Search results for: exploratory data analysis

Authors: Sourabh Jain, Sukhvir Singh Jain, Gaurav V. Jain

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Intelligent Transportation System is an integrated application of communication, control and monitoring and display process technologies for developing a user–friendly transportation system for urban areas in developing countries. In fact, the development of a country and the progress of its transportation system are complementary to each other. Urban traffic has been growing vigorously due to population growth as well as escalation of vehicle ownership causing congestion, delays, pollution, accidents, high-energy consumption and low productivity of resources. The development and management of urban transport in developing countries like India however, is at tryout stage with very few accumulations. Under the umbrella of ITS, urban corridor management strategy have proven to be one of the most successful system in accomplishing these objectives. The present study interprets and figures out the performance of the 27.4 km long Urban Bus route having six intersections, five flyovers and 29 bus stops that covers significant area of the city by causality analysis. Performance interpretations incorporate Passenger Boarding and Alighting, Dwell time, Distance between Bus Stops and Total trip time taken by bus on selected urban route.

Keywords: congestion, dwell time, passengers boarding alighting, travel time

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Suglo Tohari Luri

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Data analysis is an important aspect of business performance. With the application of artificial intelligence within databases, selecting a suitable database engine for an application design is also very crucial for business data analysis. The application of business intelligence (BI) software into some relational databases such as Neo4j has proved highly effective in terms of customer data analysis. Yet what remains of great concern is the fact that not all business organizations have the neo4j business intelligence software applications to implement for customer data analysis. Further, those with the BI software lack personnel with the requisite expertise to use it effectively with the neo4j database. The purpose of this research is to demonstrate how the Neo4j program code alone can be applied for the analysis of e-commerce website customer visits. As the neo4j database engine is optimized for handling and managing data relationships with the capability of building high performance and scalable systems to handle connected data nodes, it will ensure that business owners who advertise their products at websites using neo4j as a database are able to determine the number of visitors so as to know which products are visited at routine intervals for the necessary decision making. It will also help in knowing the best customer segments in relation to specific goods so as to place more emphasis on their advertisement on the said websites.

Keywords: data, engine, intelligence, customer, neo4j, database

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Authors: Fadzayi M.Maruza

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The conversation about diversity in the African academy focuses on the need for an international and ethnically diverse population of scholars and students. Operationalising the concept of cognitive diversity offers us an opportunity to broaden our conception of who can know and who can proclaim knowledge by availing new understandings of what knowledge is and how it is made. Limited attention is paid to the value of diversity generated by cognitive disabilities in the African academy. The inclusion of persons with minds labelled disabled in African academia requires an epistemology of disability to reform the still dominant notion of the expert and scholar as an able-bodied and hyper-rational in African academia. This review wants to explore how cognitive disabilities have been represented in higher education research in Africa or has the African academy reinforced ignorance by promoting an able-bodied academia. The review aims to tackle its exploratory objective by using Malcom Tights framework. The main questions this paper would focus on are: (I)What are the major disability themes and concerns discussed in the disability-related articles? (II)What are the major methods or methodologies used to address the topic in the papers? (III)What are the levels of analysis the papers focus on? (IV)How do higher education researchers define and represent cognitive disabilities in higher education research in Africa? To answer the exploratory questions that are aimed at mapping the disability-related higher education research landscape, Malcolm Tights’ framework is seen as most appropriate. In addition to a thematic categorization, that shall be made after reviewing of published empirical studies on disability in African higher education from the period 2010 – 2017. A synthesis of the findings and implications of African disability studies relating to students with cognitive disabilities in the African Academy will be provided using the categories suggested by Tight as a benchmark. Data for the proposed work shall be taken from well-reputed higher education journals between 2010 and 2017.Using the keyword ‘Disability’ in the titles, abstracts and keywords section of journal articles, a selection of disability-focused higher education articles shall be compiled for analysis regarding cognitive disability. It has to be noted as a limitation that the word Disability might not be sufficient to investigate the topic for there can be many more specific disabilities concerns the researchers would discuss. Therefore, the paper is only intended to give a bird’s eye view of cognitive disability in higher education research and therefore is not comprehensive. The paper is expected to shed some light for me, as a beginning researcher, and other researchers like myself as to what has been the focus of higher education researchers about cognitive disability in the African academy. Keywords: Cognitive diversity, cognitive disability, disability, higher education.

