Search Results (13,277)

Background and objective: Child illness remains a significant public health challenge in low- and middle-income countries, including Bangladesh, with complex multifactorial causes extending beyond biomedical factors. This qualitative study explored perceived causes of child illness from the perspectives of caregivers and healthcare providers in rural and urban Bangladesh. Methods: Twenty-three in-depth interviews with primary caregivers, grandmothers, healthcare providers, and a group discussion with four community representatives revealed four primary categories of perceived illness causes. Results: Individual causes included maternal illness, forgetfulness, and knowledge gaps that affected caregiving practices, leading to missed vaccinations, poor hygiene and feeding practices. Socio-cultural causes included supernatural beliefs, intra-household power dynamics, domestic violence, maternal work burdens, early marriage, adolescent motherhood, and dowry practices. Economic causes included irregular income, rising food prices, and marketing of unhealthy products. Environmental causes included poor housing ventilation, inadequate waste management, heat wave exposure, urban air pollution, and water contamination, causing respiratory and waterborne diseases. Conclusions: These findings illustrate that child illness results from complex interactions between individual, socio-cultural, economic, and environmental causes. Potential interventions can address these multifaceted causes through comprehensive approaches including caregiver education, maternal empowerment strategies, economic support programs, and household environment improvements. Full article

China’s North–South Transition Zone is a critical ecological transition region, marked by complex environments, climatic sensitivity, and transitional characteristics. To investigate the effects of individual rainfall events on runoff generation and sediment yield across different slopes and land uses within this zone, the study collected data from slope runoff plots (20 m in length and 5 m in width, measured as horizontal projection) at three monitoring stations (Luoshan, Lushan, Shanzhou) between 2014 and 2023. Rainfall events were classified via K–means clustering. Regression and correlation analyses were applied to reveal the effects of rainfall characteristics, slope gradient and land use type (grass land, dry land, forest land, bare land and natural vegetation) on runoff and sediment. The results indicate that: (1) The most frequent rainstorms were Type C (short, low–intensity, low–volume, low–erosivity events). (2) The runoff depth of bare land is 3.6, 2.3, and 2 times that of forest land, dry land, and natural vegetation, respectively. Similarly, its sediment concentration is 134, 13, and 16 times higher, respectively. Grassland, however, showed markedly lower levels of both runoff and sediment. (3) Rainfall intensity was significantly correlated with runoff and sediment across slopes. Runoff depth depended mainly on rainfall amount. While Type A (prolonged, high–intensity) caused peak runoff, Type D (moderate but intense and erosive) yielded the highest sediment. (4) Sediment reduction efficiency (sediment reduction compared to bare land under identical conditions) consistently surpassed runoff reduction across all land types, with grassland showing the highest efficiency for both. For soil and water conservation, grass–planting was the most effective measure on 10° and 15° slopes, whereas both afforestation and grass–planting were optimal on the 25° slopes. Full article

Backfilling mining methods control the surrounding pressure and ground subsidence by backfilling goaf and managing the ground pressure, providing a safety guarantee for mining in complex environments and serving as a key means of achieving the deep mining of metal minerals. However, in the design of backfill strength, material mix ratios are determined under indoor standard constant temperature and humidity conditions, which differ significantly from the in situ curing environment. Strength measurements obtained from field samples are notably higher than those from indoor test specimens. To address this issue, this study designed a curing device simulating the in situ thermal-hydraulic multi-field environment of the mining site and tested the strength and porosity of the backfill under different curing temperatures, curing pressures, and pore water pressures. The results indicate that curing pressure and pore water pressure significantly altered the pore structure of the specimens. Specifically, when the curing pressure increased to 750 kPa, the maximum pore diameter decreased from 3110.52 nm to approximately 2055 nm, accompanied by a continuous reduction in porosity. Pore water pressure exhibited a positive linear correlation with specimen porosity, which increased continuously as the pore water pressure rose. With increasing curing temperature, the strength of the backfilled specimens first increased and then decreased, reaching a maximum at 45 °C. As the curing pressure increased, the strength of the backfilled specimens rose, but the rate of increase gradually slowed. With increasing pore water pressure, the strength of the backfilled specimens showed a gradual decreasing trend. Full article

