Search results for: predicting factors

Authors: Seba Mohanty, Jitendra Mahakud

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Capital ratio is one of the major indicators of the stability of the commercial banks. Pertinent to its pervasive importance, over the years the regulators, policy makers focus on the maintenance of the particular level of capital ratio to minimize the solvency and liquidation risk. In this context, it is very much important to identify the relationship between capital and risk and find out the factors which determine the capital ratios of commercial banks. The study examines the relationship between capital and risk of the commercial banks operating in India. Other bank specific variables like bank size, deposit, profitability, non-performing assets, bank liquidity, net interest margin, loan loss reserves, deposits variability and regulatory pressure are also considered for the analysis. The period of study is 1997-2015 i.e. the period of post liberalization. To identify the impact of financial crisis and implementation of Basel II on capital ratio, we have divided the whole period into two sub-periods i.e. 1997-2008 and 2008-2015. This study considers all the three types of commercial banks, i.e. public sector, the private sector and foreign banks, which have continuous data for the whole period. The main sources of data are Prowess data base maintained by centre for monitoring Indian economy (CMIE) and Reserve Bank of India publications. We use simultaneous equation model and more specifically Two Stage Least Square method to find out the relationship between capital and risk. From the econometric analysis, we find that capital and risk affect each other simultaneously, and this is consistent across the time period and across the type of banks. Moreover, regulation has a positive significant impact on the ratio of capital to risk-weighted assets, but no significant impact on the banks risk taking behaviour. Our empirical findings also suggest that size has a negative impact on capital and risk, indicating that larger banks increase their capital less than the other banks supported by the too-big-to-fail hypothesis. This study contributes to the existing body of literature by predicting a strong relationship between capital and risk in an emerging economy, where banking sector plays a majority role for financial development. Further this study may be considered as a primary study to find out the macro economic factors which affecting risk and capital in India.

Keywords: capital, commercial bank, risk, simultaneous equation model

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Authors: Samuel Asiedu-Addo, Jonathan Annan, Yarhands Dissou Arthur

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The study aims to explore the relational effect of lecturers’ personal attribute on student’s interest in statistics. In this study personal attributes of lecturers’ such as lecturer’s dynamism, communication strategies and rapport in the classroom as well as applied knowledge during lecture were examined. Here, exploratory research design was used to establish the effect of lecturer’s personal attributes on student’s interest. Data were analyzed by means of confirmatory factor analysis and structural equation modeling (SEM) using the SmartPLS 3 program. The study recruited 376 students from the faculty of technical and vocational education of the University of Education Winneba Kumasi campus, and Ghana Technology University College as well as Kwame Nkrumah University of science and Technology. The results revealed that personal attributes of an effective lecturer were lecturer’s dynamism, rapport, communication and applied knowledge contribute (52.9%) in explaining students interest in statistics. Our regression analysis and structural equation modeling confirm that lecturers personal attribute contribute effectively by predicting student’s interest of 52.9% and 53.7% respectively. The paper concludes that the total effect of a lecturer’s attribute on student’s interest is moderate and significant. While a lecturer’s communication and dynamism were found to contribute positively to students’ interest, they were insignificant in predicting students’ interest. We further showed that a lecturer’s personal attributes such as applied knowledge and rapport have positive and significant effect on tertiary student’s interest in statistic, whilst lecturers’ communication and dynamism do not significantly affect student interest in statistics; though positively related.

Keywords: student interest, effective teacher, personal attributes, regression and SEM

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Authors: Karwan Khudhir

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A cross-sectional study was carried out to determine the prevalence of musculoskeletal disorders (MSDs) and psychosocial factors associated with it, among Kurdistan nursing professionals. Simple random sampling was used to select 220 nurses and data were collected by self-administrative questionnaire. Results of the study showed that the overall prevalence of MSDs among Kurdistan nurses was 74% in different body regions and, by body regions, neck pain was reported to be the highest complaint of twelve-month MSDs (48.4%) compared to other body parts. Logistic regression analysis indicated 6 variables that are significantly associated with musculoskeletal disorders: smoking (OR=19.472, 95% CI: 5.396, 70.273), BMI (OR= 5.106, 95% CI: 1.735, 15.025), physical activity (OR=8.639, 95% CI: 3.075, 24.271), psychological demand (OR=6.685, 95% CI: 3.318, 13.468), social support (OR=3.143, 95% CI: 1.202, 4.814) and job satisfaction (OR=2.44, 95% CI: 1.04, 5.63). Prevention strategies and health education which emphasizes on psychosocial risk factors and how to improve working conditions should be introduced.

