Search Results (86)

Agricultural rivers are often silent receivers of microplastics (MPs) from diffuse, non-point sources; however, their pollution dynamics during rainfall events remain poorly understood. In this study, MP transport was investigated at three sampling points in an agricultural river catchment, where mulching films are used, and sewage sludge is not applied. Sampling was conducted in the Umeda River and its tributaries during six sampling events. MP flux exhibited a strong positive correlation with river discharge (L–Q relationship; n = 1.49–1.61, R² = 0.67–0.87). The L–Q model indicates that a tenfold increase in discharge results in approximately a 600-fold increase in MP flux and a 1000-fold increase in total suspended solid flux. MP abundance during rainfall was up to four times higher than that during baseflow, ranging from 73 ± 64 to 200 ± 111 particles/m³, while peak flux reached 6736 particles/s, with an MP mass of 811 mg/s. Regarding particle characteristics, rainfall enhanced the heterogeneity of MPs, although fragments and polyethylene/polypropylene polymers remained consistently dominant across all hydrological stages. First-flush behavior was observed at HU, with more than half of the total MP mass exported within the initial 50% of the event flow volume. These findings help to inform mitigation strategies that should prioritize a reduction in upstream plastic inputs in order to effectively manage MP transport in agricultural rivers. Full article

This study investigates the spatial and economic differentiation of organic farming across the European Union by analyzing regional specialization patterns using Location Quotients (LQ). The results reveal a highly heterogeneous landscape shaped by the interaction of agro-ecological conditions, production traditions, market development, and structural characteristics of national agricultural systems. Six distinct regional models of organic farming are identified: the Nordic–Baltic cereal–forage model, the Alpine–Central European grassland model, the Mediterranean permanent-crop model, the Central–Eastern European raw-material model, the Western European intensive horticultural model, and the island-based niche-specialization model. Regression analyses show that overall organic specialization is strongly associated with market development, whereas the structure of organic crop production is primarily determined by agro-ecological and structural factors rather than consumer demand or purchasing power. These findings highlight the strong embeddedness of organic farming within long-term regional development pathways and underscore the need for regionally differentiated policy instruments within the Common Agricultural Policy. Effective support measures should be tailored to dominant crop types, production systems, and comparative advantages across Member States. Full article

This entry introduces an integrated model of reading that situates the Lexical Quality Hypothesis (LQH) within the Reading Systems Framework (RSF). The LQH posits that skilled reading depends on high-quality lexical representations—precise and flexible mappings of orthographic, phonological, morpho-syntactic, and semantic features—stored in the mental lexicon. These representations facilitate automatic word identification, accurate meaning retrieval, and efficient word-to-text integration (WTI), forming the foundation of text comprehension. Extending this micro-level perspective, the RSF positions lexical quality (LQ) within a macro-level cognitive architecture where the lexicon bridges word identification and reading comprehension systems. The RSF integrates multiple knowledge systems (linguistic, orthographic, and general world knowledge) with higher-order processes (sentence parsing, inference generation, comprehension monitoring, and situation model construction), emphasizing the bidirectional interactions between lower-level lexical knowledge and higher-order text comprehension. Central to this model is WTI, a dynamic mechanism through which lexical representations are incrementally incorporated into a coherent mental model of the text. This integrated model carries important implications for theory refinement, empirical investigation, and evidence-based instructional practices. Full article

This study examines the relationship between industrial agglomeration and carbon emissions in China’s forestry industry, using panel data from 30 provincial-level regions between 2009 and 2020. The industrial agglomeration level is measured by the Location Quotient (LQ), which is calculated based on regional employment shares to reflect the concentration of the forest products industry. This study finds that LQ exhibits a multiplicative effect—meaning that its influence on carbon emissions amplifies through interactive mechanisms of scale, technology diffusion, and spatial concentration. Four carbon indicators—carbon emissions from export products, carbon emission intensity, energy intensity, and energy structure cleanliness—are analyzed. Employing a threshold regression model, the study identifies nonlinear effects of agglomeration on carbon outcomes. The estimated threshold value (LQ = 0.7122) divides the process into three stages: (1) an embryonic stage (LQ < 0.7122) with rising emissions and declining efficiency; (2) a growth stage (around LQ ≈ 0.7122) with simultaneous increases in emissions and efficiency; and (3) a mature stage (LQ > 0.7122) where emissions decline as efficiency improves. These results reveal that the environmental effects of forestry industrial agglomeration evolve nonlinearly across development stages. Full article

