Search Results (5,160)

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological prototypes, operational mechanisms, and core aspects of biomimetic design. Typical water-collecting biological surfaces in nature exhibit distinctive structure–function synergy: spider silk achieves directional droplet transport via periodic spindle-knot structures, utilizing Laplace pressure difference and surface energy gradient; the desert beetle’s back features hydrophilic microstructures and a hydrophobic waxy coating, forming a fog-water collection system based on heterogeneous wettability; cactus spines enhance droplet transport efficiency through the synergy of gradient grooves and barbs; and shorebird beaks enable rapid water convergence via liquid bridge effects. These biological prototypes provide vital inspiration for the design of biomimetic water collection materials. Drawing on biological mechanisms, researchers have developed diverse biomimetic water collection materials. This review offers a theoretical reference for their structural design and performance enhancement, highlighting bio-inspiration’s core value in high-efficiency water collection material development. Additionally, this paper discusses challenges and opportunities of these materials, providing insights for advancing the engineering application of next-generation high-efficiency biomimetic water collection materials. Full article

This study investigates the characteristics and causes of a low-level wind shear (LLWS) event induced by downward momentum transport at Xining Airport, China on 5 April 2023. By utilizing Doppler Wind Lidar (DWL), Automated Weather Observing System (AWOS), and ERA5 reanalysis data, the detailed structure and synoptic-scale mechanisms of the event were analyzed. The LLWS manifested as a non-convective, meso-γ scale (2–20 km) directional wind shear, characterized by horizontal variations in wind direction. The system moved from northwest to southeast and persisted for approximately three hours. The shear zone was characterized by westerly flow to the west and easterly flow to the east, with their convergence triggering upward motion. The Range Height Indicator (RHI) and Doppler Beam Swinging (DBS) modes of the DWL clearly revealed the features of westerly downward momentum transport. Diagnostic analysis of the synoptic-scale environment reveals that a developing 300-hPa trough steered the merging of the subtropical and polar front jets. This interaction provided a robust source of momentum. The secondary circulation excited in the jet entrance region promoted active vertical motion, facilitating the exchange of momentum and energy between levels. Simultaneously, the development of the upper-level trough led to the intrusion of high potential vorticity (PV) air from the upper levels (100–300 hPa) into the middle troposphere (approximately 500 hPa), which effectively transported high-momentum air downward and dynamically induced convergence in the low-level wind field. Furthermore, the establishment of a deep dry-adiabatic mixed layer in the afternoon provided a favorable thermodynamic environment for momentum transport. These factors collectively led to the occurrence of the LLWS. This study will further deepen the understanding of the formation mechanism of momentum-driven LLWS at plateau airports, and provide a scientific basis for improving the forecasting and warning of such hazardous aviation weather events. Full article

The rapid mobilization of sediment stored behind dams, in amounts that are large relative to mean annual sediment loads, can jumpstart river restoration but can also adversely impact habitat, infrastructure, land, and water use upstream of, within, and downstream of the former impoundment. A wide range of geomorphic and engineering assessment tools were applied to help manage sediment-related risks associated with the removal of two dams from the Elwha River in Washington State and the release of roughly 21 million m³ of sediment. Each of these tools had its strengths and weaknesses, which are explored here. The processes of sediment erosion, transport and deposition were complex. No one model was able to fully simulate all these with the accuracy necessary for predicting the magnitude and timing of coarse and fine sediment release from the reservoir. Collectively, however, the model outputs provided enough information to guide the adaptive sediment management process during dam removal. When the complexity of the morphodynamic responses to dam removal and the associated risks exceeded the capacity of any one tool to adequately assess, synoptic forecasting proved useful. The lessons learned on the Elwha have provided insights into how to use a variety of modeling techniques to address sediment management issues as dam removal scale, complexity and risk increase. Full article

