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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...

BackgroundTranscriptome−wide association study (TWAS) contributes to discovering novel susceptibility genes related to diseases. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to characterize cellu...

Background and objectives: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest x-ray interpre...

BackgroundAutistic children experience enduring difficulties with emotion regulation and daily participation. Emotion-oriented play-learning tools (EPLTs, sensory play-learning tools for supporting emotion and self-regul...

To bridge the gap in visualization and interactivity found in traditional chemistry tools, this study developed an AR-powered comic using differentiated instruction to help students navigate the spatial complexities of m...

This research examines differentiation strategies in Arabic language teaching within the framework of the Merdeka Curriculum, relying on existing literature reviews to identify challenges and successes discussed in previ...

Abstract Background Blended learning, integrating traditional teaching with digital resources, has shown promise in medical education. The Radiology Assistant platform offers structured, case-based content that may addre...

University physical education (PE) is increasingly expected to foster physical literacy by integrating movement competence with cognitive understanding and intrinsic motivation. However, rigorous evidence regarding the e...

Technology-based learning is increasingly integrated into school environments, yet its role in students' moral and psychosocial development remains underexplored. This study investigates the relationship between technolo...

With the increasing integration of artificial intelligence (AI) across industries, there is a growing need to transform traditional teaching methods into more innovative, technology-driven, and practice-oriented approach...

Adaptive learning systems require effective mechanisms to model students’ evolving learning states and provide personalized learning path recommendations. Traditional rule-based approaches are limited in capturing...

Abstract Background Carotid atherosclerosis is a kind of systemic atherosclerosis in the carotid arteries. It remains one of the leading causes of ischemic stroke. However, the efficiency of treatment is insufficient. Th...

Optimizing modern factories requires dynamic, data-driven decision support aligned with Industry 5.0’s human-centric principles. This research introduces the ‘immersive cognitive factory twin’, a novel framework le...

Wildfires are among the most destructive natural disasters and pose significant risks to ecosystems, infrastructure, and human lives. This study focuses on wildfire susceptibility mapping in Los Angeles County, a region ...

Abstract Background Accurate and timely estimation of nitrogen nutrition index (NNI) is critical for assessing crop nitrogen (N) status and implementing precision N management. While machine learning (ML) techniques comb...

Cadmium poses severe threats to ecosystems and human health due to its high toxicity and bioaccumulative nature. The global regulatory limits for Cd are often low at 0.1-0.6 mg kg−1, which is 1 to 2 orders of magnitude...

Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates...

Silicon photonics enables compact integrated photonic devices with versatile functionalities and mass manufacturing capability. However, the optimization of high-performance free-form optical devices is still challenging...

ObjectiveTo develop and internally validate an interpretable, non-invasive machine learning framework to predict detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).MethodsThis retrospective c...