Different Types Of Data Research Articles

On modelling and analysis of voxel-based force prediction for a 3-axis CNC machining

ABSTRACT Determination of cutting forces is the main requirement for understanding the machining process and optimising its parameters for achieving higher productivity and surface finish. This paper presents an exploratory study and the development of a model to estimate cutting forces for a 3-axis CNC milling process using a voxel-based CAD model. The developed algorithm takes the NC code, workpiece/tool material properties, and the tool geometry data as inputs. The cutting tool engagement with the workpiece is computed using a discretized (voxelized) model. The calculated voxel engagement was finally used to calculate the cutting forces using the analytical method. The algorithm was implemented and tested for various case studies and the in-house experimental data for different types of end mill tools. Finally, the effect of variation in the size of the voxel and the number of flutes was studied. The model showed a good correlation and was found to be accurate (~80%) and robust.

Design and Implementation of a Historical German Firm-level Financial Database

Broad, long-term financial, and economic datasets are scarce resources, particularly in the European context. In this article, we present an approach for an extensible data model that is adaptable to future changes in technologies and sources. This model may constitute a basis for digitized and structured long-term historical datasets for different jurisdictions and periods. The data model covers the specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence, achieving extensibility. Furthermore, we outline a relational implementation of this approach based on historical German firm and stock market data from 1920 to 1932.

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

In recent years, with the in-depth research on the privacy protection methods of We-media network, various types of big data analysis technologies gradually protect the network privacy data, but there are still problems of low intelligence and poor protection in the existing research. Based on this, this paper first uses big data technology and artificial intelligence deep learning algorithm to complete the construction of different types of self-media control databases. Then, it analyzes the common privacy data types of We-media network, constructs an optimization protection model based on secondary identification and verification strategy, and forms a data query system. Finally, simulation experiments are conducted to verify whether the constructed network privacy protection model can realize the intelligent protection of network privacy algorithms from different dimensions. In the process of privacy protection of experimental data at different stages, the internal correlation differences of different types of protection algorithm strategies are obvious in the multidimensional analysis of specific databases. For different types of factor data in different types of We-media networks, the protection rate of the We-media network privacy protection model designed in this study has reached more than 95%. The research results show that the self-media network privacy protection model based on big data and artificial intelligence deep learning technology can realize protection from the aspects of gateway verification and data encryption and has high accuracy and reliability.

Abstract 1201: GenomeCruzer: A tool for interactive 3D visualization and analysis of multidimensional cancer omics

Abstract In cancer genomics, integrative analysis of data obtained from biological samples of patient cohorts requires handling large groups of patients, each with molecular data of different type for thousands of genes, such as expression, mutation, copy number, and others. Currently available tools allow visualization and analysis of such complex data, however forcing the user to work with partial views due to the amount of information. Also, these systems usually involve somewhat laborious procedures, so that for a complete analysis of the information it is often necessary to use many tools in sequence. Here we present GenomeCruzer, a software for 3D interactive visualization and analysis of multidimensional omic data sets (e.g. gene expression, methylation, copy number alteration). GenomeCruzer is particularly useful to quickly achieve clinical and biological insight, providing easy integration and interactive visualization of genomic data of any kind. GenomeCruzer has the dual objectives of representing large amounts of genomic data in an easily readable way, allowing interactive analysis and providing visual results in real-time. Powerful graphics also allow simultaneous display of different types of values for the same objects, through multiple dimensions and customizable visual metaphors. The user can navigate this 3D environment and interact with the different elements, which constitute a representation of the starting datasets, building analysis paths and using statistical tools e.g. for the comparison of groups to highlight correlations. In conclusion, GenomeCruzer enables users without bioinformatics background to browse, analyse and interpret complex omics datasets for multiple groups of patients and genes simultaneously, extracting clinical correlates and biological knowledge. Citation Format: Claudio Isella, Riccardo Corsi, Jamal Elhasnaoui, Andrea Bertotti, Luca Vezzadini, Enzo Medico. GenomeCruzer: A tool for interactive 3D visualization and analysis of multidimensional cancer omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1201.

