Search results for: logistic regression

Authors: Tianyu Chen

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The No Child Left Behind Act (NCLB) of 2001 was a major federal education policy that aimed to improve academic outcomes for all students in the United States. This study examines whether NCLB improved college readiness, measured by access to higher education, for different demographic groups. Using data from the Integrated Public Use Microdata Series (IPUMS) from 2003-2008, regression analyses explore the relationship between gender, race, family income, and region with occupational education score. The results indicate that NCLB implementation had a positive effect on college access for women and Asian students compared to other groups. Higher family income was also associated with an increased likelihood of pursuing higher education, especially for families in the South. While NCLB intended to close achievement gaps, disparities in college readiness remained five years after implementation. Further research could examine longer-term trends and additional factors influencing the policy's effectiveness across student subgroups. This study provides evidence that simply holding schools accountable for test scores may not sufficiently improve equitable educational outcomes. More targeted support of disadvantaged groups may be needed to fulfill the goal of "no child left behind."

Keywords: no child left behind act, college readiness, achievement gaps, educational equity

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Authors: Shatabdi Goon

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OBJECTIVE: To determine the prevalence and associations between breakfast skipping and health status for university professionals in Bangladesh. DESIGN: A cross-sectional descriptive study design was performed using information on respondent’s sociodemographic status and eating behavior. Factors associated with breakfast skipping were identified using multivariate regression models. SETTINGS: Data obtained from a representative sample (n 120) of university professionals randomly selected from two distinct universities in Dhaka city, Bangladesh. SUBJECT: A total number of one hundred and twenty university professionals with a mean age of 29 years. RESULT: Results indicated that approximately 35.8% of the sample skipped breakfast. Gender was the only statistically significant sociodemographic variable, with females skipping at over two times the rate of males (OR 95% CI: 1.9; 0.90-4.13). The reasons given for skipping breakfast were almost exclusively habit (39.5%), work pressure (23.2%) and lack of time (16.2%). Skippers were significantly more likely to be obese (OR 2.4; 95% CI 1.02- 5.7), less energetic (OR 3.5; 95% CI 1.5-8.6), associated with health problems (OR 4.3; 95% CI 1.8- 10.17) and eating tendency of fast food (OR 2.5; 95% CI 1.13 - 5.5). Gastric and heart burn (X2=4.19, p<0.05) and high blood pressure (X2=5.027, p<0.05) were detected among 34.9% and 27.9 % of those employees respectively identified as breakfast skippers and they showed significantly high prevalence. CONCLUSION: Breakfast skipping is highly prevalent among university professionals with significant association of different health problems in Bangladesh. Health promotion strategies should be used to encourage all adults to eat breakfast regularly.

Keywords: breakfast, healthy lifestyle, breakfast skipping, health status, university professionals

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Authors: Ibrahim Alkandi

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This study aims at understanding how banks can utilize their resources and capabilities to achieve a competitive advantage. The resource-based approach has been applied to assess the resources and capabilities as well as how the management perceives them as sources of competitive advantages. A quantitative approach was implemented using cross-sectional data. The research population consisted of Top managers in financial companies in Saudi Arabia, and the sample comprised 79 managers. The resources were sub divided into tangible and intangible. Among the variables that will be assessed in the research include propriety rights, trademark which is the brand, communication as well as organizational culture. To achieve the objective of the research, Multivariate analysis through multiple regression was used. The research tool used is a questionnaire whose validity is also assessed. According to the results of the study, there is a significant relationship between bank’s performance and the strategic management of propriety rights, trademark, administrative and financial skills as well as bank culture. Therefore, the research assessed four aspects, among the variables in the model, in relation to the strategic performance of these banks. The aspects considered were trademark, communication, administrative and leadership style as well as the company’s culture. Hence, this paper contributes to the body of literature by providing empirical evidence of the resources influencing both banks’ market and economic performance.

Keywords: competitive advantage, Saudi banks, strategic management, RBV

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Authors: Rachida Ouysse

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This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are cross sectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components (PC) method is consistent for any path of the panel dimensions, it is inefficient as the errors are treated to be homoskedastic and uncorrelated. The new CnPC exploits the assumption of bounded cross-sectional dependence, which defines Chamberlain and Rothschild’s (1983) approximate factor structure, as an explicit constraint and solves a constrained PC problem. The CnPC method is computationally equivalent to the PC method applied to a regularized form of the data covariance matrix. Unlike maximum likelihood type methods, the CnPC method does not require inverting a large covariance matrix and thus is valid for panels with N ≥ T. The paper derives a convergence rate and an asymptotic normality result for the CnPC estimators of the common factors. We provide feasible estimators and show in a simulation study that they are more accurate than the PC estimator, especially for panels with N larger than T, and the generalized PC type estimators, especially for panels with N almost as large as T.

