Search Results (6,031)

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

A multidisciplinary study was conducted to reconstruct the paleoenvironmental evolution of Maladroxia Bay, one of the principal bays of the islet of Sant’Antioco in southwestern Sardinia, over the past eight millennia. As part of an archaeological landscape project, this study explores the paleogeography and environment of the bay from a diachronic perspective to gain insights into the Holocene relative sea level history, shoreline displacements, and the environmental conditions during different phases. This study is based on an analysis of four sediment cores in conjunction with a chronological model that is based on radiocarbon dates. Four relative sea level indicators were produced. These are the first such indicators from the early and middle Holocene for the island of Sant’Antioco. The results indicate that in the early Holocene, the area was a terrestrial, fluvial environment without marine influence. In the 6th millennium BCE, the rising sea level and marine transgression resulted in the formation of a shallow inner lagoon. It reached its maximum extent in the middle of the 5th millennium BCE. Afterwards, a gradual transition from lagoon to floodplain, and a seaward shift of the shoreline occurred. The lagoon potentially served as a valuable source of food and resources during the middle Holocene. During the Nuragic period (2nd to 1st millennium BCE), the Bay of Maladroxia was very similar to how it is today. Its location was ideal for use as an anchorage, due to the calm and sheltered conditions that prevailed. Full article

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity. Full article

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier years, and they have performed well in certain areas of Natural Language Processing (NLP). Today, an arduous task that arises is how to estimate the quality of the produced text. This process depends on the aspects of text that you need to assess, varying from correct grammar and syntax to more intriguing aspects such as coherence and semantical fluency. Although the performance of LLMs is high, the challenge is whether LLMs can cooperate with rule-based NLG/NLU technology by leveraging their assets to overcome LLMs’ weak points. This paper presents the basics of these two families of technologies and the applications, strengths, and weaknesses of each approach, analyzes the different ways of evaluating a machine-generated text, and, lastly, focuses on a first-level approach of possible combinations of these two approaches to enhance performance in specific tasks. Full article

We adapt previously developed conceptual and numerical models of ciguateric food chains on the Great Barrier Reef, Australia, to model the bioaccumulation of ciguatoxins (CTXs) in parrotfish, the simplest food chain with only two trophic levels. Our model indicates that relatively low (1 cell/cm²) densities of Gambierdiscus/Fukuyoa species (hereafter collectively referred to as Gambierdiscus) producing known concentrations of CTX are unlikely to be a risk of producing ciguateric fishes on the Great Barrier Reef unless CTX can accumulate and be retained in parrotfish over many months. Cell densities on turf algae equivalent to 10 Gambierdiscus/cm² producing known maximum concentrations of Pacific-CTX-4 (0.6 pg P-CTX-4/cell) are more difficult to assess but could be a risk. This cell density may be a higher risk for parrotfish than we previously suggested for production of ciguateric groupers (third-trophic-level predators) since second-trophic-level fishes can accumulate CTX loads without the subsequent losses that occur between trophic levels. Our analysis suggests that the ratios of parrotfish length-to-area grazed and weight-to-area grazed scale differently (allometrically), where the area grazed is a proxy for the number of Gambierdiscus consumed and hence proportional to toxin accumulation. Such scaling can help explain fish size–toxicity relationships within and between trophic levels for ciguateric fishes. Our modelling reveals that CTX bioaccumulates but does not necessarily biomagnify in food chains, with the relative enrichment and depletion rates of CTX varying with fish size and/or trophic level through an interplay of local and regional food chain influences. Our numerical model for the bioaccumulation and transfer of CTX across food chains helps conceptualize the development of ciguateric fishes by comparing scenarios that reveal limiting steps in producing ciguateric fish and focuses attention on the relative contributions from each part of the food chain rather than only on single components, such as CTX production. Full article

In order to study how indigenous microorganisms can enhance the strength properties of granite residual soil in the Hanzhong area, two Bacillus species that produce urease were isolated from the local soil. The two Bacillus species are Bacillus subtilis and Bacillus tequilensis, and they were used for the solidification and improvement of the granite residual soil. Unconfined compressive strength tests, scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses were systematically used to analyze the influence and mechanism of different cementation solution concentrations on the improvement effect. It has been found that with the growth of cementing fluid concentration, the unconfined compressive strength of improved soil specimens shows an increasing tendency, reaching its highest value when the cementing solution concentration is 2.0 mol/L. Among different bacterial species, curing results vary; Bacillus tequilensis demonstrates better performance across various cementing solution concentrations. The examination of failure strain in improved soil samples indicates that brittleness has been successfully alleviated, with optimal outcomes obtained at a cementing solution concentration of 1.0 mol/L. SEM and XRD analyses show that calcium carbonate precipitates (CaCO₃) are formed in soil samples treated by both strains. These precipitates effectively bond soil particles, verifying improvement effects on a microscopic level. The present study proposes an environmentally friendly and economical method for enhancing engineering applications of granite residual soil in Hanzhong area, which holds significant importance for projects such as artificial slope filling, subgrade filling, and foundation pit backfilling. Full article