Anisotropic compact stars in presence of electromagnetic field, quintessence field and cosmological constant in f(G) gravity

In this work, we have discussed the anisotropic compact stars in the framework of modified Gauss–Bonnet gravity, named by f(G) theory of gravity. We have found the equations of motion of stars with respect to spherically symmetric metric, describing interior geometry of the compact stellar object, anisotropic mode of the matter distribution in the presence of electromagnetic field and quintessence field. We have taken the metric coefficients in the form [Formula: see text] and [Formula: see text] as well as a particular form of [Formula: see text], where the powers and coefficients of r are constants. The unknown constants are evaluated by matching between interior and exterior Reissner–Nordström–de Sitter metric. We have analyzed some physical aspects like energy density, radial and tangential pressures, anisotropic parameter of matter distribution, energy conditions, mass-radius relation, compactness factor, surface redshift, and stability of our resulting solutions through graphical behavior by comparing to the observational data of different types of compact stars like 4U1820-30, Vela X-1 and SAXJ1808.4-3658. We conclude that all physical attributes maintain their experimental ranges which deliver our anisotropic compact star models are viable and also stable upto certain region in [Formula: see text] gravity theory.

Viral Infectivity Inhibition and Viral Biological Elements Destruction using Safe and Low Power Electrons Generated by Life Restoration Device (LRD): An In Vitro Study

Background: Recently, different side effects have been observed after using antiviral drugs before activation of the immune system. Therefore, it is very important to use effective and non-invasive therapy with fewer side effects for infected virus treatment. Methods: In this study, we designated a new device termed a Life Restoration Device (LRD). The main function of LRD is to generate electric frequencies with lower and safer potential. These frequencies can effectively destroy the biological elements in the viruses, such as nucleic acid materials and viral cell membranes, but not the cellular plasma membrane of the infected eukaryotic cells. Results: A designated glass tube was prepared for this purpose. The infected cell culture was located in the cell culture media, and propagated viruses were poured into the glass tube. Additionally, two nickel-coated copper rods were inserted into both ends of the tube inside the cell culture media. Afterward, the two nickel-coated copper rods were connected to the LRD. Using LRD, lower potential electric frequencies were generated and applied for 30 min and 60 min time points. The treatment of the cell culture containing MERS-CoV and SARS-CoV-2 with LRD for 30 min significantly reduced the viral infectivity by 83% and 22%, respectively. After 60 min of treatment with LRD, the infectivity of MERS-CoV and SARS-CoV-2 viruses was reduced by 21% and 1%, respectively. Furthermore, HIV and HBV-infected blood showed a 95.5% and 100% viral inhibition rate after 2 h exposure to LRD. Additionally, based on the results of the electron microscopy of treated H5N1 virus and western blot analysis data of different types of viruses, the nucleic acid components of the treated viruses were reduced compared to the non-treated viruses. The low-power electric frequencies produced by LRD can reduce the fluidi ty and osmosis of the viral envelope but not the plasma membrane of the infected cells. Conclusion: Treatment of different types of pathogenic viruses with electric stimulation produced by LRD is a new alternative to safe therapy but needs further investigations. The results of this study are important to develop an effective, safe, and alternative viral therapy.

Optimization of Oil Productivity from the Ultra−Depth Strike−Slip Fault−Controlled Carbonate Reservoirs in Northwestern China

