Data Analysis Tools Research Articles

Intersection Properties of Finite Disk Collections

In this article, we study the intersection of a finite collection of disks in Euclidean space by examining spheres of various dimensions and their poles (extreme values with respect to canonical projections) contained within the intersection’s boundary. We derive explicit formulae for computing these extreme values and present two applications. The first application involves computing the smallest common rescaling factor for the radii of the disk system, which brings the system to a single point of intersection. This calculation allows us to compute the generalized Čech filtration, a crucial tool for the topological data analysis of weighted point clouds. The second application focuses on determining the minimal Axis-Aligned Bounding Box (AABB) for the intersection of a finite collection of disks in Euclidean space, addressing a significant problem in computational geometry. We consider that this work aims to contribute to the fields of topological data analysis and computational geometry by providing new tools for analyzing complex geometric structures.

Budget Absorption Analysis of Financial Performance in Production Forest Areas in Karawang Regency

This research comprehensively explores and analyzes the causal relationship between budget planning, budget execution, and the government's internal control system on budget absorption, as well as the impact of budget absorption on financial performance. The research method used is a quantitative description technique involving 200 respondents as a sample. Path analysis is used as a quantitative data analysis tool in this study. The research results show several important findings, namely: 1) Budget planning has a significant influence on budget absorption in the forestry production area in Karawang. 2) Budget compliance has an impact on budget absorption in the Karawang production forest area. 3) The government's internal management system has an impact on budget absorption in the Karawang production forest area. 4) Budget absorption has an impact on financial performance in the Karawang production forest area. 5) Budget absorption plays a role as a mediator between budget planning and financial performance in the production forest area of Karawang Regency. 6) Budget absorption plays a role as a mediator between household implementation and financial performance in the production forest area of Karawang Regency. 7) Budget absorption plays a role as a mediator between the government's internal management system and financial performance in the Karawang production forest area

Pengaruh Online Customer Review, Pengetahuan Produk, dan Kualitas Produk Terhadap Keputusan Pembelian Produk Scarlett Whitenning pada Mahasiswa Universitas Pembangunan Nasional Veteran Jawa Timur

This study aims to determine the effect of online customer reviews, product knowledge, and product quality on the decision to purchase scarlett whitening products for students at the "Veteran" National Development University, East Java. The population in this study were students of the Faculty of Economics and Business UPN "Veteran" East Java with a total sample of 98 respondents. The data collection method in this study uses a survey method and uses partial least squares as a data analysis tool. From the results of the study it was found that online customer reviews, product knowledge and product quality have a major influence on the decision to purchase scarlett whitening products, which means that the better the reviews, product knowledge, and also the quality of scarlett whitening products, the better the student's decision to make a purchase..

Pengaruh Pendapatan Asli Daerah, Dana Alokasi Umum, Dana Bagi Hasil dan Tingkat Inflasi terhadap Belanja Modal di Provinsi Bengkulu Tahun 2018-2022

The purpose of this study is to determine how the impact of capital expenditure in Bengkulu Province from 2018 to 2022 is related to local original income (PAD), general allocation funds (DAU), profit-sharing funds (DBH), and inflation. The study population consisted of local original income, general allocation funds, profit-sharing funds, inflation rates, and capital expenditures. Ten cities and regencies in Bengkulu Province. This study uses secondary data on the implementation of the 2018–2022 Bengkulu Provincial APBD budget obtained from the Directorate General of Financial Balance. WarpPLS software version 7.0 will be used as a data analysis tool to review research data. The conclusion of this study shows that although the general allocation fund does not have a significant effect and the inflation rate has a significant negative influence on capital expenditure, local original income, and profit-sharing funds have a significant positive influence.

Passionfruit Genomic Database (PGD): a comprehensive resource for passionfruit genomics

Passionfruit (Passiflora edulis) is a significant fruit crop in the commercial sector, owing to its high nutritional and medicinal value. The advent of high-throughput genomics sequencing technology has led to the publication of a vast amount of passionfruit omics data, encompassing complete genome sequences and transcriptome data under diverse stress conditions. To facilitate the efficient integration, storage, and analysis of these large-scale datasets, and to enable researchers to effectively utilize these omics data, we developed the first passionfruit genome database (PGD). The PGD platform comprises a diverse range of functional modules, including a genome browser, search function, heatmap, gene expression patterns, various tools, sequence alignment, and batch download, thereby providing a user-friendly interface. Additionally, supplementary practical tools have been developed for the PGD, such as gene family analysis tools, gene ontology (GO) terms, a pathway enrichment analysis, and other data analysis and mining tools, which enhance the data’s utilization value. By leveraging the database’s robust scalability, the intention is to continue to collect and integrate passionfruit omics data in the PGD, providing comprehensive and in-depth support for passionfruit research. The PGD is freely accessible via http://passionfruit.com.cn.

