Browse Articles

Research Trends, Visualization, and Dynamics of Scientific Publications on Silver Nanoparticles in Dentistry: A Scientometric Approach

Dynamic encryption based secure key management for Vehicle-to-Vehicle communication in autonomous driving

Bayesian probabilistic projections of proportions with limited data: An application to subnational contraceptive method supply shares

Engaging the private sector in contraceptive method supply is critical for creating equitable, sustainable, and accessible healthcare systems. To achieve this, it is essential to understand where women obtain their modern contraceptives. While national-level estimates provide valuable insights into overall trends in contraceptive supply, they often obscure variation within and across subnational regions. Addressing localised needs has become increasingly important as countries adopt decentralised models for family planning services. Decentralization has also underscored the need for reliable subnational estimates of key family planning indicators. The absence of regularly collected subnational data has hindered effective monitoring and decision-making. To bridge this gap, we propose a novel approach that leverages latent attributes in Demographic and Health Survey (DHS) data to produce Bayesian probabilistic projections of contraceptive method supply shares (the proportions of modern contraceptive methods supplied by public and private sectors) with limited data. Our modelling framework is built on Bayesian hierarchical models. Using penalized splines to track public and private supply shares over time, we leverage the spatial nature of the data and incorporate a correlation structure between recent supply share observations at national and subnational levels. This framework contributes to the domain of subnational estimation of proportions in data-sparse settings, outperforming comparable and previous approaches. As decentralization continues to reshape family planning services, producing reliable subnational estimates of key indicators is increasingly vital for researchers and policymakers.

Effect of Hydroxyapatite Seashell Nanofiller on Some Properties of Autopolymerized Acrylic Material for Orthodontic Appliances

Hybrid optimization-based quantum-driven multi-relational graph attention networks for enhanced cyber attack detection in medical IoT networks

CMAP-Fusion: A cross-modal feature selection and model pruning framework for laboratory and imaging data

Cross-modal fusion of medical imaging and laboratory data is a key pathway for accurate diagnosis of diseases, yet it is constrained by issues such as the modal heterogeneity gap, accumulation of feature redundancy, and efficiency imbalance. Existing methods struggle to balance precision and clinical adaptability, and some rely on simulated data leading to limited generalization ability. To address these challenges, we propose the Cross-Modal Alignment-Pruning Fusion model (CMAP-Fusion), which achieves optimization through modular collaboration of “encoding alignment → redundant pruning → fusion prediction”: ViT-B/16 is used to complete imaging feature extraction and dimension alignment, the SmartTrim dynamic pruning module screens key features and reduces redundancy, and the Cross-Modal Transformer (CMT) mines deep associations between dual modalities. Experiments on the COVID-19 Radiography Dataset, ISIC Skin Cancer Dataset, and ChestX-ray14 Dataset demonstrate that the model achieves accuracies of 95.3%, 89.7%, and 93.6% respectively, representing an improvement of 3.1% to 4.1% compared with optimal baselines. Meanwhile, the number of parameters is reduced by 44.2%, computational complexity is decreased by more than 43%, and cross-modal similarity and feature sparsity are significantly superior to baselines. This model realizes the synergistic optimization of “precision-efficiency-generalization,” providing an efficient solution for medical cross-modal fusion. In the future, we will expand to multi-source modalities and multi-disease scenarios, strengthen clinical multi-center validation, further improve the model’s interpretability and clinical acceptance, and facilitate the lightweight deployment of medical AI.

Effect of Over-the-counter Whitening Products on Postbleaching Enamel Surface Roughness and Shade Recovery: An In Vitro Study

On moisture prediction of thin-plate tobacco dryer outlet materials via transformer model

Research on the development of an automated system for psychology questionnaire generation based on large language models

This study reimagined the psychology questionnaire development process using large language model ((LLM) technology, aiming to overcome the protracted preparation cycles and significant human bias inherent in traditional scale development. We developed a specialized fine-tuning scheme for a corpus of 169 professional psychological questionnaires. By integrating instruction fine-tuning with human feedback reinforcement, we significantly enhanced the adaptability of the Qwen-2.5 and GLM-4 models for demanding professional psychological assessment tasks. The optimized models demonstrated remarkable gains across key dimensions: text generation quality (BLEU-4 increased by 0.05, ROUGE-L by 0.057), scientific rigor (logical consistency improved by 28.6%), and cultural adaptability (achieving over 85% accuracy in cross-regional expression conversion). This research solidly supports the feasibility of leveraging LLM technology to drive research paradigm transformation in psychology, offering crucial methodological support for developing efficient, intelligent psychological measurement tools.

