Search Results (881)

Morphological traits, as core components of functional traits, are fundamental in determining environmental adaptability. However, under climate warming, the adaptive morphological changes and associated ecological risks of locust populations migrating to higher altitudes remain poorly understood. Here, we investigated Calliptamus italicus, the dominant locust species in the desert steppes of the Ili River Basin, to explore the response patterns of its morphological functional traits along an altitudinal gradient and their relationships with environmental factors. Morphological measurements revealed that forewing area, width, and length, as well as hindwing width, exhibited highly significant positive correlations with altitude (p < 0.01); in contrast, body length, head width, head height, pronotum length, pronotum width, hind femur length, and hind tibia length displayed significant negative correlations with altitude (p < 0.05). All morphological indicators presented highly significant sexual dimorphism (p < 0.001). Ratio analysis showed that the pronotum width-to-head width ratio (M/C), pronotum height-to-head width ratio (H/C), and forewing length-to-hind tibia length ratio (E/F) were significantly positively correlated with the altitudinal gradient (p < 0.05), with all ratios exhibiting significant sexual differences (p < 0.05). Random Forest analysis showed that PC1 (75.5% of variation) reflected traits for feeding, jumping, and reproduction, whereas PC2 (5.6%) represented flight-related traits, with significant sexual dimorphism. This study demonstrates that trait variation in C. italicus along an altitudinal gradient is closely linked to environmental factors. Our findings provide critical data for predicting habitat adaptation responses in locust populations, thereby enhancing the precision and efficacy of locust plague management and contributing to the conservation and restoration of desert steppe ecosystems. Full article

The Third Plague Pandemic originated in 19th-century Yunnan, China, yet the confluence of factors that enabled the pandemic strain Yersinia pestis 1.ORI to emerge and spread globally remains unclear. Using a One Health framework, this study investigates how human-driven ecological and socioeconomic changes disrupted zoonotic barriers in Yunnan. We conduct an interdisciplinary historical analysis, triangulating evidence from Qing dynasty gazetteers, environmental reconstructions, and biological data on plague ecology, including host–vector dynamics, to model conditions for spillover and spread and to build a convergent, validated case. The analysis identifies a mid-19th-century convergence that created a high-risk interface: widespread deforestation from mining and agriculture, rapid population growth, increased synanthropic rat densities, and the turmoil of the Panthay Rebellion. Socioeconomic stressors—labour migration into mining valleys, currency devaluation undermining food security, and comorbidities such as malnutrition, heavy metal contamination, and opium use—may have further increased host susceptibility. This socio-ecological context catalysed spillover and establishment of the 1.ORI strain in commensal rat populations. The findings show the pandemic’s origin reflects spatiotemporal convergence rather than a single cause, while noting uncertainty in quantifying historical ecological and health parameters; the case offers a framework for assessing contemporary pandemic risks. It underscores how layered pressures operate across timescales. Full article

Yersinia pestis is the etiological agent of plague, a severe and often fatal disease in humans when left untreated. Because of the high genetic clonality of Y. pestis, high-resolution genotyping assays are necessary to differentiate between individual strains. Here, we report on the development and validation of a robust and reproducible core-genome multilocus sequence typing (cgMLST) assay for Y. pestis comprising 3139 gene targets, enabling high-resolution typing at the strain level. The assay was validated using 222 publicly available Y. pestis genomes, including 45 recently sequenced outbreak isolates from Madagascar and 21 isolates from Mongolia. The cgMLST analysis revealed primary clustering aligned with known biovar-associated branches and sub-branches. Additional geographically structured sub-clusters illustrate its application for regional diversification analysis. Yersinia pestis strains from different geographic regions were clearly distinguished, consistent with spatial clustering. Within the analyzed dataset, closely related or epidemiologically linked strains differed by zero to three alleles, suggesting this range as an operational reference for identifying highly similar isolates. The cgMLST showed clustering patterns concordant with previously described single-nucleotide polymorphism (SNP) assays. It therefore provides a standardized high-resolution typing approach, with demonstrated applicability for outbreak investigations, source tracking, and comparative genomic surveillance of Y. pestis. Full article

