Mining Analysis Research Articles

Gender Equality Research: A Bibliometric Analysis of Trends in Turkey

Gender disparity persists in both developed and developing nations, despite several attempts to achieve gender parity. This paper explores the evolution and importance of gender equality research, with a focus on Turkey, through bibliometric analysis. It also examines significant subjects, patterns, and academic contributions in the discussion of gender equality using a dataset of 12,539 publications from the fields of business, economics, and management published between 2000 and 2023. Advanced bibliometric methods such as text mining and social network analysis have revealed a discernible increase in gender-related publications over the last 20 years. The United States, the United Kingdom, and Sweden have all made significant contributions. The research provides a list of relevant research publications and keywords related to gender equality, women's representation, and gender differences in work. In addition, it highlights the departure from formal theories of equality and the shift towards more nuanced perspectives that consider the complex interplay among social, economic, and cultural factors. Coordinating gender equality with wider development agendas, particularly the Sustainable Development Goals (SDGs), is a crucial point of guidance for academics, practitioners, and policymakers, as the paper makes clear. In the twenty-first century, gender inequality poses many complicated problems that require interdisciplinary collaboration and continued research into understudied areas including the intersections of gender with other social identities.

Hotelling rule in non-renewable resources: A bibliometric and systematic literature review analysis

This research aimed to provide a comprehensive mapping of studies on the application of the Hotelling rule in non-renewable resources from 1970 to 2024, identifying the most prominent research topics and trends, key characteristics, evolution, and directions for future studies. To achieve this, the following research questions were addressed using a mixed-method approach, including bibliometric analysis, text mining, and content analysis: (i) How has research on the Hotelling rule in non-renewable resources evolved? (ii) What are the most relevant research topics and trends in Hotelling rule research? and (iii) What future directions should studies on the Hotelling rule in non-renewable resources pursue? A total of 197 journal articles extracted from the Scopus database were analyzed to map the main body of literature. Four main research clusters were identified: (1) theoretical and empirical perspectives on the Hotelling rule, (2) the Hotelling rule in non-renewable resource management, (3) economic policy and sustainability within the Hotelling framework, and (4) resource depletion and pricing under the Hotelling rule. Four key areas for future research were proposed, focusing on diversification to other non-renewable resources, evaluation of policies and geographical contexts, technological innovations and government policies, and sector-specific applications of the Hotelling rule. These directions are crucial for promoting sustainability in the management of non-renewable resources over time.

Exploring the Role of HR Analytics in Enhancing Talent Acquisition Strategies

Hiring and selecting the right candidate is a critical business process in today’s cutthroat competition. HR Analytics, also known as Human Resource Analytics, provides unique insights into talent acquisition and helps organisations to improve their strategies based on fact. This research paper aims to establish the use of the HR Analytics in improving the talent acquisition practices since this will help in identifying the different candidates sourcing, screening, selection, and onboarding processes. In doing so, the work employs the study of different techniques of HR Analytics including but not limited to the predictive analytics, data mining, and talent market analysis with the main view of understanding how different techniques can be used to give the organizations competitive advantage in talent management and attraction. The research will focus on a few real-world solutions such as the use of HR Analytics for social media talent acquisition, the use of prediction models for screening and the use of data analytics for selection. Also, the paper will discuss the problems and drawbacks that may be encountered while adopting HR Analytics in recruiting. In it, we will highlight the data quality issue, the privacy issue, and the need that HR professionals must have skills and knowledge to use the HR Analytics tools. Lastly, this research aims at contributing to the existing body of knowledge that seeks to help organizations that aim at increasing on their Strategic Human Resources Management Talent Acquisition approach based on the design and use of HR Analytics. Day by day organizations are becoming more rational and by using this technique organizations can arrive to better decisions about employer branding and, consequently, about attracting and retaining the best talent in today’s job market.

