Search Results (1,519)

Essential oils are multicomponent natural products with potential neurobehavioral activity, but integrated comparative studies remain limited. This study compared the essential oils of Satureja brevicalyx, Peperomia dolabriformis, and Rosmarinus officinalis in relation to their chemical profiles, predicted target interactions, preliminary acute oral safety, anxiolytic-like and antidepressant-like effects, antagonist-sensitive behavioral patterns, and exploratory serum biomarkers. Oils were characterized by GC-MS, and their constituents were screened by molecular docking against anxiety-, depression-, sleep-, and stress-related targets. Independent cohorts of male BALB/c mice received oral essential oils (25–100 mg/kg) and were assessed in anxiety-related, depression-related, and locomotor behavioral paradigms, including the elevated plus maze, light–dark box, marble burying, tail suspension, forced swim, and open field tests. Flumazenil and WAY-100635 were used to examine whether the behavioral responses were sensitive to γ-aminobutyric acid type A (GABA-A)/benzodiazepine- and serotonin 1A (5-HT1A)-related pharmacological modulation, respectively. In a preliminary 24-h acute oral toxicity screen, no mortality was observed up to 5000 mg/kg. The three oils produced anxiolytic-like and antidepressant-like effects without reducing spontaneous locomotor activity. Within its experimental block, S. brevicalyx showed the most consistent flumazenil-sensitive anxiolytic-like pattern and FDR-significant reductions in corticosterone and TNF-α, together with increased IL-4. P. dolabriformis showed a broader predicted multitarget docking profile and antagonist-sensitive behavioral attenuation compatible with mixed pathway participation. R. officinalis produced significant but more moderate behavioral effects. WAY-100635 partially attenuated the antidepressant-like effects of all three oils. These findings support differentiated but convergent functional neurobehavioral profiles among the oils. The docking, antagonist, and biomarker results should be interpreted as hypothesis-generating evidence of possible pathway involvement, supporting further validation in chronic stress models, receptor-specific assays, pharmacokinetic studies, and expanded safety evaluations. Full article

Synthetic microbial community (SynCom) biosensors are emerging from the convergence of whole-cell biosensing, synthetic ecology, and computational design. Conventional whole-cell biosensors (WCBs) use a single microbial chassis to convert analyte recognition into optical, electrochemical, gaseous, or growth-linked outputs. This compact architecture supports low-cost and field-oriented detection, but it can be limited by cellular burden, narrow dynamic range, environmental interference, and difficulty in interpreting multicomponent signals. Natural microbial consortia provide an ecological template in which sensing, transformation, stress tolerance, and response are distributed across interacting populations. SynCom biosensors seek to translate this logic into engineered platforms with defined members, assigned functional roles, designed communication, and interpretable readouts. This review traces the transition from WCBs to natural consortia and engineered multicellular biosensors, emphasizing functional partitioning, signal routing, community control, and artificial intelligence (AI)-assisted design. AI is discussed as a practical tool for narrowing design space, predicting interactions, decoding complex biosignals, and supporting adaptive operation. Key challenges remain in community stability, orthogonal communication, data quality, biosafety, standardization, and real-sample validation. Future progress will depend on parsimonious community design, reliable containment, quantitative validation, and computational workflows that connect community composition with sensing performance. Full article