Effectiveness of Intracanal Medicament Removal in Root Canals Using Four Irrigation Techniques

Using machine learning for early identification of At-risk students

Development of a Loop-Mediated Isothermal Amplification (LAMP) for the screening of Candida auris

Background Candida auris is an emerging multidrug-resistant yeast associated with invasive infections, healthcare-associated outbreaks, and high mortality, and is often misidentified by conventional diagnostic methods. Rapid, accurate, and scalable screening tools are essential for effective infection control, particularly in high-risk settings. Materials and methods We developed a multiplex loop-mediated isothermal amplification (LAMP) assay that combines a broad-range Candida Pan target with a C. auris –specific target in a single isothermal reaction. Assay conditions were optimized for primer ratio and temperature, and analytical sensitivity was evaluated using serial dilutions of culture-derived C. albicans and C. auris DNA, as well as contrived specimens consisting of urine, swab, and whole-blood matrices. Clinical performance was assessed using 35 Candida -positive clinical specimens (blood, urine, ear swabs) and 94 non-infectious controls. Results were compared with Candida Pan qPCR and C. auris qPCR. Cross-reactivity was tested against common bacterial isolates. Results Under optimized conditions (1:1 primer ratio, 64 °C), the assay allowed species-level discrimination, with C. auris positive for both Pan and auris channels and C. albicans positive only for the Pan channel. The C. auris -specific LAMP probe detected approximately 10²–10³ cells/mL in culture-derived and contrived specimens, showing a 1–2 log improvement over C. auris qPCR (10⁴–10⁵ cells/mL), while the Pan LAMP channel detected C. auris at around 10⁵ cells/mL. In clinical specimens, Pan LAMP detected Candida spp. in 34/35 cases (97.14%) versus 32/35 (91.14%) for Pan qPCR. All C. auris –positive specimens (9/9) were detected by the multiplex LAMP assay, compared with 6/9 (66.7%) by Pan qPCR. All 94 non-infectious controls and all bacterial isolates tested negative, indicating 100% clinical specificity and absence of cross-reactivity. Conclusion The multiplex Candida Pan/ auris LAMP assay provides a rapid, highly sensitive, and specific alternative to qPCR for C. auris screening, while preserving broad Candida detection in a single isothermal reaction. Its improved analytical and clinical sensitivity suggests strong potential for use in active surveillance and infection-control programs, particularly in settings where timely identification and containment of C. auris are critical.

Exposing the potential of XAI-based causal discovery for analysing unstable rock slopes

Anemia among HIV‑positive women in LMICs: Multilevel analysis of recent DHS survey

Introduction Anemia affects over 30% of women of reproductive age globally, with the highest burden in low‑ and middle‑income countries, and it poses additional risks for women living with human immunodeficiency virus (HIV), including disease progression and reduced survival. Although previous studies report prevalence among HIV‑positive women ranging from 37.8% to 55.8%, most evidence comes from hospital‑based or high‑income settings, leaving a gap in population‑level data. Addressing this gap is critical, as women in low- and middle-income countries (LMICs) often face overlapping vulnerabilities such as nutritional deficiencies, limited healthcare access, and high HIV burden. Using nationally representative Demographic and Health Surveys, this study aims to estimate anemia prevalence and identify associated factors among HIV‑positive women to inform targeted interventions and integrated management strategies. Methods A cross‑sectional study was conducted using Demographic and Health Survey data collected between 2022 and 2024 from nine countries in sub-Saharan Africa and Asia, including 1,446 HIV‑positive women aged 15–49 years. Hemoglobin concentration was used to classify anemia based on World Health Organization (WHO) thresholds. Individual and community‑level factors were examined, and weighted data were analyzed using multilevel logistic regression to account for clustering. Associations were reported as adjusted odds ratios with 95% confidence intervals. Results Among HIV‑positive women in Africa and Asia, the prevalence of anemia was 50.62% (95% confidence interval (CI): 48.04–53.20%), with 19.29% classified as mild, 26.28% as moderate, and 5.05% as severe. Prevalence varied widely across countries, ranging from 71.43% in Mali to 12.00% in Tajikistan. Educational status [adjusted odds ration (AOR) = 0.43; 95% CI: 0.22–0.81], media exposure [AOR = 0.41; 95% CI: 0.19–0.87], contraceptive use [AOR = 2.37; 95% CI: 1.35–4.17], and iron supplementation during pregnancy [AOR = 2.17; 95% CI: 1.04–4.55] were significantly associated with anemia. Conclusions Anemia remains a major public health concern among HIV‑positive women, driven by reproductive, nutritional, and socio‑behavioral factors. Strengthening antenatal and HIV care programs, integrating family planning services, and promoting adherence to iron supplementation are critical strategies to reduce anemia risk. Tailored health communication and nutritional interventions, alongside future longitudinal studies, are essential to establish causal pathways and inform targeted interventions.

