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Integrative molecular and physiological insights into the phytotoxic impact of liquid crystal monomer exposure and the protective strategy in plants.
Plant Biotechnol J
January 2025
Key Laboratory of Cultivation and Protection for Non-Wood Forest Trees, Ministry of Education, Central South University of Forestry and Technology, Changsha, China.
Liquid crystal monomers (LCMs), the integral components in the manufacture of digital displays, have engendered environmental concerns due to extensive utilization and intensive emission. Despite their prevalence and ecotoxicity, the LCM impacts on plant growth and agricultural yield remain inadequately understood. In this study, we investigated the specific response mechanisms of tobacco, a pivotal agricultural crop and model plant, to four representative LCMs (2OdF3B, 5CB, 4PiMeOP, 2BzoCP) through integrative molecular and physiological approaches.
View Article and Find Full Text PDFElectrochemical Sensor for Hydrogen Leakage Detection at Room Temperature.
Sensors (Basel)
January 2025
Laboratory of Sensors/Actuators and Energy Harvesting, National Institute for Research and Development in Electrical Engineering ICPE-CA, 030138 Bucharest, Romania.
The use of hydrogen as fuel presents many safety challenges due to its flammability and explosive nature, combined with its lack of color, taste, and odor. The purpose of this paper is to present an electrochemical sensor that can achieve rapid and accurate detection of hydrogen leakage. This paper presents both the component elements of the sensor, like sensing material, sensing element, and signal conditioning, as well as the electronic protection and signaling module of the critical concentrations of H.
View Article and Find Full Text PDFAn FPGA-Based SiNW-FET Biosensing System for Real-Time Viral Detection: Hardware Amplification and 1D CNN for Adaptive Noise Reduction.
Sensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
View Article and Find Full Text PDFComparative Analysis of Edge Detection Operators Using a Threshold Estimation Approach on Medical Noisy Images with Different Complexities.
Sensors (Basel)
December 2024
Directorate for Railways, Nemanjina 6, 11000 Belgrade, Serbia.
The manuscript conducts a comparative analysis to assess the impact of noise on medical images using a proposed threshold value estimation approach. It applies an innovative method for edge detection on images of varying complexity, considering different noise types and concentrations of noise. Five edges are evaluated on images with low, medium, and high detail levels.
View Article and Find Full Text PDFBA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals.
Sensors (Basel)
December 2024
Information Network Center, Chengdu University, Chengdu 610106, China.
Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction.
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