Keywords: Kurdistan Region, Iraq, musculoskeletal disorders, nurses, psycho-social factors

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Authors: Ahsan Ali Khan

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During past few years, researchers are emphasizing more on the need of safety in construction industry. This need of safety is an important issue in developing countries. As due to development they are facing huge construction growth. This research is done to evaluate labor safety condition in construction industry of Pakistan. The research carried out through questionnaire survey at different construction sites. Useful data are gathered from these sites which then factor analyzed resulting in five factors. These factors reflect that most of the workers are aware of the safety need, but they divert this responsibility towards management and claim that the work is more essential for management instead of safety. Moreover, those work force which is unaware of safety state that there is lack of any training and guidance from upper management which lead to many unfavorable events on construction sites. There is need of implementation safety activities by management like training, formulation of rules and policies. This research will be helpful to divert management attention towards safety need so they will make efforts for safety of their manpower—the workers.

Keywords: labor's safety, management role, Pakistan, safety factors

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Authors: Dongjoo Chin

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The purpose of this study was to investigate the effectiveness of an Early Intensive Behavioral Intervention (EIBI) program on infants with autism spectrum disorder (ASD) and to explore the factors predicting the effectiveness of the program, focusing on the infant's age, language ability, problem behaviors, and parental stress. 19 pairs of infants aged between 2 and 5 years who have had been diagnosed with ASD, and their parents participated in an EIBI program at a clinic providing evidence-based treatment based on applied behavior analysis. The measurement tools which were administered before and after the EIBI program and compared, included PEP-R, a curriculum evaluation, K-SIB-R, K-Vineland-II, K-CBCL, and PedsQL for the infants, and included PSI-SF and BDI-II for the parents. Statistical analysis was performed using a sample t-test and multiple regression analysis and the results were as follows. The EIBI program showed significant improvements in overall developmental age, curriculum assessment, and quality of life for infants. There was no difference in parenting stress or depression. Furthermore, measures for both children and parents at the start of the program predicted neither PEP-R nor the degree of improvement in curriculum evaluation measured six months later at the end of the program. Based on these results, the authors suggest future directions for developing an effective intensive early intervention (EIBI) program for infants with ASD in Korea, and discuss the implications and limitations of this study.

Keywords: applied behavior analysis, autism spectrum disorder, early intensive behavioral intervention, parental stress

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Authors: O. Sunantha, S. Zhenfeng, S. Phattraporn, A. Zeeshan

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The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. While it is recognized that SOC predictors vary significantly when derived from remote sensing data and environmental variables, identifying the specific parameters most suitable for accurately estimating SOC in diverse agricultural areas remains a challenge. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors' impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation.

Keywords: factors influencing SOC estimation, remote sensing data, environmental variables, machine learning

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Authors: Aisha Arshad, Samina Naz Mukry, Tahir Shamsi

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Background: Immune thrombocytopenia (ITP) is an autoimmune disorder. Besides platelets counts, immature platelets fraction (IPF) can be used as tool to predict megakaryocytic activity in ITP patients. The clinical biomarkers like Neutrophils to lymphocytes ratio (NLR) and platelet to lymphocytes ratio(PLR) predicts inflammation and can be used as prognostic markers.The present study was planned to assess the ratios in ITP and their utility in predicting prognosis after treatment. Methods: A total of 111 patients of ITP with same number of healthy individuals were included in this case control study during the period of January 2015 to December 2017.All the ITP patients were grouped according to guidelines of International working group of ITP. A 3cc blood was collected in EDTA tube and blood parameters were evaluated using Sysmex 1000 analyzer.The ratios were calculated by using absolute counts of Neutrophils,Lymphocytes and platelets.The significant (p=<0.05) difference between ITP patients and healthy control groups was determined by Kruskal wallis test, Dunn’s test and spearman’s correlation test was done using SPSS version 23. Results: The significantly raised total leucocytes counts (TLC) and IPF along with low platelets counts were observed in ITP patients as compared to healthy controls.In ITP groups,very low platelet count with median and IQR of 2(3.8)3x109/l with highest mean and IQR IPF 25.4(19.8)% was observed in newly diagnosed ITP group. The NLR was high with prognosis of disease as higher levels were observed in P-ITP. The PLR was significantly low in ND-ITP ,P-ITP, C-ITP, R-ITP and compared to controls with p=<0.001 as platelet were less in number in all ITP patients. Conclusion: The IPF can be used in evaluation of bone marrow response in ITP. The simple, reliable and calculated NLR and PLR ratios can be used in predicting prognosis and response to treatment in ITP and to some extend the severity of disease.

