RFID Application of Smart Grid for Asset Management

RFID technology research has resolved practical application issues of the power industry such as assets management, working environment control, and vehicle networking. Also it provides technical reserves for the convergence of ERP and CPS. With the development of RFID and location-based services technology, RFID is converging with a variety of sensing, communication, and information technologies. Indoor positioning applications are under rapid development. Micromanagement environment of the assets is a useful practice for the RFID and positioning. In this paper, the model for RFID applications has been analyzed in the microenvironment management of the data center and electric vehicle batteries, and the optimization scheme of enterprise asset management is also proposed

Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data

Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare. However, the traditional process for analyzing such datasets demands substantial human effort and expertise for the data selection, processing, and analysis. To address this challenge, we introduce a novel framework, a Team of AI-made Scientists (TAIS), designed to streamline the scientific discovery pipeline. TAIS comprises simulated roles, including a project manager, data engineer, and domain expert, each represented by a Large Language Model (LLM). These roles collaborate to replicate the tasks typically performed by data scientists, with a specific focus on identifying disease-predictive genes. Furthermore, we have curated a benchmark dataset to assess TAIS's effectiveness in gene identification, demonstrating our system's potential to significantly enhance the efficiency and scope of scientific exploration. Our findings represent a solid step towards automating scientific discovery through large language models.Comment: 18 pages, 2 figures; added contac

Ameliorative action of “daitongxiao” against hyperuricemia includes the “uric acid transporter group”

This study aimed to investigate the potential mechanisms involved in the therapeutic effects of daitongxiao (DTX) on hyperuricemia (HUA). DTX was administered to two animal models of HUA via gavage feeding: HUA quail model (a uricotelic animal with urate oxidase deficiency), treated continuously for 35 days post-HUA induction, and HUA rats (an animal with active urate oxidase), treated continuously for 28 days post-HUA induction. HUA was induced in quail by administering a solution of sterile dry yeast powder via gavage feeding, while in rats, it was induced by intragastric gavage feeding of a solution of adenine and ethambutol hydrochloride. DTX improved overall health; increased bodyweight; reduced renal index, serum urate levels, serum xanthine oxidase activity, blood urea nitrogen, and creatinine; and enhanced urinary and fecal uric acid (UA) excretion in these two animal models. The results of hematoxylin and eosin and hexamine silver staining of kidney sections revealed that DTX significantly mitigated HUA-induced renal structural damage and inflammatory response. The results of quantitative real-time polymerase chain reaction, Western blotting, and immunofluorescence analyses revealed that DTX downregulated the renal expression levels of glucose transporter 9 (GLUT9) and upregulated the renal expression levels of organic anion transporters (OAT1 and OAT3) in both HUA models. Thus, the findings of this study suggest that DTX suppresses the progression of HUA by modulating the expression of the UA transporter group members

Atomic-layered Au clusters on α-MoC as catalysts for the low-temperature water-gas shift reaction

The water-gas shift (WGS) reaction (where carbon monoxide plus water yields dihydrogen and carbon dioxide) is an essential process for hydrogen generation and carbon monoxide removal in various energy-related chemical operations. This equilibrium-limited reaction is favored at a low working temperature. Potential application in fuel cells also requires a WGS catalyst to be highly active, stable, and energy-efficient and to match the working temperature of on-site hydrogen generation and consumption units. We synthesized layered gold (Au) clusters on a molybdenum carbide (α-MoC) substrate to create an interfacial catalyst system for the ultralow-temperature WGS reaction. Water was activated over α-MoC at 303 kelvin, whereas carbon monoxide adsorbed on adjacent Au sites was apt to react with surface hydroxyl groups formed from water splitting, leading to a high WGS activity at low temperatures

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing

Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing