Search Results (500)

The coastal sandy land in northeast Hainan Province is typical for this land type, also exhibiting strong sand activity. This study is based on wind speed, wind direction, and sediment transport data obtained at a field meteorological station using an omnidirectional sand accumulation instrument from 2020 to 2024, studying the coastal aeolian environment and sediment transport distribution characteristics in the region. Its findings provide a theoretical basis for comprehensively analyzing the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results show the following: (1) The annual average threshold wind velocity for sand movement in the study area is 6.84 m/s, and the wind speed frequency (frequency of occurrence) is 51.54%, dominated by easterly (NE, ENE) and southerly (S, SSE) winds. (2) The drift potential (DP) refers to the potential amount of sediment transported within a certain time and spatial range, and the annual drift potential (DP) and resultant drift potential (RDP) of Mulan Bay from 2020 to 2024 were 550.82 VU and 326.88 VU, respectively, indicating a high-energy wind environment. The yearly directional wind variability index (RDP/DP) was 0.59, classified as a medium ratio and indicating blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 249.45°, corresponding to a WSW direction, indicating that the sand in Mulan Bay is generally transported in the southwest direction. (3) When the measured data extracted from the sand accumulation instrument in the study area from 2020 to 2024 were used for statistical analysis, the results showed that the total sediment transport rate (the annual sediment transport of the observation section) in the study area was 110.87 kg/m·a, with the maximum sediment transport rate in the NE direction being 29.26 kg/m·a. These results suggest that when sand fixation systems are constructed for relevant infrastructure in the region, the construction direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

Constructing heterojunctions can combine the superior performance of different two-dimensional (2D) materials and eliminate the drawbacks of a single material, and modulating heterojunctions can enhance the capability and extend the application field. Here, we investigate the physical properties of the heterojunctions formed by the contact of different atom planes of Janus MoSSe (JMoSSe) and graphene (Gr), and regulate the Schottky barrier of the Gr/JMoSSe heterojunction by the number of layers and the electric field. Due to the difference in atomic electronegativity and surface work function (WF), the Gr/JSMoSe heterojunction formed by the contact of S atoms with Gr exhibits an n-type Schottky barrier, whereas the Gr/JSeMoS heterojunction formed by the contact of the Se atoms with Gr reveals a p-type Schottky barrier. Increasing the number of layers of JMoSSe allows the Gr/JMoSSe heterojunction to achieve the transition from Schottky contact to Ohmic contact. Moreover, under the control of an external electric field, the Gr/JMoSSe heterojunction can realize the transition among n-type Schottky barrier, p-type Schottky barrier, and Ohmic contact. The physical mechanism of the layer number and electric field modulation effect is analyzed in detail by the change in the interface electron charge transfer. Our results will contribute to the design and application of nanoelectronics and optoelectronic devices based on Gr/JMoSSe heterojunctions in the future. Full article

Background: Salmonella is a bacterium that causes foodborne infections. This study characterized two strains isolated from cheese and beef in Brazil using whole-genome sequencing (WGS). Objectives: We evaluated their antimicrobial resistance profiles, virulence factors, plasmid content, serotypes and phylogenetic relationships. Methods: DNA was extracted and sequenced on the NovaSeq 6000 platform; the pangenome was assembled using the Roary tool; and the phylogenetic tree was constructed via IQ-TREE. Results and Discussion: For contextualization and comparison, 3493 Salmonella genomes of Brazilian origin from NCBI were analyzed. In our isolates, both strains carried the aac(6′)-Iaa_1 gene, while only Schwarzengrund harbored the qnrB19_1 gene and the Col440I_1 plasmid. Cerro presented the islands SPI-1, SPI-2, SPI-3, SPI-4, SPI-5 and SPI-9, while Schwarzengrund also possessed SPI-13 and SPI-14. Upon comparison with other Brazilian genomes, we observed that Cerro and Schwarzengrund represented only 0.40% and 2.03% of the national database, respectively. Furthermore, they revealed that Schwarzengrund presented higher levels of antimicrobial resistance, a finding supported by the higher frequency of plasmids in this serovar. Furthermore, national data corroborated our findings that SPI-13 and SPI-14 were absent in Cerro. A virulence analysis revealed distinct profiles: the cdtB and pltABC genes were present in the Schwarzengrund isolates, while the sseK and tldE1 family genes were exclusive to Cerro. The results indicated that the sequenced strains have pathogenic potential but exhibit low levels of antimicrobial resistance compared to national data. The greater diversity of SPIs in Schwarzengrund explains their prevalence and higher virulence potential. Conclusions: Finally, the serovars exhibit distinct virulence profiles, which results in different clinical outcomes. Full article

