Search Results (1,310)

The Strait of Malacca faces persistent maritime safety challenges due to high vessel density and complex navigational conditions. Current risk assessment methods often lean towards treating static accident analysis and dynamic traffic modeling separately, although some nascent hybrid approaches exist. However, these hybrids frequently lack the capacity for comprehensive, real-time factor integration. This study proposes an integrated framework coupling accident hotspot identification with vessel traffic network analysis. The framework combines trajectory clustering using improved DBSCAN with directional filters, Kernel Density Estimation (KDE) for accident hotspots, and Fuzzy Analytic Hierarchy Process (FAHP) for multi-factor risk evaluation, acknowledging its subjective and region-specific nature. The model was trained and tuned exclusively on the 2023 dataset (47 incidents), reserving the 2024 incidents (24 incidents) exclusively for independent, zero-information-leakage validation. Results demonstrate superior performance: Area Under the ROC Curve (AUC) improved by 0.14 (0.78 vs. 0.64; +22% relative to KDE-only), and Precision–Recall AUC (PR-AUC) improved by 0.16 (0.65 vs. 0.49); both p < 0.001. Crucially, all model tuning and parameter finalization (including DBSCAN/Fréchet, FAHP weights, and adaptive thresholds) relied solely on 2023 data, with the 2024 incidents reserved exclusively for independent temporal validation. The model captures 75.2% of reported incidents within 20% of the study area. Cross-validation confirms stability across all folds. The framework reveals accidents concentrate at network bottlenecks where traffic centrality exceeds 0.15 and accident density surpasses 0.6. Model-based associations suggest amplification through three pathways: environmental-mediated (34%), traffic convergence (34%), and historical persistence (23%). The integrated approach enables identification of both where and why maritime accidents cluster, providing practical applications for vessel traffic services, risk-aware navigation, and evidence-based safety regulation in congested waterways. Full article

Maize (Zea mays L.) is the most extensively cultivated food crop in China, and current studies on maize general combining ability (GCA) focus primarily on the genetic basis of traits. However, the dynamic trends and underlying genetic loci associated with GCA for yield-related traits during breeding remain underexplored. This study was designed to investigate the changing trends of the general combining ability (GCA) and the frequency of elite alleles among 218 major maize inbred lines from Northeast China, spanning the 1970s to the 2010s. PH6WC and PH4CV were used as testers to develop 436 hybrid combinations via the North Carolina design II (NCII) method, and these combinations were evaluated across three environments. We further analyzed the combining ability (particularly the GCA) of 16 yield-related traits and their dynamic trends during breeding, grouped into three age periods (AGE1: 1960s–1970s; AGE2: 1980s–1990s; AGE3: 2000s–2010s). We also screened for genetic loci associated with the GCA effects of these traits. Results show that breeding selection significantly affected the GCA of six yield-related traits (ear length (EL), tassel branch number (TBN), tassel main axis length (TL), kernel length (KL), stem diameter (SDR), and hundred kernel weight (HKW)). Specifically, the mean TBN_GCA value decreased from 2.51 in AGE1 to −1.28 in AGE3, and the mean HKW_GCA increased from −1.58 in AGE1 to 0.36 in AGE3. Yield per plant GCA (YPP_GCA) was positively correlated with the GCA values of EL, ear diameter (ED), kernel row number (KRN), kernel number per row (KNPR), and HKW. Association analysis identified 38 single nucleotide polymorphisms (SNP_S) related to GCA. The T/T alleles for TBN were absent in AGE1, emerged in AGE2 (1980s–1990s), and persisted in AGE3—consistent with the decreasing trend of TBN_GCA from AGE1 to AGE3. For HKW, the A/A alleles not only exhibited higher GCA (effectively improving the HKW_GCA of inbred lines) but also showed an 11% increase in allelic frequency from AGE1 to AGE3. Taken together, these results suggest that the accumulation of elite alleles is the primary factor driving the GCA improvement during maize breeding in Northeast China. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