High-voltage bushings, the fault-prone key electrical components of transformers, are critical for real-time and high-accuracy fault monitoring and management. Intelligent fault detection via infrared images is plagued by low classification accuracy due to massive interference from similar tubular objects and small target characteristics. This study proposes a lightweight deep learning model, MTrip–YOLO, an improved YOLO11n integrated with Mamba-2 (Structured State Space Duality, SSD) and Triplet Attention, to achieve efficient fault monitoring in complex backgrounds. The training and validation dataset comprises open-source images, on-site data from a substation, and field-collected infrared images, categorized into four types: normal bushings, poor contact, oil shortage, and high dielectric loss faults. Mamba-2 captures the long-range global context of infrared features with its linear-complexity long-range modeling capability to enhance feature extraction, while Triplet Attention suppresses complex background radiation noise through cross-dimensional interaction without dimensionality reduction, enabling the model to focus on small targets and accurately classify bushings from morphologically similar strip-shaped objects. Experimental results show that MTrip–YOLO achieves a top mAP50 of 91.6% and a minimal parameter count of 1.9 M, outperforming Faster R-CNN, RT-DETR, and YOLO26n across all evaluated metrics and being potentially suitable for edge deployment on UAV-mounted or handheld infrared platforms, pending hardware validation on embedded computing devices. Ablation experiments verify the independent contributions of Mamba-2 (0.8027% mAP50 improvement) and Triplet Attention (0.89327% mAP50 improvement), with a synergistic effect from their combination. MTrip–YOLO provides a potential edge-deployable solution for high-voltage bushing fault monitoring, offering important application value for the intelligent operation and maintenance of substations. Full article

The proliferation of digital assets has catalyzed a profound decoupling between intangible property and traditional inheritance jurisprudence. Under the existing legal framework in Taiwan, practitioners must rely on the testamentary forms prescribed in Article 1189 of the Civil Code, which are fundamentally ill equipped to handle cryptographic assets. Specifically, Notarized Wills (Article 1191) necessitate full disclosure to a notary, creating a “Privacy–Security Paradox” where revealing private keys exposes assets to misappropriation. Conversely, while Sealed Wills (Article 1192) offer confidentiality, they are plagued by risks of physical degradation and technical non-executability. This study proposes zkWill, an EVM-compatible decentralized testamentary framework designed to bridge these structural gaps. By leveraging Zero-Knowledge Proofs (ZKPs), zkWill achieves a state of “blind compliance,” verifying that a sealed will meets the statutory requirements of the Civil Code without disclosing its underlying content. The system integrates the Permit2 protocol for secure asset migration and combines AES-256 encryption with IPFS to immunize testaments against centralized storage failures. Unlike conventional services that demand custodial trust, zkWill employs decentralized oracles to trigger automated execution, ensuring legacy distribution without compromising wallet private keys. Empirical data from the Arbitrum Sepolia testnet confirms that the framework maintains constant verification efficiency and a judicially resilient audit trail, providing a paradigm that harmonizes legal pragmatism with cryptographic security for digital inheritance. Full article

Millimeter-wave radar has gradually gained popularity as a sensor mode for Human Activity Recognition (HAR) in recent years because it preserves the privacy of individuals and is resistant to environmental conditions. Nevertheless, the fast inference of high-dimensional and sparse 4D radar data is still difficult to perform on low-resource edge devices. Current models, including 3D Convolutional Neural Networks and Transformer-based models, are frequently plagued by extensive parameter overhead or quadratic computational complexity, which restricts their applicability to edge applications. The present paper attempts to resolve these issues by introducing RadarSSM as a lightweight spatiotemporal hybrid network in the context of radar-based HAR. The explicit separation of spatial feature extraction and temporal dependency modeling helps RadarSSM decrease the overall complexity of computation significantly. Specifically, a spatial encoder based on depthwise separable 3D convolutions is designed to efficiently capture fine-grained geometric and motion features from voxelized radar data. For temporal modeling, a bidirectional State Space Model is introduced to capture long-range temporal dependencies with linear time complexity $O\left(T\right)$, thereby avoiding the quadratic cost associated with self-attention mechanisms. Extensive experiments conducted on public radar HAR datasets demonstrate that RadarSSM achieves accuracy competitive with state-of-the-art methods while substantially reducing parameter count and computational cost relative to representative convolutional baselines. These results validate the effectiveness of RadarSSM and highlight its suitability for efficient radar sensing on edge hardware. Full article

