Search results for: predicting factors

Authors: Won-Seok Kim, Hyung-Ik Shin

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Self-rated health (SRH) is a subjective assessment of individual health and has been identified as a strong predictor for mortality and morbidity. However few studies have been directed to the factors associated with SRH in persons with disabilities (PWD). We used data of 7th Korean national survey for 5307 PWD in 2008. Multiple logistic regression analysis was performed to find out independent risk factors for poor SRH in PWD. As a result, indicators of physical condition (poor instrumental ADL), socioeconomic disadvantages (poor education, economically inactive, low self-rated social class, medicaid in health insurance, presence of unmet need for hospital use) and social participation and networks (no use of internet service) were selected as independent risk factors for poor SRH in final model. Findings in the present study would be helpful in making a program to promote the health and narrow the gap of health status between the PWD.

Keywords: disabilities, risk factors, self-rated health, socioeconomic disadvantages, social networks

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Authors: Thanatip Lerttanaporn, Tuanjai Somboonwiwat, Charoenchai Khompatraporn

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The automotive parts industry is one of the vital sectors in Thai economy and now is facing a greater competition from ASEAN Economic Community (AEC). This article identifies important factors that impact the competitiveness of Thai automotive parts industry. There are eight groups of factors with a total of 58 factors. Due to a variety of factors, the Exploratory Factor Analysis and Principle Component Analysis have been applied to classify factors into groups or principle components. The results show that there are 15 groups and four of them are critical, covering 80% of important value. These four critical groups are then used to formulate strategies to improve the competitiveness of the Thai automotive parts industry.

Keywords: factor analysis, Thai automotive parts, principle components, exploratory factor, ASEAN economic community

Procedia PDF Downloads 254

Authors: Sharon Cayzer-Haller, Shir Ginosar-Yaari, Ariel Knafo-Noam

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The 21st century emerged as the digital century, and it is marked by a wide range of technological developments and changes, followed by potential changes in human communication skills. This technological revolution has changed human means of communication in many different ways: children and adolescents are spending much of their time in front of screens, participating in all sorts of online activities (even more so since the outbreak of COVID-19). The current study focuses on the role of values in adolescents' online bystanders' behavior. Values are cognitive, abstract representations of desirable goals that motivate behavior, and we hypothesized finding significant associations between specific values and differential online bystanders' feelings and behavior. Data was collected through online questionnaires that measured the participants' values, using Schwartz's short version of the Portrait Values Questionnaire (Schwartz, 2012). Participants’ online behavior was assessed in a questionnaire addressing reactions to situations of cyber shaming and cyberbullying, and specifically positive feelings and pro-social behavior (e.g., more supportive reactions) toward the victims, as opposed to different offensive behavioral reactions (such as laughing at the victim or ignoring the situation). Participants were recruited with a commercial research panel company, and 308 Israeli adolescents' values and online behavior were examined (mean age 15.2). As hypothesized, results show significant associations between self-transcendence values (universalism and benevolence) and conservation values (conformity, tradition, and security). These two groups of values were positively correlated with pro-social bystanders' feelings and behavior. On the opposite side of the values scale, the value of power was negatively associated with the participants' pro-social behavior, and positively associated with offensive behavioral reactions. Further research is needed, but we conclude that values serve as crucial guiding factors in directing adolescents' online feelings and behavior.

Keywords: adolescents, values, cyberbullying, online behavior, power

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Authors: Md. Shahriar Sabuz

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Different social factors influence individuals to migrate from their native lands. This qualitative study was designed to analyze the main social factors that have a significant role in the movement of people across borders. In this study, two research questions, i.e., ‘Which social factors of a country significantly influence the persons' decision to migrate from their homeland?’ and ’2: do different social factors of a country influence the process of international migration?" were formulated and relevant data were analyzed to get the logical answer to these two questions. Data analysis revealed that people migrate in large numbers due to deplorable and unsafe social conditions in their home countries. Sometimes migration occurs due to a lack of basic facilities in native countries. It is quite significant to know that these social conditions create a sense of deprivation and insecurity in individuals, and they move to other lands to get a sense of achievement and greater security for themselves and their whole families. This study is significant and distinct from previous studies in that it provides comprehensive information about the major social factors responsible for international migrations and their role in influencing an individual's proclivity to migrate. Besides this, it greatly opens new horizons of research and analysis for other researchers working on the agenda of international migration.

