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UV-C irradiation enhances the dual-use potential of Desmodesmus quadricauda and Scenedesmus dimorphus for food and biodiesel production
Biomimetic macrophage membrane-coated nanoparticles for targeted cardioprotection against myocardial ischaemia-reperfusion injury
Clinical registry metadata as a hidden bottleneck in AI-driven drug discovery: a computational audit of translational phase data in glioma research
Development and properties of composite coatings for surface repair of cement concrete pavements
Design of three-mode free-form nanostructured optical fibers: comparison of dense and convolutional neural networks in Generative Inverse Design Networks approach
Abstract We report a numerical study on the inverse design of the internal structure of weakly coupled three-mode fibers. We explored a new class of optical fibers – free-form nanostructured fibers (FFNFs) – operating at 1550 nm for potential application in Mode-Division Multiplexing (MDM) systems. The fiber geometries were generated and optimized within the Generative Inverse Design Networks (GIDNs) framework using convolutional neural networks (CNNs) and fully connected dense neural networks (DNNs). The objective of the optimization was to maximize the minimal effective refractive index separation Min|Δ n eff | between supported modes, ensuring weak intermodal coupling. The proposed free-form nanostructured fiber designed with the CNN achieved a minimum modes separation of Min|Δ n eff | = 2.15 × 10 − 3 , exceeding that of a reference three-mode elliptical-core fiber (Min|Δ n eff | = 2.085 × 10 − 3 ). In contrast, the best DNN-optimized structure reached Min|Δ n eff | = 1.99 × 10 − 3 . The results demonstrate that the CNN-based inverse design yields fiber geometries outperforming conventional designs. The proposed methodology can be extended to higher-order mode systems and can include additional fiber properties crucial for telecommunication purposes.
Research on intelligent generation of volleyball training strategies combining YOLOv5 + DeepSORT trajectory data and a key point CNN model
Machine learning for scalable obstructive sleep apnea risk screening using digital phenotyping from wearable devices and clinical scales
Interacting dust-acoustic multi-soliton and periodic waves in strongly coupled dusty plasma with ion-drag force
Identification and characterization of intracerebral hemorrhage events in elderly veterans with alzheimer’s disease in the veterans affairs healthcare system
Abstract Cerebral amyloid angiopathy (CAA) and hypertensive (HTN) small vessel disease are causes of spontaneous intracerebral hemorrhage (ICH). We identified ICH rates in patients with all-cause mild cognitive impairment (MCI) or Alzheimer’s disease (AD), and explored feasibility of using location-based approach to differentiate CAA- and HTN-related ICH using comorbidities in electronic health records (EHRs). This administrative study combined Veterans Affairs Healthcare System plus Centers for Medicare and Medicaid Services (VAHS/CMS) databases. Patients with MCI/AD aged ≥ 50 years (2016–2023) were 1:1 matched to non-MCI/AD controls. Inpatient primary discharge International Classification of Diseases-10th Edition (ICD-10) codes identified acute ICH; anatomical location within codes classified events as likely CAA- or HTN-related ICH. Incidence rates of ICH after MCI/AD were summarized. Cluster analysis of variables related to ICH was used to describe whether data-driven groupings matched clinician-postulated classifications. The MCI/AD cohort ( n = 747,475) and controls were aged 77.7 ± 10.1 years (96% men, 75–76% White, 87–88% non-Hispanic). Demographic- and comorbidity-adjusted rates of ICH/1000 person-years were 0.84 (overall), 1.05 (MCI/AD), and 0.68 (non-MCI/AD). Adjusted events/1000 person-years were higher in MCI/AD vs. non-MCI/AD cohorts: CAA-related ICH, 0.19 vs. 0.12 (Incidence Rate Ratio (IRR) 1.63; P < 0.001); HTN-related ICH, 0.16 vs. 0.12 (IRR 1.35; P < 0.001); non-specific-ICH, 0.68 vs. 0.43 (IRR 1.59; P < 0.001). Hierarchical clustering analysis of our cohorts revealed an association of CAA-related ICH with older age, cardiovascular and rheumatic disorders, and an association of HTN-related ICH with cerebrovascular disease, hypertension and diabetes. In sum, the estimated incidence of ICH over the study period was 0.84/1000 person-years. CAA-related ICH incidence in MCI/AD was 63% greater than that in controls. Outcomes from cluster analysis are consistent with ICD-based CAA- vs. HTN-related ICH classifications. These findings support future exploration of using ICD coding-based ICH event identification in EHRs and claims databases for epidemiological studies.
