Search Results (27,263)

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets. Full article

Collagen is well-known as an essential and structural protein in the body and is classified into many types, with different roles. Type I collagen is the most abundant, offering firmness, elasticity, and resistance to the skin. Starting from natural resources such as calf, American buffalo hide, turkey, and perch skin, this research aims to develop a comparative study between the porous matrices obtained from collagen, extracted in the form of gel, with potential medical use. The extracted collagen gels were analyzed for their proximate analysis. The structural conformation of the gels was confirmed using circular dichroism measurements. The extracted collagen gels were dried using a freeze dryer in the form of porous matrices, and structural analyses were performed using FT-IR. Further, the collagen scaffolds were assessed for biocompatibility using an XTT assay. The water swelling behavior, the morphology, and the thermal stability of the collagen matrices were determined. The collagen porous matrices presented good antimicrobial activity, especially COLL_P, which presented the highest inhibition zone, making them suitable for biomedical uses. Overall, this study provides a method for producing collagen matrices from various sources for biomedical applications. Full article

Hybridization and polyploidization are key evolutionary forces shaping fish biodiversity. But their interaction with environmental factors, such as temperature, remains poorly understood. This study examined how maternal genome composition and incubation water temperature influence the hatching success, ploidy structure, morphology and early growth of polyploid Cobitis larvae. Crosses were performed using triploid Cobitis females with three genomic compositions (EEN, EET and ETN), representing combinations of genomes from C. elongatoides (E), C. taenia (T) and C. tanaitica (N), and diploid C. taenia males as sperm donors. Fertilized eggs were incubated at 18 °C, 22 °C and 28 °C. Triploid and tetraploid offspring occurred in comparable proportions on average across all groups, but developmental abnormalities were significantly more observed in tetraploid larvae. Females with EET and ETN genomes achieved higher hatching success than those with the EEN genome. Temperature had a pronounced effect on developmental timing and success: hatching occurred earliest at 28 °C, but survival decreased and abnormalities were most frequent. These results highlight genome- and temperature-dependent trade-offs in early development of Cobitis hybrids, providing new insight into reproductive dynamics and the potential resilience of polyploid systems under climate warming. Full article

Supramolecular hydrogels formed by the self-assembly of low-molecular-weight (LMW) agents are promising next-generation biomaterials for drug delivery, tissue engineering, and regenerative medicine. Phenylalanine (Phe) derivatives have emerged as a privileged class of LMW supramolecular gelators due to their strong propensity to self-assemble into emergent hydrogel networks with demonstrated biocompatibility. We have previously reported a series of cationic Phe-derived gelators in which fluorenylmethoxycarbonyl (Fmoc) phenylalanine (Phe), 3-fluorophenylalanine (3F-Phe), and pentafluorophenylalanine (F₅-Phe) are functionalized at the C-terminus with diaminopropane (DAP). These gelators (Fmoc-Phe-DAP, Fmoc-3F-Phe-DAP, and Fmoc-F₅-Phe-DAP) are water-soluble and undergo spontaneous self-assembly and gelation upon an increase in the ionic strength of the solution caused by addition of sodium chloride. Herein, we report the effects of pH on the self-assembly and gelation of Fmoc-Phe-DAP, Fmoc-3F-Phe-DAP, and Fmoc-F₅-Phe-DAP. We also describe the effects that pH has on the emergent properties of these hydrogel networks, including assembly morphology and hydrogel viscoelasticity. These studies indicate that pH has varying effects on the properties of the hydrogels that are also dependent on the molecular structure of the Fmoc-Phe-DAP derivative. Fmoc-Phe-DAP hydrogels are highly sensitive to changes in solvent pH, forming strong hydrogels only near neutral pH. In contrast, hydrogels of Phe derivatives with fluorinated side chains (Fmoc-3F-Phe-DAP and Fmoc-F5-Phe-DAP) have consistent emergent viscoelastic properties across a wider range of acidic to basic pH values. Full article

