Data Analysis Software Package Research Articles

The development of the soderberg electrolyzer electromagnetic field’s state monitoring system

This study is devoted to improving the economic efficiency of the cell, due to the field of the generated electromagnetic field’s accurate diagnostics. To solve this problem, the authors had developed a hardware-software complex for electromagnetic field diagnostics. This complex includes a measurement device and a software package for data collection and analysis. On the laboratory prototype of the aluminum electrolysis complex, a study was carried out on the formation and structure of the electromagnetic field. A number of experiments have been carried out showing the degree of formation of the electromagnetic field by the anode, the relationship of electromagnetic fields in the inter-anode space has been shown. Based on the results of the studies, conclusions were drawn about the possibility of diagnosing the current state of the anode, determining the direction of rotation of aluminum in the electrolytic cell and estimating the life of the anode and its burnout time.

Methods for the Design and Analysis of Analytical Ultracentrifugation Experiments.

Analytical ultracentrifugation experiments play an integral role in the solution-phase characterization of biological macromolecules and their interactions. This unit discusses the design of sedimentation velocity and sedimentation equilibrium experiments performed with a Beckman Proteomelab XL-A or XL-I analytical ultracentrifuge and with a Beckman Optima AUC. Instrument settings and experimental design considerations are explained, and strategies for the analysis of experimental data with the UltraScan data analysis software package are presented. Special attention is paid to the strengths and weaknesses of the available detectors, and guidance is provided on how to extract maximum information from analytical ultracentrifugation experiments. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.

Competitiveness of modes of transport in Kazakhstan: influencing factors and rating

This article discusses the competitiveness of various modes of transport in Kazakhstan and provides an assessment of the main factors influencing the choice of transportation modes within the realm of economics. The comprehensive rating system aimed at evaluating the competitiveness of different transportation modes in Kazakhstan. Drawing upon various perspectives and analytical methodologies, this system proves to be a valuable tool for politicians and stakeholders seeking to optimize Kazakhstan transportation system. The primary objective of this study is to examine which of the selected factors has the most significant impact on Kazakhstan's decisions regarding the choice of transportation vehicles. The thorough analysis of key literature sources was carried out and a methodology based on our findings was selected. The methodological approach involves the creation of multi-factor regression models utilizing the EViews10 data analysis software package. The research dataset encompasses data from the Bureau of National Statistics of the Republic of Kazakhstan's Agency for Strategic Planning and Reforms for the years 2013 to 2022. Calculations derived from our research revealed that the selected factors have varying effects on different types of transportation. As a result of this study, it becomes possible to formulate recommendations for state policies in the realm of transportation infrastructure and its implications for economic and social development, with a specific focus on enhancing vehicle capacity. The study's findings are of relevance to transportation companies and governmental entities responsible for managing the transportation system.

Effects of D-CAG chemotherapy regimen on cognitive function in patients with acute myeloid leukaemia: A resting-state functional magnetic resonance imaging study.

Post-chemotherapy cognitive impairment, also known as 'chemobrain', is a common neurotoxic complication induced by chemotherapy, which has been reported in many cancer survivors who have undergone chemotherapy. In this study, we aimed to explore the effects of D-neneneba dicitabine, C-nenenebb cytarabine, A-aclamycin, G-granulocyte colony-stimulating factor (D-CAG) chemotherapy on cognitive function in patients with acute myeloid leukaemia (AML) and its possible central mechanisms. Twenty patients with AML and 25 matched healthy controls (HC) were enrolled in this study. The cognitive function of patients before and after D-CAG chemotherapy was evaluated by the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog). The resting-state functional magnetic resonance imaging data were collected from all patients before and after chemotherapy intervention, as well as HC. Then, resting-state functional magnetic resonance imaging data were preprocessed using DPABI software package and regional homogeneity (ReHo) values of brain regions were calculated. Finally, ReHo values between groups were compared by Resting-State fMRI Data Analysis software package with t-tests and Alphasim method was performed for multiple comparison correction. Moreover, associations between ReHo values of altered brain regions and the scores of FACT-Cog were analysed by Pearson correlation. The total FACT-Cog scores and four factor scores of AML patients increased significantly after treatment. ReHo values showed no significant changes in patients before treatment when compared with HC. Compared with HC, ReHo values of the right middle frontal gyrus, inferior frontal gyrus (opercular part), middle occipital gyrus, and left praecuneus decreased significantly, while ReHo values of the left inferior temporal gyrus, right middle temporal gyrus, and hippocampus increased significantly in patients after treatment. Compared with patients before treatment, ReHo values decreased significantly in the right middle frontal gyrus, inferior frontal gyrus (opercular part), and middle and inferior occipital gyri of patients after treatment. In addition, ReHo values of the right inferior frontal gyrus (opercular part) were negatively correlated with the total scores of FACT-Cog and factor scores of perceived cognitive impairment in patients after treatment. There were also negative correlations between ReHo values of the right middle frontal gyrus and perceived cognitive impairment scores. The present study confirmed that D-CAG chemotherapy might cause impaired subjective self-reported cognitive functioning in AML patients, which might be related to the decreased function of certain regions in the right prefrontal lobe. These findings provided further understanding of the mechanisms involved in post-chemotherapy cognitive impairment and would help develop new therapeutic strategies for 'chemobrain' in AML patients.

Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising Live Twitter Data

Qualitative data analysis software (QDAS) packages that support live data extraction are a relatively recent innovation. Little has been written concerning the research implications of differences in such QDAS packages’ functionalities, and how such disparities might contribute to contrasting analytical opportunities. Consequently, early-stage researchers may experience difficulties in choosing an apt QDAS for Twitter analysis. In response to both methodological gaps, this paper presents a software comparison across the four QDAS tools that support live Twitter data imports, namely, ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™. The authors’ QDAS features checklist for these tools spotlights many differences in their functionalities. These disparities were tested through data imports and thematic coding that was derived from the same queries and codebook. The authors’ resultant QDAS experiences were compared during the first activity of a broad qualitative analysis process, ‘organising data’. Notwithstanding large difference in QDAS pricing, it was surprising how much the tools varied for aspects of qualitative research organisation. Notably, the quantum of data extracted for the same query differed, largely due to contrasts in the types and amount of data that the four QDAS could extract. Variations in how each supported visual organisation also shaped researchers’ opportunities for becoming familiar with Twitter users and their tweet content. Such disparities suggest that choosing a suitable QDAS for organising live Twitter data must dovetail with a researcher’s focus: ATLAS.ti accommodates scholars focused on wrangling unstructured data for personal meaning-making, while MAXQDA suits the mixed-methods researcher. QDA Miner’s easy-to-learn user interface suits a highly efficient implementation of methods, whilst NVivo supports relatively rapid analysis of tweet content. Such findings may help guide Twitter social science researchers and others in QDAS tool selection. Future research can explore disparities in other qualitative research phases, or contrast data extraction routes for a variety of microblogging services.

Stable volumes for persistent homology

This paper proposes a stable volume and a stable volume variant, referred to as a stable sub-volume, for more reliable data analysis using persistent homology. In prior research, an optimal cycle and similar ideas have been proposed to identify the homological structure corresponding to each birth-death pair in a persistence diagram. While this is helpful for data analysis using persistent homology, the results are sensitive to noise. The sensitivity affects the reliability and interpretability of the analysis. In this paper, stable volumes and stable sub-volumes are proposed to solve this problem. For a special case, we prove that a stable volume is the robust part of an optimal volume against noise. We implemented stable volumes and sub-volumes on HomCloud, a data analysis software package based on persistent homology, and show examples of stable volumes and sub-volumes.

A scoping review on deep learning for next-generation RNA-Seq. data analysis.

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature's size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.

Operational Capacity of County Governments to Plan on Performance of Devolved Health Sector in Kenya

In Kenya, devolution of health services has resulted in the decentralization of many health services from the national government to local governments in order to improve the health of citizens. Despite political support of such a system and initiatives to increase devolution, the performance of the devolved health sector in Kenya has not improved as expected. The was meant to determine how citizen participation influence organizational performance of devolved health care sector in Kenya. The current study utilized cross-sectional research design because it involved the collection of data in 47 counties across the country and focused on several selected officials who gave information regarding governance factors influencing devolved health care sector in Kenya. Since the study utilized census sampling, the 47 county referral hospitals were targeted. Further purposive sampling was used to arrive at the 188 selected respondents who became the sample size of the study. Descriptive statistics was used to outline the fundamental characteristics regarding the collected information in the research. The study combined the elements of both quantitative and qualitative methods of data collection. The primary data was collected using questionnaires whose content was appropriate to test the hypothesis and address the research questions. Statistical Package for Social Sciences (SPSS) data analysis software package was employed to examine the quantitative data using the descriptive statistics. Analysis of Variance (ANOVA) table techniques and beta coefficient tables were utilized in the presentation of regression analysis outcomes. The findings of the study revealed that operational capacity of county governments to plan and management transparency had positive significant relationship with organizational performance of devolved health care sector. The findings suggest that there is need for the health care sector function to be devolved to the counties to allow for more citizen inclusivity in decision making, timely response to health care needs and government planning.