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

The accurate identification of pulmonary nodules is critical for the early diagnosis of lung diseases; however, this task remains challenging due to inadequate feature representation and limited localization sensitivity. Current methodologies often utilize channel attention mechanisms and intersection over union (IoU)-based loss functions. Yet, they frequently overlook spatial context and struggle to capture subtle variations in aspect ratios, which hinders their ability to detect small objects. In this study, we introduce an improved YOLOV11 framework that addresses these limitations through two primary components: a spatial squeeze-and-excitation (SSE) module that concurrently models channel-wise and spatial attention to enhance the discriminative features pertinent to nodules and explicit aspect ratio penalty IoU (EAPIoU) loss that imposes a direct penalty on the squared differences in aspect ratios to refine the bounding box regression process. Comprehensive experiments conducted on the LUNA16, LungCT, and Node21 datasets reveal that our approach achieves superior precision, recall, and mean average precision (mAP) across various IoU thresholds, surpassing previous state-of-the-art methods while maintaining computational efficiency. Specifically, the proposed SSE module achieves a precision of 0.781 on LUNA16, while the EAPIoU loss boosts mAP@50 to 92.4% on LungCT, outperforming mainstream attention mechanisms and IoU-based loss functions. These findings underscore the effectiveness of integrating spatially aware attention mechanisms with aspect ratio-sensitive loss functions for robust nodule detection. Full article

A Stackelberg equilibrium–based Model Reference Adaptive Control (MSE) method is proposed for spacecraft Pursuit–Evasion (PE) games with incomplete information and sequential decision making under a non–zero–sum framework. First, the spacecraft PE dynamics under $J_2$ perturbation are mapped to a dynamic Stackelberg game model. Next, the Riccati equation solves the equilibrium problem, deriving the evader’s optimal control strategy. Finally, a model reference adaptive algorithm enables the pursuer to dynamically adjust its control gains. Simulations show that the MSE strategy outperforms Nash Equilibrium (NE) and Single–step Prediction Stackelberg Equilibrium (SSE) methods, achieving 25.46% faster convergence than SSE and 39.11% lower computational cost than NE. Full article

Background: Automated semi-solid extrusion (SSE) material deposition is a promising new technology for preparing personalized medicines for different patient groups and veterinary applications. The technology enables the preparation of custom-made oral elastic gel tablets of active pharmaceutical ingredient (API) by using a semi-solid polymeric printing ink. Methods: An automated SSE material deposition method was used for generating chewable gel tablets loaded with propranolol hydrochloride (-HCl) at three different API content levels (3.0 mg, 4.0 mg, 5.0 mg). The physical appearance, surface morphology, dimensions, mass and mass variation, process-derived solid-state changes, mechanical properties, and in-vitro drug release of the gel tablets were studied. Results: The inclusion of API (1% w/w) in the semi-solid CuraBlend^TM printing mixture decreased viscosity and increased fluidity, thus promoting the spreading of the mixture on the printed (material deposition) bed and the printing performance of the gel tablets. The printed gel tablets were elastic, soft, jelly-like, chewable preparations. The mechanical properties of the gel tablets were dependent on the printing ink composition (i.e., with or without propranolol HCl). The maximum load for the final deformation of the CuraBlend™-API (3.0 mg) gel tablets was very uniform, ranging from 73 N to 80 N. The in-vitro dissolution test showed that more than 85% of the drug load was released within 15–20 min, thus verifying the immediate-release behavior of these drug preparations. Conclusions: Automated SSE material deposition as a modified 3D printing method is a feasible technology for preparing customized oral chewable gel tablets of propranolol HCl. Full article