Sustaining rice productivity under the dual constraints of freshwater scarcity and low-temperature stress represents a pressing challenge for high-latitude japonica rice systems worldwide. There is an urgent need to develop coupled irrigation–agronomic management strategies that jointly safeguard yield stability and water use efficiency (WUE) in cold-region rice production. In this study, a two-year field experiment was conducted in 2024–2025 on albic soil (Albic Luvisols, WRB; θfc 38.2% v/v, pH 5.80, clayey texture with poor permeability and a propensity for subsurface waterlogging) in the Sanjiang Plain, Heilongjiang Province, China (47°15′ N, 133°28′ E), with nine coupled “irrigation regime × auxiliary practice” treatments, comprising conventional continuous flooding, four-level controlled irrigation (CI) at lower thresholds of 60%, 70%, 75%, and 80% θfc, and their combinations with film mulching (FM) or a humic-acid-based soil amendment (SA). An interpretable machine-learning diagnostic framework was developed, with elastic net (EN) as the primary analytical model and random forest (RF) as a nonlinear control, to simultaneously identify core yield predictors and outlier treatments. The principal findings were: (i) The soil-amendment-coupled 75% θfc CI treatment (SACI) increased grain yield by 12.3% and reduced water input by 17.0% relative to conventional continuous flooding, with WUE reaching 1.801 kg m⁻³, a 35.3% gain over the control (p < 0.05); these improvements were consistent across both individual years (year × treatment interaction: p = 0.601; inter-year rank correlation ρ = 0.967). Lowering the CI threshold below 75% θfc significantly reduced grain yield through diminished effective-panicle retention. (ii) Multi-method consensus analysis (Kendall’s W = 0.871, p < 0.01) identified root volume at the milk stage as the most strongly and consistently associated statistical predictor of yield formation, with convergent mechanistic support from independent rhizosphere evidence (Eh, TTC reductive activity). Definitive causal validation awaits isotope-tracing experiments. (iii) The film-mulching × continuous-flooding treatment (FMCG) was diagnosed as a yield-response outlier (permutation test p = 0.003), three in situ rhizosphere measurements (redox potential, root TTC-reducing activity, and rhizosphere temperature) supported the proposed mechanism of hot–anoxic rhizospheric inhibition. Methodologically, this study develops a four-level evidence convergence framework that integrates intra-model self-consistency, cross-model (EN vs. RF) consensus, independent rhizosphere evidence, and distribution-free permutation testing, with Jackknife+ conformal prediction and companion Monte Carlo simulations (1000 replicates) used to quantify the reliability boundaries under small-sample conditions (n = 27). These findings provide an evidence-based irrigation–soil co-management strategy for cold-region rice production in Northeast China, and the proposed diagnostic paradigm offers a generalizable, reliability-quantified methodological template for interpretable small-sample modeling in multifactorial coupled field experiments. Full article

The anti-floating punching failure of basement slabs subjected to groundwater uplift remains insufficiently understood due to the complex stress state and lack of applicable design guidance. This study investigates the punching behavior of a damaged basement slab in Shenzhen, China, using a three-dimensional finite element model developed in LS-DYNA with the Concrete Damage Plasticity (CDP) model. The model was validated against field observations and experimental data, with a prediction error of less than 8%. The results show that anti-floating punching failure evolves from crack initiation in the anchorage zone to damage propagation and final penetration. Increasing the slab thickness from 400 mm to 600 mm significantly alleviated tensile damage concentration and improved stress redistribution. Increasing the concrete compressive strength from 20 MPa to 60 MPa enhanced punching resistance and delayed crack development, but promoted localized brittle failure. Enlarging the foundation pad from CT-6 to CT-9 effectively reduced stress concentration and improved the overall anti-punching performance, whereas the influence of column size was limited. A comparative assessment of three reinforcement measures further revealed their respective applicability under different engineering conditions. The study clarifies the anti-floating punching mechanism of basement slabs and provides a theoretical basis for the anti-floating design and reinforcement optimization of underground structures. Full article

Tension–tension fatigue testing of polymer matrix composites (PMCs) conducted per ASTM D3479/D3479M using rectangular specimens is widely plagued by premature crack initiation and propagation at the edges of reinforcing grip tabs, which leads to severe underestimation of the material’s actual fatigue life. While dog-bone specimen geometries have been universally adopted to mitigate this issue, and benchmark studies have validated their ability to completely eliminate grip-region failures in low-to-intermediate-strength PMCs, our preliminary work identified a critical unaddressed limitation: standardized dog-bone configurations produce highly unreliable fatigue characterization results for T800-grade and higher-strength carbon fiber-reinforced polymer (CFRP) laminates, with experimentally measured fatigue lives deviating significantly from predictions derived from classical laminate theory. To resolve this discrepancy and enable accurate fatigue performance quantification for high-strength CFRP laminates, the present work focuses specifically on the transition region geometry of dog-bone specimens, which we hypothesized to be the source of spurious premature failures in high-strength laminate testing. The study is bounded to tension–tension fatigue loading regimes relevant to high-performance structural applications of T800-grade and above CFRP laminates, with the core objective of developing an optimized geometry that eliminates premature non-gauge-section failures. First, statistical analysis of a large dataset of preliminary tests confirmed that transition region geometric parameters exert a non-negligible effect on the measured fatigue performance of advanced high-strength fiber-reinforced polymer laminates; stress concentrations induced by non-optimized geometries were identified as the root cause of premature non-gauge-section failures even in conventional dog bone specimens. We then systematically varied transition region geometric parameters, performed finite element stress modeling to quantify full-field stress distributions for each candidate geometry, and conducted parallel tension–tension fatigue tests on all designed configurations to cross-validate simulation outputs and experimental performance. Our results demonstrate that the optimized dog-bone configuration developed in this work completely eliminates all spurious non-gauge-section failure modes. Fatigue lives measured using the optimized geometry show excellent agreement with classical laminate theory predictions, enabling robust, repeatable quantification of the intrinsic fatigue life of high-strength CFRP laminates. The proposed configuration addresses the longstanding reliability gap associated with standardized dog-bone geometries for high-strength fiber-reinforced polymer fatigue characterization. Full article