A series of novel functional polycarbonates, specifically poly(solketal glycidyl ether carbonate-co-propylene carbonate)s with varying compositions, were synthesized through the ring-opening copolymerization of solketal glycidyl ether, propylene oxide, and carbon dioxide. The reaction was catalyzed by rac-(salcy)Co^IIIX complexes with bis(triphenylphosphine)iminium salts as co-catalysts, achieving high selectivity. The resulting terpolymers exhibited number-average molecular weights ranging from 2 × 10⁴ to 1 × 10⁵ and a narrow, bimodal molecular weight distribution, with dispersities of 1.02–1.07 for each mode. Interestingly, the addition of a small amount of water to the reaction mixture yielded a terpolymer with a unimodal molecular weight distribution and a dispersity of 1.11. Subsequent acidic hydrolysis of the solketal protective groups produced poly(glyceryl glycerol carbonate-co-propylene carbonate). All terpolymers were amorphous, with T_g near or below room temperature. The hydroxyl-functional polycarbonates underwent cyclodepolymerization under milder conditions compared to polycarbonates with protected hydroxyl groups. Full article

Background: TRPC6 is recognized as a therapeutically relevant cation channel, whose activation is governed by specific ligand–pocket interactions. Methods: An integrated in silico workflow was employed, comprising structure-based docking, 100-nanosecond molecular dynamics (MD) simulations, and MM-GBSA calculations. Benzoic-acid–based compounds were designed and prioritized for binding to the TRPC6 pocket, using a known literature agonist as a reference for benchmarking. Results: Within the compound series, BT11 was found to exhibit a representative interaction profile, characterized by a key hydrogen bond with Trp680 (~64% occupancy), persistent salt-bridge interactions with Lys676 and Lys698, and π–π stacking with Phe675 and Phe679. A favorable docking score (−11.45 kcal/mol) was obtained for BT11, along with a lower complex RMSD during MD simulations (0.6–4.8 Å), compared with the reference compound (0.8–7.2 Å). A reduction in solvent-accessible surface area (SASA) after ~60 ns was also observed, suggesting decreased water penetration. The most favorable binding energy was predicted for BT11 by MM-GBSA (−67.72 kcal/mol), while SOH95 also ranked highly and slightly outperformed the reference. Conclusions: These convergent computational analyses support the identification of benzoic-acid–derived chemotypes as potential TRPC6 ligands. Testable hypotheses are proposed, along with structure–activity relationship (SAR) guidelines, to inform experimental validation and guide the design of next-generation analogs. Full article

Alzheimer’s disease (AD) is a complex pathological process that incurs significant societal costs, yet effective treatments have not yet been developed. Novel compounds targeting β-amyloid, based on the amyloid cascade hypothesis, have failed to demonstrate clinical efficacy. Among natural products with diverse mechanisms, components contained in Astragali radix have shown anti-dementia effects in various preclinical studies, including improved cognitive function, reduced β-amyloid levels, and decreased insulin resistance. This study administered a water-extracted roasted Astragali radix (RA) to 3-month-old female 5xFAD mice for 3 months, observing changes in cognitive behavior, blood glucose, and neural signaling. RA lowered glucose levels, improved working memory, fear avoidance memory, and spatial memory, and reduced anxiety behavior in 5xFAD mice. In the hippocampus, the protein expression of BDNF and p-CREB/CREB was increased, while p-JNK/JNK was decreased. The effects of RA were similar to unroasted Astragali radix in 5xFAD mice, with some components being more abundant. Therefore, RA enhances its taste and aroma, making it suitable for long-term consumption in the form of tea, which could be effective in preventing neurodegenerative diseases such as dementia. Full article

4,7-di(N-morpholyl)-1,10-phenanthroline (morphen) was introduced for the first time as a ligand for the construction of metal–organic frameworks. The obtained MOF compound has the crystallographic formula {[Tb₂(morphen)₂Br₂(chdc)₂]}_n (1; chdc^2– = trans-1,4-cyclohexanedicarboxylate) and is based on binuclear {Tb₂(N^N)₂Br₂(OOCR)₄} carboxylate blocks, interlinked by ditopicchdc linkers into a layered coordination network with sql topology. Purity and integrity of the as-synthesized 1 were confirmed by common characterization techniques, such as PXRD, CHN, IR, and TGA. Compound 1 was found to be hydrolytically stable and possessing typical green emission for Tb(III) complexes. Exploiting its high stability, luminescent 1@PVA films were successfully prepared from 1 and polyvinyl alcohol (PVA) through the water solution drying approach. Full article