Keywords: International migration, social factors, income inequality, social discrimination

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Authors: Chao Sima, Shanaz Ghandhi, Sally A. Amundson, Michael L. Bittner, David J. Brenner

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In a large-scale radiologic emergency, potentially affected population need to be triaged efficiently using various biomarkers where personal dosimeters are not likely worn by the individuals. It has long been established that radiation injury can be estimated effectively using panels of genetic biomarkers. Furthermore, the rate of radiation, in addition to dose of radiation, plays a major role in determining biological responses. Therefore, a better and more accurate triage involves estimating both the dose level of the exposure and the length of time of that exposure. To that end, a large in vivo study was carried out on mice with internal emitter caesium-137 (¹³⁷Cs). Four different injection doses of ¹³⁷Cs were used: 157.5 μCi, 191 μCi, 214.5μCi, and 259 μCi. Cohorts of 6~7 mice from the control arm and each of the dose levels were sacrificed, and blood was collected 2, 3, 5, 7 and 14 days after injection for microarray RNA gene expression analysis. Using a generalized linear model with penalized maximum likelihood, a panel of 244 genes was established and both the doses of injection and the number of days after injection were accurately predicted for all 155 subjects using this panel. This has proven that microarray gene expression can be used effectively in radiation biodosimetry in predicting both the dose levels and the length of exposure time, which provides a more holistic view on radiation exposure and helps improving radiation damage assessment and treatment.

Keywords: caesium-137, gene expression microarray, multivariate responses prediction, radiation biodosimetry

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Authors: Rachel C. F. Sun

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This study aimed to examine the similarities and differences between teachers’ and students’ causal explanations of classroom misbehavior. In-depth semi-structured interviews were conducted with twelve teachers and eighteen Grade 7-9 students. The qualitative data were analyzed, in which the attributed causes of classroom misbehavior were categorized into student, family, school, and peer factors. Findings showed that both interviewed teachers and students shared similarity in attributing to student factors, such as ‘fun and pleasure seeking’ and ‘attention seeking’ as the leading causes of misbehavior. However, the students accounted to school factors, particularly ‘boring lessons’ as the next attributed causes, while the teachers accounted to family factors, such as ‘lack of parent demandingness’. By delineating the factors at student, family, school, and peer levels, these findings help drawing corresponding implications for preventing and mitigating misbehavior in school.

Keywords: causal explanation, misbehavior, student, teacher

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Authors: Hyoup-Sang Yoon

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The quality of video transmitted by mobile ad hoc networks (MANETs) can be influenced by several factors, including protocol layers; parameter settings of each protocol. In this paper, we are concerned with understanding the functional relationship between these influential factors and objective video quality in MANETs. We illustrate a systematic statistical design of experiments (DOE) strategy can be used to analyse MANET parameters and performance. Using a 2k factorial design, we quantify the main and interactive effects of 7 factors on a response metric (i.e., mean opinion score (MOS) calculated by PSNR with Evalvid package) we then develop a first-order linear regression model between the influential factors and the performance metric.

Keywords: evalvid, full factorial design, mobile ad hoc networks, ns-2

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Authors: Sadia Munir, Diana White, Aya Albahri, Pratiwi Hastania, Eltahir Mohamed, Mahmood Khan, Fathima Mohamed, Ayat Kadhi, Haila Saleem