Keywords: high dimensionality, unknown factors, principal components, cross-sectional correlation, shrinkage regression, regularization, pseudo-out-of-sample forecasting

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Authors: Solmaz Jahed Shiran, Elizabeth Taylor, John Hearne

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Over the past decade a growing body of research, known as mobility biography approach has emerged that focuses on changes in travel behaviour over the life course of individuals. Mobility biographies suggest that changes in travel behaviour have a certain relation to important key events in life courses such as residential relocation, workplace changes, marriage and the birth of children. Taking this approach as the theoretical background, this study uses data from the Household, Income and Labor Dynamics Survey in Australia (HILDA) to model a set of life course events and their interaction with the commute time. By analysing longitudinal data, it is possible to assign different key events during the life course to change a person’s travel behaviour. Changes in the journey-to-work travel time is used as an indication of travel behaviour change in this study. Results of a linear regression model for change in commute time show a significant influence from socio-demographic factors like income and age, the previous home-to-work commute time and remoteness of the residence. Residential relocation and job change have significant influences on commute time. Other life events such as birth of a child, marriage and divorce or separation have also a strong impact on commute time change. Overall, the research confirms previous studies of links between life course events and travel behaviour.

Keywords: life course events, residential mobility, travel behaviour, commute time, job change

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Authors: J. Cabral, C. Barreto Carvalho, C. da Motta, M. Sousa

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Community integration is a construct that an increasing body of research has shown to have a significant impact in well-being and recovery of people with psychiatric problems. However, there are few studies that explore which factors can be associated and predict community integration. Moreover, community integration has been mostly studied in minority groups, and currently literature on the definition and manifestation of community integration in the more general population is scarce. Thus, the current study aims to characterize community integration and explore possible predictor variables in a sample of participants with psychiatric problems (PP, N=183) and a sample of participants from the general population (GP, N=211). Results show that people with psychiatric problems present above average values of community integration, but are significantly lower than their healthy counterparts. It was also possible to observe that community integration does not vary in terms of the socio-demographic characteristics of both groups in this study. Correlation and multiple regression showed that, among several variables that literature present as relevant in the community integration process, only three variables emerged as having the most explanatory value in community integration of both groups: sense of community, basic needs satisfaction and submission. These results also shown that those variables have increased explanatory power in the PP sample, which leads us to emphasize the need to address this issue in future studies and increase the understanding of the factors that can be involved in the promotion of community integration, in order to devise more effective interventions in this field.

Keywords: community integration, mental illness, predictors, psychiatric problems

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Authors: Mustafa K. Yilmaz, Ahmet Kaplan, Murat Guven, Vildan Kesici

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Aviation industry influences the social and economic growth across the countries. Further, airline companies are highly affected by social, political, and financial crises and show a high degree of cyclicity in operational performance. Hence, this paper investigates the effects of available seat kilometers (ASK), revenue per kilometer (RPK), passenger load factor (PLF) as well as socio-political crisis on the number of passengers carried (PC) by Turkish Airlines company over the period of 2010M1-2018M12. To conduct the analysis, we employ fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) techniques using monthly data. We use ASK, RPK, PLF as independent variables to identify the determinants of the PC, as a dependent variable. We also test the effect of the socio-political crisis. The results reveal that there is a significant and negative relationship between ASK and PC, while the relationship between RPK and PC is positive and significant. We also find that there is an insignificant relationship between PLF and PC. Further, we also find a negative effect of the crisis on the PC. These findings show although the crisis had an immediate effect on the operational performance of Turkish Airlines, the company recovered from the crisis and cope with the situation very promptly. Thus, this proves the resilience and agile management ability of the company.

Keywords: airline industry, operational performance, air traffic, socio-political crisis

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Authors: Ibrahim Makram Ibrahim Salib

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Agriculture plays an essential role in providing food for the world's population. It also offers numerous benefits to countries, including non-food products, transportation, and environmental balance. Precision agriculture, which employs advanced tools to monitor variability and manage inputs, can help achieve these benefits. The increasing demand for food security puts pressure on decision-makers to ensure sufficient food production worldwide. To support sustainable agriculture, unmanned aerial vehicles (UAVs) can be utilized to manage farms and increase yields. This paper aims to provide an understanding of UAV usage and its applications in agriculture. The objective is to review the various applications of UAVs in agriculture. Based on a comprehensive review of existing research, it was found that different sensors provide varying analyses for agriculture applications. Therefore, the purpose of the project must be determined before using UAV technology for better data quality and analysis. In conclusion, identifying a suitable sensor and UAV is crucial to gather accurate data and precise analysis when using UAVs in agriculture.