Anomaly detection in smart power grids with graph-regularized MS-SVDD: a multimodal subspace learning approach

Abstract Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applications. In this paper, we address this gap by proposing a generalized Multimodal Subspace Support Vector Data Description (MS-SVDD) model with graph-embedded regularization. The method projects data from multiple modalities into a shared low-dimensional subspace while preserving modality-specific structure through Laplacian regularizers. Our approach is evaluated on a three-modality dataset derived from smart grid event time series, using a dedicated preprocessing pipeline for constructing one-class classification training samples. The results demonstrate that our graph-embedded MS-SVDD improves robustness of event detection compared to conventional approaches, highlighting the potential of integrating graph priors with multimodal subspace learning for advancing anomaly detection in critical infrastructure. More broadly, this work contributes to the wider field of AI by illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.

Sustainable preservation of dried fish using hybridized plant extracts: HPLC-based quantification of aflatoxins and vitamins, and assessment of broad-spectrum antimicrobial efficacy

This research aimed to preserve the nutritional qualities of fish flesh during prolonged storage, by utilizing bioactive compounds derived from plant extracts and oils. Fish flesh (tissues) was treated with lime, ginger, turmeric, banana peel extracts, and oils – including their hybridized forms. The dried fish samples nutritional quality, and microbial population were measured using established protocols. Results obtained revealed that the treatments played significant roles, in enriching the fish nutritional quality and also inhibiting microbial growth during storage. It was observed that the treatments played a substantial role, in the preservation of the nutritional integrity of dried fish (mostly the proteins and vitamins), during the extended storage duration. Also, most of the hybridized treatments displayed synergistic mechanistic effects, which resulted in enhanced antimicrobial and nutrient retention actions. Conspicuously, the lime extract played a critical role in potentiating the treatments antimicrobial efficacy (T8 to T13). The results revealed that the control sample had higher bacterial and fungal growth at week 8; whereas, treatments T8 to T13 displayed superior antimicrobial activities. Notably, T8 sample exhibited the best antimicrobial action, recording the lowest counts – Staphylococcus spp. (52 CFU/g), Salmonella spp. (14 CFU/g), Bacillus spp. (3 CFU/g), Listeria spp. (5 CFU/g), Aspergillus spp. (black mold) (11 CFU/g), Penicillium spp. (7 CFU/g), Aspergillus spp. (flavus group) (37 CFU/g), and Rhizopus spp. (14 CFU/g). It was also observed that, most of the combined treatments were able to retard aflatoxin formation in the fish tissues, to a safer level of lower than 4 µg/kg. This study’s findings have revealed the sustainability aspects, predominantly the conversion of agricultural products into value-added bio-additives in food processing and preservation.

Dual mitigation of extruded plastic sack waste in green concrete via microsilica and recycled polypropylene fibers

Vulnerabilities, extreme weather and temporal tensions as experienced by construction workers in the Swedish construction sector

Aim Climate change poses increasing risks to outdoor occupations, including construction work. This study explores how vulnerability is constructed in narratives of manual labour within the Swedish construction sector, particularly under extreme weather conditions. Methods Drawing on 16 qualitative interviews with Swedish construction workers, the study adopts a social constructionist lens to explore how vulnerability is shaped and experienced. Findings The findings identify multiple, intersecting forms of vulnerability—bodily, hierarchical, material, social, and market-driven—exacerbated by climate-related challenges such as high temperatures, heavy rainfall, and strong winds. Crucially, the analysis highlights how extreme weather disrupts temporal rhythms, widening the gap between scheduled work plans and the actual time needed to complete tasks. Conclusion The paper concludes that vulnerability arises not only from direct exposure to adverse weather, but also through indirect social, material, and temporal dynamics inherent in the construction sector and exacerbated by climate change.

Optimizing venous thromboembolism risk management through the PDCA cycle: a single-center before-and-after controlled study

Digitizing microscope slide-based natural history collections: A protocol using slide scanner technology

Natural history collections contain millions of microscope slides documenting global microscopic biodiversity, yet these materials remain largely undigitized and are vulnerable to deterioration and loss. Recent advances in slide scanner technology, originally developed for medical pathology, offer new opportunities for comprehensive digitization of slide-based collections. Here we present an optimized protocol for digitizing diverse microscope slide specimens, using the Hamamatsu NanoZoomer S20 slide scanner, developed while imaging slides at the Smithsonian National Museum of Natural History. We provide specimen-specific recommendations for scanning parameters, including scan area, focal points, Z-stack configuration, and file management workflows. Scanning times range from 41 seconds for small invertebrates to 18 minutes for palynological samples, with final compressed file sizes of 0.15-28 GB. High-resolution images (0.23 μm/pixel) captured diagnostic morphological features across all specimen types, including pollen, diatoms, radiolarians, plant and fungi tissues, and invertebrates. Using this method, we estimated that just the NMNH’s paleo-palynology slide collection contains approximately 4.3 billion individual specimens, 30 times more than the current estimated size of the entire NMNH collection. Slide scanning enables 3D data capture, facilitates remote collaboration, improves reproducibility of taxonomic identifications, and creates permanent digital records that mitigate risks of physical deterioration. This protocol provides practical guidance for institutions looking to digitize slide-based collections to preserve and unlock their full research potential.