Data Analysis System Research Articles

Retraction Note: Intelligent Crime Prevention and Control Big Data Analysis System Based on Imaging and Capsule Network Model

Retraction Note: Intelligent Crime Prevention and Control Big Data Analysis System Based on Imaging and Capsule Network Model

Performance of JT-60SA Thomson Scattering data analysis system

We have developed signal processing routines for the Thomson Scattering measurement, which is planned for use in the next campaign of the JT-60SA large-scale tokamak experiment.This paper provides the data analysis system and its performance evaluated in terms of computation time and error.The sequential routine of determining the scattered light intensity from a simulated signal, including noise data from a 1 Gs/s high-speed sampling digitizer, and determining the electron temperature and density was tested for the first time on an actual machine.The data analysis system ensures that electron temperature and density can be calculated with reasonable relative errors and within a realistic time frame for operations on JT-60SA.

Mortality associated with polymyalgia rheumatica in the United States in the 1999-2020 period: a multiple-cause-of-death study.

Multicause-of-death methods were used to analyze mortality and leading causes of death associated with polymyalgia rheumatica (PMR) in the United States from 1999 to 2020. We analyzed mortality data from the Centers for Disease Control and Prevention (CDC) Data analysis system and selected death certificates that listed PMR as the cause of death based on the International Statistical Classification of Diseases and Related Health Problems (ICD-10) category code. Relevant mortality rates, number of deaths and historical trends were analyzed. The number of PMR-related deaths and age-standardized mortality rate (ASMR) trend charts were made using Excel 2010 version and trend lines were added. Over the last 22 years, the total number of PMR-related deaths in the United States was 15,421 women (89.8%), a ratio of about 1:9 men to women. When PMR is listed as the underlying cause of death, the ASMR for women and men (per 100,000 people) is approximately 1.8-5.1:1, and when it is listed as the non-underlying cause of death, it is 1.8-3.3:1. PMR deaths are more frequent in individuals aged 70 years and above, with patients aged 80 years and above being most affected. Among different ethnicities, the highest number of deaths was found in Caucasians, followed by Black or African American. When it comes to causes of death, heart disease still ranks first, followed by cancer. In addition, we also found that when PMR combined with malignant tumors as a multiple cause of death, the number of female deaths was higher than that of male deaths, the overall number of deaths of both showed an upward trend, and the overall ASMR of both showed a downward trend. In the past 22 years, we have observed a low mortality rate of PMR in the United States. However, for patients with PMR, especially elderly women, medical workers should be vigilant and pay attention to whether they are combined with other complications, such as malignant neoplasms, and make timely diagnosis and treatment to further reduce the mortality rate of patients with PMR.

Ambulatory Long Block: A Model of Precision Education and Assessment for Internal Medicine Residents.

High-quality precision education (PE) aims to enhance outcomes for learners and society by incorporating longitudinal data and analytics to shape personalized learning strategies. However, existing educational data collection methods often suffer from fragmentation, leading to gaps in understanding learner and program performance. In this article, the authors present a novel approach to PE at the University of Cincinnati, focusing on the Ambulatory Long Block, a year-long continuous ambulatory group-practice experience. Over the last 17 years, the Ambulatory Long Block has evolved into a sophisticated data collection and analysis system that integrates feedback from various stakeholders, as well as learner self-assessment, electronic health record utilization information, and clinical throughput metrics. The authors detail their approach to data prioritization, collection, analysis, visualization, and feedback, providing a practical example of PE in action. This model has been associated with improvements in both learner performance and patient care outcomes. The authors also highlight the potential for real-time data review through automation and emphasize the importance of collaboration in advancing PE. Generalizable principles include designing learning environments with continuity as a central feature, gathering both quantitative and qualitative performance data from interprofessional assessors, using this information to supplement traditional workplace-based assessments, and pairing it with self-assessments. The authors advocate for criterion referencing over normative comparisons, using user-friendly data visualizations, and employing tailored coaching strategies for individual learners. The Ambulatory Long Block model underscores the potential of PE to drive improvements in medical education and health care outcomes.

Determination of Gain Scheduling Parameters for Loss of a Feedwater Pump Transient Mitigation Using Neural Networks

Transient mitigation for nuclear power plants is essential for safe operation. The fourth industrial revolution brings with it the potential for data-based predictive maintenance and identifying remaining time of life for degrading components. An improvement to predictive maintenance would be to address continued operation with faulty components between the time of identification and eventual replacement. The ability to perform data analysis and contemporary digital control systems allows for data-driven control system actions. A methodology is developed herein to train a neural network that can map desired system performance and current plant component capability to control system settings. Simulations of plant transients were recorded and used to train a neural network. This neural network was tested with different target performance goals. The results show that the trained neural network recommended settings that affected the control system response so as to meet the target performance goals.

