Search Results (26,075)

This study proposes a bivariate distribution with Exponentiated Gumbel (BEG) marginals to estimate return levels of annual maximum daily rainfall (AMDR) in Mexico. We analyze 181 gauging stations across two contrasting climates (Coahuila, Tabasco) and compare BEG against Generalized Extreme Value (GEV), Gumbel (G), and Exponentiated Gumbel (EG). Parameters are estimated by maximum likelihood. Model selection uses AICc (primary) and BIC (tie-breaker), both computed from the same maximized log-likelihood. On a per-station basis, BEG yields the lowest AICc for 70% of samples. Differences in return levels become more pronounced at high non-exceedance probabilities. Monte Carlo reliability checks show that BEG reduces bias and mean squared error (MSE) relative to univariate fits. Using L-moments to delineate homogeneous regions and fitting all BEG pairs confirms these results. A worked example (station 5001) shows that bootstrap 95% CIs for BEG are narrower than for EG, illustrating reduced marginal-quantile uncertainty under joint estimation. Together, BEG provides a robust, dependence-aware tool for regional frequency analysis of extreme rainfall. Full article

Background and objectives: Given the limited number of studies evaluating the relationship of ABO blood groups and Premature coronary artery disease (PCAD) as well as the lack of relevant literature in Saudi Arabia, a study to assess the association of ABO blood groups and PCAD in Saudi population was crucial. Methods: This is a retrospective comparative study, where controls are healthy individuals and cases are divided into: patients younger than 51 years (PCAD) with confirmed coronary artery disease and patients ≥ 51 years (CAD) with confirmed coronary artery disease, whose data are retrieved from 2015 to 2022. Severity of the disease is assessed by vessel score and Gensini score. Results: We have collected a total of 1167 samples; 466 individuals served as controls (39.9%), 346 were PCAD cases (29.6%), and 355 were CAD patients (30.4%). No significant overall difference was found in ABO distribution among healthy, PCAD, and CAD individuals, although blood group A is more common in PCAD and CAD patients than in healthy controls. Among males, there is a statistically significant difference in ABO distribution across healthy, PCAD, and CAD groups, with a higher frequency of blood group A and a lower frequency of O in patients compared to controls (A = 19.7%, 28.1%, 28.4%, B = 17.5%, 19.0%, 18.6%, O = 60.0%, 48.3%, 50.2%, AB = 2.8%, 4.6%, 2.8%, p = 0.041, respectively). Additionally, the difference in ABO is not statistically significant between the healthy females, PCAD female patients, and CAD female patients (A = 25.5%, 31.3%, 25.7%, B = 20.7%, 13.3%, 20.0%, O = 47.2%, 53.0%, 51.4%, AB = 6.6%, 2.4%, 2.9%, p = 0.541, respectively). The result reveals the severity of coronary vessel occlusion in PCAD group by using Gensini score as follows: A: 52.81 ± 31.30, B: 66.94 ± 45.57, O: 43.06 ± 32.95, AB: 49.00 ± 49.40 with p value = 0.131. Conclusions: The present findings suggest that higher frequency of blood group “A” was found among male patients with PCAD and CAD compared to other blood groups. In addition, blood group “O” is less associated with male PCAD and CAD in Saudi population. Identification of ABO blood groups might assist in the genetic screening as well as guiding prophylaxis for premature CAD. Full article

The soil–water characteristic curve (SWCC) is essential for understanding hydraulic behavior in geotechnical applications involving coarse granular materials. However, existing models often overlook the coupled effects of key factors. This study systematically investigates the influence of grain size distribution, density, and contact angle on the SWCC using a numerical approach that combines the discrete element method (DEM) with an enhanced pore morphology method incorporating locally variable contact angles (Lvca-PMM). The results show that smaller uniformity coefficients (C_u), larger median grain sizes (D₅₀), higher porosity (φ), and larger contact angles (θ) shift the SWCC to the left, reducing both the air entry value (Ψ_a) and residual suction (Ψ_r). Specifically, linear relationships were identified between Ψ_a, Ψ_r, C_u, φ, and cos(θ), while a power-law relationship was observed with D₅₀. Furthermore, the interaction of these factors plays a critical role, where a change in one property can amplify or diminish the effects of others. Based on these findings, empirical equations for predicting Ψ_a and Ψ_r were developed, offering practical tools for engineers to efficiently estimate the SWCC. This research provides deeper insight into the water retention properties of coarse soils and supports the optimized design of granular fills and drainage systems.  Full article