Keywords: cognitive disability, cognitive diversity, disability, higher education

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Authors: Saleem Z. Ramadan

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In this paper, a model is proposed to determine the life distribution parameters of the useful life region for the PV system utilizing a combination of non-parametric and linear regression analysis for the failure data of these systems. Results showed that this method is dependable for analyzing failure time data for such reliable systems when the data is scarce.

Keywords: masking, bathtub model, reliability, non-parametric analysis, useful life

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Authors: Iyad Ismail, Fathilatul Abdul Hamid

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This research was mainly investigated to recognize the extent of big data analysis by external auditors. This paper adopts grounded theory as a framework for conducting a series of semi-structured interviews with eighteen external auditors. The research findings comprised the availability extent of big data and big data analysis usage by the external auditors in Palestine, Gaza Strip. Considering the study's outcomes leads to a series of auditing procedures in order to improve the external auditing techniques, which leads to high-quality audit process. Also, this research is crucial for auditing firms by giving an insight into the mechanisms of auditing firms to identify the most important strategies that help in achieving competitive audit quality. These results are aims to instruct the auditing academic and professional institutions in developing techniques for external auditors in order to the big data analysis. This paper provides appropriate information for the decision-making process and a source of future information which affects technological auditing.

Keywords: big data analysis, external auditors, audit reliance, internal audit function

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Authors: Yu-Ming Wang, Jia-Hong Jian

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The Internet and digital publication technologies have brought dramatic impacts on how people collect, organize, disseminate, access, store, and use information. More and more governments, schools, and organizations spent huge funds to develop digital libraries. A digital library can be regarded as a web extension of traditional physically libraries. People can search diverse publications, find out the position of knowledge resources, and borrow or buy publications through digital libraries. People can gain knowledge and students or employees can finish their reports by using digital libraries. Since the considerable funds and energy have been invested in implementing digital libraries, it is important to understand the evaluative criteria from the users’ viewpoint in order to enhance user acceptance. This study develops a list of user acceptance criteria for digital libraries. An initial criteria list was developed based on some previously validated instruments related to digital libraries. Data were collected from user experiences of digital libraries. The exploratory factor analysis and confirmatory factor analysis were adopted to purify the criteria list. The reliabilities and validities were tested. After validating the criteria list, a user survey was conducted to collect the comparative importance of criteria. The analytic hierarchy process (AHP) method was utilized to derive the importance of each criterion. The results of this study contribute to an e understanding of the criteria and relative importance that users evaluate for digital libraries.

Keywords: digital library, user acceptance, analytic hierarchy process, factor analysis

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Authors: Wanqing He

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The current study explores aspects of exploratory behaviors and social capacities in urban Chinese infants to examine whether these factors mediate the link between infant walking and receptive and productive vocabularies. The linkage between the onset of walking and language attainment proves solid, but little is known about the factors that drive such link. This study examined whether joint attention, gesture use, and object activities mediate the association between locomotion and language development. Results showed that both the frequency (p = .05) and duration (p = .03) of carrying an object are strong mediators that afford opportunities for word comprehension. Also, accessing distal objects may be beneficial to infants’ language expression. Further studies on why object carrying may account for word comprehension and why infants with autism could not benefit from walking onset in terms of language development may yield valuable clinical implications.

Keywords: exploratory behaviors, infancy, language acquisition, motor development, social communicative skills

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Amos Ekow Coffie

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This exploratory study investigates the impact of AI-driven health communications and media reportage on health outcomes in rural Ghana, focusing on rural communities within Accra. Despite the potential of AI-driven health communications in improving health outcomes, its adoption in rural Ghana is hindered by infrastructure challenges, digital literacy, and cultural factors. Media reportage plays a crucial role in shaping health perceptions and behaviors, but its impact is limited by inadequate health reporting, lack of specialized health journalists, and limited access to health information. This study aims to explore the integration of AI-driven health communications into media practices in rural Ghana, addressing the following research questions: How do AI-driven health communications impact health outcomes in rural Ghana? What role does media reportage play in shaping health perceptions and behaviors in Accra? How can AI-driven health communications and media reportage be optimized to improve health outcomes in rural Ghana? Using a mixed-methods approach, this study will combine surveys, interviews, and content analysis to investigate the impact of AI-driven Health Communication and media reportage on health outcomes in rural areas in Ghana. AI-driven health communications is the use of artificial intelligence (AI) technologies to design, deliver, and evaluate health messages, interventions, and campaigns. The study's findings will contribute to the development of effective health communication strategies, addressing the significant health disparities in rural areas in Ghana.

