Search Results (3,201)

In recent years, the increasing frequency of intense rainfall events has led to a surge in landslide occurrences, posing severe threats to human safety and ecological integrity. This study utilizes the Universal Distinct Element Code (UDEC) for discrete element numerical simulations, combined with field observation-based mechanism analysis, to examine the primary drivers of landslide formation: rainfall and underground mining. Focusing on the Zengziyan landslide in Chongqing as a case study, the research investigates the underlying instability mechanisms. The findings indicate that mining activities primarily compromise slope stability by modifying rock structures, diminishing supporting forces, and creating goaf areas. Notably, these goaf zones generate an overhanging effect on the overlying rock mass, promoting crack initiation and the propagation of structural planes. Under rainfall conditions, groundwater infiltration and elevated pore water pressure exert a more substantial destabilizing influence, markedly accelerating rock mass sliding and collapse. The analysis reveals that rainfall predominantly governs landslide initiation and evolution, particularly during the triggering and rapid acceleration phases of slope instability. The outcomes of this research offer valuable insights for post-mining slope management and monitoring, as well as for developing landslide early warning systems in rainy conditions. Full article

(1) Background: Oat (Avena sativa L.) is a crop that is widely used in human nutrition, while it also plays an important role in animal husbandry as a high-quality forage crop. However, this crop is particularly susceptible to combined biotic stressors, including insect pests (Agriotes lineatus) and fungal infections (Fusarium spp.). These stresses act synergistically: root damage caused by wireworms increases the plant’s susceptibility to fungal infection, while pathogens further limit nutrient uptake and root system development. In recent years, the reduced efficacy of chemical pesticides against both insect pests and fungal pathogens has highlighted the need for alternative strategies in oat protection, leading to an increased focus on developing bacterial bio-inoculants as sustainable and effective biocontrol agents. (2) Methods: This study aimed to identify bacterial strains capable of suppressing wireworms (Agriotes lineatus) and Fusarium spp. in oats, while simultaneously promoting plant growth. Bacterial isolates were screened for key Plant Growth Promoting (PGP) and biocontrol traits, including IAA and siderophore production, phosphate solubilization, and the presence of toxin- and antibiotic-coding genes. (3) Results: The highest insecticidal effect against wireworms was recorded for Bacillus velezensis BHC 3.1 (63.33%), while this isolate also suppressed the growth of F. proliferatum for 59%, F. oxysporum for 65%, F. poae for 71%, and F. graminearum for 15%. The most effective Bacillus strains (with insecticidal and antifungal activity) were identified and tested in two pot experiments, where their ability to enhance plant growth in the presence of insects and fungi was evaluated under semi-controlled conditions. An increase in plant biomass, grain yield, and nitrogen content was observed in oat inoculated with B. velezensis BHC 3.1 and B. thuringiensis BHC 2.4. (4) Conclusions: These results demonstrate the strong potential of both strains as multifunctional bio-inoculants for enhancing oat growth and mitigating the adverse effects of wireworm damage and Fusarium infection. Full article

Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning. This scoping review synthesizes 32 studies (2023–2025) purposively sampled from 259 records to map personalization mechanisms and effectiveness signals in higher-education CS contexts. We identify five application domains—intelligent tutoring, personalized materials, formative feedback, AI-augmented assessment, and code review—and analyze how design choices shape learning outcomes. Designs incorporating explanation-first guidance, solution withholding, graduated hint ladders, and artifact grounding (student code, tests, and rubrics) consistently show more positive learning processes than unconstrained chat interfaces. Successful implementations share four patterns: context-aware tutoring anchored in student artifacts, multi-level hint structures requiring reflection, composition with traditional CS infrastructure (autograders and rubrics), and human-in-the-loop quality assurance. We propose an exploration-firstadoption framework emphasizing piloting, instrumentation, learning-preserving defaults, and evidence-based scaling. Four recurrent risks—academic integrity, privacy, bias and equity, and over-reliance—are paired with operational mitigation. Critical evidence gaps include longitudinal effects on skill retention, comparative evaluations of guardrail designs, equity impacts at scale, and standardized replication metrics. The evidence supports generative AI as a mechanism for precision scaffolding when embedded in exploration-first, audit-ready workflows that preserve productive struggle while scaling personalized support. Full article