Objectives: To systematically analyze the expression profiles of long non-coding RNAs (lncRNAs) in serum-derived exosomes from patients with age-related hearing loss (ARHL), and to further identify key regulatory lncRNAs involved in the pathogenesis and progression of ARHL. Methods: Peripheral blood samples were collected from patients with ARHL and age-matched normal-hearing controls. Serum was separated and exosomes were extracted. The exosomes were identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and Western blot. Subsequently, total RNA was extracted from the purified exosomes for lncRNA transcriptome sequencing. Based on the sequencing results, we identified differentially expressed lncRNAs and mRNAs and conducted multi-dimensional functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway database (Reactome), and Disease Ontology (DO). Finally, four key mRNAs (THAP2, ZNF225, MED12, and RNF141) and four differentially expressed lncRNAs (DE-lncRNAs), namely MSTRG.150961.7, ENSG00000273015, MSTRG.336598.1, and ENSG00000273493, were experimentally verified by quantitative real-time polymerase chain reaction (RT-qPCR) technology. Results: Exosomes were successfully isolated from serum and confirmed by particle size, morphological examination, and the expression of exosome-labeled proteins. A total of 2874 DE-lncRNAs were identified, among which 988 were downregulated and 1886 were upregulated. Similarly, 2132 DE-mRNAs were detected, among which 882 were downregulated and 1250 were upregulated. GO analysis revealed significant enrichment in biological processes such as “phospholipid binding”, “phosphatidylinositol binding”, “phosphatase binding”, “phosphatidylinositol bisphosphate binding”, “phosphatidylinositol-4,5-bisphosphate binding”, “phosphatidylinositol-3,5-bisphosphate phosphatase activity”. KEGG is significantly enriched in signaling pathways including “Wnt signaling pathway”, “Hippo signaling pathway”, “Cushing syndrome”, and “Nucleocytoplasmic transport”. The functional annotations of Reactome were significantly enriched in biomolecular pathways including “tRNA processing”, “Cellular response to heat stress”, “Extra-nuclear estrogen signaling”, “Metabolism of non-coding RNA”, and “CTNNB1 T41 mutants aren’t phosphorylated”. DO is significantly enriched in diseases or pathological conditions such as “hepatitis”, “bacterial infectious disease”, “cystic fibrosis”, and “vasculitis”. Conclusions:THAP2, ZNF225, MED12, and RNF141 may serve as potential candidate biomarker for ARHL. Additionally, lncRNA MSTRG.150961.7, lncRNA MSTRG.336598.1, and lncRNA ENSG00000273493 may play significant roles in the pathogenesis of this condition. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

Arid and semi-arid soils represent extreme habitats where microbial life is constrained by high temperature, low water availability, salinity, and nutrient limitation, yet these ecosystems harbor unique bacterial communities that sustain key ecological processes. To explore the diversity and functional potential of prokaryotic assemblages in Algerian drylands, we compared soils from three contrasting sites: The Oasis of Djanet (RM1), the hyper-arid Tassili of Djanet desert (RM2), and the semi-arid El Ouricia forest in Sétif (RM3). Physicochemical analyses revealed strong environmental gradients: RM2 exhibited the highest pH (8.66), electrical conductivity (11.7 dS/m), and sand fraction (56%), whereas RM3 displayed the greatest moisture (10.9%), organic matter (7.6%), and calcium carbonate (20.7%) content, with RM1 generally showing intermediate levels. High-throughput 16S rRNA gene sequencing generated >60,000 effective reads per sample with sufficient coverage (>0.99). Alpha diversity indices indicated the highest bacterial richness and diversity in RM2 (Chao1 = 3144, Shannon = 10.0), while RM3 showed lower evenness and the dominance of a few taxa. Across sites, 66 phyla and 551 genera were detected, dominated by Actinobacteriota (38–45%) and Chloroflexi (13–44%), with Proteobacteria declining from RM1 (17.5%) to RM3 (3.3%). Venn analysis revealed limited overlap, with only 58 operational taxonomic units shared among all sites, suggesting highly habitat-specific communities. Predictive functional profiling (PICRUSt2, Tax4Fun, FAPROTAX) indicated metabolism as the dominant functional category (≈50% of KEGG Level-1), with carbohydrate and amino acid metabolism forming the metabolic backbone. Notably, transport functions (ABC transporters), lipid metabolism, and amino acid degradation pathways were enriched in RM2–RM3, consistent with adaptation to osmotic stress, nutrient limitation, and energy conservation under aridity. Collectively, these findings demonstrate that Algerian arid and semi-arid soils host diverse, site-specific bacterial communities whose functional repertoires are strongly shaped by soil chemistry and climate, highlighting their ecological and biotechnological potential. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

Scylla paramamosain is a commercially important crab species widely cultured in China. However, artificial breeding remains limited by the high mortality of ovigerous females and asynchronous embryo hatching. In vitro embryo hatching has emerged as a promising alternative, yet its practical feasibility and underlying molecular mechanisms have not been systematically investigated. In this study, we examined the developmental characteristics of S. paramamosain embryos under different temperature regimes and hatching modes, evaluated embryo viability following maternal death, and compared transcriptomic profiles of Zoea I larvae between in vitro and maternal hatching. Our results demonstrated that temperature had a pronounced effect on embryogenesis and survival, with 27–30 °C identified as the optimal range for development and hatching. Both low and high temperature extremes markedly reduced embryo survival. Developmental trajectories were largely comparable between in vitro and maternal hatching, confirming the reliability and feasibility of the in vitro approach. Embryos collected within 4 h after maternal death exhibited high hatching success, whereas those obtained after 8 h failed to hatch. Transcriptomic analysis revealed 3505 differentially expressed genes, including 1933 upregulated and 1572 downregulated, which were significantly enriched in pathways related to cell cycle regulation, energy metabolism, immune defense, and ion transport. These findings implied that in vitro embryos could maintain developmental competence by stabilizing genomic integrity, reallocating energy resources, and activating stress responsive mechanisms. This study provides the first comprehensive evidence supporting the feasibility of in vitro embryo hatching in S. paramamosain and offers practical insights for optimizing temperature regimes, improving the utilization of maternal resources, and advancing large scale seedstock production in crab aquaculture. Full article