Keywords: AI Driven Health Communication, Media Reporting, Rural Areas, Communication Channels

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Authors: Alon Cohen, Jeffrey Kantor, Shalom Levy

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The use of the audit risk model in auditing has faced limitations and difficulties, leading auditors to rely on a conceptual level of its application. The qualitative approach to assessing risks has resulted in different risk assessments, affecting the quality of audits and decision-making on the adoption of CAATTs. This study aims to investigate risk factors impacting the implementation of the audit risk model and propose a complementary risk-based instrument (KRIs) to form substance risk judgments and mitigate against heightened risk of material misstatement (RMM). The study addresses the question of how risk factors impact the implementation of the audit risk model, improve risk judgments, and aid in the adoption of CAATTs. The study uses a three-stage scale development procedure involving a pretest and subsequent study with two independent samples. The pretest involves an exploratory factor analysis, while the subsequent study employs confirmatory factor analysis for construct validation. Additionally, the authors test the ability of the KRIs to predict audit efforts needed to mitigate against heightened RMM. Data was collected through two independent samples involving 767 participants. The collected data was analyzed using exploratory factor analysis and confirmatory factor analysis to assess scale validity and construct validation. The suggested KRIs, comprising two risk components and seventeen risk items, are found to have high predictive power in determining audit efforts needed to reduce RMM. The study validates the suggested KRIs as an effective instrument for risk assessment and decision-making on the adoption of CAATTs. This study contributes to the existing literature by implementing a holistic approach to risk assessment and providing a quantitative expression of assessed risks. It bridges the gap between intuitive risk evaluation and the theoretical domain, clarifying the mechanism of risk assessments. It also helps improve the uniformity and quality of risk assessments, aiding audit standard-setters in issuing updated guidelines on CAATT adoption. A few limitations and recommendations for future research should be mentioned. First, the process of developing the scale was conducted in the Israeli auditing market, which follows the International Standards on Auditing (ISAs). Although ISAs are adopted in European countries, for greater generalization, future studies could focus on other countries that adopt additional or local auditing standards. Second, this study revealed risk factors that have a material impact on the assessed risk. However, there could be additional risk factors that influence the assessment of the RMM. Therefore, future research could investigate other risk segments, such as operational and financial risks, to bring a broader generalizability to our results. Third, although the sample size in this study fits acceptable scale development procedures and enables drawing conclusions from the body of research, future research may develop standardized measures based on larger samples to reduce the generation of equivocal results and suggest an extended risk model.

Keywords: audit risk model, audit efforts, CAATTs adoption, key risk indicators, sustainability

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Authors: Wang Lin, Li Zhiqiang

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The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: behavior pattern, cooperative learning, data analyze, k-means clustering algorithm

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Authors: Henok Asfaw Hailu, P. D. Rwelamila

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Creating a suitable environment for successful projects needs a rethink of the organisational design of the parent organisations. A Project-based organisation (PBO) is a unique organizational form suitable for implementing and managing business activities around projects. A construction firm is inherently a PBO as it executes most of its activities through projects. PBO design and development require an empirical foundation. This study aimed to fill this gap by developing a conceptual model to help transform Ethiopian construction firms (ECFs) into full-fledged PBOs by assimilating the required PBO characteristics. The study used an exploratory QUAL-quant research design approach. A thematic content analysis was performed to analyse the qualitative (Interviews) research data. Means, standard deviations, frequencies, percentages, one-way ANOVA, and Pearson correlation were used to analyse the quantitative data. A transformational conceptual model was proposed and illustrated that transformation needs to begin by assessing the environment, strategic documents, and PBO characteristics. Assimilating missing PBO characteristics into ECFs is vital to realise organisations’ transformation into full-fledged PBOs.

