Search results for: partial least squares regression

Authors: Jeeban Amgain

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This paper investigates the impact of the size and composition of government expenditure and tax on GDP per capita growth in 36 Asian economies over the period of 1991-2012. The research employs the technique of panel regression; Fixed Effects and Generalized Method of Moments (GMM) as well as other statistical and descriptive approaches. The finding concludes that the size of government expenditure and tax revenue are generally low in this region. GDP per capita growth is strongly negative in response to Government expenditure, however, no significant relationship can be measured in case of size of taxation although it is positively correlated with economic growth. Panel regression of decomposed fiscal components also shows that the pattern of allocation of expenditure and taxation really matters on growth. Taxes on international trade and property have a significant positive impact on growth. In contrast, a major portion of expenditure, i.e. expenditure on general public services, health and education are found to have significant negative impact on growth, implying that government expenditures are not being productive in the Asian region for some reasons. Comparatively smaller and efficient government size would enhance the growth.

Keywords: government expenditure, tax, GDP per capita growth, composition

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Authors: Davod Ahmadigheidari

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The main objective of this study was to explore prevalence of anthropometric indicators as well the factors associated with the anthropometric indications in Mali. Data on 2015, downloaded from the website of Unicef, were analyzed. A total of 16,467 women (ages 15-49 years) and 16,467 children (ages 0-59 months) were selected for the sample. Different statistical analyses, such as descriptive, crosstabs and binary logistic regression form the basis of this study. Child anthropometric indicators (i.e., wasting, stunting, underweight and BMI for age) were used as the dependent variables. SPSS Syntax from WHO was used to create anthropometric indicators. Different factors, such as child’s sex, child’s age groups, child’s diseases symptoms (i.e., diarrhea, cough and fever), maternal education, household wealth index and area of residence were used as independent variables. Results showed more than forty percent of Malian households were in nutritional crises (stunting (42%) and underweight (34%). Findings from logistic regression analyses indicated that low score of wealth index, low maternal education and experience of diarrhea in last two weeks increase the probability of child malnutrition.

Keywords: Mali, wasting, stunting, underweight, BMI for age and wealth index

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Authors: Wei Gao

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The current study investigates the change of listener responses in social conversations of the second language (L2) speakers who are staying abroad with Chinese L1 speakers in Australian universities and how their long-term stay abroad impacted their design for L2 recipient actions. There is a limited amount of empirical work on L2 English listener response acquisition, particularly regarding the influence of long-term stay abroad in English-speaking countries. Little is known whether the development of L2 listener responses and the improvement of interactional competence is affected by the prolonged residency in the target L2 country. Forty-eight participants were recruited, and they participated in the designed speaking task through Computer-Mediated Communication. Results showed that long-term stay abroad Chinese L1 speakers demonstrated an English-like pattern of listener responses in communication. Long-term stay abroad experience had a significant impact on L2 English listener responses production and organization in social conversation. Long-term stay abroad L1 Chinese speakers had an active and productive response in listenership than their non-stay abroad counterparts in terms of frequency and placement in producing listener responses. However, the L2 English listener response production only occurred to be partial in response tokens, such as backchannels and reactive expressions, also in resumptive openers' employment. This study shows that L2 English listener responses could be acquired during a long-term stay abroad in English-speaking countries but showed partial acquisition in collaborative finishes production. In addition, the most prominent finding was that Chinese L1 speakers changed their overall listener responses pattern from L1 Chinese to L2 English. The study reveals specific interactional changes in English L2 listener responses acquisition. It generates pedagogical implications for cross-cultural communication and L2 pragmatics acquisition during a long-term stay abroad.

Keywords: listener responses, stay abroad, interactional competence, L2 pragmatics acquisition

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Authors: Mustafa M. Donma, Orkide Donma