In-orbit performance of LE onboard Insight-HXMT in the first 5 years

The low-energy X-ray telescope (LE) is a main instrument of the Insight-HXMT mission and consists of 96 swept charge devices covering the 1–10 keV energy band. The energy gain and resolution are continuously calibrated by analyzing Cassiopeia A (Cas A) and blank sky data, while the effective areas are also calibrated with the observations of the Crab Nebula. In this paper, we present the evolution of the in-orbit performances of LE in the first 5 years since launch. The Insight-HXMT data analysis software package (HXMTDAS) is utilized to extract the spectra of Cas A, blank sky, and Crab Nebula using different good time interval selections. We fit a model with a power-law continuum and several Gaussian lines to different ranges of Cas A and blank sky spectra to get peak energies of their lines through xspec. After updating the energy gain calibration in CALibration DataBase (CALDB), we rerun the Cas A data to obtain the energy resolution. An empirical function is used to modify the simulated effective areas so that the background-subtracted spectrum of the Crab Nebula can best match the standard model of the Crab Nebula. The energy gain, resolution, and effective areas are calibrated every month. The corresponding calibration results are duly updated in CALDB, which can be downloaded and used for the analysis of Insight-HXMT data. Simultaneous observations with NuSTAR and NICER can also be used to verify our derived results. LE is a well-calibrated X-ray telescope working in 1–10 keV band. The uncertainty of LE gain is less than 20 eV in 2–9 keV band, and the uncertainty of LE resolution is less than 15 eV. The systematic errors of LE, compared to the model of the Crab Nebula, are lower than 1.5% in 1–10 keV.

Extending WiMDA for the data analysis of µ −SR experiments

A software extension to the Windows Muon Data Analysis software package WiMDA has been developed for the analysis of µ −SR data, which has been dubbed Negative-WiMDA. In designing Negative-WiMDA, some key features were considered: it should be easily accessed from the main analysis window of WiMDA; it should be able to account for multiple elements in a sample; it should be able to subtract signals treated as unwanted background, allowing the user to focus on a particular element of interest; it should be able to handle transverse-field (TF) data, and perhaps most importantly, it should be intuitive to use. The main features of Negative-WiMDA are presented here with a few examples of their use.

Data Combination: Interferometry and Single-dish Imaging in Radio Astronomy

Modern interferometers routinely provide radio-astronomical images down to subarcsecond resolution. However, interferometers filter out spatial scales larger than those sampled by the shortest baselines, which affects the measurement of both spatial and spectral features. Complementary single-dish data are vital for recovering the true flux distribution of spatially resolved astronomical sources with such extended emission. In this work, we provide an overview of the prominent available methods to combine single-dish and interferometric observations. We test each of these methods in the framework of the CASA data analysis software package on both synthetic continuum and observed spectral data sets. We develop a set of new assessment tools that are generally applicable to all radio-astronomical cases of data combination. Applying these new assessment diagnostics, we evaluate the methods’ performance and demonstrate the significant improvement of the combined results in comparison to purely interferometric reductions. We provide combination and assessment scripts as add-on material. Our results highlight the advantage of using data combination to ensure high-quality science images of spatially resolved objects.

The Deuterium Calculator: An Open-Source Tool for Hydrogen-Deuterium Exchange Mass Spectrometry Analysis.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful protein footprinting technique to study protein dynamics and binding; however, HDX-MS data analysis is often challenging and time-consuming. Moreover, the HDX community is expanding to investigate multiprotein and highly complex protein systems which further complicates data analysis. Thus, a simple, open-source software package designed to analyze large and highly complex protein systems is needed. In this vein, we have developed "The Deuterium Calculator", a Python-based software package for HDX-MS data analysis. The Deuterium Calculator is capable of differential and nondifferential HDX-MS analysis, produces standardized data files according to recommendations from the International Conference on Hydrogen-Exchange Mass Spectrometry (IC-HDX) to increase transparency in data analysis, and generates Woods' plots for statistical analysis and data visualization. This standard output can be used to perform time dependent deuteration studies and for the study of protein folding kinetics or differential uptake. Moreover, The Deuterium Calculator is capable of performing these analyses on large HDX-MS data sets (e.g., LC-HDX-MS from cell lysate digest). The Deuterium Calculator is freely available for download at https://github.com/OUWuLab/TheDeuteriumCalculator.git. Data are available via ProteomeXchange with identifier PXD036813.

