Search Results (5,701)

Moyamoya disease is a progressive steno-occlusive cerebrovascular disorder in which cerebral perfusion may become highly dependent on systemic arterial pressure, arterial carbon dioxide tension, and collateral flow. Encephaloduroarteriosynangiosis (EDAS) is an indirect revascularization procedure that promotes neovascularization over weeks to months but does not immediately augment cerebral blood flow intraoperatively. Anesthetic management therefore requires preservation of cerebral oxygen delivery during a period of persistent physiologic vulnerability. This narrative review presents a practical perioperative framework for EDAS anesthesia, emphasizing maintenance of mean arterial pressure near baseline or modestly above baseline, avoidance of hypotension and hypovolemia, normoxia, normothermia, and careful regulation of carbon dioxide. Hyperventilation should be avoided because hypocapnia can reduce cerebral blood flow through vasoconstriction, while excessive hypercapnia may contribute to regional maldistribution or steal physiology. Raw electroencephalography may provide cortical ischemia surveillance where available, whereas somatosensory evoked potentials, motor evoked potentials, near-infrared spectroscopy, and transcranial Doppler should be considered adjunctive and institution-dependent. A structured algorithm that integrates hemodynamics, ventilation, oxygen delivery, anesthetic depth, neuromonitoring, and surgical communication may support the timely recognition and correction of intraoperative hypoperfusion. Full article

Water erosion is a critical degradation process that reduces fertility and agricultural sustainability, especially in semi-arid regions. The Universal Soil Loss Equation (USLE) allows for the quantification of this phenomenon using factors such as rainfall erosivity (R) and topography (length-slope, LS). In this study, both factors were estimated and analyzed in the Cañitas sub-basin, located in the semi-desert area of the state of Zacatecas, Mexico, characterized by irregular precipitation and limited data availability. The objective of this study is to estimate and analyze the R factor and LS factor to evaluate their influence on soil water erosion processes. Records from five meteorological stations (1986–2022) were used, along with the Modified Fournier Index (MFI) and Geographic Information Systems (GIS) tools, generating spatial maps of rainfall erosivity and topography. An average R factor of 81.69 MJ∙mm/ha∙h∙year was estimated, consistent with the values obtained using the MFI. The LS factor shows that the northwestern area of the study zone has the most extensive and steepest slopes (up to 20). This study analyzes the R and LS factors to identify areas vulnerable to water erosion and to understand the influence of climate and topography in a semi-arid region, which can serve as a reference for planning conservation actions and managing watersheds in semi-arid areas with high climatic variability. Full article

Global economic policy uncertainty has emerged as a significant challenge for developing regions, with Sub-Saharan Africa particularly vulnerable due to its fragile economies and social systems that rely on external support. This study examines the effect of global economic policy uncertainty on social sustainability and how this relationship is moderated by governance effectiveness and natural resource rents. These relationships were examined using 27 years of panel data from 45 Sub-Saharan African countries, spanning 1997 to 2023. The Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCMG), and the two-step difference Generalized Method of Moments (GMM) estimators are advanced methods for analyzing data and estimating relationships among variables. The study found that global economic policy uncertainty had a significant negative effect on social sustainability. Furthermore, the study revealed that governance effectiveness and natural resource rents positively and significantly moderate the relationship between global economic policy uncertainty and social sustainability. These findings have significant implications for policy and governance, highlighting the critical need for governments, especially in developing and resource-dependent regions, to strengthen institutional capacity and fiscal frameworks in order to manage the adverse effects of global economic policy uncertainty. They underscore the importance of developing responsive, transparent, and accountable governance structures that can effectively allocate resources toward social priorities even during periods of external economic volatility. Full article

The aims of this study were to determine the geographical range and habitats of the Verbascum species in the Arabian Peninsula and to assess their conservation status at national, regional, and global levels by using the International Union for Conservation of Nature (IUCN) Red List categories and criteria. Verbascum is represented by 16 species with four varieties in the area of the study, and most of these species are endemic to Saudi Arabia, Yemen, Oman, and the United Arab Emirates (UAE). This study is based on an ecogeographic survey, which was conducted using herbaria collections, literature sources, and fieldwork. The findings showed that the genus is distributed in three main regions in the Arabian Peninsula, which include northern Saudi Arabia, the Asir and Yemen highlands, and the Hajar mountains in Oman and the UAE. In addition, most species of Verbascum in the region are at high risk of extinction. Nine taxa are threatened, four of which are assessed as Critically Endangered, four as Endangered, and one as Vulnerable. Furthermore, four species are assessed as Near Threatened, while another five species are assessed as of Least Concern. Threats to the Verbascum species in the region are overgrazing, suburban and agricultural expansion, climate change, invasive species, recreational activities, tourism, war, and civil unrest, leading to human intrusion and disturbances. Some important strategies for conserving and managing Verbascum species on the Arabian Peninsula are recommended here. Full article