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and application by enabling tailored control over design, pore geometry, materials, and integration. This perspective work examines the opportunities and challenges associated with utilizing additive manufacturing as an enabling battery manufacturing technology. Recent advances in the additive fabrication of electrodes, electrolytes, separators, and integrated devices are examined, exhibiting the potential to acheive electrochemical performance, design adaptability, and sustainability. At the same time, key challenges—including materials formulation, reproducibility, economic feasibility, and regulatory uncertainty—are discussed as limiting factors that must be addressed for achieving the expected results. Rather than being viewed as a replacement for conventional gigafactory-scale production, additive manufacturing is positioned as a complementary fabrication technique that can deliver value in niche, distributed, and application-specific contexts. This work concludes by outlining research and policy priorities that could accelerate the maturation of 3D-printed batteries, stressing the importance of hybrid manufacturing, multifunctional printable materials, circular economy integration, and carefully phased timelines for deployment. Moreover, by enabling customized form factors, improved device–user interfaces, and seamless integration into smart, automated environments, additive manufacturing has the potential to significantly enhance user experience across emerging battery applications. In this context, this perspective provides a grounded assessment of how additive fabrication methods may contribute to next-generation battery technologies that not only improve electrochemical performance but also enhance user interaction, reliability, and seamless integration within automated and control-driven systems. Full article

Since the beginning of the 21st century, major epidemics and pandemics such as SARS, H1N1pdm09, Ebola, and COVID-19 have repeatedly challenged global systems of disease diagnostics and control. These crises exposed the weaknesses of traditional diagnostic models, including long turnaround times, uneven resource distribution, and supply chain bottlenecks. As a result, there is an urgent need for more advanced diagnostic technologies and integrated diagnostics strategies. Our review summarizes key lessons learned from four recent major outbreaks and highlights advances in diagnostic technologies. Among these, molecular techniques such as loop-mediated isothermal amplification (LAMP), transcription-mediated amplification (TMA), recombinase polymerase amplification (RPA), and droplet digital polymerase chain reaction (ddPCR) have demonstrated significant advantages and are increasingly becoming core components of the detection framework. Antigen testing plays a critical role in rapid screening, particularly in settings such as schools, workplaces, and communities. Serological assays provide unique value for retrospective outbreak analysis and assessing population immunity. Next-generation sequencing (NGS) has become a powerful tool for identifying novel pathogens and monitoring viral mutations. Furthermore, point-of-care testing (POCT), enhanced by miniaturization, biosensing, and artificial intelligence (AI), has extended diagnostic capacity to the front lines of epidemic control. In summary, the future of epidemic and pandemic response will not depend on a single technology, but rather on a multi-layered and complementary system. By combining laboratory diagnostics, distributed screening, and real-time monitoring, this system will form a global diagnostic network capable of rapid response, ensuring preparedness for the next global health crisis. Full article

Northern artificial forests play a vital role in enhancing carbon sequestration and ecosystem services, yet quantitative evidence on how different management measures affect understory biodiversity and productivity remains limited. This study focused on Larix gmelinii var. principis-rupprechtii (Mayr) Pilg. plantations in Weichang, Hebei Province, and compared three forest management regimes: target-tree management, homogeneous management, and un-managed stands. We systematically examined understory plant diversity indices (Shannon, Simpson, Margalef, Gleason, and Pielou), shrub–herb layer biomass, soil organic carbon (SOC), and total nitrogen (TN), and employed correlation analysis and random forest modeling to identify the main driving factors. Results showed that target-tree management significantly enhanced both understory biodiversity and shrub–herb biomass, followed by homogeneous management, while unmanaged stands had the lowest values. Differences in SOC and TN among treatments were not significant. Stand structural factors were the dominant drivers: stand density and basal area were negatively correlated with diversity and biomass, while community evenness (Pielou) was positively correlated with biomass. Random forest analysis further indicated that basal area and stand density had the highest relative importance, followed by evenness, whereas soil factors contributed less. Mechanistically, target-tree management improved light availability and spatial distribution by reducing stand density, thereby increasing evenness and promoting biomass accumulation. Overall, optimizing stand structure, rather than merely increasing species richness, proved more effective in simultaneously enhancing biodiversity and productivity in light-limited Larix plantations. From a management perspective, target-tree management combined with density regulation and structural optimization is recommended to achieve near-natural management goals and enhance multiple ecological functions. Full article