Keywords: neutrophils, platelets, lymphocytes, infection

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Authors: Karl Owen L. Suan, Juliet Marie S. Lambayan, Floramay P. Salo-Curato

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Objective: To predict the outcome among patients admitted with community-acquired pneumonia (ages 3 months to 59 months old) admitted in Visayas Community Medical Center using the Respiratory Index of Severity in Children (RISC). Design: A cross-sectional study design was used. Setting: The study was done in Visayas Community Medical Center, which is a private tertiary level in Cebu City from January-June 2013. Patients/Participants: A total of 72 patients were initially enrolled in the study. However, 1 patient transferred to another institution, thus 71 patients were included in this study. Within 24 hours from admission, patients were assigned a RISC score. Statistical Analysis: Cohen’s kappa coefficient was used for inter-rater agreement for categorical data. This study used frequency and percentage distribution for qualitative data. Mean, standard deviation and range were used for quantitative data. To determine the relationship of each RISC score parameter and the total RISC score with the outcome, a Mann Whitney U Test and 2x2 Fischer Exact test for testing associations were used. A p value less of than 0.05 alpha was considered significant. Results: There was a statistical significance between RISC score and clinical outcome. RISC score of greater than 4 was correlated with intubation and/or mortality. Conclusion: The RISC scoring system is a simple combination of clinical parameters and a reliable tool that will help stratify patients aged 3 months to 59 months in predicting clinical outcome.

Keywords: RISC, clinical outcome, community-acquired pneumonia, patients

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Authors: Anjali Malik

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This paper presents a review of the literature on behavioral addictions with a particular focus on understanding online gaming habits among adolescents. Extant researches yielded many different sets of antecedent factors for developing pathological online gaming behavior. This paper draws findings from the most-cited publications most closely associated with factors explaining why individuals develop such kind of problematic behavior. What emerges as central to understanding this phenomenon is the presence of multiple variable causes that take into account the individual, the environment and their interaction to explain the risk behavior such as pathological online gaming. In addition to that role of some mediating factors and pull factors has also been discussed, along with the consequences on personal, social and academic performance resulting from such kind of addictive behavior. The paper also makes recommendations for future research including developing a deeper understanding of the phenomena studied here by examining the relative contribution of these multiple-risk contexts.

Keywords: pathological gaming, gaming addiction, adolescents, behavior

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Authors: Mai Al-Subaie

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This research suggests that women in traditional families of Saudi Arabia are divided into two groups, the one who conform to the society and the new type of women that has been emerged due to the changing and development of the culture, who do not want to conform to the rules. The factors underlying the differences were explored by using a test and an interview. That concluded some of the main factors that were a real effect of why some women still want to follow the society and traditional rules, and other want to break free.

Keywords: conformity, non conformity, females, Saudi Arabia

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Authors: Genesis F. Dela Cruz, Liza C. Costa

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The study is concerned with the Risk factors of dropping out among Grade VII students for SY 2012-2013. A total of 87 Grade VII Students-At-Risk-of-Dropping Out (SARDOs) were involved in this study. The descriptive survey method was used in this study. A 50-item questionnaire was used in data gathering. Expert validation was done to determine the validity and reliability of the instrument. The study used Chi Square, Kruskal Wallis Test and Mann Whitney Test in the statistical treatment of data. The study revealed that the respondents are within the standard age limit for Grade VII students in the Philippines which is 13 years old. Males more than females usually becomes SARDOs. SARDOs come from low economic status and complete families contrary to the common belief that they came from single-parent families. The study also showed that parent’s involvement in educating their children on family-related factors contributed to the very good perception on the family related factors. Based on age, there are no significant differences in their perception of the four major recognized risk factors for dropping out among all ages. There are no significant differences in their perception of the family, individual and community related factors for dropping out based on sex. However, females have a more favorable perception when it comes to school related factors. No significant differences in their perception of dropping out were also noted when they are classified according to distance of school from home. The respondents do not differ in their perception on family, individual and community related factors when they are classified according to type of family. When surveyed regarding the respondents’ reason for being absent, it was found out that laziness and being late are the two major reasons. Respondents also perceived remedial and tutorial classes as school-initiated intervention measure to prevent school disengagement or dropping out.

