Search Results (33,740)

Muskmelon (Cucumis melo) is a widely cultivated and economically important fruit crop that is severely affected by Fusarium wilt caused by Fusarium oxysporum f. sp. melonis (race 1.2) (Fom). Conventional management practices have shown limited effectiveness and pose environmental and health risks; therefore, sustainable and eco-friendly alternatives are required to manage this disease. In the present study, 23 endophytic fungal isolates belonging to eight genera were isolated from Ecballium elaterium and screened to determine antifungal potential against Fom using an in vitro antagonistic assay. Two endophytic isolates (Fusarium sp. EeR4 and Fusarium clavum EeR24) exhibited an inhibitory effect against Fom on quarter-strength PDA plates. In growth chamber experiments, F. clavum EeR24-colonized melon seedlings and significantly protected plants from wilting compared to non-colonized pathogen-challenged seedlings. Under greenhouse conditions, F. clavum EeR24 significantly improved morphological and physiological traits, including plant height, weight, number of leaves, membrane stability, photosynthesis, stomatal conductance, and transpiration, in Cucumis melo. Endophytic colonization improved catalase (56%), guaiacol peroxide (47%), and superoxide dismutase activity (25%), and increased flavonoid and phenolic content by 11–59% compared to non-colonized Fom-challenged plants. Lipid peroxidation significantly decreased by 37% and proline accumulation increased by 70% in colonized plants compared to non-colonized plants. Histochemical analysis also indicated that endophytic colonization considerably reduced the levels of H₂O₂, O₂⁻, malondialdehyde, and cell mortality in Fom-challenged plants. In addition, the culture filtrate and organic residues of F. clavum EeR24 inhibited the mycelial growth of Fom by 52–58%, respectively. Furthermore, a study on spatial colonization of the endophyte and the pathogen using GFP and RFP tagging indicated that both the endophyte and the pathogen simultaneously colonized the root tissues of C. melo; however, the endophyte significantly reduced the pathogenicity of Fom. These results suggest that endophytic F. clavum EeR24 may be developed as an effective biocontrol agent for the management of Fusarium wilt in melon plants under field conditions. Full article

Shoreline change detection from remote sensing imagery remains challenging in environments subject to water level fluctuations, as remotely sensed shoreline positions reflect instantaneous hydrodynamic states rather than true geomorphic change. In the Great Lakes, seasonal and short-term water level variations can produce apparent shoreline shifts unrelated to sediment dynamics. Reliable calibration with bathymetry and water level data can mitigate this effect, but such data are often unavailable or difficult to obtain for many coastal and lacustrine systems worldwide. To address this limitation, we proposed a morphology-based framework that quantifies geometric change between successive shoreline curves using a discrete Fréchet distance, a modified Euclidean distance and a Union distance metric. Rather than relying solely on cross-shore displacements, the approach leverages shape similarity to differentiate water-level-driven shifts from true morphological change. We evaluated the framework across three spatial scales (100 m, 500 m, and 1000 m) along 125 km of southwestern Lake Michigan coastline using 2010 and 2020 aerial imagery, benchmarking against water-level-calibrated DSAS erosion hotspots. The Fréchet distance improved monotonically with scale, achieving strong agreement at 1000 m (F1 = 0.84, Spearman $\rho$ = 0.79) but limited reliability at 100 m. While individual morphology-based metrics appeared competitive with or inferior to uncalibrated DSAS at each scale, the union of both distances substantially outperformed uncalibrated DSAS at management-relevant scales (F1 of 0.64 vs. 0.50 at 500 m and 0.79 vs. 0.42 at 1000 m), reflecting the complementary nature of shape-based and displacement-based detection. The Patient Rule Induction Method (PRIM) further identified gentle nearshore slopes and moderate separation from engineered structures as the geomorphic conditions under which the morphology-based and calibrated erosion indicators converged most closely (in-box F1 = 0.92 at 1000 m and 0.72 at 500 m). These results suggest that the proposed framework, particularly the complementary union of both metrics, provides a practical, calibration-free alternative for multiscale shoreline change screening in lacustrine and microtidal, data-limited environments, while local-scale applications still benefit from explicit water-level correction. Full article

