Search Results (25,422)

Aldosterone excess, particularly in the context of primary aldosteronism, is associated with adverse cardiovascular outcomes. Historically considered a condition of resistant hypertension with hypokalaemia, patients with primary aldosteronism often experienced prolonged diagnostic delay with significant end-organ damage involving the renal, cardiovascular, and central nervous systems at diagnosis. Emerging research has revealed a wide spectrum of renin-independent aldosteronism, ranging from subclinical disease with normal or mildly elevated BP to overt disease marked by resistant hypertension and cardiovascular complications. Subclinical forms of primary aldosteronism have been identified across all age groups, and it is increasingly linked to early signs of adverse cardiac remodelling, even in young adults. Notably, adverse cardiac remodelling was independent of blood pressure. Furthermore, primary aldosteronism confers excess cardiovascular morbidity and mortality compared to blood-pressure-matched essential hypertension. Importantly, these risks can be mitigated through timely diagnosis and treatment with mineralocorticoid receptor antagonists. In this narrative review, we explore the cardiovascular consequences of aldosterone excess, discuss the pathophysiological mechanisms underlying cardiac remodelling, and examine the implications of renin-independent aldosteronism for cardiovascular risk across the lifespan. Full article

This study presents a novel approach to determine the composition of masonry mortars and their types from cement, lime, and cement–lime using an artificial neural network (ANN). It also allows the preparation of mortar recipes for the conservation of historical masonry objects with properties similar to the original ones, but using currently available raw materials. An ANN was trained using a set of cement, lime, and cement–lime mortars with known compositions. The properties chosen for the ANN’s analysis included total porosity, specific density, insoluble residue content, silicone (SiO₂) content, calcium (CaO) content, Si/Ca ratio in grout, and compressive strength. The use of ANNs allows for the determination of mortar composition with a validation error of less than 5% and a method of classification of the type of mortar that gives correct answers in more than 93% of cases, proving the usefulness of ANNs in determining the type and composition of masonry mortars relevant for the conservation of historical masonry structures. Full article

This study presents real-time measurements of particulate matter (PM₁, PM₂.₅, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently exceeded the 1000 ppm guideline, with peak values reaching 3018 ppm and 2715 ppm in lecture spaces, whereas one workshop environment maintained levels well below limits (mean = 668 ppm). PM concentrations varied widely: PM₁₀ reached 541.5 µg/m³ in a carpeted amphitheater, significantly surpassing the 50 µg/m³ legal daily limit, while a well-ventilated classroom exhibited lower levels despite moderate occupancy (PM₁₀ max = 116.9 µg/m³). Elevated PM values were strongly associated with flooring type and occupant movement, not just activity type. Notably, window ventilation during breaks reduced CO₂ concentrations by up to 305 ppm (p < 1 × 10⁻⁴⁷) and PM₁₀ by over 20% in rooms with favorable layouts. These findings highlight the importance of ventilation strategy, spatial orientation, and surface materials in shaping indoor air quality. The study emphasizes the need for targeted, non-invasive interventions to reduce pollutant exposure in historic university buildings where mechanical ventilation upgrades are often restricted. Full article

Historic earthen architecture—particularly rammed earth—is underrepresented in digital heritage initiatives despite its widespread historical use and vulnerability to degradation. This paper presents a novel methodology for integrating semantic, geometric, and geospatial information from earthen heritage into a unified digital environment, bridging Heritage Building Information Modeling (HBIM) and Geographic Information Systems (GIS) through an object-relational database. The proposed workflow enables automated and bidirectional data exchange between Revit (via Dynamo scripts) and open-source GIS tools (QGIS and PostgreSQL/PostGIS), supporting semantic alignment and spatial coherence. The method was tested on seven fortified rammed-earth sites in the southwestern Iberian Peninsula, chosen for their typological and territorial diversity. Results demonstrate the feasibility of multiscale documentation and analysis, supported by a structured database populated with geometric, semantic, diagnostic, and environmental information, enabling enriched interpretations of construction techniques, material variability, and conservation status. The approach also facilitates the integration of HBIM datasets into broader territorial management frameworks. This work contributes to the development of scalable, open-source digital tools tailored to vernacular heritage, offering a replicable strategy for bridging the gap between building-scale and landscape-scale documentation in cultural heritage management. Full article