Keywords: drop-out, guidance and counseling, school initiated intervention, students at risk of dropping out

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Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

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Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: Cristina Sá, Carlos Gomes, António Martins

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The reasons that explain different behaviors towards tax obligations in similar countries are not completely understood yet. The main purpose of this paper is to identify and compare the factors that influence tax morale levels in Portugal and Spain. We use data from European Values Study (EVS). Using a sample of 2,652 individuals, a factor analysis was used to extract the underlying dimensions of tax morale of Portuguese and Spanish taxpayers. Based on a factor analysis, the results of this paper show that sociological and behavioral factors, psychological factors and political factors are important for a good understanding of taxpayers’ behavior in Iberian Peninsula. This paper added value relies on the analyses of a wide range of variables and on the comparison between Portugal and Spain. Our conclusions provided insights that tax authorities and politicians can use to better focus their strategies and actions in order to increase compliance, reduce tax evasion, fight underground economy and increase country´s competitiveness.

Keywords: compliance, tax morale, Portugal, Spain

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Authors: Iván González, Pablo Andrés Maya, Sebastián Jaén

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Eco-industrial parks (EIPs) work as networks for the exchange of by-products, such as materials, water, or energy. This research identifies the relevant factors in the formation of EIPs in different industrial environments around the world. Then an aggregation of these factors is carried out to reduce them from 50 to 17 and classify them according to 5 fundamental axes. Subsequently, the Vester Sensitivity Model (VSM) systemic methodology is used to determine the influence of the 17 factors on an EIP system and the interrelationship between them. The results show that the sequence of effects between factors: Trust and Cooperation → Business Association → Flows → Additional Income represents the “backbone” of the system, being the most significant chain of influences. In addition, the Organizational Culture represents the turning point of the Industrial Symbiosis on which it must act correctly to avoid falling into unsustainable economic development. Finally, the flow of Information should not be lost since it is what feeds trust between the parties, and the latter strengthens the system in the face of individual or global imbalances. This systemic understanding will enable the formulation of pertinent policies by the actors that interact in the formation and permanence of the EIP. In this way, it seeks to promote large-scale sustainable industrial development, integrating various community actors, which in turn will give greater awareness and appropriation of the current importance of sustainability in industrial production.

Keywords: critical factors, eco-industrial park, industrial symbiosis, system methodology

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Authors: Anita Kéri

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Satisfying foreign student needs has been in the center of research interest in the past several years. Higher education institutions have been exploring factors influencing foreign student satisfactionto stay competitive on the educational market. Even though foreign student satisfaction and loyalty are topics investigated deeply in the literature, the academic years of 2020 and 2021 have revealed challenges never experienced before. With the COVID-19 pandemic, new factors have emerged that might influence foreign student satisfaction and loyalty in higher education. The aim of the current research is to shed lights on what factors influence foreign student satisfaction and loyalty in the post-pandemic educational era and to reveal if the effects of factors influencing satisfaction and loyalty have changed compared to previous findings. Initial results show that students are less willing to participate in online surveys during and after the pandemic. The return rate of the survey instrument is below 5%. Results also reveal that there is a slight difference in what factors have significant effects on school-related and non-school-related satisfaction and overall loyalty, measured pre- and post-pandemic times. The results of the current study help us determine what factors higher education institutions need to consider when planning the future service affordances for their foreign students that might influence their satisfaction and loyalty.