Keywords: agriculture land, agriculture land loss, Kabul city, urban land expansion, urbanization agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models drone, precision agriculture, farmer income

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Authors: Amanda Rithieli Pereira Dos Santos, Rute Quelvia De Faria, Álvaro De Oliveira Cardoso, Anderson Rodrigo Da Silva, Érica Leão Fernandes Araújo

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The study of the kinetics of drying has great importance for the mathematical modeling, allowing to know about the processes of transference of heat and mass between the products and to adjust dryers managing new technologies for these processes. The present work had the objective of studying the kinetics of drying of soybean seeds and adjusting different statistical models to the experimental data varying cultivar and temperature. Soybean seeds were pre-dried in a natural environment in order to reduce and homogenize the water content to the level of 14% (b.s.). Then, drying was carried out in a forced air circulation oven at controlled temperatures of 38, 43, 48, 53 and 58 ± 1 ° C, using two soybean cultivars, BRS 8780 and Sambaíba, until reaching a hygroscopic equilibrium. The experimental design was completely randomized in factorial 5 x 2 (temperature x cultivar) with 3 replicates. To the experimental data were adjusted eleven statistical models used to explain the drying process of agricultural products. Regression analysis was performed using the least squares Gauss-Newton algorithm to estimate the parameters. The degree of adjustment was evaluated from the analysis of the coefficient of determination (R²), the adjusted coefficient of determination (R² Aj.) And the standard error (S.E). The models that best represent the drying kinetics of soybean seeds are those of Midilli and Logarítmico.

Keywords: curve of drying seeds, Glycine max L., moisture ratio, statistical models

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Authors: Galih Mediana

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Boarding school can helps students to turn all good qualities into habits. The process of forming one's personality can be done in various ways. In addition to gaining general knowledge at school during learning hours, teachers can instill values in students which can be done while in the dormitory when the learning process has ended. This shows the important role that must be played by boarding school’s teachers. Transformational leadership and a culture of innovation are things that can instill innovative behavior in teachers. This study aims to determine the effect of perceptions of transformational leadership and a culture of innovation on innovative work behavior among Islamic boarding school teachers. Respondents in this study amounted to 70 teachers. To measure transformational leadership, a modified measuring tool is used, namely the Multifactor Leadership Questionnaire (MLQ) by Bass (1985). To measure innovative work behavior, a measurement tool based on dimensions from Janssen (2000) is used. The innovation culture in this study will be measured using the innovation culture factor from Dobni (2008). This study uses multiple regression analysis to test the hypothesis. The results of this study indicate that there is an influence of perceptions of transformational leadership and innovation culture factors on innovative work behavior in Islamic boarding school’s teachers by 57.7%.

Keywords: transformational leadership, innovative work behavior, innovation culture, boarding school, teacher

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Authors: Wei Yaming

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Objective: In order to compare the stress of employees sent overseas with (GRAS) before and after, we used the resource-oriented music listening intervention in this study. We also collected pertinent experimental data. Methods: The experiment involved 47 employees who were sent abroad by the Chinese side. They completed the stress scale test and documented it before the intervention. They tested for stress after five interventions and performed one-on-one interviews. Quantitative data and SPSS software were used to analyze relationships between stress reduction and resource-oriented music listening, as well as Pearson's correlation, multiple regression levels, and ANOVA. For the qualitative analysis, content analysis of one-on-one interviews was performed. Results: A comparison of data from before and after demonstrates how resource-focused music listening activities can lessen and relieve stress in remote workers. In the qualitative study, stress is broken down into six categories: relationship stress, health stress, emotional stress, and frustration stress. External pressures include work pressure and cultural stress. And it has been determined that listening to music that is resource-oriented can better ease internal stress (health, emotion, and dissatisfaction). Conclusion: The Guide Resource-oriented Music Listening (GROML) Program appears to have had some effect on the participants' stress levels. The resources that the participants encountered while listening to music are bravery, calm, letting go, and relaxing.