The infection cycle of the Influenza A Virus (IAV) typically requires host factors to regulate replication and proliferation. However, the roles of these factors remain undiscovered. This study focuses on Sorting Nexin 10 (SNX10), which is involved in regulating membrane trafficking and endosomal stabilization. Our previous study identified that SNX10 facilitates the replication of human coronavirus OC43 through enhancing clathrin-mediated endocytosis. In our present study, we found that SNX10 significantly promoted IAV infection in host cells. The conditional knockout of Snx10 in mice lungs prolonged survival following IAV challenge. Mechanistically, SNX10 facilitated the production of acidic endosomal vesicles and promoted the accumulation of pro-viral autophagic structures, a process supported by the specific interaction between SNX10 and the viral NP and M2 protein of IAV. Blocking SNX10-mediated acidic endosomal vesicles and autophagosome formation demonstrated antiviral effects. Moreover, IAV infection increased SNX10 protein levels by suppressing its ubiquitination, suggesting that SNX10 could serve as a potential host-derived antiviral drug target. Full article

Nile tilapia (Oreochromis niloticus) is a major aquaculture species worldwide, yet bacterial infections remain a critical constraint to production sustainability. Although pathogen-associated dysbiosis has been widely described, most studies have focused exclusively on bacterial communities, overlooking the multi-kingdom nature of the intestinal microbiota. This study evaluated the impact of experimental Providencia vermicola infection on both prokaryotic and microeukaryotic intestinal communities under controlled conditions. Using 16S (V3–V4) and 18S (V9) rRNA amplicon sequencing, we compared healthy and infected fish and assessed taxonomic, structural, and predicted functional changes. Infection was associated with significant compositional shifts, including increased relative abundances of Bacteroidota and Proteobacteria and decreased relative abundances of Fusobacteriota and Patescibacteria. Concomitantly, microeukaryotic groups such as Protalveolata, Nematozoa, and Phragmoplastophyta were significantly reduced. Functional prediction revealed metabolic pathway reconfiguration consistent with infection-associated ecological disturbance. Together, these results suggest that the pathogen challenge is associated with coordinated changes in the intestinal microbiota as an integrated system across multiple microbial kingdoms rather than as isolated bacterial shifts. This study supports ecosystem-level interpretations of dysbiosis and highlights the importance of incorporating cross-domain analyses into health assessment strategies in aquaculture species. Full article

Open-pit mining operations rely heavily on visual inspection to identify indicators of slope instability such as surface cracks. Early identification of these geotechnical hazards enables timely safety interventions to protect both workers and assets in the event of slope failures or landslides. While computer vision (CV) approaches offer a promising avenue for autonomous crack detection, their effectiveness remains constrained by the scarcity of labelled geotechnical datasets. Deep learning (DL)-based models, in particular, require large amounts of representative training data to generalize to unseen conditions; however, collecting such data from operational mine sites is limited by safety, cost, and data confidentiality constraints. To address this challenge, this study proposes a novel hybrid game engine–generative artificial intelligence (AI) framework for large-scale dataset generation without requiring real-world training data. Leveraging a parameterized virtual environment developed in Unreal Engine 5 (UE5), the framework generates realistic images of open-pit surface cracks and enhances their fidelity and diversity using StyleGAN2-ADA. The synthesized datasets were used to train the YOLOv11 real-time object detection model and evaluated on a held-out real-world dataset of open-pit slope imagery to assess the effectiveness of the proposed framework in improving model generalizability under extreme data scarcity. Experimental results demonstrated that models trained using the proposed framework consistently outperformed the UE5 baseline, with average precision (AP) at intersection over union (IoU) thresholds of 0.5 and [0.5:0.95] increasing from 0.792 to 0.922 (+16.4%) and 0.536 to 0.722 (+34.7%), respectively, across the best-performing configurations. These findings demonstrate the effectiveness of hybrid generative AI frameworks in mitigating data scarcity in CV applications and supporting the development of scalable automated slope monitoring systems for improved worker safety and operational efficiency in open-pit mining. Full article

