Search Results (4,701)

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

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. 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

This study analyzes the differences in the selection of teaching methodologies, assessment instruments, and educational values in Master’s Theses (TFMs) written within the Geography and History specialization of a Teacher Training Master’s program in Spain. The aim is to examine how these pedagogical components vary according to the gender of the author and the educational level targeted by the instructional proposals. A mixed-methods approach was applied combining statistical analysis (Chi-square and ANOVA tests) with qualitative content analysis of 54 anonymized TFMs. The results indicate that while gender-related differences were not statistically significant in most categories, qualitative patterns emerged: female authors tended to adopt more reflective, participatory approaches (e.g., oral expression, gender visibility), whereas male authors more often used experiential or gamified strategies. Significant differences by educational level were found in the use of gamification, inquiry-based learning, and project-based learning. A progressive increase in methodological complexity was observed from lower secondary to upper levels. In terms of educational values, interdisciplinarity and inclusion were most frequently promoted, with critical perspectives such as historical memory and gender visibility more prevalent at the Baccalaureate level. These findings underscore the TFM’s role as a space for pedagogical innovation, reflective practice, and value-driven teacher identity formation. Full article

 Porcine epidemic diarrhea virus (PEDV) continues to circulate globally, causing substantial economic losses to the swine industry. Historically, PEDV strains are classified into the classical G1, epidemic G2, and S-INDEL genotypes. Among these genotypes, the highly virulent and prevalent G2 genotype has been extensively studied. However, recent clinical outbreaks in China necessitate a reevaluation of the epidemiological and evolutionary dynamics of circulating strains. This study analyzed 37 newly sequenced S genes and public sequences to characterize the genetic variations of S-INDEL strains. Our analysis revealed that S-INDEL strains are endemic throughout China, with a phylogenetic analysis identifying two distinct clades: clade 1, comprising early endemic strains, and clade 2, representing a recently dominant, geographically restricted lineage in China. While inter-genotypic recombination has been documented, our findings also demonstrate that intra-genotypic and intra-clade recombination events contributed significantly to the emergence of clade 2, distinguishing its evolutionary pattern from clade 1. A comparative analysis identified 22 clade-specific amino acid changes, 11 of which occurred in the D0 domain. Notably, mutations at positively selected sites—113 and 114 within the D0 domain, a domain associated with pathogenicity—were specific to clade 2. A phylodynamic analysis indicated Germany as the epicenter of S-INDEL dispersal, with China acting as a sink population characterized by localized transmission networks and frequent recombination events. These results demonstrate that contemporary S-INDEL strains, specifically clade 2, exhibit unique recombination patterns and mutations potentially impacting virulence. Continuous surveillance is essential to assess the pathogenic potential of these evolving recombinant variants and the efficacy of vaccines against them.  Full article

Background: Maxillary sinus puncture (MSP), once a cornerstone for diagnosing and treating acute rhinosinusitis (ARS), has declined with the rise in less invasive techniques. This study explores MSP trends, its association with age, and long-term effects on quality of life using data from the Study of Health in Pomerania (SHIP). Methods: Data from SHIP-START-2 (n = 2332), SHIP-START-3 (n = 1717), and SHIP-TREND-0 (n = 4420) cohorts were analyzed to assess MSP prevalence, demographic correlations, and quality- of-life impacts using SNOT-20-D, EQ-5D-3L, and SF-12. Results: MSP prevalence was higher in older SHIP-START cohorts (11.2% in START-2) compared to SHIP-TREND-0 (9.5%), reflecting its historical decline. The procedure was more frequently reported by participants aged > 60 years (e.g., 14.0% in START-2) than by younger groups (<40 years: 3.5% in START-2). MSP was associated with increased SNOT-20-D scores across cohorts (e.g., +0.28 in START-2, p < 0.001) and minor reductions in EQ-5D-3L and SF-12 mental health scores, indicating greater symptom burden but limited general health impact. The age- and time-related decline in MSP highlights its diminishing role in modern practice. Conclusions: While MSP offers diagnostic insights and serves as an indicator for ARS, its modest impact on long-term quality-of-life underscores the need for alternative, minimally invasive techniques for sinonasal conditions. Full article

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, and Google Earth Engine (GEE) were used to assess flood-prone zones across North Macedonia’s watersheds. The presented GEE-based assessment was accomplished by a custom script that automates the FFPI calculation process by integrating key factors derived from publicly available sources. These factors, which define susceptibility to torrential floods, include slope (Copernicus GLO-30 DEM), land cover (Copernicus GLO-30 DEM), soil type (SoilGrids), vegetation (ESA World Cover), and erodibility (CHIRPS). The spatial distribution of average FFPI values across 1396 small catchments (10–100 km²) revealed that a total of 45.4% of the area exhibited high to very high susceptibility, with notable spatial variability. The CHIRPS rainfall data (2000–2024) that combines satellite imagery and in situ measurements was used to estimate peak 24 h runoff and discharge. To improve the accuracy of CHIRPS, the data were adjusted by 30–50% to align with meteorological station records, along with normalized FFPI values as runoff coefficients. Validation against 328 historical river flood and flash flood records confirmed that 73.2% of events aligned with moderate to very high flash flood susceptibility catchments, underscoring the model’s reliability. Thus, the presented cloud-based scenario highlights the potential of the GEE’s efficacy in scalability and robustness for flash flood modeling and regional risk management at national scale. Full article

