Search results for: hierarchal regression analysis

Authors: Arpine Maghakyan

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The purpose of this paper is to study the relationship between the level of industry digitalization and audit fees, especially, the relationship between Big 4 auditor fees and industry digitalization level. On the one hand, automation of business processes decreases internal control weakness and manual mistakes; increases work effectiveness and integrations. On the other hand, it may cause serious misstatements, high business risks or even bankruptcy, typically in early stages of automation. Incomplete automation can bring high audit risk especially if the auditor does not fully understand client’s business automation model. Higher audit risk consequently will cause higher audit fees. Higher audit fees for clients with high automation level are more highlighted in Big 4 auditor’s behavior. Using data of US firms from 2005-2015, we found that industry level digitalization is an interaction for the auditor quality on audit fees. Moreover, the choice of Big4 or non-Big4 is correlated with client’s industry digitalization level. Big4 client, which has higher digitalization level, pays more than one with low digitalization level. In addition, a high-digitalized firm that has Big 4 auditor pays higher audit fee than non-Big 4 client. We use audit fees and firm-specific variables from Audit Analytics and Compustat databases. We analyze collected data by using fixed effects regression methods and Wald tests for sensitivity check. We use fixed effects regression models for firms for determination of the connections between technology use in business and audit fees. We control for firm size, complexity, inherent risk, profitability and auditor quality. We chose fixed effects model as it makes possible to control for variables that have not or cannot be measured.

Keywords: audit fees, auditor quality, digitalization, Big4

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Authors: Shujie Liu

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This study aimed to investigate the relationship between teachers’ perceived transformational leadership behaviors and their job satisfaction in China after controlling for teacher self-efficacy. Hierarchical regression analysis (HRA) technique was employed to examine factors’ contributions to teacher job satisfaction with a sample of Chinese high school teachers. The finding of this study provided evidence that teachers’ perceived transformational leadership behaviors accounted for a large percentage (44.9%) of the variance in Chinese teachers’ job satisfaction. Uniquely, school principals’ sense of power was a negative significant predictor of teacher job satisfaction, meaning that the more teachers perceived their principals’ sense of power, the lower of their job satisfaction. Furthermore, this study provided evidence that teacher self-efficacy significantly contributes to teacher job satisfaction. Specifically, teachers’ self-efficacy on student engagement was found to be a significant predictor of teacher job satisfaction. The conclusions were discussed in terms of Chinese cultures. The authors pointed out that how to make teachers involved in school policy making is a challenge for China and that more shared leadership is needed in Chinese schools.

Keywords: Chinese teachers, teacher job satisfaction, teacher self-efficacy, transformational leadership

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP.

Keywords: Chinese restaurant process, Dirichlet prior, infinite mixture model, PCR stutter

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Authors: Anatoliy Kharkhurin

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The study argues that variations in creative performance may be stipulated by cross-cultural differences in perception of the creativity construct. In Experiment 1, 50 Russian and 50 Emirati college students received structured imagination test that requires producing a drawing of an alien creature. In Experiment 2, 53 Russian and 53 Emirati college students (different from Experiment 1) on 5-point Likert-type scale evaluated the level of creativity of the drawings produced in the Experiment I. Repeated-measure ANOVA revealed an interaction between the country where the drawings were produced and the country where they were evaluated. Russians evaluated their country mates’ drawings as more creative than the Emiratis evaluated their country mates’ drawings. Regression analysis revealed that the creativity level of the drawings was positively predicted by the Russians’ evaluation and negatively predicted by the Emiratis’ evaluation. Finally, the evaluation of the drawings by the Russians predicted divergent thinking performance.