Three-dimensional (3D) crop phenotyping is increasingly used to capture crop structure, but its value for crop protection is conditional rather than automatic. 3D approaches are operationally justified only when reconstructed geometry adds decision-relevant information beyond simpler 2D, spectral, scalar, or conventional baselines. This review examines 3D crop phenotyping through a reconstruction–trait–task–maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development. The literature is organized through four connected lenses: reconstruction routes that generate crop geometry, 3D traits that may alter protection reasoning, decision pathways that link traits to intervention variables, and maturity levels that distinguish static 3D models, validated phenotypic traits, process-coupled systems, protection outputs, and outcome-updated decision twins. The strongest decision-facing evidence currently comes from canopy-based dose adjustment, deposition prediction, drift reduction, and related spraying applications in which 3D traits are linked to intervention variables and field-facing comparators. Disease, stress, and architecture-aware modelling provide important but more heterogeneous evidence, while many point-cloud datasets, segmentation pipelines, neural reconstruction methods, and agricultural digital-twin frameworks remain upstream of practical crop-protection decisions because they do not yet connect 3D measurements to validated protection labels, comparator baselines, decision thresholds, intervention outputs, or outcome updating. A central conclusion is that high-fidelity 3D representation should not be conflated with decision-twin maturity. Protection-oriented digital twins require explicit coupling among synchronized crop geometry, functional or epidemiological models, decision rules, and recorded field outcomes. This review therefore identifies the evidence and reporting priorities needed to move 3D crop phenotyping toward validated, deployment-oriented, and feedback-aware crop-protection support. Full article

This paper presents a detailed case study of a semi-top-down excavation carried out for the Haowang Tower project in Shaoxing, China, where thick soft clay deposits dominate the subsurface profile. The excavation, covering approximately 10,000 m² in plan area and reaching a depth of 12.35 m, posed significant challenges due to the presence of sensitive adjacent utilities and roads. In response, an integrated design–construction strategy was adopted, combining soldier pile retaining walls with a permanent first-floor slab serving as horizontal bracing. Several innovative structural features—including load-transfer beams, stress-reinforced strips, and soil molds—were introduced to address the specific demands of the semi-top-down method in soft ground. A multi-stage numerical analysis framework was implemented, employing the Hardening-Soil (HS) model within 2D and 3D finite element analyses (PLAXIS), alongside the subgrade reaction method (FRWS2006). Predicted wall deflections, ground settlements, and structural forces were systematically compared with field measurements. The 3D analysis showed good agreement for wall deflections (within 5% of the maximum measured value), validating the approach’s effectiveness. However, the analysis over-predicted ground settlements (e.g., sewage pipe settlement was over-predicted by 60%), indicating a need for more refined settlement prediction models or parameter calibration. Based on this finding, a correction factor of 0.6–0.7 is proposed for settlement prediction when using HS parameters derived from standard drained tests. The results also highlight the importance of spatial effects and the critical role of construction sequencing. This study offers both practical insights and validated numerical tools for similar deep excavations in urban soft clay environments. Full article