Keywords: resource-oriented, music listening, oversea dispatch employees, work stress

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Authors: Chalachew Kassaw, Henok Ababu, Bethelhem Sileshy, Lulu Abebe, Birhanie Mekuriaw

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Background: Psychological discomfort is a state of emotional distress caused by everyday stressors and obligations that are difficult to manage. Orthopedic trauma has a wide range of effects on survivors' physical health, as well as a variety of mental health concerns that impede recovery. Psychiatric and behavioral conditions are 3-5 times more common in people who have undergone physical trauma, and they are a predictor of poor outcomes. Despite the above facts, there is a shortage of research done on the subject. Therefore, this study aimed to determine the magnitude of psychological distress and associated factor among patients attending orthopedic treatment at Gedeo zone, South Ethiopia 2022. Methods: A cross-sectional study was undertaken at Dilla University Referral Hospital from October –November 2022. The data was collected via a face-to-face interview, and the Kessler psychological distress scale (K-10) was used to assess psychological distress. A total of 386 patients receiving outpatient and inpatient services at the orthopedic unit were chosen using a simple random selection technique. A Statistical Package for the Social Science version 21 (SPSS-21) was used to enter and evaluate the data. To find related factors, bivariate, and multivariate logistic regressions were used. Variables having a p-value of less than 0.05 were deemed statistically significant. Result: A total of 386 participants with a response rate of 94.8% were included in the study. Out of all respondents, 114 (31.4%) of the individuals have experienced psychological distress. Independent variables such as Females [Adjusted odds ratio (AOR)=5.8, 95%CI=(4.6-15.6)], Average monthly income of <3500 birrs [Adjusted odds ratio (AOR) =4.8, 95% CI=(2.4-9.8) ], Current history of substance use [Adjusted odds ratio (AOR) =2.6, 95% CI=(1.66-4.7)], Strong social support [Adjusted odds ratio (AOR)=0.4, 95% CI= 0.4(0.2-0.8)], and Poor sleep quality (PSQI score>5) [Adjusted odds ratio (AOR)=2.0, 95%CI= 2.0(1.2-2.8)] were significantly associated with psychological distress. Conclusion: The prevalence of psychological distress was high. Being female, having poor social support, and having a high PSQI score were significantly associated factors with psychological distress. It is good if clinicians emphasize orthopedic patients, especially females and those having poor social support and low sleep quality symptoms.

Keywords: psychological distress, orthopedic unit, Dilla University hospital, Dilla Town, Southern Ethiopia

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Authors: Joseph E. Estevez, Mahdi Ghazizadeh, James G. Ryan, Ajit D. Kelkar

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Radiation shielding is an obstacle in long duration space exploration. Boron Nitride Nanotubes (BNNTs) have attracted attention as an additive to radiation shielding material due to B10’s large neutron capture cross section. The B10 has an effective neutron capture cross section suitable for low energy neutrons ranging from 10-5 to 104 eV and hydrogen is effective at slowing down high energy neutrons. Hydrogenated BNNTs are potentially an ideal nanofiller for radiation shielding composites. We use Molecular Dynamics (MD) Simulation via Material Studios Accelrys 6.0 to model the Young’s Modulus of Hydrogenated BNNTs. An extrapolation technique was employed to determine the Young’s Modulus due to the deformation of the nanostructure at its theoretical density. A linear regression was used to extrapolate the data to the theoretical density of 2.62g/cm3. Simulation data shows that the hydrogenated BNNTs will experience a 11% decrease in the Young’s Modulus for (6,6) BNNTs and 8.5% decrease for (8,8) BNNTs compared to non-hydrogenated BNNT’s. Hydrogenated BNNTs are a viable option as a nanofiller for radiation shielding nanocomposite materials for long range and long duration space exploration.

Keywords: boron nitride nanotube, radiation shielding, young modulus, atomistic modeling

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Authors: Jayprakash Yadav, Nivedita Patra

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Polyhydroxybutyrate (PHB) is an interesting material in the field of medical science, pharmaceutical industries, and tissue engineering because of its properties such as biodegradability, biocompatibility, hydrophobicity, and elasticity. PHB is naturally accumulated by several microbes in their cytoplasm during the metabolic process as energy reserve material. PHB can be extracted from cell biomass using halogenated hydrocarbons, chemicals, and enzymes. In this study, a cheaper and non-toxic solvent, acetone, was used for the extraction process. The different parameters like acetone percentage, and solvent pH, process temperature, and incubation periods were optimized using the Response Surface Methodology (RSM). RSM was performed and the determination coefficient (R2) value was found to be 0.8833 from the quadratic regression model with no significant lack of fit. The designed RSM model results indicated that the fitness of the response variable was significant (P-value < 0.0006) and satisfactory to denote the relationship between the responses in terms of PHB recovery and purity with respect to the values of independent variables. Optimum conditions for the maximum PHB recovery and purity were found to be solvent pH 7, extraction temperature - 43 °C, incubation time - 70 minutes, and percentage acetone – 30 % from this study. The maximum predicted PHB recovery was found to be 0.845 g/g biomass dry cell weight and the purity was found to be 97.23 % using the optimized conditions.