Background and Objectives: Chronic obstructive pulmonary disease (COPD) is a systemic condition in which muscle wasting, malnutrition, and altered fluid balance strongly influence prognosis. While spirometry remains essential for diagnosis and staging, it often fails to reflect the heterogeneity of outcomes. Multifrequency bioelectrical impedance analysis (MF-BIA) enables the assessment of body composition and fluid distribution, offering additional prognostic information. This systematic review aimed to evaluate the prognostic significance of MF-BIA in COPD, with emphasis on outcomes such as mortality, exacerbations, and hospital admissions. Materials and Methods: We systematically searched PubMed, Web of Science and Scopus from inception to 29 April 2025. The earliest record retrieved was published in 1996 but was excluded during screening. Studies including COPD patients in whom MF-BIA-derived parameters were related to clinical outcomes were eligible. Risk of bias was assessed using the Newcastle–Ottawa Scale. Data on design, population, methodology, and endpoints were extracted and narratively synthesized due to heterogeneity. The review protocol was not registered. Results: Eight studies were included. Phase angle (PhA) consistently showed prognostic value, being inversely related to mortality and rehospitalizations. Fat-free mass index (FFMI) was integrated into multidimensional models, but its independent role was inconsistent. Parameters describing fluid distribution, such as Extracellular Water/Total Body Water ratio, also appeared relevant, though interpretation was often limited by the absence of consistent consideration of underlying cardiac disease. Conclusions: MF-BIA provides useful prognostic insights in COPD patients, particularly through PhA. It may refine risk stratification beyond spirometry, yet further prospective studies with standardized methods are needed to confirm its independent value. Heterogeneity of methods and small sample sizes remain important limitations. Full article

As a critical ecological barrier in the arid and semi-arid regions of northwestern China, the spatio-temporal evolution of vegetation carbon sequestration in the Hexi Corridor is of great significance to the ecological security of this region. Based on multi-source remote sensing and meteorological data, this study integrated second-order partial correlation analysis, ridge regression, and other methods to reveal the spatio-temporal evolution patterns of Gross Primary Productivity (GPP) in the Hexi Corridor from 2003 to 2022, as well as the response characteristics of GPP to air temperature, precipitation, and Vapor Pressure Deficit (VPD). From 2003 to 2022, GPP in the Hexi Corridor showed an overall increasing trend, the spatial distribution of GPP showed a pattern of being higher in the east and lower in the west. In the central oasis region, intensive irrigation agriculture supported consistently high GPP values with sustained growth. Elevated air temperatures extended the growing season, further promoting GPP growth. Due to irrigation and sufficient soil moisture, the contributions of precipitation and VPD were relatively low. In contrast, desert and high-altitude permafrost areas, constrained by water and heat limitations, exhibited consistently low GPP values, which further declined due to climate fluctuations. In desert regions, high air temperatures intensified evaporation, suppressing GPP, while precipitation and VPD played more significant roles. This study provides a detailed analysis of the spatio-temporal change patterns of GPP in the Hexi Corridor and its response to climatic factors. In the future, the Hexi Corridor needs to adopt dual approaches of natural restoration and precise regulation, coordinate ecological security, food security, and economic development, and provide a scientific paradigm for carbon neutrality and ecological barrier construction in arid areas of Northwest China. Full article

Persistent regional inequalities in economic resource distribution pose a major obstacle to inclusive and sustainable development. In peripheral economies, structural vulnerabilities have been reinforced by long-standing reliance on extractive industries. This study examines the economic structure of Chile’s Atacama Region over a twenty-year period (2003–2023), focusing on sectoral specialization, structural concentration, and their implications for sustainability, territorial resilience, and long-term development. Expanding on prior empirical work (2011–2021), the research adopts a strategic diagnostic approach informed by endogenous growth theory and territorial knowledge management. Three structural indicators—the Theil, Entropy, and Herfindahl–Hirschman indexes—are applied to assess sectoral inequality, productive diversity, and value-added distribution. The results reveal a persistently concentrated and mining-dependent economy, with limited diversification, undermining sustainability and resilience. These findings highlight structural weaknesses that hinder progress toward a knowledge-based and sustainable economy, emphasizing the urgency of diversification strategies that reduce mining dependence and foster inclusive growth. By explicitly linking structural diagnostics to the Sustainable Development Goals (SDGs 8, 9, and 10), this study contributes empirical evidence for designing sustainability-oriented policies and advancing knowledge-driven development pathways in resource-dependent regions. Full article