Confidence-guided outlier refinement and collaborative embedding for unsupervised person re-identification
Abstract Unsupervised person re-identification techniques have developed rapidly in recent years. Nonetheless, they still face challenges such as unstable pseudo-label quality and insufficient feature representation, particularly when handling outlier points and complex backgrounds. To address these issues, this paper proposes a joint optimization algorithm of Multi-level Confidence Outlier Refinement (MLCOR) and Collaborative Embedding Method (CEM), which aims to improve the discriminative nature of the embedding space and optimize the accuracy of pseudo-labels. Specifically, Multi-level Confidence Outlier Refinement evaluates the confidence level of outlier points by analyzing the distance relationship between outlier points and their neighboring samples, and classifies them into multiple confidence levels. We design a weighted voting strategy for low-confidence samples to correct the pseudo-labeling of low-confidence points by using the label distribution of neighboring samples, thus reducing noise interference and clustering errors and improving the accuracy of pseudo-labeling. Meanwhile, the Collaborative Embedding Method jointly optimizes global and local features, establishing an effective synergy between global category differentiation and local fine-grained feature learning. By integrating multi-level similarity relationships, this approach not only strengthens the model’s ability to capture subtle differences between samples but also significantly enhances the model’s boundary awareness. Experimental results demonstrate that the proposed method achieves outstanding performance on multiple standard datasets, significantly improving both clustering accuracy and pseudo-label precision, while also exhibiting strong domain generalization and robustness in complex environments.
Utilization of advanced machine learning models for analysis of pharmaceutical cocrystals by prediction of solubility parameters using Dragonfly algorithm optimization
Parametrising harmonic strengths in Raman driven spin noise spectra of neutral atoms
Cover cropping differentially shapes soil fungal and bacterial communities in citrus orchards
The role of cultivation technology in shaping the health-promoting potential of potato tubers (Solanum tuberosum L.)
Application of personalized nursing based on dynamic monitoring of oral pH in oral management of patients with hematological diseases undergoing chemotherapy
Anionic surfactant reformed carbon composite voltammetric sensor for the efficient and sensitive detection of carmoisine in food samples
Abstract Carmoisine (CM), an azo dye, imparts vibrant red color when added to the food, beverage, cosmetic and pharmaceutical products. The excessive intake of CM causes various health effects and hence it is essential to develop appropriate methods for the determination and quantification of CM in food samples. In this current work, a rapid detection and analysis of CM was performed via cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques by harnessing sodium dodecyl sulfate modified composite sensor (SDSMCS). The morphological features and elemental composition of bare carbon nanotube graphite composite sensor (BCNGCS) and SDSMCS surfaces were compared and the difference between the working sensors was assessed. The active surface area was computed as 0.031 cm 2 for BCNGCS and 0.049 cm 2 for SDSMCS surfaces by employing CV technique. Key insights into the charge transfer process involved in the oxidation of CM was obtained by electrochemical impedance spectroscopy (EIS). The modified sensor demonstrated improved electrochemical features at optimal experimental conditions. The analysis validated that the electro oxidation of CM proceeds through adsorption-controlled kinetics at the surface of SDSMCS and exhibited a significantly low limit of detection (LOD) of 0.009 µM by CV and 0.073 µM by DPV technique, in the linear range of 0.1 µM to 1.1 µM and 0.1 µM to 0.9 µM, respectively. The attributes like reproducibility and repeatability of the developed SDSMCS were evaluated and the respective relative standard deviation (RSD) of 4.95% and 4.90% verified that the fabricated sensor is reliable for detecting CM in various commercial food samples with a recovery rate exceeding 97%. Surfactant- reformed carbon-based sensors provide a cost-effective and reliable platform that aligns with the evolving trends in field of electrochemical sensing.
Evaluation of practical equivalent seismic input methods for fault-crossing bridges considering dynamic and permanent ground displacement
Associations of lifestyle and biological factors with body image among reproductive-aged women: a cross-sectional study
Abstract This study aimed to identify lifestyle and biological factors associated with body image among reproductive-aged women, focusing on physical activity, sedentary time, sleep, BMI, premenstrual symptoms (PMS), and sleep quality. A secondary aim was to explore interrelationships among these factors to contextualise their combined association with body image. In this cross-sectional online survey, 291 women aged 18–45 years participated (mean age: 27.16 ± 0.36 years). Validated questionnaires were used to assess body image (Body Appreciation Scale), physical activity (Global Physical Activity Questionnaire), sleep quality (Sleep Quality Scale), BMI, and PMS (Premenstrual Assessment Form-Short Form). Data were analysed with IBM SPSS 28.0 using multivariable linear regression and correlation analyses ( p < 0.05). Higher BMI, greater PMS severity, poorer sleep quality and longer sedentary time were significantly associated with lower body appreciation. Correlations were found between PMS and sleep quality ( r = 0.309; p < 0.001), PMS and work-related moderate activity ( r = 0.119; p = 0.043), and sleep quality and work-related moderate activity ( r = 0.169; p = 0.004). No significant associations were observed for recreational physical activity with PMS ( r =-0.041; p = 0.544) or sleep quality ( r =-0.040; p = 0.498). Body image in reproductive-aged women was associated with both lifestyle behaviours and biological symptom burden. The findings suggest that supporting positive body image may benefit from integrated approaches addressing sedentary behaviour, sleep quality and menstrual health alongside weight-related factors.