Estuaries represent quintessential coupled human–natural systems (CHNS) where the dynamic interplay between ecological processes and anthropogenic pressures (e.g., shipping, water use exploitation) challenges conventional static spatial planning approaches. Focusing on the Yangtze River Estuary—a globally significant yet intensely utilized ecosystem—this study develops an adaptive management (AM)-oriented spatial planning framework for estuarine protected areas. Our methodology integrates systematic identification of optimal zones using multi-criteria assessments of biodiversity indicators (e.g., flagship species habitats), ecological metrics (e.g., ecosystem services), and management considerations; delineation of a three-tier adaptive zoning system (Control–Functional–Seasonal) to address spatiotemporal pressures; and dynamic management strategies to mitigate human-environment conflicts. The proposed phased conservation boundary (Phase I: 664.38 km²; Phase II: 1721.94 km²) effectively balances ecological integrity with socio-economic constraints. Spatial–temporal analysis of shipping activities over five years demonstrates minimal operational interference, confirming the framework’s efficacy in reconciling conservation and development priorities. By incorporating ecological feedback mechanisms into spatial planning, this work advances a transferable model for governing contested seascapes, contributing to CHNS theory through practical tools for adaptive, conflict-sensitive conservation. The framework’s implementation in the Yangtze context provides empirical evidence that science-driven, flexible spatial planning can reduce sectoral conflicts while maintaining ecosystem functionality, offering a replicable pathway for sustainable water management of similarly complex human–natural systems worldwide. Full article

Given the significant impact of concrete’s compressive strength on structural service life, the development of accurate and efficient prediction methods is critically important. A hybrid machine learning modeling method based on the Whale Optimization Algorithm (WOA)-optimized XGBoost algorithm is proposed. Using 1030 sets of concrete mix proportion data covering eight key parameters—cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age—the predictive performance of four models (linear regression, random forest, XGBoost, and WOA-XGBoost) was systematically compared. The results demonstrate that the WOA-XGBoost model achieved the highest goodness of fit (R² = 0.9208, MSE = 4.5546), significantly outperforming the other models, and exhibited excellent generalization capability and robustness. Feature importance and SHAP analysis further revealed that curing age, cement content, and water content are the key variables affecting compressive strength, with blast furnace slag showing a significant marginal diminishing effect. This study provides a high-precision data-driven tool for optimizing mix proportions and predicting the strength of complex-component concrete, offering significant application value in promoting the resource utilization of industrial waste and advancing the development of green concrete. Full article

Volcanic rocks are rich in high-quality oil and gas and represent a key future exploration target. However, bottom water coning in complex reservoirs severely reduces extraction efficiency. This study uses numerical simulation to analyze how permeability, oil layer thickness, interlayers, and horizontal well parameters affect water coning. Results indicate that a permeability ratio of 0.02 delays water breakthrough and increases displacement efficiency by 20%. An oil–water volume ratio of 20 improves displacement efficiency and stabilizes water cut rise. Recovery increases by 15% when the oil layer exceeds 25 m in thickness, with coning expanding significantly at 35 m. A 200 m interlayer can reduce water cut rise by 30%. Optimal well spacing of 300–400 m increases recovery by 10–15%, while a horizontal section of 300–500 m improves reserve recovery. Placing the well 35 m from the top of the layer slows water cut rise. Full article

