Data Analysis Approach Research Articles

MIMR: Development of a Web-Based System for miRNA and mRNA Integrated Analysis

The human body is a complex network of systems that is harmonized with multiple biological components. To understand these interactions is very challenging. With rapid development of advanced sequencing technologies, massive amounts of data such as mRNA, miRNA are rapidly accumulated. The integrated analysis of mRNA–miRNA has brought an extensive understanding of complex biological systems and pathological mechanisms. MicroRNAs (miRNAs) are small non-coding RNAs that intricately regulate target gene products, resulting in the inhibition of gene expression. While these miRNAs play crucial roles in essential biological processes—ranging from immunity and metabolism to cell death—their specific impacts on diseases remain unknown. Recent studies have been focused on the integration of miRNA and mRNA expression to reveal the underlying biological pathways and mechanisms responsible for disease manifestation. We proposed a novel approach for integrative analysis of miRNA and mRNA expression data and developed MIMR (Integrative Analysis of miRNA and mRNA), a web-based application that leverages the Random Walk with Restart (RWR) algorithm. MIMR incorporates both direct and indirect interactions by utilizing protein–protein interaction (PPI) networks and experimentally validated mRNA–miRNA target interactions. MIMR provides comprehensive results, including novel pathological pathways associated with a specific disease and interactive network diagrams representing the mRNAs and miRNAs. We applied it to Alzheimer and breast cancer data and successfully identified the novel biological pathways related to these diseases. In summary, MIMR will offer a deeper insight into the hidden mechanisms of diseases and identify potential therapeutic strategies through integrated analysis of miRNAs and mRNAs.

Analisis Pengaruh Pembiayaan LPEI Konvesional dan Syariah, Variabel Ekonomi Makro Terhadap Pertumbuhan Ekspor Indonesia

The purpose of this study is to evaluate the relationship between funding provided by LPEI in conventional and sharia forms to export growth, using two separate models to compare the two types of funding. This study also examines the relationship between inflation and the rupiah exchange rate on Indonesia's export growth. The analysis uses monthly data from 2015 to 2022 and ARDL approach in data analysis, with the help of EViews 12 to estimate the long-run and short-run impact of these variables on export growth. The results of this study show that in the short run, neither conventional nor sharia LPEI financing has a significant impact on export growth. The value of exports in the previous period and the previous two periods has a negative effect on current export growth. In contrast, export growth is positively affected by the value of exports in the previous three and four periods. Changes in the rupiah exchange rate in the previous period have a positive effect on export growth in the Islamic LPEI financing model, while changes in the inflation rate have no significant effect on export growth. In the long run, both conventional and Islamic LPEI financing do not have a major impact on export growth. The positive impact on export growth in the Islamic LPEI financing model is caused by fluctuations in the rupiah exchange rate in the long run. Changes in inflation do not have a significant impact on export growth based on other macroeconomic variables.

Augmented Reality Adoption: Exploring Security Concerns and Consumer Resourcefulness in the Era of Society 5.0

ABSTRACTSociety 5.0, building its foundation on industry 4.0, defines the concept of super smart cities as places where humans can adapt novel technologies such as augmented reality (AR) in their daily lives. AR brought its consumers an immersive and hybrid experience, blurring the boundaries between the real and virtual worlds. This study explores the user adoption of AR, an interactive technology, in the e‐commerce domain in the present era of Society 5.0. This study seeks to address the question of what factors drive the consumer's adoption of AR while shopping online and how security concern and technostress (TS) impact their intention towards its use and its actual usage behaviour. This research offers a novel perspective and fills a gap in the existing literature by integrating consumers' security concerns and TS, previously unexplored factors, in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The ability of the UTAUT model, over the earlier technology adoption models, to incorporate context‐specific factors makes it significantly relevant for studying AR adoption, a technology blending the virtual and real world. A modified UTAUT‐based questionnaire assessed relevant factors influencing this behaviour. This study employed a quantitative, cross‐sectional survey design to apprehend a broad range of users and to investigate consumer perceptions and adoption of AR in online shopping. Purposive sampling ensured that respondents were aligned with the research context and were knowledgeable about the use of AR in the e‐commerce domain and its relevant applications, thereby strengthening the validity of the study findings. A two‐staged partial least square structural equation modelling–artificial neural network (PLS‐SEM–ANN) data analysis approach was used to determine the study's findings. The meaningful responses collected from 457 respondents were analysed using this targeted approach for in‐depth insights. The synthesis of the study's analysis suggests that personal innovativeness towards information technology and security concerns are critical drivers of intention and actual usage. Interestingly, TS and social influence emerged as non‐significant, indicating a nuanced view of the UTAUT model in this context. A possible explanation for their insignificance lies in the present generation's ability to rapidly adapt to new technologies and make autonomous decisions, reducing reliance on peer influence. By understanding these factors, the research seeks to pave the way for the efficacious integration of AR within the e‐commerce domain. It further emphasizes the significance of consumer resourcefulness, a gap in current research, for bridging the intention‐usage gap. This focus on consumers’ personal innovativeness towards IT, security concerns and consumer resourcefulness provides valuable insights for e‐commerce platforms seeking to drive successful AR adoption. The study explores how well AR will be accepted and used, informing scholarly researchers and businesses on making AR‐integrated online shopping successful.