The Maximum Entropy (MaxEnt) model, integrated with ArcGIS (a geographic information system), was employed to project potential species distribution under current conditions and future climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) for the 2050s, 2070s, and 2090s. Model optimization involved testing 1160 parameter combinations. The optimized model (FC = LQ, RM = 0.1) exhibited significantly improved predictive performance, with an average AUC of 0.967. Under current conditions, the estimated core suitable habitat spans 35.62 × 10⁴ km², primarily located in southern China. Future projections indicated a non-linear trajectory: an initial contraction of total suitable area by mid-century, followed by a substantial expansion by the 2090s, particularly under high-emission scenarios. Simultaneously, the distribution centroid shifted northwestward. The primary factors influencing distribution were the annual mean temperature (Bio1, 41.1%) and the precipitation of the coldest quarter (Bio19, 20.0%). These findings establish a critical scientific basis for developing climate-adaptive conservation strategies, including the identification of priority climate refugia in Fujian province, China, and planning for assisted migration to northwestern regions. Full article

Stock return prediction in emerging markets remains difficult due to the gap between theoretical efficiency and empirical irregularities. This study assesses the statistical and economic performance of Linear Regression, Ridge Regression, Random Forest, and XGBoost in forecasting 5-day and 21-day returns for six LQ45 stocks (2016–2025). Momentum, volatility, trend, and volume indicators are used as predictors, while model performance is evaluated using MAE, RMSE, R², and backtested trading metrics that include transaction costs. All models yield near-zero or negative R², directional accuracy of 49–54%, and AUC around 0.50–0.53, indicating weak signals overshadowed by noise. XGBoost offers the lowest statistical errors, but Ridge Regression achieves slightly better risk-adjusted outcomes (Sharpe 0.1232), although every strategy underperforms Buy & Hold. SHAP results show volatility and volume features as most influential, but with minimal absolute impact. Overall, the LQ45 market exhibits semi-efficiency: patterns exist but fail to translate into profitable trading once real-world frictions are considered, underscoring the gap between statistical predictability and economic viability in algorithmic trading. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth). Full article

Rapid urbanization in China has reshaped retail spatial structures, creating challenges of accessibility and service equity. This study employs a Geographic Information Systems (GIS)-based analytical framework to examine the spatial distribution and driving mechanisms of retail outlets in Taiyuan, a resource-based transition city in central China. Using 2023 Point of Interest (POI) data and a 2 km × 2 km grid system, kernel density estimation (KDE), Average Nearest Neighbor (ANN) Analysis, Location Quotient (LQ), and spatial autocorrelation were applied to identify clustering patterns and functional specialization. The GeoDetector (Word version, downloaded 2025) model further quantified the explanatory power of twelve natural, social, economic, and transportation variables. Results reveal a polycentric retail structure, with high-density clusters in Yingze and Xiaodian districts and under-supply in Jiancaoping and Jinyuan. Population density, nighttime light (NTL) intensity, and school distribution emerged as the strongest drivers, while topography constrained expansion. By integrating GIS-based spatial statistics with GeoDetector, the study demonstrates a transferable framework for analyzing urban retail spatial patterns. The findings extend retail geography to transition cities and provide practical guidance for optimizing retail allocation, enhancing service equity, and supporting spatial decision-making for sustainable urban development. Full article