Impact of hands-on training in STATA for data management and data analysis: How much knowledge gained among health-care professionals?

Objectives: Data management and statistical analysis are integral parts of any research. But there is difficulty in cleaning, organizing, and maintaining data in a systematic and meaningful way for accurate analysis without any tool. Hence, there is a need to learn a tool such as SPSS, STATA, R, SAS and so many for understanding the data. This study focused on the impact of short-course training on STATA on healthcare eye professionals and found an improvement in their competency to use computer-based statistical software packages for data management and analysis and identify the major challenges for participants and suggestions to improve the training. Materials and Methods: We organized a 10-day short course on data management and data analysis using STATA. A pre and post-test evaluation was done every day to find the impact. The questionnaire covered several areas of research methodology from data management to statistics. Categorical data were described in frequency and percentage whereas continuous data was described in median and interquartile range (IQR). Wilcoxon signed-rank test was applied to find out the significant improvement in the knowledge. Results: The 10 days cumulative median score for pre- and post-test was 43 (IOR = 39-47.5) and 72.5 (IOR = 69-77) respectively and was statistically significant (P = 0.012). Male participants, younger age groups, experience, and prior knowledge were identified as major determinants to improve the knowledge and performance in the usage of STATA. We observed that advanced training in the usage of STATA is highly beneficial to people interested/involved in research to gain autonomy in data management and analysis. Conclusion: The evaluation of the training confirms that hands-on training in the use of STATA software does have a significant impact on the knowledge, self-efficacy, and attitude of the learner. Since Stata is very user-friendly, we recommend that researchers and medical students should be offered a comprehensive training course in STATA.

Industry 4.0 research in the maritime industry: a bibliometric analysis

Industry 4.0 (I4.0) is currently a major topical concern in the literature on maritime industry. Maritime 4.0 is the area concerned with the integrated implementation of digital processes and technologies in the maritime industry. There is, however, a shortage of review studies that can reflect the research directions and various important issues in Maritime 4.0. The purpose of this study is to explore and categorize academic publications concerned with Maritime 4.0 to identify its current research status, main subject categories, emerging trends, current gaps, and future research directions. To provide a comprehensive review of the publications, the study used bibliometric analysis to conduct citation analysis (publication distribution, productive journals, and cross-country collaborations), cooccurrence of the subject categories, cocitation analysis, and keyword frequency analysis. In doing so, the study utilized the Web of Science (WoS) dataset and software packages for data analysis. Finally, the study proposed a research framework composed of five themes to link current research topics with future research trends in Maritime 4.0. The five themes observed were “digitalization technology,” “methodology and techniques,” “management,” “safety and security,” and “environmental issues.” The results revealed that, although scholars and practitioners focused on Maritime 4.0, this area remained underdeveloped. Many scopes under the five themes were left unexplored, such as emerging paradigms of Internet of things (IoT) and blockchain under the theme of “digitalization,” hybrid models (e.g., game theory and genetic algorithms) under the theme of “methodology and techniques,” and social responsibility under the theme of “environmental issues.”

Utjecaj zadovoljstva poslom na posvećenost zaposlenika u neprofitnim organizacijama

Given that, in the realization of the mission of a non-profit organization, employees play the most important role, in this research we questioned the employees' job satisfaction and their dedication to the organization. The problem question was aimed at examining the influence of job satisfaction on organizational commitment. Data were collected through a questionnaire from respondents in the state of Bosnia and Herzegovina. The sample consisted of employees of the Islamic Community in Bosnia and Herzegovina. The statistical software package for data analysis -SPSS -was used to analyze the collected attitudes of 602 employees. The main research hypothesis was: H1. Job satisfaction has a direct impact on the organizational commitment of employees of the Islamic Community in Bosnia and Herzegovina. The obtained results confirmed the main hypothesis. The research also confirmed that within the framework of the functioning of the aforementioned non-profit organization, it would be important to research other topics in the field of human resource management.

