Search Results (1,747)

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

In the past three decades, localized research has highlighted shifts in rainfall patterns and temperature trends in central Ethiopia, a region vital for agriculture and economic activities and heavily dependent on climate conditions to sustain livelihoods and ensure food security. However, comprehensive analyses of long-term climate data remain limited for this area. Understanding local climate trends is essential for enhancing agricultural resilience in the study area, a region heavily dependent on rainfall for crop production. This study analyzes historical rainfall and temperature patterns over the past 30 years and projects future climate conditions using downscaled CMIP6 models under SSP4.5 and SSP8.5 scenarios. Results indicate spatial variability in rainfall trends, with certain areas showing increasing rainfall while others experience declines. Temperature has shown a consistent upward trend across all seasons, with more pronounced warming during the short rainy season (Belg). Climate projections suggest continued warming and moderate increases in annual rainfall, particularly under SSP8.5 by the end of the 21st century. It is concluded that both temperature and rainfall are projected to increase in magnitude by 2080, with higher Sen’s slope values compared to earlier periods, indicating a continued upward trend. These findings highlight potential breaks in agricultural calendars, such as shifts in rainfall onset and cessation, shortened or extended growing seasons, and increased risk of temperature-induced stress. This study highlights the need for localized adaptation strategies to safeguard agriculture production and enhance resilience in the face of future climate variability. Full article

The cessation of traditional mountain grazing has emerged as a principal driver of habitat degradation and the local extinction of Parnassius apollo (Linnaeus, 1758) in Serbia. While previous studies have cited multiple contributing factors, our research provides evidence that the abandonment of extensive livestock grazing has triggered vegetation succession, the disappearance of the larval host plant (Sedum album), and a reduction in microhabitat heterogeneity—conditions essential for the persistence of this stenophagous butterfly species. Through satellite-based analysis of vegetation dynamics (2015–2024), we identified clear structural differences between habitats that currently support populations and those where the species is no longer present. Occupied sites were characterized by low levels of exposed soil, moderate grass coverage, and consistently high shrub and tree density, whereas unoccupied sites exhibited dense encroachment of grasses and woody vegetation, leading to structural instability. Furthermore, MODIS-derived indices (2010–2024) revealed a consistent decline in vegetation productivity (GPP, FPAR, LAI) in succession-affected areas, alongside significant correlations between elevated land surface temperatures (LST), thermal stress (TCI), and reduced photosynthetic capacity. A wildfire event on Mount Stol in 2024 further exacerbated habitat degradation, as confirmed by remote sensing indices (BAI, NBR, NBR2), which documented extensive burn scars and post-fire vegetation loss. Collectively, these findings indicate that the decline of P. apollo is driven not only by ecological succession and climatic stressors, but also by the abandonment of land-use practices that historically maintained suitable habitat conditions. Our results underscore the necessity of restoring traditional grazing regimes and integrating ecological, climatic, and landscape management approaches to prevent further biodiversity loss in montane environments. Full article

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

Relict fern species, confined to microhabitats with stable historical conditions, are especially vulnerable to climate change. The Alboran Arc hosts a unique relict fern flora, including Culcita macrocarpa, Diplazium caudatum, and Pteris incompleta, and functions as a major Pleistocene refuge. This study assesses the population trends and climate sensitivity of these species in Los Alcornocales Natural Park using annual abundance time series for a decade, empirical survival projections, and principal component analysis to identify key climatic drivers. Results reveal distinct climate response clusters among populations, though intra-specific variation highlights the importance of local conditions. Climate change is already impacting population viability, especially for P. incompleta, which shows high sensitivity to rising maximum temperatures and prolonged heatwaves. Climate-driven models forecast more severe declines than empirical ones, particularly for C. macrocarpa and P. incompleta, with the latter showing a projected collapse by the mid-century. In contrast, D. caudatum exhibits moderate vulnerability. Crucially, the divergence between models underscores the impact of conservation efforts: without reinforcement and reintroduction actions, projected declines would likely be more severe. These results project a decline in the populations of the studied ferns, highlighting the urgent need to continue implementing both in situ and ex situ conservation measures. Full article

Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators—including dew point depression, critical temperature–humidity zones, and damp effect indices—were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials. Full article