Keywords: acetone, PHB, RSM, halogenated hydrocarbons, extraction, bacillus subtilis.

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Authors: Racheli Magnezi, Inbal Gazit, Michal Rassin, Joseph Barr, Orna Tal

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An effective process for transmitting patient critical information is essential for patient safety and for improving communication among healthcare staff. Previous studies have discussed handover tools such as SBAR (Situation, Background, Assessment, Recommendation) or SOFI (Short Observational Framework for Inspection). Yet, these formats lack flexibility, and require special training. In addition, nurses and physicians have different procedures for handing over information. The objectives of this study were to establish a universal, structured tool for handover, for both physicians and nurses, based on parameters that were defined as ‘important’ and ‘appropriate’ by the medical team, and to implement this tool in various hospital departments, with flexibility for each ward. A questionnaire, based on established procedures and on the literature, was developed to assess attitudes towards the most important information for effective handover between shifts (Cronbach's alpha 0.78). It was distributed to 150 senior physicians and nurses in 62 departments. Among senior medical staff, 12 physicians and 66 nurses responded to the questionnaire (52% response rate). Based on the responses, a handover form suitable for all hospital departments was designed and implemented. Important information for all staff included: Patient demographics (full name and age); Health information (diagnosis or patient complaint, changes in hemodynamic status, new medical treatment or equipment required); and Social Information (suspicion of violence, mental or behavioral changes, and guardianship). Additional information relevant to each unit included treatment provided, laboratory or imaging required, and change in scheduled surgery in surgical departments. ICU required information on background illnesses, Pediatrics required information on diet and food provided and Obstetrics required the number of days after cesarean section. Based on the model described, a flexible tool was developed that enables handover of both common and unique information. In addition, it includes general logistic information that must be transmitted to the next shift, such as planned disruptions in service or operations, staff training, etc. Development of a simple, clear, comprehensive, universal, yet flexible tool designed for all medical staff for transmitting critical information between shifts was challenging. Physicians and nurses found it useful and it was widely implemented. Ongoing research is needed to examine the efficiency of this tool, and whether the enthusiasm that accompanied its initial use is maintained.

Keywords: handover, nurses, hospital, critical information

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Authors: Samuel Ato Dadzie

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The objective of this study is to examine the influence of eclectic paradigm on motivational strategy of foreign subsidiaries in Ghana. This study uses binary regression model, and the analysis was based on 75 manufacturing investments made by MNEs from different countries in 1994–2008. The results indicated that perceived market size increases the probability of foreign firms undertaking a market seeking (MS) in Ghana, while perceived cultural distance between Ghana and foreign firm’s home countries decreased the probability of foreign firms undertaking an market seeking (MS) foreign direct investment (FDI) in Ghana. Furthermore, extensive international experience decreases the probability of foreign firms undertaking a market seeking (MS) foreign direct investment (FDI) in Ghana. Most of the studies done by earlier researchers were based on the advanced and emerging countries and offered support for the theory, which was used in generalizing the result that multinational corporations (MNCs) normally used the theory regarding investment strategy outside their home country. In using the same theory in the context of Ghana, the result does not offer strong support for the theory. This means that MNCs that come to Sub-Sahara Africa cannot rely much on eclectic paradigm for their motivational strategies because prevailing economic conditions in Ghana are different from that of the advanced and emerging economies where the institutional structures work.

Keywords: foreign subsidiary, motives, Ghana, foreign direct investment

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Authors: Adeola Adejumo, Oluwole David Adebayo, Muraina Kamilu Olanrewaju

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The purpose of this study was to investigate the influence of some psychological factors (i.e, school climate, parental involvement and classroom interaction) on the learning gains of university undergraduates in Mathematics in Ibadan, Nigeria. Three hundred undergraduates who are on open distance learning education programme in the University of Ibadan and thirty mathematics lecturers constituted the study’s sample. Both the independent and dependent variables were measured with relevant standardized instruments and the data obtained was analyzed using multiple regression statistical method. The instruments used were school climate scale, parental involvement scale and classroom interaction scale. Three research questions were answered in the study. The result showed that there was significant relationship between the three independent variables (school climate, parental involvement and classroom interaction) on the students’ learning gain in mathematics and that the independent variables both jointly and relatively contributed significantly to the prediction of students’ learning gain in mathematics. On the strength of these findings, the need to enhance the school climate, improve the parents’ involvement in the student’s education and encourage students’ classroom interaction were stressed and advocated.