Do-It-Yourself, Low-Cost Pop-Up Usability Labs for Learning Experience Designers

This article introduces the concept of pop-up usability labs as a practical and cost-effective solution for conducting usability testing in real-world learning experience design (LXD) contexts. Various configurations of pop-up usability labs are presented, including budget-friendly, portable, semi-permanent, and mobile setups, along with recommendations for hardware and software requirements. The importance of selecting appropriate software packages for usability data analysis is emphasized, with suggestions for low-cost, no-cost, and open-source options for quantitative and qualitative data analyses. Pop-up usability labs offer numerous advantages, such as accommodating limited funding and reaching participants in rural areas. However, they also present challenges that require LXD professionals to become familiar with the equipment and data analysis methods. Despite these limitations, pop-up usability labs provide a viable, resource-efficient approach for LXD professionals to conduct usability testing in real-world settings, identify and address design flaws related to usability, and embrace more human-centered design practices in the development of educational and learning technology products, systems, or services.

Auditors' Perceptions toward Challenges and Attitudes of Adopting International Financial Reporting Standards

The objective of this study is to examine auditors' perceptions on the challenges and attitudes toward the implementation of the International Financial Reporting Standards (IFRS). In this study, questionnaires are collected from auditors. The questionnaire is composed of 13 items that are measured using Likert scale. The responses were used to measure participants' perceived challenges and attitudes towards IFRS adoption. The survey ends with two open-ended questions to supplement the quantitative component of the study in providing insights, via participants' voices, into the challenges of IFRS adoption. For the quantitative component, the Statistical Package for Social Sciences (SPSS) has been utilized and a qualitative data analysis software package (NVivo) is used to analyze qualitative data collected from the open-ended questions. Analysis of both quantitative and qualitative data indicates that auditors support the adoption of IFRS. The qualitative component highlighted the importance of training auditors on the implementation of IFRS to ensure effective integration of the accounting standards. Training, knowledge sharing and professional practice are identified as key factors in facilitating effective adoption of IFRS. The findings suggest that adopting IFRS necessitates the development of well-thought-out training programs that consider the implementation process with a local insight.

Preventive Health Behaviours among Adolescents and Their Parents during the COVID-19 Outbreak in the Light of the Health Beliefs Model

This article analysed the relationship between the preventive health behaviours of parents and teenagers during the COVID-19 outbreak, taking the Health Beliefs Model (HBM) as a point of reference. We assumed that parents' behaviours may be a cue to action for adolescents, looking at their preventive health behaviours regarding vaccination against COVID-19, as well as vaccination intention (among unvaccinated people); wearing protective masks where it is compulsory and where it is not obligatory; and maintaining physical distance and disinfecting hands in public places. The collected data were statistically analysed using the Statistica version 13.3 software package for advanced statistical data analysis. Descriptive statistics and correlation for non-parametric data (Spearman's correlation) were used. Research on a sample of 201 parents and their children revealed that young people engage in preventive behaviour less frequently than parents, but that the likelihood of such behaviour increases if they have a parent's cue to action. When formulating recommendations, we considered the gender of the surveyed parents, as the questionnaire was mainly completed by women, which may be an indicator of the unequal involvement in addressing the topic of the pandemic and preventive health behaviours, including attitudes towards vaccines.

Genome-Wide Association Study of Complex Traits in Maize Detects Genomic Regions and Genes for Increasing Grain Yield and Grain Quality

This review describes the current status of genome-wide association study (GWAS) of the major crops in maize (Zea mays L.) concentrate on performing association mapping as a novel method in associating genetic and complex traits, current strategy in analyzing of phenotype and genotype data to identify population structure and linkage disequilibrium. GWAS has an important role in food security because this method identified many crucial genomic regions of important traits in the most commercialize crops of the world, such as maize. These complex traits including yield, grain quality, biofortification, biotic and abiotic resistance. GWAS has many advantages correlated with reducing genotyping cost and research time, increasing mapping resolution and larger allele number. Meanwhile, GWAS has two main limitations related to population size and the number of markers. There are many software packages for data analysis in GWAS. The most commonly software that was used in GWAS especially in this crop is TASSEL because frequently updated. Recently, many research papers concentrated on GWAS in maize. GWAS analysis accelerated identification of genetic regions, candidate genes within these genomic regions and their metabolomic analysis correlated to the complex traits in maize for increasing grain yield and grain quality to fulfill the market demands.

Using Qualitative Data Analysis Software to Support Digital Research Workflows

While software packages such as NVivo are typically considered useful for qualitative data analysis, in actuality they are powerful and flexible enough to serve as the central component of a robust digital research workflow. Various features of these packages can be used across the entire research process from reviewing the literature to writing up the study’s findings. This article provides an overview of available qualitative data analysis software (QDAS) packages, including how to: use software tactics to enact research design strategies; select an appropriate QDAS package; report the use of software in the research report; and consider implications of QDAS use for human resource development scholars.