Keywords: creativity, culture, cross-cultural, perception, production

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: Teka Haile, Behailu Hawulte, Solomon Alemayehu

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Background: HIV virological failure still remains a problem in HV/AIDS treatment and care. This study aimed to describe the prevalence and identify the factors associated with viral non-suppression among HIV-positive adult patients on antiretroviral therapy in Woliso Town, Oromia, Ethiopia. Methods: A retrospective cross-sectional study was conducted among 424 HIV-positive patient’s attending antiretroviral therapy (ART) in Woliso Town during the period from August 25, 2020 to August 30, 2020. Data collected from patient medical records were entered into Epi Info version 2.3.2.1 and exported to SPSS version 21.0 for analysis. Logistic regression analysis was done to identify factors associated with viral load non-suppression, and statistical significance of odds ratios were declared using 95% confidence interval and p-value < 0.05. Results: A total of 424 patients were included in this study. The mean age (± SD) of the study participants was 39.88 (± 9.995) years. The prevalence of HIV viral load non-suppression was 55 (13.0%) with 95% CI (9.9-16.5). Second-line ART treatment regimen (Adjusted Odds Ratio (AOR) = 8.98, 95% Confidence Interval (CI): 2.64, 30.58) and routine viral load testing (AOR = 0.01, 95% CI: 0.001, 0.02) were significantly associated with virological non-suppression. Conclusion: Virological non-suppression was high, which hinders the achievement of the third global 95 target. The second-line regimen and routine viral load testing were significantly associated with virological non-suppression. It suggests the need to assess the effectiveness of antiretroviral drugs for epidemic control. It also clearly shows the need to decentralize third-line ART treatment for those patients in need.

Keywords: virological non-suppression, HIV-positive, ART, Woliso town, Ethiopia

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Authors: A. Kumar, V. Devadas

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India has the most year-round favorable sunny conditions along with the second-highest solar irradiation in the world, the country holds the potential to become the global solar hub. The solar and wind-based generation capacity has skyrocketed in India with the successful effort of the Ministry of Renewable Energy, whereas the potential of rooftop based solar power generation has yet to be explored for proposed solar cities in India. The research aims to analyze the gap in the energy scenario in Jodhpur City and proposes interventions of solar energy generation systems as a catalyst for urban development. The research is based on the system concept which deals with simulation between the city system as a whole and its interactions between different subsystems. A system-dynamics based mathematical model is developed by identifying the control parameters using regression and correlation analysis to assess the gap in energy sector. The base model validation is done using the past 10 years timeline data collected from secondary sources. Further, energy consumption and solar energy generation-based projection are made for testing different scenarios to conclude the feasibility for maintaining the city level energy independence till 2031.

Keywords: city, consumption, energy, generation

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Authors: Mohammad Abu Nassar, Mahmoud Al Khalilah, Hussein Abu Nassar

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The purpose of this study is to examine whether corporate tax avoidance practices can impact firm value in the Jordanian context. The study employs a quantitative approach using s sample of (124) industrial and services companies listed on the Amman Stock Exchange for the period from 2010 to 2019. Multiple linear regression analysis has been applied to test the study's hypothesis. The study employs effective tax rate and book-tax difference to measure tax avoidance and Tobin's Q factor to measure firm value. The results of the study revealed that tax avoidance practices, when measured using effective tax rates, do not significantly impact firm value. When the book-tax difference is used to measure tax avoidance, the study results showed a negative impact on firm value. The result of the study has not supported the traditional view of tax avoidance as a transfer of wealth from the government to shareholders for industrial and services companies listed on the Amman Stock Exchange, indicating that Jordanian firms should not use tax avoidance strategies to enhance their value.

Keywords: tax avoidance, effective tax rate, book-tax difference, firm value, Amman stock exchange

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Authors: Abdul-Monim Batiha, Manar AlAzzam, Mohammed ALBashtawy, Loai Tawalbeh, Ahmad Tubaishat, Fadwa N. Alhalaiqa

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Background: Obesity is the major modifiable risk factor for many chronic illnesses especially high blood pressure. Objectives: To evaluate the relationship between anthropometric indices and high blood pressure, and which one was most strongly correlated with high blood pressure in Jordanian population. Methods: A cross-sectional study was conducted with a total 622 students and workers from three Jordanian universities. Results: Nearly half of the participant are overweight (34.7%) and obese (15.4%) and hypertension was detected among 138 (22.2%) of the participants. Linear correlation was significant (p<0.01) between both systolic blood pressure and diastolic blood pressure for all anthropometric indices, except for A body shape index and diastolic blood pressure was significant at p< 0.05. Stepwise multiple linear regression analysis was used to assess the influence of age and anthropometric measurements. Conclusions: The waist circumference was the only independent predictor of hypertension, showing that this simple measurement may be an importance marker of high blood pressure in Jordanian population.

