Search Results (36,621)

In the era of digital economy, manufacturing industry transcends geographical space to build virtual networks. Revealing the complex network characteristics and driver paths of virtual agglomeration is of great significance for accelerating the digitalization of manufacturing. First, this paper explains the formation mechanism and proposes the model of virtual agglomeration; moreover, the paper identifies complex network characteristics. Finally, this paper constructs a driving path framework based on the “Technology–Organization–Environment” theory, and uses fuzzy set qualitative comparative analysis to identify paths. The results show that the technological platform foundation plays a core role in enhancing the level of virtual agglomeration. Differentiated combinations of organizational and environmental conditions also have a positive impact. This study provides theoretical support and practical reference for cities to accelerate virtual agglomeration according to local conditions. Full article

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

Bodanella lauterborni W.M. Zimmermann and Heribaudiella fluviatilis (Areschoug) Svedelius are members of brown algae (Phaeophyceae) that exclusively inhabit freshwater habitats. Heribaudiella fluviatilis is the most frequently reported freshwater brown alga, widely distributed in the Northern Hemisphere. In contrast, B. lauterborni, one of the rarest algae globally, has been reported in only four glacial Alpine lakes and has not been observed in nature for nearly 50 years. Since 2019, the species has been considered locally extinct at its type locality, and its presence in the other three lakes is also questionable. Here, we report the occurrence of B. lauterborni in three springs on the Vlasina Plateau (Southeast Serbia), being the first finding of the species in Southeast Europe and the fifth discovery globally in environmental conditions not previously described for the species. We also provide detailed data on the morphology, ecology, and biogeography of B. lauterborni and H. fluviatilis. Additionally, we report the non-obligate association Hildenbrandio rivularis-Heribaudielletum fluviatilis discovered in two rivers. Our findings significantly expand the known ecological and geographical range of phaeophytes, highlighting Southeast Europe as a refugium for freshwater Phaeophyceae biodiversity. Full article

Creating a dependable approach for identifying both the mass of a shuttle car and how material is distributed in it removes the need for equipment operators to manually engage the flight chain. The quantification of environmental and installation conditions and the extent of influence considering their combined contribution towards inaccurate or exclusive measurements are to that degree limited. This experimental study investigated how two different strain transducers—installed in a force-shunt configuration—respond to thermo-mechanical loads when used to determine load distribution and position. Initial observations indicated that thermal effects at the installation site contributed to measurement inaccuracies or exclusive readings. The investigation quantified the impact of environmental and installation variables on measurement accuracy and found this influence to be indirectly linked to the mechanical properties of the substrate to which the strain transducers were mounted. Mounting bolt torque was determined to exert a negligible effect on strain measurement accuracy for the custom-built strain transducers. Nonetheless, both transducers failed to consistently return to the selected baseline at the start of experiments since thermal dependence persisted at the balanced state following the first cycle of loading. The research indicated that the custom-built force-shunt strain transducers are an effective means for mapping the profile and location of coal in shuttle cars, provided that the systems are subjected to continuous and cyclic rebalancing to maintain accuracy. Full article

Background/Objectives: The use of physical and mechanical restraints and seclusion in psychiatric facilities to manage violent and aggressive behaviours has long been a contentious issue, balancing patient safety with ethical considerations. With advancements in psychiatry and increased understanding of mental illness, there have been expectations that such interventions would no longer be required; however, their use persists in clinical practice. Management policies differ across countries and are largely influenced by legal frameworks. This study aimed to identify the factors influencing the incidence of these interventions across two psychiatric facilities in Saudi Arabia and to examine associations among inpatient variables. Methods: A prospective cohort study was conducted over six months (September 2021–March 2022) across two psychiatric facilities in Saudi Arabia (Eradah Complex, n = 1120; King Fahd University Hospital (KFUH), n = 268). Data from 333 restriction events were analysed using descriptive statistics, chi-square tests, and negative binomial regression to calculate incidence rates and explore associated factors. Results: The findings revealed a complex interplay of factors related to patient characteristics and clinical and environmental conditions within the facilities. Key contributing variables included symptom deterioration and the duration of observation required. Longer observation periods were associated with certain diagnoses, particularly schizophrenia and mood disorders. Conclusions: Restraints and seclusion remain influenced by multiple interacting factors within psychiatric settings. These findings highlight the need to reduce their use and ensure they are applied cautiously, with greater emphasis on minimising patient trauma and promoting safer, person-centred care. 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