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The examination of both serum lipid fractions and body’s lipid composition are quite informative during the evaluation of obesity stages. Within this context, alterations in lipid parameters are commonly observed. The variations in the fat distribution of the body are also noteworthy. Total cholesterol (TC), triglycerides (TRG), low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C) are considered as the basic lipid fractions. Fat deposited in trunk and extremities may give considerable amount of information and different messages during discrete health states. Ratios are also derived from distinct fat distribution in these areas. Trunk-to-leg fat ratio (TLFR) and trunk-to-appendicular fat ratio (TAFR) are the most recently introduced ratios. In this study, lipid fractions and TLFR, as well as TAFR, were evaluated, and the distinctions among healthy, obese (OB), and morbid obese (MO) groups were investigated. Three groups [normal body mass index (N-BMI), OB, MO] were constituted from a population aged 6 to 18 years. Ages and sexes of the groups were matched. The study protocol was approved by the Non-interventional Ethics Committee of Tekirdag Namik Kemal University. Written informed consent forms were obtained from the parents of the participants. Anthropometric measurements (height, weight, waist circumference, hip circumference, head circumference, neck circumference) were obtained and recorded during the physical examination. Body mass index values were calculated. Total, trunk, leg, and arm fat mass values were obtained by TANITA Bioelectrical Impedance Analysis. These values were used to calculate TLFR and TAFR. Systolic (SBP) and diastolic blood pressures (DBP) were measured. Routine biochemical tests including TC, TRG, LDL-C, HDL-C, and insulin were performed. Data were evaluated using SPSS software. p value smaller than 0.05 was accepted as statistically significant. There was no difference among the age values and gender ratios of the groups. Any statistically significant difference was not observed in terms of DBP, TLFR as well as serum lipid fractions. Higher SBP values were measured both in OB and MO children than those with N-BMI. TAFR showed a significant difference between N-BMI and OB groups. Statistically significant increases were detected between insulin values of N-BMI group and OB as well as MO groups. There were bivariate correlations between LDL and TLFR (r=0.396; p=0.037) as well as TAFR values (r=0.413; p=0.029) in MO group. When adjusted for SBP and DBP, partial correlations were calculated as (r=0.421; p=0.032) and (r=0.438; p=0.025) for LDL-TLFR as well as LDL-TAFR, respectively. Much stronger partial correlations were obtained for the same couples (r=0.475; p=0.019 and r=0.473; p=0.020, respectively) upon controlling for TRG and HDL-C. Much stronger partial correlations observed in MO children emphasize the potential transition from morbid obesity to metabolic syndrome. These findings have concluded that LDL-C may be suggested as a discriminating parameter between OB and MO children.

Keywords: children, lipid parameters, obesity, trunk-to-leg fat ratio, trunk-to-appendicular fat ratio

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Authors: Arun Dhakal, Geoff Cockfield, Tek Narayan Maraseni

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This paper attempts to fulfil the gap in measuring adoption in agroforestry studies. It explains the derivation of an index of adoption rate in a Nepalese context and examines the factors affecting adoption of agroforestry-based land management practice (AFLMP) in the Dhanusha District of Nepal. Data about the different farm practices and the factors (bio-physical, socio-economic) influencing adoption were collected during focus group discussion and from the randomly selected households using a household survey questionnaire, respectively. A multivariate regression model was used to determine the factors. The factors (variables) found to significantly affect adoption of AFLMP were: farm size, availability of irrigation water, education of household heads, agricultural labour force, frequency of visits by extension workers, expenditure on farm inputs purchase, household’s experience in agroforestry, and distance from home to government forest. The regression model explained about 75% of variation in adoption decision. The model rejected ‘erosion hazard’, ‘flood hazard’ and ‘gender’ as determinants of adoption, which in case of single agroforestry practice were major variables and played positive role. Out of eight variables, farm size played the most powerful role in explaining the variation in adoption, followed by availability of irrigation water and education of household heads. The results of this study suggest that policies to promote the provision of irrigation water, extension services and motivation to obtaining higher education would probably provide the incentive to adopt agroforestry elsewhere in the terai of Nepal.

Keywords: agroforestry, adoption index, determinants of adoption, step-wise linear regression, Nepal

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Authors: Abdullah Alaklabi, Ibrahim A. Arif, Sameera O. Bafeel, Ahmad H. Alfarhan, Anis Ahamed, Jacob Thomas, Mohammad A. Bakir