Background: Breast cancer (BC) remains the most prevalent malignancy among women worldwide, with one in eight at risk during their lifetime. Platinum-based chemotherapeutic drugs, despite of their binding to the DNA of cancer cells, are plagued by toxicity and resistance, necessitating the need for safer and more effective alternatives, such as organometallic complexes. Both synthetic organometallic complexes and natural compounds have attracted attention in this regard. Organotin(IV) complexes are promising chemotherapeutics due to their structural versatility and bioactivity, while vitamins such as Vitamin D (VD) and Vitamin E (VE) exhibit antiproliferative, anti-inflammatory, and antioxidant properties, making them valuable candidates for combination therapy. Methodology: In this study, six novel organotin(IV) dithiocarbamate complexes [LMe₃Sn (Complex 1), LBu₃Sn (Complex 2), LPh₃Sn (Complex 3), LMe₂SnCl (Complex 4), LBu₂SnCl (Complex 5), and L₂Me₂Sn (Complex 6), where L = (E)-4-styrylpiperazine-1-carbodithioate], were synthesized and characterized by FT-IR, ¹H-, ¹³C-NMR, and elemental analysis. Results: Structural studies confirmed penta- and hexacoordination geometries. In silico docking against six BC-related proteins identified Complexes 2 and 4 with both vitamins as promising candidates, exhibiting strong binding affinities, with stable interaction profiles. However, integration of pharmacokinetic, antioxidant, and anti-inflammatory analyses highlighted Complex 4 with both vitamins as the most potent candidate owing to its superior ADME characteristics and balanced biological properties. Subsequent in vitro assays confirmed these findings, as Complex 4 demonstrated strong cytotoxic activity against both MCF-7 (>1.16-fold) and MDA-MB-231 (>1.46-fold) cell lines, surpassing the efficacy of cisplatin. Remarkably, co-administration of VD or VE with Complex 4 further enhanced its anticancer potential, with Chou–Talalay combination index values < 1 (0.66–0.91) indicating a synergistic interaction. Conclusions: Collectively, these results identify Complex 4 as a promising lead compound, and its synergistic activity with natural vitamins may promote cell death, likely through apoptosis induction and modulation of oxidative stress, underscoring its potential as an effective and less toxic therapeutic strategy for breast cancer management. Full article

The electrochemical reduction of carbon dioxide (CO₂RR) into value-added chemicals using renewable electricity is a pivotal strategy for achieving a sustainable carbon cycle. However, this process is plagued by intrinsic challenges, including poor product selectivity, competing hydrogen evolution, and catalyst instability. Metal–organic frameworks (MOFs), with their highly designable periodic structures, atomically dispersed active sites, and tunable pore microenvironments, have emerged as a uniquely versatile platform to address these issues. This review articulates a multi-scale design philosophy that enables precise steering of the CO₂RR pathway. We systematically elaborate on hierarchical tuning strategies, beginning with molecular-scale engineering of active sites (metal nodes and organic ligands) to define intrinsic activity and intermediate binding. This is synergistically integrated with the optimization of electronic structure and charge transport to overcome conductivity bottlenecks, meso-scale modulation of crystal morphology and defects to enhance mass transport and site accessibility, and the construction of heterogeneous interfaces for tandem catalysis and synergistic effects. Through this coherent, cross-scale design framework, MOF-based catalysts demonstrate exceptional capability in the precise control of reaction pathways, leading to remarkably selective synthesis of target high-value products, from C₁ compounds (CO, HCOOH, CH₄, CH₃OH) to C₂⁺ species (C₂H₄, C₂H₅OH) and urea. Finally, we outline future directions centered on dynamic mechanistic understanding, electrode engineering for industrial current densities, and stability enhancement, thereby providing a comprehensive material design guideline to advance CO₂RR technology. This work positions MOFs as a quintessential tunable catalytic platform for the sustainable conversion of CO₂. Full article

The Turpan–Hami Basin, a critical energy hub in northwestern China, is plagued by frequent ground collapses induced by extensive mining over karst geology, threatening ecology and safety. Current hazard assessment methods, mainly single linear or traditional machine learning models, fail to capture the complex nonlinear interactions inherent to this coupled geo-mining environment. This study addresses this gap by establishing a multi-dimensional “Geology-Mining-Hydrology-Environment” index system comprising 14 critical factors—including lithology, goaf distribution, mining intensity, and their interaction terms. A coupled gradient boosting decision tree and logistic regression (GBDT-LR) model, optimized for the multi-factor coupling characteristics of ground collapse in arid mining basins, was applied for the hazard assessment. The results reveal a distinct spatial pattern of “core agglomeration with multi-level gradient differentiation.” Extremely high-hazard areas, covering 9.21% of the area, are concentrated in the core mining areas northwest of Turpan and southwest of Hami, while high-hazard areas (4.63%) form surrounding belts. The GBDT-LR model (AUC = 0.871) demonstrated significantly superior performance over a single logistic regression model (AUC = 0.813), proving its enhanced capability to identify high-hazard areas by modeling complex factor interactions. This work provides an essential scientific foundation for implementing zonal hazard management and prioritizing disaster prevention projects in key areas of the basin. Full article