Private set intersection (PSI) is a fundamental cryptographic task that allows two mutually distrusting parties, each holding a private set of elements, to jointly compute the intersection of their sets. It ensures a symmetric information structure where neither party gains any knowledge about the other’s elements beyond those in the shared intersection. Traditional PSI protocols are primarily designed for static settings, which limits their applicability and efficiency in dynamic scenarios where input sets continuously evolve. To address this challenge, the notion of updatable PSI (UPSI) was introduced, enabling repeated PSI computations over changing inputs while preserving the symmetric privacy guarantees between participants. Despite the numerous recent advancements in UPSI research, it still suffers from significant communication overhead. In this paper, we address this challenge by introducing LcUPSI (low-communication UPSI), a new updatable PSI protocol that achieves remarkably low communication overhead. We formally prove that LcUPSI is secure in the semi-honest model. Furthermore, we compare the LcUPSI protocol with the state-of-the-art UPSI protocol BMSTZ24 (ASIACRYPT). The results demonstrate that LcUPSI significantly reduces communication overhead, highlighting its advantages in low-bandwidth conditions. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

Ship detection in remote sensing imagery serves as a cornerstone of modern maritime surveillance. Existing visible light detectors suffer from severe performance degradation in adverse environmental conditions (e.g., fog, low light) due to domain gaps. Traditional global enhancement methods often lack adaptability, leading to “negative transfer”—where artifacts are introduced into clean images or mismatched with degradation types. To address these challenges, we propose CLEAR (Cognitive Large Language Model (LLM)-Empowered Adaptive Restoration) framework. Inspired by the dual-process theory of cognition, we introduce a dynamic switching mechanism between fast perception and deep reasoning. Rather than processing all images indiscriminately, it utilizes a hybrid gating mechanism to efficiently filter nominal samples, triggering Vision–Language Model (VLM) only when necessary to diagnose degradation and dispatch targeted restoration operators. Extensive experiments on the constructed HRSC-Robust dataset demonstrate that CLEAR achieves an overall mean Average Precision (mAP) at 0.5 Intersection-over-Union (IoU) of 86.92%, outperforming the baseline by 7.74%. Notably, it establishes a “fail-safe” mechanism for optical degradations. By adaptively resolving fog and low-light, it effectively mitigates detector blindness—exemplified by a doubled Recall rate (52.52%) in dark scenarios. Furthermore, a confidence-based sparse triggering strategy ensures operational efficiency, maintaining a throughput of ~11.8 FPS in nominal conditions. This work validates the potential of VLMs for interpretable and robust remote sensing tasks. Full article

The large-scale integration of distributed energy resources poses numerous challenges to distribution networks. At present, multi-terminal flexible interconnection has become a key development trend for active distribution networks integrated with high-penetration distributed energy resources. Conventional unified power flow controllers (UPFCs) are mainly designed for high-voltage transmission networks and lack distribution-adapted control strategies, making it difficult for them to meet the networking requirements for multi-terminal interconnection. Moreover, most existing studies still focus on two-terminal devices, soft open points and improved UPFC topologies for transmission networks. Existing multi-port schemes mostly adopt only shunt-side structures without series compensation branches, which fail to regulate voltage magnitude and phase difference, thus failing to suppress closing inrush currents and mitigate busbar voltage sags. Meanwhile, such schemes struggle with three-phase imbalance, feeder load imbalance and bidirectional power flow fluctuations in distribution networks, and lack adaptive power allocation capability among multiple ports. To solve the above problems, this paper proposes a VSC-based series–shunt hybrid multi-terminal flexible interconnection converter. The proposed topology consists of one series-side VSC and n − 1 shunt-side VSCs connected through a common DC capacitor; it removes the shunt-side transformer, and effectively reduces cost and volume, while achieving phase shifting, voltage regulation and power flow control. Meanwhile, dual closed-loop PI cross-decoupling control and a flexible closing strategy are adopted to independently regulate the active and reactive power of each feeder, adapt to three-phase imbalance and load imbalance conditions, suppress inrush currents, and realize flexible power mutual support among multiple ports, thereby significantly enhancing adaptability to distribution networks. Full article

