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To address the sensitivity of traditional Two-Dimensional Principal Component Analysis (2DPCA) to outliers and the breakdown of variance-reconstruction equivalence under noise, we propose an Angle-Aware Weighted 2DPCA (A...

This study aimed to evaluate the performance of deep learning models for segmenting the augmented bone following transalveolar sinus floor elevation (TSFE). Cone-beam computed tomography (CBCT) data from 103 patients und...

BackgroundPersonal characteristics, income, social capital, and immigration policies influence immigrants’ social integration (SI) in European Union (EU) countries. However, few studies have used machine learning (ML) ...

Purpose – This study investigates how recontextualizing EFL reading instruction through local-wisdom-based Problem-Based Learning (PBL) supports students’ reading comprehension and learning experiences. Methodology ...

Autonomous microrobotic navigation in life sciences is highly challenging due to environmental disturbances, unpredictable phenomena, diverse obstacles, and dynamic changes. These significantly limit motion of microrobot...

Abstract Accurate prediction of cascading faults in power grids can ensure system stability and prevent large-scale outages. Existing methods typically require learning effective statistical information from datasets wit...

The low visual contrast between dermatological lesions and normal skin, coupled with the masking effect of surface artefacts, presents two major obstacles to precise automated skin lesion segmentation. In order to addres...

Abstract Background Delirium is a neuropsychiatric syndrome associated with serious complications. Residents of nursing homes are particularly vulnerable to developing delirium. Nurses play a key role in the prevention, ...

Abstract Roadside areas are major sources of traffic-related air pollutant emissions. This study identified the spatial distribution of roadside pollutants using Unmanned Aerial Vehicle (UAV) measurements and predicted t...

Background and purpose: Accurate segmentation of the urethra is crucial for safe focal dose escalated radiotherapy, while prostate zone identification is important for prostate cancer diagnosis. Manual delineations on ma...

IntroductionTo optimize radiotherapy treatment and minimize toxicities, effective segmentation of organs-at-risk (OARs) and clinical target volume (CTV) is essential. Deep learning (DL) models can achieve high segmentati...

IntroductionFlood risk assessment has become increasingly important in regions vulnerable to climate-induced disasters. This study addresses the need for a robust decision-support framework by proposing a hybrid multi-cr...

Despite achieving high volumetric overlap, pixel-based deep learning segmentation approaches do not ensure true vascular connectivity, leading to fragmentations structurally analogous to Branch Retinal Artery Occlusions....

Reliable trajectory similarity estimation underpins applications such as map matching, mobility anomaly detection, and travel behavior analysis. However, traditional distance-based methods are computationally expensive a...

Abstract This paper explores the rapid evolution of virtual classrooms over the past decade. It examines their usefulness in language teaching, especially as teachers' roles change. The study investigates how virtual lea...

ABSTRACT With the development of unmanned aerial vehicle and satellite technology, the application of tiny object detection in remote sensing images is becoming increasingly widespread. Although significant progress has ...

Abstract A growing body of research suggests that sports training enhances high-level cognitive functions, including sensory perception, sensorimotor skills, executive control, memory, attention, and processing speed. Ho...

BackgroundStudents with disabilities in South African higher education continue to encounter persistent institutional, structural and attitudinal barriers, despite a progressive policy landscape. These challenges are par...

Digital twins are increasingly used in wireless networks to provide geometry- and channel-aware replicas that support prediction and control, while large language models (LLMs) offer strong instruction-following capabili...

Abstract Background: Marker-based training is a widely used type of positive reinforcement training believed to improve learning outcomes by providing a precise and consistent marker for desired behavior. However, resear...