Computational power density and interconnection between transistors have grown to be the dominant challenges for the continued scaling of complementary metal-oxide-semiconductor (CMOS) technology due to limited integration density and computing power. Herein, we designed a novel, hardware-efficient, interconnect-free microelectromechanical 7:3 compressor using three microbeam resonators. Each resonator is configured with seven equal-weighted inputs and multiple driven frequencies, thus defining the transformation rules for transmitting resonance frequency to binary outputs, performing summation operations, and displaying outputs in compact binary format. The device achieves low power consumption and excellent switching reliability even after 3 × 10 repeated cycles. These performance improvements, including enhanced computational power capacity and hardware efficiency, are paramount for moderately downscaling devices. Finally, our proposed paradigm shift for circuit design provides an attractive alternative to traditional electronic digital computing and paves the way for multioperand programmable computing based on electromechanical systems.
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http://dx.doi.org/10.1038/s41378-023-00508-0 | DOI Listing |
J Comput Chem
January 2025
Departmento de Química, Facultad de Ciencias, Universidad de Tarapacá, Arica, Chile.
Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data.
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January 2025
Department of Integrated Clinical Procedures, School of Dentistry, Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil.
Aim: This study aimed to explore the possible bidirectional interrelations between fructose-induced metabolic syndrome (MS) and apical periodontitis (AP).
Methodology: Twenty-eight male Wistar rats were distributed into four groups (n = 7, per group): Control (C), AP, Fructose Consumption (FRUT) and Fructose Consumption and AP (FRUT+AP). The rats in groups C and AP received filtered water, while those in groups FRUT and FRUT+AP received a 20% fructose solution mixed with water to induce MS.
Sensors (Basel)
January 2025
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Computer-Aided Design and Test (CADT) Research Group, McMaster University, Hamilton, ON L8S 4L8, Canada.
A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China.
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.
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