Keywords: school climate, parental involvement, ODL, learning gains, mathematics

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Authors: Salem Abdalla

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Objective: Dipyrone is a widely used, well tolerated analgesic drug which, however, is compromised by agranulocytosis as an adverse effect. Subsequent to no enzymatic hydrolysis, the primary metabolic step is N-demethylation of 4-methylaminoantipyrine (4-MAA) to 4-aminoantipyrine (4-AA). The aim of the present study was to identify the cytochrome P-450 enzyme (CYP) mediating this reaction. Methods: We identified the relevant CYP using virus expressed isolated rat liver microsomes with chemical inhibition studies. The substrate of 4-methylaminantipyrine was employed at six different concentrations (25, 50, 100, 400, 800, and 1200 µmol/l) with varying concentrations of selective inhibitors of CYP1A2 (furafylline, fluvoxamine), CYP3A4 (ketoconazole), CYP2A6 (coumarin), CYP2D6 (quinidine), CYP2C19 (omeprazole, fluvoxamine, tranylcypromine), CYP2C9 (sulfaphenazole), and CYP1A1 (alpha-naphthoflavone). 4-MAA and 4-AA were analyzed by HPLC, and enzyme kinetic parameters (Km and Vmax) were determined by regression (Sigma plot 9.0). Results: The N-demethylation of 4-MAA by microsomes prepared from baculovirus-expressing human CYP was pronounced with CYP2C19. Intrinsic clearances of the most active enzymes were 0.092, 0.027, and 0.026 for the CYP enzymes 2C19, 2D6, and 1A2, respectively. Metabolism by rat liver microsomes was strongly inhibited by omeprazole (IC50 of 0.05). Conclusion: The enzyme CYP2C19 apparently has an important role in N-demethylation of 4-methylaminoantipyrine which should be further analyzed in clinical studies and which may also be interesting concerning the agranulocytosis.

Keywords: dipyrone, 4-methylaminoantipyrine (4-MAA), 4- aminoantipyrine (4-AA), metabolism, human CYP2C19

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Ali H. Muhammad

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This paper investigates both instrumental and emotional family support on employee job satisfaction and perception of work-family conflict. Instrumental family support is manifested in family behavior that contributes to the reduction of employee’s family responsibilities and keeping the physical home environment in a proper shape. Emotional family support includes the encouragement and praise that the employee receives from his family and families for the employee’s work problem and their role in assisting the employees in dealing with these problems. The paper suggests that instrumental and emotional family support increases employee’s job satisfaction. Furthermore, the study proposes that family support decreases employee’s perception of work-family conflict. In addition, this study examines the reliability and validity of the family support index developed by Lynda King and her colleagues in 1995. Confirmatory factor analysis is used to test the validity of the instrument in an Arab business setting. A paper-pencil questionnaire was used to collect data from a random sample of 70 Kuwaiti employees working in the oil sector. Data were analyzed using factor analysis, reliability tests, and regression analysis. Results confirmed the research hypothesis. Family support had a positive effect on job satisfaction. Furthermore, family support significantly contributed to the reduction of employee perception of work-family conflict.

Keywords: family support, job satisfaction, work-family conflict, Kuwait oil sector

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Authors: Vesna Karapetkovska Hristova, M. Ayaz Ahmad, Julijana Tomovska, Biljana Bogdanova Popov, Blagojce Najdovski

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The average total concentrations of heavy metals; (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], and zinc [Zn]) were analyzed in soil and vegetables samples collected from the different region of Macedonia during the years 2010-2012. Basic soil properties such as pH, organic matter and clay content were also included in the study. The average concentrations of Cd, Cu, Ni, Pb, Zn in the A horizon (0-30 cm) of agricultural soils were as follows, respectively: 0.25, 5.3, 6.9, 15.2, 26.3 mg kg-1 of soil. We have found that neural networking model can be considered as a tool for prediction and spatial analysis of the processes controlling the metal transfer within the soil-and vegetables. The predictive ability of such models is well over 80% as compared to 20% for typical regression models. A radial basic function network reflects good predicting accuracy and correlation coefficients between soil properties and metal content in vegetables much better than the back-propagation method. Neural Networking / soft computing can support the decision-making processes at different levels, including agro ecology, to improve crop management based on monitoring data and risk assessment of metal transfer from soils to vegetables.