Keywords: project-based organization, organizational design, dimensions, construction firms

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Authors: Lindy Van Vliet, Saloni Phadke, Anthea Nelson, Ann Gallant

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Holistic and patient-centred palliative care and support require an integrated system of care that includes health and social service providers working together to ensure that patients and families have access to the care they need. The objective of this study is to further explore and understand the benefits and challenges of mobilizing multidisciplinary care teams for health care professionals and social service providers. Drawing on an interpretivist, exploratory, qualitative design, our multidisciplinary research team (medicine, nursing and social work) conducted interviews with 15 health care and social service providers in the Ottawa region. Interview data was audio-recorded, transcribed, and analyzed using a reflexive thematic analysis approach. The data deepens our understandings of the facilitators and barriers posed by multidisciplinary care teams. Three main findings emerged: First, the data highlighted the benefits of multidisciplinary care teams for both patient outcomes and quality of life and provider mental health; second, the data showed that the lack of a system-wide integrated communication system reduces the quality of patient care and increases provider stress while working in multidisciplinary care teams; finally, the data demonstrated the existence of implicit hierarchies between disciplines, this coupled with different disciplinary perspectives of palliative care provision can lead to friction and challenges within care teams. These findings will have important implications for the future of palliative care as they will help to facilitate and build stronger person-centred/relationship-centred palliative care practices by naming the challenges faced by multidisciplinary palliative care teams and providing examples of best practices.

Keywords: public health palliative care, palliative care nursing, care networks, integrated health care, palliative care approach, public health, multidisciplinary work, care teams

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Authors: Davoud Maleki, Neda Zamani

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The present study aims at exploring the effective accountability model of Islamic Azad University using a qualitative approach of grounded theory. The data of this study were obtained from semi-structured interviews with 25 professors and scholars in Islamic Azad University of Tehran who were selected by theoretical sampling method. In the data analysis, the stepwise method and Strauss and Corbin analytical methods (1992) were used. After identification of the main component (balanced response to stakeholders’ needs) and using it to bring the categories together, expressions and ideas representing the relationships between the main and subcomponents, and finally, the revealed components were categorized into six dimensions of the paradigm model, with the relationships among them, including causal conditions (7 components), main component (balanced response to stakeholders’ needs), strategies (5 components), environmental conditions (5 components), intervention features (4 components), and consequences (3 components). Research findings show an exploratory model for describing the relationships between causal conditions, main components, accountability strategies, environmental conditions, university environmental features, and that consequences.

Keywords: accountability, effectiveness, Islamic Azad University, grounded theory

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Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Sibusiso Buthelezi, Rugira Regis Marie-Modeste, Deliwe Rene Phetlhu

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Introduction: Mother-to-child transmission persists owing to inadequate implementation of prevention of mother-to-child transmission programmes, particularly in rural areas. To achieve a target of zero new HIV infection in children born from women living with HIV, the involvement of mothers and caregivers is undeniable. Therefore, there is a need to explore the views of the mothers because of the role they play in their own right when it comes to preventing their children from contracting HIV by consistently adhering to the guidelines of the prevention of mother-to-child transmission programme. Thus, this study sought to explore and describe the perceptions of mothers on their role in the prevention of HIV to their children exposed to HIV and further explore their perceptions of the childcare received in the healthcare facility. Methods: The study was conducted in November-December 2019 in Ngaka Modiri Molema in North West Province in South Africa. A qualitative exploratory, descriptive research design was used. Purposive sampling was used to select the mothers of children exposed to HIV during the mother`s clinic attendance. Data collection was done through semi-structured individual interviews with mothers of children exposed to HIV. Colaizzi`s method of data analysis was used to analyse data in this study. Results: Seven themes emerged from data analysis, namely: health benefits from coming to the healthcare facility, communication, information needs, attitude of healthcare workers, healthcare administration system, the role of a mother, and disclosure of HIV status. Conclusion: This study revealed systematic gaps that exist in the programme, which hinder the childcare services of children exposed to HIV and socio-economically related hindrances. Mothers’ roles, such as exclusive breastfeeding, taking their own medication, and child follow-up visits, remain inadequate. The study findings show that there is a need to develop a contextual-tailored intervention strategy that would improve the implementation of prevention of mother-to-child transmission in rural areas.

