Search Results (36)

To investigate the associations between single and mixed exposure to air pollutants in underground coal mine environments and the risk of metabolic syndrome (MetS) among workers, we conducted a cross-sectional study. Multivariable logistic regression, Bayesian kernel machine regression (BKMR), and quantile-based g-computation (Qgcomp) were used to evaluate the joint effects of mixed pollutant exposures and to identify the major contributing components. In addition, untargeted urinary metabolomics analysis was performed to explore the potential biological mechanisms. After adjusting for confounding factors, logistic regression analysis revealed that exposure to coal dust (CD), carbon monoxide (CO), carbon dioxide (CO₂), and nitrogen dioxide (NO₂) was associated with an increased risk of MetS. Furthermore, BKMR and Qgcomp models consistently indicated a significant positive association between mixed pollutant exposure and MetS risk, with CD and NO₂ identified as the primary components with the strongest statistical contribution. CD exposure was mainly associated with central obesity and hypertension, whereas NO₂ exposure was primarily linked to elevated blood glucose and triglyceride levels. MetS patients exhibited significant alterations in urinary metabolic profiles, with more than 113 differentially abundant metabolites identified. Notably, leukotrienes were positively correlated with NO₂ exposure, while acylcarnitines were associated with CD exposure. Pathway enrichment analysis indicated significant disturbances in pyrimidine and arachidonic acid metabolism. These findings provide important evidence for developing comprehensive occupational health strategies in mining environments. Full article

Lardizabala biternata is an endemic vine and the sole species of the monotypic genus Lardizabala, distributed across central–southern Chile and producing edible fruits traditionally harvested from wild populations. Despite its cultural relevance, potential agronomic value, and evolutionary singularity, its conservation status remains poorly understood because of incomplete distributional knowledge and historical nomenclatural inconsistencies. Here, we conducted a preliminary record-based spatial conservation assessment of L. biternata using a taxonomically and geographically curated occurrence database integrated with Kernel Density Estimation (KDE), Extent of Occurrence (EOO), Area of Occupancy (AOO), overlap analyses with Chile’s protected-area network (SNASPE) and the Chilean Winter Rainfall–Valdivian Forests biodiversity hotspot, and ecological representativeness assessments based on mapped vegetation-class units. Our analyses revealed substantial spatial and ecological conservation gaps. Most documented occurrence records were located outside protected areas, while KDE-derived occurrence-record density patterns were concentrated within the biodiversity hotspot, a region characterized by high endemism and intense anthropogenic pressure. Although EOO indicated a relatively broad geographic extent, AOO indicated restricted occupancy based on documented records. In addition, several mapped vegetation-class units intersecting the 95% KDE-derived occurrence-record density area lacked formal conservation coverage entirely. Together, these findings suggest that L. biternata may warrant greater conservation attention than previously recognized. This study provides the first integrated record-based spatial conservation assessment of the species and establishes a baseline framework for future conservation evaluations, ecological studies, and ex situ conservation strategies. Full article

Two-objective optimization requires both reliable front approximation and explainable non-dominated extraction. This study develops a theoretical and computational method that maps sampled objective vectors to rasterized objective-space images and processes their Pareto structure through supervised neural approximation, a deterministic convolutional extractor, and exploratory reinforcement search. Network I reconstructs a high-density sampled occupancy image from sparse samples, whereas Network II approximates the sampled Pareto-front boundary. The principal algorithmic contribution is a fixed cross-correlation kernel derived from the two-objective dominance quadrant and coupled with a cell archive that preserves original vectors and resolves raster collisions through exact dominance checks. Under the stated coordinate convention, central inversion relates the dominating and dominated displacement quadrants, translation-equivariant cross-correlation applies the same local relation across the grid, and minimization selects only the improvement-directed boundary. Experiments on SCH, FON, POL, KUR, and ZDT synthetic benchmarks assess front-geometry recovery and deterministic extraction on grids from 127 × 127 to 2048 × 2048; the reinforcement-learning results on SCH are interpreted as exploratory feasibility evidence. The present evidence is therefore confined to synthetic benchmarks. The method provides a benchmark-based methodological foundation for future multi-criterion model-selection and calibration research, including option-pricing model research in financial mathematics and quantitative economic analysis. Full article