Tourism plays an increasingly important role in promoting economic growth and rural revitalization in ecologically fragile regions. However, tourism systems in Tibet–related areas of Sichuan, China, are highly vulnerable to natural disasters, ecological degradation, and regional development imbalances, posing challenges to sustainable tourism development. This study aims to evaluate tourism system resilience and identify its key influencing factors from a sustainability perspective. Based on the regional characteristics of Tibet-related areas in Sichuan, a comprehensive evaluation framework is constructed covering four subsystems: tourism infrastructure and scale, economy, society, and ecology. An integrated entropy weight–analytic hierarchy process (AHP) model, coupling coordination model, and obstacle degree model are employed to assess tourism system resilience and examine subsystem interactions using panel data from 2011 to 2020. The results indicate that: (1) the resilience levels of tourism subsystems show no clear spatial or temporal regularity across the study areas; (2) ecological resilience remains significantly lower than tourism, economic, and social resilience, representing the weakest component of the tourism system; (3) the coupling coordination among subsystems remains at a low level, suggesting insufficient synergy for sustainable regional development; and (4) ecological constraints are the primary limiting factors affecting overall tourism system resilience. This study contributes to sustainable tourism research by revealing the critical role of ecological governance and subsystem coordination in enhancing tourism resilience in ecologically sensitive regions. Policy implications include strengthening ecological protection, improving tourism infrastructure, promoting digital tourism marketing, and advancing rural revitalization to achieve long-term sustainable development. However, this study is limited by data availability and the spatial scope of the selected case-study areas, which may affect the generalizability of the findings. Full article

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the Berrechid plain represents a relevant case study illustrating both the practical and theoretical challenges of groundwater governance. The aquifer is heavily exploited to satisfy agricultural, industrial, and domestic needs. This study develops a hybrid “grey-box” modeling approach for predicting groundwater depth (GWD) fluctuations under climate change (CC). Unlike conventional black-box machine learning models, our framework combines a deterministic physical engine with a stochastic machine learning corrector. The physical component simulates aquifer mass balance using the Hargreaves method for evapotranspiration, linear drainage, climate memory via exponential decay, and an anthropogenic trend parameter (xi). The machine learning component—XGBoost with quantile regression—is trained exclusively on physical model residuals and predicts the 5th, 50th, and 95th percentiles, providing explicit 90% confidence intervals. Hydrological states (dry, normal, wet) are identified via K-means clustering for context-aware correction. The model is calibrated using historical data (1972–2019) and validated using blocked time-series cross-validation. Climate projections under the RCP 4.5 and RCP 8.5 scenarios were used to forecast GWD up to 2100. At piezometer 3933/20, the best performance was achieved, with an RMSE of 0.347 m and a KGE of 0.742 during the validation period. The proposed approach is suitable for seasonal GWD forecasting and offers practical value for water managers and decision-makers in the Berrechid region. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

Background/Objectives: The genes encoding HSPA1A, HSPA1B, and HSPA1L, located in the MHC class III region at 6p21.3–22.1, a region implicated in susceptibility to schizophrenia, are critical regulators of neurodevelopmental processes and contribute to synaptic neuroprotection. This study investigated whether the HSPA1A, HSPA1B, and HSPA1L genes are associated with schizophrenia. Methods: We sequenced the coding regions of HSPA1A, HSPA1B, and HSPA1L from 100 patients with schizophrenia to identify genetic variants. Further, we conducted a genetic association analysis of three SNPs (rs9469057, rs142416335, and rs2075800) in the HSPA1L gene in 519 patients with schizophrenia and 1492 healthy controls from the Taiwan Biobank. We analyzed the function of the HSPA1L protein via immunoblotting. Results: We identified 17 coding variants, including 8 missense and 9 synonymous mutations, in 100 patients with schizophrenia. Three variants (HSPA1L^p.Ala8Pro, HSPA1L^p.Ala8Thr, and HSPA1L^p.Glu602Lys) in the HSPA1L gene did not exhibit any significant differences in allele or genotype frequencies between patients and control subjects. Notably, one ultra-rare missense mutation, HSPA1L^p.Val262Met, was not documented in the control sample in Taiwan BioBank. Immunoblotting revealed HSPA1L^p.Val262Met mutant with decreased protein expression in SH-SY5Y cells compared with the wild type. Conclusions: While common variants in the HSPA1A, HSPA1B, and HSPA1L genes do not seem to be significant genetic risk factors for schizophrenia in this cohort, the ultra-rare mutation, HSPA1L^p.Val262Met, significantly reduces protein expression. These preliminary findings suggest that a potential loss-of-function or reduced expression of the HSPA1L gene may be a predisposing factor contributing to schizophrenia vulnerability in certain individuals. However, the finding should be replicated in other independent samples. The in vitro and in vivo impacts of the associated mutation at the HSPA1L gene on the pathophysiology of schizophrenia are worthy of future investigation. Full article

