Search Results (4,284)

Extreme precipitation events severely impact agriculture, reducing yields and land use efficiency. The spatiotemporal distribution of Total Column Water Vapor (TCWV), the primary gaseous form of water, directly influences sustainable agricultural management. This study, through multi-source data fusion, employs methods including the Mann–Kendall test, sliding change-point detection, wavelet transform, pixel-scale trend estimation, and linear regression to analyze the spatiotemporal dynamics of global TCWV from 1959 to 2023 and its impacts on agricultural systems, surpassing the limitations of single-method approaches. Results reveal a global TCWV increase of 0.0168 kg/m²/year from 1959–2023, with a pivotal shift in 2002 amplifying changes, notably in tropical regions (e.g., Amazon, Congo Basins, Southeast Asia) where cumulative increases exceeded 2 kg/m² since 2000, while mid-to-high latitudes remained stable and polar regions showed minimal content. These dynamics escalate weather risks, impacting sustainable agricultural management with irrigation and crop adaptation. To enhance prediction accuracy, we propose a novel hybrid model combining wavelet transform with LSTM, TCN, and GRU deep learning models, substantially improving multidimensional feature extraction and nonstationary trend capture. Comparative analysis shows that WT-TCN performs the best (MAE = 0.170, R² = 0.953), demonstrating its potential for addressing climate change uncertainties. These findings provide valuable applications for precision agriculture, sustainable water resource management, and disaster early warning. Full article

Background/Objectives: Mumps remains a relevant vaccine-preventable disease globally, especially in settings where immunization coverage fluctuates or vaccine-induced immunity wanes. This study aimed to assess long-term trends in mumps incidence, vaccination coverage, clinical outcomes, and demographic characteristics in the Autonomous Province of Vojvodina (AP Vojvodina), Serbia, over a 47-year period. Methods: We conducted a retrospective observational study using surveillance data from the Institute of Public Health of Vojvodina. Analyses included annual mumps incidence rates (1978–2024), coverage with mumps-containing vaccines (MuCVs; 1986–2024), monthly case counts, and individual-level case data for the 1997–2024 period. Variables analyzed included age, month of notification, gender, vaccination status, presence of clinical complications, and the method used for case confirmation. Results: Following the introduction of MuCV in 1986, the mumps incidence markedly declined, with limited resurgences in 2000, 2009, and 2012. Between 1997 and 2024, a total of 1358 cases were reported, with 62.7% occurring in males. Over time, the age distribution shifted, with adolescents and young adults being increasingly affected during the later (2011–2024) observed period. In 2012, the highest age-specific incidence was observed among individuals aged 10–19 and 20–39 years (49.1 and 45.5 per 100,000, respectively). Vaccination coverage for both MuCV doses was suboptimal in several years. The proportion of unvaccinated cases decreased over time, while the proportion with unknown vaccination status increased. Mumps-related complications—such as orchitis, pancreatitis, and meningitis—were rare and predominantly affected unvaccinated individuals: 84.2% of orchitis, 40.0% of pancreatitis, and all meningitis cases. Only two pancreatitis cases (40.0%) were reported after one MMR dose, while fully vaccinated individuals (two doses) had one orchitis case (5.3%) and no other complications. Laboratory confirmation was applied more consistently from 2009 onward, with 49.6% of cases confirmed that year (58 out of 117), and, in several years after 2020, only laboratory-confirmed cases were reported, indicating improved diagnostic capacity. Conclusions: Despite substantial progress in controlling mumps, gaps in vaccine coverage, waning immunity, and incomplete vaccination records continue to pose a risk for mumps transmission. Strengthening routine immunization, ensuring high two-dose MuCV coverage, improving vaccination record keeping, and enhancing laboratory-based case confirmation are critical. Consideration should be given to booster doses in high-risk populations and to conducting a seroepidemiological study to estimate the susceptible population for mumps in AP Vojvodina. Full article

This work introduces a new family of multivariate hybrid special polynomials, motivated by their growing relevance in mathematical modeling, physics, and engineering. We explore their core properties, including recurrence relations and shift operators, within a unified structural framework. By employing the factorization method, we derive various governing equations such as differential, partial differential, and integrodifferential equations. Additionally, we establish a related fractional Volterra integral equation, which broadens the theoretical foundation and potential applications of these polynomials. To support the theoretical development, we carry out computational simulations to approximate their roots and visualize the distribution of their zeros, offering practical insights into their analytical behavior. Full article