Traditional villages in ecologically fragile and multi-ethnic frontier regions are increasingly threatened by rapid urbanization and socio-economic transformation. Northwestern Yunnan, located in the longitudinal valleys of the Hengduan Mountains, represents a key cultural landscape of plateau agropastoral civilization and ethnic interaction, yet its spatial organization and clustering mechanisms remain insufficiently understood. This study develops a four-dimensional analytical framework integrating four dimensions—spatial morphology (village distribution patterns and density), geomorphological conditions (elevation, slope, and terrain features), cultural attributes (ethnic composition and historical-cultural corridors), and architectural typologies (dominant residential structure types) to examine 246 officially recognized traditional villages. Using GIS-based spatial statistics, kernel density estimation (KDE), spatial autocorrelation, and a hierarchical overlay model, the study identifies the spatial structure (distribution patterns and density gradients), environmental adaptability (relationships with elevation, slope, and hydrological conditions), and multidimensional clustering characteristics (integrated clustering intensity across four analytical dimensions) of settlements. The results reveal a highly uneven and a statistically significant clustered spatial pattern (R = 0.606, Moran’s I = 0.251, p < 0.05) characterized by a “two corridors–six clusters–multiple nodes” structure. Settlement distribution demonstrates strong coupling with mid-elevation plateau basins, river valley systems, and trade-cultural corridors shaped by the Ancient Tea Horse Road. Multidimensional integration further classifies villages into three typologies—comprehensive, specialized, and general clusters—reflecting different levels of coordination among spatial, environmental, cultural, and architectural dimensions. These findings reveal the spatial regularities and multidimensional clustering characteristics of officially recognized traditional villages in Northwestern Yunnan, and suggest that environmental setting, historical corridors, and cultural-architectural features jointly shape the current recognized heritage landscape. The proposed framework provides a context-sensitive basis for differentiated heritage conservation and rural management in mountainous multi-ethnic regions. Full article

Perovskite solar cells (PSCs) have quickly achieved certified energy conversion efficiency reaching a certified record of 27.3% for single-junction cells, while having a low mass, thin-film form factor and high specific power, which are attractive for space energy systems. However, their long-term reliability in extraterrestrial environments is not adequately ensured by terrestrial qualification routes, and standardized space-related test protocols remain insufficiently developed. This review critically summarizes the current understanding of the degradation of PSCs under the influence of key environmental factors in space—ionizing and non-ionizing radiation, thermal vacuum exposure and thermal cycling, and ultraviolet radiation AM0, as well as atmospheric oxygen in low orbits. The central task of the work is to develop and justify the need to create specialized PSCs test protocols for space applications, since existing ground standards do not reflect the multifactorial nature and extreme orbital loads. It has been shown that thermal vacuum accelerates ion migration, interphase reactions, and degassing, while AM0 UV and atomic oxygen introduce additional photochemical and oxidative mechanisms of destruction; at the same time, stressors often act synergistically and are not detected by single-factor tests. Next, the limitations of the current IEC and ISOS are discussed and an approach to their expansion is formulated through the ISOS-T-Space and ISOS-LC-Space protocols, which integrate high vacuum, AM0 lighting, extended temperature ranges and controlled particle irradiation. It is concluded that the development and interlaboratory validation of such space-oriented protocols is a key condition for the correct qualification of PSCs and targeted optimization of materials and interfaces to meet the requirements of space energy. 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