This paper presents a method to design a preview controller with virtual disturbance and an active suspension system for ride comfort improvement and motion sickness mitigation. Quarter-car and half-car models are selected as the vehicle model. With those models, an LQ optimal preview controller is designed in the discrete-time domain. In the controller, feedback controllers are designed with LQ static output feedback (SOF) control. In real driving environments, it is hard to exactly measure a bump profile, which causes performance deterioration. To cope with difficulties and uncertainties in measuring a real bump, a virtual disturbance is used instead of a real bump. In the LQ optimal preview controller, the virtual disturbance, used for the feedforward control, is optimized with a simulation-based optimization method. To show the effectiveness of the proposed method, a simulation is performed on a vehicle simulation package. The simulation results show that the LQ SOF controller decreases the vertical acceleration and pitch rate of the sprung mass by 28% and 66%, respectively, whereas the preview controllers with the optimized virtual disturbance yield reductions of 41% and 84%, respectively. Those results demonstrate that the proposed preview controller with the optimized virtual disturbance can effectively enhance ride comfort and mitigate motion sickness. Full article

Backgrounds: Non-alcoholic fatty liver disease (NAFLD) is a multifaceted metabolic disorder that has become a prominent public health problem worldwide. As a traditional Chinese medicine formula, the ErChen decoction (ECD) possesses significant effects on metabolic syndrome. Methods: To determine whether ECD can relieve lipid accumulation and insulin resistance (IR) in liver cells, NAFLD and IR cell models were established by treating HepG2 cells with free fatty acids and an overdose of insulin, respectively. Bioinformatics and experimental evidence demonstrated that ECD could ameliorate NAFLD by modulating multiple pathways. The optimal combination of the key compounds in ECD was identified by the orthogonal experiment. Results: For lipid homeostasis, ECD suppressed de novo lipogenesis and reduced the cholesterol level by activating the AMPK signaling pathway. Concurrently, ECD enhanced hepatic β-oxidation by inducing PPARα-mediated upregulation of ACOX-1 and CPT-1α. ECD also resolved hepatic insulin resistance by activating the IRS1-Akt-FoxO1 pathway. The combined treatment with 100 μM liquiritin (LQ), 200 μM glycyrrhizic acid (GA) and 200 μM hesperidin (HEN) exhibited the best effect in reducing TG content in NAFLD model cells. Conclusions: ECD exhibited superior activities in activating the AMPK signaling pathway compared to the optimal compound combination. The comparison between the ECD and its key compounds demonstrated the superior synergistic effects of the herbs in ECD. Full article

In recent years, interest in the application of ontologies in various domains of knowledge has grown significantly. Ontologies are widely used in a myriad of areas, such as artificial intelligence, data integration, knowledge management, and the semantic web, to name but a few. However, despite the widespread adoption, there exist a range of problems associated with ontologies, such as the complexity and cognitive challenges associated with ontology engineering, design, and development. One of the solutions to these challenges is to reuse existing ontologies rather than developing new ontologies afresh for new applications. The reuse of ontologies that describe a knowledge domain is a complex task consisting of many aspects. One of the key aspects involves ranking ontologies to aid in their selection. Various techniques have been proposed for this task, but many of them fall short in their expressiveness and ability to capture the cognitive aspects of human-like decision-making processes. Furthermore, much of the existing research focuses on an objective approach to ontology ranking, but it is unquestionable that a wide range of aspects pertaining to the quality of an ontology simply cannot be captured in a quantitative manner. Existing ranking models fail to provide a robust and flexible canvas for facilitating qualitative ontology ranking and selection for reuse. To address the aforementioned shortcomings of existing ontology ranking approaches, this study proposes a novel algorithm for ranking ontologies that extends the Elimination and Choice Translating Reality (ELECTRE) multi-criteria decision-making method with the Linguistic q-Rung Orthopair Fuzzy Set (Lq-ROFS-ELECTRE II), allowing the expression of uncertainty in a more robust and precise manner. The new Lq-ROFS-ELECTRE II algorithm was applied to rank a set of 19 ontologies of the machine learning (ML) domain. The ML ontologies were evaluated using a set of seven qualitative criteria extracted from the Ontometric framework. The proposed Lq-ROFS-ELECTRE II algorithm was then applied to rank the 19 ontologies in light of the seven criteria. The ranking results obtained were compared against the quantitative rankings of the same 19 ontologies using the traditional ELECTRE II algorithm, and confirmed the validity of the ranking performed by the proposed Lq-ROFS-ELECTRE II algorithm and its effectiveness in the task of ontology ranking. Furthermore, a comparative analysis of the proposed Lq-ROFS-ELECTRE II against existing MCDM methods and other existing fuzzy ELECTRE II methods displayed its superior modeling capabilities that allow for more natural decision evaluation from subject experts in real-world applications and allow the decision-maker to have much flexibility in expressing their preferences. These capabilities of the Lq-ROFS-ELECTRE II algorithm make it applicable not only in ontology ranking, but in any domain where there exist decision-making scenarios that comprise multiple conflicting criteria under uncertainty. Full article