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Diospyros mespiliformis (Hochst. ex A.DC.; Ebenaceae) is a large deciduous medicinal plant. This plant species is currently listed as endangered in Saudi Arabia. Molecular identification of this plant species based on short sequence regions (571 and 664 bp) of plastid rbcL (ribulose-1, 5-biphosphate carboxylase) gene was investigated in this study. The endangered plant specimens were collected from Al-Baha, Saudi Arabia (GPS coordinate: 19.8543987, 41.3059349). Phylogenetic tree inferred from the rbcL gene sequences showed that this species is very closely related with D. brandisiana. The close relationship was also observed among D. bejaudii, D. Philippinensis and D. releyi (≥99.7% sequence homology). The partial rbcL gene sequence region (571 bp) that was amplified by rbcL primer-pair rbcLaF-rbcLaR failed to discriminate D. mespiliformis from the closely related plant species, D. brandisiana. In contrast, primer-pair rbcL1F-rbcL724R yielded longer amplicon, discriminated the species from D. brandisiana and demonstrated nucleotide variations in 3 different sites (645G>T; 663A>C; 710C>G). Although D. mespiliformis (EU980712) and D. brandisiana (EU980656) are very closely related species (99.4%); however, studied specimen showed 100% sequence homology with D. mespiliformis and 99.6% with D. brandisiana. The present findings showed that rbcL short sequence region (664 bp) of plastid rbcL gene, amplified by primer-pair rbcL1F-rbcL724R, can be used for authenticating samples of D. mespiliforformis and may provide help in authentic identification and management process of this medicinally valuable endangered plant species.

Keywords: Diospyros mespiliformis, endangered plant, identification partial rbcL

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Authors: Peter Temitope Agboola

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Food insecurity continues to affect a large number of U.S households during this coronavirus COVID-19 pandemic. The pandemic has threatened the livelihoods of people, making them vulnerable to severe hardship and has had an unanticipated impact on the U.S economy. This study attempts to identify the food insecurity status of households and the determinant factors driving household food insecurity. Additionally, it attempts to discover the mitigation measures adopted by households during the pandemic in the city of Huntsville, Texas. A structured online sample survey was used to collect data, with a household expenditures survey used in evaluating the food security status of the household. Most survey respondents disclosed that the COVID-19 pandemic had affected their life and source of income. Furthermore, the main analytical tool used for the study is descriptive statistics and logistic regression modeling. A logistic regression model was used to determine the factors responsible for food insecurity in the study area. The result revealed that most households in the study area are food secure, with the remainder being food insecure.

Keywords: food insecurity, household expenditure survey, COVID-19, coping strategies, food pantry

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Authors: Ali Soroush, Mehdi Mirzaei Alavijeh, Touraj Ahmadi Jouybari, Fazel Zinat-Motlagh, Abbas Aghaei, Mari Ataee

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Diabetes is a disease with long cardiovascular, renal, ophthalmic and neural complications. It is prevalent all around the world including Iran, and its prevalence is increasing. The aim of this study was to determine the factors related to self-care behavior based on health belief model among sample of Iranian diabetic patients. This cross-sectional study was conducted among 301 type 2 diabetic patients in Gachsaran, Iran. Data collection was based on an interview and the data were analyzed by SPSS version 20 using ANOVA, t-tests, Pearson correlation, and linear regression statistical tests at 95% significant level. Linear regression analyses showed the health belief model variables accounted for 29% of the variation in self-care behavior; and perceived severity and perceived self-efficacy are more influential predictors on self-care behavior among diabetic patients.

Keywords: diabetes, patients, self-care behaviors, health belief model

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Authors: Hamed Rashid Sarand, Kemal Selçuk Öğüt

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Speed is one of the fundamental variables of road traffic flow that stands as an important evaluation criterion for traffic analyses in several aspects. In particular, varieties of speed variable, such as average speed, free flow speed, optimum speed (capacity speed), acceleration/deceleration speed and so on, have been explicitly considered in the analysis of not only road safety but also road capacity. In the purpose of realizing 'road speed – maximum speed difference across lanes' and 'road flow rate – maximum speed difference across lanes' relations on freeway traffic, this study presents a case study conducted on a basic freeway segment of O-2 in Istanbul. The traffic data employed in this study have been obtained from 5 remote traffic microwave sensors operated by Istanbul Metropolitan Municipality. The study stretch is located between two successive freeway interchanges: Ümraniye and Kavacık. Daily traffic data of 4 years (2011-2014) summer months, July and August are used. The speed data are analyzed into two main flow areas such as uncongested and congested flows. In this study, the regression analyses were carried out in order to examine the relationship between maximum speed difference across lanes and road speed. These investigations were implemented at uncongested and congested flows, separately. Moreover, the relationship between maximum speed difference across lanes and road flow rate were evaluated by applying regression analyses for both uncongested and congested flows separately. It is concluded that there is the moderate relationship between maximum speed difference across lanes and road speed in 50% cases. Additionally, it is indicated that there is the moderate relationship between maximum speed difference across lanes and road flow rate in 30% cases. The maximum speed difference across lanes decreases as the road flow rate increases.

