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Abnormal Operation Detection of Automated Orchard Irrigation System Actuators by Power Consumption Level.
Sensors (Basel)
January 2025
Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea.
Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system.
View Article and Find Full Text PDFFew-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation.
Entropy (Basel)
January 2025
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China.
Graph anomaly detection is crucial in many high-impact applications across diverse fields. In anomaly detection tasks, collecting plenty of annotated data tends to be costly and laborious. As a result, few-shot learning has been explored to address the issue by requiring only a few labeled samples to achieve good performance.
View Article and Find Full Text PDFDetecting anomalies in smart wearables for hypertension: a deep learning mechanism.
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFClinical findings of candidate stallions presented for licensing at all German Warmblood horse-breeding associations in 2018-2020.
Equine Vet J
January 2025
Clinic for Horses, University of Veterinary Medicine Hannover, Hannover, Germany.
Background: There is very little information available about the health status of young stallions from the German Warmblood population that will, once licensed, shape the future of equestrian sport and horse breeding.
Objectives: To evaluate the prevalence of clinical findings at licensing examinations of candidate stallions and the influences of season of birth, age at licensing, year of licensing, and the evaluator on the distribution of recorded findings.
Study Design: Retrospective observational study.
Protocol for genetic analysis of population-scale ultra-low-depth sequencing data.
STAR Protoc
January 2025
BGI Research, Shenzhen 518083, China; Shenzhen Key Laboratory of Transomics Biotechnologies, BGI Research, Shenzhen 518083, China. Electronic address:
Non-invasive prenatal testing (NIPT) not only enables the detection of chromosomal anomalies in fetuses but also generates vast amounts of ultra-low-depth sequencing data, which can be leveraged for population genomic studies. Here, we present a protocol designed for massive ultra-low-depth sequencing datasets. We detail the steps for data processing, quality control, and genotype imputation, followed by genome-wide association study (GWAS) and post-GWAS analyses.
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