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article

Therapeutic phlebotomy remains a key intervention in the management of erythrocytosis and iron overload disorders, particularly polycythemia vera (PV) and hereditary hemochromatosis. Despite its historical origins as an ancient practice, venesection continues to be recommended in international guidelines for the reduction of hematocrit and iron burden, thereby mitigating thrombotic and organ-related complications. However, the evolving landscape of targeted pharmacologic therapies is reshaping the therapeutic paradigm. This review examines the current role of therapeutic phlebotomy, with a particular focus on PV, outlining its physiological rationale, clinical benefits, and well-documented limitations—including iron deficiency, procedural burden, and incomplete hematocrit control between sessions. Comparative insights are provided between phlebotomy and red cell apheresis, highlighting differences in efficacy, tolerability, and accessibility. The emergence of disease-modifying agents—such as interferons, JAK inhibitors, hepcidin mimetics, and epigenetic modulators like givinostat and bomedemstat—promises more sustained hematologic control with the potential to reduce or eliminate the need for repeated phlebotomies. While phlebotomy remains indispensable in early-stage or low-risk PV, its future utility will likely shift toward complementary or bridge therapy in the context of individualized, pharmacologically driven strategies, redefining the role of phlebotomy in the era of precision medicine. Full article

We present a novel derivation of the spacetime metric generated by matter, without invoking Einstein’s field equations. For static sources, the metric arises from a relativistic formulation of D’Alembert’s principle, where the inertial force is treated as a real dynamical entity that exactly compensates gravity. This leads to a conformastatic metric whose geodesic equation—parametrized by proper time—reproduces the relativistic version of Newton’s second law for free fall. To extend the description to moving matter—uniformly or otherwise—we apply a Lorentz transformation to the static metric. The resulting non-static metric accounts for the motion of the sources and, remarkably, matches the weak-field limit of general relativity as obtained from the linearized Einstein equations in the de Donder (or Lorenz) gauge. This approach—at least at Solar System scales, where gravitational fields are weak—is grounded in a new dynamical interpretation of the Equivalence Principle. It demonstrates how gravity can emerge from the relativistic structure of inertia, without postulating or solving Einstein’s equations. Full article

Over the past 70 years, Central Asia has emerged as a globally recognized water security hotspot due to its unique geographic location and uneven distribution of water resources. In arid and semi-arid regions, understanding runoff dynamics under climate change is essential for ensuring regional water security. This study addresses the data-sparse Central Asian region by applying the ISIMIP3b multi-scenario analysis framework, selecting three representative global hydrological models. Using model intercomparison, trend analysis, and geographically weighted regression, we assess the spatiotemporal evolution of runoff from 1950 to 2080 and investigate the spatial heterogeneity of runoff responses to precipitation and temperature. The results show that under the historical scenario, all models consistently identify similar spatial pattern of runoff, with higher values in southeastern mountainous regions and lower values in western and central regions. However, substantial differences exist in runoff magnitude, with regional annual means of 10, 26, and 68 mm across the three models, respectively. The spatial disparity of runoff distribution is projected to increase under higher SSP scenarios. During the historical period, most of Central Asia experienced a slight decreasing trend in runoff, but the overall trends were −0.022, 0.1, and 0.065 mm/year, respectively. In contrast, future projections indicate a transition to increasing trends, particularly in eastern regions, where trend magnitudes and statistical significance are notably greater than in the west. Meanwhile, the spatial extent of significant trends expands under high-emission scenarios. Precipitation exerts a positive influence on runoff in over 80% of the region, while temperature impacts exhibit strong spatial variability. In the WaterGAP2-2e and MIROC-INTEG-LAND models, temperature has a positive effect on runoff in glaciated plateau regions, likely due to enhanced snow and glacier melt under warming conditions. This study presents a multi-model framework for characterizing climate–runoff interactions in data-scarce and environmentally sensitive regions, offering insights for water resource management in Central Asia. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of the system. First, this study establishes a dynamic model of the hydrogen–electric hybrid vehicle, a static input–output model of the hybrid power system, and an aging model. Next, a speed prediction method based on an Autoregressive Integrated Moving Average (ARIMA) model is designed. This method fits a predictive model by collecting historical speed data in real time, ensuring the robustness of speed prediction. Finally, based on the speed prediction results, an adaptive Equivalence Factor (EF) method using a GA is proposed. This method comprehensively considers fuel consumption and the economic costs associated with the aging of the hydrogen–electric hybrid system, forming a total operating cost function. The GA is then employed to dynamically search for the optimal EF within the cost function, optimizing the system’s economic performance while ensuring real-time feasibility. Simulation outcomes demonstrate that the proposed energy management strategy significantly enhances both the durability and fuel economy of the fuel cell hybrid vehicle. Full article