Nanotechnologies bring a rapid paradigm shift in hard and soft bone tissue regeneration (BTR) through unprecedented control over the nanoscale structures and chemistry of biocompatible materials to regenerate the intricate architecture and functional adaptability of bone. This review focuses on the transformative analyses and prospects of current and next-generation nanomaterials in designing bioactive bone scaffolds, emphasizing hierarchical architecture, mechanical resilience, and regenerative precision. Mainly, this review elucidated the innovative findings, new capabilities, unmet challenges, and possible future opportunities associated with biocompatible inorganic ceramics (e.g., phosphates, metallic oxides) and the United States Food and Drug Administration (USFDA) approved synthetic polymers, including their nanoscale structures. Furthermore, this review demonstrates the newly available approaches for achieving customized standard porosity, mechanical strengths, and accelerated bioactivity to construct an optimized nanomaterial-oriented scaffold. Numerous strategies including three-dimensional bioprinting, electro-spinning techniques and meticulous nanomaterials (NMs) fabrication are well established to achieve radical scientific precision in BTR engineering. The contemporary research is unceasingly decoding the pathways for spatial and temporal release of osteoinductive agents to enhance targeted therapy and prompt healing processes. Additionally, successful material design and integration of an osteoinductive and osteoconductive agents with the blend of contemporary technologies will bring radical success in this field. Furthermore, machine learning (ML) and artificial intelligence (AI) can further decode the current complexities of material design for BTR, notwithstanding the fact that these methods call for an in-depth understanding of bone composition, relationships and impacts on biochemical processes, distribution of stem cells on the matrix, and functionalization strategies of NMs for better scaffold development. Overall, this review integrated important technological progress with ethical considerations, aiming for a future where nanotechnology-facilitated bone regeneration is boosted by enhanced functionality, safety, inclusivity, and long-term environmental responsibility. Therefore, the assimilation of a specialized research design, while upholding ethical standards, will elucidate the challenge and questions we are presently encountering. Full article

Background/Objectives: The expansion of National Health Insurance (NHI) coverage for dental implants in South Korea has substantially increased implant placements among older adults. While implants offer functional and esthetic benefits, their lack of periodontal ligaments alters occlusal force distribution, potentially increasing biomechanical stress on adjacent or opposing teeth. This study aimed to investigate the association between the increased number of dental implants and the incidence of cracked teeth following the introduction of implant insurance. Methods: A retrospective analysis was conducted using the Clinical Data Warehouse of Seoul St. Mary’s Dental Hospital. Patients who underwent molar crown restorations between 2014 and 2022 were included. The incidence and clinical features of cracked teeth were compared before (2014–2015) and after (2016–2022) the introduction of implant insurance. Statistical analyses assessed differences in symptom presentation, pulp status, and treatment outcomes. Results: Among 5044 molars restored with crowns, 1692 were diagnosed with cracks. The incidence of cracked teeth significantly increased after NHI coverage for implants (25.5% vs. 32.6%, p < 0.001). Cases after insurance implementation showed fewer signs and symptoms at initial presentation (67.4% vs. 50.0%, p < 0.001), reduced irreversible pulpitis (37.2% vs. 25.8%, p < 0.001), and increased preservation of pulp vitality (46.9% vs. 57.8%, p < 0.001). These shifts may reflect changes in occlusal adjustment practices and earlier clinical intervention. Conclusions: The findings suggest a temporal link between increased implant placement and the rising incidence of cracked teeth. Implant-induced occlusal changes may contribute to this trend. Careful occlusal evaluation and follow-up are essential after implant placement, and further prospective studies are warranted to confirm causality and refine prevention strategies. Full article

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

Background: The COVID-19 pandemic has profoundly altered the clinical and microbiological landscape of respiratory tract infections (RTIs), potentially reshaping pathogen distribution and antimicrobial resistance (AMR) profiles across care settings. Objectives: The objective of this study was to assess temporal trends in respiratory bacterial pathogens, antimicrobial resistance, and polymicrobial infections across three pandemic phases—pre-COVID (2018–2019), COVID (2020–2022), and post-COVID (2022–2024)—in hospitalized and ambulatory patients. Methods: We retrospectively analyzed 1827 respiratory bacterial isolates (hospitalized patients, n = 1032; ambulatory patients, n = 795) collected at a tertiary care center in Northern Italy. Data were stratified by care setting, anatomical site, and pandemic phase. Species identification and susceptibility testing followed EUCAST guidelines. Statistical analysis included chi-square and Fisher’s exact tests. Results: In hospitalized patients, a significant increase in Pseudomonas aeruginosa (from 45.5% pre-COVID to 58.6% post-COVID, p < 0.0001) and Acinetobacter baumannii (from 1.2% to 11.1% during COVID, p < 0.0001) was observed, with 100% extensively drug-resistant (XDR) rates for A. baumannii during the pandemic. Conversely, Staphylococcus aureus significantly declined from 23.6% pre-COVID to 13.7% post-COVID (p = 0.0012). In ambulatory patients, polymicrobial infections peaked at 41.2% during COVID, frequently involving co-isolation of Candida spp. Notably, resistance to benzylpenicillin in Streptococcus pneumoniae reached 80% (4/5 isolates) in hospitalized patients during COVID, and carbapenem-resistant P. aeruginosa (CRPA) significantly increased post-pandemic in ambulatory patients (0% pre-COVID vs. 23.5% post-COVID, p = 0.0014). Conclusions: The pandemic markedly shifted respiratory pathogen dynamics and resistance profiles, with distinct trends observed in hospital and community settings. Persistent resistance phenotypes and frequent polymicrobial infections, particularly involving Candida spp. in outpatients, underscore the need for targeted surveillance and antimicrobial stewardship strategies. Full article