Existing long-form video recommendation systems primarily rely on rating prediction or click-through rate estimation. However, the former is constrained by data sparsity, while the latter fails to capture actual viewing experiences. The accumulation of mid-playback abandonment behaviors undermines platform stickiness and commercial value. To address this issue, this paper seeks to improve viewing engagement. Grounded in Expectation-Confirmation Theory, this paper proposes the Long-Form Video Viewing Engagement Prediction (LVVEP) method. Specifically, LVVEP estimates user expectations from storyline semantics encoded by a pre-trained BERT model and refined via contrastive learning, weighted by historical engagement levels. Perceived experience is dynamically constructed using a GRU-based encoder enhanced with cross-attention and a neural tensor kernel, enabling the model to capture evolving preferences and fine-grained semantic interactions. The model parameters are optimized by jointly combining prediction loss with contrastive loss, achieving more accurate user viewing engagement predictions. Experiments conducted on real-world long-form video viewing records demonstrate that LVVEP outperforms baseline models, providing novel methodological contributions and empirical evidence to research on long-form video recommendation. The findings provide practical implications for optimizing platform management, improving operational efficiency, and enhancing the quality of information services in long-form video platforms. Full article

Background: Mango seed kernels, an agro-industrial waste byproduct, constitute approximately 40–50% of the fruit’s weight and serve as a substantial source of starch. There are only a few reported studies on the pharmaceutical applications of Mango Seed Kernel Starch (MSKS) and drug carriers produced from this source. This study aims to isolate starch from mango seed kernels (MSKS), prepare drug-loaded mango seed kernel starch nanoparticles (MSKSNPs), and study the in vitro transdermal permeation. Methods: The MSKS was prepared using the alkaline method and freeze-dried. The prepared starch was analyzed for physicochemical properties relative to corn starch. The mango seed kernel starch nanoparticles (MSKSNPs) were prepared using mild alkali hydrolysis and the ultrasonication method. The model drug selected for this study was diclofenac sodium (DS), a commonly prescribed non-steroidal anti-inflammatory drug. Results: The average particle size of the drug-loaded nanoparticles was 140.0 ± 3.6 nm, with a PDI of 0.42 ± 0.03. The Transmission Electron Microscopy images confirmed the globular structure of MSKSNPs. X-ray Diffraction revealed that the diclofenac crystal size decreased to 14 nm from 33 nm in the pure drug, confirming the amorphous nature of MSKSNPs. The drug-loaded MSKSNPs showed a % encapsulation efficiency of 92.4 ± 3.7 and % drug loading of 31.08 ± 0.96. The cumulative drug released from MSKSNPs after 6 h, 12 h, and 24 h was found to be 25.58 ± 1.30, 59.68 ± 2.98, and 127.5 ± 6.4 μg/cm^2, respectively, which was more than the ethanolic drug solution with statistical significance (p-value < 0.01) along with enhanced skin retention. Conclusions: MSKSNPs were efficiently synthesized using mild alkali hydrolysis and ultrasonication, showing enhanced transdermal delivery. Skin retention was significantly higher in MSKSNPs (p-value < 0.05). The cytotoxic studies revealed that both formulations exhibit similar dose-dependent cytotoxicity, with no significant difference (p > 0.05) in their potency under the tested conditions. Full article

Edible coatings enriched with antioxidants offer a promising approach to prolong the shelf life of oxidation-sensitive foods such as nuts. Nonetheless, not all formulations provide the expected protection, and understanding why is equally important. The aim of this study was to assess the effect of an Arabic gum/gelatin/ascorbyl palmitate (GAR/GEL/AP) coating on the quality of hazelnut kernels during storage at 23 °C and ~40% relative humidity. The coating was applied by dipping hazelnuts in a 20% ethanolic solution containing GAR/GEL 75/25 blend (10% w/w), glycerol (1% w/w), Tween 80 (0.25% w/w), and AP (2% w/w), followed by drying. Control (uncoated) and coated hazelnuts were stored in plastic containers and evaluated at 1, 2, 4, 8, and 16 weeks for weight loss, moisture content, hardness, color, 2,2-diphenyl-1-picrylhydrazyl radical (DPPH*) scavenging activity, acid and peroxide values, and thiobarbituric acid reactive substances (TBARS) level. Coated hazelnuts showed higher initial moisture content (8.17%), stabilizing at 4.80% after one week, compared to 3.35% in uncoated samples. This increased moisture led to greater storage-related weight loss. The coating darkened the nuts and reduced their yellow hue. It had no significant effect on hardness, peroxide value, or TBARS index, but notably enhanced the antiradical potential. After 16 weeks, coated nuts had an acid value ~10 mg KOH/g lower than the control. In conclusion, the coating improved antioxidant capacity and reduced hydrolytic, but not oxidative, rancidity in hazelnuts. Therefore, further optimization of the coating formulation or application method is necessary to more effectively improve the shelf life of hazelnuts. Full article