The Tourism Area Life Cycle shaped tourism research for decades, but its concepts Product Life Cycle and Carrying Capacity remain problematic. We apply a Path Dependence frame under an Urban Growth Machine Theory lens to explore the effects of growth pressure and resource undersupply on the decline and rejuvenation of Japan’s former premier hot spring resort Atami. We conduct structured data collection utilizing sampling and coding methods to collect quantitative and qualitative data from primary and secondary sources, reconstructing Atami’s development paths. Findings suggest that growth pressure conflicted with local supply such as land, water, labor and created negative externalities, most notably high prices. Decision makers’ uncompromising focus on growth aggravated displacement of key actors, disrupting local communities and undermining the human agency needed for small-scale product innovation; empowered associations obstructing promotion and diversification efforts; encouraged extreme specialization depriving Atami of new independent businesses; and drove local opposition to major new projects, thereby stalling product renewal. The framework helped recontextualize Atami’s recovery and demonstrated the value of directly incorporating factors of capacity into analysis. Results link displacement to long-term sustainability risks affecting ‘replaceable’ resorts reliant on innovation. Unencumbered access to local resources for residents (housing, training) is proposed as mitigation. Full article

Enterococcus spp. produce diverse bioactive molecules used for biotechnological purposes or as probiotic agents for livestock and human health. The main aim of this study was to decipher the genetic traits using whole-genome sequencing (WGS) of a bacteriocinogenic Enterococus faecium 1702 strain showing diverse probiotic traits. Genetic traits of the strain were determined by performing WGS using the NovaSeq6000 platform followed by consecutive sequence analysis using appropriate software. WGS showed that the genome of the E. faecium 1702 strain has a size of 2,621,416 bp, with a GC content of 38.03%. The strain belonged to the sequence type ST722 not known as a human clonal lineage. The strain was free of genes encoding clinically relevant antibiotic resistance; in addition, genes encoding sensu stricto virulence factors, plasmids, and prophages were not detected. Annotations through the Prokaryotic Genomes Automatic Annotation Pipeline (PGAP) tool revealed 2413 coding sequencing entries (CDC) out of 2521 predicted chromosomal genes. The functional annotation of the whole genome through the KEGG database using KofaScan revealed several genes related to several biological activities, including metabolic process, carbohydrate metabolism, amino acid metabolism, and nucleotide metabolism. The strain harbored three entero-bacteriocins (enterocins) encoded by entA, entB, and entX (enterocin X-alpha and X-beta) genes. Interestingly, the strain harbored the ansB, glsA, and arcA genes encoding L-asparaginase, L-glutaminase, and arginine deiminase, respectively, known for their anticancer activities. E. faecium 1702 harbored the gadB, gadC, and gadR genes implicated in gamma(γ)-aminobutyric acid (GABA) production, which is known for its analgesic, anti-anxiety, hypotensive, diuretic, and antidiabetic effects. The WGS findings and phenotypic traits of E. faecium 1702 revealed significant features that allow for its use as a probiotic or for biotechnological and pharmaceutical applications. Full article

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network. Full article

Accurate grading of corn kernels is critical for seed certification, directional seeding, and breeding, yet it is still predominantly performed by manual inspection. This work introduces CornViT, a three-stage Convolutional Vision Transformer (CvT) framework that emulates the hierarchical reasoning of human seed analysts for single-kernel evaluation. Three sequential CvT-13 classifiers operate on $384\times 384$ RGB images: Stage 1 distinguishes pure from impure kernels; Stage 2 categorizes pure kernels into flat and round morphologies; and Stage 3 determines the embryo orientation (up vs. down) for pure, flat kernels. Starting from a public corn seed image collection, we manually relabeled and filtered images to construct three stage-specific datasets: 7265 kernels for purity, 3859 pure kernels for morphology, and 1960 pure–flat kernels for embryo orientation, all released as benchmarks. Head-only fine-tuning of ImageNet-22k pretrained CvT-13 backbones yields test accuracies of 93.76% for purity, 94.11% for shape, and 91.12% for embryo-orientation detection. Under identical training conditions, ResNet-50 reaches only 76.56 to 81.02 percent, whereas DenseNet-121 attains 86.56 to 89.38 percent accuracy. These results highlight the advantages of convolution-augmented self-attention for kernel analysis. To facilitate adoption, we deploy CornViT in a Flask-based web application that performs stage-wise inference and exposes interpretable outputs through a browser interface. Together, the CornViT framework, curated datasets, and web application provide a deployable solution for automated corn kernel quality assessment in seed quality workflows. Source code and data are publicly available. Full article