The largest ultra−deep (>7000 m) strike−slip fault−controlled oilfield was found in the tight Ordovician carbonates in the Tarim Basin. Because oil production wells in the Fuman Oilfield generally have bottom water, a reasonable production design is of significant importance for extending the water−free oil recovery period and improving oil recovery. However, there is no economical and effective quantitative method to determine a reasonable production capacity and a corresponding reasonable work system for stable−production wells in fault−controlled fractured carbonate reservoirs. In this contribution, we integrated the dynamic and static data of different types of wells in the Fuman Oilfield. It was found that there is a positive relationship between well−controlled dynamic reserves and reasonable productivity, and the productivity corresponding to the upper limit of the reserve can be defined as reasonable productivity. Further, we proposed a correlation equation between well−controlled dynamic reserves and reasonable productivity that shows the reasonable productivity of a stable well from the well−controlled dynamic reserve, which was obtained by combining the well productivity test and analogous well productivity test methods. The relationship between the reasonable productivity per unit nozzle and the rational productivity was obtained according to the determined matching relationship between the reasonable productivity of the stable−production well and the nozzle. The application results show that it can not only quickly determine the reasonable production capacity of stable−production wells but also deepen the understanding of the quantitative characteristics of the production capacity of the Fuman Oilfield. It also provides insight in the rational production allocation of new wells and optimize the development design, which supports the 1500 × 104 bbl/year oil production from the ultra−deep fractured carbonate reservoirs along the strike−slip fault zones in the Tarim Basin.

Slat Chain Conveyor with V-Block Assembly

Chain conveyors are used for moving products down an assembly line and/or around a manufacturing or warehousing facility. Chain conveyors are primarily used to transport heavy unit loads, e.g. pallets, grid boxes, and industrial containers. These conveyors can be single or double chain strand in configuration. The load is positioned on the chains, the friction pulls the load forward. Chain conveyors are generally easy to install and have very minimum maintenance for users. Aimed to provide most segmented consumption and sales data of different types of Chain Conveyor, downstream consumption fields and competitive landscape in different regions and countries around the world, this report analyzes the latest market data from the primary and secondary authoritative source. The report also tracks the latest market dynamics, such as driving factors, restraining factors, and industry news like mergers, acquisitions, and investments. It provides market size (value and volume), market share, growth rate by types, applications, and combines both qualitative and quantitative methods to make micro and macro forecasts in different regions or countries.

GIS-Based Urban Agglomeration Landscape Dynamic Observation and Simulation Prediction Algorithm

In order to improve the intelligence level of urban agglomeration landscape, this paper conducts dynamic observation and simulation prediction of urban agglomeration landscape based on geographic information system. There are many characteristics of GIS technology itself that can be well matched with urban landscape research, so in the process of actually studying the green space landscape pattern, GIS technology has a very large space to play. The first is to use GIS technology to collect basic data of different types of scenic spots. Raster data and vector data are two important data in GIS technology. Among them, vector data with point, line, and surface morphology can relatively express the landscape ecology. Raster data is to determine the specific position of each pixel through the vertical and horizontal row and column relationship of the pixel, which is relatively simple in structure and easy to topology. According to the characteristics of the urban agglomeration landscape, the urban agglomeration landscape is reconstructed on the virtual simulation platform using GIS technology. It can meet the long-term sustainable development of the ecological environment of the scenic area. The research of this paper mainly belongs to the scope of practical research. Taking the existing research results as the first point of experimental research, combined with the relevant research results of design, the dynamic observation and simulation prediction algorithm of urban agglomeration landscape are constructed.

Prediction of News Popularity Based on Deep Neural Network

Due to the timeliness and short life cycle of news, the postrelease prediction is limited, and the prerelease prediction also faces huge challenges due to the diversity and difficulty of defining influencing factors. This paper uses the foreclosure. This paper proposes a news popularity prediction method based on GRU deep neural network. Firstly, a web crawler was designed to obtain news data of different types and structures from 10 information security portal websites in China. After data preprocessing, the Word2Vec method was used to extract features, extract key news sentences and construct a subset of content features. Establish a GRU neural network regression prediction model to predict hot news on the Internet. The experimental results show that, compared with the traditional processing method, the model can process the multi-source rough data set in this paper and greatly reduce the prediction error. At the same time, because the threshold recurrent unit structure used in this paper is simpler than the long-short-term memory network structure, it can shorten the prediction time and improve the computing performance.

Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning

The performance of photovoltaic (PV) cell is affected by the model structure and corresponding parameters. However, these parameters are adjustable and variable, which play an available role in regarding to the efficiency and effectiveness of PV generation. Due to strong non-linear characteristics, existing PV model parameters identification methods cannot easily obtain accurate solutions. To tackle this, this paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOL), named DOLADE. In DOLADE, the opposite learning method expands the current elite population and the population of poor performance, improving the particles’ exploration capability. In the process of particles work, the searching area of particles is adjusting dynamically so that the particles’ exploitation capability is enhanced. The experimental data of different types of PV are tested, respectively. Three PV models are used to verify the new strategy’s accuracy and effectiveness. The proposed DOLADE is compared with several general advanced algorithms, and comprehensive experimental results are demonstrated. The results illustrate that DOLADE well extracts optimal parameters for each PV cell model and brought great competition in terms of accuracy, reliability, and computational efficiency in solving the problem.

Modeling of composite sorption isotherm for stratum corneum

Equilibrium water sorption in stratum corneum (SC) is considered by treating it as a biocomposite with two main phases, namely, corneocytes and lipids. To validate the rule of mixtures for the individual phase sorption isotherms, a new flexible fitting model is introduced by accounting for characteristic features observed in the variations of the thermodynamic correction factors corresponding to the individual sorption isotherms. The comparison of the model fitting performance with that of the five-parameter Park's model shows a remarkably good ability to fit experimental data for different types of sorption isotherms. The effect of the lipids content on the variance of the composite sorption isotherm of stratum corneum is highlighted. The sensitivity analysis reveals that for the typical water content 20–30 wt%, which corresponds to the SC in a stable condition, the sensitivity of the composite sorption isotherm to the variation of the lipids content on dry basis is predominantly positive and sufficiently small. The good agreement observed between the experimental sorption isotherm for SC and the composite isotherm, which is based on the rule of mixtures for the individual phase sorption isotherms, yields a plausible conclusion (hypothesis) that the corneocytes–lipids mechanical interaction during unconstrained swelling of the SC membrane in the in vitro laboratory experiment is negligible.

Energy-Efficient Ground Traversability Mapping Based on UAV-UGV Collaborative System

With the development of science and technology, robots have been widely used in smart cities. The traversability mapping of environment perception is the prerequisite for robots to perform tasks. To save the energy consumption of traversability mapping for unmanned ground vehicle (UGV), we fusion a wide range of aerial images and a small amount of ground images to provide vision for UGV. Current map fusion methods are usually constrained by homogeneous model of robotic systems and lack of diverse sensors. As a result, they cannot work well in heterogeneous collaborative robotic systems that consist of aerial and ground robots. In this paper, we use heterogeneous robot systems, including UGV and unmanned aerial vehicles (UAV) to build an occupancy grid map that can be used for navigation. To fuse sensor data of different types, we propose a Collaborative Map Fusion algorithm based on Multi-task Gaussian Process Classification (MTGPC) using heterogeneous robotic systems. Besides, probabilistic model is exploited in traversability mapping, so the active perception can be used to build the map efficiently. Our system is tested in real scenes and can achieve an accuracy of more than 70%. The map fusion using active perception is better than map fusion using random strategy in terms of speed and accuracy. To our knowledge, this is the first work that can build the occupancy grid map using sparse data points sampled from aerial images and ground lidar map.

3D GIS Semi-automatized Modelling Procedure for the Conservation of the PHiM: Heritage Municipal Buildings of Seville (Spain). A New Dimension for Urban Cultural Data Management