A robust approach for regression analysis of panel count data with time-varying covariates

A robust approach for regression analysis of panel count data with time-varying covariates

Applying triple collocation for verifying wind resource measurements and reanalysis data

Abstract Dual comparisons or dual collocation, respectively, is a standard approach in wind data analysis, and particularly in the wind energy resource assessment context, for comparing and relating two time series from different sources describing the same quantity as e.g. the wind speed at a specific height over a certain period of time. This comparison can be used to validate a data source, or to derive a correction for one of the two datasets. Though being widely used, dual collocation comes with inherent flaws which are partly unknown or ignored. A major problem is that dual collocation requires the definition of a reference in advance. Triple collocation may instead allow for a more objective and comprehensive evaluation, where (three) suitable data sources are available, and possibly help to identify which datasets are most suited to represent the relevant wind resource. In this study, we apply the triple collocation methodology to two typical wind resource assessment test cases. Results are promising in providing more information on the analysed datasets and improving the use of different types of data for wind resource assessment in the future.

Minga as a placemaking tool in peripheral neighbourhoods. Co-design experience in Calderon, Quito*

ABSTRACT This article explores the role of local vernacular socio-spatial practices in co-design and placemaking in Calderon, a peripheral urban parish in Quito, Ecuador. Using a case-study method and transformative paradigm theory, the research employs a mixed-methods approach for data collection and analysis. The focus is on ‘Minga’, a collaborative community effort manifested in two forms: ‘community mingas’ organised autonomously by communities, and ‘megamingas’ coordinated by public institutions. Community mingas enhance residents’ sense of belonging and pride, empowering them in shaping public spaces through a bottom-up approach. In contrast, megamingas exhibit top-down organisation with limited community engagement, raising concerns about social impacts. The study underscores mingas’ potential for social cohesion, cultural expression, and sustainable development in urban design. It emphasises the nuanced understanding needed for fostering active participation and addresses challenges such as sustaining community ownership. Despite yielding immediate benefits in public space enhancement, sustaining long-term engagement is crucial. The study concludes that mingas offer a valuable avenue for residents to actively contribute to public space improvement, fostering shared responsibility and long-term sustainable development in low-income neighbourhoods.

Application of an implementation premortem: A novel qualitative approach leveraging prospective hindsight to enhance barbershop health interventions for Black men.

Barbershop-based efforts to promote health among Black men have been uniquely successful. Despite the success of these efforts, the emphasis on outcomes as opposed to how these outcomes can be achieved has created a gap in the literature. The present study addresses this gap by describing implementation-related program priorities that Black men identify for barbershop-based interventions. Twenty-three Black men participated in implementation premortem activities. Participants were: (i) given details about a barbershop-based health promotion effort, (ii) told that the effort had failed, and (iii) were asked to identify potential sources of program failure and strategies to overcome those challenges. The frequency of program priorities was calculated based on the Exploration, Preparation, Implementation, and Sustainment (EPIS) Framework, and focus group data were analyzed using an inductive thematic qualitative data analytic approach. Data analyses occurred in three phases: (i) excerpts were grouped by Phase, Domain, and Construct of the EPIS Framework; (ii) themes within each excerpt were then identified using an inductive approach; and (iii) themes were organized into conceptually similar and parsimonious categories. Participants suggested that the biggest source of program failure was that more attention needed to be paid to Program Fit during the Implementation Phase. Participants reported concerns with the accessibility and convenience of the effort. Additional participant concerns included economic issues, community ownership, climate, staffing processes, cultural sensitivity, engagement, and trust. The implementation of health promotion programs for Black men should pay particular attention to how the intervention fits the needs, lives, and contexts of potential participants.

Cultural profiles evaluation and their impact on the performance of production systems in Colombian companies using data analytics

Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.