Keywords: maximum speed difference, regression analysis, remote traffic microwave sensor, speed differentiation, traffic flow

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Authors: Ebru Dural, M. Zulfu Asık

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Laminated glass is a kind of safety glass, which is made by 'sandwiching' two glass sheets and a polyvinyl butyral (PVB) interlayer in between them. When the glass is broken, the interlayer in between the glass sheets can stick them together. Because of this property, the hazards of sharp projectiles during natural and man-made disasters reduces. They can be widely applied in building, architecture, automotive, transport industries. Laminated glass can easily undergo large displacements even under their own weight. In order to explain their true behavior, they should be analyzed by using large deflection theory to represent nonlinear behavior. In this study, a nonlinear mathematical model is developed for the analysis of laminated glass cylindrical shell which is free in radial directions and restrained in axial directions. The results will be verified by using the results of the experiment, carried out on laminated glass cylindrical shells. The behavior of laminated composite cylindrical shell can be represented by five partial differential equations. Four of the five equations are used to represent axial displacements and radial displacements and the fifth one for the transverse deflection of the unit. Governing partial differential equations are derived by employing variational principles and minimum potential energy concept. Finite difference method is employed to solve the coupled differential equations. First, they are converted into a system of matrix equations and then iterative procedure is employed. Iterative procedure is necessary since equations are coupled. Problems occurred in getting convergent sequence generated by the employed procedure are overcome by employing variable underrelaxation factor. The procedure developed to solve the differential equations provides not only less storage but also less calculation time, which is a substantial advantage in computational mechanics problems.

Keywords: laminated glass, mathematical model, nonlinear behavior, PVB

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Authors: Fuchun Li, Hong Xiao

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We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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Authors: Dalson Figueiredo, Enivaldo Rocha, Ranulfo Paranhos, José Alexandre

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This paper analyzes the effects of institutional capacity on corruption. Methodologically, the research design combines both descriptive and multivariate statistics to examine two original datasets based on secondary data. In particular, we employ a principal component model to estimate an indicator of institutional capacity for both state audit institutions and subnational judiciary courts. Then, we estimate the effect of institutional capacity on two dependent variables: (1) incidence of administrative irregularities and (2) time elapsed to judge corruption cases. The preliminary results using ordinary least squares, negative binomial and Tobit models suggest the same conclusions: higher the institutional audit capacity, higher is the probability of detecting a corruption case. On the other hand, higher the institutional capacity of state judiciary, the lower is the time to judge corruption cases.

Keywords: institutional capacity, corruption, state level institutions, evidence from Brazil

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Authors: Nur Sahver Uslu, Ali̇ Hakan Büyüklü

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Maintenance, Repair, and Overhaul (MRO) costs are significant in the aviation industry. On the other hand, companies that provide MRO services to the aviation industry but are not dominant in the sector, need to determine the right strategies for sustainable profitability in a competitive environment. This study examined the operational real data of a small medium enterprise (SME) MRO company where analytical methods are not widely applied. The company's customers were divided into two categories: airline companies and non-airline companies, and the variables that best explained profitability were analyzed with Logistic Regression for each category and the results were compared. First, data reduction was applied to the transformed variables that went through the data cleaning and preparation stages, and the variables to be included in the model were decided. The misclassification rates for the logistic regression results concerning both customer categories are similar, indicating consistent model performance across different segments. Less profit margin is obtained from airline customers, which can be explained by the variables part description, time to quotation (TTQ), turnaround time (TAT), manager, part cost, and labour cost. The higher profit margin obtained from non-airline customers is explained only by the variables part description, part cost, and labour cost. Based on the two models, it can be stated that it is significantly more challenging for the MRO company, which is the subject of our study, to achieve profitability from Airline customers. While operational processes and organizational structure also affect the profit from airline customers, only the type of parts and costs determine the profit for non-airlines.

