Search Results (3,010)

Accurate time series forecasting of sea surface temperature (SST) is essential for understanding the ocean climate system and large-scale ocean circulation, yet it remains challenging due to regime-dependent variability and correlated errors across heterogeneous prediction models. This study addresses these challenges by formulating SST ensemble time series forecasting aggregation as a stochastic, sample-adaptive weighting problem. We propose a diffusion-conditioned ensemble framework in which heterogeneous base forecasters generate out-of-sample SST predictions that are combined through a noise-conditioned weighting network. The proposed framework produces convex, sample-specific mixture weights without requiring iterative reverse-time sampling. The approach is evaluated on short-horizon global SST forecasting using the Global Ocean Data Assimilation System (GODAS) reanalysis as a representative multivariate dataset. Under a controlled experimental protocol with fixed input windows and one-step-ahead prediction, the proposed method is compared against individual deep learning forecasters and conventional global pooling strategies, including uniform averaging and validation-optimized convex weighting. The results show that adaptive, diffusion-weighted aggregation yields consistent improvements in error metrics over the best single-model baseline and static pooling rules, with more pronounced gains in several mid- to high-latitude regimes. These findings indicate that stochastic, condition-dependent weighting provides an effective and computationally practical framework for enhancing the robustness of multivariate time series forecasting, with direct applicability to global SST prediction from large-scale geophysical reanalysis data. Full article

This study investigates the characterisation of particulate matter (PM) across different size fractions (TSP, PM₁₀, PM₄, PM_2.5, and PM₁) in Porto, Portugal, over a 2-year period. Sampling was conducted at two heights (ground level and rooftop), integrating real-time measurements and filter-based analyses to evaluate seasonal and spatial variations. Elemental composition was determined using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), enabling detailed assessments of 30 chemical elements. Meteorological parameters, including temperature, precipitation, wind speed, and direction, were analysed to understand their influence on PM concentrations. Results indicate that significant seasonal trends, with higher PM concentrations observed during autumn and winter, were associated with low boundary layer height, promoting greater mixing of particles, enhanced deposition, and higher anthropogenic emissions, with average seasonal TSP values ranging from 0.001 to 0.059 µg m⁻³. Elemental analysis revealed distinct profiles at ground and rooftop levels, with Ba, Cu, Pb, Mg, and Na among the most frequently detected elements; ground-level samples showed stronger contributions from local sources, such as traffic, while rooftop samples reflected regional and long-range transport. Meteorological factors, such as precipitation and wind speed, exhibited negative correlations with PM concentrations, underscoring their role in atmospheric washing. These findings highlight the complex interplay of local and regional factors in shaping PM dynamics and emphasise the importance of multi-level monitoring for effective air-quality management. Full article

This paper analyzes the impact of corruption on foreign direct investment (FDI) in the Portuguese-speaking African countries (PALOP) economies between 2006 and 2018. The focus lies on Angola, Cape Verde, Guinea-Bissau, and Mozambique since, according to Transparency International, they exhibit intermediate to low levels on the Corruption Perceptions Index. Despite sharing historical and cultural ties, as former Portuguese colonies, no research has focused on the impact of corruption on FDI in the PALOP economies, to the best of our knowledge. To accomplish this, we use an Instrumental Variables Fractional Probit Regression applied to data from the World Bank Enterprise Surveys, which gather information for 2180 firms. The results show that, on average, corruption does not significantly affect FDI in PALOP economies. Trade, credit, and firm size emerge as key FDI determinants, while investment levels and tax rates are not relevant. Corruption has negligible effects on FDI in manufacturing but boosts FDI in services. Interestingly, while corruption has no significant effect on FDI for small and medium firms, a positive, significant impact is revealed for large firms. Finally, corruption’s overall FDI impact is the same across PALOP countries, except in Angola, where it negatively influences FDI compared to Mozambique. Full article