Keywords: anthropometric indices, Jordan, blood pressure, cross-sectional study, obesity, hypertension, waist circumference

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Authors: Sadhana Ghnayem

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This article examines the relationship between several socio-economic and background variables of Arab-Israeli families and their effect on the conflict management style of forcing, where teenage children are expected to obey their parents without questioning. The article explores the inter-generational gap and the desire of Arab-Israeli parents to force their teenage children to obey without questioning. The independent variables include: the sex of the parent, religion (Christian or Muslim), income of the parent, years of education of the parent, and the sex of the teenage child. We use the dependent variable of “Obedience Without Questioning” that is reported twice: by each of the parents as well as by the children. We circulated a questionnaire and collected data from a sample of 180 parents and their adolescent child living in the Galilee area during 2018. In this questionnaire we asked each of the parent and his/her teenage child about whether the latter is expected to follow the instructions of the former without questioning. The outcome of this article indicates, first, that Christian-Arab families are less authoritarian than Muslims families in demanding sheer obedience from their children. Second, female parents indicate more than male parents that their teenage child indeed obeys without questioning. Third, there is a negative correlation between the variable “Income” and “Obedience without Questioning.” Yet, the regression coefficient of this variable is close zero. Fourth, there is a positive correlation between years of education and obedience reported by the children. In other words, more educated parents are more likely to demand obedience from their children.  Finally, after running the regression, the study also found that the impact of the variables of religion as well as the sex of the child on the dependent variable of obedience is also significant at above 95 and 90%, respectively.

Keywords: conflict, religion, conflict management style, obedience

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Authors: Velly Anatasia

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The worldwide annual expenditure for cosmetics is estimated at U.S. $18 billion and many players in the field are competing aggressively to capture more and more markets. Players in the cosmetics industry strive to be the foremost by establish customer loyalty. Furthermore, customer loyalty is portrayed by brand loyalty. Therefore, brand loyalty is the key determine of winning the competition in tight market. This study examines the influence of brand loyalty on cosmetics buying behavior of female consumers in Jakarta as capital of Indonesia. The seven factors of brand loyalty are brand name, Product quality, price, design, promotion, servicesquality and store environment. The paper adopted descriptive analysis, factor loading and multiple regression approach to test the hypotheses. The data has been collected by using questionnaires which were distributed and self-administered to 125female respondents accustomed using cosmetics. The findings of this study indicated that promotion has shown strong correlation with brand loyalty. The research results showed that there is positive and significant relationship between factors of brand loyalty (brand name, product quality, price, design, promotion, services quality and store environment) with cosmetics brand loyalty.

Keywords: brand loyalty, brand name, product quality, service quality, promotion

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Authors: Xitong Liu, Yutong Huang, Shu-Ching Yang

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The phenomena of hypergamous and hypogamous marriages have become popular due to the imbalanced sex ratio caused by Chinese social preference for sons. Our research explores the spillover effect of husbands' lifestyles on their wives' marital satisfaction in China. Both personal and spouse lifestyle elements are utilized to develop regression models to study husbands' spillover effects on women's marital satisfaction. With data from China Family Panel Study and Stata for analysis, we tested our hypothesis that both smoking and substance use by a spouse will negatively impact women's marital satisfaction. Our empirical findings suggest that substance use has negative implications on marriage satisfaction. In particular, husbands' substance use is more critical to wives' marriage satisfaction than wives' behaviours. Conversely, another behavior indicating bad habits, the number of times the spouse drank alcohol, had no significant effect on the wife's marital satisfaction. We concluded our investigation and provided future implications for scholars in the family economics field.

