Data Analysis Tools Research Articles

PEDL+: protein-centered relation extraction from PubMed at your fingertip.

Relation extraction (RE) from large text collections is an important tool for database curation, pathway reconstruction, or functional omics data analysis. In practice, RE often is part of a complex data analysis pipeline requiring specific adaptations like restricting the types of relations or the set of proteins to be considered. However, current systems are either non-programmable web sites or research code with fixed functionality. We present PEDL+, a user-friendly tool for extracting protein-protein and protein-chemical associations from PubMed articles. PEDL+ combines state-of-the-art NLP technology with adaptable ranking and filtering options and can easily be integrated into analysis pipelines. We evaluated PEDL+ in two pathway curation projects and found that 59% to 80% of its extractions were helpful. PEDL+ is freely available at https://github.com/leonweber/pedl.

Status of Agricultural Farmers in the Republic of Serbia in Relation to Banking Institutions

Summary This paper examines the registered agricultural holdings (RAHs) in the Republic of Serbia. The present research aims to assess the current condition of RAHs in Serbia and highlight the considerable opportunities that banks and insurance companies possess to promote loans and insurance to small-scale farms. The data on Serbian RAHs were collected for the period 2018–2021. Descriptive statistics served as the primary tool for data analysis. The findings obtained indicate that the small-scale producers in Serbia face restricted access to financial services from banks, which in turn hampers their operational and investment capacities.

Does Reputation Lead to Student Loyalty? The Case of a Private Higher Education Institution, a State University, and a Local College

"Research Aims: The current research aims to analyse (1) the direct interrelationships of higher education institutions' (HEIs) reputation, student satisfaction, and student loyalty; (2) the mediating effect of student satisfaction on the relationship between HEI reputation and student loyalty; and (3) the moderating role of HEI type (private HEI [PHEI], state university/college [SUC], local university/college [LUC]) on the links between HEI reputation and student satisfaction, HEI reputation and student loyalty, and student satisfaction and loyalty. Design/Methodology/Approach: All hypothesised relationships were analysed using partial least squares – structural equation modelling (PLS-SEM). Research Findings: The results revealed that (1) HEI reputation has a significant effect on student satisfaction and loyalty; (2) student satisfaction mediates the relationship between HEI reputation and loyalty; (3) a significant difference exists in the effect of reputation on student satisfaction between PHEIs and SUCs; and (4) significant difference exists on the effect of student satisfaction on loyalty between PHEIs and SUCs, and between PHEIs and LUCs. Theoretical Contribution/Originality: The current study lends credence to using the S-O-R model as an appropriate framework in conducting predictive-causal studies that employ the PLS-SEM as the tool for data analysis. Managerial Implication in the South East Asian Context: The current study presents findings which may be used by policymakers and administrators in crafting effective, goal-directed marketing plans for higher education institutions. Research Limitation & Implications: One limitation of this study is the geographic location of its sample. All of the participants were students in the Province of Pampanga, Philippines. Keywords: reputation, student satisfaction, student loyalty, higher education, multigroup analysis"

Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity

This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study.

Digital Literacy Level and Career Satisfaction of Academics

Technology affects educational institutions like every other field. Higher education institutions, which are the main source of new knowledge creation and dissemination, are now dominated by digital technologies. The acquisition of digital literacy skills, which is among the 21st-century competencies, is possible with the positive attitudes and skills of academics, who are instructors in higher education institutions, towards the use of digital technologies. As role models in strengthening digital literacy, academics should develop themselves in line with strategies that include elements of the digital age and more active learning, and be able to transfer these strategies to educational practices. Otherwise, it will not be possible to increase the quality of education and it is inevitable for academicians to experience career dissatisfaction with the feeling of failure. Despite the importance of the subject, digital literacy of academicians has not been adequately addressed in the literature and the relationship between academics' digital literacy levels and career satisfaction has not been focused. Based on this gap, in this study, the concept of "digital literacy" is handled specifically for academics working in universities operating in Istanbul, Turkey, and it is aimed to contribute to the literature by revealing the relationship between academics' digital literacy and career satisfaction. The cross-sectional survey method, a quantitative research technique, was utilized in the research as a data collection and analysis tool. For this purpose, the data collected through questionnaires from 304 academics with a simple random sampling method were analyzed. As a result of the analysis, it was found that academics' digital literacy explained the career satisfaction variable by 28.1% at a 0.01 significance level. Finally, the significant positive relationship between academics' digital literacy and career satisfaction can be interpreted as that academics who cannot develop digital literacy skills and transfer these skills to teaching environments will not be able to achieve career satisfaction by experiencing a sense of failure because they cannot provide an education in line with the expectations of the digital age. In addition, according to the research findings, it was found that the digital literacy and career satisfaction levels of the academicians differ according to age, working time, academic title, and having a personal web page.