This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods. Full article

Accurate evaluation of the operational status of smart meters, as the critical interface between the power grid and its users, is essential for ensuring fairness in power transactions. This highlights the importance of implementing rotation management practices based on meter status. However, the traditional expiration-based rotation method has become inadequate due to the extended service life of modern smart meters, necessitating a shift toward status-driven targeted management. Existing multifactor comprehensive assessment methods often face challenges in balancing accuracy and interpretability. To address these limitations, this study proposes a novel method for analyzing the status of smart meter groups using an interpretable random forest model. The approach incorporates an expert-knowledge-guided grouping assessment strategy, develops a multi-source heterogeneous feature set with strong correlations to meter status, and enhances the random forest model with the SHAP (SHapley Additive exPlanations) interpretability framework. Compared to conventional methods, the proposed approach demonstrates superior efficiency and reliability in predicting the failure rates of smart meter groups within distribution network areas, offering robust support for the maintenance and management of smart meters. Full article

The spatiotemporal evolution of the regional water use structure holds significant theoretical value for optimizing regional water resource allocation, adjusting industrial structures, and achieving sustainable water resource development. Shandong Province, located at the lowest reach of the Yellow River Basin in China, is a major economic, agricultural, and populous province, as well as a region with one of the most prominent water supply–demand imbalances in the country. As a result, exploring how water use patterns change over time and space in this region has become crucial. Using analytical methods like the Lorenz curve, Gini coefficient, cluster analysis, and spatial statistics, we examine shifts in Shandong’s water use structure from 2001 to 2023. We find that while agriculture remained the largest water consumer over this period, industrial, household, and ecological water use steadily increased, signaling a move toward more balanced resource distribution. Across Shandong’s 16 regions (cities), the water use patterns varied considerably, particularly in terms of agriculture, industry, and ecological needs. Among these, agricultural, industrial, and domestic water use were distributed relatively evenly, whereas ecological water use showed greater regional disparities. These results may have the potential to guide policymakers in refining water allocation strategies, improving industrial planning, and boosting the water use efficiency in Shandong and the country ore broadly. Full article

In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand and local photovoltaic (PV) generation. We employ the alternating direction method of multipliers (ADMM) to solve the dispatch problem in a distributed manner. Demand response in a 141-bus test system serves as our case study, demonstrating the effectiveness of our approach in shifting power loads to periods of high PV generation. Our results indicate remarkable reductions in the total carbon emission by utilizing more distributed PV generation. Full article

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO₂ concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions. Full article

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article

This study investigates the thermal stability and microstructural evolution of the solid electrolyte medium used in DLyte^® dry electropolishing and dry anodizing processes. Samples were thermally aged between 30 °C and 45 °C to simulate Joule heating during industrial operation. Visual and SEM analyses revealed shape deformation and microcrack formation at temperatures above 40 °C, potentially reducing particle packing efficiency and electrolyte performance. Particle size distribution shifted from bimodal to trimodal upon aging, with an overall size reduction of up to 39.5% due to dehydration effects, impacting ionic transport properties. Weight-loss measurements indicated a diffusion-limited dehydration mechanism, stabilizing at 15–16% mass loss. Fourier transform infrared analysis confirmed water removal while maintaining the essential sulfonic acid groups responsible for ionic conductivity. In dry anodizing tests on titanium, aged electrolytes enhanced process efficiency, producing TiO₂ films with improved optical properties—color and brightness—while preserving thickness and uniformity (~70 nm). The results highlight the need to carefully control thermal exposure to maintain electrolyte integrity and ensure consistent process performance. Full article

Trachypteris picta (Pallas, 1773) is a significant pest that can cause serious damage to poplars and willows. To assess the impact of climate change on the suitable habitats of T. picta, this study conducted a comparative analysis of its global suitable habitats using climatic factors, global land use type, and global vegetation from different periods, in combination with the maximum entropy (MaxEnt) model. The results indicate that the annual mean temperature (Bio01), mean temperature of the coldest quarter (Bio11), precipitation of the coldest quarter (Bio19), and isothermality (Bio03) are the four most important climate variables determining the distribution of T. picta. Under the current climate conditions, the highly suitable areas are primarily located in southern Europe, covering an area of 2.22 × 10⁶ km². Under future climate scenarios, the suitable habitat for T. picta is expected to expand and shift towards higher latitudes. In the 2050s, the SSP5-8.5 scenario has the largest suitable area compared to other scenarios, while the SSP2-4.5 scenario has the largest suitable area in the 2090s. In addition, the centroids of the total suitable areas are expected to shift toward higher latitudes under future climate conditions. The results of this study provide valuable data for the monitoring, control, and management of this pest. Full article