Soil erosion in mountain environments is governed by the interaction of climatic drivers, surface conditions, and geomorphic connectivity. Recently, disturbance by large herbivores has been recognized as a potentially important but poorly quantified geomorphic driver. However, the combined effects of freeze–thaw processes and ungulate disturbance on sediment production remain unclear. This study provides quantitative field-based evidence linking deer activity to hillslope sediment flux in a montane forest catchment in central Japan. A six-year dataset (2019–2025), including climatic conditions, deer detections from camera traps, understory vegetation cover, and hillslope sediment flux (<9.5 mm) was analyzed. Multiple regression analysis was conducted using daily sediment flux as the response variable and maximum 1 h rainfall, freeze–thaw frequency, and daily deer detections as explanatory variables. The results showed that deer detections had a significant positive effect on sediment flux, whereas rainfall intensity and freeze–thaw frequency did not exhibit strong independent effects. Particle-size analysis further indicated that eroded sediment was markedly coarser than the surface soil, suggesting that short-term climatic drivers alone did not control sediment transport. These findings demonstrate that biotic disturbance by large herbivores can play a dominant role in hillslope sediment flux under cold, high-elevation conditions by modifying surface conditions and sediment connectivity. From a sustainability perspective, these results highlight the importance of managing deer populations to maintain ecosystem stability, prevent land degradation, and support sustainable forest and watershed management under changing environmental conditions. Full article

Understanding the reproductive physiology of marine fish is critical for sustainable fisheries management, particularly under environmental variability. This study evaluated seasonal changes in body parameters (condition factor, Kn, and gonadosomatic index, GSI, as proxies for body condition and reproductive status, respectively) and biochemical composition (P, proteins; G, glucose; L, lipids; fatty acids; and bioenergetic ratios L/P, LG, all as proxy of integrated biochemical condition) of female gonads in Strangomera bentincki, a key pelagic species in the Humboldt Current System (HCS) off south-central Chile. Moreover, environmental factors (sea surface temperature and chlorophyll-a) were also analyzed to explore their influence on the FA profile of gonads. Female body parameters showed significant seasonal variations, with high values of Kn and GSI in autumn and spring, respectively. The biochemical composition also revealed significant seasonal variation in protein and glucose content, with the highest protein levels in winter and elevated glucose in autumn. While total lipid and energy content remained relatively stable across seasons, the L/P and L/G ratios presented seasonal variations. Similarly, the fatty acid composition showed pronounced seasonal differences, particularly with increased polyunsaturated fatty acids (e.g., DHA) in winter. The SST was the environmental factor with the greatest influence on the seasonal variations in the gonadal FA profile. Altogether, these findings suggest a partial capital breeding strategy in S. bentincki, where reproductive investment depends on both accumulated reserves and environmental conditions during reproduction. This study underscores the importance of incorporating reproductive biochemical indicators into ecosystem-based fisheries management models to improve assessments of stock health and reproductive potential. Full article

Alkali-activated cementitious materials (AACMs) are recognized as promising green building materials and a viable alternative to traditional cement due to their low carbon footprint, high durability, and superior mechanical properties. These materials primarily utilize industrial by-products such as coal gangue, steel slag, and gasification slag. The alkali activation process offers an environmentally friendly pathway for the construction industry. To address the need for the large-scale utilization of bulk solid wastes, this study established a ternary solid waste synergy system comprising coal gangue, steel slag, and gasification slag. The preparation and performance optimization of AACMs based on this system were investigated. An optimal mix proportion was identified through orthogonal experiments, and the influence of various factors on the mechanical properties at different curing ages was analyzed. The results indicate that the fluidity of all AACMs meets the requirements for general backfilling applications. Among the alkali activators, Na₂SO₄ had the smallest effect on fluidity. Under single-activator conditions, sodium silicate (water glass) and sodium hydroxide exerted a greater influence on strength development compared to anhydrous sodium sulfate. For the composite activator system, the significance of parameters affecting compressive strength followed the order: silicate modulus > alkali activator content. The maximum 28-day unconfined compressive strength reached 7.653 MPa with a mix proportion of 55% coal gangue, 45% steel slag, and 5% gasification slag, as well as a silicate modulus of 1.2 and a water glass content of 8%. This represents increases of 540.95% and 299.25% compared to the non-activated group and single-activator groups, respectively. Microstructural analysis revealed that the enhanced integrity and strength of AACMs are attributed to pore-filling by hydration products, predominantly C–S–H and C–A–S–H gels. This study successfully developed high-performance AACMs based on a coal gangue–steel slag–gasification slag ternary system, elucidating the critical regulatory role of silicate modulus in composite activators and the underlying microstructural strengthening mechanisms. The findings provide a theoretical foundation and technical support for the high-value, large-scale utilization of bulk industrial solid wastes in building materials. Full article