Keywords: Asian/Pacific Islander families, family economics, housework/division of labor, spillover

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Authors: Silvia Portarena, Chiara Anselmi, Chiara Baldacchini, Enrico Brugnoli

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Extra virgin olive oil (EVOO) is a complex matrix mainly composed by fatty acid and other minor compounds, among which carotenoids are well known for their antioxidative function that is a key mechanism of protection against cancer, cardiovascular diseases, and macular degeneration in humans. EVOO composition in terms of such constituents is generally the result of a complex combination of genetic, agronomical and environmental factors. To selectively improve the quality of EVOOs, the role of each factor on its biochemical composition need to be investigated. By selecting fruits from four different cultivars similarly grown and harvested, it was demonstrated that Raman spectroscopy, combined with chemometric analysis, is able to discriminate the different cultivars, also as a function of the harvest date, based on the relative content and composition of fatty acid and carotenoids. In particular, a correct classification up to 94.4% of samples, according to the cultivar and the maturation stage, was obtained. Moreover, by using gas chromatography and high-performance liquid chromatography as reference techniques, the Raman spectral features further allowed to build models, based on partial least squares regression, that were able to predict the relative amount of the main fatty acids and the main carotenoids in EVOO, with high coefficients of determination. Besides genetic factors, climatic parameters, such as light exposition, distance from the sea, temperature, and amount of precipitations could have a strong influence on EVOO composition of both major and minor compounds. This suggests that the Raman spectra could act as a specific fingerprint for the geographical discrimination and authentication of EVOO. To understand the influence of environment on EVOO Raman spectra, samples from seven regions along the Italian coasts were selected and analyzed. In particular, it was used a dual approach combining Raman spectroscopy and isotope ratio mass spectrometry (IRMS) with principal component and linear discriminant analysis. A correct classification of 82% EVOO based on their regional geographical origin was obtained. Raman spectra were obtained by Super Labram spectrometer equipped with an Argon laser (514.5 nm wavelenght). Analyses of stable isotope content ratio were performed using an isotope ratio mass spectrometer connected to an elemental analyzer and to a pyrolysis system. These studies demonstrate that RR spectroscopy is a valuable and useful technique for the analysis of EVOO. In combination with statistical analysis, it makes possible the assessment of specific samples’ content and allows for classifying oils according to their geographical and varietal origin.

Keywords: authentication, chemometrics, olive oil, raman spectroscopy

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Authors: Stefano Frassinelli, Alessandro Niccolai, Riccardo E. Zich

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Diving is an important Olympic sport. In this sport, the effective performance of the athlete is related to his capability to interact correctly with the springboard. In fact, the elevation of the jump and the correctness of the dive are influenced by the vibrations of the board. In this paper, the vibrations of the springboard will be analyzed by means of typical tools for vibration analysis: Firstly, a modal analysis will be done on two different models of the springboard, then, these two model and another one will be analyzed with a time analysis, done integrating the equations of motion od deformable bodies. All these analyses will be compared with experimental data measured on a real springboard by means of a 6-axis accelerometer; these measurements are aimed to assess the models proposed. The acquired data will be analyzed both in frequency domain and in time domain.

Keywords: springboard analysis, modal analysis, time domain analysis, vibrations

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Authors: M. Tahir Demirsel, Mustafa Atsan

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Many elements of the environment in which businesses operate today leave them faced with unexpected threats and opportunities. One of the major threats is business crisis. The crisis is a state of affairs in a business wherein the executives must take urgent and unprecedented action to try to save the business from failure. In order to survive in the business environment, organizations should be prepared for the potential crises. Technological developments, uncertainty in the market and the intense competition increase the probability of encountering a crisis for organizations. Therefore, by acting proactively to predict crisis, to detect signals of crisis and be prepared for a crisis by taking necessary precautions accordingly, is of great importance for businesses. In this context, the objective of this study is to reveal that how much organizations are proactive and can predict the future crises and investigate whether they are prepared for possible crises or not. The research was conducted on 222 business executives in one of the major industrial zones of Turkey, Konya Organized Industrial Zone (KOS). The findings are analyzed through descriptive statistics and multiple regression analysis. According to the results, it has been observed that organizations cannot predict the crisis signals and are not prepared for potential crises.