In CO₂ flooding technology, the injection tubing string is prone to intense fluid–structure interaction (FSI) vibrations and water hammer effects during transient start-up and shutdown processes, which seriously threaten injection safety. This study is based on a four-equation FSI model and employs the method of characteristics (MOC) and numerical simulations to analyze the dynamic responses of fluid velocity, pressure, axial vibration velocity, and additional stress in the tubing string during start-up and shutdown processes. The results indicate that the most severe vibrations occur within 12 s after pump start-up, with a significant increase in the amplitude of axial additional stress. Increasing the injection rate leads to a notable rise in the peak water hammer pressure. Extending the shutdown time effectively reduces impact loads. This research provides an important theoretical basis for the safe design and operational control of the CO₂ injection wells. It is recommended to adopt operational strategies such as low rate, slow start-up, and reasonably extended shutdown times to mitigate vibration hazards. Full article

Hydraulic fracturing is a technique employed to weaken rock formations during hard rock excavation. This study aims to investigate the impact of hydraulic fracturing on crack propagation in rock walls and its subsequent effect on the load borne by roadheaders during the cutting of pre-cracked rock. A three-dimensional model for the crack growth process in rock walls under hydraulic fracturing is developed using the CFD-DEM (Computational Fluid Dynamics–Discrete Element Method) two-way fluid–structure coupling approach. The results indicate that crack propagation under hydraulic fracturing occurs in four distinct phases: the initiation of the main crack, the further development of the main crack, the fine cracking phase, and the retardation of the main crack with the subsequent expansion of secondary cracks. The study analyzes the influence of pore size and water injection pressure on crack growth. It is observed that an increase in pore size and injection pressure within a certain range results in a nonlinear increase in crack propagation. Specifically, when the hydraulic fracturing aperture expands from 85 mm to 100 mm, the number of fracture bonds increases by 56.2%. Similarly, as water injection pressure rises from 25 MPa to 40 MPa, the number of broken bonds increases by 153.9%. The force exerted on rock with pre-existing cracks is found to be 9.05% lower compared to unfractured rock, with the average forces in the Z and Y directions reduced by 11.46% and 7.2%, respectively. However, the average force in the X direction increases by 5.49%. These findings provide a valuable reference for optimizing hydraulic fracturing procedures in hard rock excavation. Full article

The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols. Full article

Recently developed quantitative structure–activity relationship (QSAR) prediction uses machine learning techniques with analytical signals from the full scan of mass spectra as input, and does not need exhaustive structural determination to assess unknown compounds. The QSAR approach assumes that a mass spectral pattern reflects the structure of a target chemical. However, the relationship between the spectrum and structure is complex, and requirement of its interpretation could restrict further development of QSAR prediction methods based on analytical signals. In this study, whether gas chromatography-electron-impact ionization-mass spectrometry (GC-EI-MS) data contain meaningful structural information that assists QSAR prediction was determined by comparing it with the traditional molecular descriptor used in QSAR prediction. Four molecular descriptors were used: ECFP6, topological descriptor in CDK, MACCS key, and PubChem fingerprint. The predictive performance of QSAR based on analytical and molecular descriptors was evaluated in terms of molecular weight, log K_o-w, boiling point, melting point, water solubility, and two oral toxicities in rats and mice. The influential variables were further investigated by comparing analytical-descriptor-based and linear regression models using simple indicators of the mass spectrum. The investigation indicated that the analytical and molecular descriptors preserved structural information differently. However, their performance was comparable. The analytical-descriptor-based approach predicted the physicochemical properties and toxicities of structurally unknown chemicals, which was beyond the scope of the molecular-descriptor-based approach. The QSAR approach based on analytical signals is valuable for evaluating unknown chemicals in many scenarios. Full article