Application-specific instruction set processors (ASIPs) often employ specialized hardware to improve performance, but this introduces complexity in resource management and programming. Existing compiler solutions, including LLVM’s default schedulers, lack fine-grained structural conflict analysis for complex arithmetic logic unit (ALU) instructions, leading to suboptimal performance or runtime errors. This limitation becomes critical when targeting very long instruction word (VLIW) architectures with instruction fusion units that exhibit pipeline-stage-level resource contention. In this paper, we propose a compile-time instruction scheduling method that models sub-cycle resource usage and analyzes both data and structural dependencies at fine granularity. Unlike coarse-grained resource tables used in existing compilers, our approach tracks functional unit occupancy at the pipeline stage level, enabling precise detection of structural hazards in complex execution units. We implement this scheduler as a backend pass in the LLVM compiler framework and validate it on the Sayram VLIW processor for wireless communication. Experimental results show that our approach achieves 100% scheduling correctness while improving execution efficiency by 23% on average compared with in-order scheduling, with benefits up to 38% for highly parallel kernels such as PRACH, and reducing average running time by 66% compared with atomic execution. Full article

Railway marshalling yard inbound operations are affected by irregular train arrivals, finite receiving-yard capacity, service interruptions, and boundary states carried across operating days. This study develops a hybrid fluid–queueing and simulation framework for analyzing congestion evolution in the arrival–technical-operation–hump-disassembly process. The framework integrates continuous arrival-input construction, boundary-state stability validation (PSSBV), and daily gated integer discrete-event simulation (DGDES). Circular kernel density estimation and observed train-level sequences represent non-stationary arrivals; PSSBV determines operationally reasonable initial conditions; and DGDES captures finite-capacity admission, FIFO outside holding, parallel technical operations, hump disassembly, and service-interruption windows. The framework is evaluated under deterministic and stochastic service-time conditions using continuous-operation data from a large Chinese marshalling yard and field-observed occupancy and waiting-time indicators. The results show that congestion is driven less by daily arrival volume alone than by the interaction of concentrated arrivals, pre-disassembly backlog, residual in-yard occupancy, and insufficient hump-disassembly clearance capacity. The simulated outside holding rate follows fluctuations in the field-based track–time load ratio and is closely associated with disassembly waiting time, system sojourn time, and pre-disassembly queue length. Lagged analysis indicates that previous-day saturation and residual workload can increase next-day outside holding risk. Stochastic service times produce similar high-risk patterns while increasing variability and tail-risk exposure. The proposed threshold–probability diagnostic framework supports rapid congestion-risk identification and dispatching-oriented control under continuous and uncertain operating conditions. Full article

A formal thermodynamic mapping is established between the attractive Fermi–Hubbard model and the repulsive Bose–Hubbard model at finite temperature and at imaginary chemical potential $\mu =i\theta$. By utilizing a large N-expansion, it is shown that the partition functions of the two models are related by a plain shift $\theta \to \theta +\pi$. This condition maps the BCS–BEC crossover of attractive fermions to a Bose–Fermi crossover (fermion-like occupation) of repulsive bosons. A central feature of this correspondence is the thermal kernel $g(\beta E,\varphi )$ (with $\beta$ the inverse absolute temperature, E the energy scale, and $\varphi$ the phase angle), whose analytic continuation $g_B(\beta E,\varphi )=g_F(\beta E,\varphi +\pi )$ governs the bosonic (B) and fermionic (F) sectors. Interestingly, the particular angles $\varphi =2\pi /3$ and $4\pi /3$ for fermions correspond to $\varphi =\pi /3$ and $5\pi /3$ for bosons, marking the boundaries of an universal thermal window. It is further argued that the present mechanism shows how an emergent, fermionization-like phenomenon can occur at finite interaction strength through a thermodynamic effect induced by the imaginary chemical potential. It is emphasized that this does not imply a transmutation of quantum statistics at the operator level, but rather a thermodynamic exclusion-like behavior driven by the imaginary chemical potential, unlike the Tonks–Girardeau limit, where fermionization arises from an infinite repulsive interaction and anyonic or Floquet-engineered systems where transmutation emerges from modified statistics or dynamics. Effectively, the phase $\varphi$ is a statistical parameter; by twisting the thermal phase, it generates fermion-like behavior without hard-core constraints or infinite repulsion through purely thermodynamic mechanisms. The gap equation and number equation for the bosonic model are derived, highlighting the role of the imaginary chemical potential as a statistical regulator. The results obtained here provide a unified framework for understanding crossovers in interacting lattice systems. Full article