In this study, a strain of green microalga adapted to the extreme environmental conditions of the Tibetan Plateau was isolated from the Lalu Wetland. The isolate was identified and tentatively designated as Micractinium sp. LL-1. Following the inoculation of strain LL-1 into tannery wastewater, the ammonia nitrogen concentration was rapidly reduced, achieving a removal efficiency of 98.7%. The maximum accumulated biomass reached 1641.68 mg/L and 1461.28 mg/L. Integrated transcriptomic and label-free quantitative proteomic approaches were employed to systematically investigate the molecular response mechanisms of LL-1 under tannery wastewater stress. Transcriptomic analysis revealed that differentially expressed genes were enriched in pathways related to cell proliferation, morphogenesis, intracellular transport, protein synthesis, photosynthesis, and redox processes. Proteomic analysis indicated that LL-1 enhances cellular and enzymatic activities, strengthens regulatory capacity, modulates key metabolic pathways, and upregulates stress-responsive proteins. Under tannery wastewater stress, LL-1 exhibits dynamic adaptation involving signal perception and metabolic reconfiguration through the coordinated regulation of multiple pathways. Specifically, ribosomal translation and nucleic acid binding regulate biosynthetic capacity; the redistribution of energy metabolism boosts photosynthetic carbon fixation and ATP generation; and membrane transport coupled with antioxidant mechanisms mitigates stress-induced damage. Collectively, this study provides theoretical insights into microalgal adaptation to complex wastewater environments and offers potential targets for strain improvement and wastewater valorization. Full article

Under the requirements of ecological civilization and rural revitalization strategies in China, studying and evaluating the development potential of rural recreational resources surrounding the urban areas of megacities is of great significance for promoting the integrated development of urban and rural areas. Based on the collection and processing of multi-source datasets, this paper proposes corresponding evaluation methods for the development potential of three types of rural recreational resources (nature-historical culture-village). It combines AHP-entropy weight combination weighting, GIS spatial analysis, and Graphab network connectivity analysis to explore and evaluate the potential of rural recreational resources within the Zhengzhou urban area, which is in Central China. It quantifies the contribution degree and development priority of potential points to the overall recreational network. The results show that the recreational resources in rural areas are abundant and have great development potential. High potential points of the natural category are concentrated in the western shallow mountainous and hilly areas, with convenient transportation and a high green coverage rate, suitable for developing as suburban forest parks. High-potential points of historical sites are close to the urban area, and should be integrated and connected with the urban leisure corridors, suitable for developing as suburban cultural parks. High-potential points of villages are suitable for creating composite stations (homestay, study, folk customs) and developing into key nodes of the recreational network. Potential points with high contribution to the overall recreational network should be prioritized for development. In the future, the optimization and development of rural recreational resources can be achieved through four paths of overall planning, key promotion, brand driving, and network collaboration. Full article

Raccoon dog fur is a commercially valuable animal product. As the scale of raccoon dog breeding continues to expand, ensuring the health of these animals has become an urgent priority. The gut microbiota plays a central role in regulating animal health; however, current research on the composition of raccoon dog gut microbiota remains limited. This study aimed to characterize changes in the gut microbiota of suckling raccoon dogs across different stages, providing a foundation for future scientific feeding practices. Fecal samples of eight lactating raccoon dogs were collected and tested for microbiota on days 14, 21, and 45. Our results showed that the richness and diversity of microbiota increased with age in suckling raccoon dogs, peaking on the 45th day. Significant separation between groups was observed in both PCoA and NMDS analyses. UPGMA analysis indicated temporal fluctuations in gut microbiota composition. At the phylum level, Firmicutes and Bacteroidetes were the dominant taxa across all stages. LEfSe analysis at the genus level showed that Bacteroides was the most enriched taxon on the 14th day, Fusobacterium on the 21st day, and Prevotella_9 on the 45th day. Tax4Fun and PICRUSt analyses identified metabolism and genetic information processing as the primary functional roles of the gut microbiota. Further investigation suggested that the microbiota may benefit raccoon dogs through membrane transport, carbohydrate metabolism, amino acid metabolism, and energy metabolism. These findings establish a theoretical basis for improving the survival rate of suckling raccoon dogs and developing scientifically informed feeding and management protocols. Full article