Hybrid natural/synthetic fiber laminates were examined as a practical process to cut mass, reduce material footprint, and meet structural demands while addressing sustainability targets. Yet direct, like-for-like comparisons generated under a single process and accompanied by durability measurements were limited, leaving design choices uncertain. This study aimed to fabricate and benchmark five representative laminates—C1: flax/epoxy, C2: jute/glass/epoxy, C3: hemp/carbon/epoxy, C4: flax/glass/bio-epoxy, and C5: kenaf/basalt/polyester—under a controlled hot-press schedule with a fixed cavity and verified fiber volume fraction. Panels were characterized using ASTM D3039 tension, ASTM D790 flexure, instrumented impact, 168 h water immersion, and thermogravimetric mass retention. The results were normalized to enable direct multi-criteria comparison, and a model was calibrated to predict tensile strength. C3 delivered the highest strengths (tension ≈ 120 MPa; flexure ≈ 126 MPa), while C5 showed the greatest impact capacity (≈60 kJ/m²). End-of-test water uptake at 168 h was C1 ≈ 3.4%, C2 ≈ 2.6%, C3 ≈ 1.4%, C4 ≈ 2.1%, and C5 ≈ 2.3%. The tensile predictor was fitted to panel means, with an R² of 0.988, and maintained an R² of 0.96 under leave-one-configuration-out testing. These results indicated that carbon-containing hybrids played the most critical roles in terms of stiffness, with kenaf/basalt being most suitable for stiffness-critical components at a similar density, and flax/glass with a bio-resin maximized the sustainability score while maintaining adequate strength. Future research should focus on enhancing specific strength at high renewable content through interface treatments, and extended modeling to improve flexure and impact responses. Full article

In this work, nanofibers and cellulose nanocrystals from the native Amazonian bamboo Guadua weberbabeuri were used in structural cementitious composites. Through the preparation of bamboo nanofibers—bleached cellulose pulp (BCP) and cellulose nanocrystals (CNC), as well as obtaining shredded bamboo (SB) and delignified cellulose pulp (DCP)—the additions corresponding to the additive nanomaterials were characterized with physical tests such as water absorption, specific mass, void index, and dimensional variation. A mechanical tensile strength test was carried out at 28 days, with an incorporation content of 0.40% of mass in relation to the cement. The results indicated, in relation to the control, improvement in the physical properties, especially in the additions with nanofibers and cellulose nanocrystals. For the mechanical tensile strength tests, the indicator allowed an increase of 14.60% with the addition of nanofibers and 12.70% in the addition of nanocrystals. Therefore, with the execution carried out, it could be seen that the incorporation was able to generate optimization in the joint performance of the materials under analysis, reinforcing the practices and ideals arising from civil engineering, nanotechnology, and sustainability. Full article

To address the issues of zero-valent iron Fe(0) passivation and limited nitrogen and phosphorus removal in constructed wetlands (CWs), this study investigated the enhancement effect of two carbon materials—activated carbon (AC) obtained through high-temperature pyrolysis and biochar (BC) obtained through low-temperature pyrolysis—when coupled with Fe(0). Four systems were set up: control (CW-C), Fe(0) alone (CW-Fe), Fe(0) with AC (CW-FeAC), and Fe(0) with BC (CW-FeBC). Evaluations covered wastewater treatment performance, microbial community structure, and functional gene abundance. Results showed that iron–carbon coupling significantly improved nitrogen and phosphorus removal, with the CW-FeAC system performing best, achieving 58% total nitrogen (TN) and 90% total phosphorus (TP) removal. This enhancement was attributed to AC’s high conductivity, which strengthened iron–carbon micro-electrolysis, accelerated Fe(0) corrosion, and enabled continuous Fe²⁺/Fe³⁺ release, supplying electrons for denitrification and phosphorus precipitation. Microbial analysis indicated that iron–carbon coupling markedly reshaped community structure, enriching key genera such as Thiobacillus (33.8%) and Geobacter (12.5%) in CW-FeAC. Functional gene analysis further confirmed higher abundances of denitrification (napA/narG→nirS→nosZ) and iron metabolism genes (feoA/feoB), suggesting enhanced nitrogen-iron cycling. This study clarifies the mechanisms by which iron–carbon coupling improves nitrogen and phosphorus performance in CWs and highlights the superiority of AC over BC in facilitating electron transfer and functional microorganism enrichment, providing a basis for the design of enhanced CW systems treating low-carbon-nitrogen-ratio wastewater, such as secondary effluent or lightly polluted surface water. Full article