To address the issue of reducing noise in images containing mixed noise, a Volterra filtering method based on a Bat algorithm with velocity weight perturbation is proposed to optimize kernel parameters. The structural advantages of the Volterra filter (predictive performance, linear and nonlinear terms) are used to reduce the noise in these images. The dynamic velocity inertia-weight perturbation mechanism is used to improve the Bat algorithm’s optimization ability, while the kernel-parameter optimization and the noise reduction abilities of the Volterra filter are further improved. Theoretical analysis and experimental results show that the high-density mixed noise, comprising Gaussian and salt-and-pepper noise, can be filtered effectively by the proposed algorithm. Compared to traditional image-denoising methods, the proposed method outperforms other algorithms in removing mixed noise from images while preserving edge details. Within a specific noise intensity range, the greater the intensity of mixed noise in the image, the better the noise reduction performance of this filtering method. The method proposed in this paper is less affected by noise intensity. When the number of bats in the population and the number of iterations reach a certain value, the algorithm exhibits good convergence and stability. Full article

The quality of groundwater, a crucial freshwater resource in cold regions, directly affects human health. This study used groundwater quality monitoring data collected in the central Songnen Plain in 2014 and 2022 as a case study. The improved DRASTICL model was used to assess the vulnerability index, while water quality indicators were selected using a random forest algorithm and combined with the entropy-weighted groundwater quality index (E-GQI) approach to realize water quality assessment. Furthermore, self-organizing maps (SOM) were used for pollutant source analysis. Finally, the study identified the synergistic migration mechanism of NH₄⁺ and Cl⁻, as well as the activation trend of As in reducing environments. The uncertainty inherent to health risk assessment was considered by developing a kernel density estimation–trapezoidal fuzzy number–Monte Carlo simulation (KDE-TFN-MCSS) model that reduced the distribution mis-specification risks and high-risk misjudgment rates associated with conventional assessment methods. The results indicated that: (1) The water chemistry type in the study area was predominantly HCO₃⁻–Ca²⁺ with moderately to weakly alkaline water, and the primary and nitrogen pollution indicators were elevated, with the average NH₄⁺ concentration significantly increasing from 0.06 mg/L in 2014 to 1.26 mg/L in 2022, exceeding the Class III limit of 1.0 mg/L. (2) The groundwater quality in the central Songnen Plain was poor in 2014, comprising predominantly Classes IV and V; by 2022, it comprised mostly Classes I–IV following a banded distribution, but declined in some central and northern areas. (3) The results of the SOM analysis revealed that the principal hardness component shifted from Ca²⁺ in 2014 to Ca²⁺–Mg²⁺ synergy in 2022. Local high values of As and NH₄⁺ were determined to reflect geogenic origin and diffuse agricultural pollution, whereas the Cl⁻ distribution reflected the influence of de-icing agents and urbanization. (4) Through drinking water exposure, a deterministic evaluation conducted using the conventional four-step method indicated that the non-carcinogenic risk (HI) in the central and eastern areas significantly exceeded the threshold (HI > 1) in 2014, with the high-HI area expanding westward to the central and western regions in 2022; local areas in the north also exhibited carcinogenic risk (CR) values exceeding the threshold (CR > 0.0001). The results of a probabilistic evaluation conducted using the proposed simulation model indicated that, except for children’s CR in 2022, both HI and CR exceeded acceptable thresholds with 95% probability. Therefore, the proposed assessment method can provide a basis for improved groundwater pollution zoning and control decisions in cold regions. Full article

We developed a model that classifies normal and abnormal brain MRI images. This study initially referenced a small-scale real patient dataset (98 normal and 155 abnormal MRI images) provided by the National Institute of Aging (NIA) to illustrate the class imbalance challenge. However, all experiments and performance evaluations were conducted on a larger synthetic dataset (10,000 images; 5000 normal and 5000 abnormal) generated from the National Imaging System (NIS/AI Hub). Therefore, while the NIA dataset highlights the limitations of real-world data availability, the reported results are based exclusively on the synthetic dataset. In the preprocessing step, all MRI images were normalized to the same size, and data augmentation techniques such as rotation, translation, and flipping were applied to increase data diversity and reduce overfitting during training. Based on deep learning, we fine-tuned our own CNN model and a ResNet-50 transfer learning model using ImageNet pretrained weights. We also compared the performance of our model with traditional machine learning using SVM (RBF kernel) and random forest classifiers. Experimental results showed that the ResNet-50 transfer learning model achieved the best performance, achieving approximately 95% accuracy and a high F1 score on the test set, while our own CNN also performed well. In contrast, SVM and random forests showed relatively poor performance due to their inability to sufficiently learn the complex characteristics of the images. This study confirmed that deep learning techniques, including transfer learning, achieve excellent brain abnormality detection performance even with limited real-world medical data. These results highlight methodological potential but should be interpreted with caution, as further validation with real-world clinical MRI data is required before clinical applicability can be established. Full article