빅데이터 분석을 활용한 중소기업, 소상공인 ESG 경영 이슈 분석

In the context of experiencing unexpected external factors like COVID-19, businesses are facing a significant demand for change. The management focus on ESG has emerged as a practical issue concerning the survival and sustainability of companies.
 Existing research has primarily concentrated on empirical analysis based on ESG evaluation systems, with relatively little attention given to analyzing key ESG-related keywords. This study aims to fill the gap in such research.
 To achieve this, the study utilized big data from news channels operating on major portal sites such as Naver, Daum, and Google. Through the application of big data analysis tools, the study performed the extraction and analysis of ESG-related keywords targeting small and medium-sized enterprises (SMEs) and small businesses.
 The ESG keywords extracted through the use of big data analysis tools contribute to the ongoing discussions about integrated approaches to ESG evaluation criteria. Additionally, they provide foundational data necessary for developing differentiated ESG strategies based on the size of the companies. As a result, this study holds significance in deriving both academic and practical insights.

Optimization of Productive Cash Waqf Funds on the Human Development Index

In this research, we will examine the optimization of cash waqf funds in order to increase the human development index of Ketawang Gondanglegi Village. The aim of this research is to find out the pattern of optimization (utilization) of waqf funds and to analyze the impact resulting from the utilization of cash waqf funds in increasing the human development index. This research was conducted in Ketawang Village, Gondanglegi District, Malang Regency with an implementation period of 8 months from May to December 2019. Data collection methods used interview, observation and documentation techniques while the data analysis tool used was Multiple Linear Regression. The research results show that cash waqf can have an impact on all areas including religious facilities, health, education and the economy if it is managed optimally and on target, namely in accordance with the proportion of needs or urgency of an area.

SINIRLARIN ÖTESİNDEKİ DİJİTAL İZLER: GÖÇ KRİZİ ÜZERİNE SİSTEMATİK BİR DERLEME

This study aimed to systematically examine the studies conducted and published on immigrants, asylum seekers, and refugees by using big data written in English. Articles were searched on Scholar, The Web of Science, ProQuest, Science Direct, PubMed and Scopus databases. The concept set centered around the concepts of immigration and big data was used in the surveys. In accordance with the PRISMA protocol principles, 49 articles were examined according to the inclusion and exclusion criteria among 258 articles obtained from the relevant databases until the end of December 2022. The reviewed articles were categorized under the headings of “topics examined”, “dataset”, “analyses”, “software used” and “key findings”. The studies provide indications on how to obtain information about this population, which is difficult to reach group especially due to its massiveness, using big data tools. In the findings, it has been seen that studies based on big data on immigrants, asylum seekers and refugees contribute to facilitating the integration of these groups into the target country. Also, it has been revealed that these studies may lead to undesirable results in terms of violating the confidentiality of research groups, producing labeling, and increasing surveillance for these groups. In addition to these, it has been found that these studies have methodological handicaps in terms of representativeness, accuracy, excessive homogenization, and easy generalization. It is thought that the findings of the study will shed light on the international migration and refugee policies to be carried out using big data analysis tools.

Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes†

Comprehensive SummaryThe electrical tunneling sensors have excellent potential in the next generation of single‐molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities. Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information. Despite the extraordinarily large amount of data that has been gathered, it is still difficult to correlate signal transformations with molecular processes, which creates great obstacles for signal analysis. Machine learning is an effective tool for data analysis that is currently gaining more significance. It has demonstrated promising results when used to analyze data from single‐molecule electrical measurements. In order to extract meaningful information from raw measurement data, we have combined intelligent machine learning with tunneling electrical signals. For the purpose of analyzing tunneling electrical signals, we investigated the clustering approach, which is a classic algorithm in machine learning. A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering. Additionally, customized statistical algorithms were designed. It has been proven to efficiently gather molecular information and enhance the effectiveness of data analysis.

Indoor UWB Positioning and Position Tracking Data Set

Indoor positioning has become a hot topic in various fields, such as industry, healthcare, and commerce. Ultra-wideband (UWB) radio technology provides a cost-effective solution for range-based positioning, offering exceptionally high accuracy and precision. Its ultra-high temporal resolution enables range measurements with accuracy of a few centimeters. To develop and evaluate range-based positioning systems, we collected measurements in four different indoor environments using eight fixed devices and one mobile positioning device. To eliminate the fluctuation of walking speed from the data, we pre-defined a path in each indoor environment, similar to the human walking path, which was sampled at equidistant positions. We collected multiple range measurements and channel impulse response (CIR) data at each tag position on the path. The resulting dataset supports the development of range-based positioning and position tracking algorithms with various combinations of network topologies and anchor-tag combinations. We have also provided a full set of data analysis tools that enable the reproducibility of results and serve as a basis for further development of range-based UWB positioning algorithms.