Keywords: aircraft, aircraft components, aviation, data analytics, data science, gini index, maintenance, repair, and overhaul, MRO, logistic regression, profit, variable clustering, variable reduction

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: Suryapeta Harini

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Road surface roughness is one of the essential aspects of the road's functional condition, indicating riding comfort in both the transverse and longitudinal directions. The government of India has made maintaining good surface evenness a prerequisite for all highway projects. Pavement distress data was collected with a Network Survey Vehicle (NSV) on a National Highway. It determines the smoothness and frictional qualities of the pavement surface, which are related to driving safety and ease. Based on the data obtained in the field, a regression equation was created with the IRI value and the visual distresses. The suggested system can use wireless acceleration sensors and GPS to gather vehicle status and location data, as well as calculate the international roughness index (IRI). Potholes, raveling, rut depth, cracked area, and repair work are all affected by pavement roughness, according to the current study. The study was carried out in one location. Data collected through using Bump integrator was used for the validation. The bump integrator (BI) obtained using deflection from the network survey vehicle was correlated with the distress parameter to establish an equation.

Keywords: roughness index, network survey vehicle, regression, correlation

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Authors: S. Sulochana Singha

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Dhiyan or Dehan is the name of the community and language spoken by the Koch-Rajbangshi people of Barak Valley of Assam. Ethnically, they are Mongoloids, and their language belongs to the Indo-Aryan language family. However, Dhiyan is absent in any classification of Indo-Aryan languages. So the classification of Dhiyan language under the Indo-Aryan language family is completely based on the shared typological features of the other Indo-Aryan languages. Typologically, Dhiyan is an agglutinating language, and it shares many features of Indo-Aryan languages like presence of aspirated voiced stops, non-tonal, verb-person agreement, adjectives as different word class, prominent tense and subject object verb word order. Reduplication is a productive word-formation process in Dhiyan. Besides it also expresses plurality, intensification, and distributive. Generally, reduplication in Dhiyan can be at the morphological or lexical level. Morphological reduplication in Dhiyan involves expressives which includes onomatopoeias, sound symbolism, idiophones, and imitatives. Lexical reduplication in the language can be formed by echo formations and word reduplication. Echo formation in Dhiyan is formed by partial repetition from the base word which can be either consonant alternation or vowel alternation. The consonant alternation is basically found in onset position while the alternation of vowel is basically found in open syllable particularly in final syllable. Word reduplication involves reduplication of nouns, interrogatives, adjectives, and numerals which further can be class changing or class maintaining reduplication. The process of reduplication can be partial or complete whether it is lexical or morphological. The present paper is an attempt to describe some aspects of the formation, function, and usage of reduplications in Dhiyan which is mainly spoken in ten villages in the Eastern part of Barak River in the Cachar District of Assam.

Keywords: Barak-Valley, Dhiyan, Indo-Aryan, reduplication

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: Mina Zaky Sarofim Zaky

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Experiential marketing is a form of marketing that offers a unique integration of experiential and entertainment elements into a product or service. Experiential marketing is defined as an unforgettable experience that penetrates the customer's mind. Customer satisfaction is also defined as the emotional response to the experience provided with the purchased product or service. Experiential marketing activities can, therefore, affect the level of customer satisfaction and loyalty. In this context, the study aims to determine the relationship between experiential marketing, customer satisfaction and customer loyalty in cosmetic products in Konya. The least squares method (PLS) was used to analyze the research data. Existing research has shown that experiential marketing is a significant predictor of customer satisfaction and customer loyalty, and that experiential marketing has a positive impact on customer satisfaction and customer loyalty.

Keywords: internet, marketing, tourism, tourism management corporate responsibility, employee organizational performance, internal marketing, internal customer experiential marketing, customer satisfaction, customer loyalty, social sciences

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: Atul C. Mahajan, A. K. Chakravarty, V. Jamuna, C. S. Patil, Neeraj Kashyap, Bharti Deshmukh, Vijay Kumar