In response to the challenge of dynamic adaptability in operational safety assessment for UAVs operating in complex operational environments, this study proposes a novel operational safety assessment method based on an Improved Support Vector Machine. An operational safety assessment index system encompassing four dimensions—operator, UAV platform, flight environment, flight mission—is constructed to provide a comprehensive foundation for evaluation. The method introduces a dynamic weighted information entropy mechanism based on a sliding window, overcoming the static features and delayed response of traditional SVM methods. Additionally, it integrates Gaussian and polynomial kernel functions to significantly enhance the generalization capability and classification accuracy of the SVM model in complex operational environments. Experimental results show that the proposed model demonstrates superior performance on test samples, effectively improving the accuracy of operational safety assessment for the Reconnaissance–Strike UAV in complex operational environments, and offering a novel methodology for UAV safety assessment. Full article

In promoting the “dual-carbon goals” and sustainable development strategy, analyzing the spatio-temporal response mechanism of landscape patterns to carbon emissions is a critical foundation for achieving carbon emission reductions. However, existing research primarily targets urbanized zones or individual ecosystem types, often overlooking how landscape pattern affects carbon emissions across entire watersheds. This research examines spatial–temporal characteristics of carbon emissions and landscape patterns in China’s Yellow River Basin, utilizing Kernel Density Estimation, Moran’s I, and landscape indices. The Geographically and Temporally Weighted Regression model is used to analyze the impact of landscape patterns and their spatial–temporal changes, and recommendations for sustainable low-carbon development planning are made accordingly. The findings indicate the following: (1) The overall carbon emissions show a spatial pattern of “low upstream, high midstream and medium downstream”, with obvious spatial clustering characteristics. (2) The degree of fragmentation in the upstream area decreases, and the aggregation and heterogeneity increase; the landscape fragmentation in the midstream area increases, the aggregation decreases, and the diversity increases; the landscape pattern in the downstream area is generally stable, and the diversity increases. (3) The number of patches, staggered adjacency index, separation index, connectivity index and modified Simpson’s evenness index are positively correlated with carbon emissions; landscape area, patch density, maximum number of patches, and average shape index are negatively correlated with carbon emissions; the distribution of areas positively or negatively correlated with average patch area is more balanced, while the spread index shows a nonlinear relationship. (4) The effects of landscape pattern indices on carbon emissions exhibit substantial spatial heterogeneity. For example, the negative impact of landscape area expands upstream, patch density maintains a strengthened negative effect downstream, and the diversity index shifts from negative to positive in the upper reaches but remains stable downstream. This study offers scientific foundation and data support for optimizing landscape patterns and promoting low-carbon sustainable development in the basin, aiding in the establishment of carbon reduction strategies. Full article

Durum wheat is a vital wheat species cultivated worldwide for human consumption, ranking second to bread wheat. The Ethiopian durum wheat allele pool shows wide gene diversity; however, limited improvement work has been done to exploit this diversity. Thus, this study aimed to assess the genetic variability, heritability, and interrelationship among different phenotypic traits in 210 recombinant inbred lines (RILs) using an alpha lattice design with two replications. The analysis of variance revealed a significant difference for all the measured traits. The phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variation (GCV) for all the characters, which reflects that the existing range of variability within the genotypes was not only due to the varying influence of genotype but also the environment. A correlation analysis disclosed that grain yield was positively related to the traits of plant height and 1000-kernel weight, suggesting that selecting these traits could enhance yield. Path analysis revealed that days to booting, maturity, and 1000-kernel weight directly affect grain yield. Among the measured traits, early developmental traits revealed higher broad-sense heritability. The findings of this study highlight high genetic diversity among Ethiopian durum wheat genotypes, opening up opportunities to integrate these materials into future wheat-breeding programs through introgression with other germplasm sources in Ethiopia and beyond. Full article