Keywords: children exposed to HIV, mothers’ role to prevent MTCT, mothers’ perceptions on childcare, PMTCT in rural areas

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Authors: Jonathan K. Baeza, Bryan Monzón, Yair Cruz, José J. Castro

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While agriculture is essential for feeding the world, it's also heavily reliant on herbicides. The overuse of these chemicals poses a significant global problem. In this context, 2,4-dichlorophenoxyacetic acid (2,4-D) is one of the most commonly used herbicides, especially in grain crops. This study investigates the removal of 2,4-D from water using an advanced oxidation process, specifically the homogeneous photo-fenton process. We used iron salts and hydrogen peroxide as primary reactants under controlled conditions: 24 ultraviolet LEDs, a commercial herbicide called 'hierbamina,' an initial 2,4-D concentration of 100 mg/L, a pH of 2.5, and a reaction volume of 350 ml. Through exploratory experiments and analytical techniques like UV-vis spectrophotometry and HPLC chromatography, we found that the concentrations of iron and hydrogen peroxide are critical for optimizing the process. Surprisingly, temperature didn't significantly affect the degradation rate. However, ultraviolet light was essential; without it (the classic Fenton process), the degradation efficiency was much lower. We also conducted plant toxicity tests, which showed a significant reduction in the toxicity of the treated wastewater. Additionally, using a high-resolution FT-ICR-MS mass spectrometer, we searched for 2,4-D and its toxic byproduct, 2,4-dichlorophenol (2,4-DCP), but found neither at the end of the reaction. These results indicate a degradation efficiency of over 99% in all exploratory tests with a 90-minute reaction time. However, we need to complement these results with a total organic carbon (TOC) analysis to measure the extent of contaminant mineralization. These analyses will be conducted in future research once we've optimized the reaction conditions.

Keywords: agriculture, herbicide, photo-fenton, water

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: S. Turkan

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In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: A. Camões, M. Ferreira Dias, M. Amorim

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In recent years, the social economy is increasingly seen as a viable means to address social problems. Social enterprises, as well as public projects and initiatives targeted to meet social purposes, offer organizational models that assume heterogeneity, flexibility and adaptability to the ‘real world and real problems’. Despite the growing popularity of social initiatives, decision makers still face a paucity in what concerns the available models and tools to adequately assess its sustainability, and its impacts, notably the nature of its contribution to economic growth. This study was carried out at the local level, by analyzing the social impact initiatives and projects promoted by the Municipality of Albergaria-a-Velha (Câmara Municipal de Albergaria-a-Velha -CMA), a municipality of 25,000 inhabitants in the central region of Portugal. This work focuses on the challenges related to the qualifications and employability of citizens, which stands out as one of the key concerns in the Portuguese economy, particularly expressive in the context of small-scale cities and inland territories. The study offers a characterization of the Municipality, its socio-economic structure and challenges, followed by an exploratory analysis of multiple sourced data, collected from the CMA's documental sources as well as from privileged informants. The purpose is to conduct detailed analysis of the CMA's social projects, aimed at characterizing its potential impact for the model of qualifications and employability of the citizens of the Municipality. The study encompasses a discussion of the socio-economic profile of the municipality, notably its asymmetries, the analysis of the social projects and initiatives, as well as of data derived from inquiry actors involved in the implementation of the social projects and its beneficiaries. Finally, the results obtained with the Better Life Index will be included. This study makes it possible to ascertain if what is implicit in the literature goes to the encounter of what one experiences in reality.

Keywords: measurement, municipalities, social economy, social impact

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Authors: Abi Miller, Eleanor Wilson, Claire Diver