Keywords: pandemic, COVID-19, satisfacion, loyalty, service quality, higher education

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Authors: Sri Lakshmi Kanniah, Mohd Naz’ri Mahrin

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More and more businesses and services are depending on software to run their daily operations and business services. At the same time, cyber-attacks are becoming more covert and sophisticated, posing threats to software. Vulnerabilities exist in the software due to the lack of security practices during the phases of software development. Implementation of secure software development practices can improve the resistance to attacks. Many methods, models and standards for secure software development have been developed. However, despite the efforts, they still come up against difficulties in their deployment and the processes are not institutionalized. There is a set of factors that influence the successful deployment of secure software development processes. In this study, the methodology and results from a systematic literature review of factors influencing the implementation of secure software development practices is described. A total of 44 primary studies were analysed as a result of the systematic review. As a result of the study, a list of twenty factors has been identified. Some of factors that affect implementation of secure software development practices are: Involvement of the security expert, integration between security and development team, developer’s skill and expertise, development time and communication between stakeholders. The factors were further classified into four categories which are institutional context, people and action, project content and system development process. The results obtained show that it is important to take into account organizational, technical and people issues in order to implement secure software development initiatives.

Keywords: secure software development, software development, software security, systematic literature review

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Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

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This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

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Authors: Mehrdad Karimi, Farshad Negintaji

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This study has examined the appearance of identity in the urban landscape and its effects on the natural factors. For this purpose, the components of place identity, emotional attachment, place dependence and social bond which totally constitute place attachment, measures it in three domains of cognitive (place identity), affective (emotional attachment) and behavioral (place dependence and social bond). In order to measure the natural factors, three components of the absolute elements, living entities, natural elements have been measured. The study is descriptive and the statistical population has been Yasouj, a city in Iran. To analyze the data the SPSS software has been used. The results in two level of descriptive and inferential statistics have been investigated. In the inferential statistics, Pearson correlation coefficient test has been used to evaluate the research hypotheses. In this study, the variable of identity is in high level and the natural factors are also in high level. These results indicate a positive relationship between place identity and natural factors. Development of environment and reaching the quality level of the personality or identity will develop the individual and society.

Keywords: identity, place identity, landscape, urban landscape, landscaping

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: Habib Honari

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The purpose of this study is an investigation of the factors affecting entrepreneurship in sport from the point of view of experts in this field. This study is a descriptive analytic one and was conducted as a survey and statistical sample consisted of 64 subjects including top managers and sport management professors at physical education organization. Data is collected by research designed questionnaire. Its reliability (α=.95) is obtained after its validity confirmation (by professors). In this article the most important factors affecting sport entrepreneurship, both as an interdisciplinary field in the world, are studied. Initially, infrastructures are identified for entrepreneurial opportunities in sports and related problems become known so that identifying factors for social, cultural, and economical development to entrepreneurs will be a smooth path, because sport entrepreneurship, given its effective roles in business development, welfare, health development, and participation in various aspects of society, can also play a crucial role in the development of the country. Finally, some solutions for developing entrepreneurial sport are introduced.

Keywords: sport entrepreneurship, entrepreneurial opportunities, entrepreneurial barriers, interdisciplinary

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Authors: Farrin Nayebzadeh, Mohammadreza Eslami Amirabadi

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According to technological progress and urban development, the cities of the world are growing to become metropolitans, living in which can be enthusiastic, entertaining and accessibility to the facilities like education, economic factors, hygiene and welfare is high. On the other hand, there are some problems that have been ignored in planning for such high quality of life, most important of which, is human health. Two aspects of human health are physical health and mental health, that are closely associated. Human mental health depends on two important factors: Biological factor and environmental factor. Air pollution is one of the most important environmental risk factors that affects mental health. Psychological and toxic effects of air pollution can lead to psychiatric symptoms, including anxiety and changes in mood, cognition, and behavior, depression and also children's mental disorders like hyperactivity, aggression and agitation. Increased levels of some air pollutants are accompanied by an increase in psychiatric admissions and emergency calls and, in some studies, by changes in behavior and a reduction in psychological well-being. Numerous toxic pollutants interfere with the development and adult functioning of the nervous system. Psychosocial stress can cause symptoms similar to those of organic mental disorders. These factors can cause resonance of psychiatric disorders. So, in cities of developing countries, people challenge with mental health problems due to environmental factors especially air pollution that have not been forecasted in urban planning.