Keywords: crisis preparedness, crisis signals, industrial organizations, proactivity

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Authors: M. Tassi, E. Dotsika, P. Karalis, A. Trantalidou, A. Mazarakis Ainian

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The Kythnos Island in Greece is of particular archaeological interest, as it has been inhabited from the 12th BC until the 7th AD. From island excavations, numerous faunal and human skeletal remains have been recovered. This work is the first attempt at the paleoenvironmental reconstruction of the island via stable isotope analysis. Specifically, we perform 13C and 18O isotope analysis in faunal bone apatite in order to investigate the climate conditions that prevailed in the area. Additionally, we conduct 13C and 15N isotope analysis in faunal bone collagen, which will constitute the baseline for the subsequent diet reconstruction of the ancient Kythnos population.

Keywords: stable isotopes analysis, bone collagen stable isotope analysis, bone apatite stable isotope analysis, paleodiet, palaeoclimate

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Cavidan Yakupoglu, Kurt Rohloff

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In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here.

Keywords: lattice cryptography, fully homomorphic encryption, parameter selection, LWE, RLWE

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

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Authors: Mohd Najib Mansor, Nur Syamilah Md. Noor, Abdul Rahim Anuar, Shazida Jan Mohd Khan, Ahmad Zubir Ibrahim, Badariah Hj Din, Abu Sufian Abu Bakar, Kalsom Kayat, Wan Nurmahfuzah Jannah Wan Mansor

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This study aimed and focused on the behavior of youth entrepreneurs’ especially entrepreneurial self-efficacy and the performance in micro SMEs in Malaysia. Entrepreneurship development calls for support from various quarters, and mostly the need exists to initiate a youth entrepreneurship culture and drive amongst the youth in the society. Although backed up by the government and non-government organizations, micro-entrepreneurs are still facing challenges which greatly delay their progress, growth and consequently their input towards economic advancement. Micro-entrepreneurs are confronted with unique difficulties such as uncertainty, innovation, and evolution. Reviews on the development of entrepreneurial characteristics such as need for achievement, internal locus of control, risk-taking and innovation and have been recognized as highly associated with entrepreneurial behavior. The data in this study was obtained from the Department of Statistics, Malaysia. A random sampling of 830 respondents was distributed to 14 states that involve of micro-entrepreneurs. The study adopted a quantitative approach whereby a set of questionnaire was used to gather data. Multiple regression analysis was chosen as a method of analysis testing. The result of this study is expected to provide insight into the factor affecting entrepreneurial behavior and performance of youth entrepreneurs in micro SMEs. The finding showed that the Malaysian youth entrepreneurs do not have the entrepreneurial self-efficacy within themselves in order to accomplish greater success in their business venture. The establishment of entrepreneurial schools to allow our youth to be exposed to entrepreneurship from an early age and the development of special training focuses on the creation of business network so that the continuous entrepreneurial culture is crafted.

Keywords: youth entrepreneurs, micro entrepreneurs, entrepreneurial self-efficacy, entrepreneurial performance

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Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif

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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.

Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence

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Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

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Authors: Emilio Galdeano-Gómez, Ángeles Godoy-Durán, Juan C. Pérez-Mesa, Laura Piedra-Muñoz