The uplift of the Qinghai–Tibet Plateau has shaped a unique extreme environment, fostering distinct endemic aquatic organisms. Schizopygopsis pylzovi, a vulnerable endemic fish in the upper Yellow River, is a key model for studying the biogeographic patterns of plateau fish. To assess its genetic characteristics and evolutionary dynamics, we comprehensively evaluated 11 geographic populations of S. pylzovi using two complementary mitochondrial markers, the conserved COI gene and the fast-evolving D-loop region. A total of 142 COI and 143 D-loop sequences were analyzed, and sequences alignment, haplotype network construction, AMOVA, and neutrality tests were performed. AMOVA revealed that genetic variation was mainly distributed within populations, indicating weak population differentiation. Neutrality tests and mismatch distribution analysis suggested historical and recent population expansion events. Our findings highlight the value of joint analysis using COI and D-loop markers in revealing the genetic structure of S. pylzovi, provide new insights into the impact of plateau uplift on fish evolution, and establish a scientific basis for the conservation of this vulnerable species. Full article

Seawater intrusion presents a significant risk to coastal aquifers, particularly in low-lying locations where groundwater resources are intensively exploited. This study assesses the vulnerability of the Keta Strip aquifer in Southeastern Ghana to seawater intrusion using the GALDIT model; a widely applied index-based approach that evaluates seawater intrusion risk based on six key hydrogeological indicators: groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater level above sea level (L), distance from the shoreline (D), impact of existing intrusion (I), and aquifer thickness (T). These parameters were analyzed using data from 105 monitoring wells within a Geographic Information System (GIS) environment. The resulting vulnerability index was spatially grouped into four categories: low, moderate, high, and very high vulnerability. Results indicate that very high and high vulnerability regions are predominantly clustered along the coastal margins and central portions of the study area, driven mainly by low hydraulic gradients, proximity to the shoreline, and high hydraulic conductivity. Moderate vulnerability zones dominate inland areas, while low vulnerability zones are limited and confined to northern sections. Sensitivity analysis reveals that hydraulic head (L) and distance from shoreline (D) are the most influential parameters, whereas TDS exhibits relatively low contribution to overall vulnerability. The findings highlight the critical role of hydrogeological controls and anthropogenic pressures in shaping seawater intrusion risk and provide a scientific basis for sustainable groundwater management in the Keta Strip and similar coastal environments. Full article

Coastal typhoon deformation plays a critical role in determining typhoon tracks, intensity changes, precipitation and related flooding, storm surges, and typhoon waves, and thus is highly associated with coastal disaster patterns. This study proposes a three-level framework for typhoon wind field modeling through the integration of geometric characterization with physical-informed reconstruction. At its core, an elliptical fitting method is developed based on second-order moments to quantify the structural asymmetries. This geometric fitting method is incorporated into the reconstruction method of Holland–Miyazaki, creating a physically consistent model capable of simulating typhoon deformation processes during landfall. Validation through high-resolution Weather Research and Forecasting (WRF) simulations of Typhoon Chan-hom (2015) demonstrates the framework’s effectiveness, capturing elliptical eyewall deformation with aspect ratios exceeding 1.5, primarily driven by coastal topography and surface friction interactions. The method is further validated through Typhoon Mitag (2019), with mean wind component errors below 1 m/s, the average correlation coefficients surpassing 0.9, and wind direction mean absolute errors largely below 10°. This research provides a practical framework for quantifying and characterizing the wind field deformation during typhoon landfall in coastal regions, thereby supporting ther operational forecasting and disaster reduction in vulnerable coastal regions. Full article