Keywords: soft computing approach, total concentrations, heavy metals, agricultural soils

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Authors: Vivienne Langhorne, Helen Sharpe

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Body dissatisfaction begins developing in middle childhood, with wide-ranging implications for mental health and well-being. Previous research on parent behavior has focused on the role of explicit parent behaviors in adolescent and young adult body dissatisfaction, leaving a gap in understanding how implicit parent behaviors relate to body dissatisfaction in childhood. The current study investigated how implicit parent behavior (such as modeling own body dissatisfaction and dieting) relates to parent and child body dissatisfaction. It was hypothesized that implicit behavior would be directly related to parent and child body dissatisfaction and mediate the relationship between the two. Furthermore, this study aimed to examine child gender as a potential moderator in this mediation, as research shows that boys and girls experience body dissatisfaction differently. This study analyzed survey responses on parent body dissatisfaction, implicit behavior, and child body dissatisfaction measures from a sample of 166 parent-child dyads with children between the ages of 6 to 9 years old. Regression analyses revealed that parent body dissatisfaction is related to both parent-implicit behavior and child body dissatisfaction. However, implicit behavior did not mediate the relationship between the two body dissatisfaction variables. Additionally, the results of moderated mediation indicated there were no child gender differences in the strength of the association between parental implicit behaviors and child body dissatisfaction. These findings highlight the need for further research into the mechanisms behind parent and child body dissatisfaction to better understand the process through which intergenerational transmission occurs.

Keywords: body dissatisfaction, implicit behaviour, middle childhood, parenting

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Authors: Ningombam Linthoingambi Devi, Amrendra Kumar

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The long term sampling at one of the most populated city in Indo-Gangetic region shows higher mass concentration of atmospheric aerosol (PM₂.₅) during spring season (144.70µg/m³), summer season (91.96 µg/m³), the autumn season (266.48µg/m³) and winter season (367.09 µg/m³) respectively. The concentration of PM₂.₅ in Patna across the year shows much higher than the limit fixed by the national ambient air quality level fixed by central pollution control board India (CPCB, India) and World Health Organization (WHO). Different water-soluble cation (Na⁺, K⁺, Ca²⁺, NH₄⁺ , and Mg²⁺) and anion (Cl⁻, NO₃⁻ , and SO₄²⁻) species were detected in PM₂.₅. Results show the significantly higher loaded of water-soluble ions during winter and spring seasons. The acidity of the atmosphere was revealed and calculated using selected major cations (K⁺, Ca²⁺ , and NH₄⁺) and anions (SO₄²⁻, and NO₃⁻). A regression correlation was analyzed to check the significant linkage between the acidity and alkalinity ions. During the winter season (r² = 0.79) and spring season (r² = 0.64) shows good significant correlation between the cations and anions. The ratio of NO₃⁻/SO₄²⁻ indicates the sources of secondary pollutants were mainly influenced by industrial and vehicular emission however SO₄²⁻ mostly emitted from industries during the winter season.

Keywords: aerosols, inorganic species, source apportionment, Indo-Gangetic region

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Authors: Ali Kassem, Esam Nada, Amro Abdelhamed, Shigeo Horie

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Objective: Diabetes mellitus (DM) is associated with macrovascular complications, including coronary artery disease (CAD), and microvascular complications that contribute to the pathogenesis of erectile dysfunction (ED). On the other hand, silent myocardial ischemia (SMI) is more common in diabetic patients and is a strong predictor of cardiac events and mortality in diabetic and non-diabetic patients. Recently, Multidetector computed tomographic coronary angiography (MDCT-CA) has become a reliable non-invasive imaging modality for screening diabetic patients for SMI. We aim to evaluate the presence of SMI using (MDCT-CA) in patients with type 2DM having ED. Methods: This study evaluated 20 patients (mean age 61.45 ± 10.7 years), with DM and ED without any history of angina or angina equivalent. ED was tested with the Sexual Health Inventory for Men score, erection hardness score (EHS), and maximal penile circumferential change by an erect meter. Results: Of twenty studied patients, coronary artery stenosis was detected in 13 (65%) patients in the form of one-vessel disease (n = 6, 30%), two-vessel disease (n = 2, 10%), and three-vessel disease (n = 5, 25%). Maximum coronary artery stenosis was positively correlated with age (P < 0.016,) and negatively correlated with EHS (P <04). Multivariate regression analysis using age and EHS showed that age was the only independent predictor of SMI (P <04). Conclusion: MDCT-CA is a useful tool to identify SMI in patients with diabetes mellitus and ED. One should consider the possibility of SMI especially in elderly patients with DM who have ED.