High energy consumption in winter greenhouses poses a challenge to agricultural sustainability in Northern China, where heating costs typically account for 40–60% of total operating expenses. This study integrated a non-linear cost–volume–profit (CVP) analysis and data envelopment analysis (DEA) to balance cucumber yields with escalating energy costs. A single-season, single-factor experiment was conducted using insulated greenhouse compartments to evaluate four night temperature gradients (10 °C, 13 °C, 16 °C, and 19 °C). Results showed that although the 19 °C treatment (T3) achieved the highest marketable yield, it was associated with lower economic return because heating costs increased disproportionately. Among the four tested nighttime temperatures, the 16 °C treatment (T2) showed the most favorable observed combination of yield, net profit, and DEA-based efficiency indicators under the present experimental conditions. However, because the experiment was conducted in a single season within a compartment-based greenhouse system and the CVP relationship was fitted using treatment-level means, this result should be interpreted as a preliminary and condition-specific finding rather than as definitive evidence of a universal optimum temperature. Accordingly, the integrated bio-economic framework presented here is best viewed as an analytical prototype that merits further validation across multiple seasons, cultivars, and greenhouse systems. Full article

Despite significant advancements in communication systems, inherent limitations persist in providing reliable data transmission for emerging applications with massive data exchanges. Semantic communication offers promising solutions by extracting and transmitting meaningful information rather than raw bit sequences. However, it faces challenges from high mobility and dynamic channel conditions in wireless environments. In this paper, we design a ground-to-air network architecture that integrates a rotary-wing unmanned aerial vehicle (UAV) and ground terminals to maximize semantic transmission efficiency while maintaining low energy consumption. This approach leverages the high mobility of the UAV for flexible deployment and the data reduction capabilities of semantic communication. Therefore, we formulate a multi-objective optimization problem to simultaneously balance the total semantic transmission rate and the UAV propulsion energy consumption by jointly optimizing the UAV hovering position, semantic encoding lengths, and resource block (RB) allocation. The problem is complex, with mixed continuous and discrete variables, which necessitates an advanced optimization method. To address these challenges, we propose a novel greedy-enhanced adaptive multi-objective cellular genetic algorithm (GEAMOCell), which utilizes an adaptive neighborhood selection mechanism to balance exploration and exploitation, and employs a crowding-guided archive feedback mechanism to maintain population diversity. The simulation results demonstrate that the proposed GEAMOCell algorithm outperforms baseline algorithms in terms of convergence, semantic transmission rate, and energy efficiency. Full article

Xanthomonas campestris pv. campestris (Xcc) is the causal agent of black rot, one of the most destructive bacterial diseases on crucifer crops, resulting in yield losses of up to 90%. The aim of this study was to identify novel endophytic bacteria from cabbages with potential biocontrol agents against Xcc. A total of sixty-five isolates were evaluated for plant growth-promoting characters and antagonistic activity, from which ten were selected for in planta assays and subsequently validated under field conditions. Pseudomonas synxantha BR25/2 consistently demonstrated the highest efficacy, reducing disease severity by 81.12% in in planta trials and 33.5% in field trials, thereby comparing to copper-based control measures. Additionally, Pseudomonas synxantha BR25/2 significantly enhanced yield parameters, including a 31.8% increase in head weight under field conditions. Whole-genome sequencing identified biosynthetic gene clusters, including siderophores, phenazines, and non-ribosomal peptide synthetases, notably a coronatine-like NRPS and a fengycin-like betalactone, suggesting an extensive antimicrobial potential of metabolites. This represents the first report of P. synxantha exhibiting control over Xcc. For commercial application, large-scale fermentation and encapsulation techniques are recommended to overcome shelf-life challenges, providing a sustainable microbial solution for crucifer production. Full article