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article

Sustainable building design is significantly impacted by the local climate response knowledge ingrained in traditional architecture. However, its integration and dissemination with contemporary green technologies are limited by the absence of a comprehensive quantitative analysis of the regulation of its humid and temperature environment. The Ganzhou Wei family compound from China’s wind–heat environmental regulation systems are examined in this study. We statistically evaluate the synergy between spatial morphology, material qualities, and microclimate using field data with Thsware and Ecotect software in a multiscale simulation framework. The findings indicate that the compound’s special design greatly controls the thermal and wind conditions. Cold alleyways and courtyards work together to maximize thermal environment regulation and encourage natural ventilation. According to quantitative studies, courtyards with particular depths (1–4 m) and height-to-width ratios (e.g., 1:1) reduce wind speed loss. A cool alley (5:1 height–width ratio) creates a dynamic wind–speed–temperature–humidity balance by lowering summer daytime temperatures by 2.5 °C. It also serves as a “cold source area” that moderates temperatures in the surrounding area by up to 2.1 °C. This study suggests a quantitative correlation model based on “spatial morphology–material performance–microclimate response,” which offers a technical route for historic building conservation renovation and green renewal, as well as a scientific foundation for traditional buildings to maintain thermal comfort under low energy consumption. Although based on a specific geographical case, the innovative analytical methods and strategies of this study are of great theoretical and practical significance for promoting the modernization and transformation of traditional architecture, low-carbon city construction, and sustainable building design. Full article

Recreational diving depends on healthy marine ecosystems, yet it can harm biodiversity through species displacement and habitat damage. Bombinhas, a biodiverse diving hotspot in southern Brazil, faces growing threats from human activity and climate change. This study assessed the ecological structure of Sepultura Beach (2018) for potential diving trails, comparing it with historical data from Porto Belo Island. Using visual censuses, transects, and photo-quadrats across six sampling campaigns, researchers documented 2419 organisms from five zoological groups, identifying 14 dominant species, including Haemulon aurolineatum and Diplodus argenteus. Cluster analysis revealed three ecological zones, with higher biodiversity at the site’s edges (Groups 1 and 3), but these areas also hosted endangered species like Epinephelus marginatus, complicating trail planning. A major concern was the widespread bleaching of the zoanthid Palythoa caribaeorum, a key ecosystem engineer, likely due to rising sea temperatures (+1.68 °C from 1961–2018) and declining chlorophyll-a levels post-2015. Comparisons with past data showed a 0.33 °C increase in species’ thermal preferences over 17 years, alongside lower trophic levels and greater ecological vulnerability, indicating tropicalization from the expanding Brazil Current. While Sepultura Beach’s biodiversity supports diving tourism, conservation efforts must address coral bleaching and endangered species protection. Long-term monitoring is crucial to track warming impacts, and adaptive management is needed for sustainable trail development. The study highlights the urgent need to balance ecotourism with climate resilience in subtropical marine ecosystems. Full article

Classical thermodynamics omits rigidity, which property distinguishes solids from gases and liquids. By accounting for rigidity (i.e., Young’s elastic modulus, ϒ), we recently amended historical formulae and moreover linked heat capacity, thermal expansivity, and ϒ. Further exploration is motivation by the importance of classical thermodynamics to various applied sciences. Based on heat performing work, we show here, theoretically, that density times sublimation enthalpy divided by the molar mass (ρΔH_sub/M, energy per volume), depends linearly on ϒ (1 GPa = 10⁹ J m⁻³). Data on diverse metals, non-metallic elements, chalcogenides, simple oxides, alkali halides, and fluorides with cubic structures validate this relationship at ambient conditions. Furthermore, data on hcp metals and molecular solids show that ρΔH_sub/M is proportional to ϒ for anisotropic materials. Proportionality constants vary only from 0.1 to 0.7 among these different material types (>100 substances), which shows that the elastic energy reservoir of solids is large. Proportionality constants depend on whether molecules or atoms are sublimated and are somewhat affected by structure. We show that ductility of refractory, high-ϒ metals affect high-temperature determinations of their ΔH_sub. Our results provide information on sublimation processes and subsequent gas phase reactions, while showing that elasticity of solids is the key parameter needed to assessing their energetics. Implications are highlighted. Full article

Background and Objectives: Although the use of allografts in revision anterior cruciate ligament reconstruction is associated with theoretical advantages, it has historically led to poorer clinical results and lower survival rates. However, the heterogeneity of the available literature makes it difficult to elucidate the effectiveness of allographs, as most of the studies published do not make any reference to some of the key aspects related to the processing of the allograft employed. The present study analyzed the clinical results and the survival of allografts in patients undergoing revision anterior cruciate ligament reconstruction with a well-characterized, single type of allograft. Materials and Methods: This was a retrospective observational study analyzing a series of patients undergoing revision anterior cruciate ligament reconstruction with an Achilles tendon allograft with a bone block (FlexiGraft, LifeNet Health), subjected to low-dose irradiation at dry ice temperatures. Preoperative and follow-up clinical variables (IKDC, pain, hop test, and YBT scores) were recorded. Survival was analyzed using the Kaplan–Meier methodology. Results: A total of 39 patients (34 male, 5 female) were included in the study. The mean patient age was 37.3 years and mean postoperative follow-up was 78.7 months. Forty-one percent of patients were competitive athletes, and all of the patients in the sample exhibited preoperative instability. The mean allograft thickness was 9.2 mm. During surgery, 51.3% of patients required meniscus repair and 20.5% had to be treated for chondral defects. At the last follow-up visit, 92.3% of the subjects presented with IKDC grade A and 7.7% with IKDC grade B. The mean subjective IKDC score was 0.79 and mean pain intensity was 1.15 according to the VAS scale. Limb symmetry, as measured by the various hop tests and the Y balance test, were within the safety range, with 74.4% of patients succeeding in returning to their previous level of sport. Ten-year survival was estimated at 97.4%. Conclusions: Allografts obtained and processed following the current regulations governing patient selection and graft harvesting, which are additionally processed without recourse to chemical procedures and sterilized at less than 2 MRad in dry ice conditions, represent an effective and safe alternative in revision anterior cruciate ligament reconstruction. Full article