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Preeclampsia is a major complication of pregnancy. Prediction and management of preeclampsia is a challenge for obstetricians. To our knowledge, no major progress has been achieved in the prevention and early detection of preeclampsia. There is very little known about the clear treatment path of this disorder. Preeclampsia puts both mother and baby at risk of several short term- and long term-health problems later in life. There is huge health service cost burden in the health care system associated with preeclampsia and its complications. Preeclampsia is divided into two different types. Early onset preeclampsia develops before 34 weeks of gestation, and late onset develops at or after 34 weeks of gestation. Different genetic and environmental factors, prognosis, heritability, biochemical and clinical features are associated with early and late onset preeclampsia. Prevalence of preeclampsia greatly varies all over the world and is dependent on ethnicity of the population and geographic region. To authors best knowledge, no published data on preeclampsia exist in Qatar. In this study, we are reporting the incidence of preeclampsia in Qatar. The purpose of this study is to compare the incidence and risk factors of both early onset and late onset preeclampsia in Qatar. This retrospective longitudinal cohort study was conducted using data from the hospital record of Women’s Hospital, Hamad Medical Corporation (HMC), from May 2014-May 2016. Data collection tool, which was approved by HMC, was a researcher made extraction sheet that included information such as blood pressure during admission, socio demographic characteristics, delivery mode, and new born details. A total of 1929 patients’ files were identified by the hospital information management when they apply codes of preeclampsia. Out of 1929 files, 878 had significant gestational hypertension without proteinuria, 365 had preeclampsia, 364 had severe preeclampsia, and 188 had preexisting hypertension with superimposed proteinuria. In this study, 78% of the data was obtained by hospital electronic system (Cerner) and the remaining 22% was from patient’s paper records. We have gone through detail data extraction from 560 files. Initial data analysis has revealed that 15.02% of pregnancies were complicated with preeclampsia from May 2014-May 2016. We have analyzed difference in the two different disease entities in the ethnicity, maternal age, severity of hypertension, mode of delivery and infant birth weight. We have identified promising differences in the risk factors of early onset and late onset preeclampsia. The data from clinical findings of preeclampsia will contribute to increased knowledge about two different disease entities, their etiology, and similarities/differences. The findings of this study can also be used in predicting health challenges, improving health care system, setting up guidelines, and providing the best care for women suffering from preeclampsia.

Keywords: preeclampsia, incidence, risk factors, maternal

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Authors: Babak Darvish Rouhani, Mohd Nazri Mahrin, Fatemeh Nikpay, Pourya Nikfard, Maryam Khanian Najafabadi

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Enterprise Architecture (EA) is a strategy that is employed by enterprises in order to align their business and Information Technology (IT). EA is managed, developed, and maintained through Enterprise Architecture Implementation Methodology (EAIM). The effectiveness of EA implementation is the degree in which EA helps to achieve the collective goals of the organization. This paper analyzes the results of a survey that aims to explore the factors that affect the effectiveness of EAIM and specifically the relationship between factors and effectiveness of the output and functionality of EA project. The exploratory factor analysis highlights a specific set of five factors: alignment, adaptiveness, support, binding, and innovation. The regression analysis shows that there is a statistically significant and positive relationship between each of the five factors and the effectiveness of EAIM. Consistent with theory and practice, the most prominent factor for developing an effective EAIM is innovation. The findings contribute to the measuring the effectiveness of EA implementation project by providing an indication of the measurement implementation approaches which is used by the Enterprise Architects, and developing an effective EAIM.

Keywords: enterprise architecture, enterprise architecture implementation methodology, implementation methodology, factors, EA, effectiveness

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment

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Authors: Mohammad Matin

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Nowadays, one of the basic problems of higher education is a significant decline in the quality of education and reduction in efficiency of training. These research and studies are aiming to assess affecting factors of the erosion of academic quality, including educational environmental and content, social and economic factors, elements of the training, elements of education, family factors, from the perspective of students. The result of such improper competition, totally, has led to the decline of education quality in higher education centers, and in many aspects. The results showed a significant difference between male and female students' perspective for two areas of social and economic factors.