Data Analytics for Undergraduate Tax Students: An Alteryx Case Study for MACRS Depreciation

ABSTRACT The increasing use of data analytics in the accounting profession necessitates development of instructional cases that teach students how to use data analytics tools to solve common accounting problems. While there has been an emphasis on data analytics cases for graduate education, we recognized a need for such cases at the undergraduate level to prepare students for internships, careers, and graduate education. This case meets this important need by introducing Alteryx in a typical tax setting—the calculation of Modified Accelerated Cost Recovery System (MACRS) depreciation. The learning objectives are to: (1) extend knowledge of data analytics; (2) develop skills using Alteryx to extract, transform, and load data; (3) demonstrate understanding of tax rules for computing depreciation; and (4) develop effective written communication skills.

Unlocking Insights: A Cloud Tool for Data Visualisation in a Smart Meter Project

Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation on smart meters. Two stages were carried out in order to analyse and process the data using a cloud-based tool to support data visualisation and to understand and facilitate effective decision-making. This resulted in a remarkable 27.39% increase in average data quality, thanks to the authenticity and reliability of the data obtained through the 4G LTE network on smart meters. It also had a significant impact on the percentage of data read and displayed, with an increase of 63.70%. Finally, the percentage of useful data when applying the tool in the cloud also increased by 47.30%. This allows end users to visualise and understand the behaviour of electricity meters with an LTE network through a customised dashboard.

Technology advancement and international law in marine policy, challenges, solutions and future prospective

The use of technology and international law in marine policy is becoming increasingly vital as we seek to address the challenges facing our oceans. Technology has revolutionized our ability to monitor, understand, and manage marine ecosystems, providing valuable tools for data collection, analysis, and decision-making. International law, ocean governance ensuring marine environment represented by the frameworks such as the United Nations Convention on the Law of the Sea (UNCLOS), plays a crucial role in governing the use of technology and establishing guidelines for the sustainable management of marine resources. This paper explores the intersection of technology and international law in marine policy, highlighting the transformative impact of technology on our understanding of the oceans and the legal frameworks that shape our approach to marine conservation. It examines the benefits of technology in enabling evidence-based decision-making and fostering international collaboration, while also addressing the legal and regulatory complexities that arise. The paper emphasizes the need for ongoing dialogue and the development of international legal frameworks to adapt to emerging technologies and discourse ethical considerations, and by harnessing the power of technology and international law, we can work towards effective marine policies that ensure the conservation and sustainable use of our oceans for future generations.

Trends In The Valuation Of Indian Currency Since 1991

This paper aims to explore the dynamics of trends in currency valuation by studying the relationship between inflation and exchange rates after the period of Economic Reforms in India. The scope of the paper also explores the interconnection of exchange rates and inflow of FDI in the country and the balance of trade. We examine trends in inflation through the Wholesale Price Index and Consumer Price Index, over the sample period. We aim to use statistical tools, namely correlation, the standard deviation to indicate the relationship between the variables in addition to the tools of data analysis, to extricate the desired results.

Research Trends in Translanguaging: A Systematic Review of Master Theses and Doctoral Dissertations

This study aims to review master theses and doctoral dissertations which were conducted upon translanguaging and catalogued on the official website of the Council of Higher Education, the National Theses Database in Türkiye. The theses and dissertations were analyzed in terms of emerging themes, research design, participants, data collection, and data analysis tools. There were 10 master theses and seven doctoral dissertations in the database. The gathered data were subjected to summative content analysis. According to the findings, while individual differences were the most studied theme in the master theses, it was the teaching methodology in the doctoral dissertations. Moreover, a mixed method was the most preferred research design, and tertiary level students were the most common participants in both groups. Finally, interviews were the most employed data collection tools while qualitative content analysis and descriptive statistics were the most preferred data analysis tools in both groups. Further educational implications are discussed.

Federated learning backdoor attack detection with persistence diagram

Federated learning (FL) is an emerging decentralized machine learning method that allows multiple clients to participate in a joint mission by merging the local models into a global model. While FL provides the ultimate data privacy for each participant, the service providers can hardly verify the validity of local datasets, which gives malicious clients an opportunity to undermine the functionality of the global model, i.e., perpetrate the backdoor attack. To find the potential attackers among the clients, we propose a topological data analysis tool called Persistence Homology (PH). PH reveals the correlation between topological properties and the neurons' status, which tells us whether the model is well generalized or overfits on some specific samples. We trained a classifier based on the PH features of neural network models, eventually composing a secure federated learning mechanism. The results illustrated that our method can detect malicious clients with different types of backdoor attacks with high accuracy, even under the highly unbalanced non-i.i.d. data distribution condition.