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Eco-efficiency is receiving ever-increasing interest as an indicator of sustainability, as it links environmental and economic performances in productive activities. In agriculture, these indicators and their determinants prove relevant due to the close relationships in this activity between the use of natural resources, which is generally limited, and the provision of basic goods to society. In this context, various analyses have focused on eco-efficiency by considering individual family farms as the basic production unit. However, not only must the measure of efficiency be taken into account, but also the existence of a series of factors which constitute socio-economic, political-institutional, and environmental determinants. Said factors have been studied to a lesser extent in the literature. The present work analyzes eco-efficiency at a micro level, focusing on small-scale family farms as the main decision-making units in horticulture in southeast Spain, a sector which represents about 30% of the fresh vegetables produced in the country and about 20% of those consumed in Europe. The objectives of this study are a) to obtain a series of eco-efficiency indicators by estimating several pressure ratios and economic value added in farming, b) to analyze the influence of specific social, economic and environmental variables on the aforementioned eco-efficiency indicators. The present work applies the method of Data Envelopment Analysis (DEA), which calculates different combinations of environmental pressures (water usage, phytosanitary contamination, waste management, etc.) and aggregate economic value. In a second stage, an analysis is conducted on the influence of the socio-economic and environmental characteristics of family farms on the eco-efficiency indicators, as endogeneous variables, through the use of truncated regression and bootstrapping techniques, following Simar-Wilson methodology. The results reveal considerable inefficiency in aspects such as waste management, while there is relatively little inefficiency in water usage and nitrogen balance. On the other hand, characteristics, such as product specialization, the adoption of quality certifications and belonging to a cooperative do have a positive impact on eco-efficiency. These results are deemed to be of interest to agri-food systems structured on small-scale producers, and they may prove useful to policy-makers as regards managing public environmental programs in agriculture.

Keywords: data envelopment analysis, eco-efficiency, family farms, horticulture, socioeconomic features

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Authors: Nannapus Saramad, Rewwadee Petsirasan, Jom Suwanno

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Background: This retrospective study design was to describe average blood pressure, blood pressure level, target blood pressure control rate post-stroke BP control in the year following discharge from Sichon hospital, Sichon District, Nakhon Si Thammarat province. The secondary data analysis was employed from the patient’s health records with patient or caregiver interview. A total of 232 eligible post-acute ischemic strokes in the year following discharge (2017-2018) were recruited. Methods: Data analyses were applied to identify the relationship values of single variables were determined through univariate analyses: The Chi-square test, Fisher exact test, the variables found to have a p-value < 0.2 were analyzed by the binary logistic regression Results: Most of the patients in this study were men 61.6%, an average age of 65.4 ± 14.8 years. Systolic blood pressure levels were in the grade 1-2 hypertension and diastolic pressure at optimal and normal at all times during the initial treatment through the present. The results revealed 25% among the groups under the age of 60 achieved BP control; 36.3% for older than 60 years group; and 27.9% for diabetic group. The multivariate analysis revealed the final relationship of four significant variables: 1) receiving calcium-channel blocker (p =.027); 2) medication adherence of antihypertensive (p = .024) 3) medication adherence of antiplatelet ( p = .020); and 4) medication behavior ( p = . 010) . Conclusion: The medical nurse and health care provider should promote their adherence to behavior to improve their blood pressure control.

Keywords: acute ischemic stroke, target blood pressure control, medication adherence, recurrence stroke

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Authors: Ammar Amsyar Abdul Haddi, Mohd Hasni Jaafar

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Heavy metals are elements naturally presented in the environment that can cause adverse effect to health. But not much literature was found on effects toward lung function, where impairment of lung function may lead to various lung diseases. The objective of the study is to explore the lung function impairment, urinary heavy metal level, and its associated factors among the community in Klang valley, Malaysia. Sampling was done in Kuala Lumpur suburb public and housing areas during community events throughout March 2019 till October 2019. respondents who gave the consent were given a questionnaire to answer and was proceeded with a lung function test. Urine samples were obtained at the end of the session and sent for Inductively coupled plasma mass spectrometry (ICP-MS) analysis for heavy metal cadmium (Cd) and lead (Pb) concentration. A total of 200 samples were analysed, and of all, 52% of respondents were male, Age ranging from 18 years old to 74 years old with a mean age of 38.44. Urinary samples show that 12% of the respondent (n=22) has Cd level above than average, and 1.5 % of the respondent (n=3) has urinary Pb at an above normal level. Bivariate analysis show that there was a positive correlation between urinary Cd and urinary Pb (r= 0.309; p<0.001). Furthermore, there was a negative correlation between urinary Cd level and full vital capacity (FVC) (r=-0.202, p=0.004), Force expiratory volume at 1 second (FEV1) (r = -0.225, p=0.001), and also with Force expiratory flow between 25-75% FVC (FEF25%-75%) (r= -0.187, p=0.008). however, urinary Pb did not show any association with FVC, FEV1, FEV1/FVC, or FEF25%-75%. Multiple linear regression analysis shows that urinary Cd remained significant and negatively affect FVC% (p=0.025) and FEV1% (p=0.004) achieved from the predicted value. On top of that, other factors such as education level (p=0.013) and duration of smoking(p=0.003) may influencing both urinary Cd and performance in lung function as well, suggesting Cd as a potential mediating factor between smoking and impairment of lung function. however, there was no interaction detected between heavy metal or other influencing factor in this study. In short, there is a negative linear relationship detected between urinary Cd and lung function, and urinary Cd is likely to affects lung function in a restrictive pattern. Since smoking is also an influencing factor for urinary Cd and lung function impairment, it is highly suggested that smokers should be screened for lung function and urinary Cd level in the future for early disease prevention.