Analysis of Promotion Strategies Through Advertising Media in Increasing Consumer Loyalty

UD. Talenta faces several weaknesses in marketing catering services, such as a lack of diversification in promotional methods which still rely on traditional methods and word of mouth, which limits market reach. In addition, limited access to technology and the internet hinders the use of digital platforms for promotions, while increasingly tight competition with similar competitors causes difficulties in distinguishing ourselves, so that sales levels experience fluctuations, especially in the midst of unstable economic conditions. This research uses a qualitative approach with methods. field study. Data collection techniques were carried out by means of interviews, observation and documentation using the Milles and Huberman data analysis approach. The research results showed that UD. Talenta relies on marketing strategies that include promotion through social media, print, word of mouth recommendations, and local event participation to expand the reach of its services. Despite facing resource constraints and intense competition, advertising strategies, especially on social media, are considered quite effective in expanding the market and increasing consumer loyalty. Promotional effectiveness is influenced by message clarity, visual appeal, and brand image consistency, although challenges remain in maintaining customer loyalty and overcoming budget constraints. UD. Talenta continues to innovate and evaluate strategies to improve its advertising campaigns. To strengthen consumer loyalty, UD. Talenta need to focus on clarity of information in advertisements, as well as improving the consistency and quality of advertisements with content innovation that is creative and relevant to consumer needs.

The Role of Agricultural Inputs on Crop Productivity: The Case of Zigam Woreda

In spite of the importance of agriculture in Ethiopia, it is characterized by low productivity and has been unable to produce sufficient quantities of output to feed the country’s population. In light of this various development strategies has been undertaken to improve the performance of agriculture. Intensification of agriculture through the use of new agricultural technologies has been emphasized over the last three decades. This study attempted to examine the contribution of agricultural input for crop productivity. The data for the study was collected from 91 sample farmers. This study was the study used both primary and secondary data. In this study researcher was used simple random sampling techniques. This research was use cross sectional approach and econometric method of data analysis to investigate the role of age, sex, land size, labor force, fertilizer, improved seed, extension service, and access to credit, education level and pesticides for crop production by collecting data from the household. In econometric method of data analysis researcher was used ordinary least square (OLS) Model. The econometric result show that land size, labor force, improved seed, fertilizer, credit service, extension service and education level have positive and significant effect on crop production. However, pesticide has a negative and significant impact on crop production. From the explanatory variables, education level has a higher coefficient. This indicates education level is more significant for crop production. According regression result <i>R</i><sup><i>2</i></sup> is 0.97, which implies 97% of output function is explained by the selected ten (10) explanatory variables. The policy implication is that to reduce farmers resistant to use farm inputs and to create knowledge about the optimal input use educate and training of farmers is necessary.

A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging.

Single-cell RNA sequencing has enabled the study of aging at a molecular scale. While substantial progress has been made in measuring age-related gene expression, the underlying patterns and mechanisms of aging transcriptomes remain poorly understood. To address this gap, we propose a physics-inspired, data-analysis approach to extract additional insights from single-cell RNA sequencing data. By considering the genome as a many-body interacting system, we leverage central idea of the Renormalization Group to construct an approach to hierarchically describe aging across a spectrum of scales for the gene expresion. This framework provides a quantitative language to study the multiscale patterns of aging transcriptomes. Overall, our study demonstrates the value of leveraging theoretical physics concepts like the Renormalization Group to gain new biological insights from complex high-dimensional single-cell data.

MACHINE-LEARNING TECHNIQUES IN MULTIPLE SCLEROSIS PREDICTION USING EEG

The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), which resulted in a costly, time-consuming and non-reproducible process. The application of Machine Learning (ML) in MS diagnosis has been getting a lot of attention in the last few years due to the volume of scientific data, the heterogeneity of disease courses, and the variety of diagnostic methods. The EEG has the capability to detect important changes in the brain’s inherent electrical activity, which are influenced by changes in the neural network connections associated with inflammatory demyelinating and neural damage characteristic of the MS. Utilizing multimodal machine learning over the clinically available data may be a contemporary strategy with amazing potential to facilitate early diagnosis. Considering recent EEG investigations as well as the accessibility to their datasets would be the initial steps in utilizing the ML in MS diagnosis. This paper provides a systematic review of the latest techniques for MS diagnostic based on PRISMA guidelines with the prospect of ML application in their investigations. The goal is to find if EEG could be considered a robust and accurate technique for MS diagnosis. In accordance with PRISMA guidelines, we consider 111 papers. In our review, 404 people are considered including 209 with MS and 195 healthy controls. As a result, we generated an updated investigation looking at the ML approaches as well as utilizing EEG as an accurate, but less often used method to help with early diagnosis of MS. We summarize, analyze, discuss, and synthesize the recently published works, current trends, and open research issues. The review also points out knowledge gaps about the necessity of validating results and addressing constraints such as limited sample number. Our investigation proves that the precision of the supervised strategies such as [Formula: see text]NN and SVM is typically higher than that of the unsupervised strategies. On the other hand, by utilizing various techniques such as splitting EEG signal sub-bands, signal windowing, and identifying effective features from the data analysis approaches, we have been able to achieve significant classification accuracy higher than 99%. According to the high degree of accuracy of the results, this approach is becoming the focal point of the research works. The high diagnostic accuracy of the proposed machine learning techniques on the EEG signal analysis shows its potential capacity to become a more widespread procedure as the MS diagnostic techniques. To develop the reliability of these methods, gathering, and analyzing more EEG signals from MS patients and creating more suitable EEG protocols are essential in future studies. In the same way, for the dynamic and online analyses of the EEG signals, in-depth studies are needed to determine more effective EEG protocols and machine-learning techniques.