It is widely acknowledged that General Matrix Multiplication (GEMM) serves as a foundational kernel across numerous application domains. Complex numbers exhibit distinctive mathematical properties that enable their widespread adoption across engineering computing scenarios, including signal processing and signal transformation. This study investigates high-efficiency CGEMM, namely, complex-valued GEMM, for NPU hardware, broadening the application scope of NPUs beyond mainstream low-precision AI computation workloads. The major contributions of this study are as follows: (i) numerical precision and hardware utilization of the 3M and 4M decomposition schemes on Ascend NPUs are analyzed, and the 4M method is selected as the preferred CGEMM implementation under our tested hardware constraints to fit the bandwidth limitations of modern accelerators for both precision-sensitive and performance-critical matrix computation scenarios; (ii) a complete high-performance CGEMM design based on the 4M scheme tailored for Ascend NPUs is proposed, with an AIC/AIV dual-stream pipeline scheduling strategy equipped to coordinate padding operations, matrix–matrix multiplications, and element-wise instructions across multi-level memory hierarchies and compute units; (iii) a fine-grained task scheduling and assignment mechanism is implemented to maximize Cube core occupancy across diverse matrix dimensions, improving hardware utilization for various computation workloads. Our experimental measurements show that the proposed CGEMM achieves a competitive hardware utilization rate of 83.6% across all tested matrix configurations, enabling efficient exploitation of available computing resources. Meanwhile, we observe a measured average speedup of 1.14× relative to the AscendSipBoost implementation tested on an identical Ascend NPU, alongside a measured 3.17× speedup compared with cuBLAS running on the Nvidia GPU platform adopted in our experiments across all evaluated matrix sizes. These results reflect the promising capability of Ascend NPUs for high-precision complex-valued computing workloads within the tested experimental setup. Full article

Understanding the internal spatial structure of widely distributed species is fundamental for biogeographic theory and conservation practice, yet such structure is often masked by extent-based range metrics. We investigated the spatial organisation of Pseudopus apodus (Pallas, 1775), the largest limbless lizard of the Western Palearctic, using 3967 occurrence records spanning 1843–2025. Spatial point pattern analysis revealed a pronounced deviation from complete spatial randomness (Clark–Evans R = 0.105, p < 0.001), with strong fine-scale clustering. Kernel density estimation identified a clear core–periphery organisation: high-density core areas accounted for 30% of records but occupied only 22% of the total extent of occurrence (EOO). The discrepancy between EOO (~8.9 million km²) and area of occupancy (AOO; ~8944 km²) spanned three orders of magnitude, emphasising that only a small fraction of the species’ geographic envelope is actively occupied. Spatial heterogeneity was high (coefficient of variation ≈ 0.99), and core and peripheral occurrences were significantly segregated along both latitudinal and longitudinal gradients. The proportion of core records showed a weak positive temporal trend most plausibly attributable to sampling effort, particularly the recent expansion of citizen-science contributions, rather than to ecological processes. These findings demonstrate that P. apodus exhibits a compact spatial core embedded within a broad, sparsely occupied periphery, underscoring the limitations of EOO-based metrics in the conservation assessment of widely distributed reptiles. We emphasise that this structure is characterised using the density of occurrence records and therefore describes the observed spatial organisation of the available data rather than directly measured population density; ecological interpretations are accordingly framed as hypotheses requiring independent validation. Full article