This research explores the possibilities resulting from the use of three-dimensional (3D) models designed in GIS environments for their application to the management and conservation of historical architectonic heritage. This 3D modelling work is one of the strategic actions of the recently finished Master Plan for Conservation of Heritage Municipal Buildings (PD-PHiM) for the City of Seville (Spain). This plan deals with the analysis of a group of 115 municipally owned buildings of high heritage interest that include different typologies, chronologies scales, and uses. This investigation has complemented and continued the initial work begun by the Seville Spatial Data Infrastructure (ide.SEVILLA) in the field of 3D mapping of urban environments and its publication as institutional open data. The implemented improvements started on an initial diagnosis of a preliminary urban model, which reached a level of detail (LOD) of 2, as defined by the CityGML standard, in only 20% of the registered assets in the PD-PHiM database. The proposed methodology has achieved the automation of most of the process of building 3D geo-referenced models to increase the percentage of assets that reach the LOD2 to 75%. The initial information comes from the use of institutional spatial data of different types and sources: Light Detection and Ranging (LiDAR), Spanish Cadastre Office, and so on. Additionally, the generated entities have been linked to a complex, multidisciplinary and multiscale database, designed within the framework of the strategic actions of the PD-PHiM. The contributions of the proposal, especially in the automation of processes, imply a considerable saving of resources in comparison with other methods in which the modelling is eminently carried out manually. Thus, they are complementary to those that are related to the use of 3D modelling software intended for other purposes, with the consequent incompatibilities and hard interoperability procedures with GIS environments that this implies.

Assessing the interconnectedness and systemic risk contagion in the Chinese banking network

PurposeIn this paper, the authors use the balance sheet data to investigate the interconnectedness and risk contagion effects in China's banking sector. They firstly study the network structure and centrality of the interbank network. Then, they investigate how and to what extent the credit shock and liquidity shock can lead to the risk propagation in the banking network.Design/methodology/approachReferring to the theoretical framework by Haldane and May (2011), this paper uses the network topology theory to analyze the contagion mechanism of credit shock and liquidity shock. Centrality measures and log-log plot are used to evaluate the interconnectedness of China's banking network.FindingsThe network topology has shown clustering effects of large banks in China's financial network. If the Industrial and Commercial Bank of China (ICBC) is in distress, the credit shock has little impact on the Chinese banking sector. However, the liquidity shock has shown more substantial effects than that of the credit shock. The discount rate and the rollover ratio play significant roles in determining the contagion effects. If the credit shock and liquidity shock coincide, the contagion effects will be amplified.Research limitations/implicationsThe results of this paper reveal the network structure of China's interbank market and the resilience of banking system to the adverse shock. The findings are valuable for regulators to make policies and supervise the systemic important banks.Originality/valueThe balance sheet data of different types of banks are used to construct a bilateral exposure matrix. Based on the matrix, this paper investigates the knock-on effects of credit shock triggered by the debt default in the interbank market, the knock-on effects of liquidity effects, which is featured by “fire sale” of bank assets, and the contagion effects of combined shocks.

Using biological networks to integrate, visualize and analyze gene-disease interactions

Nowadays, data integration methods have been widely used to build models and to represent interactions between the data. They are showing high efficiency. Recent technologies permitted the research community to perform complex analysis on cell structures and it’s functioning system. The tremendous amount of data collected from a biological system encouraged the exploration of new hypothesis. However, the manipulation of heterogenous data require additional efforts to find the model that handles perfectly data of different type. In this paper we present our method to create a unified model and to integrate gene-disease interactions. We will talk about stat of the art methods in data integration, and how we built our network based on omics layers. Moreover, we will present the overall framework we followed to extract important interactions by visually interpreting the generated graph, and the betweenness centrality of nodes. We compared our findings to the medical literature to explain the topology of our generated network. Some genes revealed as important nodes due to the fact holding many interactions and being connected to several syndromes.