Background/Objectives. Flavonoids are a vast class of phenolic substances. To date, approximately 6000 plant-origin flavonoids have been discovered, with many of them being used in drug therapy. Therapeutic flavonoids are commonly formulated to conventional “one-size-fits-all” dosage forms, such as conventional tablets or hard capsules. However, the current trends in pharmacy and medicine are centred on personalised drug therapy and drug delivery systems (DDSs). Therefore, 3D printing is an interesting technique for designing and preparing novel personalised pharmaceuticals for flavonoids. The aim of the present study was to develop aqueous polyethylene oxide (PEO) gel inks loaded with rutin for semisolid extrusion (SSE) 3D printing. Methods. Rutin (a model substance for therapeutic flavonoids), Tween 80, PEO (MW approx. 900,000), ethanol, and purified water were used in PEO gels at different proportions. The viscosity and homogeneity of the gels were determined. The rutin–PEO gels were printed with a bench-top Hyrel 3D printer into lattices and discs, and their weight and effective surface area were investigated. Results. The key SSE 3D-printing process parameters were established and verified. The results showed the compatibility of rutin as a model flavonoid and PEO as a carrier polymer. The rutin content (%) and content uniformity of the 3D-printed preparations were assayed by UV spectrophotometry and high-performance liquid chromatography (HPLC). Conclusions. The most feasible aqueous PEO gel ink formulation for SSE 3D printing contained rutin 100 mg/mL and Tween 80 50 mg/mL in a 12% aqueous PEO gel. The 3D-printed dosage forms are intended for the oral administration of flavonoids. Full article

This study focuses on exploring potential inhibitors of the Marburg virus interferon inhibitory domain protein (MARV-VP35), which is responsible for immune evasion and immunosuppression during viral manifestation. A combination of in silico techniques was applied, including structure-based pharmacophore virtual screening, molecular docking, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, molecular dynamics (MD), and molecular stability assessment of the identified hits. The docking scores of the 14 selected ligands ranged between −6.88 kcal/mol and −5.28 kcal/mol, the latter being comparable to the control ligand. ADMET and drug likeness evaluation identified Mol_01 and Mol_09 as the most promising candidates, both demonstrating good predicted antiviral activity against viral targets. Density functional theory (DFT) calculations, along with relevant quantum chemical descriptors, correlated well with the docking score hierarchy, and molecular electrostatic potential (MEP) mapping confirmed favorable electronic distributions supporting the docking orientation. Molecular dynamics simulations further validated complex stability, with consistent root mean square deviation (RMSD), root mean square fluctuation (RMSF), and secondary structure element (SSE) profiles. These findings support Mol_01 and Mol_09 as viable candidates for experimental validation. Full article

Forecasting financial time series is challenging due to their intrinsic nonlinearity, high volatility, and complex dependencies across temporal scales. This study introduces MSGformer, a novel hybrid architecture that integrates multi-scale graph neural networks (MSGNet) with Transformer encoders to capture both local temporal fluctuations and long-term global trends in high-frequency financial data. The MSGNet module constructs multi-scale representations using adaptive graph convolutions and intra-sequence attention, while the Transformer component enhances long-range dependency modeling via multi-head self-attention. We evaluate MSGformer on minute-level stock index data from the Chinese A-share market, including CSI 300, SSE 50, CSI 500, and SSE Composite indices. Extensive experiments demonstrate that MSGformer significantly outperforms state-of-the-art baselines (e.g., Transformer, PatchTST, Autoformer) in terms of MAE, RMSE, MAPE, and R². The results confirm that the proposed hybrid model achieves superior prediction accuracy, robustness, and generalization across various forecasting horizons, providing an effective solution for real-world financial decision-making and risk assessment. Full article