Climate change has intensified over recent decades, prompting shifts in forest management strategies, particularly in the Sudetes region of Poland, where native species like Norway spruce (Picea abies), European beech (Fagus sylvatica), and silver fir (Abies alba) have historically dominated. To address these changes, non-native species such as Douglas fir (Pseudotsuga menziesii) have been introduced as potential alternatives. This study, conducted in the Jugów and Świerki forest districts, compared the soil properties and water retention capacities of Douglas fir (Dg) and Norway spruce (Sw) stands (age classes from 8–127 years) in the Fresh Mountain Mixed Forest Site habitat. Field measurements included temperature, humidity, organic matter content, water capacity, and granulometric composition. Results indicate that, in comparison to Sw stands, Dg stands were consistently linked to soils that were naturally finer textured. The observed hydrological changes were mostly supported by these textural differences: In all investigated circumstances, Dg soils demonstrated greater water retention, displaying a water capacity that was around 5% higher. In addition to texture, Dg stands showed reduced soil water repellency and a substantially greater organic matter content (59.74% compared to 27.91% in Sw), which further enhanced soil structure and moisture retention. Conversely, with increasing climatic stress, Sw soils, with coarser textures and less organic matter, showed decreased water retention. The study highlights the importance of species selection in sustainable forest management, especially under climate change. Future research should explore long-term ecological impacts, including effects on microbial communities, nutrient cycling, and biodiversity, to optimize forest resilience and sustainability. Full article

Chromium slag sites pose severe environmental risks due to hexavalent chromium (Cr(VI)) contamination, characterized by high mobility and toxicity. This study focused on chromium-contaminated soil from a historical chromium slag site in North China, where long-term accumulation of chromate production residues has led to serious Cr(VI) pollution, with Cr(VI) accounting for 13–22% of total chromium and far exceeding national soil risk control standards. To elucidate Cr(VI) transformation mechanisms and elemental linkages, a combined approach of macro-scale condition experiments and micro-scale analysis was employed. Results showed that acidic conditions (pH < 7) significantly enhanced Cr(VI) reduction efficiency by promoting the conversion of CrO₄²⁻ to HCrO₄⁻/Cr₂O₇²⁻. Among reducing agents, FeSO₄ exhibited the strongest effect (reduction efficiency >30%), followed by citric acid and fulvic acid. Temperature variations (−20 °C to 30 °C) had minimal impact on Cr(VI) transformation in the 45-day experiment, while soil moisture (20–25%) indirectly facilitated Cr(VI) reduction by enhancing the reduction of agent diffusion and microbial activity, though its effect was weaker than chemical interventions. Soil grain-size composition influenced Cr(VI) distribution unevenly: larger particles (>0.2 mm) in BC-35 and BC-36-4 acted as main Cr(VI) reservoirs due to accumulated Fe-Mn oxides, whereas BC-36-3 showed increased Cr(VI) in smaller particles (<0.074 mm). μ-XRF and correlation analysis revealed strong positive correlations between Cr and Ca, Fe, Mn, Ni (Pearson coefficient > 0.7, p < 0.01), attributed to adsorption–reduction coupling on iron-manganese oxide surfaces. In contrast, Cr showed weak correlations with Mg, Al, Si, and K. This study clarifies the complex factors governing Cr(VI) behavior in chromium slag soils, providing a scientific basis for remediation strategies such as pH adjustment (4–6) combined with FeSO₄ addition to enhance Cr(VI) reduction efficiency. Full article

In response to the challenges faced by traditional short-term electricity load forecasting models during holiday load predictions, a novel approach based on multifactor analysis and the Backpropagation–Gated Recurrent Unit (BP-GRU) model is proposed. Firstly, the impacts of temperature, humidity, and the type of date on the electricity load curve are examined. The study clarifies the positive and negative effects of temperature at different times on load forecasting and reveals the non-dependence characteristics of historical data on holiday load curves. Secondly, the performance of various model weight parameter optimization algorithms is assessed, leading to the establishment of a combined GRU and BP model. The operational workflow of the BP-GRU model is introduced. Lastly, the effectiveness of the proposed forecasting model is verified using actual load data. Compared to existing models, the proposed approach exhibits a significant advantage in achieving higher accuracy in holiday load predictions. Full article