Genome Mining Analysis Uncovers the Previously Unknown Biosynthetic Capacity for Secondary Metabolites in Verrucomicrobia.

Bacteria of the phylum Verrucomicrobia is widely distributed in diverse ecological environments. Their limited cultivability has greatly caused the significant knowledge gap surrounding their secondary metabolites and their mediating ecological functions. This study delved into the diversity and novelty of secondary metabolite biosynthetic gene clusters (BGCs) of Verrucomicrobia by employing a gene-first approach to investigate 2323 genomes. A total of 7552 BGCs, which encompassed 3744 terpene, 805 polyketide, 773 non-ribosomal peptide gene clusters, and 1933 BGCs of other biosynthetic origins, were identified. They were further classified into 3887 gene cluster families (GCFs) based on biosynthetic gene similarity clustering, of which only six GCFs contained reference biosynthetic gene clusters in the Minimum Information about a Biosynthetic Gene Cluster (MIBiG), indicating the striking novelty of secondary metabolites in Verrucomicrobia. Notably, 37.8% of these gene clusters were harbored by unclassified species of Verrucomicrobia phyla, members of which were highly abundant in soil environments. Furthermore, our comprehensive analysis also revealed Luteolibacter and Methylacidiphilum as the most prolific genera in terms of BGC abundance and diversity, with the discovery of a conservative and new NRPS-PKS BGC in Luteolibacter. This work not only unveiled the biosynthetic potential and genetic diversity of secondary metabolites of Verrucomicrobia but also provided a fresh insight for the exploration of new bioactive compounds.

Text mining approach in chicken meat welfare

The aim of the study was to analyze the aspects affecting broiler welfare with the use of Text Mining technique. This approach converts text into numerical data and analyzes word frequency distributions, enabling the extraction of useful information and the identification of relationships between elements. Text mining has limitations, i.e. ambiguity and context sensitivity, making it difficult to capture nuanced meanings. The search was carried out with Scopus using the term "Welfare" with the keywords "Chicken", "Broiler", "Broiler chicken", and "Chicken meat", to create a database of abstracts. Text Mining and Topic Analysis were performed on the abstracts (1228 documents) using the Software R 4.3.1., analyzing also the weight of bigram and trigram. Publications on broiler welfare are present in the bibliography since 1990's, but in the last 10 years, for the interest of public opinion, the numbers of publications significantly increased (76.5% of all documents published). USA, Brazil, and Europe-27 published 60% of the documents found. The works were published in a high number of journals, but 37% of them are published in only 4 journals (Poultry Science, Animals, Applied Animal Behavior Science and Animal Welfare). Text Mining analysis identified key terms related to the slaughter phase, housing management, and environmental conditions such as light quality and quantity. Moreover, a high correlation was found between some terms, underlying the importance of the effects of rearing, slaughter phases and litter management on broiler welfare. Most of the countries focused their research on some specific topics identified by Topic Analysis, mainly genetic selection, feeding, stocking density, slaughter, and consumer perceptions. Poultry Science published the highest number of papers (18%) and the topics more investigated were growing performance, transport and slaughter, and litter management. In conclusion, the high number of publications on chicken welfare underlines the importance of broiler welfare both in Europe and in other countries, even if it is difficult to identify common research topics among the geographic areas and the evolution over the time.

A look at the possible impact of Apple's new policy on third-party apps and the data food chain of iOS users

The research presents and discusses that Apple's new policy of applying the privacy application framework "App Tracking Transparency" to Apple's IOS14 system has implications for third-party companies in the data food chain as well as users. Users has paid attention to private information including consumption habits, spending power, product preferences, price sensitivity. We found that Apple's new Privacy Application Framework gives Apple absolute dominance in the data food chain of smart media users, Apple and the Apple ecosystem, third-party application platforms, and downstream marketing companies that provide data mining analysis for third-party applications, changing the traditional equal relationship between third-party application platforms and their direct access to user data.

