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Vegetation optical depth (VOD), as a key parameter of vegetation, plays an important role in the process of vegetation growth and change. The spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technolog...

To fully exploit the advantages of GNSS-R small satellites (large quantity, short revisit cycle) and address key challenges of multisource GNSS-R data—track-wise distribution, uneven spatial coverage, and observat...

Abstract Alkaline water electrolysis represents a promising pathway for green hydrogen production, yet comprehensive multi-physics simulation remains computationally prohibitive for practical design optimization. This st...

Abstract Accurate prediction of terrestrial water cycle variables like soil moisture (SM) and evapotranspiration (ET) is crucial, yet existing Deep Learning (DL) models face limitations. Many adopt Single-Task Learning (...

Introduction We developed an innovative, active learning workshop to introduce foundations of point-of-care ultrasound (POCUS) physics and instrumentation. The workshop design aimed to reduce cognitive load and encourage...

Purpose – This study critically examines the rapid expansion of artificial intelligence (AI) in digital learning, with a specific focus on how these technologies intersect with metacognitive skills and digital literacy...

The identification of exoplanets within habitable zones remains a central objective in modern astrophysics, particularly with the availability of large-scale photometric datasets from space-based missions such as the Tra...

Wildfires are natural hazards that threaten ecosystems, communities, and economies, and they have shown a significant increase in frequency and intensity in recent years. Accurate wildfire spread prediction is critical f...

Background and Purpose: Deep learning segmentation (DLS) and planning (DLP) models in radiotherapy treatment planning increase consistency and reduce planning time. However, many DL segmentations and plans are manually a...

Manual Gross Tumor Volume (GTV) delineation for radiotherapy is time‑consuming and prone to inter‑observer variability. Deep Learning (DL)-based auto‑segmentation offers faster and more consistent solutions, but tr...

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 Physical sciences are essential to the predictive modeling of complex environmental systems, particularly environmental monitoring and sustainability applications, as they provide the deterministic foundation re...

Voltage stability in modern smart grids faces increasing challenges due to the widespread use of renewable energy and diminished reactive-power margins. While power flow analysis remains the most precise method, it is of...

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...

IntroductionVision-based fall analysis has advanced rapidly, yet a key bottleneck remains: visually similar motions can correspond to markedly different physical outcomes, as subtle differences in contact mechanics and p...

Multimodal remote sensing image registration is a fundamental step for multisource data fusion and geospatial analysis. However, nonlinear radiometric, textural, and geometric discrepancies across modalities challenge in...

Artificial intelligence (AI) models developed for Global Navigation Satellite System Reflectometry (GNSS-R) observations have demonstrated competitive performance in estimating geophysical parameters, especially ocean su...

Few-shot object detection (FSOD) recently garnered increasing attention in remote sensing images (RSIs). Although FSOD has achieved success in natural sense, the large size and complex backgrounds of RSIs make FSOD espec...

Measurements from global navigation satellite system reflectometry (GNSS-R) have proven effective for applications in sea surface height (SSH). Deep learning-based methods for spaceborne GNSS-R SSH retrieval can effectiv...

Intelligent mining area detection is fundamental for ecological monitoring and sustainable development of mining areas. However, current remote sensing detection lacks high-quality multimineral rotated object detection d...

This review addresses the urgent need for scalable, accurate, and reproducible remote sensing solutions following the February 2023 Türkiye earthquakes. It synthesizes the contributions of five peer-reviewed studi...

Modern communication environments ranging from smart mobility infrastructures to digitally integrated health care systems demand adaptable and trustworthy network security. In this situation, software-defined networks (S...

Digital health increasingly relies on artificial intelligence for clinical decision-making, patient management, and distributed learning, yet two persistent challenges remain: the computational complexity of optimization...

Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are l...