Remote Sensing and Geographic Information Systems for Precision Agriculture: A Review

Precision agriculture aims to optimize crop production and minimise environmental impacts by using information technology, remote sensing, satellite positioning systems, and proximal data gathering. This review paper examines current applications and future directions of remote sensing and geographic information systems (GIS) for precision agriculture. Remote sensing provides data on crop health, soil conditions, water status, and yield which can guide variable rate applications within fields. Satellite and aerial platforms allow multispectral and hyperspectral imaging for vegetation indices analysis, crop classification, and stress detection. GIS technology integrates these data layers to model and map variations, develop prescription maps, and analyse spatial relationships. Key research frontiers include high-resolution satellite and drone data for within-field analysis, better integration of proximal and remote sensing, online nutrient and yield monitors, real-time prescription modelling, and predictive analytics using machine learning. Adoption continues to increase with better data analytics tools and greater economic returns realized. Remote sensing and GIS provide an integral platform for variable rate technologies, predictive modelling, and data-driven decision-making for precision agriculture.

Exploring JASP as a data analysis tool in L2 research: a snapshot

This paper explores the potential of JASP (Jeffreys’s Amazing Statistics Program, https://jasp-stats.org/) as a robust statistical tool in advancing Second Language Acquisition (SLA) research, with a specific emphasis on its application within the domain of L2 writing. Second language writing proficiency is a complex and multifaceted skill, demanding rigorous empirical investigation to uncover nuanced patterns and insights. JASP, known for its user-friendly interface and advanced statistical capabilities, emerges as a promising instrument for researchers seeking to unravel the intricacies of L2 writing development. The paper begins by providing an overview of the features embedded in JASP and continues to discuss some of the extant research within the field of SLA that implements JASP as a data analysis tool. Follows is a detailed description of the use of JASP in two L2 writing papers, in which the data analysis decisions are discussed. Furthermore, the discussion delves into the practical implications of utilizing JASP in L2 writing research, including its ability to accommodate small sample sizes, handle complex interactions, and facilitate transparent and reproducible analyses. The paper concludes by advocating for the widespread adoption of JASP in SLA research, positing that its integration holds the promise of advancing our understanding of the intricacies of L2 writing development and contributing to the refinement of pedagogical approaches in second language education.

Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. Different imputation methods were employed in the experimentation with various missing value imputation techniques, and the performances of machine learning (ML) models were compared. A hybrid model that integrates ML and sample-based statistical techniques for missing value imputation is being proposed. Furthermore, the continuation involved the dataset with the best missing value imputation, chosen based on ML model performance for subsequent feature engineering and hyperparameter tuning. K-means clustering and principal component analysis were applied in our study. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, K-fold cross-validation was implemented.

The Effect of Warehousing in Logistics Performance of Shipping Firms in Nigeria

The demand for businesses to develop new methods to generate value and distribute it to consumers is rising in the highly challenging global market in recent times. This has forced the development of more effective and efficient warehousing techniques in the shipping industry. This study empirically investigated the role of warehousing in the logistics performance of shipping companies in Nigeria. Data were obtained from registered shipping enterprises and warehouse operators in Nigeria to test the hypotheses. Descriptive and inferential statistics were tools for data analysis. The conclusions of the investigation indicated that warehousing impacts shipping Companies' logistics performance significantly. Based on the findings from the results, it was established that if warehousing activities are successfully implemented in shipping companies, their logistics performance will be improved rapidly. Therefore, it was recommended that logistics managers and warehousing operators should devise strategies for improving their warehousing activities to achieve effective and efficient logistics performance.

MiCId GUI: The Graphical User Interface for MiCId, a Fast Microorganism Classification and Identification Workflow with Accurate Statistics and High Recall.

Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment. Another paramount factor to consider is the necessity of accurate statistical significance to properly control the proportion of false discoveries among the identified microorganisms, and antimicrobial-resistant proteins, and to provide robust biomass estimation. Recently, we have developed Microorganism Classification and Identification (MiCId) workflow that assigns accurate statistical significance to identified microorganisms, antimicrobial-resistant proteins, and biomass estimation. MiCId's workflow is also computationally efficient, taking about 6-17 minutes to process a tandem mass-spectrometry (MS/MS) experiment using computer resources that are available in most laptop and desktop computers, making it a portable workflow. To make data analysis accessible to a broader range of users, beyond users familiar with the Linux environment, we have developed a graphical user interface (GUI) for MiCId's workflow. The GUI brings to users all the functionality of MiCId's workflow in a friendly interface along with tools for data analysis, visualization, and to export results.