Keywords: diabetes mellitus, erectile dysfunction, microvascular, silent ischemia

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Authors: Ella Tyuryumina, Alexey Neznanov

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This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

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Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

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Authors: Sanja Podunavac-Kuzmanović, Denis Kučević, Strahinja Kovačević, Milica Karadžić, Lidija Jevrić

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The present study describes the application of chemometric methods in analysis of milk samples which were collected in a conventional dairy farm and an organic dairy farm in AP Vojvodina, Republic of Serbia. The chemometric analysis included the application of univariate regression modeling and Analysis of Variance (ANOVA) method. The ANOVA was used in order to determine the differences in fatty acids content in the milk samples from conventional and organic farm. The results of the ANOVA testing indicate that there is a highly statistically significant difference between the content of fatty acid (saturated fatty acid vs. unsaturated fatty acids) in different dairy farming. Besides, the linear univariate models have been obtained as a result of modeling the linear relationships between the milk fat content and saturated fatty acids content, and the linear relationships between the milk fat content and unsaturated fatty acids content. The models obtained on the basis of the milk samples which originate from the organic farming are statistically better than the models based on the milk samples from conventional farming.

Keywords: hemometrics, milk, organic farming, quality control

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Authors: A. J. Francisco, K. C. Gallosa, R. J. Gasacao, J. R. Ros, B. J. Viado

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The study focuses on the use of quality indicators (QI)s based on the standards made by the (IFCC), that could effectively identify and minimize errors occurring throughout the total testing process (TTP), in order to improve patient safety. The study was conducted through a survey questionnaire that was given to a random sample of 19 respondents (eight privately-owned and eleven government-owned hospitals), mainly CMTs, MTs, and Supervisors from UST-affiliated hospitals. The pre-analytical laboratory errors, which include misidentification errors, transcription errors, sample collection errors and sample handling and transportation errors, were considered as variables according to the IFCC WG-LEPS. Data gathered were analyzed using the Mann-Whitney U test, Percentile, Linear Regression, Percentage, and Frequency. The laboratory performance of both hospitals is High level. There is no significant difference between the laboratory performance between the two stated variables. Moreover, among the four QIs, sample handling and transportation errors contributed most to the difference between the two variables. Outcomes indicate satisfactory performance between both variables. However, in order to ensure high-quality and efficient laboratory operation, constant vigilance and improvements in pre-analytical QI are still needed. Expanding the coverage of the study, the inclusion of other phases, utilization of parametric tests are recommended.

Keywords: pre-analytical phase, quality indicators, laboratory performance, pre-analytical error

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

Abstract:

Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: Chiranjit Das, Sanjay Jharkharia

Abstract:

This study is focused on transport mode selection under lead time variability and emissions constraint. In order to reduce the carbon emissions generation due to transportation, organization has often faced a dilemmatic choice of transport mode selection since logistic cost and emissions reduction are complementary with each other. Another important aspect of transportation decision is lead-time variability which is least considered in transport mode selection problem. Thus, in this study, we provide a comprehensive mathematical based analytical model to decide transport mode selection under emissions constraint. We also extend our work through analysing the effect of lead time variability in the transport mode selection by a sensitivity analysis. In order to account lead time variability into the model, two identically normally distributed random variables are incorporated in this study including unit lead time variability and lead time demand variability. Therefore, in this study, we are addressing following questions: How the decisions of transport mode selection will be affected by lead time variability? How lead time variability will impact on total supply chain cost under carbon emissions? To accomplish these objectives, a total transportation cost function is developed including unit purchasing cost, unit transportation cost, emissions cost, holding cost during lead time, and penalty cost for stock out due to lead time variability. A set of modes is available to transport each node, in this paper, we consider only four transport modes such as air, road, rail, and water. Transportation cost, distance, emissions level for each transport mode is considered as deterministic and static in this paper. Each mode is having different emissions level depending on the distance and product characteristics. Emissions cost is indirectly affected by the lead time variability if there is any switching of transport mode from lower emissions prone transport mode to higher emissions prone transport mode in order to reduce penalty cost. We provide a numerical analysis in order to study the effectiveness of the mathematical model. We found that chances of stock out during lead time will be higher due to the higher variability of lead time and lad time demand. Numerical results show that penalty cost of air transport mode is negative that means chances of stock out zero, but, having higher holding and emissions cost. Therefore, air transport mode is only selected when there is any emergency order to reduce penalty cost, otherwise, rail and road transport is the most preferred mode of transportation. Thus, this paper is contributing to the literature by a novel approach to decide transport mode under emissions cost and lead time variability. This model can be extended by studying the effect of lead time variability under some other strategic transportation issues such as modal split option, full truck load strategy, and demand consolidation strategy etc.

Keywords: carbon emissions, inventory theoretic model, lead time variability, transport mode selection

Procedia PDF Downloads 400