Keywords: higher education, decline, the quality of education, student

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Authors: Tallataf Rasheed, Adnan Rashdi, Ahmad Naeem Akhtar

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The accurate spectrum sensing is a fundamental requirement of dynamic spectrum access for deployment of Cognitive Radio Network (CRN). To acheive this requirement a Reliability factors based Fuzzy Logic (RFL) Scheme for Spectrum Sensing has been proposed in this paper. Cognitive Radio User (CRU) predicts the presence or absence of Primary User (PU) using energy detector and calculates the Reliability factors which are SNR of sensing node, threshold of energy detector and decision difference of each node with other nodes in a cooperative spectrum sensing environment. Then the decision of energy detector is combined with Reliability factors of sensing node using Fuzzy Logic. These Reliability Factors used in RFL Scheme describes the reliability of decision made by a CRU to improve the local spectrum sensing. This Fuzzy combining scheme provides the accuracy of decision made by sensornode. The simulation results have shown that the proposed technique provide better PU detection probability than existing Spectrum Sensing Techniques.

Keywords: cognitive radio, spectrum sensing, energy detector, reliability factors, fuzzy logic

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Authors: Al-Tammar M. Shahah

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Background and Objective: A novel pneumonia caused by coronavirus disease 2019 (COVID-19), which is spreading domestically and internationally, has been identified by the Chinese city of Wuhan since the end of December 2019. Limited studies examined the psychological experience such as anxiety, depression, and stress during Corona pandemic. Moreover, to the best of author's knowledge, there is no study to date has examined the psychological resilience and mental health during Corona pandemic in Kuwait. Therefore, the present research investigates the role of psychological resilience in predicting psychological distress among Kuwaiti adults during Corona pandemic. Method: Kuwaiti citizens (N = 735) completed an online survey, which includes four scales the Hospital Anxiety and Depression Scale HADS (anxiety and depression), the Connor-Davidson Resilience Scale (CD-RISC-25), and the Perceived Stress Scale (PSS). A cross-sectional correlational design was used. Results: A high level of stress was observed, with 59% reported moderate to severe stress. In contrast, low levels of anxiety and depression were observed; with 70% reporting no anxiety symptoms and 74% report no depression symptoms. Psychological resilience was negatively correlated with anxiety, depression, and stress, consistent with previous studies. As expected, resilience was found to account for significant variance in anxiety and stress after controlling for quarantine variables and demographic variables. Conclusion: The findings suggest that increasing psychological resilience might help reduce psychological distress after confronting with stressful live events in Kuwaiti citizen.

Keywords: anxiety, corona, depression, psychological resilience, stress

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Authors: Sulaiman Al-Hinai, Setyawan Widyarto

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The aim of this study is to identify the critically dominating factors on the delays of road constructions in the GCC countries and their effects on project delivery in Arab countries. Towards the achieved of the objectives the study used the empirical literature from the all relevant online sources and database as many as possible. The findings of this study have summarized and short listed of the success factors in the two categories such as internal and external factors have caused to be influenced to delay of road constructions in the Arab regions. However, in the category of internal factors, there are 63 factors short listed from seven group of factors which has revealed to effects on the delay of road constructions especially, the consultant related factors, the contractor related factors, designed related factors, client related factors, labor related factors, material related issues, equipment related issues respectively. Moreover, for external related factors are also considered to summarized especially natural disaster (flood, hurricanes and cyclone etc.), conflict, war, global financial crisis, compensation delay to affected property owner, price fluctuated, unexpected ground conditions (soil and high-water level), changing of government regulations and laws, delays in obtaining permission from municipality, loss of time by traffic control and restrictions at job site, problem with inhabitant of community, delays in providing service from utilities (water and electricity’s) and accident during constructions accordingly. The present study also concluded the effects of above factors which has delay road constructions through increasing of cost and overrun it, taken overtime, creating of disputes, going for lawsuits, finally happening of abandon of projects. Thus, the present study has given the following recommendations to overcome of above problems by increasing of detailed site investigations, ensure careful monitoring and regular meetings, effective site management, collaborative working and effective coordination’s, proper and comprehensive planning and scheduling and ensure full and intensive commitment from all parties accordingly.