Keywords: air pollution, environmental factors, mental health, psychiatric disorder

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Authors: Kaowen Grace Chang

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Urban green spaces play an important role in promoting wellbeing through the sense of relaxation for urban residents. Among many designing factors, what the principal ones that could effectively influence people’s sense of relaxation? And, what are the relationship between the sense of relaxation and those factors? Regarding those questions, there is still little evidence for sufficient support. Therefore, the purpose of this study, based on individual responses to environmental information, is to investigate the landscape factors that relate to well-being through the sense of relaxation in mixed-use urban environments. We conducted the experimental design and model construction utilizing choice-based conjoint analysis to test the factors of plant arrangement pattern, plant trimming condition, the distance to visible automobile, the number of landmark objects, and the depth of view. Through the operation of balanced fractional orthogonal design, the goal is to know the relationship between the sense of relaxation and different designs. In a result, the three factors of plant trimming condition, the distance to visible automobile, and the depth of view shed are significantly effective to the sense of relaxation. The stronger magnitude of maintenance and trimming, the further distance to visible automobiles, and deeper view shed that allow the users to see further scenes could significantly promote green space users’ sense of relaxation in urban green spaces.

Keywords: urban green space, landscape planning and design, sense of relaxation, choice model

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Sweta Pandey, Samridhi Dhyani, Susmita Chaudhuri

Abstract:

Klebsiella pneumoniae bacteria is a global threat to human health due to an increase in multi-drug resistance among strains. The hypervirulent strains of Klebsiella pneumoniae is a major trouble due to their association with life-threatening infections in a healthy population. One of the major virulence factors of hyper virulent strains of Klebsiella pneumoniae is the T6SS (Type six secretary system) which is majorly involved in microbial antagonism and causes interaction with the host eukaryotic cells during infections. T6SS mediates some of the crucial factors for establishing infection by the bacteria, such as cell adherence, invasion, and subsequent in vivo colonisation. The antibacterial activity and the cell invasion property of the T6SS system is a major requirement for the establishment of K. pneumoniae infections within the gut. The T6SS can be an appropriate target for developing therapeutics. The T6SS consists of an inner tube comprising hexamers of Hcp (Haemolysin -regulated protein) protein, and at the top of this tube sits VgrG (Valine glycine repeat protein G); the tip of the machinery consists of PAAR domain containing proteins which act as a delivery system for bacterial effectors. For this study, immune response to recombinant VgrG protein was generated to establish this protein as a potential immunogen for the development of therapeutic leads. The immunogenicity of the selected protein was determined by predicting the B cell epitopes by the BCEP analysis tool. The gene sequence for multiple domains of VgrG protein (phage_base_V, T6SS_Vgr, DUF2345) was selected and cloned in pMAL vector in E. coli. The construct was subcloned and expressed as a fusion protein of 203 residue protein with mannose binding protein tag (MBP) to enhance solubility and purification of this protein. The purified recombinant VgrG fusion protein was used for mice immunisation. The antiserum showed reactivity with the recombinant VgrG in ELISA and western blot. The immunised mice were challenged with K. pneumoniae bacteria and showed bacterial clearance in immunised mice. The recombinant VgrG protein can further be used for studying downstream signalling of VgrG protein in mice during infection and for therapeutic MAb development to eradicate K. pneumoniae infections.

Keywords: immune response, Klebsiella pneumoniae, multi-drug resistance, recombinant protein expression, T6SS, VgrG

Procedia PDF Downloads 103

Authors: Krzysztof J. Czarnocki, F. Silveira, E. Czarnocka, K. Szaniawska

Abstract:

Research background: Problems related to the occupational health and decreasing level of safety occur commonly in the construction industry. Important factor in the occupational safety in construction industry is scaffold use. All scaffolds used in construction, renovation, and demolition shall be erected, dismantled and maintained in accordance with safety procedure. Increasing demand for new construction projects unfortunately still is linked to high level of occupational accidents. Therefore, it is crucial to implement concrete actions while dealing with scaffolds and risk assessment in construction industry, the way on doing assessment and liability of assessment is critical for both construction workers and regulatory framework. Unfortunately, professionals, who tend to rely heavily on their own experience and knowledge when taking decisions regarding risk assessment, may show lack of reliability in checking the results of decisions taken. Purpose of the article: The aim was to indicate crucial parameters that could be modeling with Risk Assessment Model (RAM) use for improving both building enterprise productivity and/or developing potential and safety climate. The developed RAM could be a benefit for predicting high-risk construction activities and thus preventing accidents occurred based on a set of historical accident data. Methodology/Methods: A RAM has been developed for assessing risk levels as various construction process stages with various work trades impacting different spheres of enterprise activity. This project includes research carried out by teams of researchers on over 60 construction sites in Poland and Portugal, under which over 450 individual research cycles were carried out. The conducted research trials included variable conditions of employee exposure to harmful physical and chemical factors, variable levels of stress of employees and differences in behaviors and habits of staff. Genetic modeling tool has been used for developing the RAM. Findings and value added: Common types of trades, accidents, and accident causes have been explored, in addition to suitable risk assessment methods and criteria. We have found that the initial worker stress level is more direct predictor for developing the unsafe chain leading to the accident rather than the workload, or concentration of harmful factors at the workplace or even training frequency and management involvement.