In automated container terminals (ACTs), rolling bearings of equipment serve as crucial power transmission components, and their performance directly determines the operational efficiency, reliability, and service life of the entire equipment. Rolling bearing fault detection and diagnosis are key means to improve production efficiency, reduce the safety risks, and achieve sustainable development of equipment in ACTs. However, existing rolling-bearing diagnosis models are vulnerable to environmental noise and interference, depressing accuracy and raising misclassification, and they seldom achieve both noise robustness and a lightweight design; robustness usually increases complexity, while compact networks degrade under low signal-to-noise ratios. Therefore, this paper proposes a noise-robust, lightweight, and interpretable deep learning framework for fault detection and diagnosis of rolling bearings in automated container terminal (ACT) equipment. The framework comprises four coordinated components, including Time-Domain Filter, Frequency-Domain Filter, Physical-Feature Extraction module, and Classification module, whose joint optimization yields complementary time–frequency representations and physics-aligned features, and fuses into robust diagnostic decisions under noisy and non-stationary environments. The first component highlights impulsive transients, the second component emphasizes harmonic and sideband modulation, the third module introduces two differentiable and rolling bearing-signal-informed objectives to align learning with characteristic bearing signatures by weighted-average kurtosis and an Lp/Lq-based envelope-spectral concentration index, and the last module integrates multi-layer convolutional neural networks (CNN) and Deep Kolmogorov–Arnold Networks (DeepKAN). Finally, two public datasets are employed to estimate the model’s performance, and results indicate that the proposed method outperforms others. Full article

To explore a more personalized approach to radiation therapy for adjuvant whole-breast irradiation in triple-negative breast cancer (TNBC), we analyzed the cell lines BT549 and MDA-MB-231 as in vitro models for radiobiological characterization. The local disease-free survival (LSR) values were determined for both cell lines’ median, maximum, and minimum α and β parameters to achieve an LSR probability of close to 100% in a five-fraction schedule. Based on these findings, fifteen treatment plans were created for BC to simulate the proposed dose schedule. For the MDA-MB-231 cell line, the α/β ratios were 3.79 Gy (minimum), 15 Gy (maximum), and 7 Gy (median). For the BT-549 cell line, the α/β ratios were 5.95 Gy (minimum), 22.93 Gy (maximum), and 16.51 Gy (median). To achieve an LSR probability of close to 100%, the required doses per fraction were 5.2 Gy, 5.3 Gy, and 7.3 Gy for MDA-MB-231 and 8 Gy, 9.1 Gy, and 9.9 Gy for BT-549. We selected the highest dose per fraction, 9.9 Gy × 5, to simulate the worst-case scenario. To achieve 100% cell death effectiveness in TNBC, it is likely that higher radiation doses are required—doses that are not feasible within the setting of adjuvant whole-breast irradiation. Our model, which relies on the intrinsic biological features of the tumor, paves the way to reach more tailored RT plans and to improve the classic LQ model. Full article