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Background: UK Head and Neck Cancer incidence and prevalence were rising related to better treatment outcomes and changed demographics. More people of working-age now survive Head and Neck Cancer. For individuals, work provides income, purpose, and social connection. For society, work increases economic productivity and reduces welfare spending. In the UK, a cancer diagnosis is classed as a disability and more disabled people leave the workplace than non-disabled people. Limited evidence exists on return-to-work after Head and Neck Cancer, with no UK qualitative studies. Head and Neck Cancer survivors appear to return to work less when compared to other cancer survivors. This study aimed to explore the effects of Head and Neck Cancer disability on survivors’ return-to-work experience. Methodologies: This was an exploratory qualitative study using a critical realist approach to carry out semi-structured one-off interviews with Head and Neck Cancer survivors who had returned to work. Interviews were informed by an interview guide and carried out remotely by Microsoft Teams or telephone. Interviews were transcribed verbatim, pseudonyms allocated, and transcripts anonymized. Data were interpreted using Reflexive Thematic Analysis. Findings: Thirteen Head and Neck Cancer survivors aged between 41 -63 years participated in interviews. Three major themes were derived from the data: changed identity and meaning of work after Head and Neck Cancer, challenging and supportive work experiences and impact of healthcare professionals on return-to-work. Participants described visible physical appearance changes, speech and eating challenges, mental health difficulties and psycho-social shifts following Head and Neck Cancer. These factors affected workplace re-integration, ability to carry out work duties, and work relationships. Most participants experienced challenging work experiences, including stigmatizing workplace interactions and poor communication from managers or colleagues, which further affected participant confidence and mental health. Many participants experienced job change or loss, related both to Head and Neck Cancer and living through a pandemic. A minority of participants experienced strategies like phased return, which supported workplace re-integration. All participants, bar one, wanted conversations with healthcare professionals about return-to-work but perceived these conversations as absent. Conclusion: All participants found returning to work after Head and Neck Cancer to be a challenging experience. This appears to be impacted by participant physical, psychological, and functional disability following Head and Neck Cancer, work interaction and work context.

Keywords: disability, experience, head and neck cancer, qualitative, return-to-work

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Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: Susmita Ghosh, Bhaskar Bhowmick

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Market is an important factor for start-ups to look into during decision-making in product development and related areas. Emerging country markets are more uncertain in terms of information availability and institutional supports. The literature review of market uncertainty reveals the need for identifying factors representing the market uncertainty. This paper identifies factors for market uncertainty using Exploratory Factor Analysis (EFA) and confirms the number of factor retention using an alternative factor retention criterion, ‘Parallel Analysis’. 500 entrepreneurs, engaged in start-ups from all over India participated in the study. This paper concludes with the factor structure of ‘market uncertainty’ having dimensions of uncertainty in industry orientation, uncertainty in customer orientation and uncertainty in marketing orientation.

Keywords: uncertainty, market, orientation, competitor, demand

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Authors: Santosh Kumar Sharma, Imran Ahmed Khan

Abstract:

Context: In the field of management education, it has been observed that students often have discrepancies between their career anchors and their chosen domain of specialization. This misalignment creates challenges for students during their summer internships and job placements in the corporate sector. The outcome is that some students opt to change their career track or even leave the management profession altogether. This situation poses a significant concern in terms of the overall human capital in the industry. However, there is a notable lack of substantial literature addressing this specific context. Therefore, this current study aims to contribute to the global discourse on management education and its impact on human resource management. Research Aim: The objective of this study is to analyze the deviation between career anchors and domain specialization in the context of management education in India. Methodology: This study adopts an exploratory approach. Data is collected from a substantial sample of post-graduate students who are currently pursuing management education from a renowned business school in India. The data collection process is followed by a descriptive analysis. Findings: The findings of this research contribute to the professional development of management students by highlighting the significance of aligning career anchors with their chosen domain of specialization. This alignment is crucial for enhancing human capital, which in turn impacts various factors within the Indian economy. Theoretical Importance: This study addresses the gap in the existing literature by exploring the relationship between career anchors and domain specialization in management education. By shedding light on this issue, it contributes to theoretical knowledge in the field and provides insights into the importance of career alignment within the management profession.

Keywords: management education, specialization, human resource management, India

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

Abstract:

Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

Abstract:

Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Ruchi Yadav, Shivani Pandey, Prachi Srivastava

Abstract:

Microarrays provide a rich source of data on the molecular working of cells. Each microarray reports on the abundance of tens of thousands of mRNAs. Virtually every human disease is being studied using microarrays with the hope of finding the molecular mechanisms of disease. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation. A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. One of the most popular platforms for microarray analysis is Bioconductor, an open source and open development software project based on the R programming language. This paper describes specific procedures for conducting quality assessment, visualization and preprocessing of Affymetrix Gene Chip and also details the different bioconductor packages used to analyze affymetrix microarray data and describe the analysis and outcome of each plots.

Keywords: microarray analysis, R language, affymetrix visualization, bioconductor

Procedia PDF Downloads 480