Intensified human activities in recent years, such as wastewater discharge and agricultural non-point source pollution have led to a decline in lake water quality, especially in the middle and lower reaches of the Yangtze River Basin, which threaten the stability of lake water ecosystems. Therefore, it is necessary to conduct a scientific assessment of the water eco-environmental quality of shallow lakes and implement targeted management measures. Considering the characteristics of shallow lakes, major ecological and environmental issues, and current standards and guidelines, an indicator system method was employed to establish a water eco-environmental quality evaluation system tailored for typical shallow lakes in the middle and lower reaches of the Yangtze River Basin. This evaluation system comprises three criteria layers (aquatic organism, habitat quality, and water quality) and 10 indicator layers. Using survey data from 2022 to 2024 for evaluation, the results showed that the water eco-environmental quality of Lake Gehu was rated as poor, with the lowest score for macrophyte coverage and the highest score for riparian vegetation coverage. This indicates that the shoreline restoration project in Lake Gehu was effective, while the lake water quality still needs improvement. Remedial measures include increasing aquatic vegetation coverage, reducing nitrogen and phosphorus pollution loads, and controlling the occurrence of algal blooms. This evaluation system combines field surveys with remote sensing monitoring data, fully considering historical and current conditions, and can guide local authorities in evaluating lake water environmental quality. The constructed evaluation system is applicable for the assessment of shallow lakes in the middle and lower reaches of Yangtze River Basin. It provides a scientific basis for the continuous improvement of eco-environmental quality and the construction of Beautiful Lakes Initiative, contributing to the management and protection of lake ecosystems. Full article

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life. Full article

Climate change has intensified the occurrence of wildfires, increasing their frequency and intensity worldwide, and threatening sustainable development through ecological and socioeconomic impacts. Understanding the driving factors behind wildland–urban interface (WUI) fire events and predicting future wildfire hazards in WUI areas are essential for effective wildfire management and sustainable land-use planning. In this study, we developed a WUI fire hazard prediction model for China using machine learning techniques. Diagnostic attribution analysis was employed to identify key drivers of WUI fire hazards. The results revealed that the random forest-based WUI fire hazard model outperformed other models, demonstrating strong generalization capability. SHapley Additive exPlanations analysis revealed that meteorological factors are the primary drivers of WUI fire hazards. These factors include temperature, precipitation, and relative humidity. We further examined the evolution of WUI fire hazards under historical and future climate change scenarios. During the historical baseline period (1985–2014), regions classified as moderate and high hazards were concentrated in southern China. These regions include East China, South Central China, and Southwest China. Climate change exacerbates future WUI fire hazards in China. Projections under the high emission scenario (SSP5–8.5) indicate a rapid increase in WUI fire hazard indices in northern China by the end of the 21st century. Concurrently, the gravity center of high hazard areas is predicted to shift northward, accompanied by a substantial expansion in their area coverage. These findings highlight an urgent need to reorient fire management resources toward northern China under high-emission scenarios. Our findings establish a predictive framework for WUI fire hazards and emphasize the urgency of implementing climate-adaptive management strategies aligned with future hazard patterns. These strategies are critical for enhancing sustainability by reducing fire-related ecological and socioeconomic losses in WUI areas. Full article

Mining activities have long-term impacts on the environment even after the active stage. Historical mines developed in the 19th and 20th centuries for tin, copper, and mainly iron ore are located in the Pitkäranta area (Karelia, Russia). These objects are considered in our research as natural–anthropogenic sites of long-term water–rock interaction. Waters from flooded mines are the subject of this research. Redox conditions, pH, dissolved oxygen content, conductivity, and water temperature were determined during field work. The chemical composition of natural waters was determined by ICP-MS, ICP-AES, ion chromatography, potentiometric titration, and spectrophotometry. Our investigation showed that the mine waters are fresh and predominantly calcium–magnesium hydrocarbonate; most samples showed elevated sulfate ion contents. Circumneutral pH values and the absence of extremely high concentrations of heavy metals indicate neutral mine drainage. However the calculation of the accumulation coefficient showed the highest levels for siderophile elements relative to the corresponding data of the geochemical regional background. Moreover, zinc has the highest content in the series of heavy metal(loid)s considered. The maximum concentration of zinc was determined in the water of one of the shafts of the Lupikko mine, i.e., 5205 µg/L. The accumulation of heavy metals occurs in the process of long-term interaction of water–rock–organic matter under conductive redox conditions. Overall, the research highlighted the relevance of investigating the geochemistry of historical mines in the Pitkäranta area both from the perspective of environmental safety and the preservation of mining sites for scientific and educational purposes. Full article

Dam displacement serves as a critical visual indicator for assessing structural integrity and stability in dam engineering. Data-driven displacement forecasting has become essential for modern dam safety monitoring systems, though conventional approaches—including statistical models and basic machine learning techniques—often fail to capture comprehensive feature representations from multivariate environmental influences. To address these challenges, a bidirectional gated recurrent unit (BiGRU)-enhanced neural network is developed, incorporating sliding window mechanisms to model time-dependent hysteresis characteristics. The BiGRU’s architecture systematically integrates historical temporal patterns through overlapping window segmentation, enabling dual-directional sequence processing via forward–backward gate structures. Validated with four instrumented measurement points from a major concrete gravity dam, the proposed model exhibits significantly better performance against three widely used recurrent neural network benchmarks in displacement prediction tasks. These results confirm the model’s capability to deliver high-fidelity displacement forecasts with operational stability, establishing a robust framework for infrastructure health monitoring applications. Full article