The Ionian Sea plays a crucial role as a crossroads for various Mediterranean water masses, making it a significant factor in the seawater budgets, biogeochemistry, and biodiversity of the subbasins of the Mediterranean Sea. In recent years, numerous theories have been proposed in an effort to better understand the complex hydrography and dynamics of the Ionian. These theories primarily focus on the variability of the basin’s near-surface circulation, which is characterized by a recurring reversal that occurs over a period of 10–13 years. This variability is often attributed to internal processes and/or boundary forcing, as waters of Atlantic origin enter the basin from its western boundary. In this study, we utilize temperature–salinity profiles and absolute dynamic topography data provided by the Copernicus database to examine long-term changes in the vertical structure of the basin and their relationships with changes in the horizontal near-surface circulation. Our findings show that the vertical dependency of the density field of the basin undergoes significant fluctuations over interannual and decadal time scales, which induce important buoyancy changes throughout the water column and determine changes in the structure of the first baroclinic mode. However, no changes in the basin-averaged hydrographic structure can be related to the near-surface current reversals. These reversals are mainly associated with deformations of the main isopycnal surface, intended as the region of maximum buoyancy over the water column, suggesting that they do not impact the hydrographic properties of the various Ionian water masses. Instead, they alter their routes and relative volumes within different parts of the basin. Full article

Crude oil emulsions continue to pose significant challenges across production, transportation, and refining due to their inherent stability and complex interfacial chemistry. Their persistence is driven by the synergistic effects of asphaltenes, resins, acids, waxes, and fine solids, as well as operational factors such as temperature, pH, shear, and droplet size. These emulsions increase viscosity, accelerate corrosion, hinder catalytic activity, and complicate downstream processing, resulting in substantial operational, economic, and environmental impacts—underscoring the necessity of effective demulsification strategies. This review provides a comprehensive examination of emulsion behavior, beginning with their formation, classification, and stabilization mechanisms and progressing to the fundamental processes governing destabilization, including flocculation, coalescence, Ostwald ripening, creaming, and sedimentation. Separation techniques are critically assessed across chemical, thermal, mechanical, electrical, membrane-based, ultrasonic, and biological domains, with attention to their efficiency, limitations, and suitability for industrial deployment. Particular emphasis is placed on hybrid and emerging methods that integrate multiple mechanisms to improve performance while reducing environmental impact. By uniting fundamental insights with technological innovations, this work highlights current progress and identifies future directions toward greener, more efficient oil–water separation strategies tailored to diverse petroleum operations. Full article

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research. Full article

The rapid expansion of global shipping has led to continuously increasing vessel traffic density, making high-accuracy ship trajectory prediction particularly critical for navigational safety and traffic management optimization in complex waters such as ports and narrow channels. However, existing methods still face challenges in medium-to-long-term prediction and nonlinear trajectory modeling, including insufficient accuracy and low computational efficiency. To address these issues, this paper proposes an enhanced Informer model (WOA-Informer) based on the Whale Optimization Algorithm (WOA). The model leverages Informer to capture long-term temporal dependencies and incorporates WOA for automated hyperparameter tuning, thereby improving prediction accuracy and robustness. Experimental results demonstrate that the WOA-Informer model achieves outstanding performance across three distinct trajectory patterns, with an average reduction of 23.1% in Root Mean Square Error (RMSE) and 27.8% in Haversine distance (HAV) compared to baseline models. The model also exhibits stronger robustness and stability in multi-step predictions while maintaining a favorable balance in computational efficiency. These results substantiate the effectiveness of metaheuristic optimization for strengthening deep learning architectures and present a computationally efficient, high-accuracy framework for vessel trajectory prediction. Full article

Empirical green’s functions (EGFs) can be extracted from the cross-correlation of ambient ocean noise and serve as the foundation for passive ocean acoustic tomography (POAT). A critical challenge in POAT is the accurate identification of propagation paths, especially in shallow water and short-range scenarios where multipath arrivals often overlap. Traditional methods relying on absolute arrival time delays are rather sensitive to environmental variability and measurement uncertainty. In this study, we propose a path identification method based on time delay differences between extracted acoustic paths, which exhibit lower sensitivity to sound speed profile (SSP) perturbations than absolute time delays. This approach provides a more robust and stable metric for distinguishing coherent arrivals. We further analyze how accumulation time and hydrophone spacing influence the extraction of coherent wavefronts and identify trade-offs in resolution and stability. The effectiveness of the proposed method is validated through both field experiments and Bellhop simulations, demonstrating consistent time delay difference patterns and improved arrival stability. The findings suggest that time delay difference-based path identification enhances robustness and provides practical guidance for optimizing POAT deployments in complex shallow water environments. Full article