In medium-sized cities, daily travel often follows routine patterns, which may lead to suboptimal route choices. This study examines such trips and evaluates them to assess the influence of travel information. The research is motivated by the growing importance of sustainable urban mobility and the need to address traffic congestion, environmental concerns, and inefficient transportation choices in the city of Volos, Greece. To achieve that, a survey of two phases was performed. First, self-reported and GPS data of an examined group of 96 participants from the University of Thessaly, Volos, Greece, were collected. The data were used to evaluate the daily trips in terms of travel time, cost, and environmental friendliness. Second, a stated preference survey was designed, targeting motorized vehicle users of the examined group. The survey investigated the extent to which shared information on social media can be used to recommend a different route than the usual one or convince them to shift to a sustainable way of transportation. The analysis shows that travelers are more inclined to accept the recommended route after receiving travel information; however, this effect does not translate into choosing a sustainable mode of transport. We also found that women are more likely to change routes than men. Full article

Pervasive microplastics (MPs) and per- and polyfluoroalkyl substances (PFAS) frequently co-occur across aquatic and terrestrial environments due to shared sources, transport pathways, and persistence, yet their interaction-driven effects on environmental fate, bioavailability, and toxicity remain incompletely resolved. This review critically synthesizes current knowledge on the environmental co-occurrence of MPs and PFAS, the physicochemical mechanisms governing their interactions, and the resulting ecological and toxicological consequences across aquatic, terrestrial, and biological systems. Emphasis is placed on sorption and desorption processes; environmental modifiers such as pH, salinity, dissolved organic matter (DOM), and aging; and biological responses under combined exposure scenarios. Across laboratory and field studies, MPs–PFAS co-exposure is frequently associated with altered PFAS partitioning and enhanced organismal uptake, with reported bioaccumulation increases of up to ~2.5-fold relative to PFAS-only exposures. These changes are often accompanied by amplified oxidative stress, immune dysregulation, metabolic disturbance, and reproductive impairment, particularly in aquatic invertebrates and early life stages of fish. Evidence further indicates that the magnitude and direction of combined effects depend on polymer type, particle size, surface aging, and biological context, underscoring the highly system-specific nature of MPs–PFAS interactions. By integrating findings from environmental monitoring, laboratory toxicology, and mechanistic and modeling studies, this review identifies key knowledge gaps related to nanoplastics detection, environmentally realistic exposure conditions, sorption reversibility, and mixture toxicity assessment. Collectively, these insights highlight limitations in current single-contaminant risk frameworks and underscore the importance of incorporating MPs-mediated PFAS transport and bioavailability into exposure assessment and regulatory evaluation. Full article

When considering the adsorption effect, traditional experimental methods have faced significant challenges in obtaining the solute transport parameters for composite pollutants. Based on the adsorption test data of three types of composite pollutants collected from the Web of Science and China National Knowledge Infrastructure databases from 2014 to 2024, this study employed four commonly used machine learning models, that is, Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Decision Tree (DT) models, to establish adsorption isotherms of pollutants with liquid-phase equilibrium concentration as the horizontal coordinate and solid-phase adsorption capacity as the vertical coordinate, and systematically investigated the adsorption characteristics of combined pollutants in the porous aquifer. Subsequently, the Mean Square Errors (MSEs) and coefficients of determination, two commonly used evaluation metrics for regression models in machine learning, were chosen to estimate the prediction effect of datasets. Combined with the convection–diffusion equation, the adsorption kinetic parameters under the mutual interference of composite pollutants, namely, the retardation factor, were solved. The results show that for the adsorption isotherms of heavy metal composite pollutants, organic composite pollutants, and heavy metal and organic combined composite pollutants, SVM, BPNN, and RF models have the best prediction effect, respectively, and their MSEs are 0.032, 0.001, and 0.018. The adsorption isotherm fitting results indicate that the heavy metal composite pollutants and organic composite pollutants conform to the Freundlich model. The retardation factor of organic composite pollutants is significantly higher than that of heavy metal composite pollutants. Full article

Cerebrospinal fluid (CSF) is a key biological matrix that reflects the physiological and pathological states of the central nervous system (CNS). It supports brain function by regulating ionic balance, facilitating molecular transport, and clearing metabolic waste. In this article, we present a standardized protocol for CSF collection along with an integrative multiplatform metabolomic workflow that combines proton nuclear magnetic resonance spectroscopy (¹H-NMRS) and high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS). Integrating these complementary analytical modalities enhances metabolite coverage and improves analytical robustness, enabling a more comprehensive and reliable characterization of the CSF metabolome. This workflow supports the discovery of potential biomarkers and advances our understanding of neurochemical alterations within the CNS. Full article