Keywords: Arab GCC countries, critical success factors, road constructions delay, project management

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Authors: Mai Ghazal, Ahmed Hammad

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Cost overruns in construction projects are considered as worldwide challenges since the cost performance is one of the main measures of success along with schedule performance. To overcome this problem, studies were conducted to investigate the cost overruns' factors, also projects' historical data were analyzed to extract new and useful knowledge from it. This research is studying and analyzing the effect of some factors causing cost overruns using the historical data from completed construction projects. Then, using these factors to estimate the probability of cost overrun occurrence and predict its percentage for future projects. First, an intensive literature review was done to study all the factors that cause cost overrun in construction projects, then another review was done for previous researcher papers about mining process in dealing with cost overruns. Second, a proposed data warehouse was structured which can be used by organizations to store their future data in a well-organized way so it can be easily analyzed later. Third twelve quantitative factors which their data are frequently available at construction projects were selected to be the analyzed factors and suggested predictors for the proposed model.

Keywords: construction management, construction projects, cost overrun, cost performance, data mining, data warehousing, knowledge discovery, knowledge management

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Authors: Khalid A. Salman, Shereen Wagih, Tariq Munshi, Musaed Almalki, Safwan Zatari, Zahid Khan

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Aim: To measure exposure levels to family members and caregivers of Saudi patients treated with low dose I131 therapy, and household radiation exposure rate to predict different factors that can affect radiation exposure. Patients and methods: All adult self dependent patients with hyperthyroidism or cancer thyroid referred for low dose radioactive I131 therapy on outpatient basis are included. Radiation protection procedures are given to the participant and family members in details. TLD’s were dispensed to each participant in sufficient quantity for his/her family members living in the household. TLD’s are collected at fifth days post-dispense from patients who agreed to have a home visit during which the household is inspected and level of radiation contamination of surfaces was measured. Results: Thirty-two patients were enrolled in the current study, with a mean age of 43.1± 17.1 years Out of them 25 patients (78%) are females. I131 therapy was given in twenty patients (63%) for cancer thyroid of and for toxic goiter in the remaining twelve patients (37%), with an overall mean I131 dose of 24.1 ± 7.5mCi that is relatively higher in the former. The overall number of household family members and helpers of patients are 139, out of them77 are females (55.4%) & 62 are males (44.6%) with a mean age of 29.8± 17.6. The mean period of contact with the patient is 7.6 ±5.6hours. The cumulative radiation exposure shows that radiation exposure to all family members is below the exposure constraint (1mSv), with a range of 109 to 503uSv, and a mean value of 220.9±91 uSv. Numerical data shows a little higher exposure rate for family members of those who receive higher dose of I131 (patients with thyroid cancer) and household members who spent longer time with the patient, yet, the difference is statistically insignificant (P>0.05). Besides, no significant correlation was found between the degree of cumulative exposure of the family members to their gender, age, socioeconomic standard, educational level and residential factors. In the 21 home visits all data from bedrooms, reception areas and kitchens are below hazardous limits (0.5uSv/h) apart from bathrooms that give a slightly higher reading of 0.57±0.39 uSv/h in those with cancer thyroid who receive a higher radiation dose. A statistically significant difference was found between radiation exposure rate in bathrooms used by the patient versus those used by family members only, with a mean value of exposure rate of 0.701±0.21 uSv/h and 0.17±0.82 uSv/h respectively, with a p-value of 0.018 (<0.05). Conclusion: Family members of patients treated with low dose I131 on outpatient basis have a good compliance to radiation protection instruction if given properly with a cumulative radiation exposure rate evidently beyond the radiation exposure constraints of 1 mSv. Given I131 dose, hours spent with the patient, age, gender, socioeconomic standard, educational level and residential factors have no significant correlation with the cumulative radiation exposure. The patient bathroom exhibits more radiation exposure rate, needing more strict instructions for patient bathroom use and health hygiene.

Keywords: family members, radiation exposure, radioactive iodine therapy, radiation safety

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Authors: Atziri Moreno Vite, David Silva Gutiérrez

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Oil companies have become the key player in the world energy scenario thanks to their strong control of the level of hydrocarbon reserves and production. The present research aims to identify the main factors that explain the results of these companies through an in-depth review of the specialized literature and to analyze the results of these companies by means of econometric analysis with techniques such as Data Envelopment Analysis (DEA). The results show the relevance and impact of factors such as the level of employment or investment of the company.