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The selection of Sahiwal bulls on the basis of dams best lactation milk yield under breeding programme in herd of the country neglecting fertility traits leads to deterioration in their performances and economy. The goal of this study was to explore polymorphism of CRISP2 gene and their association with semen traits (Post Thaw Motility, Hypo-osmotic Swelling Test, Acrosome Integrity, DNA Fragmentation and capacitation status), scrotal circumference, expected predicted difference (EPD) for milk yield and fertility. Sahiwal bulls included in present study were 60 bulls used in breeding programme as well as 50 young bulls yet to be included in breeding programme. All the Sahiwal bulls were found to be polymorphic for CRISP2 gene (AA, AG and GG) present within exon 7 to the position 589 of CRISP2 mRNA by using PCR-SSCP and Sequencing. Semen analysis were done on 60 breeding bulls frozen semen doses pertaining to four season (winter, summer, rainy and autumn). The scrotal circumference was measured from existing Sahiwal breeding bulls in the herd (n=47). The effect of non-genetic factors on reproduction traits were studied by least-squares technique and the significant difference of means between subclasses of season, period, parity and age group were tested. The data were adjusted for the significant non-genetic factors to remove the differential environmental effects. The adjusted data were used to generate traits like Waiting Period (WP), Pregnancy Rate (PR), Expected Predicted Difference (EPD) of fertility, respectively. Genetic and phenotypic parameters of reproduction traits were estimated. The overall least-squares means of Age at First Calving (AFC), Service Period (SP) and WP were estimated as 36.69 ± 0.18 months, 120.47 ± 8.98 days and 79.78 ± 3.09 days respectively. Season and period of birth had significant effect (p < 0.01) on AFC. AFC was highest during autumn season of birth followed by summer, winter and rainy. Season and period of calving had significant effect (p < 0.01) on SP and WP of sahiwal cows. The WP for Sahiwal cows was standardized based on four developed predicted model for pregnancy rate 42, 63, 84 and 105 days using all lactation records. The WP for Sahiwal cows were standardized as 42 days. A selection criterion was developed for Sahiwal breeding bulls and young Sahiwal bulls on the basis of EPD of fertility. The genotype has significant effect on expected predicted difference of fertility and some semen parameters like post thaw motility and HOST. AA Genotype of CRISP2 gene revealed better EPD for fertility than EPD of milk yield. AA genotype of CRISP2 gene has higher scrotal circumference than other genotype. For young Sahiwal bulls only AA genotypes were present with similar patterns. So on the basis of association of genotype with seminal traits, EPD of milk yield and EPD for fertility status, AA and AG genotype of CRISP2 gene was better for higher fertility in Sahiwal bulls.

Keywords: expected predicted difference, fertility, sahiwal, waiting period

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Authors: Raeed Alanazi

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Introduction: Nursing students have a crucial role to play in the inhibition of infectious diseases and, therefore, must be trained in infection control and prevention modules prior to entering clinical settings. Simulations have been found to have a positive impact on infection control skills and the use of standard precautions. Aim: The purpose of this study was to use the four sources of self-efficacy in explaining the level of clinical self-efficacy and adherence to infection control practices in Saudi nursing students during simulation practice. Method: A cross-sectional design with convenience sampling was used. This study was conducted in all Saudi nursing schools, with a total number of 197 students participated in this study. Three scales were used simulation self- efficacy Scale (SSES), the four sources of self-efficacy scale (SSES), and Compliance with Standard Precautions Scale (CSPS). Multiple linear regression was used to test the use of the four sources of self-efficacy (SSES) in explaining level of clinical self-efficacy and adherence to infection control in nursing students. Results: The vicarious experience subscale (p =.044) was statistically significant. The regression model indicated that for every one unit increase in vicarious experience (observation and reflection in simulation), the participants’ adherence to infection control increased by .13 units (β =.22, t = 2.03, p =.044). In addition, the regression model indicated that for every one unit increase in education level, the participants’ adherence to infection control increased by 1.82 units (beta=.34= 3.64, p <.001). Also, the mastery experience subscale (p <.001) and vicarious experience subscale (p = .020) were shared significant associations with clinical self-efficacy. Conclusion: The findings of this research support the idea that simulation-based learning can be a valuable teaching-learning method to help nursing students develop clinical competence, which is essential in providing quality and safe nursing care.

Keywords: simulation-based learning, clinical self-efficacy, infection control, nursing students

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Authors: Müzeyyen Balçikanli, Erdoğan Özbay, Hakan Tacettin Türker, Okan Karahan, Cengiz Duran Atiş

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In this research, effect of curing time (TC), curing temperature (CT), sodium concentration (SC) and silicate modules (SM) on the compressive strength, chloride ion permeability, and water absorption capacity of alkali activated slag (AAS) concretes were investigated. For maximization of compressive strength while for minimization of chloride ion permeability and water absorption capacity of AAS concretes, best possible combination of CT, CTime, SC and SM were determined. An experimental program was conducted by using the central composite design method. Alkali solution-slag ratio was kept constant at 0.53 in all mixture. The effects of the independent parameters were characterized and analyzed by using statistically significant quadratic regression models on the measured properties (dependent parameters). The proposed regression models are valid for AAS concretes with the SC from 0.1% to 7.5%, SM from 0.4 to 3.2, CT from 20 °C to 94 °C and TC from 1.2 hours to 25 hours. The results of test and analysis indicate that the most effective parameter for the compressive strength, chloride ion permeability and water absorption capacity is the sodium concentration.