This study explores the use of a homemade mung bean protein extract solution (MP) as the moisture source in high-moisture extrusion to produce pea–mung bean composite textured protein (PMP). Single-factor experiments assessed the effects of MP addition amount (30–70%), screw speed (140–220 rpm), and extrusion temperature (140–180 °C) on the textural, physicochemical, and structural properties, followed by optimization using response surface methodology (RSM). MP addition amounts between 50% and 60% promoted higher surface hydrophobicity, a higher disulfide bond content, more ordered secondary structures, and a higher intrinsic fluorescence, accompanied by improved water- and oil-holding capacities, bulk density, and texturization degree (p < 0.05). Screw speeds of 160–180 rpm enhanced texturization and texture via increased shear and reduced residence time, whereas higher extrusion temperatures darkened the color (Maillard browning) and reduced texturization and the bulk density. RSM found that the optimal conditions were 53% MP, 160 rpm, and 150 °C, yielding a theoretical maximum texturization degree of 1.55, which was experimentally validated (1.53 ± 0.02). These findings support MP as an effective green moisture source to tailor the structure and functionality of pea-based high-moisture extrudates. Future work will integrate calibrated SME, sensory evaluation, and application testing in meat-analog formats. Full article

With increasing attention to health, plant protein products have gained significant market potential due to their growing consumer demand. This study researches the influence of ultrasonic treatment on the structure and function of pea–oat composite protein gel (POPG) to enhance its elasticity and thermal stability. The ultrasonic treatment parameters were regulated to power (200–600 W for 30 min) and ultrasonic time (20–40 min at 400 W) during the preparation of POPG, and the properties and structure, including gel strength, rheological analysis, water-holding capacity (WHC), thermal characteristics, fluorescence performance, and microstructure, were further evaluated. The results showed that the POPG samples exhibited optimal values in WHC, gel strength, surface hydrophobicity, free sulfhydryl amount, and endogenous fluorescence at 400 W ultrasonic for 30 min compared with the untreated POPG. Rheological analysis indicated that POPG displayed the highest storage modulus and improved viscoelasticity. Ultrasonication resulted in an augmentation in β-sheet content, hence creating a more compact network structure. DSC and TGA revealed improved thermal stability, while SEM and CLSM exhibited a homogeneous and firm gel structure of POPG. This research offers the theory that ultrasonic technology can improve the performance of plant-based composite gels. Full article

The southeastern United States (US) is known for its highly productive forests, but they are under intense threat from increasing climate-induced windstorms like hurricanes and tornadoes. This study explored the effectiveness of unmanned aerial vehicles (UAVs) equipped with Light Detection and Ranging (LiDAR) to detect, classify, and map windstorm damage in ten pine-dominated forest stands (10–20 acres each). Three classification techniques, Random Forest (RF), Maximum Likelihood (ML), and Decision Tree (DT), were tested on two datasets: RGB imagery integrated with LiDAR-derived Canopy Height Model (CHM) and without LiDAR-CHM. Using LiDAR-CHM integrated datasets, RF achieved an average Overall Accuracy (OA) of 94.52% and a kappa coefficient (k) of 0.92, followed by ML (average OA = 89.52% and k = 0.85), and DT (average OA = 81.78% and k = 0.75). The results showed that RF consistently outperformed ML and DT in classification accuracy across all sites. Without LiDAR-CHM, the performance of all classifiers significantly declined, underscoring the importance of structural data in distinguishing among the classification categories (downed trees, standing trees, ground, and water). These findings highlight the role of UAV-derived LiDAR-CHM in improving classification accuracy for assessing the impact of windstorm damage on forest stands. Full article

Access to clean, hygienic, and sufficient potable water is a concern in many countries. To ensure this, asset management, planning, and structured pipe renewal are crucial in providing an adequate level of service. However, there is a significant backlog in municipal pipe renewal, which needs to be addressed to raise the standard of potable water supply to an acceptable level in countries across most continents. Therefore, the objective of this research was to improve decision-making to reduce this backlog. Competent personnel are a scarce resource and not easily replaced. Standardized decision-making is considered an efficient approach to addressing the shortage of skilled personnel in pipe renewal. However, its effectiveness depends on its adaptability to the varying complexity and scale of such projects during implementation. This research is based on a literature review that explores decision theories, project definitions, and project models, and compares the typical characteristics of pipe renewal projects with those of other infrastructure projects. The research highlights that structured and standardized decision-making processes are essential to ensure appropriate asset management of the pipe network and sufficient pipe renewal. The main outcome of this research is a tailored project model that supports better front-end decision-making in pipe renewal projects through improved information flow. Full article