Students' cognition of artificial intelligence in education

As artificial intelligence (AI) becomes more widely used in classrooms, it is important to learn more about students' perspectives on its usefulness and the factors that affect their performance. In order to better understand how students think about AI in the classroom, this experiment builds a more precise random forest model to foretell how they will do on AI-related tasks. Kaggle's "Students' Perceptions of AI Survey Analysis" dataset was used in the study as it contained important information about students' AI-related attitudes, expertise, and learning objectives. Engineering techniques were incorporated into the methodology as part of the feature selection process once the data had been collected and preprocessed. In order to process, visualize, and model the data, we used data analytic tools including Pandas, Matplotlib, Seaborn, and Scikit-learn. Students' AI knowledge, information availability, attitudes, and recognition of AI's impact on many sectors were all exposed through exploratory data analysis. The results were interpreted and analyzed in the debate, which also highlighted the merits and shortcomings of the approach and its possible limitations. Notably, the random forest model outperformed other methods in forecasting students' performance on the AI exam task.

Digital Repository as a Service (D-RaaS): Enhancing Access and Preservation of Cultural Heritage Artifacts

The employment of technology and digitization is crucial for cultural organizations to establish and sustain digital repositories for their cultural heritage artifacts. This exploitation is also essential in facilitating the presentation of cultural works and exhibits to a broader audience. Consequently, in this work, we propose a custom-developed digital repository that functions as software-as-a-service (SaaS), primarily promoting the safe storage, display, and sharing of cultural materials; enhancing accessibility; and fostering a deeper understanding and appreciation of cultural heritage. The proposed digital repository service is designed as a multitenant architecture, which enables organizations to expand their reach, enhance accessibility, foster collaboration, and ensure the preservation of their content. Moreover, our technology stack incorporates robust and reliable backend technologies, such as Django, to ensure data security and efficient management. Meanwhile, the frontend is powered by Angular, which guarantees a user-friendly and engaging interface for exploring and interacting with cultural materials. Specifically, this project aims to assist each cultural institution in organizing its digital cultural assets into collections and feeding other digital platforms, including educational, museum, pedagogical, and games, through appropriate interfaces. The creation of this digital repository offers a cutting-edge and effective open-access laboratory solution. It allows organizations to have a significant influence on their audiences by fostering cultural understanding and appreciation. Additionally, it facilitates the connection between different digital repositories and national/European aggregators, promoting collaboration and information sharing. By embracing this innovative solution, cultural institutions can benefit from shared resources and features, such as system updates, backup and recovery services, and data analytics tools, attributes that are currently provided by the platform.

Analisis Kelayakan Usaha Rumah Potong Hewan Ditinjau Dari Aspek Teknis Dan Sumber Daya Manusia

The research aims to determine the feasibility of slaughterhouse businesses in East Java, in terms of technical and human resource aspects. This research uses descriptive qualitative methods. Sampling was carried out using purposive sampling technique and data collection used primary data. The data analysis tool used is cross tabulation. The objects of this research are the slaughterhouse in Krian, Sidoarjo and the poultry slaughterhouse in Jombang (CV. RAFIKA FOODS INDONESIA). The results of the analysis from this research on the technical aspects of ruminant slaughterhouses are 86% and poultry slaughterhouses are 84% in compliance with Minister of Agriculture Regulation number 13 of 2010. In the human resources aspect ruminant slaughterhouses and poultry slaughterhouses have complied with Minister of Agriculture Regulation No. 13 2010. From these results, it is stated that ruminant slaughterhouses and poultry slaughterhouses are feasible to run.
 
 Keywords: Technical, Human Resource Aspect. Slaughterhouse

An enhanced topological analysis for Lamb waves based SHM methods

Topological data analysis (TDA) is a powerful and promising tool for data analysis, but yet not exploited enough. It is a multidimensional method which can extract the topological features contained in a given dataset. An original TDA-based method allowing to monitor the health of structures when equipped with piezoelectric transducers (PZTs) is introduced here. Using a Lamb wave based Structural Health Monitoring (SHM) approach, it is shown that with specific pre-processing of the measured time-series data, the TDA (persistent homology) for damage detection and classification can be greatly improved. The TDA tool is first applied directly in a traditional manner in order to use homology classes to assess damage. After that, another method based on slicing the temporal data is developed to improve the persistence homology perception and to leverage topological descriptors to discriminate different damages. The dataset used to apply both methods comes from experimental campaigns performed on aeronautical composite plates with embedded PZTs where different damage types have been investigated such as delamination, impacts and stiffness reduction. The proposed approach enables to consider a priori physical information and provides a better way to classify damages than the traditional TDA approach. In summary, this article demonstrates that manipulating the topological the features of PZTs time-series signals using TDA provides an efficient mean to separate and classify the damage natures and opens the way for further developments on the use of TDA in SHM.