Keywords: oil companies, performance, determinants, productive

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Noppadol Sritragool

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The objectives of this research were to investigate the decision of passengers who choose to fry with low cost airlines and to study marketing factors which have the influence to the decision to choose each low cost airlines. This paper was a quantitative research technique. A total of 400 low cost airlines’ passengers were interviewed via English questionnaire to collect the respondents’ opinions. The findings revealed that respondents were male and female at a similar proportion. The majority had at least an undergraduate degree, have a lower management level jobs, and had income in the range of 25,000 -35,000 baht per month.. In addition, the findings also revealed that the first three marketing factors influencing the decision of the respondents to choose low-cost airlines were low price, direct flight, and online system.

Keywords: decision to choose, marketing factors, low-cost airlines

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Authors: Zanjbeel Tabassum

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The main purpose of any economic investment is to get profit at the end. As the investment in large organizations bears complexities, investors are influenced to invest in small or medium enterprises. With the increase of global competition in terms of quality and productivity, these small and medium-sized enterprises (SMEs) are trying to convert to modern production practices using Six Sigma. But this concept is still lacking in Pakistani SMEs. There are some critical success factors which influence the successful implementation of Six Sigma. Through this paper, an attempt has been made to identify various CSF for successful implementation of Six Sigma in Pakistani SMEs with the help of a structured survey. On the basis of responses to the questionnaire, factor analysis is performed on the selected critical success factors (from literature) to prioritize the critical factors and those are rated by calculating descriptive statistics. This paper will provide a base for Pakistani SMEs and future researchers working in six sigma implementation and help them to prepare a road map to eradicate the hurdles in six sigma implementation.

Keywords: critical success factors, SMEs, Six Sigma, CSF

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Authors: Peter S. Kim

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The increasing availability of music facilitated by new technology and open sourcing has eliminated many traditional limiting factors in musical taste, creating a culture of choice. This study tested 191 international subjects, mostly young adults more decisively shaped by emerging technologies like Facebook, the platform for the study. Using an aggregated Big Five personality test, subjects were asked to self-report on questions related to extraversion, agreeableness, conscientiousness, neuroticism, and openness. Subsequently, subjects listened to five pairs of musical works reflecting opposite extremes of one of five musical qualities: tempo (fast/slow), complexity (simple/complex), degree of dissonance (tonal/atonal), familiarity (familiar/unfamiliar), and extra-musical significance (significant/not significant). Subjects were then asked to record listening times and preferences among the selections. Strikingly, this study shows a relatively high positive correlation between agreeableness and musical preferences (predicting preferences for simple, familiar, and fast music), as compared to extraversion, openness, conscientiousness, and neuroticism. Thus, this research suggests that the not yet well-understood relationship between personality traits and musical qualities merits further study.

Keywords: music perception, psychology, cognition, musical preference

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Authors: Jo Rhodes, Bruce Bergstrom, Peter Lok, Vincent Cheng

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The aim of this study is to determine key factors and processes for multinationals (MNCs) to develop an effective stakeholder engagement and sustainable development framework. A qualitative multiple-case approach was used. A triangulation method was adopted (interviews, archival documents and observations) to collect data on three global firms (MNCs). 9 senior executives were interviewed for this study (3 from each firm). An initial literature review was conducted to explore possible practices and factors (the deductive approach) to sustainable development. Interview data were analysed using Nvivo to obtain appropriate nodes and themes for the framework. A comparison of findings from interview data and themes, factors developed from the literature review and cross cases comparison were used to develop the final conceptual framework (the inductive approach). The results suggested that stakeholder engagement is a key mediator between ‘stakeholder network’ (internal and external factors) and outcomes (corporate social responsibility, social capital, shared value and sustainable development). Key internal factors such as human capital/talent, technology, culture, leadership and processes such as collaboration, knowledge sharing and co-creation of value with stakeholders were identified. These internal factors and processes must be integrated and aligned with external factors such as social, political, cultural, environment and NGOs to achieve effective stakeholder engagement.

