Search Results (20,983)

Thermal monocular depth estimation can provide more robust depth predictions than RGB-based methods under nighttime and adverse weather conditions. However, when trained with projected LiDAR supervision, depth models often retain structural errors in sky regions, long-range areas, and object boundaries because LiDAR measurements are sparse or missing in such regions. To address this limitation, we propose a thermal monocular depth estimation framework that incorporates propagation-based refinement. To make this refinement applicable across different base models, we further design a multi-scale feature adapter that converts heterogeneous multi-scale features with different spatial resolutions and channel dimensions into a unified representation. As a result, the same refinement architecture can be used across different base models without model-specific refiner redesign. On the multispectral stereo (MS2) dataset, the proposed method improves both BTS (big-to-small) and NeWCRFs (neural window fully connected CRFs), reducing the meter-based error metrics SqRel from 0.380 to 0.369 and RMSE from 3.163 to 3.126 for BTS, and reducing SqRel from 0.331 to 0.328 and RMSE from 2.937 to 2.924 for NeWCRFs. Qualitative results further show that the proposed method alleviates mixed-depth artifacts and abnormal depth patterns in regions lacking reliable depth supervision. Full article

Extreme weather events have raised the frequency of power outages, posing critical challenges to the sustainability and resilience of modern power systems. In such cases, distributed energy resources (DERs) can effectively support the re-establishment of sustainable power supply for critical loads within the distribution network and reduce power outage losses. In this paper, a sustainable fault recovery framework based on an intentional islanding scheme is proposed to partition the distribution system in order to optimize the priority restoration of critical loads, while taking the operational constraints of the system into consideration. In addition, a blockchain-based P2P insurance mechanism is applied to mitigate the outage losses of the network’s users with a higher degree of security and transparency. By linking technical restoration decisions with financial risk-sharing mechanisms, the proposed framework improves economic sustainability and social equity among network users. For this purpose, a multi-layer, multi-objective optimization algorithm is proposed for optimal partitioning of the distribution network, management of DERs, and demand side management of flexible loads in order to minimize the outage losses and the insurance premium, while maintaining satisfactory performance of the network. To validate the feasibility of the proposed algorithm, the 45-node distribution network of Alexandria, Egypt is used. The results show that a reduction in peak load, outage losses, and operational costs are achieved, with an overall saving of 17.34%, in addition to a premium reduction of 41.3%. These results highlight the effectiveness of the proposed framework in enhancing the environmental, economic, and operational sustainability of distribution systems under outage conditions. Full article

While check dams are crucial for debris flow mitigation, they face increasing failure risks under extreme weather and seismic activities. Their collapse can severely amplify debris flow magnitude, yet quantitative understanding of this amplification mechanism remains limited. Based on field investigations in southern Gansu, China, and a total of 12 flume experiments (comprising 11 distinct scenarios and 1 representative repeatability test), this study quantitatively assesses the amplification effect of dam breaches under varying channel slopes, check dam types, and bed conditions. Results indicate that dam-breach debris flow evolution comprises three stages: material initiation and deposition, breaching and material release, and recession. Crucially, dam breaching shifts the initiation mode from progressive retrogressive erosion to a near-instantaneous release of mass and potential energy. Compared to no-dam scenarios, breaches amplified peak discharge, erosion rate, and downstream inundated area by factors of 1.65–3.04, 1.44–1.55, and 2.14–2.77, respectively. This amplification is driven by the rapid initial release of material and energy, compounded by erosional entrainment during the transport phase. Furthermore, check dam type and channel slope act as key controlling factors. By revealing how check dams transition from protective structures to hazard sources, this study provides quantitative experimental evidence for optimizing dam design and advancing resilient disaster risk reduction strategies in mountainous regions. Full article