Recent bryological surveys conducted at the Khaybar White Volcano site (northwest Saudi Arabia) led to the documentation of 51 bryophyte species, including five liverworts and 46 mosses. Representing approximately 30% of the national bryophyte flora within less than 0.3% of the country’s surface, this site emerged as a regional hotspot of bryological diversity. A systematic catalog was compiled, presenting the biogeography, local distribution, demography, fertility, taxonomy and ecology of all recorded taxa. Notably, two Arabian endemics—Crossidium deserti and Tortula mucronifera—were identified in Khaybar, alongside six previously unknown on the Arabian Peninsula (Anoectangium euchloron, Geheebia erosa, Grimmia capillata, Molendoa sendteriana, Pterygoneurum subsessile, and Ptychostomum torquescens) and six species newly recorded for Saudi Arabia (Anoectangium aestivum, Husnotiella revoluta, Syntrichia pagorum, Tortella nitida, Tortula lindbergii, and Tuerckheimia svihlae). These findings highlighted the conservation value of Khaybar, whose unique geothermal microhabitats (active fumaroles) supported a suite of tropical and thermophilous species otherwise absent in northern Arabia, such as Fissidens sciophyllus, and Plagiochasma eximium. Comparative analysis with the AlUla region revealed a comparable species richness despite Khaybar’s smaller area and indicated substantial ecological divergence. While AlUla’s bryoflora was primarily associated with lithological heterogeneity, Khaybar’s was shaped by geothermal activity. Conservation recommendations emphasize the vulnerability of these specialized bryophyte communities to grazing, trampling, and climate change, and call for long-term monitoring, regulated access, and integration into national biodiversity management strategies. Full article

This paper presents an evaluation of the use of deep learning architectures for forecasting electrical energy consumption in residential environments. The main contribution of this study lies in the development and assessment of a hybrid forecasting framework that integrates multiscale temporal analysis and weather data, enabling evaluation of predictive performance across different temporal granularities, forecast horizons, and aggregation levels. Single and hybrid models were compared, trained with high-resolution data from a single residence, both considering only endogenous variables and including exogenous variables (weather data). The results showed that, among all models tested in this study, the hybrid LSTM + GRU model achieved the highest predictive performance, with R² values of 94.62% using energy data and 95.25% when weather variables were included. Intermediary granularities, particularly the 6 steps, offered the best balance between temporal detail and predictive robustness for the tests performed. Furthermore, short-time windows aggregation (1 to 5 min) showed better accuracy, while the inclusion of weather data in scenarios with larger aggregation windows and longer horizons provided additional gains. The results reinforce the potential of hybrid deep learning models as effective tools for forecasting residential electricity consumption, with possible practical applications in energy management, automation, and integration of distributed energy resources. Full article

The separable heat pipe, with its highly efficient heat transfer and flexible layout features, has become an innovative solution to the heat dissipation problem of batteries, especially suitable for the directional heat dissipation requirements of high-energy-density battery packs. However, most of the number–value models currently studied examine the flow of refrigerant working medium within the pump as an isentropic or isothermal process and are unable to effectively analyze the heat transfer characteristics of different internal regions. Based on the laws of energy conservation, momentum conservation, and mass conservation, this study establishes a steady-state mathematical model of the pump-driven microchannel-separated heat pipe. The influence of factors—such as the phase state change in the working medium inside the heat exchanger, the heat transfer flow mechanism, the liquid filling rate, the temperature difference, as well as the structural parameters of the microchannel heat exchanger on the steady-state heat transfer and flow performance of the pump-driven microchannel-separated heat pipe—were analyzed. It was found that the influence of liquid filling ratio on heat transfer quantity is reflected in the ratio of change in the sensible heat transfer and latent heat transfer. The sensible heat transfer ratio is higher when the liquid filling is too low or too high, and the two-phase heat transfer is higher when the liquid filling ratio is in the optimal range; the maximum heat transfer quantity can reach 3.79 KW. The decrease in heat transfer coefficient with tube length in the single-phase region is due to temperature and inlet effect, and the decrease in heat transfer coefficient in the two-phase region is due to the change in flow pattern and heat transfer mechanism. This technology has the advantages of long-distance heat transfer, which can adapt to the distributed heat dissipation needs of large-energy-storage power plants and help reduce the overall lifecycle cost. Full article