Slope Stability Analysis of Open-Pit Mine Considering Weathering Effects

Weathering processes gradually alter the physical and mechanical attributes of slope materials, weakening the structural integrity and stability of slopes. This paper presents an in-depth analysis of slope stability in an open-pit mine, emphasizing the pivotal role of weathering effects in determining slope stability. To accurately capture the impact of weathering on slope stability, a comprehensive analysis model was developed, incorporating field observations, laboratory testing, and numerical simulations. The effects of weathering on the mechanical properties of black shale were studied through extensive laboratory tests. The uniaxial compressive strength, shear strength, and modulus of elasticity significantly decreased with increasing weathering, indicating a heightened vulnerability to slope failure. The correlation function between mechanical parameters and weathering time was obtained, providing the basis for evaluating the stability of mine slopes. It was found that more severe weathering conditions were strongly correlated with elevated risks of slope failure, including landslides and collapses. Based on these findings, practical recommendations are provided for slope reinforcement and management strategies, aimed at mitigating slope failure risks and ensuring the safe and efficient operation of the mine. By incorporating weathering effects into slope stability analysis, mine operators can make informed decisions that account for the dynamic nature of slope materials and their susceptibility to weathering, thereby improving overall mine performance and safety.

Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction

Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.

Technological Scanning for Foresight: The case of Metaverse applications for Healthcare

The process of foresight, which allows companies and organizations to build scenarios and inform the creation and sustainment of their competitive advantage, relies on the integration of several steps. Scanning is a crucial step of foresight, as it informs and influences the results of the whole process and, thus, the strategic decision-making of the company. Sources and methods of scanning for foresight analysis can be diverse and lead to different results, although few studies investigate such differences: more specifically, the informative power of academic and non-academic articles and reports has not been assessed yet. This study aims to shed novel light on how the different analysis methods of full reading of records and text mining analysis isolate and gather forces of change differently, based on the source analyzed. The study’s empirical context is the metaverse and its application in healthcare. We find that each source and method by itself is unable to fully gather the whole set of forces of change; however, each source presents some topics that are specific to the target readers of the source, and each methodology presents some advantages as well as some limitations. From the comparison of the results, theoretical and managerial implications are drawn.

Big-Data Technology in Finance: Quality of Stock-Trading Information in Vietnamese Stock Market

Quantitative finance is a field of applied research where the data is important information investors’ profitability and financial risk of financial assets in the processes of decision-making. And financial institutions and securities have applied big-data technologies in stock market to exploit useful information for investors using mobile stock-trading apps. The standardization of database from the raw data of companies’ financial statements, statistical reports and stock-trading data support to enhance data mining analysis by predictive patterns with high feasibility for useful information for investors’ decision-making. And quantitative tools of predictive patterns for financial advanced indexes test expected market return and favourable market timing for buying or selling stocks in stages of market cycle and prediction of market risk before market-trend crash. On the development of securities’ mobile stock-trading apps, the addition of new advanced indexes increases the quality of stock-trading information in Vietnamese stock market generally, seeks competitive advantages of market segments for securities particularly when they change big-data technology in mobile stock-trading apps. This contribution has a better using FinTech combined with stock-trading algorithms to provide more useful and confident information of optimal profitability and financial risk management that individual and institutional investors catch up the trend of big-data technology.

Honor in the Mediterranean: A Comparative Narrative Analysis of Malena (2000) and Mine (1982)

Honor in the Mediterranean: A Comparative Narrative Analysis of Malena (2000) and Mine (1982)

Epigenetic silencing of ZIC4 unveils a potential tumor suppressor role in pediatric choroid plexus carcinoma