Precipitation forecasting is essential for disaster prevention, water resource management, and socio-economic resilience. The field has evolved from numerical weather prediction (NWP) and optical-flow-based methods toward data-driven deep learning approaches that can exploit larger observational datasets and model complex nonlinear relationships. Against this background, this study evaluates multi-station temporal forecasting models within a single-year, station-based proof-of-concept benchmark under unified data conditions. We adapt the Transformer and Informer architectures to this meteorological setting, rigorously preprocess the AWS dataset to avoid data leakage, and select predictive variables using complementary linear and nonlinear relevance criteria. Model performance is assessed using continuous and categorical precipitation metrics, including the Critical Success Index (CSI). The results show that the Informer outperforms the recurrent neural network (RNN) baselines and achieves the lowest mean MAE and RMSE together with the highest mean CSI among the evaluated models while using substantially fewer parameters than the standard Transformer. However, its sample-wise absolute error distribution remains statistically comparable to that of the standard Transformer. Overall, this study establishes a single-year, station-based proof-of-concept benchmark for comparing architectures in very-short-term (1–5 h ahead) precipitation forecasting. Full article

This study investigates the development of high-strength biofiber cement boards with enhanced thermal insulation properties by utilizing recycled biofibers derived from cement packaging bags, combined with a water-based adhesive to enhance the curing efficiency of Portland cement through a cementation–curing process. This approach reduces waste from cement packaging and other biofiber residues through recycling, thereby promoting environmental sustainability. Moreover, it does not require the use of additional chemicals for the disposal or treatment of fiber waste, nor does it require the incineration of biofiber waste. Recycled biofiber from cement bags, composed primarily of cellulose (60 wt%), lignin (15 wt%), and hemicellulose (10 wt%), serves as a reinforcing phase, while the cement and adhesive mixture functions as a strong binding matrix. The fabrication of composite materials using undamaged cement bag fibers preserves fiber integrity and enables a well-ordered one-dimensional (1D) fiber alignment, which promotes more effective reinforcement than two-dimensional (2D) or three-dimensional (3D) orientations, in accordance with the rule of mixtures. In addition, the incorporation of a water-based PVAc adhesive accelerates the curing rate of the cement phase, promoting the formation of a strong interconnected network structure, and facilitates a more complete curing process. The physical, mechanical, chemical, and thermal properties of the biofiber cement boards were evaluated in accordance with relevant industrial standards, including TISI 878:2023, BS 874, ASTM C1185, ASTM D570, ASTM C518, ISO 8301, and JIS A1412. The results indicate that an optimal cement mortar to water-based adhesive ratio of 1:2, combined with an increased number of biofiber sheet layers, significantly enhances material performance, particularly in Formulas (7)–(9). Among these, Formula (9) exhibits the lowest water absorption (0.0835 ± 0.0102%), the highest tensile strength (19.489 ± 0.670 MPa), the highest flexural strength (20.867 ± 2.505 MPa), the highest Young’s modulus (5735.068 ± 387.032 MPa), and low thermal conductivity (0.152 W/m.K). The resulting boards demonstrate strong bonding ability, enhanced resistance to fire, moisture, and weathering, and a longer service life compared to lower cement-to-adhesive ratios (1:1 and 1:0). These findings demonstrate the potential of recycled biofiber composites, combined with water-based adhesives, as sustainable alternative materials for thermal insulation and structural applications, including ceilings and walls in building construction. Full article

Extreme heat is a major weather-related cause of death and is expected to intensify in European cities. We quantified Milan-specific temperature–mortality relationships, defined impact-based heat thresholds around the minimum mortality temperature (MMT) and identified vulnerable subgroups using individual-level risk factors. We conducted a time-stratified case-crossover study including 2230 natural deaths among Milan residents aged ≥75 years occurring between 15 July and 15 September 2022. The MMT (29 °C) was used as the reference temperature [odds ratio (OR) = 1], and mortality risks were evaluated across high-impact (1.20 < OR ≤ 1.50, ≥35 °C) maximum temperature (Tmax) days. Compared with MMT days, mortality was higher on high-impact days (OR 1.44), with somewhat larger estimates among adults aged ≥85 years (OR 1.63) and men (OR 1.50). Disability (OR 1.51) and socioeconomic deprivation (OR 1.89) were also associated with higher vulnerability, with relatively higher estimates observed in women aged ≥85 years and in men with comorbidities or living alone. Overall, the findings suggest that extreme heat may have had a greater impact on the oldest old and on socially or clinically vulnerable groups, highlighting the possible relevance of targeted heat–health interventions and neighborhood-focused prevention strategies. Full article

