Search Results (1,638)

Curling is a strategic sport in which shot decisions involve both expected rewards and inherent risks; expected-score distributions (ESDs)—probability distributions over possible final scores—capture this uncertainty as a risk-aware strategic indicator. Although unified frameworks predicting ESDs across all shots have been proposed, their internal behavior and learned output characteristics have not been systematically examined. We construct an all-shot ESD prediction framework grounded entirely in real-match board configurations drawn from World Curling Federation championship events and conduct a shot-wise analysis along two complementary axes: accuracy, the model’s reproduction of its training targets, and plausibility, the validity of those targets at aggregate and in-match scales. Accuracy degrades monotonically with shot number; only shot 16 admits comparison with an independent reference, while intermediate-shot labels are bootstrap rollouts of the next-shot model. An independent ground-truth probe at the first backward step (shot 15,500 boards × 95 contexts) bounds the consequence of this bootstrap structure: the resulting deviation is a small selection-induced hammer-underestimation bias common to both FiLM and concatenation chains, whose magnitude is more than an order of magnitude larger than the FiLM chain versus concatenation chain stage difference, and full multi-step verification beyond the first step is structurally out of reach. Within this scope the aggregate ESD shares the gross shape of the empirical end-score distribution and the qualitative hammer/non-hammer ordering, with a hammer-favorable offset attributed—after disentangling intent from execution on the shot-percentage 100% subset—to a label execution-noise envelope that is tighter than the realized play of top-tier matches. Within real matches the chain responds smoothly to each delivered stone and, at the directly validated terminal shot, assigns mean probability $\approx 0.71$–$0.72$ to the realized end score under intended execution (against $0.28$ for a marginal-frequency baseline), transferring from senior to junior populations. The framework is best read as an internally coherent chain anchored to a directly validated final-shot calibration, statistically plausible under intended execution with a clear boundary at execution failure. Two structural limitations remain: rare high-magnitude outcomes are scarce in real data and produce a heavy upper tail of accuracy errors, and a single fixed execution-noise envelope that is tighter than top-tier realized play accounts for the aggregate hammer-side offset and motivates recalibration against real-match execution statistics. Full article

As a natural event that poses a serious threat to human life, property, and the natural ecology, the effective identification, assessment, and early prevention of geological hazards are crucial. Hunan Province in China is a region with a high incidence of geological hazards, exhibiting complex chain-generated characteristics due to the influence of terraced topography, heavy rainfall, and human activities. Existing landslide monitoring methods have insufficient ability to capture weak deformation at small spatial scales, making it challenging to identify landslide disaster precursors in this region effectively. This paper proposes a multi-resolution SAR collaborative monitoring method using SBAS-InSAR technology for wide-area screening, followed by a joint PS/DS-InSAR processing framework to identify weak deformation signals at small spatial scales. Using 2441 registered geohazard sites in the work area as the background dataset, wide-area InSAR monitoring and remote-sensing interpretation delineated 180 suspected geohazard target areas. Field investigation confirmed 83 of the 180 candidate target zones as active hidden-danger points, corresponding to a field-confirmed rate of 46.11% among the interpreted candidates. Full article

The occupational health implications of solar photovoltaic (PV) recycling remain critically under-investigated. This study assessed occupational exposure across the PV recycling value chain in Taiwan, evaluating primary mechanical dismantling and downstream reuse sectors (glass milling and controlled low-strength material [CLSM] batching). Area and personal samples were analyzed for total dust, respirable dust, and trace heavy metals. Results indicated that primary mechanical crushing yielded relatively low ambient dust and negligible toxic heavy metal (e.g., Pb, Cd) aerosols, attributed to the macroscopic ductility of metallic ribbons and EVA shock-absorbing properties. Conversely, a critical “hazard transfer” phenomenon was empirically identified downstream, where intensive secondary grinding and aggregate blending in the downstream reuse sector (glass milling and CLSM batching) systematically shifted the aerodynamic particle size distribution, causing the respirable dust fraction to surge to 38.9–72.6%. The pursuit of zero-waste material circularity inadvertently amplifies highly dispersive, respirable dust hazards in downstream sectors, necessitating targeted occupational exposure controls. Full article