The Analysis of Financial Rewards on Employee Engagement in the Public Sector: Cross-Cultural as a Moderating Variables

Cross-cultural research on the influence of individuality and collectivism on employee engagement is lacking. This study aims to analyse the impact of employee financial rewards on employee engagement with a focus on individualistic and collectivistic cultures as moderating variables in a public sector organisation. This research employed standardised face-to-face open-ended interviews. The research instrument is made from one theme and five open-ended questions to address the objectives of this study. The constant comparative data analysis approach was employed in conjunction with content analysis. The researcher categorized the data based on their disparities as well as their similarities. This study found that the municipality offers its employees financial rewards; however, due to inequality, unfairness, favouritism, and political influence, employees are not rewarded fairly, leading to employees being unhappy and disengaged from their work and the organisation. The municipality does not practice either a collectivistic or individualistic culture. A poor imbalance between individualistic and collectivistic cultures reduces the impact of financial rewards on employee engagement. This study's findings emphasise that the effectiveness of financial rewards on employee engagement depends on the organisation's practices and the equilibrium of individualistic and collectivistic cultures.

CSR disclosure, financial performance, and ownership: evidence from China

PurposeThis study investigates the relationship between corporate social responsibility (CSR) disclosure and financial performance (FP), and ascertains whether ownership structure (OS) moderates the CSR disclosure–FP nexus.Design/methodology/approachWe distinctly employed the well-established approach of panel data analysis to examine the comprehensive dataset of Shanghai A-share listed firms from 2008 to 2017 with 20,236-full sample and 4,190-disclosed sample firm-year observations. To test the hypotheses, the study used panel regression analysis. The study used CSR disclosure as an explanatory variable and accounting-based performance measures: return on equity (ROE) and earning per share (EPS) as dependent variables. In addition, we used CSR score to determine the extent of disclosure by each firm and employed matched pair sample analysis to check for the robustness of the earlier obtained results.FindingsThe findings indicate significant positive association among CSR disclosure and CSR score with ROE and EPS. Further, the CSR disclosure–FP nexus is more pronounced when the OS moderates it.Research limitations/implicationsThe results of this study lack generalizability due to its unique setting. A limitation of this paper is that our sample period only covers 2008–2017. Future studies can extend our research to a more recent period to test whether our findings remain valid in other periods.Practical implicationsOur findings suggest stronger CSR disclosure measures to enhance the image of businesses in the eyes of stakeholders. The study findings are consistent and confirm the theoretical basis (stakeholder theory) that Chinese listed firms can be more beneficial from disclosing CSR related information, and they should put more emphasis on the improvement of CSR disclosure.Originality/valueThis research offers empirical evidence that sheds light on the importance of OS as the moderating effect on the nexus of CSR disclosure–FP measures. In doing so, this study’s findings contribute to the literature significantly, along with the regulators and shareholders.

Understanding tourists’ ambassador role of their origin places from perspective of residents’ attitudes towards tourists

Recognizing the pivotal role of tourists as ambassadors for their hometown is imperative, as it relates to the dynamic host-guest relationship, thereby significantly influencing the sustainable tourism progress in both their originating locations and the visited destinations. To delve deeper into this ambassadorial role, this study employed the tripartite model of resident attitude, which bridges the intricate interplay between residents’ cognitive, affective, and conative attitudes towards tourists. 204 U.S. people have been enrolled through Amazon’s Mechanical Turk to finish semi-structured questionnaire about their attitudes towards Chinese outbound tourists. Leximancer, a computer-assisted qualitative data analysis approach was applied. Results demonstrated that neutral or positive cognitive-attitudes U.S. residents held towards Chinese tourists exceeded negative cognitive attitudes. Further analysis inferred that it was the interpretation or attribution of cognitive attitudes that would induce residents’ different emotional feelings and behavioral intention. Positive interpretation would lead to positive feeling (i.e., admiration) and positive intention to visit China. Negative interpretation would lead to negative feeling (i.e., contempt) and negative intention to visit China. This research provided implications for destination management organization of tourists’ origin place to incorporate outbound tourists as their place ambassadors.