Ocean Temperature Profiling Lidar: Analysis of Technology and Potential for Rapid Ocean Observations

Development of ocean measurement technologies can improve monitoring of the global Ocean Heat Content (OHC) and Heat Storage Rate (HSR) that serve as early-warning indices for climate-critical circulation processes such as the Atlantic Meridional Overturning Circulation and provide real-time OHC assessments for tropical cyclone forecast models. This paper examines the potential of remotely measuring ocean temperature profiles using a simulated Brillouin lidar for calculating ocean HSR. A series of data analysis (‘Nature’) and Observational Systems Simulation Experiments (OSSEs) were carried out using 26 years (1992–2017) of daily mean temperature and salinity outputs from the ECCOv4r4 ocean circulation model. The focus of this study is to compare various OSSEs carried out to measure the HSR using a simulated Brillouin lidar against the HSR calculated from the ECCOv4r4 model results. Brillouin lidar simulations are used to predict the probability of detecting a return lidar signal under varying sampling strategies. Correlations were calculated for the difference between sampling strategies. These comparisons ignore the measurement errors inherent in a Brillouin lidar. Brillouin lidar technology and instruments are known to contain numerous, instrument-dependent errors and remain an engineering challenge. A significant decrease in the ability to measuring global ocean HSRs is a consequence of measuring ocean temperature from nadir-pointing instruments that can only take measurements along-track. Other sources of errors include the inability to fully profile ocean regions with deep mixed layers, such as the Southern Ocean and North Atlantic, and ocean regions with high light attenuation levels.

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.

System of complex data analysis of thematic sites ISCAD IS

Objectives. Currently, the main source of information is the Internet. The huge amount of information available on the Internet makes it urgent to comprehensively analyze data from open Internet sources.The goal of this work is to create a multi-purpose, modifiable cluster for in-depth analysis of data from Internet sources, the main objectives of which are to identify the most important publications in a certain subject area, thematic analysis of these publications, identifying the leader of a scientific direction and determining trends in the development of areas and interaction of groups of people.Methods. To solve this problem, a methodology was developed for constructing a multi-purpose cluster using technologies for quickly constructing a thematic graph database, a knowledge graph, methods and models of machine learning for in-depth analysis of data.Results. A system for comprehensive analysis of data from thematic sites ISKAD IS has been developed, a methodology for quickly constructing a thematic graph database and a comprehensive technology for in-depth analysis of data from Internet sources and analysis of data from the most important well-known world sites have been tested.Conclusion. An IT environment has been created for the rapid construction of thematic graph databases. The results of using the technology for quickly constructing graph databases are shown using examples of the work of ISKAD IS.

Analyzing the role of big data and its effects on the retail industry

The expansion of social media, technology, and online shopping will eventually speed up massive development of retail industry. Retailing organizations are trying to figure out how the usage of big data analysis may enable them to make the correct decisions as we see Big Data as one of the buzzwords sweeping the IT world by force. Therefore, the current study’s objective is to investigate and assess the function of big data and its consequences on the retail business with the support of employees and managers from the Indian retail sector. The main factors covered in the study include big data technologies like data sources, data analysis, data storage, data security and privacy, real-time processing, and messaging system. This study employs a quantitative technique and performs Partial Least Square Structural Equation Modeling (PLS-SEM) to analyze the effects of big data in the retail sector. The study findings revealed that data analysis, real-time processing, and messaging system significantly impact the performance of the retail industry whereas, data source, data storage, and data security and privacy do not significantly impact the performance of the retail industry. Various aspects discussed in the study revolving around big data technology are important factor and demands the maximum attention of the organization such as inventory utilization, customer involvement, and current value in the retail sector. The study recommends that in order to succeed, businesses of all sizes must make sure they are competitive. As internet businesses engage with their customers in real time, the rivalry heats up. Big Data analysis may benefit merchants in the following ways.

RABiT-III: an Automated Micronucleus Assay at a Non-Specialized Biodosimetry Facility.