Rapid urbanisation has reshaped Shanghai’s ecological land base and intensified fragmentation of its green infrastructure (GI). This study evaluates the spatiotemporal evolution of Shanghai’s GI network from 2000 to 2020 using a resilience-informed patch-corridor-connectivity assessment. In this study, resilience is not just an explanatory label but a measurable structural criterion. Morphological Spatial Pattern Analysis (MSPA) was used to identify core patches; patch importance was evaluated using delta Probability of Connectivity (dPC); a Minimum Cumulative Resistance (MCR) model was used to derive potential corridors; and a gravity model was used to classify corridor importance. The results show that important ecological corridors increased from 22 in 2000 to 33 in 2010 and 68 in 2020, while the total area of the MSPA core class declined and north–south connectivity remained uneven. The key finding is not the growth of corridor number itself, but the mismatch between corridor densification and contraction of major source patches. This mismatch indicates a structural vulnerability that would be overlooked by a conventional network-optimisation reading. Therefore, based on the results of indicator-based resilience assessment, this study proposes a planning scheme that combines core-area conservation, corridor continuity, redundancy improvement, and cross-regional connectivity enhancement. Full article

The deployment of large-scale antenna arrays (ELAAs) in sixth-generation (6G) networks extends wireless communications into the near-field regime, facilitating integrated sensing and communications while introducing security requirements. To ensure secure near-field transmission and sensing accuracy, this paper proposes a framework that jointly minimizes the Cramér–Rao Bound (CRB), guarantees quality-of-service (QoS) for ordinary users, and ensures the covertness of a primary user through an explicit vulnerable-region constraint. The nonconvex problem is addressed through an iterative approach integrating semidefinite relaxation (SDR), alternating optimization (AO), and successive convex approximation (SCA). Numerical results demonstrate sensing performance, QoS satisfaction, and accurate vulnerable-region control. Full article

Background and Objectives: With the rising number of geriatric surgical patients, postoperative cognitive dysfunction (POCD) has become a major concern, linked to impairments in memory, attention, and executive function. POCD increases morbidity, prolongs hospitalization, and diminishes quality of life. This review examines the mechanisms underlying POCD, with emphasis on neuroinflammation, blood–brain barrier (BBB) disruption, and oxidative stress, and evaluates the impact of anesthetic techniques on cognitive outcomes in the elderly. Materials and Methods: This narrative review used a targeted literature search to identify relevant clinical, translational, and mechanistic evidence on POCD in older surgical patients. The evidence was synthesized qualitatively, with attention to heterogeneity in study populations, anesthetic techniques, cognitive assessment methods, and follow-up duration. Results: Neuroinflammation, BBB compromise, oxidative stress, perioperative stress responses, and patient vulnerability appear to contribute to POCD. Evidence comparing anesthetic techniques remains heterogeneous. Some studies suggest associations between general anesthesia, volatile agents, and early postoperative cognitive changes, whereas other comparative and randomized studies do not demonstrate consistent long-term cognitive differences between general, regional, neuraxial, volatile, and intravenous anesthetic approaches. Regional and neuraxial techniques may reduce anesthetic or opioid exposure in selected patients, but they should not be interpreted as definitively superior for POCD prevention. Adjunctive and multimodal strategies, including dexmedetomidine and non-opioid analgesics, show potential benefits, although evidence remains variable. Conclusions: Individualized anesthetic planning, early risk stratification, avoidance of excessive anesthetic depth, hemodynamic optimization, multimodal analgesia, and postoperative recovery strategies may help reduce modifiable contributors to POCD. Current evidence does not support a definitive hierarchy of anesthetic techniques for preventing POCD, and further high-quality studies are needed. Full article

Hungary is experiencing more frequent and severe droughts due to climate change, with 60% of its arable land in the vulnerable Great Hungarian Plain. Drought events in 2012 and 2022 reduced maize yields by more than 50% in some regions. This review synthesizes studies (2000–2025) on remote sensing capabilities, climate change impacts, and farmer adaptation in Hungarian agriculture. Remote sensing technologies (Sentinel, Landsat, MODIS) and indices (NDVI, VCI, LST, TCI) achieve high accuracy (often >80%) in drought detection under validated conditions, yet technical and financial barriers limit uptake among smallholder farmers. Climate projections indicate that a 2 °C temperature rise by 2050 will expand drought-affected areas. Farmer adaptation varies sharply by farm size: large farms (>100 ha) adopt precision agriculture (65% uptake), while smallholders (<10 ha) rely on crop rotation and drought-resistant varieties. Although substantial support is provided through the EU Common Agricultural Policy, institutional fragmentation and weak extension services—which reach only 32% of farmers—undermine its effectiveness. Bridging this gap requires integrating accessible remote sensing tools with targeted smallholder support and reformed extension services. Full article