An improved depleting sand fracture model was derived in this work using Finite Element Methods, taking into consideration the effect of pore pressure and production on in situ stresses. Sets of governing equations from the commercial finite element simulator COMSOL Multiphysics were used to obtain a model that compares well with the existing fracture model, mainly based on the Mohr–Coulomb failure criterion. The model uniquely couples reservoir depletion-induced stress evolution with fracture initiation and propagation within a unified finite element framework. A constant overburden load was used since its value majorly depends on depth, and the formation is assumed to be fixed at the bottom. The reservoir is assumed to be depleting at a constant rate with no water injection to assist pressure, with an average porosity of 25% and an average permeability of 251 mD at the beginning of production. The reservoir compacted during production, and in turn, porosity and permeability were reduced over the years of observation. Fracturing was observed to be much easier for the depleted reservoir, since horizontal stresses, which might have created friction, are reduced during reservoir production, signifying that for depleted reservoirs, a small fracture pressure is required. Created fractures are observed to propagate in the direction of the maximum horizontal stress and perpendicular to the direction of the minimum horizontal stress. Full article

Condition monitoring of rolling bearings is essential for ensuring the reliability of mechanical systems operating under severe or insufficient lubrication conditions. This study proposes a fault diagnosis framework that integrates tribological interpretation of wear phenomena, acoustic emission (AE) signal analysis, and machine learning, based on bearing life tests conducted under dry conditions as an accelerated wear environment to capture damage progression within a practical experimental time. Unlike conventional studies relying on artificially introduced defects, this work focuses on AE signals obtained from bearings in which damage initiates and progresses through actual wear processes. Life tests were conducted using deep groove ball bearings under two radial load conditions. The temporal evolution of the coefficient of friction, AE signals, and surface damage was analyzed. Although the coefficient of friction was the most sensitive indicator of wear progression, its direct measurement is impractical for in-service applications. Frequency-domain analysis revealed that AE counts per second and band-specific AE energy exhibit early changes consistent with the evolution of the friction coefficient. Using these physically interpretable AE features, a fully connected neural network was developed to classify bearing conditions into normal, early-stage damage, and damage progression. The proposed model achieved an average classification accuracy of approximately 85%, demonstrating the effectiveness of AE-based machine learning for bearing fault diagnosis under real wear progression conditions rather than artificial defect scenarios. Full article

Driven by advancements in artificial intelligence technology, pedestrian trajectory prediction is shifting from traditional machine learning methods toward autonomous decision-making frameworks based on neural networks. However, the spatiotemporal uncertainty of pedestrian movement results in low accuracy of existing prediction models. To address this issue, we propose a multi-source perception fusion system based on INS-GNSS wearable devices. By integrating high-precision inertial measurement units (IMUs) and multi-mode global navigation satellite systems (GNSS), we enhance localization and prediction accuracy. For localization, we introduce a Gait Adaptive UKF (Gait-AUKF) that identifies pedestrian gait patterns and motion states by fusing multi-sensor data. An adaptive algorithm effectively suppresses trajectory drift and improves tracking accuracy. For trajectory prediction, we propose a pedestrian trajectory prediction framework based on a multi-source fusion attention mechanism. A GRU encoder extracts pedestrian trajectory features from historical motion data. An attention mechanism assigns varying weights to trajectory features across different scales. An LSTM decoder and A* path planning algorithm constrain spatiotemporal paths to generate future pedestrian trajectories. Experimental results demonstrate that compared to UKF and AKF, the Gait-AUKF reduces eastward error by 30%, northward error by 26.27%, and vertical error by 49.08%. The complete prediction framework achieves a 68.54% reduction in average position error (APE) and a 70.42% reduction in direction error (DE) compared to LSTM and Transformer models. Ablation experiments demonstrate that the integrated Gait-AUKF algorithm and A* path planning algorithm enhance model decision performance. After incorporating these algorithms, the model’s ADE decreased by 68.49% and FDE by 71.86%. Full article