Oral lichen planus (OLP) is a chronic, T cell-mediated inflammatory disorder classified by the World Health Organization as an oral potentially malignant disorder (OPMD). Despite decades of research, its precise etiology remains incompletely understood and involves a complex interplay between genetic predisposition, environmental triggers, and autoimmune-like responses. This review provides a comprehensive update on OLP pathogenesis, emphasizing the role of CD8 positive cytotoxic T lymphocyte-driven basal keratinocyte apoptosis and the skewing of the T-cell receptor (TCR) repertoire. We highlight the significance of the epidermal growth factor receptor (EGFR) signaling pathway as a molecular bridge between chronic inflammation and epithelial proliferation. Furthermore, we discuss a stepwise therapeutic approach that prioritizes the management of the oral microenvironment—specifically Candida colonization and periodontal health—before escalating to immunosuppressive agents. Finally, we explore emerging precision medicine frontiers, including IL-17/IL-23 inhibitors and JAK inhibitors, alongside traditional Japanese Kampo medicine (Hange-shashin-to) and systemic adjuncts like Cepharanthine, offering a contemporary perspective on modern OLP management. This integrative framework redefines OLP not merely as a chronic inflammatory disorder, but as an immunologically sustained, microenvironment-driven, potentially malignant condition. Full article

To in situ and efficiently degrade irreversible membrane contaminants under mild conditions, SiC ceramic membranes (CMs) were imparted a catalytic ozonation functionality. A spinel-type CoFe₂O₄ catalyst was fabricated via a citrate-assisted sol–gel method and subsequently impregnated into the macropores of SiC ceramic membranes through a urea-assisted one-step combustion technique. The as-prepared catalytic membranes (CoFe₂O₄-CM) were systematically characterized by SEM, EDS, XRD and XPS techniques, and the catalytic ozonation performance was evaluated in an integrated catalytic ozonation–membrane separation system (CoFe₂O₄-CM/O₃). A flux recovery rate (FRR) of 93.33% was achieved at an ozone concentration of 70.27 mg·L⁻¹ within 30 min, indicating that a catalytic self-cleaning membrane was successfully developed. The possible catalytic reaction mechanism was elucidated by identifying reactive oxygen species generated using free radical quenching tests and electron paramagnetic resonance (EPR) analysis. This study offers a promising and environmentally friendly strategy for ceramic membrane cleaning in various membrane separation fields. Full article

Amid the global transition to carbon neutrality, tidal current energy has become a strategic sustainable energy resource due to its high predictability, power density, and environmental compatibility. Horizontal-axis turbines show great potential for marine energy harvesting, yet the large-scale commercialization of tidal turbines is severely hindered by complex wake dynamics and the lack of reliable, efficient prediction tools for out-of-distribution (OOD) operating conditions. Traditional high-fidelity CFD methods are computationally prohibitive for engineering optimization, while conventional data-driven surrogate models suffer from poor extrapolation performance, extrapolation collapse near training parameter boundaries, and the absence of uncertainty quantification. To address these bottlenecks, this study focuses on the OOD extrapolation of wake flow prediction across tip speed ratio (TSR) distributions for a single horizontal-axis tidal turbine. A CFD-generated spatiotemporal benchmark dataset is constructed for comparative OOD evaluation across various TSR conditions with 9504 total samples. A novel physics-constrained Fourier neural operator framework named TSR-FNO is proposed to improve OOD generalization. The model integrates TSR–Lipschitz regularization to suppress extrapolation collapse and Monte Carlo Dropout to provide reliable uncertainty estimation. Extensive experiments demonstrate that the proposed method effectively reduces prediction error in unseen TSR regimes, mitigates performance degradation in far-field extrapolation, and produces well-calibrated uncertainty estimates consistent with actual prediction confidence. This work provides a data-driven surrogate modeling strategy for fast and reliable wake prediction on a common CFD-generated benchmark, supporting the efficient design, array layout optimization, and engineering deployment of tidal current energy systems. Full article