Keywords: safety climate, occupational health, civil engineering, productivity

Procedia PDF Downloads 318

Authors: Emmanuel Carlo Belara, Albert John Dela Merced, Mark Anthony Dominguez, Diomari Erasga, Jerome Ferrer, Bernard Ombrog

Abstract:

Social distancing errors are a common prevalence between vaccinated and unvaccinated in the Filipino community. This study aims to identify and relate the factors on how they affect our daily lives. Observed factors include memory, attention, anxiety, decision-making, and stress. Upon applying the ergonomic tools and statistical treatment such as t-test and multiple linear regression, stress and attention turned out to have the most impact to the errors of social distancing.

Keywords: vaccinated, unvaccinated, socoal distancing, filipinos

Procedia PDF Downloads 201

Authors: Nwobu Obiamaka, Samuel O. Faboyede

Abstract:

This study examines the factors influencing career choice of undergraduate accounting students in selected Nigerian universities. The manner in which students of accounting perceive the factors that drive them into pursuing a career in accounting is important to the profession. The study made use of primary data collected from undergraduate accounting students in their final year in selected Nigerian universities. The data was collected using a survey instrument (questionnaire), copies of which were made and administered to the respondents (undergraduate accounting students in selected Nigerian universities). The finding suggests that the major factors influencing undergraduate accounting students to opt for a career in accounting include pressure from peers and monetary reward. The findings from the study have crucial policy implications for admission officers in tertiary institutions as well as the accounting profession in Nigeria.

Keywords: accounting, career, choice, undergraduate

Procedia PDF Downloads 278

Authors: Muhammad Faizan Rehman Qureshi, Ahmed Al-Kaisy

Abstract:

Accurate estimation of traffic loads is critical for pavement and bridge design, among other transportation applications. Given the disproportional impact of heavier axle loads on pavement and bridge structures, truck and heavy vehicle traffic is expected to be a major determinant of traffic load estimation. Further, heavy vehicle traffic is also a major input in transportation planning and economic studies. The traditional method for estimating heavy vehicle traffic primarily relies on AADT estimation using Monthly Day of the Week (MDOW) adjustment factors as well as the percent heavy vehicles observed using statewide data collection programs. The MDOW factors are developed using daily and seasonal (or monthly) variation patterns for total traffic, consisting predominantly of passenger cars and other smaller vehicles. Therefore, while using these factors may yield reasonable estimates for total traffic (AADT), such estimates may involve a great deal of approximation when applied to heavy vehicle traffic. This research aims at assessing the approximation involved in estimating heavy vehicle traffic using MDOW adjustment factors for total traffic (conventional approach) along with three other methods of using MDOW adjustment factors for total trucks (class 5-13), combination-unit trucks (class 8-13), as well as adjustment factors for each vehicle class separately. Results clearly indicate that the conventional method was outperformed by the other three methods by a large margin. Further, using the most detailed and data intensive method (class-specific adjustment factors) does not necessarily yield a more accurate estimation of heavy vehicle traffic.

Keywords: traffic loads, heavy vehicles, truck traffic, adjustment factors, traffic data collection

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Authors: Clement C. Aladi, Zhaoxia Yi

Abstract:

This study investigates factors influencing the implementation of flexible curricula in e-learning in Information Systems (IS) higher education. Drawing from curriculum theorists and contemporary literature, and using the Technology, Pedagogy, and Content Knowledge (TPACK) framework, it explores teacher-related challenges and their impact on curriculum flexibility implementation. By using the PLS-SEM, the study uncovers these factors and hopes to contribute to enhancing curriculum flexibility in delivering online and blended learning in IS higher education.

Keywords: flexible curriculum, online learning, e-learning, technology

Procedia PDF Downloads 54