In this study, we statistically analyze the performance of a threshold-based multiple optical signal selection scheme (TMOS) for wavelength division multiplexing (WDM) and adaptive coded modulation (ACM); this is achieved using free space optical (FSO) communication between mobile platforms in maritime environments with fog and 3D pointing errors. Specifically, we derive a new closed-form expression for a composite probability density function (PDF) that is more appropriate for applying various algorithms to FSO systems under the combined effects of fog and pointing errors. We then analyze the outage probability, average spectral efficiency (ASE), and bit error rate (BER) performance of the conventional detection techniques (i.e., heterodyne and intensity modulation/direct detection). The derived analytical results were cross-verified using Monte Carlo simulations. The results show that we can obtain a higher ASE performance by applying TMOS-based WDM and ACM and that the probability of the beam being detected in the photodetector increased at a low signal-to-noise ratio, contrary to conventional performance. Furthermore, it has been confirmed that applying WDM and ACM is suitable, particularly in maritime environments where channel conditions frequently change. Full article

Background: Fructans are fructose-based polysaccharides with diverse biological activities; however, their direct activity on skin cells remains unresolved. This study investigated the biological activity of fructan extracted from rakkyo (Allium chinense) (RF) and examined its effects on extracellular matrix (ECM) metabolism, particularly collagen and hyaluronan synthesis, in human dermal fibroblasts. Methods: RF was prepared from fresh rakkyo bulbs by aqueous extraction, alkaline clarification, and membrane filtration. The average molecular weight and structural characteristics of RF were analyzed using size-exclusion chromatography and ¹³C NMR spectroscopy. Normal human dermal fibroblasts (NHDFs) were treated with RF by culturing cells in RF-supplemented medium (0.1–1.0 mg/mL). Cell viability and viable cell number were evaluated using the thiazolyl blue tetrazolium bromide and trypan blue exclusion assays, respectively. Expression of ECM-related genes was analyzed by qRT-PCR, and collagen and hyaluronan production were quantified by Sirius Red staining and ELISA. Results: RF had an average molecular weight of approximately 11,500 Da and consisted of nearly equal proportions of inulin- and levan-type fructans. RF (≤1 mg/mL) increased the number of viable cells and markedly upregulated collagen, type I, alpha 1 (COL1A1) and hyaluronic acid synthase 2 (HAS2) expression while downregulating Hyal1 expression. After 9 days of treatment, the cumulative production of type I collagen and hyaluronic acid increased by 3.8- and 1.3-fold, respectively, as compared with controls. Upregulation of lysyl oxidase (LOX) mRNA suggested enhanced collagen cross-linking, whereas MMP-1 showed only modest induction. Conclusions: Rakkyo-derived fructan directly stimulates collagen and hyaluronan synthesis in dermal fibroblasts, likely through regulation of ECM-related genes. These results suggest that rakkyo-derived fructan modulates ECM-related readouts in NHDFs under controlled in vitro conditions. Further validation in more complex skin models and in vivo studies is necessary. Full article

Real-time lithology identification during drilling faces challenges such as indistinct boundaries and difficulties in feature extraction. To address these, this study proposes the MSC-Transformer, a novel model integrating time-frequency features with a deep neural network. A series of drilling experiments were conducted using an intelligent drilling platform, during which triaxial vibration signals were collected from five types of rock specimens: anthracite, granite, bituminous coal, sandstone, and shale. Short-time Fourier Transform (STFT) was applied to generate multi-channel power spectral density (PSD) maps, which were then fused into a three-channel tensor to preserve directional frequency information and used as inputs to the model. The proposed MSC-Transformer combines a multi-scale convolutional (MSC) module with a lightweight Transformer encoder to jointly capture local texture patterns and global dependency features, thereby enabling accurate classification of complex lithologies. Experimental results demonstrate that the model achieves an average accuracy of 98.21 ± 0.49% on the test set, outperforming convolutional neural networks (CNNs), visual geometry group (VGG), residual network (ResNet), and bidirectional long short-term memory (Bi-LSTM) by 5.93 ± 0.90%, 2.54 ± 1.11%, 6.38 ± 2.63%, and 10.56 ± 3.11%, respectively, with statistically significant improvements (p < 0.05). Ablation studies and visualization analyses further validate the effectiveness and interpretability of the model architecture. These findings indicate that lithology recognition based on time-frequency representations of vibration signals is both stable and generalizable, offering technical support for real-time intelligent lithology identification during drilling operations. Full article