Zic family member ZIC4 is a transcription factor that has been shown to be silenced in several cancers. However, understanding the regulation and function of ZIC4 in pediatric choroid plexus tumors (CPTs) remained limited. This study employed data mining and bioinformatics analysis to investigate the DNA methylation status of ZIC4 in CPTs and its correlation with patient survival. Our results unveiled ZIC4 methylation as a segregating factor, dividing CPT cohorts into two clusters, with hyper-methylation linked to adverse prognosis. Hyper-methylation of ZIC4 was confirmed in a choroid plexus carcinoma-derived cell line (CCHE-45) by bisulfite sequencing. Furthermore, our study demonstrated that demethylating agent and a histone methyltransferase inhibitor could reverse ZIC4 silencing. RNA sequencing and proteomic analysis showed that ZIC4 over-expression influenced genes and proteins involved in immune response, antigen processing and presentation, endoplasmic reticulum stress, and metabolism. Functionally, re-expressing ZIC4 negatively impacted cell proliferation and migration. Ultimately, these findings underscore ZIC4 hyper-methylation as a prognostic marker in CPTs and shed light on potential mechanisms underlying its tumor suppressor role in CPC. This insight paves the way for novel therapeutic targets in treating aggressive CPTs.

Analysis of Correlation Features in Material Application for Architectural Design Based on Big Data Text Mining

This study conducted a big data mining and lexical co-occurrence network analysis on design texts sourced from architectural media over the past decade, systematically evaluating the application trends of materials in architectural design. This approach distinguishes itself from traditional case studies that rely on small samples of material applications in architectural design, thereby significantly enhancing the generalizability of the conclusions. The research reveals a 27.2% increase in the material–project frequency of architectural materials over the past ten years, indicating that the materiality of architecture is gradually making a comeback to counteract the trend toward architectural visualization. Within this context, material properties, material interactions, and material presentation in architectural components emerge as universal concerns in architectural design. Notably, the sustainability and energy efficiency of wood and concrete have emerged as pivotal topics in architectural practice, in addition to their structural functions. Additionally, over the past decade, the material–project frequencies of brick and stone have grown rapidly, with respective increases of 24.3% and 11.8%. Brick and stone primarily respond to the locality and style of architectural design by emphasizing their inherent texture and color, thereby enhancing the spatial perception and experience within the designs. This study lays the groundwork for quantitative research on the influence of building materials on architectural design, and it illuminates current trends and preferences in material application within architectural design, thereby empowering architects to make more informed decisions in their material selection.

Importance of Lifestyle Activities for Older Adults' Psychosomatic Functions After Driving Cessation: Interpretation by a Mixed-Methods Study.

Older adults who cease driving are more likely to experience psychosomatic decline than those who continue driving. This mixed-methods study was intended to examine differences in psychosomatic functions depending on driving status and lifestyle activities, and factors affecting engagement in additional lifestyle activities after driving cessation. The quantitative analysis included individuals aged 60 and above. Driving status, lifestyle activities, and psychosomatic functions were assessed. For qualitative analysis, semi-structured interviews were conducted with the driving-cessation group to determine the factors affecting involvement in various lifestyle activities after driving cessation. Analysis of covariance was used for quantitative data, while text mining and qualitative inductive analysis were used for qualitative data. Older adults who engaged in more lifestyle activities walked faster than those who engaged in fewer lifestyle activities, even after driving cessation. Actively using local and personal resources may increase engagement in lifestyle activities after driving cessation.

Exploring public-private partnerships in Latin America and the Caribbean using topic modeling and sentiment analysis

Collaboration between the public and private sectors is crucial for supporting resource-constrained government budgets globally. Over the past two decades, studies have recorded significant advances in public-private partnerships (PPPs). However, PPPs in agribusiness within Latin America and the Caribbean (LAC) remain underexplored. This study analyzes the primary issues related to PPPs in agribusiness across selected countries and compares sentiments regarding these partnerships within the analyzed countries. Using text mining, topic modeling, and sentiment analysis, the study found that the main issues revolve around infrastructure, impacting trade, flow, and services in regional, national, and continental agribusiness and exports. While PPPs are seen as an innovative approach to improving public services, sentiment analysis reveals that many countries express anger and disgust, primarily due to concerns about corruption and the state’s inability to manage or play an effective role in these public policies. This study highlights that integrating topic and sentiment data offers a comprehensive view that can inform more effective public policies and development strategies for PPPs in agribusiness.

