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This article presents an innovative technique for remote sensing-based mapping and predicting land degradation in the European frontier regions. The mapping is carried out by geospatial data fusion of relevant biophysica...

In patients with brain injury, conventional clinical paradigm based on macro imaging and behavioral scales fails to achieve precise localization and dynamic assessment of neural functions and network connectivity. It is ...

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...

BackgroundInfluenza poses a significant global public health threat, with its pandemic potential and seasonal variability presenting formidable challenges to prediction accuracy. This study leverages high-quality weekly ...

Malignant tumors pose a significant global health challenge. While established screening methods exist, they are largely site-specific, limiting comprehensive prevention. Multi-cancer screening, which detects multiple ca...

Abstract Building energy consumption is dominated by lighting and heating, ventilation, and air conditioning (HVAC) systems, whose operation is often inefficient due to inadequate control strategies and unreliable occupa...

Traditional electromechanical cleaning equipment remains overly dependent on human assistance when performing cleaning tasks in unstructured marine environments. With the rapid development of robotic technologies, underw...

Abstract BackgroundMainstays of pharmacoepidemiology are large, representative, behavioral surveys, which focus on many drugs with few detailed behaviors. Smaller, targeted studies measure drug-specific patte...

Soil microbial properties are increasingly recognised as a crucial metric of both soil health and soil carbon dynamics. However, these properties remain underutilised due to challenges in quantifying these properties at ...

Introduction and aims: For decades, orthodontic diagnosis and treatment have primarily depended on physical models, imaging techniques, and three-dimensional (3D) imaging technologies. Nevertheless, single-modal data exh...

Link performance functions (LPFs) underpin the estimation of travel times, delays, and congestion in transportation network models. As multimodal systems become increasingly data rich and feature connected and automated ...

To address the challenges of multi-source heterogeneity, insufficient timeliness, inconsistent quality, and scattered security management in enterprise credit data under high-timeliness business scenarios, this paper pro...

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 Air-conditioning load forecasting is important for building energy scheduling and control. The task is difficult because load data show clear periodic patterns, and sudden changes caused by events such as holida...

Abstract For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusio...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

Classifying the sweetness level of pineapples is an important part of quality control, but existing methods still face issues of subjectivity and require destructive testing. Manual assessment is often inconsistent, whil...

The dynamic range limitation is intrinsic to conventional RGB cameras, which reduces global contrast and causes the loss of high-frequency details such as textures and edges in complex, dynamic traffic environments (e.g....

The field of remote sensing change detection (RSCD) has witnessed remarkable progress. Most existing RSCD methods currently follow the encoder-feature interaction-decoder architecture. However, these methods often treat ...

Abstract Background Super-enhancers are critical cis-regulatory elements that play a central role in modulating gene expression and driving cellular identity. Their dysregulation is closely associated with the developmen...

Abstract Unmanned Aerial Vehicles (UAVs) are pivotal in tasks such as traffic monitoring and rescue operations, yet detecting small objects in UAV images remains challenging due to their diminutive size, low resolution, ...

Abstract Facial Expression Recognition (FER) demonstrates significant value in practical scenarios such as intelligent human–computer interaction. However, conventional FER methods often struggle to balance performance...

INTRODUCTION: Accurate power quality disturbances (PQDs) classification is critical for maintaining grid stability and reliability in modern power systems. However, existing deep learning methods predominantly rely on si...

Autonomous underwater vehicles (AUVs) operating in harsh, communication-limited underwater environments require robust fault diagnosis systems to ensure mission success and safety. Existing model-based approaches suffer ...