Antisense genes (usually suffixed by -AS) represent a class of long non-coding RNAs (lncRNAs) transcribed from the opposite strand of annotated human genes or exon(s). A total of ~2236 human antisense genes exist in the human genome. Their genomic locations with respect to the corresponding sense genes, their dysregulated expression patterns in cancer specimens, and clinical associations with patient outcomes reveal their potential importance in clinical settings. As of today, there lacks a comprehensive review of HNC-associated antisense genes/transcripts to help move forward the antisense field for genetic biomarker development or future drug research. In total, 2.3% (52/2236 antisense genes) of all known human antisense genes have been investigated in head and neck cancer (HNC). Thus, we perform a comprehensive review of the genomic aberrations (mutations, copy number changes, RNA-expression dysregulation, and single nucleotide polymorphisms) associated with HNC patient prognosis, disease progression, cancer cell signaling, drug sensitivity, and radio-resistance. Four antisense genes, namely HOXA10-AS, LEF1-AS1, MSC-AS1, and ZEB2-AS1, have been clinically cross-validated and have consistently demonstrated to be associated with patient outcomes in multiple independent cohorts by different research teams, with clear evidence for the prioritization of clinical biomarker development in HNC. Single nucleotide polymorphisms (SNPs) of antisense genes with evidence for HNC risk or outcomes should be further validated in different ethnic groups, for potential global HNC applications. Full article

Despite seven decades of international commitment—from the 1948 Universal Declaration of Human Rights through SDG 3.8—universal health coverage remains stubbornly out of reach. Two billion people, predominantly informal sector workers, lack access to sustainable health insurance. This entry explains the underlying cause: sustainable health insurance requires specific behavioral and institutional conditions for collective action—conditions that existing health insurance models systematically fail to satisfy, thereby structurally excluding informal populations. The Trinity Law framework formalizes these conditions as three multiplicatively interacting requirements—Trust (T), Consensus (C), and Dual Benefit (DB)—expressed as S = T × C × DB. Empirical analysis of community-based health insurance schemes across 24 countries identifies a robust trust threshold (τ* ≈ 0.68) operating as a behavioral phase transition: below this level, cooperation collapses; above it, participation becomes self-sustaining. Cross-country evidence from 274 organizations across 155 countries confirms consensus thresholds (C* ≈ 0.59), while analysis of 158,763 observations validates dual benefit mechanisms. The multiplicative structure explains why partial reforms fail: weakness in any single component drives overall sustainability toward zero. Applied to health insurance, this framework distinguishes conventional systems—Bismarckian employment-based, Beveridgean tax-financed, and commercial health insurance from sustainable systems like participatory community-based microinsurance that satisfy all three Trinity Law conditions through participatory design, transparent governance, and aligned incentives. The persistent UHC gap reflects not implementation failures but fundamental design incompatibilities that the Trinity Law makes explicit. This entry has three objectives: first, it states the Trinity Law conditions; second, it summarizes the empirical evidence for each component; third, it applies the framework to classify major health insurance models. Supporting datasets and code are available in the referenced Zenodo repositories. The term ‘law’ follows the tradition of social science regularities like the ‘law of demand’: a robust empirical pattern with strong predictive validity, not a claim to physical certainty. Full article

