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This study presents the development of a structured and contextually grounded industry internship model—PRIGEL—designed to align with Indonesia's Merdeka Belajar Kampus Merdeka (MBKM) policy. The PRIGEL model compris...

Tire and Road Wear Particles (TRWP) are pervasive environmental contaminants, yet the molecular mechanisms linking their toxic derivative, 6PPD-quinone (6PPD-Q), to bladder cancer (BLCA) progression remain obscure. This ...

Abstract Type 2 Diabetes Mellitus (T2DM) confers a significant risk for Mild Cognitive Impairment (MCI), yet robust biomarkers for early detection remain limited. In this study, 150 age-matched participants (50 Healthy C...

Employment competitiveness is widely regarded as a key indicator of higher education effectiveness, particularly in application-oriented universities where graduate employability affects institutional reputation and soci...

This study explores the development and management of campus learning spaces at the Norwegian University of Science and Technology (NTNU), focusing on the experiences and perspectives of the technical administrative staf...

This paper will offer a unified strategy to the optimization of the chassis of a Formula SAE Supra car and predict structural failures using machine learning and Finite Element Analysis (FEA). The work demonstrates that ...

Abstract The increasing frequency of natural hazards, intensified by climate change, poses substantial challenges to sustainable development worldwide. Northern Pakistan, particularly the Hunza district, is highly suscep...

Abstract Artificial intelligence (AI) has transformed social media into a powerful tool for educational learning engagements. WeChat, a Chinese social media platform, is extensively used by students for academic learning...

Phthalate plasticizers, especially diethyl phthalate (DEP), are linked to metabolic disorders, while their precise role and mechanism in diabetic nephropathy (DN) pathogenesis remain unclear. Here, we integrated transcri...

Lung diseases such as pneumonia, tuberculosis, COVID-19, and lung cancer remain significant global health challenges that demand rapid and accurate diagnosis to improve patient outcomes. This study proposes NASNet-ViT, a...

Dynamic gesture recognition is a core technology for human–computer interaction, yet existing methods face prominent limitations in hand spatial topology modeling, long–short-term temporal feature fusion, and general...

Materias / palabras clave: automated annotation; deep learning; lung adenocarcinoma; pathology; tumor-infiltrating lymphocytes

To achieve high accuracies in deep learning-based inspection of IC markings, a large number of manually labeled images of good and defective markings are needed. In many IC production lines, only a handful of labeled ima...

ObjectiveTo evaluate a novel multichannel deep learning (DL) model using contrast-enhanced ultrasound (CEUS) data with multiple regions of interest (ROIs) and time-intensity curve (TIC)-derived key frames for predicting ...

Abstract Globally groundwater depletion threatens water security with aquifer storage declining at 300 km³/year. Climate change intensifies flood frequency and magnitude causing agricultural and other losses. Punjab, I...

Abstract Background Population ageing presents increasing challenges for health systems. Identifying complex needs and planning individualized care through Comprehensive Geriatric Assessment (CGA), promoting intergenerat...

Abstract Background Thalassemia is the most prevalent single-gene disorder in Sri Lanka, imposing a significant socioeconomic and healthcare burden. Early carrier detection is essential for genetic counselling and the pr...

Abstract Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer s...

Abstract Purpose This study aimed to identify the effectiveness of free water MRI (FW-MRI) features for predicting amyloid-beta (Aβ) statuses in Alzheimer’s disease (AD) by constructing diagnostic models using machine...

Global food security relies heavily on rice production, which is continuously threatened by crop diseases. Although deep learning (DL) models have demonstrated strong diagnostic performance, their high computational and ...

Abstract Background This study aims to explore spatial heterogeneity within tumors, establish and validate an interpretable machine learning model combining habitat radiomics and deep learning, and investigate its predic...

ABSTRACT Background and Aims De novo drug design is the process of generating novel lead compounds that possess desirable pharmacological activities and optimal physicochemical properties for therapeutic development. In ...

Objectives: Deep learning networks have achieved significant progress in caries diagnosis, but automated localization and numbering of carious teeth in cone-beam computed tomography (CBCT) images remain underexplored. Th...

Abstract We developed and evaluated a deep learning system integrating segmentation and classification for automated detection of Weiss rings on fundus photographs (FPs). A U-Net segmenter and two EfficientNet-B0 classif...