Activated sludge treatment is plagued by high secondary pollution risks, and ship antifouling paint particles (APPs) as hazardous heavy metal-rich solid wastes generated from hull derusting wastewater, pose severe environmental threats and intractable disposal dilemmas. This study developed a novel pilot-scale activated sludge reduction process coupling APPs-derived carbon-based catalysts with ultrasound-Fe²⁺/peroxydisulfate (PDS) advanced oxidation. Columnar catalysts were fabricated via direct carbonization-molding using waste APPs from an 82,000 deadweight bulk carrier were used as the sole raw material to prepare columnar catalysts via direct carbonization-molding; single-factor and orthogonal experiments optimized process parameters, Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS) and X-ray Photoelectron Spectroscopy (XPS) characterized catalyst and sludge properties, free radical quenching experiments elucidated reaction mechanisms and a 90-day continuous pilot run assessed catalytic stability. The process achieved a 43.5% sludge removal rate under optimal conditions, accompanied by 100% toluene and 92.3% phenolic compound degradation, as well as efficient total phosphorus (TP) and total nitrogen (TN) removal. Mechanistic studies via characterization and quenching experiments confirmed the catalyst enhanced PDS activation through free/non-free radical synergy and accelerated Fe²⁺/Fe³⁺ redox cycling. A 90-day continuous pilot operation demonstrated excellent long-term catalytic stability, with sludge removal rate remaining above 38%. This “waste treating waste” technology realizes high-value APPs resource utilization, provides a low-carbon sludge disposal pathway, and offers a scalable solution for collaborative pollution control in the wastewater treatment and shipping industries. Full article

The cultivation of asparagus (Asparagus officinalis L.) is plagued by two serious issues: “asparagus decline” and “asparagus replant problem”. The average lifespan of an asparagus plant is 15 to 20 years. However, its productivity decreases after a few years (asparagus decline). Even when these asparagus plants are replaced with new ones, the new plants remain unproductive (asparagus replant problem). The main causes of these problems are a Fusarium infection and asparagus autotoxicity. Several reviews have been conducted on Fusarium. Despite the accumulation of evidence on asparagus autotoxicity in the literature over the past four decades, no review has focused specifically on asparagus autotoxicity. It has been reported that asparagus growth is inhibited by asparagus root residues, leachates, root exudates, and rhizosphere soils. Several phenylpropanoids, including trans-cinnamic acid, p-coumaric acid, caffeic acid, and ferulic acid, have been identified as asparagus autotoxic substances in these root residues, root exudates, rhizosphere soils, growth media, and/or plant tissues. Tryptophan, 3,4-methylenedioxycinnamic acid, and iso-agatharesinol were also identified as asparagus autotoxic substances. These substances may cause autotoxicity by disrupting phytohormone levels, cellular metabolism, impairing membrane function, and by inducing oxidative stress. Although cinnamic, p-coumaric, caffeic, and ferulic acids have been reported to act as antibiotics, these compounds have also been shown to weaken the defense mechanisms of asparagus against pathogen infection, and enhance the Fusarium pathogenicity. The presence of these autotoxic substances, coupled with a Fusarium infection, may create a vicious cycle that worsens “asparagus decline” and “asparagus replant problem”. This is the first review to focus on the asparagus autotoxicity. Full article

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion capabilities present novel solutions to these challenges. Scope: This narrative review provided a comprehensive overview of the current landscape and emerging trends of LLM applications across the entire RT workflow. Findings: LLMs demonstrated substantial clinical utility in key RT domains, including automated target volume delineation (e.g., Medformer, Radformer), dose prediction (e.g., DoseGNN), treatment planning automation (e.g., GPT-Plan), patient education, clinical decision support, medical information extraction, and prognosis assessment. These applications not only have the potential to enhance the accuracy and efficiency of RT but also facilitate the standardization of clinical pathways. However, widespread clinical adoption was impeded by critical limitations, including model hallucinations, insufficient generalizability, and unresolved issues regarding data privacy and ethical governance. Conclusions: LLMs possessed transformative potential to revolutionize radiation oncology. Future endeavors should prioritize technical refinements to mitigate model deficiencies, establish standardized evaluation benchmarks, and develop robust ethical frameworks. These concerted efforts are crucial for translating LLM research into clinical practice and advancing the era of intelligent, precision RT. Full article