Uncharted depths: Navigating the energy security potential of deep-sea mining

In the context of a global shift towards low-carbon energy systems, this paper provides an in-depth analysis of deep-sea mining's (DSM) potential role in enhancing global energy security. Addressing the growing demand for critical minerals essential for clean energy technologies, electric vehicles (EVs), and energy storage systems, the paper examines how DSM can diversify the global mineral supply and reduce reliance on geopolitically sensitive sources. It explores DSM's capacity to recalibrate energy prices, influence the competitive landscape of clean energy technologies, and shift geopolitical dynamics. The paper delves into the multi-faceted impacts of DSM on energy security, including geopolitical shifts, supply chain diversification, and environmental trade-offs. By providing a holistic view that links mineral supply security to sustainable energy transitions, this study extends beyond prior research focused mainly on the technical and environmental aspects of DSM. The findings illustrate DSM's intersection with international politics, its effect on energy pricing strategies, and the balance between resource exploitation and environmental stewardship. Strategic policy recommendations are offered to optimize DSM's benefits while minimizing its ecological impacts, aligning the emerging DSM industry with global sustainability goals. In addition to identifying challenges, the paper proposes actionable solutions, contributing a unique perspective to the discourse on DSM and energy security.

A Semantic Analysis of the User-Generated Content from Social Network Sites of Cashbuild Limited: Application of Text Mining, Machine Learning and Big Data Analytics toward the Development of a Customer-Centric Marketing Strategy

Social Network Sites (SNS) have grown rapidly over the past 15 years, which is in line with the growth of smartmobile phones. A key phenomenon of SNSs is User-Generated Content (UGC). It enables social engagements between users and their followers. UGC shares various perspectives on news, entertainment, and fun, as well as their perspectives on brands like Customer Experience (CX) and Customer Satisfaction (CS). UGC stimulates the Brand Attitudes (BA) of the readers if the user is trusted and credible. BAs influence the Purchase Intentions (PI) of the readers of UGC. Marketers like that UGC influences PI. They learn about CX and CS, which users share on the UGC. This semantic study uses Text Mining (TM) and Big Data Analysis (BDA) to extract keywords from the UGC of a building materials retail company, Cashbuild Limited (CBL) from the Republic of South Africa (RSA). The UGC was analysed using Power BI (PBI) to identify its sources and sentiments. The majority of the UGC in this study was obtained from Facebook (94.51%), followed by X.com (3.95%). The rest of the UGC was obtained from Blogs (0.79%) and Instagram (0.76%). 65.63% of the UGC was neutral, 29.74% positive, and 4.46% negative. UGC influences PI by mediating hedonic and utilitarian Brand Equity. The generation of a high-quality UGC was encouraged by CS - an outcome of CX. CX comes from the touchpoints across the CJ. Marketers want to ensure a good customer experience (CX) to achieve customer satisfaction (CS). Ultimately, CS influences future customer expectations (CE) when shared through UGC. 84 top keywords were extracted out of 16 328 hit sentences (HS) from the UGC of CBL using the MELTWATER program. The keywords were ranked to 51 frequency positions. CONCOR analysis was used to determine the connectivity and centrality of the keywords using Freeman’s coefficient and the Eigenvector value for each keyword. The results of the CONCOR analysis were used to identify 41 significant keywords based on their higher connectivity and centrality. The 41 significant keywords were matched with the 16 328 HSs. 14 keywords that matched less than 100 HSs were excluded. The remaining 27 keywords were subjected to exploratory factor analysis EFA on Statistical Program for Social Sciences (SPSS). 21 keywords were loaded on 8 factors, and the model explained 68.363% of the variance with a Kaizer Meyer Olkin (KMO) of 0.736 above 0.6. The 8 categories were compared to input from a panel of industry experts. 7 categories proposed by experts matched 7 out of 8 categories from the statistical model. 7 key themes proposed to be used in the study to develop Customer-centric Marketing Strategies (CCMS) in the building materials retail industry (BMRI) are DIY Kings, House Proud, Project Master, Renovators, Interior Image, Building Materials Inventory, and Promotions. All 5 objectives of the study were met. The study adds value to the development of CCMS learning from the UGC.