With regard to low-voltage (LV) distribution networks, the quality of distributed electricity can be compromised by the level of phase load imbalance. Consequently, numerous phase load balancing (PLB) algorithms have been proposed in the specialized literature. However, those models have been focused on the quality of the solution obtained rather than performance, which leads to reduced practical applicability for the distribution network (reduced scalability, slow convergence, and a higher computational cost). Furthermore, certain constraints regarding the electrical network and the switching operations of consumers must be integrated into the mathematical model. In this context, the proposed PLB algorithm represents an improved constrained greedy optimization (ICGO), capable of achieving fast convergence even on large datasets, with a lower computational cost. Three scenarios (30, 250, and 500 consumers), each with 20 distinct initial non-symmetries, were simulated. The results support the practical effectiveness and scalability of the ICGO: an absolute value of the neutral current below 0.63 A (99.53% relatively reduction), a current unbalance index below 0.1%, a small number of iterations (between 4 and 11 iterations), and an execution time between 0.00051 and 0.01149 s). Therefore, this research proposes an efficient PLB algorithm, with the possibility for its improvement in future work. Full article

Rice, as one of the world’s three major staple crops, provides a food source for nearly half of the global population. Timely and accurate acquisition of rice cultivation information is crucial for optimizing spatial distribution, guiding production practices, and safeguarding food security. Taking Bishan District of Chongqing as the study area, NDVI values were derived from Sentinel-2 satellite imagery to construct standard NDVI time-series curves for typical land-cover types, including paddy fields, dryland, water bodies, construction land, and forest and grassland. These curves were then used in the NDVI time-series characteristics method to identify paddy fields. First, the Euclidean distance between the standard NDVI time series of paddy fields and those of other land-cover types was calculated. The sum of these element-wise differences was used to determine the upper threshold for paddy field extraction. Second, the mean absolute deviation between elements of the rice sample dataset and the standard NDVI time series was calculated for each time step. The sum of these average deviations was used as the lower threshold to extract the initial paddy field data. On this basis, an extreme-value constraint was introduced to reduce the interference of mixed pixels from forest and grassland and construction land, effectively eliminating anomalous pixels and improving the accuracy of paddy field identification. Finally, the results were validated and compared with those from other extraction methods. The results indicate that: (1) Paddy fields exhibit distinct NDVI time-series characteristics throughout the entire growing season, which can serve as a reference standard. By calculating the Euclidean distance between the NDVI curves of other land-cover types and those of paddy fields, similarity can be quantified, enabling rice identification. (2) The extraction method based on NDVI time-series characteristics successfully identified paddy fields through the appropriate setting of thresholds. The overall accuracy and Kappa coefficient remained high, while the F1-score consistently exceeded 0.8, indicating a good balance between precision and recall. Furthermore, the bootstrap uncertainty analysis revealed narrow 95% confidence intervals across all metrics, confirming the robustness and statistical reliability of the results. Overall, the proposed method demonstrated excellent performance in paddy field classification and significantly outperformed traditional machine learning methods implemented on the GEE platform. (3) Mixed pixels considerably affected the accuracy of rice classification; however, the introduction of the extreme-value constraint effectively mitigated this influence and further improved classification results. Full article

Litchi is an important subtropical fruit, highly valued by consumers for its vibrant color and distinctive flavor. B-box (BBX) proteins, which are zinc finger transcription factors, play a crucial role in regulating plant growth, development, and stress responses. Nevertheless, the specific function of BBX genes in the development and coloration of litchi fruit remains inadequately understood. In this study, 21 LcBBX genes (designated as LcBBX1-LcBBX21) were identified within the litchi genome. These genes were categorized into five sub-families based on phylogenetic analysis and were found to be unevenly distributed across 12 chromosomes. Promoter analysis revealed a rich presence of light-responsive elements, such as the G-box, and abscisic acid (ABA) responsive elements, including ABRE, within the promoter regions of LcBBX genes. Protein–protein interaction predictions indicated that the majority of LcBBX genes have the potential to interact with the light-responsive factor HY5. Transcriptome analysis and qRT-PCR results demonstrated that LcBBX genes exhibit tissue-specific expression patterns. Notably, most LcBBX genes were highly expressed prior to fruit coloration, whereas LcBBX4 and LcBBX10 were upregulated during the fruit coloration phase. Furthermore, LcBBX1/4/6/7/15/19 were upregulated in response to light following the removal of shading. The findings suggest that LcBBX4 may directly regulate anthocyanin biosynthesis in litchi pericarp. This study provides critical insights into the molecular mechanisms underlying litchi fruit development and coloration. Full article