This paper examines tail risk spillovers and cross-market volatility forecasting between the U.S. equity market and the crude oil market. Using realized and implied volatility within a heterogeneous autoregressive (HAR) framework, we document asymmetric and time-varying tail risk transmission across the two markets. Motivated by these findings, we propose several cross-market volatility forecasting strategies, including direct information augmentation, threshold-based designs, forecast averaging, and transfer learning. The results show that incorporating cross-market information improves volatility forecasts primarily at medium and longer horizons, consistent with the forward-looking nature of implied volatility. Moreover, the relative effectiveness of different transmission mechanisms varies across markets, with transfer learning performing particularly well in the crude oil market. Overall, the findings highlight the importance of linking tail risk spillovers to volatility forecasting and demonstrate that flexible cross-market information transmission can enhance predictive performance across markets and horizons. Full article

Saline–alkali soil salinization is a global ecological crisis affecting 932 million hectares of land worldwide, posing a severe threat to food security and ecological sustainability. Traditional improvement methods, such as chemical amendments and hydraulic engineering, are limited by high costs and environmental risks, whereas microbial amendments have emerged as eco-friendly and sustainable alternatives due to their ability to regulate soil microenvironments and enhance plant stress resistance. However, a comprehensive synthesis of their core mechanisms, global application progress, and regional adaptation characteristics is still lacking, hindering the standardization and promotion of related technologies. This review, conducted in accordance with PRISMA guidelines, systematically synthesizes 112 core studies (1990–2025) retrieved from Web of Science, Scopus, and CNKI databases, focusing on three core research objects: salt-tolerant microbial communities in saline–alkali soils (dominant taxa, functional genes, metabolic characteristics), development and optimization of microbial amendments (strain screening, composite formulation, carrier selection), and mechanisms and application effects of microbial remediation (soil–plant–microbe interactions, physicochemical improvement, crop growth promotion). Key findings include the following. (1) Dominant microbial taxa (e.g., Proteobacteria, Actinobacteria) exhibit region-specific adaptation strategies, with salt tolerance thresholds and functional characteristics varying by soil type (coastal vs. inland saline–alkali soils). (2) Composite microbial amendments, especially those combined with biochar or organic fertilizers, achieve synergistic effects in desalination, alkali reduction, and fertility improvement. (3) Core mechanisms involve organic acid-mediated pH regulation, EPS-driven ion adsorption, and plant hormone-induced stress tolerance. (4) Microbial remediation technologies have been successfully applied globally (e.g., China, Africa, Americas), resulting in average crop yield increases of 15–42% and soil salinity reductions of 30–50%. This review provides a standardized technical framework for the development and application of microbial amendments, offers theoretical support for region-specific remediation strategies, identifies key challenges (e.g., strain stability, cost control) and future research directions (e.g., gene-edited strains, smart monitoring integration), and thus facilitates the industrialization and large-scale promotion of microbial remediation technologies to address global saline–alkali soil issues. Full article