Traditional enzyme-induced carbonate precipitation (EICP) technology for brick crack rehabilitation is commonly plagued by solution clogging and low repair efficiency. To overcome these technical limitations, a novel centrifugal cementation method was proposed in this study, with its core innovation lying in decoupling the EICP reaction from the masonry reinforcement process. After the complete reaction of urease with the cementation solution, a high-concentration calcium carbonate colloid was extracted via centrifugation, which was then mixed with fine sand to prepare a repair mortar for direct injection into brick cracks. The experimental results, based on a single-factor design with a fixed soybean powder concentration (180 g/L, peak urease activity), showed that the maximum flexural strength of the repaired bricks reached 2.31 MPa, recovering as much as 122.9% of that of the cracked unrepaired bricks. Furthermore, the flexural strength of the repaired bricks exhibited a significant positive correlation with the calcium carbonate content (20–100%) and curing time (3–28 days). Phase analysis indicated that the repair mortar was primarily composed of calcite and quartz. The high shear force generated by centrifugation triggered explosive nucleation of calcium carbonate, and spherical calcite particles were formed through Ostwald ripening, exhibiting a distinct characteristic of decoupling between the spherical morphology and calcite crystal phase. The centrifugal cementation method proposed in this study achieves excellent short-term repair effects for masonry structures under laboratory conditions, thus providing a novel technical approach for the crack rehabilitation of masonry structures. Full article

Ziziphus jujuba Mill. is a characteristic resource with both medicinal and edible values. At present, its lactic acid bacteria-fermented products are plagued by ambiguous variety selection and low added value. To clarify the variety-specific regulatory effects of Z. jujuba cultivars on metabolic profiles during lactic acid bacteria fermentation, this study analyzed the metabolic characteristics of fermented broths of Tan jujube, Jun jujube, and Ban jujube under a unified fermentation system using LC-MS untargeted metabolomics technology. Significantly differential metabolites were screened with the criteria of p < 0.05 and VIP > 1, and the metabolic regulatory mechanisms were further elucidated, combined with KEGG pathway enrichment analysis. The results showed that a total of 570 metabolites were identified in the three fermented broths. Tan jujube was enriched in linolenic acid, Ban jujube was rich in D-xylitol and dethiobiotin, and Jun jujube had prominent contents of S-adenosylmethionine and pyridoxine. All the aforementioned metabolites are involved in important physiological processes such as anti-inflammation and intestinal homeostasis maintenance. The differential metabolites were mainly enriched in 6 key pathways, including central carbon metabolism, ABC transporters, and phenylpropanoid biosynthesis, among which central carbon metabolism and ABC transporters were the core regulatory pathways. This study constructed an association network of Z. jujuba variety–differential metabolite–key pathway, systematically elucidated the metabolic differentiation mechanisms of fermented broths from different Z. jujuba cultivars, and provided a scientific basis for the precise selection of Z. jujuba varieties dedicated to fermentation and the targeted development of high-value-added functional fermented foods. Full article

Emergency facility location and layout are critical to the efficiency of emergency rescue and resource allocation. However, practical emergency scenarios are plagued by two key challenges: the risk of facility failure due to various uncertain factors and the presence of complex polygonal barriers (including convex and concave polygons) that hinder transportation. Existing studies often overlook concave polygonal barriers or fail to prioritize time satisfaction, a core demand in emergency response. To address these gaps, this paper proposes a reliable emergency facility location optimization model with the objective of maximizing time satisfaction, considering constraints such as capacity, cost, and demand. The model integrates three key methods: a convex hull algorithm to convert concave barriers into convex ones for simplified calculation, a path optimization algorithm to find the shortest bypass routes around barriers, and an Artificial Ecosystem Optimization (AEO) algorithm to solve the nonlinear programming model. Through numerical experiments (single-facility, multi-facility, and medium-scale scenarios) and a practical case study in the Meknès region of Morocco for ambulance deployment, the feasibility and effectiveness of the model and algorithms are verified. The results show that the model achieves high time satisfaction (all above 0.8, with most exceeding 0.9) and efficiently optimizes facility locations and resource allocation. Sensitivity analysis indicates that increased failure risk parameters ($\alpha$ and $\theta$) lead to a gradual decrease in average time satisfaction. This research provides a systematic mathematical model and practical method for emergency facility location decision-making, effectively addressing the challenges of complex barriers and facility failure. Full article