Recently, mycotoxin prediction has mainly relied on meteorological data and crop physiology. The contribution of soil characteristics as additional environmental variables remains largely unexplored. A systematic literature search was carried out to analyze the latest research (from 2020 to 2025) on the relationship between soil properties (temperature, water content, pH, and electrical conductivity), fungal communities (particularly Aspergillus and Fusarium), and different crops (mainly peanut, wheat, and maize). Measurement methodologies were analyzed, with a focus on the use of in-field soil sensors in correlation studies and predictive models. Disease incidence and mycotoxin occurrence were related to stressful soil conditions, such as different pH levels, wetness or drought, and temperatures above 25 °C. Other external variables (crop and field management) must also be considered. Laboratory equipment was primarily used in correlation studies, with limited in-field sensor implementation. Although recent predictive models included soil properties as effective inputs, they mostly relied on satellite data. However, real-time conditions and fluctuations, which can be captured by in-field soil sensors, are essential for training new functional models. To monitor soil properties, IoT technologies must be considered, but their implementation is still not sufficient to collect widespread data. Therefore, groundwork is needed to fill this gap with high-quality soil data for future in-field experimentation. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Driven by increasingly severe drought stress associated with global warming, this study investigated a synthetic microbial community, SynCom-SASW01, with strong stress tolerance and plant growth-promoting potential, and systematically elucidated its mechanisms for enhancing drought resistance in wheat (Triticum aestivum L.). Dual-site field trials demonstrated that SynCom-SASW01 significantly alleviated drought-induced growth suppression, increasing grain yields by 10.42% and 8.52% at the Hohhot and Hulunbuir sites, respectively. This improvement was primarily associated with increased effective tiller number and enhanced root vigor. Physiologically, inoculation promoted root proline and glutathione accumulation and enhanced antioxidant enzyme activities, including superoxide dismutase, thereby reducing malondialdehyde levels. Environmental analyses showed that the consortium established rhizosphere “micro-reservoirs” through exopolysaccharide secretion, improving soil relative water content and the availability of alkali-hydrolyzable nitrogen and phosphorus. High-throughput sequencing revealed that SynCom-SASW01 reshaped the endosphere microbiome through early colonization priority effects, selectively enriching beneficial taxa such as Pseudomonas. Functional prediction indicated upregulated branched-chain amino acid biosynthesis, promoting osmotic adjustment and redox homeostasis. These findings provide a microbiome-based strategy for stabilizing wheat productivity in arid regions. Full article

This study focused on burn-through leakage at girth welds of mechanically lined pipe (MLP) during field service. Field failure analysis, experimental tests, and numerical simulation were combined to investigate the process parameters of seal welding and multi-pass girth butt welding. Macroscopic metallography and energy dispersive spectroscopy (EDS) of failed specimens showed that excessive welding heat input (high current) caused severe expansion of the heat-affected zone (HAZ) and significant element dilution. The results indicated that the HAZ width of the solid-wire girth weld increased markedly from 1.312 mm to 2.247 mm under high-current conditions. Meanwhile, the Fe mass fraction in the root pass sharply increased to 33.66%, while key corrosion-resistant elements such as Cr and Ni were greatly reduced, which directly led to local pitting corrosion and perforation leakage. In addition, a moving heat source model was established in Abaqus 2024 to simulate the multi-pass welding process. The results showed that strong stress concentration developed at the groove root and the interface between the backing steel pipe and corrosion-resistant liner during repeated thermal cycles. The maximum von Mises stress reached 686.56 MPa during the second butt welding pass. After final cooling, the residual hoop tensile stress and axial tensile stress at the center of the inner surface reached 500–550 MPa and 480–510 MPa, respectively. By correlating microscopic compositional evolution with the macroscopic residual stress field, this study revealed the weld failure mechanism of MLP joints. The proposed finite element method can also be used as an efficient tool to predict the effects of welding speed, current, and voltage on residual stress, providing guidance for field welding procedure optimization and pipeline structural integrity assessment. Full article

Short-term captivity is widely used in experimental studies but may unintentionally alter host-associated microbiomes, potentially confounding biological interpretation of experimental outcomes. Here, we evaluated the effects of 35 days of captivity on the gut microbiome of Fundulus heteroclitus collected from Long Island Sound (Milford, CT, USA) using 16S rRNA gene sequencing. Comparisons between Field Control (FC) and short-term Captive Treatment (CT) groups revealed a marked reduction in microbial diversity under captive conditions. Observed richness decreased approximately five-fold (Field Control: 1026 features; Captive Treatment: 221 features), and Shannon diversity declined from 8.89 to 5.93. Beta diversity analyses based on UniFrac distances demonstrated clear separation between groups, indicating substantial shifts in community composition. Taxonomic profiling revealed reduced community complexity in captive fish, with increased dominance of Proteobacteria and loss of diverse environmental taxa. Predicted enrichment of pathways associated with stress response, altered respiration, and metabolic flexibility in captivity reflects inferred functional potential rather than direct functional activity. Given the use of pooled samples with limited biological replication, these findings should be interpreted as strong community-level patterns rather than population-level inference. Collectively, these results indicate that short-term captivity alters the F. heteroclitus gut microbiome. Full article