Background and Objectives: Sperm DNA fragmentation (SDF) has emerged as an important marker of male reproductive potential; however, its relationship with sperm kinematic performance remains incompletely understood. While conventional semen analysis primarily evaluates sperm concentration and motility, computer-assisted semen analysis (CASA) enables a more detailed assessment of sperm motility parameters, including velocity, path length, and directionality. Materials and Methods: This observational study included 183 semen samples, stratified by SDF levels into control (1–15%), mild (15.01–25%), moderate (25.01–50%), and severe (>50%) fragmentation groups. Sperm kinematic parameters were assessed using CASA, including curvilinear velocity (VCL), straight-line velocity (VSL), average path velocity (VAP), linearity (LIN), straightness (STR), and wobble (WOB). Group comparisons were performed using ANOVA or Kruskal–Wallis tests, correlation analyses were conducted using Spearman’s rank coefficient, and multivariable linear regression models were applied to evaluate the independent effect of SDF after adjustment for sperm concentration and progressive motility. Results: Parameters reflecting directional motility and movement efficiency differed significantly across increasing SDF categories. VSL, LIN, STR, and WOB showed a progressive decline with higher levels of DNA fragmentation, whereas VCL and VAP did not demonstrate a proportional decrease across groups. Correlation analysis revealed significant negative associations between SDF and VSL (ρ = −0.367, p < 0.001), VAP (ρ = −0.323, p < 0.001), and VCL (ρ = −0.202, p = 0.006), while correlations with LIN, STR, and WOB were negative but not statistically significant. Multivariable regression analysis confirmed SDF as an independent negative predictor of VSL and VAP after adjustment for conventional semen parameters, whereas the association with VCL was no longer significant. Conclusions: Increased sperm DNA fragmentation is associated with impaired sperm motility efficiency and directionality, rather than a uniform reduction in motility. These findings highlight the functional relevance of sperm DNA integrity in shaping sperm kinematic performance and support the complementary use of SDF assessment and CASA parameters in evaluating male fertility. Full article

Background/Objectives: Emergency department length of stay (ED LOS) is a key indicator reflecting emergency department crowding, patient safety, and healthcare resource efficiency. Among injured patients, ED LOS may be prolonged depending on injury severity and disposition pathways (admission and inter-hospital transfer). This nationwide study using the Korean National Emergency Department Information System (NEDIS) aimed to (1) describe the distribution and determinants of ED LOS among injured patients and (2) quantify the mediating effects of disposition (admission and transfer) on the association between injury severity measured by the International Classification of Diseases-based Injury Severity Score (ICISS) and ED LOS. Methods: We analyzed NEDIS injury-related ED visit records collected from the date of IRB approval through 12 January 2026. We conducted a retrospective observational study using NEDIS data. Of 1,048,575 injury-related ED visits, 1,035,484 visits with valid ED LOS and eligible records were included after excluding missing key variables and implausible time values. ED LOS was calculated in minutes using arrival and departure timestamps. Injury severity was assessed using ICISS (primary: based on 15 diagnoses; sensitivity: based on 20 diagnoses). Determinants of ED LOS were evaluated using gamma regression with a log link. Disposition was categorized as discharge, admission, and inter-hospital transfer; admission and transfer were modeled as binary mediators. Causal mediation analyses estimated the average causal mediation effect (ACME), average direct effect (ADE), total effect, and proportion mediated. Multiple sensitivity analyses (outlier handling, missing-data approaches, alternative log-linear modeling, and EMS arrival subgroup analyses) assessed robustness. Results: The median ED LOS was 150 min (IQR 90–260). ED LOS differed substantially by disposition: 120 min for discharged patients, 420 min for admitted patients, and 360 min for transferred patients. Overall, 17.9% of visits had an ED LOS ≥ 6 h, and prolonged stays were concentrated among admitted (≥6 h: 55.0%) and transferred (≥6 h: 45.0%) patients. In gamma regression, a 0.05 decrease in ICISS (greater severity) was associated with longer ED LOSs in the unadjusted model (Ratio 1.34) and remained significant in the fully adjusted model (Ratio 1.12, 95% CI 1.11–1.13). Admission and transfer were strong determinants of ED LOS in the final model (ratios of 2.35 and 2.05, respectively). In mediation analyses, admission mediated 36.8% of the severity–ED LOS association (ACME 0.085; ADE 0.146), and transfer mediated 14.3% (ACME 0.033; ADE 0.198). Findings were consistent across sensitivity analyses. Conclusions: In this nationwide cohort of injured patients, ED LOS showed a right-skewed distribution, with prolonged stays concentrated in admission and transfer pathways. Injury severity (ICISS) was independently associated with longer ED LOS, and a substantial proportion of this association was mediated through admission and transfer. Reducing ED LOS among severely injured patients likely requires not only streamlining diagnostic and treatment processes but also system-level interventions targeting output-stage bottlenecks, including inpatient bed operations/boarding management and transfer coordination. Full article