Improving the energy performance of existing multi-apartment residential buildings is critical for reducing energy consumption and greenhouse gas emissions in Central and Eastern Europe, where large stocks of post-war buildings with limited insulation are connected to district heating systems. This study evaluates façade insulation retrofit strategies for two representative typologies in Novi Beograd, Serbia—a high-rise tower and an elongated slab-type (‘lamella’) building—using calibrated dynamic energy models and cradle-to-use lifecycle assessment (LCA) over a 50-year service life. Models were calibrated against measured 2023–2024 heating consumption data (NMBE < 1%, CVRMSE < 15%) and normalized with Typical Meteorological Year weather for consistent scenario comparison. Retrofit scenarios applied expanded polystyrene (EPS) and cellulose insulation at 10, 12, and 15 cm thicknesses. Results show that external insulation reduces annual heating demand by approximately 19–20% compared to the uninsulated baseline (192 kWh/m²·a), with the majority of savings achieved at 10 cm and only marginal gains from additional thickness. Insulation thickness has a stronger influence on operational energy reduction than material choice, as differences between EPS and cellulose remain below 0.5%. LCA indicates 23.6–26.0% lower climate change impacts and 23.6–25.8% reduced cumulative energy demand in retrofit scenarios, with cellulose offering modest advantages due to lower embodied emissions and biogenic carbon storage. These findings support targeted envelope retrofits as an effective strategy for decarbonizing district-heated residential buildings in the region. Full article

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue. Full article

Weather is an essential part of life that affects livelihoods such as agriculture, aviation, etc. Existing systems for weather prediction use deep learning frameworks such as Recurrent Neural Networks and Long Short-term Memory. These models, however, suffer from vanishing gradients that affect the accuracy of the prediction. Using the Deep Belief Networks, we developed a model to address this. Historical weather data is obtained from the Philippine Atmospheric, Geophysical and Astronomical Services Administration for model training. The ground-level sensor data was used to normalize the inputs for the model. The resulting multiclass accuracy is 80%. A larger dataset is recommended for better performance. Full article

We analyzed wildfire hazard profiles across the Hyrcanian temperate forests of northern Iran (Guilan Province) by simulating a large set of wildfires with FlamMap MTT. We first derived geospatial data on terrain, fuel models, weather conditions, and historical wildfire occurrence (1992–2022) for the study area. We stratified fire weather conditions and fuel moisture based on the bioclimatic classification of the study area, considering observed extreme fire weather, as well as observed and random fire ignition locations for the simulations. The wildfire simulations were used to estimate burn probability (BP), conditional flame length (CFL), fire size (FS), and crown fire probability (CFP). BP ranged from 0 to 5.0 × 10⁻², with mean values of 1.3 × 10⁻³ and 1.1 × 10⁻³ for observed and random scenarios, respectively. The mean value of CFL from random ignition simulations (0.78 m) was substantially higher than that obtained in the observed ignition simulations (0.54 m), ranging from 0 to 6.75 m. We evidenced significant differences between observed and random ignition simulations for all wildfire hazard metrics. The highest wildfire hazard profiles were observed in the Cold-Mountainous bioclimatic zone under the random ignition simulations. On average, the annual number of anthropic structures threatened by wildfires ranged from 97 (observed scenario) to 123 (random scenario). This research provides detailed and spatially explicit fire hazard and exposure maps to inform fire modeling, land management, and policy actions. Full article