A Qualitative Assessment of Changes in Occupational Exposures Among Healthcare Facility Workers.

Adult-onset asthma, notably prevalent among healthcare professionals, especially nurses, is often attributed to occupational factors such as exposure to cleaning agents. Studies consistently underscore the substantial role of such exposure in work-related asthma among hospital staff. We aimed to (a) identify and characterize current practices in cleaning and aerosolized medication administration; (b) assess changes in practices since a similar 2003 study of Texas healthcare workers; and (c) identify factors contributing to diverse exposures within healthcare job categories. We conducted focus groups with 38 participants in 6 healthcare settings, analyzing current practices, changes since 2003, and factors contributing to exposure diversity. We used a three-step approach for data analysis, including sociodemographic characterization, a scissor-and-sort technique for exposure description, and qualitative content analysis. Participants were primarily healthcare providers (76%) and housekeepers/cleaners (11%) who reported exposure to aerosolized medications, cleaning products, adhesives, and solvents. Participants reported transitioning from cleaning practices to new formulas with reduced odors and shifting from spray cleansers to wipes. Personal protective equipment (PPE) used during cleaning tasks varied, with training differing among job categories. Aerosolized medication administration varied among facilities, with reported medication types and protocol changes over time. The results emphasized the significance of maintaining uniform protection, disseminating knowledge, and consistently adhering to PPE protocols in the healthcare environment. Addressing the identified gaps in comprehension and potential sources of exposure variability requires additional focus on occupational health and safety initiatives.

Analysis of Spatial Vitality Characteristics and Influencing Factors of Old Neighborhoods: A Case Study of Ya’an Xicheng Neighborhood

Amidst the pursuit of sustainable urban development, bolstering the spatial vitality of historic neighborhoods emerges as a pivotal endeavor. This study, centering on the Xicheng neighborhood in Ya’an City, employs a multifaceted data analysis approach to unravel the intricate spatial vitality patterns. By adopting a dual lens focusing on both “people” and “space”, we delve into the intricate interplay between spatial vitality, spatial perception, and various influencing factors, such as the spatial environment, business format layout, and place atmosphere. Leveraging spatial correlation analysis, we meticulously examine the extent to which these multifarious factors shape spatial vitality and perception. Our findings reveal a profound connection between the emotional dimension of perception and the distribution of spatial vitality. Notably, aspects of spatial character—green rating, accessibility, and interface richness—exert the most pronounced influence on vitality. Furthermore, the creation of a distinctive atmosphere in a place is more likely to resonate with a person’s spatial perception than the physical environment itself.

Maximization of Hydrogen Production via Methane Steam Reforming in a Wavy Microreactor by Optimization of Catalyst Coating: A Combined Computational and Data Analytics Approach

Maximization of Hydrogen Production via Methane Steam Reforming in a Wavy Microreactor by Optimization of Catalyst Coating: A Combined Computational and Data Analytics Approach

ANALYSIS OF THE IMPACT OF SOCIAL MEDIA MARKETING, SERVICE QUALITY, AND SALES ACTIVITIES ON THE INCREASE IN SALES OF PROBLEMATIC FINANCING GUARANTEES (DEFAULT) THROUGH AUCTION: CASE STUDY AT ABC ISLAMIC BANK

The study's purpose is to assess and disseminate the effect of the interaction between Social Media Marketing components, Quality, and Sales Activities on increasing the sales of customer guarantees for problem financing (losses) through auctions at ABC Service Sharia Bank. This study employs quantitative methodologies with a population of all workers of PT Bank Syariah ABC and a random sample of 30 respondents from Bank Syariah ABC employees in the Surabaya region. Data collection methods use questionnaires, case studies and interviews. The data analysis approach includes performing “Data Quality Tests”, “Classic Assumption Tests”, “Multiple Linear Regression Tests”, and “Hypothesis Testing”. Based on this study, the components of Social Media Marketing, Service Quality and Sales Activity, both on their own and in combination, have a notable influence on increasing sales of collateral for non-performing financing customers through auctions: Case Study at ABC Sharia Bank. The managerial implications for PT Bank Syariah ABC resulting from this research include optimizing the implementation or running of the three components above so that sales of customer collateral through auctions can increase significantly so that the rate of return or recovery of problematic/defaulting customers is reduced.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.