This review critically evaluates current PA6 recycling technologies, with a specific focus on caprolactam-oriented chemical recycling pathways, including hydrolysis, pyrolysis, glycolysis, ammonolysis, hydrothermal treatment, ionic-liquid-assisted depolymerization, and microwave-assisted processes. Reported caprolactam yields vary significantly depending on reaction conditions and catalyst systems, ranging from below 60 wt% in conventional hydrolysis to above 90 wt% under optimized catalytic, hydrothermal, or microwave-assisted conditions. Among these approaches, microwave-assisted hydrolysis and catalytic depolymerization have emerged as particularly promising, offering substantially reduced reaction times (minutes rather than hours), improved energy efficiency, and high monomer selectivity at moderate temperatures (typically 200–350 °C). This review integrates kinetic modeling approaches, analytical methods for monitoring depolymerization, and downstream separation considerations that govern monomer purity and recyclability. Key challenges, including energy demand, feedstock contamination, scalability, and economic competitiveness, are critically discussed in relation to industrial implementation. Overall, hydrolysis-based and microwave-assisted chemical recycling routes are the most viable pathways for closed-loop recycling of PA6. Future progress will rely on integrated reaction–separation–repolymerization designs, catalyst optimization, and process intensification to enable sustainable and industrially relevant PA6 circularity. Full article

This study illuminates fundamental questions in behavioral science through advanced machine learning methodologies applied to large-scale public opinion data. Drawing on Kahneman and Tversky’s dual-process theory and Sunstein’s nudge architecture, we employ hierarchical unsupervised clustering and supervised predictive models to detect cognitive biases—loss aversion, availability heuristic, and partisan motivated reasoning—embedded within a nationally representative survey of 5022 American respondents. Our primary methodological contribution is a hierarchical two-stage clustering framework that uncovers latent opinion structures without imposing a priori partisan categories, permitting discovery of cross-cutting cleavages invisible to conventional survey analysis. Three principal findings emerge: (1) loss aversion is empirically confirmed in prospective economic perception, with pessimists outnumbering optimists at a 1.14:1 ratio even among respondents rating current conditions positively; (2) partisan motivated reasoning produces a 13.15 percentage-point perception gap among individuals with identical financial circumstances; and (3) multi-platform digital engagement is associated with reduced partisan bias, providing evidence that challenges simple echo chamber assumptions. Crime safety perception emerges as the strongest predictor of economic bias, surpassing party affiliation, and substantiating availability heuristic dominance in political cognition. These findings carry implications for democratic accountability, platform governance, and the ethics of AI-augmented behavioral analysis in an era of affective polarization. Full article

The low participation of men in primary education is a persistent and structural phenomenon that cannot be adequately understood through homogeneous views of teachers’ motivations and experiences. This study is conducted in the Chilean context, which is characterized by a highly feminized teaching workforce and persistent challenges related to working conditions, social valuation of teaching, and teacher retention. It aims to analyze profiles of male primary school teachers, considering their motivations, perceptions, and the meanings they attribute to the teaching profession. A sequential explanatory mixed-methods design (QUAN → qual) was employed. First, 144 male in-service primary teachers completed the FIT-Choice scale and a latent class analysis was conducted. Subsequently, in-depth interviews were carried out with an intentionally selected subsample of 20 teachers, which were analyzed using qualitative content analysis. Three distinct motivational profiles were identified: altruistic, pragmatic, and critical. The qualitative findings complemented these profiles, highlighting the influence of personal trajectories and working conditions on teachers’ career choice and retention in the profession. Overall, the findings suggest that policies for training, support, and professional induction must recognize teacher heterogeneity and promote inclusive working environments, moving beyond approaches that focus exclusively on increasing the number of men in primary education. Implications for the design of policies aimed at attracting and retaining male primary school teachers are discussed. Full article