Micronuclei, detected through the cytokinesis-block micronucleus assay, are valuable indicators of ionizing radiation exposure, especially in short-term lymphocyte cultures. The peripheral human blood lymphocyte assay is recognized as a prime candidate for automated biodosimetry. In a prior project at the Columbia University Center for Radiological Research, we automated this assay using the 96-well ANSI/SLAS microplate standard format and relied on established biotech robotic systems named Rapid Automated Biodosimetry Tool (RABiT). In this study, we present the application of a similar automated biotech setup at an external high-throughput facility (RABiT-III) to implement the same automated cytokinesis-block micronucleus assay. Specifically, we employed the Agilent BRAVO liquid-handling system and GE IN Cell Analyzer 6000 imaging system in conjunction with the PerkinElmer Columbus image data storage and analysis system. Notably, this analysis system features an embedded PhenoLOGIC machine learning module, simplifying the creation of cell classification algorithms for CBMN assay image analysis and enabling the generation of radiation dose-response curves. This investigation underscores the adaptability of the RABiT-II CBMN protocol to diverse RABiT-III biotech robotic platforms in non-specialized biodosimetry centers. Furthermore, it highlights the advantages of machine learning in rapidly developing algorithms crucial for the high-throughput automated analysis of RABiT-III images.

Abstract 6475: Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health

Abstract Pancreatic cancer is the third leading cause of cancer-related death in the United States. Current therapeutic options offer a dismal overall survival with the 5-year survival at just ~12%. Analysis of the clinical and molecular underpinnings of pancreatic cancer is critical to developing both early detection methodologies as well as novel therapeutic options. The aggressiveness and deadly nature of this disease warranted the development of a data repository and analysis system dedicated to pancreatic cancer data. The Pancreatic Cancer Action Network’s (PanCAN) SPARK platform is a cloud-based data and analytics platform, powered by Velsera, that integrates real-world patient health data from PanCAN research initiatives and accelerates research by making pancreatic cancer data easier to access and use. Encompassing clinical, imaging, and genomics data from over 600 patients with pancreatic cancer within PanCAN’s Know Your Tumor ® (KYT) precision medicine service, the SPARK platform connects with petabytes of publicly available cancer data via the Cancer Genomics Cloud (CGC), also powered by Velsera. The CGC is part of NCI’s Cancer Research Data Commons (CRDC), a cloud-based data science infrastructure that connects data with analytics tools to allow researchers to share, integrate, analyze, visualize, and drive scientific discovery. Here, we demonstrate the application of these datasets by providing a case study demonstrating how to combine and enrich data to accelerate pancreatic cancer research. Currently, the genomic and proteomic data available on CRDC amounts to 402 and 304 cases of pancreatic tumor samples, respectively. We will use the capabilities of the SPARK and CGC platforms, which provide ready-to-use tools for multi-omics analysis that require no coding knowledge. Using the KYT and CRDC open-access pancreatic cancer data, we aim to demonstrate how to perform integrated analysis of data from diverse scientific domains, and share with collaborators all in one space, streamlining and increasing the potential for new scientific discoveries. Further expansion of the PanCAN and CGC datasets will undoubtedly provide a more comprehensive understanding of pancreatic cancer tumor biology. SPARK and CGC’s cloud based computation infrastructure, along with numerous available cancer datasets and easy-to-use multi-omics data processing workflows and data analytic tools will be instrumental in this process. Citation Format: Kawther Abdilleh, Zelia Worman, Rowan Beck, Cera Fisher, Divya Sain, Jack DiGiovanna, Lynn Matrisian, Sudheer Doss, Brandi Davis-Dusenbery. Leveraging real-world pancreatic cancer datasets to drive drug discovery and patient health [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6475.

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis.

The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.

Improving simulations of extreme precipitation events in China by the CMIP6 global climate models through statistical downscaling

The recently released NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is a high-resolution daily downscaled dataset derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. We comprehensively evaluated the performance of the NEX-GDDP dataset in simulating the characteristics of the climatological precipitation and extreme precipitation events across China from 1960 to 2014. We present here the projected changes in precipitation at the end of the 21st century (2070–2099) with respect to a reference period (1985–2014). We compared the NEX-GDDP dataset with both a state-of-the-art gauge data analysis system and the CMIP6 global climate models. The NEX-GDDP dataset significantly outperformed the CMIP6 simulations in replicating the climatological patterns of precipitation. It showed a closer alignment with the observed data, as evidenced by notably increased correlation coefficients and reduced model-relative errors. The NEX-GDDP dataset exceled in accurately reproducing the spatial distribution of indices of extreme precipitation, surpassing the performance of the CMIP6 simulations. The projections show an increased frequency of heavy precipitation under higher emission scenarios in both datasets, with the CMIP6 simulations consistently estimating a higher probability of heavy rain than the NEX-GDDP dataset.