Unauthorised FM broadcasting poses significant challenges to spectrum regulators globally, contributing to interference, degraded service quality, and national security threats. While traditional spectrum monitoring relies primarily on carrier frequency and power measurements, this study demonstrates that FM baseband features—specifically the multiplex (MPX) signal structure, pilot tone, and Radio Data System (RDS) subcarrier—provide robust discriminative markers for detecting non-compliant transmissions. Using a real-world dataset of 3710 pre-processed records collected across Nigeria’s capital region between 2021 and 2024, we extracted and analysed six transmission parameters: assigned frequency, band occupancy (±100 kHz), MPX overshoot percentage, pilot tone presence, and RDS indicators. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel was trained to distinguish compliant licensed stations from regulatory non-compliant transmissions—encompassing both unlicensed transmitters and technically non-compliant licensed operators—achieving 99.96% accuracy, 99.38% precision, and 99.63% recall with a false alarm rate of 0.026%. A Comparative analysis against baseline feature sets confirmed that integrating MPX, pilot, and RDS significantly improved detection robustness compared with carrier-only approaches. Results demonstrate that feature-rich baseband analysis enables scalable, cost-effective regulatory enforcement, reducing manual monitoring burden while enhancing detection reliability. This framework offers practical applicability for spectrum management agencies in resource-constrained environments where unauthorised broadcasting remains prevalent. Full article

This paper develops a finite-state mathematical framework for structural stability and regime classification in discrete-time state–event–response systems whose effective transition structure is generated endogenously by state-dependent response rules. Unlike classical structural stability theory, which focuses on qualitative persistence in smooth dynamical systems, and unlike Markov-chain analysis, which typically assumes a fixed transition kernel, the proposed framework treats the transition graph as an induced object. The model specifies a finite state space, an event-generation law, an elasticity-dependent attenuation function, and a deterministic transition mapping. Structural regimes are classified by adjacency relations, communicating components, absorbing organization, and long-run occupancy support. A Monte Carlo verification layer is used only to examine whether the analytically defined topological regimes are visible in finite-sample occupancy signatures. The results indicate that, within the finite-state setting considered here, admissible disturbance scaling changes traversal frequency without changing graph identity, whereas elasticity variation can activate or deactivate effective edges and thereby generate structurally distinct regimes. Full article

Currently, improving runway utilization under operational safety constraints has become a critical concern for small and medium airports. Existing research focuses primarily on landing-phase runway occupation time, while predictive studies on the takeoff phase remain limited. Analysis of 1749 Quick Access Recorder (QAR) records from ten airports reveals that departure runway occupation time is strongly correlated with ground speed at liftoff (0.72) and airport elevation (0.67) but weakly correlated with aircraft weight and meteorological conditions, providing guidance for feature engineering. To address the prediction of departure runway occupation time, this study proposes a TCN-FEP hybrid model. The model employs an enhanced Temporal Convolutional Network (TCN) module with multi-scale convolutions (kernel sizes 3, 5, 7) and dilated convolutions (rates 2, 4, 8) to capture multi-scale feature interactions, alongside a Feature Enhancement Projection (FEP) module that maps local features into a high-dimensional latent space for implicit relationship mining and global information integration. Experimental results demonstrate that the proposed TCN-FEP model achieves an MSE of 90.20, RMSE of 9.49, MAE of 5.84 s, MAPE of 3.80%, and R² of 0.97, outperforming Informer (MSE 117.95), Longformer (MSE 132.11), XGBoost (MSE 92.30), and LightGBM (MSE 91.45). Under 5% outlier injection, MSE increases by 7.9%, compared to 24.3% for LSTM and 18.4% for Informer. With 94% of prediction errors within ±5 s, the model’s accuracy may offer a useful reference for runway resource optimization at small and medium airports. Full article