Keywords: stakeholder, engagement, sustainable development, shared value, corporate social responsibility

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Authors: Seyed Abolhasan Naeini, Elham Ghanbari Alamooti

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Static stability of soil slopes using numerical simulation by a finite element code, ABAQUS, has been investigated, and safety factors of the slopes achieved in the case of static load of a 10-storey building. The embankments have the same soil condition but different loading distance from the slope heel. The numerical method for estimating safety factors is 'Strength Reduction Method' (SRM). Mohr-Coulomb criterion used in the numerical simulations. Two steps used for measuring the safety factors of the slopes: first is under gravity loading, and the second is under static loading of a building near the slope heel. These safety factors measured from SRM, are compared with the values from Limit Equilibrium Method, LEM. Results show that there is good agreement between SRM and LEM. Also, it is seen that by increasing the distance from slope heel, safety factors increases.

Keywords: limit equilibrium method, static stability, soil slopes, strength reduction method

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Authors: Intan Maisarah Abd Rahim, Herlina Abdul Rahim, Rashidah Ghazali

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Diabetes is a medical condition that can lead to various diseases such as stroke, heart disease, blindness and obesity. In clinical practice, the concern of the diabetic patients towards the blood glucose examination is rather alarming as some of the individual describing it as something painful with pinprick and pinch. As for some patient with high level of glucose level, pricking the fingers multiple times a day with the conventional glucose meter for close monitoring can be tiresome, time consuming and painful. With these concerns, several non-invasive techniques were used by researchers in measuring the glucose level in blood, including ultrasonic sensor implementation, multisensory systems, absorbance of transmittance, bio-impedance, voltage intensity, and thermography. This paper is discussing the application of the near-infrared (NIR) spectroscopy as a non-invasive method in measuring the glucose level and the implementation of the linear system identification model in predicting the output data for the NIR measurement. In this study, the wavelengths considered are at the 1450 nm and 1950 nm. Both of these wavelengths showed the most reliable information on the glucose presence in blood. Then, the linear Autoregressive Moving Average Exogenous model (ARMAX) model with both un-regularized and regularized methods was implemented in predicting the output result for the NIR measurement in order to investigate the practicality of the linear system in this study. However, the result showed only 50.11% accuracy obtained from the system which is far from the satisfying results that should be obtained.

Keywords: diabetes, glucose level, linear, near-infrared, non-invasive, prediction system

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Authors: Tonga Agadi Danladi, Ajao Wasiu Bamidele, Terdue Dyeko

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Recent advancement in microwave communications technologies especially in telecommunications and broadcasting have resulted in congestion on the frequencies below 10GHz. This has forced microwave designers to look for high frequencies. Unfortunately for frequencies greater than 10GHz rain becomes one of the main factors of attenuation in signal strength. At frequencies from 10GHz upwards, rain drop sizes leads to outages that compromises the availability and quality of service this making it a critical factor in satellite link budget design. Rain rate and rain attenuation predictions are vital steps to be considered when designing microwave satellite communication link operating at Ku-band frequencies (112-18GHz). Unreliable rain rates data in the tropical regions of the world like Nigeria from radio communication group of the international Telecommunication Union (ITU-R) makes it difficult for microwave engineers to determine a realistic rain margin that needs to be accommodated in satellite link budget design in such region. This work presents an empirical tool for predicting the amount of signal due to rain on DBS signal operating at the Ku-band.

Keywords: attenuation, Ku-Band, microwave communication, rain rates

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Nahid Ehsani, Amir Hossein Rezvanfar

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The main objective of this study is to investigate psychological and socio-cultural factors effective on the tendency of the villagers to use e-banking services. The current paper is an applied study considering its objectives. The main data gathering tool in the current study is a made questionnaire which is designed and executed based on the conceptual background of the subject matter and the objectives and hypotheses of the study. The statistical population of this study includes all the customers of rural branches of Agricultural Bank in Ilam Province (N=82885). Among these 120 participants were chosen through sample size determination formula and they were studied using stratified random sampling method. In the analytical statistics level the results obtained from calculating Spearman’s Correlative Coefficient showed that socio-cultural and psychological factors had a significant impact of the extent of the tendency of the villagers to use e-banking services of the Agricultural Bank at the 99% level. Furthermore, stepwise multiple regression analysis showed that both sets of psychological factors as well as socio-economic factors were able to explain 50 percent of the variance of the independent variable; namely the tendency of villagers to use e-banking services.