This study investigates the temporal dynamics, sources, and photochemical behaviour of key volatile organic compounds (VOCs) along Marylebone Road, London (1 January 2015–1 January 2023), a heavily trafficked urban area. Hourly measurements of benzene, toluene, ethylbenzene, ethene, propene, isoprene, propane, and ethyne, alongside ozone (O₃) and meteorological data, were analysed using correlation matrices, regression, cross-correlation, diurnal/seasonal analysis, wind-sector analysis, PCA (Principal Component Analysis), and clustering. Strong inter-VOC correlations (e.g., benzene–ethylbenzene: r = 0.86, R² = 0.75; ethene–propene: r = 0.68, R² = 0.53) highlighted dominant vehicular sources. Diurnal peaks of benzene, toluene, and ethylbenzene aligned with rush hours, while O₃ minima occurred in early mornings due to NO titration. VOCs peaked in winter under low mixing heights, whereas O₃ was highest in summer. Wind-sector analysis revealed dominant VOC emissions from SSW (south-southwest)–WSW (west-southwest) directions; ethyne peaked from the E (east)/ENE (east-northeast). O₃ concentrations were highest under SE (southeast)–SSE (south-southeast) flows. PCA showed 39.8% of variance linked to traffic-related VOCs (PC1) and 14.8% to biogenic/temperature-driven sources (PC2). K-means clustering (k = 3) identified three regimes: high VOCs/low O₃ in stagnant, cool air; mixed conditions; and low VOCs/high O₃ in warmer, aged air masses. Findings highlight complex VOC–O₃ interactions and stress the need for source-specific mitigation strategies in urban air quality management. Full article

Background: Pharmaceutical compounding remains a predominantly manual process with limited innovation, particularly in non-sterile applications. This study explores the implementation of an automated compounding platform based on 3D printing to enhance precision, efficiency, and adaptability in pediatric corticosteroid formulations. Methods: Personalized hydrocortisone dosage forms were prepared in a hospital pharmacy setting using a proprietary excipient base and standardized procedures, including automated dosing and syringe heating when required. Three dosage forms—3.2 mg gel tablets, 2.8 mg water-free troches, and 1.2 mg orodispersible films (ODFs)—were selected to demonstrate the platform’s versatility and to address pediatric needs for varying strengths and dosage types. All products were prepared using a reproducible semi-solid extrusion (SSE)-based workflow with the consistent API-excipient blending and automated deposition. Results: Analytical testing confirmed that all formulations met pharmacopeial criteria for mass and content uniformity. The ODF and troche forms achieved rapid drug release, exceeding 75% within 5 min, while the gel tablet showed a slower release profile, reaching 86% by 60 min. Additionally, in-process homogeneity testing across syringe printing cycles confirmed the consistent API distribution. Conclusions: The results support the feasibility of integrating automated compounding technologies into pharmacy workflows. Such systems can improve accuracy, minimize variability, and streamline the production of customized pediatric medications, particularly for drugs with poor palatability or narrow therapeutic windows. Overall, this study highlights the potential of automation to modernize non-sterile compounding, and to better support individualized therapy. Full article

Background/Objectives: Effective drug delivery systems require precise formulation and understanding of excipient impact on active pharmaceutical ingredient (API) stability and efficacy, as uncontrolled interactions can compromise outcomes. This study developed and validated a semi-solid extrusion (SSE) 3D printing method for polyvinyl alcohol (PVA)-based hydrogel disks with metronidazole (MET). These disks served as a standardized platform to assess excipient influence on MET’s antimicrobial activity, focusing on plasticizers (polyethylene glycol 400, glycerol, propylene glycol, and diethylene glycol monoethyl ether)—excipients that modify hydrogel properties for their application in printing dressing matrices—with the platform’s capabilities demonstrated using in vitro antimicrobial susceptibility testing against Bacteroides fragilis. Methods: Hydrogel inks based on PVA with added plasticizers and MET were prepared. These inks were used to 3D-print standardized disks. The MET content in the disks was precisely determined. The antimicrobial activity of all formulation variants was evaluated using the disk diffusion method against B. fragilis. Results: The incorporated plasticizers did not negatively affect the antimicrobial efficacy of MET against B. fragilis. All printed hydrogel matrices exhibited clear antimicrobial activity. The 3D-printed disks showed high repeatability and precision regarding MET content. Conclusions: SSE 3D printing is viable for manufacturing precise, reproducible MET-loaded PVA hydrogel disks. It provides a standardized platform to evaluate diverse excipient impacts, like plasticizers, on API antimicrobial performance. The tested plasticizers were compatible with MET. This platform aids rational formulation design and screening for optimal excipients in designed formulations and for various pharmaceutical applications. Full article