Blockchain Data Mining With Graph Learning: A Survey.

Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. First, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Second, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Third, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data.

Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Computational drug development for membrane protein targets.

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Leveraging OpenAI's Advanced Data Analysis Tool in Dermatology: Opportunities and Challenges

Leveraging OpenAI's Advanced Data Analysis Tool in Dermatology: Opportunities and Challenges

Optimization of Fraud Detection Model with Hybrid Machine Learning and Graph Database

Machine learning and the graph database work well together. By concentrating on the relationships between fraudsters or fraud cases, graph databases can provide an additional layer of security, while machine learning uses statistics and data analytical tools to categorize information and identify patterns within data. In doing so, it can transcend rigid rules and scale human insights into algorithms. When combined with a graph, machine learning alone can increase the accuracy of fraud signals to 90% or higher. On its own, it can reach 70–80%. Graphs also improve machine learning's explainability.

Optimization Path for Management Decision-Making of Chinese Public Hospitals Under the Background of Big Data

This study examines how Big Data might improve Chinese public hospital management. A comprehensive study examines how data diversity, storage efficiency, analytics tools, and information system complexity affect decision-making. A carefully selected quantitative dataset from Chinese public hospitals is used in the study. Analyses use structured medical records, semi-structured billing data, and unstructured patient comments. The sample size of 115 was chosen for statistical robustness and multiple regression analysis best practices, which recommend 10-20 observations per predictor variable for estimate. Multiple linear regression analysis highlights amazing correlations and stresses data diversity, storage efficiency, analytics tools, and information system sophistication in decision efficiency. The study helps healthcare executives and regulators understand the complex relationship between regression coefficients and modified R-squared value. Also evaluated are Chinese public hospitals' strengths and weaknesses. Strengths include data integration, analytics, and advanced information systems. The report emphasizes data quality and cultural transformation, which impact Big Data and decision-making. The report emphasizes data consumption and advanced analytics to empower healthcare decision-makers. This research informs Chinese public hospital strategic reforms to improve resource allocation, patient care, and efficiency. This paper demonstrates how Big Data can impact healthcare decision-making. It enriches academic discourse and guides healthcare stakeholders through modern management with relevant insights and practical advice.

A Critical Network Governance Issues in Social Sciences Discipline: A Literature Review

Some scholars have underlined network governance issues in theoretical, conceptual, operational, and practical terms. However, it is still requested to core map this issue, as past scholars have lacked attention. Therefore, this paper explores network governance’s pivotal issues and proposes a theoretical model. A systematic literature review was conducted using Scopus database sources, and NVivo 12 Plus software was used as an assistive qualitative data analysis tool. This study revealed several viewpoints. First, network governance has received much attention over the last decade. Second, some essential core key terms are linked to network governance issues, such as social, public, government, policy, actor, political, institutional, and organizational. In addition, we proposed a theoretical model that can be used as a recommendation for future studies.

RESEARCH ON THE IMPACT OF EMPLOYEES’ SOCIAL MEDIA USE ON AMBIDEXTROUS INNOVATION

With the rapid development of social media, employees' use of social media in the workplace has a significant impact on communication, interaction, and information sharing among employees and between employees and organizations. Therefore, this study selects employees as the research object, based on UGT and ambidextrous innovation theory, comprehensively considers the different use purposes of social media, and explores the influence mechanism of social media use on ambidextrous innovation from the level of employees. The understanding of the boundary conditions of employee social media use for ambidextrous innovation has deepened. By combing existing literature, designing questionnaires, collecting data, and using a variety of data analysis tools for correlation analysis, this study draws the following main conclusions: employees' use of social media and its three dimensions all have a significant positive impact on ambidextrous innovation; innovative atmosphere plays a positive moderating role in the impact of employees' social media use on ambidextrous innovation. This study verifies the positive impact of employees' use of social media and its different dimensions on ambidextrous innovation, reveals the specific effects of social media use within enterprises, and improves the research framework for social media application within enterprises.

Analysis of the Impact of the Earthquake on the Economy of Wirotaman Village, Ampelgading District, Malang Regency

The impact of an earthquake on the economic sector is very large. Mainly what happens in the economy of society with insufficient human resource capacity and invalid capital. This is like what happened in the 2021 Earthquake in Ampelgading District, Malang Regency. With the greatest damage impact occurring in Wirotaman Village. This research used qualitative descriptive methods with a research period of 3 months from 1 April - 1 July 2022. Data collection methods used interviews, observation and documentation techniques while the data analysis tool used was Multiple Linear Regression. The results of the research show that there is an impact due to the earthquake disaster on the economy of village communities. Because there is an obstacle in the production and distribution sectors. This results in widespread unemployment and poverty