Keywords: e-banking, agricultural bank, tendency, socio-economic factors, psychological factors

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Authors: Jacob J. Shila, Xiaoyu O. Wu

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As the applications of unmanned aerial vehicles (UAV) continue to increase across the world, it is critical to understand the factors that contribute to incidents and accidents associated with these systems. Given the variety of daily applications that could utilize the operations of the UAV (e.g., medical, security operations, construction activities, landscape activities), the main discussion has been how to safely incorporate the UAV into the national airspace system. The types of UAV incidents being reported range from near sightings by other pilots to actual collisions with aircraft or UAV. These incidents have the potential to impact the rest of aviation operations in a variety of ways, including human lives, liability costs, and delay costs. One of the largest causes of these incidents cited is the human factor; other causes cited include maintenance, aircraft, and others. This work investigates the key human factors associated with UAV incidents. To that end, the data related to UAV incidents that have occurred in the United States is both reviewed and analyzed to identify key human factors related to UAV incidents. The data utilized in this work is gathered from the Federal Aviation Administration (FAA) drone database. This study adopts the human factor analysis and classification system (HFACS) to identify key human factors that have contributed to some of the UAV failures to date. The uniqueness of this work is the incorporation of UAV incident data from a variety of applications and not just military data. In addition, identifying the specific human factors is crucial towards developing safety operational models and human factor guidelines for the UAV. The findings of these common human factors are also compared to similar studies in other countries to determine whether these factors are common internationally.

Keywords: human factors, incidents and accidents, safety, UAS, UAV

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Authors: Mohd Rashid bin Ab Hamid, Noor Azura binti Omar, Zainol Bin Mustafa

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Purpose: The aim of this paper is to study the encouraging factors for scholarly works contribution towards among academicians at Malaysian university. Methods: This paper uses questionnaire for data collection on the respondents’ perceptional level on the institutional repository efforts in one of the university under study. Several encouraging factors have been identified and to be measured using descriptive statistics. The factors are related to content contribution, i.e. personal factor, professional factor, organizational factor and technological factor. Findings: The study found that all these four encouraging factors did have a relation to the contribution of scholarly works in the university by the academician. Research Limitations: This study used a case study and generalization to all Malaysian universities should be well taken care of. Practical implications: The library at the university should look into these four encouraging factors in order to enhance the contribution from academician towards the repository. Originality/value: This research paper provides basic information for the knowledge management officers in the university by endeavouring more efforts in order to attract more contributions.

Keywords: institutional repository, information retrieval, information storage and retrieval

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Authors: H. A. Lopez-Aguilar, E. A. Huerta-Reynoso, J. A. Gomez, J. A. Duarte-Moller, A. Perez-Hernandez

Abstract:

Environmental performance of artisanal brick manufacture was studied by Lifecycle Assessment (LCA) methodology and Computational Fluid Dynamics (CFD) analysis in Mexico. The main objective of this paper is to evaluate the environmental impact during artisanal brick manufacture. LCA cradle-to-gate approach was complemented with CFD analysis to carry out an Environmental Impact Assessment (EIA). The lifecycle includes the stages of extraction, baking and transportation to the gate. The functional unit of this study was the production of a single brick in Chihuahua, Mexico and the impact categories studied were carcinogens, respiratory organics and inorganics, climate change radiation, ozone layer depletion, ecotoxicity, acidification/ eutrophication, land use, mineral use and fossil fuels. Laboratory techniques for fuel characterization, gas measurements in situ, and AP42 emission factors were employed in order to calculate gas emissions for inventory data. The results revealed that the categories with greater impacts are ecotoxicity and carcinogens. The CFD analysis is helpful in predicting the thermal diffusion and contaminants from a defined source. LCA-CFD synergy complemented the EIA and allowed us to identify the problem of thermal efficiency within the system.

Keywords: LCA, CFD, brick, artisanal

Procedia PDF Downloads 393