Keywords: lung function, heavy metals, community

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Authors: Usman Yakubu, Muktar Haruna

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The paper examines the impact of the audit committee on the earning quality of the listed consumer goods sector in Nigeria. The study used data collected from annual reports and accounts of the 13 sampled companies for the periods 2007 to 2018. Data were analyzed by means of descriptive statistics to provide summary statistics for the variables; also, correlation analysis was carried out using the Pearson correlation technique for the correlation between the dependent and independent variables. Regression was employed using the Generalized Least Square technique since the data has both time series and cross sectional attributes (panel data). It was found out that the audit committee had a positive and significant influence on the earning quality in the listed consumer goods companies in Nigeria. Thus, the study recommends that competency and personal integrity should be the worthwhile attributes to be considered while constituting the committee; this could enhance the quality of accounting information. In addition to that majority of the committee members should be independent directors in order to allow a high level of independency to be exercised.

Keywords: earning quality, corporate governance, audit committee, financial reporting

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Authors: Atya Nur Aisha, Pamoedji Hardjomidjojo, Yassierli

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Motivation is an important aspect which affects employee behavior to achieve performance. Working motivation tend to be unstable, it easily changing. This condition could be affected by individual factors, namely working ability, and organizational factors, such as working condition and incentives system. The purpose of this study was to examine the impact of individual and organizational factors on non-academic staff motivation. A questionnaire was designed and distributed to 150 non-academic staff of a university in Indonesia. Regression analysis was used to identify the relationship. Results revealed that individual working ability and incentives system had a positive impact on non-academic staff motivation (sig 0.001). This study provides information about practical implication for university authorities and theoretical implications for researchers who interested in exploring motivational and employee performance in a higher education context. It was proposed to increase productivity and work motivation of non-academic staff, university authorities should maintain equality and feasibility of incentives system and design a human resource development to improve employee ability.

Keywords: motivation, incentives, working ability, non-academic staff

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Authors: Mohammad Didar Hossain

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Background/Purpose: The purpose of this study was to examine the associations between productive engagements and the psychological well-being of older adults in the U.S by analyzing cross-sectional data from a secondary dataset. Specifically, this paper analyzed the associations of 4 different types of productive engagements, including current work status, caregiving to the family members, volunteering and religious strengths with the psychological well-being as an outcome variable. Methods: Data and sample: The study used the data from the Health and Retirement Study (HRS). The HRS is a nationally representative prospective longitudinal cohort study that has been conducting biennial surveys since 1992 to community-dwelling individuals 50 years of age or older on diverse issues. This analysis was based on the 2016 wave (cross-sectional) of the HRS dataset and the data collection period was April 2016 through August 2017. The samples were recruited from a multistage, national area-clustered probability sampling frame. Measures: Four different variables were considered as the predicting variables in this analysis. Firstly, current working status was a binary variable that measured by 0=Yes and 1= No. The second and third variables were respectively caregiving and volunteering, and both of them were measured by; 0=Regularly, 1= Irregularly. Finally, find in strength was measured by 0= Agree and 1= Disagree. Outcome (Wellbeing) variable was measured by 0= High level of well-being, 1= Low level of well-being. Control variables including age were measured in years, education in the categories of 0=Low level of education, 1= Higher level of education and sex r in the categories 0=male, 1= female. Analysis and Results: Besides the descriptive statistics, binary logistic regression analyses were applied to examine the association between independent and dependent variables. The results showed that among the four independent variables, three of them including working status (OR: .392, p<.001), volunteering (OR: .471, p<.003) and strengths in religion (OR .588, p<.003), were significantly associated with psychological well-being while controlling for age, gender and education factors. Also, no significant association was found between the caregiving engagement of older adults and their psychological well-being outcome. Conclusions and Implications: The findings of this study are mostly consistent with the previous studies except for the caregiving engagements and their impact on older adults’ well-being outcomes. Therefore, the findings support the proactive initiatives from different micro to macro levels to facilitate opportunities for productive engagements for the older adults, and all of these may ultimately benefit their psychological well-being and life satisfaction in later life.