Seismic excitations induce floor accelerations that can damage non-structural components and, in extreme cases, contribute to global structural failure. Although floor acceleration demands have been widely studied, their integration into probabilistic seismic performance and reliability frameworks remains limited within Performance-Based Seismic Design (PBSD). This study addresses this gap by proposing a reliability-based framework that incorporates the stochastic nature of floor accelerations through their probability density functions. Five-story steel and reinforced concrete (RC) buildings, designed according to Mexican codes, were analyzed using nonlinear dynamic simulations in PERFORM 3D under 33 ground motions corresponding to immediate occupancy (IO), life safety (LS), and collapse prevention (CP) levels. Structural reliability was quantified using the probability of failure ($p_f$) and the reliability index (β). Results show that peak accelerations occur at the roof level, with higher demands in the steel structure. For the IO level, β ranged from approximately 2.29 to values above 4.0 in steel buildings, while RC structures reached up to β ≈ 4.97. At LS and CP levels, RC buildings maintained β values generally above 3.0, whereas steel structures showed values as low as β ≈ 1.32. The Kernel distribution best captured response variability, reflecting high dispersion ($C.V.$ > 30%). The proposed framework enhances PBSD by linking acceleration demands with reliability-based decision-making. Full article

Accurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation (CV), relative change (ROC), and absolute change (AC). In this study, kernel density estimation (KDE) is employed not as a predictive model, but as a distributional tool to robustly extract representative flock weight from noisy, high-frequency scale measurements under commercial farm conditions. In the absence of physical ground-truth, our evaluation focused on the framework’s ability to consistently detect the single, representative peak in the KDE distribution. Weekly thresholds were empirically optimized for the preprocessing filters. Results show that the combined ROC + AC method consistently produced unimodal peak distributions and improved the Peak Detection Rate (PDR) from 91.2% (raw data) to 97.9%. Single-Entity Filtering, assisted by cameras, further mitigated density distortions caused by prolonged occupancy, while CV-only and ROC-only filtering yielded less stable representative values. These findings demonstrate that rigorous preprocessing is essential for reliable KDE-based weight estimation under real-world farm conditions. The proposed framework not only improves data quality and stabilizes distributions but also provides a practical foundation for real-time monitoring and AI-driven precision livestock farming models. Full article

This study develops an integrated bi-level operations–assignment model to optimise express service on the Gyeongin Line, a core corridor connecting Seoul and Incheon. The upper level jointly selects express stops and time-of-day headways under coverage constraints—a minimum share of key stations and a maximum inter-stop spacing—while the lower level assigns passengers under user equilibrium using a generalised time function that incorporates in-vehicle time, 0.5× headway wait, walking and transfers, and crowding-sensitive dwell times. Undergrounding and alignment straightening are incorporated into segment run-time functions, enabling the co-design of infrastructure and operations. Using automatic-fare-collection-calibrated origin–destination matrices, seat-occupancy records, and station-area population grids, we evaluate five rail scenarios and one intermodal extension. The results indicate substantial system-wide gains: peak average door-to-door times fall by approximately 44–46% in the AM (07:00–09:00) and 30–38% in the PM (17:30–19:30) for rail-only options, and by up to 55% with the intermodal extension. Kernel density estimation (KDE) and cumulative distribution function (CDF) analyses show a leftward shift and tail compression (median −8.7 min; 90th percentile (P90) −11.2 min; ≤45 min share: 0.0% → 47.2%; ≤60 min: 59.7% → 87.9%). The 45-min isochrone expands by ≈12% (an additional 0.21 million residents), while the 60-min reach newly covers Incheon Jung-gu and Songdo. Backcasting against observed express/local ratios yields deviations near the ±10% band (PM one comparator within and one slightly above), and the Kolmogorov–Smirnov (KS) statistic and Mann–Whitney (MW) test results confirm significant post-implementation shifts. The most cost-effective near-term package combines mixed stopping with modest alignment and capacity upgrades and time-differentiated headways; the intermodal express–transfer scheme offers a feasible long-term upper bound. The methodology is fully transparent through provision of pseudocode, explicit convergence criteria, and all hyperparameter settings. We also report SDG-aligned indicators—traction energy and CO₂-equivalent (CO₂-eq) per passenger-kilometre, and jobs reachable within 45- and 60-min isochrones—providing indicative yet robust evidence consistent with SDG 9, 11, and 13. Full article

Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO₂, total volatile organic compounds (TVOC), PM_2.5, temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO₂ surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation. Full article