The cascading damage of expressways induced by extreme heavy rainfall presents a persistent threat to transportation safety and regional sustainable development. To investigate the chain-like evolution characteristics of expressway damage caused by heavy rainfall and to identify precise strategies for mitigating disaster risks by breaking the chain. Firstly, directed causal event pairs were extracted, and clustering generalization was performed on disaster events.; the asymmetric Jaccard index was used to calculate edge weights, thereby establishing a directed causal knowledge graph of disaster chain evolution; Secondly, based on systematic risk assessment and chain-breaking priority indicators, we achieved the precise identification and quantification of critical vulnerable links; finally, we selected three typical damage cases—the ‘5·1’ case on the Meida Expressway in Guangdong, the ‘7·19’ case on the Danning Expressway in Shaanxi, and the ‘8·3’ case on the Yakang Expressway in Sichuan—for case validation, and proposed chain-breaking strategies. The research findings indicate that: (1) under specific hazard-forming environment, secondary disasters can supplant the primary causative factors to become the dominant driving nodes in chain evolution; (2) edge vulnerability and source-path diversity loss indicators respectively point to two distinct categories of high-risk edges; the comprehensive chain-breaking index compensates for the assessment blind spots of single indicators through two-dimensional weighting; (3) core vulnerabilities in disaster chains vary significantly across different regions: the Meida Expressway, the Danning Expressway, and the Yakang Expressway correspond to terminal response, pavement control node, and dual vulnerabilities at the source and structural levels, respectively, necessitating tailored chain-breaking strategies adapted to local conditions. These research findings offer a quantitative tool for infrastructure risk governance, contributing to the safety and sustainability of expressway transportation. Full article

Under the background of the steady advancement of the dual-carbon goal and the increasing improvement of the green financial system, green supply chain finance is like a bridge that closely links the capital of the financial market and the low-carbon transformation of the real economy. The following article chooses A-shares traded enterprises from 2014 to 2024 as the study sample, adopts multi-dimensional empirical methods to study the association in green supply chain finance along with corporate emission levels, and analyzes its transmission mechanisms and heterogeneity. The findings demonstrate that green supply chain finance has a substantial inhibitory impact with enterprise emission levels, a finding that remains robust across a series of tests, including parallel trend tests, placebo tests, and propensity score matching (PSM). Mechanism analysis demonstrates that green supply chain finance can indirectly reduce carbon emission intensity by improving both financing levels and supply chain stability. Looking at heterogeneity, we find that the emission-reducing effect tends to be stronger among state-owned firms, non-heavy polluters, enterprises with higher total factor productivity, and enterprises that are more financially oriented. Our theoretical value lies in clarifying the direct relationship between green supply chain finance and micro-enterprise carbon emissions, identifying two differentiated intermediary transmission paths, and defining the boundary conditions of the policy role across multiple dimensions, thereby better coordinating and promoting the digital and low-carbon transformation of enterprises. Full article

Identifying the most appropriate lag structure for double seasonal autoregressive models is a fundamental demanding task in time series analysis. Classical model selection tools such as the Akaike information criteria are well known to be inconsistent for identifying the true lag structure. At the same time, standard Bayesian formulations almost invariably assume normality that is often violated in the presence of heavy tails, isolated outliers, or contaminated observations. This paper addresses both limitations simultaneously by proposing a unified Bayesian framework for the best-subset selection of multiplicative double seasonal autoregressive models under the scale-mixtures of normal family of error distributions. Adopting an extended stochastic search variable selection, we assign mixture-normal priors to all primitive autoregressive coefficient vectors and derive tractable closed-form conditional posterior distributions for all model parameters. Building on these results, we design an efficient Markov chain Monte Carlo algorithm integrating Gibbs sampling and Metropolis–Hastings updates for simultaneous subset identification and parameter estimation under heavy-tailed errors. A comprehensive simulation study and real-data applications to hourly electricity demand in the Czech Republic and Germany demonstrate that the proposed algorithm consistently outperforms classical information criteria, particularly when innovations are heavy-tailed. Full article