Keywords: productive engagements, older adults, psychological wellbeing, productive aging

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Authors: Austyn Snowden

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Aim: To examine the construct validity of the Trait Emotional Intelligence Questionnaire Short form. Background: Emotional intelligence involves the identification and regulation of our own emotions and the emotions of others. It is therefore a potentially useful construct in the investigation of recruitment and retention in nursing and many questionnaires have been constructed to measure it. Design: Secondary analysis of existing dataset of responses to TEIQue-SF using concurrent application of Rasch analysis and confirmatory factor analysis. Method: First year undergraduate nursing and computing students completed Trait Emotional Intelligence Questionnaire-Short Form. Responses were analysed by synthesising results of Rasch analysis and confirmatory factor analysis.

Keywords: emotional intelligence, rasch analysis, factor analysis, nurse recruiters

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Authors: Amit Das

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Background and significance of the study: Generally investors and market forecasters use financial statement for investigation while it awakens contribute to investing. The main vicinity of financial accounting and reporting practices recommends a few basic financial performance indicators, namely, return on capital employed, return on assets and earnings per share, which is associated considerably with share prices. It is principally true in case of Indian pharmaceutical companies also. Share investing is intriguing a financial risk in addition to investors look for those financial evaluations which have noteworthy shock on share price. A crucial intention of financial statement analysis and reporting is to offer information which is helpful predominantly to exterior clients in creating credit as well as investment choices. Sound financial performance attracts the investors automatically and it will increase the share price of the respective companies. Keeping in view of this, this research work investigates the impact of financial performance indicators on share price of pharmaceutical companies in India which is listed in the Bombay Stock Exchange. Methodology: This research work is based on secondary data collected from moneycontrol database on September 28, 2015 of top 101 pharmaceutical companies in India. Since this study selects four financial performance indicators purposively and availability in the database, that is, earnings per share, return on capital employed, return on assets and net profits as independent variables and one dependent variable, share price of 101 pharmaceutical companies. While analysing the data, correlation statistics, multiple regression technique and appropriate test of significance have been used. Major findings: Correlation statistics show that four financial performance indicators of 101 pharmaceutical companies are associated positively and negatively with its share price and it is very much significant that more than 80 companies’ financial performances are related positively. Multiple correlation test results indicate that financial performance indicators are highly related with share prices of the selected pharmaceutical companies. Furthermore, multiple regression test results illustrate that when financial performances are good, share prices have been increased steadily in the Bombay stock exchange and all results are statistically significant. It is more important to note that sensitivity indices were changed slightly through financial performance indicators of selected pharmaceutical companies in India. Concluding statements: The share prices of pharmaceutical companies depend on the sound financial performances. It is very clear that share prices are changed with the movement of two important financial performance indicators, that is, earnings per share and return on assets. Since 101 pharmaceutical companies are listed in the Bombay stock exchange and Sensex are changed with this, it is obvious that Government of India has to take important decisions regarding production and exports of pharmaceutical products so that financial performance of all the pharmaceutical companies are improved and its share price are increased positively.

Keywords: financial performance indicators, share prices, pharmaceutical companies, India

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