelerate the weighted soft-thresholding iterations. Experiments on real ground-based observation data, together with ablation studies and sensitivity analyses, demonstrate that the proposed method effectively suppresses structured stripe interference while preserving weak stellar targets under low-SNR conditions. In addition, the acceleration module further improves computational efficiency, making the framework more suitable for practical real-time processing. Full article

The rapid increase in point density and acquisition rate of UAV laser scanning (ULS) systems has shifted the primary bottleneck of LiDAR workflows from data acquisition to post-processing, particularly during direct georeferencing of massive LiDAR measurements. This study presents a systematic evaluation of parallel computing strategies for accelerating ULS direct georeferencing while preserving geodetic accuracy. Two georeferencing models are investigated: (1) a rigorous model that strictly follows the full geodetic transformation chain from sensor owned coordinates system (SOCS) to projected map coordinates, and (2) an approximate model that incorporates meridian convergence angle compensation and preprocessing of platform trajectories to reduce per-point computational complexity. For each model, a shared-memory multicore CPU implementation based on OpenMP and a heterogeneous GPU implementation based on CUDA are designed. Experiments were conducted on seven real-world ULS datasets, ranging from 2.9 × 10⁷ to 7.0 × 10⁸ points and covering diverse terrain types. Accuracy analysis shows that, in typical urban, plain, and industrial scenarios, the approximate model achieves millimeter-level mean errors and centimeter-level RMSEs relative to the rigorous model, satisfying the requirements of most engineering surveying applications. Performance evaluation demonstrates that parallelization yields substantial speedups: OpenMP-based method achieves 7–9 times acceleration, while GPU computing attains up to 24.6 times acceleration for the rigorous model and up to 16.7 times for the approximate model. The results highlight the complementary strengths of the two models and provide practical guidance for selecting accuracy-efficiency trade-offs in large-scale ULS production workflows. Full article

The electric vehicle revolution has created an urgent need for lithium-ion battery (LIB) recycling, with projections exceeding 11 million tons of end-of-life batteries annually by 2030. However, progress toward a circular economy remains fragmented. This perspective article introduces the concept of a ‘Recycling Trilemma,’ arguing that technological advancements in material separation are systematically undermined by economic volatility and regulatory fragmentation. While current literature focuses on isolated domains—chemistry, business models, or policy—this work provides a systems-level synthesis. By analyzing the friction points between material science (e.g., binder removal, impurity sensitivity), economic realities (e.g., logistics costs, LFP profitability), and regulatory frameworks (e.g., EU vs. US divergence), we propose that true circularity requires synchronized design-for-recycling, market stabilization mechanisms, and harmonized digital product passports. The paper concludes that overcoming the trilemma demands a shift from isolated fixes to integrated, cross-sectoral coordination. Full article