The proliferation of binary vulnerabilities in the software supply chain has become a critical security challenge. Existing vulnerability detection approaches—including dynamic analysis, static analysis, and decompilation-assisted analysis—all suffer from limitations such as insufficient coverage, high false-positive and false-negative rates, or poor compatibility. Although decompilation technology can serve as a bridge connecting binary-code and source-code vulnerability detection tools, current schemes suffer from inadequate semantic restoration quality and lack of tool compatibility. To address these issues, this paper proposes LLMVulDecompiler, a binary decompilation model based on fine-tuned large language models designed to generate high-precision decompiled code that integrates directly with source-code static analysis tools. We construct a dedicated training and evaluation dataset that covers multiple compiler optimization levels (e.g., O0–O3) and a diverse set of program functionalities. We adopt a two-stage fine-tuning strategy that involves first building foundational decompilation capabilities, then enhancing vulnerability-specific features. Additionally, we design a low-cost inference pipeline and establish multi-dimensional evaluation criteria, including restoration similarity, compilation success rate, and functional correctness. Experimental results show that the model significantly outperforms baseline models in terms of average edit distance, compilation success rate, and black-box test pass rate on the HumanEval-C benchmark. In tests on 12 real-world CVE (Common Vulnerabilities and Exposures) instances, the approach achieved a detection accuracy of 91.7%, with substantially reduced false-positive and false-negative rates. This study demonstrates the effectiveness of specialized fine-tuning of large language models for binary decompilation and vulnerability detection, offering a new pathway for binary security analysis. Full article

In animals and humans, nutrients influence signaling cascades, transcriptional programs, chromatin dynamics, and mitochondrial function, collectively shaping traits related to growth, immunity, reproduction, and stress resilience. This review synthesizes evidence supporting nutrient-mediated regulation of DNA methylation, histone modifications, non-coding RNAs, and mitochondrial biogenesis, and emphasizes their integration within metabolic and developmental pathways. Recent advances in epigenome-wide association studies (EWAS), single-cell multi-omics, and systems biology approaches have revealed how diet composition and timing can reprogram gene networks, sometimes across generations. Particular attention is given to central metabolic regulators (e.g., PPARs, mTOR) and to interactions among methyl donors, fatty acids, vitamins, and trace elements that maintain genomic stability and metabolic homeostasis. Nutrigenetic evidence further shows how genetic polymorphisms (SNPs) in loci such as IGF-1, MSTN, PPARs, and FASN alter nutrient responsiveness and influence traits like feed efficiency, body composition, and egg quality, information that can be exploited via marker-assisted or genomic selection. Mitochondrial DNA integrity and oxidative capacity are key determinants of feed conversion and energy efficiency, while dietary antioxidants and mitochondria-targeted nutrients help preserve bioenergetic function. The gut microbiome acts as a co-regulator of host gene expression through metabolite-mediated epigenetic effects, linking diet, microbial metabolites (e.g., SCFAs), and host genomic responses via the gut–liver axis. Emerging tools such as whole-genome and transcriptome sequencing, EWAS, integrated multi-omics, and CRISPR-based functional studies are transforming the field and enabling DNA-informed precision nutrition. Integrating genetic, epigenetic, and molecular data will enable genotype-specific feeding strategies, maternal and early-life programming, and predictive models that enhance productivity, health, and sustainability in poultry production. Translating these molecular insights into practice offers pathways to enhance animal welfare, reduce environmental impact, and shift nutrition from empirical feeding toward mechanistically informed precision approaches. Full article

Urban forests offer a range of environmental, climatic, economic, and social benefits to citizens. However, these effects have not been systematically measured owing to the localized nature of urban forests. This study developed a framework to assess the effects of urban forest ecosystem services and elucidate the service and benefit pathways of its indicators. Two PRISMA-guided scoping reviews were conducted using Web of Science and Scopus to identify English peer-reviewed articles (2015–2024) on the effects of urban forests and indicators. The studies on the urban forest effects were analyzed to systematically code and classify the criteria, effects, methods, and techniques based on the nature-based solutions. In terms of indicators, the ecosystem service cascade was employed to organize indicators across four pathways with structures/function, service, benefit, and value. The review revealed that temperature regulation, air pollution reduction, and carbon sequestration were the most studied effects, followed by social effects; in contrast, economic benefits and sound and noise were the least studied and assessed. Furthermore, indicator pathways were found to vary by effects. Drawing on this scoping review, a standard and expanded indicator assessment framework was developed. The proposed framework provides a decision-support tool to assess urban forest performance based on evidence, facilitating link between biophysical properties and human outcomes. Full article