Background/Objectives: Osteoporosis remains a leading cause of morbidity in postmenopausal women, yet many high-risk individuals remain undiagnosed or untreated. This study aimed to assess the prevalence of osteoporosis and osteopenia, treatment patterns, and skeletal fragility indicators in a large cohort of postmenopausal women undergoing DXA screening. Methods: We analyzed data from 1669 postmenopausal women aged 40–89 years who underwent DXA evaluation. BMD status was categorized as normal, osteopenia, or osteoporosis. Treatment status was classified based on active antiosteoporotic therapy, calcium/vitamin D supplementation, hormonal therapy (historical use), or no treatment. Logistic regression models were used to explore independent predictors of osteoporosis and treatment uptake. Results: A total of 45.0% of women had osteoporosis and 43.5% had osteopenia. Despite this, 58.5% of the population, over half of women with osteoporosis, were not receiving any active pharmacologic treatment. Bisphosphonates were the most prescribed therapy (17.9%), followed by calcium/vitamin D supplements (20.6%). A prior history of fragility fractures and radiological bone lesions were significantly associated with lower BMD (p < 0.05). Historical hormone replacement therapy (HRT) use was not associated with current BMD (p = 0.699), but women with HRT use reported significantly fewer fractures (p < 0.001). In multivariate analysis, later menopause age and low BMD status predicted higher odds of receiving active treatment. Conclusions: Our findings highlight a substantial care gap in osteoporosis management, with treatment primarily initiated reactively in more severe cases. Improved screening and earlier intervention strategies are urgently needed to prevent fractures and reduce the long-term burden of osteoporosis. Full article

As the body of knowledge on feline mammary tumors (FMTs) continues to grow, their histological classification and grading systems have undergone revisions and updates to better reflect the biological behavior of these tumors. In this review, the historical evolution of these frameworks is traced and later revisited in the context of their prognostic relevance. Numerous studies have investigated clinicopathological prognostic factors in feline mammary carcinomas (FMCs); however, the heterogeneity in assessment methods, inclusion criteria for survival analysis, and the clinical endpoints considered can often complicate direct comparisons across different studies and may contribute to seemingly conflicting results. Furthermore, the small cohort size of many studies limits the robustness and transferability of their findings. This paper provides an updated overview of the epidemiological, clinical, and pathological prognostic factors of these tumors, while also highlighting current challenges, methodological limitations, and areas for future improvement. Full article

In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied. Full article

Background/Objectives: Historically, echocardiographic imaging of the right heart has been challenging because its abnormal geometry is not conducive to reproducible anatomical and functional assessment. With the development of advanced echocardiographic techniques, it is now possible to complete an integrated assessment of the right heart that has fewer assumptions, resulting in increased accuracy and precision. Echocardiography continues to be the first-line imaging modality for diagnostic analysis and the management of acute and chronic right heart failure because of its portability, versatility, and affordability compared to cardiac computed tomography, magnetic resonance imaging, nuclear scintigraphy, and positron emission tomography. Virtually all echocardiographic parameters have been well-validated and have demonstrated prognostic significance. The goal of this narrative review of the echocardiographic parameters of the right heart chambers and hemodynamic alterations associated with right ventricular dysfunction is to present information that must be acquired during each examination to deliver a comprehensive assessment of the right heart and to discuss their clinical significance in right heart failure. Methods: Using a literature search in the PubMed database from 1985 to 2025 and the Cochrane database, which included but was not limited to terminology that are descriptive of right heart anatomy and function, disease states involving acute and chronic right heart failure and pulmonary hypertension, and the application of conventional and advanced echocardiographic modalities that strive to elucidate the pathophysiology of right heart failure, we reviewed randomized control trials, observational retrospective and prospective cohort studies, societal guidelines, and systematic review articles. Conclusions: In addition to the conventional 2-dimensional echocardiography and color, spectral, and tissue Doppler measurements, a contemporary echocardiographic assessment of a patient with suspected or proven right heart failure must include 3-dimensional echocardiographic-derived measurements, speckle-tracking echocardiography strain analysis, and hemodynamics parameters to not only characterize the right heart anatomy but to also determine the underlying pathophysiology of right heart failure. Complete and point-of-care echocardiography is available in virtually all clinical settings for routine care, but this imaging tool is particularly indispensable in the emergency department, intensive care units, and operating room, where it can provide an immediate assessment of right ventricular function and associated hemodynamic changes to assist with real-time management decisions. Full article

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies. Full article

This study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R² values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning. Full article

During the transition of culture-featured district planning from static conservation to innovation-driven models, existing research remains constrained by mechanistic paradigms, reducing districts to functional containers and neglecting human perceptual interactions and meaning-production mechanisms. This study explores and quantifies the generative mechanisms of spatial communicability and cultural dissemination efficacy within human-centered frameworks. Grounded in humanistic urbanism, we analyze Harbin Central Street as a case study integrating historical heritage with contemporary vitality, developing a tripartite communicability assessment framework comprising perceptual experience, infrastructure utility, and behavioral dynamics. Machine learning-based threshold analysis reveals that spatial encoding elements govern communicability through significant nonlinear mechanisms. The conclusion shows synergies between street view-quantified greenery visibility and pedestrian accessibility establish critical human-centered design thresholds. Spatial data analysis integrating physiologically sensed emotional experiences and topologically analyzed spatial morphology resolves metric fragmentation while examining spatial encoding’s impact on interaction efficacy. This research provides data-driven decision support for sustainable urban renewal and enhanced cultural dissemination, advancing heritage sustainability. Full article