Fatigue cracking remains a critical challenge in asphalt pavement design, yet conventional prediction methods fail to capture the fundamental damage mechanisms governing failure evolution. This study proposes an innovative residual strain-based approach to predict the fatigue life of crumb rubber modified asphalt (CRMA) mixtures. Through semi-circular bending (SCB) tests under varying aging conditions and stress ratios, a modified Burgers model was employed to decompose residual strain into residual viscoelastic strain (RVES) and residual viscous-flow strain (RVFS) components. The key innovation lies in establishing the residual strain ratio (RSR) as a damage evaluation parameter, with its plateau value (PV) serving as the independent variable in a novel fatigue prediction equation. Results demonstrate that while RVES stabilizes after initial loading, RVFS accumulation drives fatigue damage progression. The RSR-defined damage factor exhibits a distinct three-stage evolution accurately characterized by the ExpAssoc model (R² > 0.97). The proposed PV-based fatigue equation achieves prediction errors below 15% when validated against field core samples, offering a mechanistically sound and practically viable alternative to conventional phenomenological approaches. Full article

Accurate prediction of overpressured formations in deepwater is important for drilling safety and reservoir evaluation. However, conventional two-step inversion workflows are affected by cumulative errors and parameter crosstalk, which limits their ability to characterize the sharp pressure-transition interfaces at the top of overpressured zones. In this study, we propose a direct prestack nonlinear inversion method for the formation pressure coefficient ($\lambda$), a dimensionless and drilling-relevant indicator of overpressure intensity. Unlike previous exact-Zoeppritz direct inversions that target effective stress or elastic moduli, here a single formation pressure coefficient drives the pressure-sensitive rock-physics chain—linking pore pressure, effective stress, and pore-space stiffness to the seismic response—thereby reducing the number of free inversion variables. This single-parameter mapping is then coupled with the exact Zoeppritz equation to build a nonlinear prestack forward operator, helping to reduce the parameter coupling and error propagation associated with conventional multiparameter inversion workflows. To describe the typical blocky structural features of overpressured strata, a nonconvex Lp-norm (0 < p < 1) regularization is introduced as a structural prior, and a decoupled optimization strategy combining the alternating direction method of multipliers (ADMM) and iteratively reweighted least squares (IRLS) is developed for a stable solution. In a single pseudo-well synthetic test, the proposed method achieved a higher correlation coefficient and lower root mean square error (RMSE) than the indirect workflow, indicating improved agreement with the reference formation-pressure-coefficient profile. Application to field seismic data from the Yinggehai Basin, South China Sea, shows that the method produces clearer pressure-transition boundaries and pressure-coefficient profiles more consistent with the available well constraints. These results suggest that, under the tested conditions, the proposed method can provide useful geophysical support for pressure prediction and the characterization of deepwater overpressured reservoirs. Full article

This study evaluates the ecological impacts of seawater desalination discharge on coastal marine ecosystems through a sequential analytical framework linking systematic literature synthesis, field-monitoring evidence, spatial analysis, and predictive ecological modeling. The novelty of the study lies in combining multi-regional evidence from Mediterranean coastal zones, Persian Gulf waters, and Pacific coastal environments with threshold-based ecological risk assessment, thereby linking discharge-related environmental stressors with biological responses and ecosystem-function alterations. The systematic review first retained 750 studies published between 2004 and 2024 for qualitative synthesis. On this basis, 59 high-quality references with sufficient numerical information were selected for the main quantitative meta-analysis, while field-monitoring data were used to support the interpretation of distance-based discharge gradients. Spatial interpolation and hierarchical modeling were then applied to evaluate exposure–response patterns and ecological threshold behavior. The results showed that desalination facilities generated measurable ecological impacts mainly within 50–200 m of discharge points, with a critical transition distance of approximately 127 m where hypersaline conditions, typically 1.5–2.0 times ambient seawater levels, were associated with marked changes in marine community structure. Benthic assemblages showed taxon-specific responses, with mollusks and echinoderms exhibiting greater sensitivity than polychaetes and small crustaceans. Marine vegetation declined strongly under combined salinity, thermal, and chemical stress, while phosphonate-based antiscalants accumulated in filter-feeding organisms and produced bioaccumulation factors up to 42.1 times ambient levels. Ecosystem-function indicators, including microbial community composition and sediment organic matter processing, remained altered up to 300 m from discharge points, indicating that functional impacts may extend beyond the primary hypersaline plume. The predictive modeling framework further demonstrated that ecological risk decreased nonlinearly with distance and varied according to discharge intensity, local hydrodynamics, and biological sensitivity. These findings indicate that conventional uniform buffer-based assessment may underestimate the ecological footprint of desalination discharge. Sustainable desalination management should therefore adopt site-specific monitoring, species-sensitive protection thresholds, improved brine-management technologies, and adaptive mitigation strategies based on real-time environmental feedback. Full article