The failure to accurately identify abnormal vibrations in protected metro areas is a serious threat to the operational safety of metro tunnels and trains, and there is currently no suitable method for effectively improving the accuracy of abnormal vibration identification. To address this issue, an accurate method for identifying abnormal vibrations in a metro reserve based on spatially correlated dense measurement points is proposed. First, by arranging distributed optical fibers along the longitudinal length of a tunnel, dynamic strain vibration signals are extracted via phase-sensitive optical time-domain reflectometry analysis, and analysis of variance (ANOVA) and Pearson correlation analysis are used to jointly downscale the dynamic strain features. On this basis, a spatial correlation between the calculated values of the features of the target measurement points to be updated and its adjacent measurement points is constructed, and the spatial correlation credibility of the dynamic strain features between the dense measurement points and the target measurement points to be updated is calculated via quadratic function weighting and kernel density estimation methods. The weights are calculated, and the eigenvalues of the target measurement points are updated on the basis of the correlation credibility weights between the adjacent measurement points. Finally, a support vector machine (SVM) and back propagation (BP) identification model for the eigenvalues of the target measurement points are constructed to identify the dynamic strain eigenvalues of the abnormal vibrations in the underground tunnel. Numerical simulations and an experiment in an actual tunnel verify the effectiveness of the proposed method. Full article

To effectively address the issues of poor ventilation, light deficiency, increased pest and disease pressure, and declining fruit quality in closed-canopy walnut orchards, this study was conducted in a standard, densely planted ‘Xinwen 185’ walnut orchard. Three treatments were established: an unthinned control (CK), a 1-year thinning treatment (T1), and a 2-year thinning treatment (T2). All parameters were uniformly investigated during the 2023 growing season to analyze the effects of thinning on orchard population structure, microenvironment, leaf physiological characteristics, fruit quality, and yield. The results demonstrated that tree thinning significantly optimized the population structure: crown width expanded by 6.22–6.76 m, light transmittance increased to 27.74–33.64%, and orchard coverage decreased from 100% to 75.94–80.51%. The microenvironment was improved: inter-row temperature increased by 2.34–4.08 °C, light intensity increased by 5.38–25.29%, and relative humidity decreased by 2.15–3.30%. Furthermore, leaf physiological functions were activated: in the T2 treatment, the chlorophyll content in outer-canopy leaves increased by 15.23% and 12.45% at the kernel-hardening and maturity stages, respectively; the leaf carbon-to-nitrogen ratio increased by 18.67%; the net photosynthetic rate (Pn) during fruit expansion increased by 34.21–46.10%; and the intercellular CO₂ concentration (Ci) decreased by 10.18–10.31%. Fruit quality and yield were synergistically enhanced: single fruit weight increased by 23.39~37.94%, and kernel weight increased by 26.79–41.13%. The total sugar content in inner-canopy fruits increased by 16.50–16.67%, while the protein and fat content in outer-canopy fruits increased by 0.69–12.50% and 0.60–2.18%, respectively. Yield exhibited a “short-term adjustment and long-term gain” pattern: the T2 treatment (after 2 years of thinning) achieved a yield of 5.26 t·ha⁻¹, which was 20.38% higher than the CK. The rates of diseased fruit and empty shells decreased by 65.71% and 93.22%, respectively, and the premium fruit rate reached 90.60%. This study confirms that tree thinning is an effective measure for improving the growing environment and enhancing overall productivity in closed-canopy walnut orchards, providing a scientific basis for sustainable orchard management and increased orchard profitability. Full article

Tourism villages represent a key breakthrough for achieving rural revitalization and integrated urban–rural development. By analyzing the spatial patterns of tourism villages in the Great Mount Huang district and their influencing factors, this study provides a scientific foundation for the high-quality development of rural tourism and for the enhancement and sustainable management of regional leisure tourism systems. Using methods such as the nearest neighbor index, kernel density, geographic detector, and geographically weighted regression analysis, the results reveal: (1) the spatial distribution of tourism villages in the Great Mount Huang district exhibits significant clustering and unevenness, forming a spatial pattern characterized by “one cluster, two cores, and three points”; Anqing City shows the most concentrated and uneven distribution of tourism villages; (2) the number of Grade A tourist attractions and cultural resources are dominant factors; tourism culture and natural environment are the most influential dimensions affecting the spatial distribution of tourism villages in the Great Mount Huang district; the development of rural tourism requires consideration of multiple aspects and factors, emphasizing multidimensional coordination; (3) the average slope and the number of Grade A tourist attractions exhibit the greatest spatial variability, while the average elevation shows the lowest spatial variability; average elevation, average slope, per capita disposable income, the number of Grade A tourist attractions, and cultural resources all show a positive correlation with the distribution of tourism villages. Full article