Background/Objectives: Temporomandibular disorders (TMDs) are a heterogeneous subset of orthodontic conditions with persistent diagnostic and reporting variability. This review compared transparency, reporting quality, and spin prevalence in TMD/TMJ (temporomandibular joint)-focused orthodontic randomized controlled trials (RCTs) versus general orthodontic RCTs. Methods: The review followed PRISMA 2020 and was registered in PROSPERO (4201024184). Searches were performed in PubMed/MEDLINE, Embase, CINAHL, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform from the earliest available records in each database up to 15 October 2025. Eligible studies were peer-reviewed human orthodontic RCTs. Five transparency indicators (funding disclosure, bias discussion, confounder consideration, protocol registration, reporting-guideline adherence) and five spin indicators (selective focus, unsupported efficacy claims, emphasis on benefits, recommendations despite nonsignificance, “trend toward significance” language) were coded dichotomously. Beta–binomial mixed-effects models compared composite scores between groups, adjusting for publication era, impact factor, and journal clustering. Results: Among 874 included trials (840 general, 34 TMD/TMJ-focused), TMD/TMJ-focused studies showed lower adjusted transparency (odds ratio (OR) = 0.58; 95% confidence interval (CI) 0.34–0.99; p = 0.047), mainly due to limited registration and incomplete guideline adherence. Predicted transparency proportions were 0.82 for general and 0.73 for TMD/TMJ-focused studies. Composite spin did not differ (OR = 1.05; 95% CI 0.68–1.62; p = 0.821), though TMD/TMJ-focused abstracts more often emphasized benefits (OR = 4.62) and recommended interventions despite nonsignificant primary outcomes (OR = 2.83). Conclusions: TMD-focused orthodontic trials exhibited lower transparency and a distinct pattern of interpretive spin, particularly a greater tendency to emphasize benefits or recommend interventions despite non-significant results, compared with general orthodontic research. Full article

Background/Objectives: The oral–gut–lung axis represents a dynamic system where exosomes carrying mRNAs and non-coding RNAs might help to regulate microbiota and human cell crosstalk to establish transcriptional regulatory networks controlling cellular biological processes and signaling pathways. Methods: We conducted a comprehensive transcriptomic analysis to characterize the molecular cargo of extracellular exosomes in the context of gut and lung cancer. Results: By analyzing gut and lung exosomes cargo with our previous transcriptomic studies from tumoral and inflammatory tissues, we found that exosomes can transport key RNAs that codify specific receptors that facilitate pathogenic interaction with microorganisms and RNAs that are part of interacting gene and transcriptional regulatory networks that control the function of differentially expresses genes, all involved in biological processes like cell cycle, plasticity and growth regulation, invasion, metastasis, microenvironmental remodeling, epigenetic, and microbial and immunological modulation, during the unlocking of phenotypic plasticity for the acquisition of the hallmarks of cancer in the oral–gut–lung axis. Conclusions: Exosomal RNA regulation of transcriptional networks represents a pivotal axis in the interplay between inflammation and cancer, offering opportunities for innovative diagnostic and therapeutic approaches. Full article

With the continuous expansion of urban metro systems, balancing passenger experience and operational efficiency has become a central concern in contemporary public transportation design. However, most existing metro service studies continue to focus on perceptual comfort or isolated usability tasks and lack an integrated, behavior-centered perspective that accounts for the full travel chain and diverse user groups. This study develops the Bi-directional Service Behavioral Experience Model (BSBEM), which systematically integrates one-way navigation behaviors and interactive operational behaviors within a unified dual-path framework to identify behavioral patterns and experiential disparities across user groups. Based on the People–Touchpoints–Environments–Messages–Services–Time–Emotion (POEMSTI) behavioral observation framework, this study employs a mixed-method approach combining video-based behavioral coding, usability testing, and subjective evaluation. An empirical study conducted at Beidajie Station on Xi’an Metro Line 2 involved three representative passenger groups: high-frequency commuters, urban leisure travelers, and special-care passengers. Multi-source data were collected to capture temporal, spatial, and interactional dynamics throughout the travel process. Results show that high-frequency commuters demonstrate the highest operational fluency, urban leisure travelers exhibit greater visual dependency and exploratory pauses, and special-care passengers are most affected by accessibility and feedback latency. Further analysis reveals a positive correlation between route complexity and interaction delay, highlighting discontinuous information feedback as a key experiential bottleneck. By jointly modeling one-way and interactive behaviors and linking group-specific patterns to concrete metro touchpoints, this research extends behavioral evaluation in metro systems and offers a novel behavior-based perspective along with empirical evidence for inclusive, adaptive, and human-centered service design. Full article