Innovation in healthcare management: drug decriminalization for reducing the health damage from crime

The article focuses on the positive impact of drug legalization on reducing crime and violence's impact on health. The authors considered the potential of an innovative approach to crime prevention and health care improvement. Criminalization may reduce drug harm, but the current criminal-justice approach to drugs is not working enough. Drug use is still widespread, public. The personal harms are significantly large. There are a lot of short- and long-term health effects from crime and violence. The World Health Organization affirms that rates of drug use are unrelated to how effectively drug laws are enforced. Thus, it is actually to find new possibilities and develop new methods to reduce crime level and its negative influence. Drug decriminalization is one of such important issues. This article aims to investigate drug decriminalization for reducing the health damage from crime in the context of innovation in healthcare management. There is a comparative analysis of some drug legalization policies: decriminalization of the use and possession of all illicit drugs (with the control of their legal supply) or legalization of the use and supply of cannabis etc. The dynamic analysis of data for different types of crime, such as unlawful acts involving controlled drugs or precursors, intentional homicide, assault, kidnapping, sexual assault, and other violent crime both in two groups of countries – with criminal-justice and innovation health care (including drug decriminalization) approaches were taken. Portugal, Switzerland, Netherlands, Czechia are among the countries of the second group, which have decriminalized drug use and possession for personal use and have invested in harm reduction programs. The research consists of data for 25 European countries for 2008-2018 (the time limit of 2018 is determined by available statistics of the statistical service of the European Union, World Health Organization, UN Office on Drugs & Crime Databases, etc.). Based on the correlation and regression analysis, it is substantiated that drug decriminalization is an important factor in reducing the health damage from crime.

Обзор опыта решения задач транспортного моделирования в лесном хозяйстве

The article systematizes the experience of solving the task of transport modeling for the ground access to the forest fires and forest resources, presented by scientific and engineering works. General characteristics of transport modeling based on the Russian and foreign experience are given; the analysis of data services and Open Source developments are the topical subjects in the development of transport projects. The main attention is paid to the review of issues of transport modeling issues in the use, protection, and reproduction of forests, taking into account the current Russian regulatory documents of the forestry. We have described the geodata sources, as well as using remote sensing data of different types and spatial resolution for the transport modeling in the forestry. The works of scientific and educational institutions represent Russian research on the forest transport modeling and forest infrastructure. They have a common understanding of forest transport accessibility as a complex issue. It is noted that the interest of researchers traditionally lies in solving environmental and economic problems of modeling. It is necessary to continue the work on the modeling for the location of fire stations, cutting areas and forest warehouses for the model territories in different regions and of different forest infrastructure’s characteristics. The development of methods and technologies to balance the infrastructural load and natural stability of forest ecosystems is described as a direction for further research. Undertaken review shows some patterns of forest transport modeling as a part of forestry using modern applied geoinformatics.

The Costs of Medical Care for Covid-19 Patients: A case study in Turkey

Objective: This study aims to calculate the medical costs of Covid-19 patients for hospitals based on the severity of clinical care. Design: The study was conducted in a hospital in Istanbul/Turkey. A micro-costing approach was performed using historical cost data for one year. All direct and indirect medical inputs were determined in quantities and monetary values for four types of Covid-19 patients in the hospital. Results: The analysis calculated the unit cost of an outpatient to be 459,99 ₺, while the cost per day for inpatient to be 1.184,63 ₺, for non-intubated in intensive care unit to be 1.938,11, for intubated in the intensive unit to be 2.393,99₺. The study also indicates that the total cost of a non-intubated patient in intensive care units is 1,54 times higher than the total cost per inpatient. An intubated patient’s cost is 2,08 times higher than an inpatient’s cost. Conclusion: This study indicates that Covid-19 patients incur significantly high costs for hospitals. The findings of the study provide empirical data for different types of patients which can be used in clinical management and can help all related governing bodies to plan their actions and make the decisions

Collection and processing of bearing vibration data for their technical condition classification by machine learning methods

An experimental research facility has been developed to receive vibration signals from mechanisms with installed rolling bearings. A control block for all equipment has been created. For the repeatability of the experiment, an external microcontroller with a programmed proportional-integral-derivative regulator was used.
 Experiments were carried out to obtain initial data for different types of bearings. The processed data were grouped and made publicly available for further research. It is proposed to solve the problem of emergency stop of the generator, arising during operation due to bearings worn, by recognizing the pre-emergency conditions of rotary rig based on the use of advanced machine learning techniques: to highlight the signs of vibration and build clusters according to the degree of worn.