Fusarium circinatum poses severe threats to agroforestry ecosystem as a globally significant pathogenic fungus. This study utilized multi-source species distribution data and environmental variables (climatic, topographic, and soil factors) to predict the global potential habitat suitability of F. circinatum and its response to future climate change using an optimized MaxEnt model (RM = 1, FC = LQ). The results indicate that the current total suitable area spans approximately 69.29 million km², with highly suitable habitats (>0.493) accounting for 15.07%, primarily concentrated in East Asia, southwestern North America, western South America, the Mediterranean coast, and eastern Australia. The distribution of F. circinatum’s suitable habitats is primarily constrained by the following environmental factors, ranked by contribution rate: coldest quarter precipitation (29.4%), coldest quarter mean temperature (18.2%), annual mean temperature (17.2%), and annual precipitation (12%). Under future climate scenarios, the suitable habitats exhibited an overall contraction and poleward shift, with the most significant decline in highly suitable areas observed under SSP370-2050s (−52.1%). The centroid of suitable habitats continuously migrated northwestward from Gombe State, Nigeria, with the maximum displacement reaching 1077.6 km by SSP585-2090s. This study reveals a latitude gradient redistribution pattern of F. circinatum driven by climate warming, providing a scientific basis for transboundary biosecurity and early warning systems. Full article

Background/Objectives: The aim of this study was to evaluate the impact of inhomogeneity correction (IC) of dose distribution on the dosimetric and radiobiological efficacy of radiation treatment for heterotopic ossification (HO). Methods: This study involved a retrospective analysis of 21 patients treated using a homogeneous dose distribution plan for hip prophylactic HO. These IC-off plans were evaluated against an IC-on dose distribution plan. Dosimetric and corresponding radiobiological parameters (gEUD, LQ-EUD, LQ, EQD2 for α/β = 3 and 10 Gy) were calculated. These parameters were compared for both treatment plans. Additionally, Monte Carlo simulations were performed using mean and standard deviation values from baseline data to generate 10,000 synthetic datasets, allowing for robust statistical modeling of variability in dose distributions and biological outcomes. Results: The homogeneous (IC-off) plans demonstrated overestimation of dose conformity and uniformity, reflected in lower HI values (0.10 ± 0.05 vs. 0.18 ± 0.05) and higher D_90%–D_98% coverage. Radiobiologically, these plans yielded higher gEUD (7.02 Gy vs. 6.80 Gy) and EQD2 values across all α/β scenarios (e.g., EQD2_{[α/β=3]_gEUD} = 14.07 Gy vs. 13.35 Gy), with statistically significant differences (p < 0.001). Although IC-on plans demonstrated steeper dose gradients (higher GIs), this came at the expense of internal dose variability and potentially compromised biological effectiveness. Conclusions: Our results suggest that plans without IC deliver suboptimal biological effectiveness if continued preferentially in routine HO prophylaxis. With advanced radiation dose calculation algorithms available in all centers, inhomogeneity-corrected doses warrant prospective validation. Full article

Background: Parvovirus B19 (B19V) can cause severe relapsing episodes of pure red cell aplasia in immunocompromised individuals, which are commonly treated with intravenous immunoglobulins (IVIGs). Few data are available on B19V intra-host evolution and the role of humoral immune selection. Here, we report the dynamics of genomic mutations and subsequent protein changes during relapsing infection. Methods: Longitudinal plasma samples from immunocompromised patients with relapsing B19V infection in the period 2011–2019 were analyzed using whole-genome sequencing to evaluate intra-host evolution. The impact of mutations on the 3D viral protein structure was predicted by deep neural network modeling. Results: Of the three immunocompromised patients with relapsing infections for 3 to 9 months, one patient developed two consecutive nonsynonymous mutations in the VP1/2 region: T372S/T145S and Q422L/Q195L. The first mutation was detected in multiple B19V IgG-seropositive follow-up samples and resolved after IgG seroreversion. Computational prediction of the VP1 3D structure of this mutant showed a conformational change in the proximity of the antibody binding domain. No conformational changes were predicted for the other mutations detected. Discussion: Analysis of relapsing B19V infections showed mutational changes occurring over time. Resulting amino acid changes were predicted to lead to a conformational capsid protein change in an IgG-seropositive patient. The impact of humoral response and IVIG treatment on B19V infections should be further investigated to understand viral evolution and potential immune escape. Full article