Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN

Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task. Therefore, it is very important to solve the data class imbalance classification problem. The SMART dataset exhibits an evident class imbalance, comprising a substantial quantity of healthy samples and a comparatively limited number of defective samples. This dataset serves as a reliable indicator of the disc's health status. In this paper, we obtain the best balanced disk SMART dataset for a specific classification model by mixing and integrating the data synthesised by multivariate generative adversarial networks (GAN) to balance the disk SMART dataset at the data level; and combine it with genetic algorithms to obtain higher disk fault classification prediction accuracy on a specific classification model

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages

A Survey of Methods for Handling Disk Data Imbalance

Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs. The Backblaze dataset, a widely used dataset related to hard discs, has a small amount of failure data and a large amount of health data, which exhibits a serious class imbalance. This paper provides a comprehensive overview of research in the field of imbalanced data classification. The discussion is organized into three main aspects: data-level methods, algorithmic-level methods, and hybrid methods. For each type of method, we summarize and analyze the existing problems, algorithmic ideas, strengths, and weaknesses. Additionally, the challenges of unbalanced data classification are discussed, along with strategies to address them. It is convenient for researchers to choose the appropriate method according to their needs

A distributed hybrid index for processing continuous range queries over moving objects

Central to many location-based services is the problem of processing concurrent continuous range queries over a large scale of moving objects. Most relevant works to this problem mainly investigate the centralized search algorithms based on a single server for handling range queries. However, due to the limited resources of a single server, these algorithms hardly can deal with an ocean of objects and extensive concurrent queries. Moreover, these approaches usually suppose either objects or queries are static but seldom consider the scenario that objects and queries are both moving simultaneously, restricting the practicability of these approaches. To resolve the above issues, we propose a distributed hybrid index (DHI) that consists of a global grid index and extensive local VR-tree indexes. DHI is apt to be deployed on a cluster of servers, and owns a good scalability to maintain numerous moving objects and concurrent range queries. Based on DHI, we further design a distributed incremental search approach, which organizes multiple servers with a publish/subscribe mechanism to calculate and monitor the results for continuous range queries in a distributed pattern. Finally, we conduct extensive experiments to fully evaluate the performance of our paper.Peer ReviewedPostprint (author's final draft

Advances in Renal Denervation in the Treatment of Hypertension

Hypertension significantly increases the risk of cardiovascular events and it is associated with high rates of disability and mortality. Hypertension is a common cause of cardiovascular and cerebrovascular accidents, which severely affect patients’ quality of life and lifespan. Current treatment strategies for hypertension are based primarily on medication and lifestyle interventions. The renal sympathetic nervous system plays an important role in the pathogenesis of hypertension, and catheter-based renal denervation (RDN) has provided a new concept for the treatment of hypertension. In recent years, studies on RDN have been performed worldwide. This article reviews the latest preclinical research and clinical evidence for RDN

The molecular clouds in a section of the third Galactic quadrant: observational properties and chemical abundance ratio between CO and its isotopologues

We compare the observational properties between $^{12}$CO, $^{13}$CO, and C$^{18}$O and summarize the observational parameters based on 7069 clouds sample from the Milky Way Imaging Scroll Painting (MWISP) CO survey in a section of the third Galactic quadrant. We find that the $^{13}$CO angular area ($A_{\rm ^{13}CO}$) generally increases with that of $^{12}$CO ($A_{\rm ^{12}CO}$), and the ratio of $A_{\rm ^{13}CO}$ to $A_{\rm ^{12}CO}$ is 0.38 by linear fitting. We find that the $^{12}$CO and $^{13}$CO flux are tightly correlated as $F_{\rm ^{13}CO}~=~0.17~ F_{\rm ^{12}CO}$ with both fluxes calculated within the $^{13}$CO-bright region. This indicates that the abundance $X_{\rm ^{13}CO}$ is a constant to be 6.5$^{+0.1}_{-0.5}$ $\times 10^{-7}$ for all samples under assumption of local thermodynamic equilibrium (LTE). Additionally, we observed that the X-factor is approximately constant in large sample molecular clouds. Similarly, we find $F_{\rm C^{18}O}~=~0.11~F_{\rm ^{13}CO}$ with both fluxes calculated within C$^{18}$O-bright region, which indicates that the abundance ratios ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ stays the same value 9.7$^{+0.6}_{-0.8}$ across the molecular clouds under LTE assumption. The linear relationships of $F_{\rm ^{12}CO}$ vs. $F_{\rm ^{13}CO}$ and $F_{\rm ^{13}CO}$ vs. $F_{\rm C^{18}O}$ hold not only for the $^{13}$CO-bright region or C$^{18}$O-bright region, but also for the entire molecular cloud scale with lower flux ratio. The abundance ratio ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ inside clouds shows a strong correlation with column density and temperature. This indicates that the ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ is dominated by a combination of chemical fractionation, selectively dissociation, and self-shielding effect inside clouds.Comment: 11 pages, 16 figures, 1 table, accepted by A

Scalable colored sub-ambient radiative coolers based on a polymer-Tamm photonic structure

Daytime radiative coolers cool objects below the air temperature without any electricity input, while most of them are limited by a silvery or whitish appearance. Colored daytime radiative coolers (CDRCs) with diverse colors, scalable manufacture, and sub-ambient cooling have not been achieved. We introduce a polymer-Tamm photonic structure to enable a high infrared emittance and an engineered absorbed solar irradiance, governed by the quality factor (Q-factor). We theoretically determine the theoretical thresholds for sub-ambient cooling through yellow, magenta, and cyan CDRCs. We experimentally fabricate and observe a temperature drop of 2.6-8.8 degrees Celsius on average during daytime and 4.0-4.4degrees Celsius during nighttime. Furthermore, we demonstrate a scalable-manufactured magenta CDRC with a width of 60 cm and a length of 500 cm by a roll-to-roll deposition technique. This work provides guidelines for large-scale CDRCs and offers unprecedented opportunities for potential applications with energy-saving, aesthetic, and visual comfort demands