Soil contamination around iron ore complexes in semiarid zones is usually described by total metal concentrations, which underestimate the labile mobile pool. At the Sokolovka-Sarbai mining and beneficiation complex (SSGPO, Northern Kazakhstan), total (EPA 3051A) and mobile (neutral NH₄OAc, pH 7) forms of ten heavy metals were analyzed in 87 site-horizon composites (29 sites × 3 pits × 3 horizons; 261 field samples). Descriptive indices flag Co as the only moderately contaminated element (I_geo 1.16) and Mn as the only one in persistent deficit (Kc_total 0.62); Co and Mn show the largest mobility-factor increments above background (Δ_MF +17.8 and +22.3 p.p.). The priority toxic elements As, Cd and Pb remain at or near depth-matched background in the total fraction (median Kc_total 0.98–1.09; I_geo < 0 for all three), although Pb shows a moderate mobile-fraction enrichment (median Kc_mobile 2.6); mercury was not among the ten metals analyzed. Factor analysis of mobile forms resolves two independent sources (F1-siderophile Cr-Ni-Fe-Mn-Co; F2-Zn-dominant, non-sphalerite) and a humus-driven sorption pool (F3), coherently localized in the dump2A-pit2 sector; mobile-fraction attribution greatly outperforms the total fraction (21 vs. 0 FDR-significant trends). The raw Mn-deficit-Co-mobility correlation (ρ = −0.54) is fully mediated by humus (partial ρ = +0.05). Total Mn deficit and enhanced Co lability are therefore interpreted as coupled consequences of a single humus-Ca-pH Kastanozem geochemistry rather than a causal “Mn-buffer depletion → Co mobilization” chain. Because the dataset is cross-sectional, this distinction remains correlational; sequential fractionation and mineralogical verification are priorities for future work. Full article

False base station (FBS) attacks exploit the attach window before the network authenticates to the device. Existing User Equipment (UE)-side detectors typically need either labeled attack data, which is scarce and does not generalize to unseen attacks, or models too heavy for the resource budget of a smartphone or embedded endpoint. This study presents a lightweight UE-side detector built on a first-order Markov chain over a four-tuple state of packet type, direction, message identifier, and access-network type. A single counting pass fits the 119 KB chain, and thresholds are derived from normal traffic, so no attack labels are consulted. The capture path requires root and Qualcomm modem diagnostic access. Attacks surface as low-probability transitions, rare field values, and anomalous pacing, fused into a per session verdict with per-message attribution. On 192 commercial, testbed, and public LTE and 5G captures, the detector flags 51 of 53 attacks at an F1 of 88.70% in leakage-free leave-one-session-out evaluation and 96.23% once calibration covers the scored sessions. In five-fold cross-validation its F1 of 86.21% trails the strongest supervised baselines by margins that are not statistically significant, and it records the lowest latency (0.46 ms) and smallest working set (8.8 MB) among the eleven detectors benchmarked. Full article

The economic value of Bombyx mori is built on two complementary substrates: the productive traits and the molecular mechanism of fibroin synthesis. Despite their importance to European sericulture, neither phenotypic nor transcriptional characterizations of Romanian breeds have been reported. Herein, we combine ten phenotypic traits with qRT-PCR quantification of fibroin light-chain (Fib-l) and fibroin heavy-chain (FIBH) expression across four B. mori breeds: the Romanian breeds Băneasa 1 (B1) and Galben de Băneasa (GB), the Japanese breed JH3, and the Chinese breed Auriu Chinez (ACH). All breeds were reared on the same artificial diet under identical thermohydrometric conditions, highlighting the genetic background as the dominant source of variation. Phenotypically, univariate testing and multivariate analyses converged on a two-cluster solution, (B1 + JH3) and (GB + ACH), consistent with genetic background and silk-pigment. However, B1 is the only breed that combines high silk-gland mass with Fib-l overexpression relative to the other three breeds (FC = 2.7–3.6, all p < 1 × 10⁻⁴) and with a Fib-l/FIBH expression ratio of 9.27, above the 1.5–2.9 observed in the other three breeds. The molecular signature identifies B1 as transcriptionally distinct and provides a candidate mechanism for superior productive parameters under artificial-diet rearing. Full article

Exhaust emission assessment of heavy-duty diesel engines is commonly based on complementary steady-state and transient procedures, represented by the World Harmonized Steady-State Cycle (WHSC) and the World Harmonized Transient Cycle (WHTC). However, under transient operation, tailpipe NO_x and CO₂ signals cannot be directly assigned to instantaneous engine operating states because the measured response is affected by transport delay, analyser dynamics, and signal dispersion within the measurement chain. This paper proposes a machine-learning-assisted dynamic synchronization framework for aligning engine operating signals with tailpipe emissions under transient conditions. The method uses actual engine torque as the primary dynamic reference and determines local effective alignment between emission readings and the engine operating states that generated them. The synchronized data are then evaluated using an XGBoost-based modelling approach to assess whether emission characteristics obtained from WHSC steady-state operation can be transferred to WHTC transient operation. The results show that the proposed synchronization improves the physical consistency of transient emission data and provides a more reliable basis for comparing stationary and dynamic test outcomes. The transferability analysis indicates good predictive consistency for CO₂, whereas NO_x shows only partial transferability, reflecting stronger transient sensitivity and more complex formation dynamics. The proposed framework supports intelligent emission-data preprocessing, data-driven interpretation of heavy-duty engine tests, and assessment of the representativeness of steady-state tests under transient operating conditions. Full article

Background: Insomnia and stress-related sleep disorders are increasingly recognized as systemic conditions linked to the microbiota–gut–brain axis (MGBA). With growing clinical interest in natural products that modulate the gut environment, this systematic review evaluates the efficacy and mechanisms of non-pharmacological interventions, specifically probiotics, prebiotics, dietary indices, and botanicals, in alleviating insomnia, restoring circadian rhythms, and modulating neurochemical markers. Methods: In strict accordance with PRISMA 2020 guidelines, we searched PubMed, ScienceDirect, Scopus, and The Cochrane Library for English language studies published from inception to March 31, 2026. Eligibility was restricted to studies with rigorously controlled designs, specifically randomized controlled trials (RCTs) and controlled in vivo animal studies. Interventions had to target the gut microbiota, with primary outcomes measuring sleep quality (subjective or objective) or sleep-related neurochemical markers. We excluded uncontrolled, single-arm, or observational designs; in vitro studies; non-original research; and studies involving subjects with severe medical or psychiatric comorbidities (e.g., cancer, ADHD, severe psychiatric disorders) to prevent confounding variables, though mild-to-moderate anxiety and depression were permitted. Risk of bias was assessed using the Cochrane RoB 2.0 and SYRCLE tools. Due to significant methodological heterogeneity, a narrative synthesis stratified by intervention and population was conducted. This review was not registered in PROSPERO. Results: A total of 56 studies (33 humans, 23 animals) met the inclusion criteria. Taxonomic nomenclature was updated to reflect 2020 reclassifications (e.g., Lactiplantibacillus plantarum). In human trials, interventions significantly improved subjective sleep metrics (PSQI, ISI). Recent additions demonstrated the efficacy of the Dietary Index for Gut Microbiota (DI-GM) and the improvement in N3 sleep latency by yeast mannan. Furthermore, whole-food patterns (e.g., the MIND diet) and Traditional Chinese Medicine (TCM) decoctions successfully enriched beneficial taxa, such as Bacteroides coprophilus, and increased short-chain fatty acid (SCFA) production. Animal models demonstrated that “psychobiotic” strains (Bifidobacterium breve, Lacticaseibacillus paracasei), prebiotics (GOS/PDX), and TCM formulas effectively restored GABA/5-HT profiles, lowered morning cortisol, and facilitated REM rebound in PCPA-induced models, while also consolidating non-rapid eye movement (NREM) sleep and downregulating clock genes (Per1/Per2). Conclusions: Psychobiotics, prebiotics, and botanicals represent a highly viable non-pharmacological strategy for treating insomnia. However, current evidence is constrained by a heavy reliance on subjective human questionnaires, short follow-up durations limiting insight into long-term stability, and a substantial translational gap between mechanistic rodent models and human clinical outcomes. Full article

The relationship between corporate digitalization and green management practices has received increasing scholarly attention, but existing empirical findings remain inconsistent. To clarify this relationship, this research conducts a meta-analysis based on 94 effect sizes from 82 empirical studies, adopting a multivariable research framework to integrate existing findings and explore the factors that contribute to the generation of heterogeneity. The findings indicate that corporate digitalization facilitates green management practices, a conclusion robust across three key dimensions: environmental performance, green innovation, and green supply chain management. Furthermore, the findings show that digitalization exerts a stronger positive effect in non-manufacturing firms, non-heavy-polluting firms, and high-tech firms, while measurement approaches emerge as a critical factor influencing empirical outcomes. These findings provide integrated evidence on the digitalization–green management relationship, clarify its key boundary conditions, and offer practical implications for firms seeking to advance low-carbon transformation through digital technologies. Full article