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

A model for predicting physical health of college students based on semantic web and deep learning under cloud edge collaborative architecture is proposed to address the issue of most physical health prediction models being unable to fully describe the characteristics of sports performance changes and having large prediction errors. Firstly, the authors design a measurement data analysis system based on cloud edge collaboration architecture to improve data analysis efficiency. Then, they preprocess the data on the edge side, such as missing samples, and extract data features using an equal dimensional dynamic GOM model. Finally, they deploy the RBFNN-SSA model in the cloud center, input the characteristics of each indicator into the model for predictive analysis, and obtain the physical health status of college students. Based on the physical health test data of Hohai University from 2018 to 2021, an experimental analysis was conducted. The results showed that all three intervention measures had significant effects on maintaining and improving the physical health level of college students.

An Integrated Hepatocyte Stability Assay for Simultaneous Metabolic Stability Assessment and Metabolite Profiling.

The determination of metabolic stability is critical for drug discovery programs, allowing for the optimization of chemical entities and compound prioritization. As such, it is common to perform high-volume in vitro metabolic stability experiments early in the lead optimization process to understand metabolic liabilities. Additional metabolite identification experiments are subsequently performed for a more comprehensive understanding of the metabolic clearance routes to aid medicinal chemists in the structural design of compounds. Collectively, these experiments require extensive sample preparation and a substantial amount of time and resources. To overcome the challenges, a high-throughput integrated assay for simultaneous hepatocyte metabolic stability assessment and metabolite profiling was developed. This assay platform consists of four parts: 1) an automated liquid-handling system for sample preparation and incubation, 2) a liquid chromatography and high-resolution mass spectrometry-based system to simultaneously monitor the parent compound depletion and metabolite formation, 3) an automated data analysis and report system for hepatic clearance assessment; and 4) streamlined autobatch processing for software-based metabolite profiling. The assay platform was evaluated using eight control compounds with various metabolic rates and biotransformation routes in hepatocytes across three species. Multiple sample preparation and data analysis steps were evaluated and validated for accuracy, repeatability, and metabolite coverage. The combined utility of an automated liquid-handling instrument, a high-resolution mass spectrometer, and multiple streamlined data processing software improves the process of these highly demanding screening assays and allows for simultaneous determination of metabolic stability and metabolite profiles for more efficient lead optimization during early drug discovery. SIGNIFICANCE STATEMENT: Metabolic stability assessment and metabolite profiling are pivotal in drug discovery to fully comprehend metabolic liabilities for chemical entity optimization and lead selection. Process of these assays can be repetitive and resource demanding. Here, we developed an integrated hepatocyte stability assay that combines automation, high-resolution mass spectrometers, and batch-processing software to improve and combine the workflow of these assays. The integrated approach allows simultaneous metabolic stability assessment and metabolite profiling, significantly accelerating screening and lead optimization in a resource-effective manner.

A non-invasive, low-cost and easy-to-operate underwater terrain monitoring method

A fast, reliable, through water and non-intrusive bed-profile measurement technique that is economical and easy to use in a wide range of laboratory experiments involving sediment transport is proposed. A consumer-grade 3-D range sensing technology which by its low cost, easy operation and relatively good accuracy is becoming an attractive alternative to expensive 3-D laser scanners. The Microsoft XBOX 360 kinect sensor 2.0 is a low-cost and highly popular depth camera, which can be used to scan 3D surface terrain of dynamic objects and has the potential to scan terrain below and above water. Through the software matching of kinect 2.0, a data storage and analysis system are formed, which makes it has the ability to obtain underwater terrain data. In addition, the correction model of water refraction error is established, which can eliminate the measurement error caused by water refraction. Up to four different water conditions, kinect 2.0 is tested. Finally, the influences of water depth, suspend load volume concentration and moving flow velocity on the measurement accuracy of kinect 2.0 are explored, and the compensation methods of measurement error caused by the above factors are given. The results of this paper can provide reference for underwater topographic survey.