Understanding the carbon emission efficiency (CEE) of maize production and its determinants is critical to supporting China’s dual-carbon goals and advancing sustainable agriculture. This study employs a super-efficiency slack-based measure model (SBM) to evaluate the CEE of five major maize-producing regions in China from 2001 to 2022. Kernel density estimation and the Dagum Gini coefficient are used to analyze spatiotemporal disparities, while a geographically and temporally weighted regression (GTWR) model explores the underlying drivers. Results indicate that the national average maize CEE was 0.86, exhibiting a “W-shaped” fluctuation with turning points in 2009 and 2016. From 2001 to 2015, the Southwestern Mountainous Region led with an average efficiency of 0.76. Post-2015, the Northern Spring Maize Region emerged as the most efficient area, reaching 0.90. Efficiency levels have generally become more concentrated across regions, though the Southern Hilly and Northwest Irrigated Regions showed higher volatility. Inter-regional differences were the primary source of overall CEE disparity, with an average annual contribution of 46.66%, largely driven by the efficiency gap between the Northwest Irrigated Region and other areas. Spatial heterogeneity was evident in the impact of key factors. Agricultural mechanization, cropping structure, and environmental regulation exhibited region-specific effects. Rural economic development and agricultural fiscal support were positively associated with CEE, while urbanization had a negative correlation. These findings provide a theoretical foundation and policy reference for region-specific emission reduction strategies and the green transition of maize production in China. Full article

Flood control infrastructure is essential for the development of cities and the population’s well-being. The goal is to protect human and economic resources by reducing the inundation area and controlling the flood level and peak discharges. Detention basins can do this by storing a large volume of water to be released after the peak discharge. By doing this, a large amount of energy is stored, which can be recovered via micro-hydropower. In addition, as the release flow is controlled and almost constant, Pumps as Turbines (PAT) could be a feasible and economic option in these cases. Thus, this study investigates the feasibility of micro-hydropower (MHP) in urban detention basins, using the Santa Lúcia detention basin in Belo Horizonte as a case study. The methodology involved hydrological modeling, hydraulic analysis, and economic and environmental assessment. The results demonstrated that PAT selection has a crucial role in the feasibility of the MHP, and exploiting rainfall with lower intensities but higher frequencies is more attractive. Using multiple PATs with different operating points also showed promising results in improving energy production. In addition to the economic benefits, the MHP in the detention basin produces minimal environmental impact and, as it exploits a wasted energy source, it also reduces the carbon footprint in the urban water cycle. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

The effect of replacing wheat with corn on the color distribution across various locations in the carcasses of broilers was assessed. One thousand two hundred ROSS 308 one-day-old male chicks were reared in an experimental farm, with ten pens per treatment, based on the primary cereal ingredient during both the starter (1–14 d) and grower (15–41 d) phases: corn and wheat. At 41 days old, slaughtering was performed. At 24 h post slaughter, color measurements were taken in 15 carcasses per treatment using a MINOLTA CM600d reflectance spectrocolorimeter in the CIEL*a*b* space. Twelve locations were measured: drumette, apterial latero-pectoral area, neck, the back at the seventh thoracic vertebra, saddle, thigh, ham, hock and around the vent, shanks, and the surface and interior of the breast. The results indicate that the color distribution in the broiler carcass is not homogeneous. Very small color differences can be found between the surface and the interior of the breast, but they differ greatly from areas where there is skin. Corn produces a darker color than wheat, though the intact skin homogenizes lightness* across the entire carcass, reducing differences between regions. Corn increased mainly yellowness, influencing color saturation. The pronounced color differences between the shank and other locations almost disappear when corn is excluded from the feed. Hue° could serve as a potential indicator of color variations in the breast resulting from differences in dietary ingredients. Full article

Background: Modification of diameter stability after the abutment retention can result in a decrease in the applied torque or affect the peri-implant tissue, compromising the longevity of the treatment. Therefore, this study aimed to investigate how different connection angles (11.5° and 16.0°) at the implant–abutment interface influence implant diameter stability under the manufacturer’s recommended torque. Methods: Eighty Morse cone-type implant specimens were divided into two groups, with different internal conicity angles: 11.5° (n = 40) and 16.0° (n = 40). Implants varied in diameter (mm): 3.5, 3.8, 4.5, and 5.0. Initial measurements of the implants’ external diameter were carried out. After these measurements, all implants received the abutment installation, and a final measurement of the external implant diameter was performed. Results: Considering the comparative analysis between the final and initial diameters, a non-significant increase in diameter, in the cervical implant region, after torque on the abutment, was observed. The torque applied to the abutments did not produce deformations in the cervical area of Morse taper implants. Conclusions: The torque applied to the abutment screw in implants with a Morse taper connection does not cause deformation in the cervical area of the implant body in implant with 11.5° and 16.0° conicity angles. Full article

Multi-laser processing is increasingly adopted in laser powder bed fusion (L-PBF) to improve productivity and enable the fabrication of larger components, but its impact on part quality and performance remains a critical concern. This study investigates the microstructure, tensile properties, and fatigue performance of components fabricated by L-PBF using single- and multiple-laser configurations. Both strategies were evaluated under varying layer thicknesses and gas flow conditions with optimized process parameters. Microstructural analysis revealed defects such as lack-of-fusion, porosity and microcracks in multiple-laser builds with reduced gas flow. However, the density and microhardness results showed negligible differences between single and multiple-laser builds. Tensile testing indicated that single-laser builds exhibited superior strength and ductility, whereas multiple-laser builds demonstrated reduced performance due to localized defects such as lack-of-fusion and microcracks. Low-cycle fatigue testing results showed that optimized multiple-laser strategies could achieve performance comparable to that of single-laser builds while improving productivity. The results also revealed that the gas flow becomes more pronounced with multiple-laser processing, where more spatter is generated due to the interactions of the lasers in a small scan area, and that reduced gas flow leads to fatigue degradation due to increased defect density. The results from this study clearly highlight the importance of gas flow, laser overlap, border optimization, and defect mitigation strategies in producing multiple-laser produced components with mechanical properties and fatigue performance comparable to those of single-laser produced L-PBF components. Full article