Path planning for large-scale agricultural fields faces challenges such as irregular field shapes, uncertain boundaries, and the need to balance path efficiency, energy consumption, and coverage quality. To address these problems, this research introduces a strategy-aware hierarchical hybrid optimization framework (HANS) for autonomous agricultural operations. This framework introduces a global principal axis extraction method based on Principal Component Analysis (PCA), utilizing the statistical distribution of field boundaries to guide path direction, thereby improving robustness against boundary noise and irregular geometries. The framework integrates Adaptive Large Neighborhood Search (ALNS) for global exploration and Tabu Search (TS) for local optimization, forming a tightly coordinated hybrid structure. The framework further employs a Pareto-set-based decision support selection strategy to solve a multi-objective optimization model encompassing machine kinematics, turning patterns, and energy-aware cost evaluation. This strategy provides three methods: weighted preference-based compromise solution selection, crowding distance-based diversified solution selection, and single-objective extreme value-based dedicated optimization solution selection. To balance the impact of path length, energy consumption, and coverage rate, we assigned equal or nearly equal weights to them (i.e., (0.33, 0.33, 0.34)). Furthermore, the framework incorporates operators and feedback learning mechanisms specific to agricultural coverage path problems to enable adaptive operator selection and reduce reliance on manual parameter tuning. Simulation results under three representative field scenarios show that compared to fixed-direction planning, HANS improves the average coverage rate by 0.51 percentage points and reduces fuel consumption by 4.34%. Compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), and Simulated Annealing (SA), the proposed method shortens the working path length by 0.37–0.83%, improves coverage rate by 0.34–1.11%, and reduces energy consumption by 0.61–1.03%, while maintaining competitive computational costs. These results demonstrate the effectiveness and practicality of HANS in large-scale autonomous farming operations. Full article

Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer’s credit limit truly raises the likelihood of default remains an open empirical question that this work seeks to answer. We applied Bayesian causal inference to estimate the causal effect of credit limits on default probability. The analysis incorporated Directed Acyclic Graphs (DAGs) for causal structure, d-separation for identification, and Bayesian logistic regression using a dataset of 30,000 credit card holders in Taiwan (April–September 2005). Twenty-two confounding variables were adjusted for, covering demographics, repayment history, and billing and payment behavior. Continuous covariates were standardized, and posterior inference was performed using NUTS sampling with posterior predictive simulations to compute Average Treatment Effects (ATEs). We found that a one-standard-deviation increase in credit limit reduces default probability by 1.44 percentage points (94% HDI: [−2.0%, −1.0%]), corresponding to a 6.3% relative decline from the baseline default rate of 22.1%. The effect was consistent across demographic subgroups, with homogeneous treatment effects observed for age, education, and gender categories, and remained robust under sensitivity analysis addressing potential unmeasured confounding. The findings suggest that increasing credit limits can causally reduce default risk, likely by enhancing financial flexibility and lowering utilization ratios. These results have practical implications for credit policy design and motivate further investigation into mechanisms and applicability across broader lending environments. These estimates are explicitly interpreted as context-specific causal effects for a pre-crisis consumer credit environment, with external validity assessed conceptually rather than assumed. Full article