East Africa is highly vulnerable to climate change due to limited adaptive capacity and strong reliance on rain-fed agriculture. Ethiopia, in particular, experiences recurrent socio-economic losses from droughts and floods. This study presents a national-scale assessment of observed (1981–2010) and projected (2041–2100) changes in extreme seasonal precipitation across Ethiopia using ten ETCCDIs. High-resolution Enhancing National Climate Services (ENACTS) observations and bias-corrected outputs from a selected ensemble of CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios are used to assess historically trends and future extreme precipitation, respectively. Historical trends show increases in extreme precipitation during the Kiremt (JJAS) season, particularly over the northwestern, western, and southwestern highlands; however, most of these increases are not statistically significant. In contrast, the Belg (FMAM) season exhibits widespread declines, which are also largely not statistically significant. Future projections suggest increases in total precipitation (PRCPTOT), heavy (R10) and very heavy rainfall days (R20), very wet days (R95p) and extremely wet days (R95p), and rainfall intensity (SDII) over northwestern, western, southwestern, and parts of northeastern Ethiopia during JJAS. During FMAM, PRCPTOT is projected to increase in the northern and northwestern regions, while decreases are expected in the northeastern and southeastern regions. The Awash and Tekeze basins emerge as key hotspots of change, indicating potential seasonal shifts and an increased likelihood of extreme weather in these regions. Despite inter-model uncertainty, the results highlight the need for flexible, uncertainty-informed adaptation strategies to enhance climate resilience in Ethiopia. Full article

Based on hourly reanalysis data during 2010–2020, the Self-Organizing Map method is used to objectively classify convective precipitation events in the Chengdu terminal area. Combined with circulation background characteristics, the results are further grouped into three typical synoptic types. Among these three types, Type 1, characterized by a pattern with strong high pressure and abundant water vapor, yields the most intense precipitation. Type 2, a pattern with moderately strong high pressure and water vapor convergence, produces the second-highest precipitation. Type 3, associated with a low trough and weak water vapor conditions, has the weakest precipitation. Two indicators of the Weather Severity Index (WSI) and Node Coverage Index (NCI), respectively describing the coverage extent of heavy precipitation over the terminal area and over key arrival and departure nodes, are established and calculated based on heavy precipitation samples. The results show that Type 1 exhibits the highest WSI and NCI values, indicating the greatest potential impact. Type 2 displays a lower WSI than Type 1 but retains a relatively higher NCI, suggesting a more directionally biased impact, whereas Type 3 records the lowest values for both indicators, indicating a relatively weak impact. The integration of synoptic weather classification and spatial impact indicators offers a reference for weather-impact identification and scenario-based operational assessment in terminal areas. However, some limitations remain in the current study. The weather classification is primarily based on reanalysis data, and the correspondence between the WSI/NCI and actual airport operational constraints requires further validation. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

The Horn of Africa (Kenya, Djibouti, Uganda, Eritrea, Somalia, Ethiopia, South Sudan, and Sudan) faces the highest rates of hunger and malnutrition in the world, exacerbated by conflict and adverse weather conditions. These factors have serious health, educational, social, and economic consequences, especially for children under five and pregnant women. In this context, we analyze each country’s progress toward Sustainable Development Goals (SDGs) 1, 2, 3, and 4, which are closely linked to the eradication of hunger, improved health, and access to quality education. Using comparable data from the United Nations 2030 Agenda up to 2019, the achievement of the SDGs is assessed through a multidimensional approach based on Pena’s P₂ distance method, constructing a composite indicator that allows for robust cross-country comparisons. This method helps identify the key measures needed to prevent future humanitarian crises in the Horn of Africa, including providing urgent assistance to these countries in vital areas such as water, nutrition, education, sanitation, and child and maternal immunization. Factors related to the work of qualified healthcare personnel in treating diseases and improving maternal and neonatal health, as well as facilitating access to basic services such as clean drinking water and sanitation and ensuring girls’ access to primary education, top the rankings in terms of their correlation with greater progress by these countries in achieving these four SDGs, which are crucial for improving the well-being of their populations. Full article

To mitigate environmental impact, specifically the CO${}_2$ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimization and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include $LSTM$, $GRU$, $DNN$, $CNN1D-LSTM$, $BiLSTM$, attention mechanisms, $DT$, $SVM$, and $XGBoost$. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), and short-term flicker severity ($P_{st}$). Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilized root mean square error ($RMSE$) as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing. Full article