One reservoir and different views on local development: text mining analyses for the Mucharz Reservoir, Poland

Abstract In this study, we analyzed the relationship between how a community views reservoir construction and the plan for local development around it. Specifically, we focused on the social perception of the Mucharz reservoir in Poland. The research hypothesis assumed that residents’ expectations of socioeconomic development around the reservoir dam determined whether they were satisfied with the project. Fifty-nine in-depth interviews with residents of villages adjacent to the newly created dam facility were conducted. The resulting data were analyzed using text mining methods. We identified three groups of people who perceived the reservoir in different ways. Most respondents perceived the reservoir positively, mostly because of economic benefits from investments, including job creation or intensive tourism development. However, these issues do not always determine whether a local community perceives a dam project positively.

Research on Impact of Investor Sentiment on Chinese Stock Market: Review and Prospect

Over time, investor behavior has become more complicated due to the unpredictability of the Chinese stock market. Major research has found that investor sentiment swings have a huge impact on the market. Therefore, investors need to observe and understand how investor sentiment affects the market in the right way. Much of the past literature has used textual data mining and event analysis to measure the impact of investor sentiment and its impact on stock market returns, pricing, and volatility. Several articles have concluded that investor sentiment is highly responsive to the Chinese stock market. Another phenomenon that has emerged on various forums and social media is that people's fear of the stock market is spreading. The results of event analysis can prove that the impact of events such as the lifting of the ban on restricted shares and the listing of companies will be very obvious and have an impact on investor sentiment. An important implication of this study is that in addition to the dominant role of Granger causality in the causality that produces negative returns, negative investor sentiment is the main factor determining the overall behavior of the stock market. When bad news breaks, investors release their pent-up emotions for a short period, resulting in a violent reaction. In addition, market sentiment can also affect trading volumes and stock prices. This has put pressure on the market. In the end, this paper expands the future research direction from the two aspects of communication mechanism and quantitative analysis.

Spatiotemporal variations and the ecological risks of microplastics in the watersheds of China: Implying the impacts of the COVID-19 pandemic

China is not only the first reported place of the COVID-19 pandemic but also is the biggest microplastic emitter in the world. Nevertheless, the impact of the COVID-19 pandemic on microplastic pollution in the watersheds of China remains poorly understood. To address this, the present study conducted a data mining and multivariate statistical analysis based on 8898 microplastic samples from 23 Chinese watershed systems before and during the COVID-19 pandemic. The results showed that the COVID-19 pandemic extensively affected the abundance, colors, shapes, polymer types, and particle sizes of microplastic in Chinese watershed systems. Before and during the COVID-19 pandemic, 77.27 % of the Chinese watershed systems observed increased microplastic abundance. Moreover, the COVID-19 pandemic itself, natural conditions (such as altitude and weather), and anthropogenic factors (such as civil aviation throughput) are highly intertwined, jointly impacting the microplastic in the watersheds of China. From the perspective of ecological risks, the COVID-19 pandemic was more likely to aggravate the microplastic pollution in the middle and down reaches of the Yangtze River Watersheds. Overall, whether before or during the COVID-19 pandemic, the main watershed systems of China still stayed at a high pollution level, which rang the alarm bell that watershed systems of China had been at serious ecological risk accused of microplastic contamination.