АРХІТЕКТУРА ПРОГРАМНОЇ СИСТЕМИ ДЛЯ ВИРІШЕННЯ ЗАДАЧІ КЛАСИФІКАЦІЇ НА ОСНОВІ ПРИВАТНИХ ДАНИХ

Data analysis and artificial intelligence systems are becoming widely used in various spheres of human life. This is confirmed by more typical cases of their use, in particular, the selection of recommendations for the user in e-commerce, the detection of spam in e-mail services, and the moderation of user comments; as well as cases of personal use of such tools (for example, chatbots ChatGPT, Google Bard, Microsoft Copilot have appeared and gained significant popularity in the last two years). One of the key elements of such systems is data, which is necessary for training and testing software systems of intelligent data analysis. A significant amount of diverse data contributes to the construction of a software system with high accuracy. Considering this, the task of choosing and preparation of datasets that can be used in the construction of such systems is important. One of the difficulties in this task is the presence of private information in the datasets, which limits their use for systems of intelligent data analysis. The paper is devoted to the development of the software system architecture for solving the classification problem based on private data. The existing methods and architectural approaches for privacy-preserving in machine learning were considered. The architecture of the software system was proposed, the characteristic feature of which is the protection of private datasets by using of functional encryption, which allows to increase the number of datasets for training publicly available data analysis and artificial intelligence systems. The proposed architecture of the software system is based on the client-server architecture and functional encryption. The components are a classifier, a generator of encryption keys, and modules of functional encryption and decryption. Prospects for further research were discussed.

Conversational agent in HCI a review

Nowadays, AI technology is developing rapidly and slowly covering all areas of life. AI facilitates different parts of our lives and provides us with a lot of help. For example, in learning, students can acquire knowledge more conveniently through AI, and buyers can find suitable goods more conveniently through AI when shopping. At the same time, more and more technology is used in the field of voice agents, which allows humans to enjoy a lot of better services. In this article, we will study to better understand "how to understand human natural language and how the repository of knowledge is built." In the article we build with examples and deep learning models (CNN and RNN) through databases. Through repeated research and analysis, we can find that there are some limitations in this paper, such as the single learning model and the insufficient elaboration of data analysis and signal system technology. But at the same time, we also found a lot of future application prospects that voice agents can develop, it can be applied in many fields, such as finance, medical care, education and so on. For example, in children's education, parents can use voice agents to set time limits and monitor their children's progress. Existing digital interactive storytelling systems have limitations in terms of available storybooks and hand-crafted issues. Voice agents are becoming more popular in everyday scenarios, and more users are adopting devices like Siri and Google Assistant. In the future, conversational agents are expected to play an important role in oral communication with users.

Development of regional pharmacy intravenous admixture services data reporting and analysis platform for enhanced quality control ability

BackgroundPharmacy intravenous admixture service (PIVAS) center has emerged as an important department of hospitals as it can improve occupational protection and ensure the safety and effectiveness of intravenous infusions. However, there is little research on the standardized capability and risk evaluation of PIVAS by using modern information technology. In this research, we established Regional Pharmacy Intravenous Admixture Services Data Reporting and Analysis Platform (RPDRAP) to improve quality control ability for PIVAS management. RPDRAP including evaluation matrix for quality control monitoring. The construction of platform is based on guidelines for the Construction and Management of PIVAS and management specifications of PIVAS in China.MethodsRPDRAP was established in 2018. This platform comprises a data collection system and a data analysis system. The data collection system consists of 67 data items. Data collection relied on online platforms through data acquisition module. The collected data were analyzed using a model with 20 indicators within the data analysis system. Fifteen hospitals, public comprehensive healthcare facilities with more than 500 beds, participated in the platform’s application evaluation.ResultsThe study revealed significant differences in PIVAS total score, supervisors, and workload between 2020 and 2022. The platform’s application results demonstrated improvements in personnel management, work efficiency, and infection control within these PIVAS. Although statistical significance was observed in only 8 out of the 25 items, most of the scores showed an increase, with a small portion remaining unchanged and no decline in scores.ConclusionsThis platform can be recommended for PIVAS homogeneous and regional efficient management. The use of this platform not only improves the quality control ability of PIVAS but also enables the management department to quickly grasp the current situation and characteristics of each PIVAS through standardized data collection and analysis.

Real-time sensor networks based on genetic algorithms application in the analysis of innovative data in cultural industry management

Against the backdrop of sustained economic development and steady improvement of China's overall national strength, the cultural industry is in a good stage of development, but it also faces problems such as data complexity and difficulty in organizing. Based on this, we propose a management innovation data analysis system based on genetic algorithm and real-time technology of the Internet of Things, and apply this method to cultural industry management to analyze relevant management data. It was found that based on the improved genetic algorithm, it can achieve better optimization results with less computational complexity. Meanwhile, genetic algorithms based on penalty functions adopt a non-uniform mutation operator, which can accelerate convergence. Compared with existing genetic algorithms, the new genetic algorithm has lower computational complexity and lower system average overhead, greatly improving work efficiency and making its work cost lower. On this basis, combined with theoretical and empirical research results, provide theoretical and practical support for the sustainable development of the industry.