African soils face increasing levels of metal pollution due to industrialization, artisanal mining activities, improper waste management, and enhanced agricultural productivity. However, unlike many organic pollutants, heavy metals do not degrade naturally and therefore persist in environmental systems for prolonged periods. Heavy metals accumulate over many decades in the soil and bioaccumulate through the food chain causing severe health complications such as cancer, kidney problems, and neurological impairment. This paper reviews the current literature on the origin, prevalence, and behavior of the main pollutants Pb, Cd, Cr, As, Hg, and Cu. The major phytoremediation methods including phytoextraction, rhizofiltration, phytostabilization, and phytovolatilization are highlighted alongside in planta screening methods for hyperaccumulating plants including Berkheya coddii (Ni) and Haumaniastrum robertii (Co). The paper evaluates various enhancement techniques such as the use of chelators, Rhizobium inoculations, and genetic modifications. The significance of these approaches in tropical and subtropical climates is discussed. The paper suggests a holistic framework involving empirical kinetic modeling, geospatial machine learning (random forest, kriging), and molecular omics in prediction modeling. Major hurdles in such predictions include lack of field-based verification of the models, biotechnology safety of genetically modified (GM) organisms, and inadequate regulations. Future perspectives emphasize community-driven phytomining, biomass recycling, and resilient phytoremediation solutions. Full article

Iron overload disrupts cellular homeostasis and drives ferroptosis through dysregulated iron metabolism. Non-coding RNAs (ncRNAs) are considered as key regulators of various biological functions and targets for a new generation of RNA therapeutics and biomarkers. However, few studies have investigated the regulatory roles of ncRNAs, particularly competitive endogenous RNAs (ceRNAs) in iron overload. This study performed whole-transcriptome sequencing to characterize the ceRNA network in ferric ammonium citrate (FAC)-induced iron-overloaded HT-1080 fibrosarcoma cells. A total of 208 differentially expressed mRNAs, 83 lncRNAs, and 170 circRNAs (q < 0.05) were identified, with hierarchical clustering revealing distinct expression patterns between control and iron-treated groups. KEGG enrichment implicated vitamin B6 metabolism (q < 0.001) and lysine degradation (q < 0.001) as key disrupted pathways. ceRNA network was conducted and further demonstrated lncRNA/circRNA-mediated regulation of ferroptosis genes via shared miRNA response elements. Notably, LINC-PINT-232 was implicated in the regulation of both ferritin heavy chain (FTH) and sequestosome 1 (SQSTM1), two ferroptosis-associated mRNAs. FTH upregulation mitigates iron toxicity through ferroxidase activity, while SQSTM1 modulates lipid peroxidation in ferroptosis. These findings provide a preliminary transcriptomic landscape for hypothesis generation regarding ncRNA-mediated regulatory mechanisms in iron overload-induced ferroptosis and offer a computational foundation for future functional and therapeutic investigations. Full article

Flow cytometry is an essential diagnostic method in hematology, and one of its main applications is the assessment of the clonality of mature B cells. We present a case report of a patient referred for the investigation of absolute lymphocytosis. The flow cytometry study revealed an increased percentage of B cells, but it could not establish B-cell clonality, based on the study of surface light chains in combination with the pattern of expression of mature B-cell markers. The diagnosis of Persistent Polyclonal B-cell Lymphocytosis (PPBL) was considered in the differential diagnosis as the mature B cells were found to be immunophenotypically memory B cells. However, due to the markedly elevated count of B cells, molecular testing with Polymerase Chain Reaction (PCR) for B-cell clonality based on IGH (Immunoglobulin Heavy Chain) gene rearrangements was performed, and it revealed the presence of two clones of B cells. Approximately one year later, the same work-up was repeated in the patient’s bone marrow aspirate. By flow cytometry, a distinct clonal B-cell population was isolated, while the molecular testing with PCR for B cell clonality based on IGH heavy-chain gene rearrangements revealed the presence of three clones of B cells. In addition, evaluation of the sample with high-dimensional mass cytometry showed the presence of four major immunophenotypically abnormal B-cell subsets. Full article