Keywords: alkali activation, slag, rapid chloride permeability, water absorption capacity

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Authors: Chung-Ming Lo, Chung-Chien Lee

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In many countries, the lifetime prevalence of shoulder pain is up to 70%. In America, the health care system spends 7 billion per year about the healthy issues of shoulder pain. With respect to the origin, up to 70% of shoulder pain is attributed to rotator cuff lesions This study proposed a computer-aided diagnosis (CAD) system to assist radiologists classifying rotator cuff lesions with less operator dependence. Quantitative features were extracted from the shoulder ultrasound images acquired using an ALOKA alpha-6 US scanner (Hitachi-Aloka Medical, Tokyo, Japan) with linear array probe (scan width: 36mm) ranging from 5 to 13 MHz. During examination, the postures of the examined patients are standard sitting position and are followed by the regular routine. After acquisition, the shoulder US images were drawn out from the scanner and stored as 8-bit images with pixel value ranging from 0 to 255. Upon the sonographic appearance, the boundary of each lesion was delineated by a physician to indicate the specific pattern for analysis. The three lesion categories for classification were composed of 20 cases of tendon inflammation, 18 cases of calcific tendonitis, and 18 cases of supraspinatus tear. For each lesion, second-order statistics were quantified in the feature extraction. The second-order statistics were the texture features describing the correlations between adjacent pixels in a lesion. Because echogenicity patterns were expressed via grey-scale. The grey-scale co-occurrence matrixes with four angles of adjacent pixels were used. The texture metrics included the mean and standard deviation of energy, entropy, correlation, inverse different moment, inertia, cluster shade, cluster prominence, and Haralick correlation. Then, the quantitative features were combined in a multinomial logistic regression classifier to generate a prediction model of rotator cuff lesions. Multinomial logistic regression classifier is widely used in the classification of more than two categories such as the three lesion types used in this study. In the classifier, backward elimination was used to select a feature subset which is the most relevant. They were selected from the trained classifier with the lowest error rate. Leave-one-out cross-validation was used to evaluate the performance of the classifier. Each case was left out of the total cases and used to test the trained result by the remaining cases. According to the physician’s assessment, the performance of the proposed CAD system was shown by the accuracy. As a result, the proposed system achieved an accuracy of 86%. A CAD system based on the statistical texture features to interpret echogenicity values in shoulder musculoskeletal ultrasound was established to generate a prediction model for rotator cuff lesions. Clinically, it is difficult to distinguish some kinds of rotator cuff lesions, especially partial-thickness tear of rotator cuff. The shoulder orthopaedic surgeon and musculoskeletal radiologist reported greater diagnostic test accuracy than general radiologist or ultrasonographers based on the available literature. Consequently, the proposed CAD system which was developed according to the experiment of the shoulder orthopaedic surgeon can provide reliable suggestions to general radiologists or ultrasonographers. More quantitative features related to the specific patterns of different lesion types would be investigated in the further study to improve the prediction.

Keywords: shoulder ultrasound, rotator cuff lesions, texture, computer-aided diagnosis

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Authors: Thi Thi Zin, Pyke Tin, Ikuo Kobayashi, Yoichiro Horii

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Today dairy farm experts and farmers have well recognized the importance of dairy cow Body Condition Score (BCS) since these scores can be used to optimize milk production, managing feeding system and as an indicator for abnormality in health even can be utilized to manage for having healthy calving times and process. In tradition, BCS measures are done by animal experts or trained technicians based on visual observations focusing on pin bones, pin, thurl and hook area, tail heads shapes, hook angles and short and long ribs. Since the traditional technique is very manual and subjective, the results can lead to different scores as well as not cost effective. Thus this paper proposes an algebraic geometric imaging approach for an automatic dairy cow BCS system. The proposed system consists of three functional modules. In the first module, significant landmarks or anatomical points from the cow image region are automatically extracted by using image processing techniques. To be specific, there are 23 anatomical points in the regions of ribs, hook bones, pin bone, thurl and tail head. These points are extracted by using block region based vertical and horizontal histogram methods. According to animal experts, the body condition scores depend mainly on the shape structure these regions. Therefore the second module will investigate some algebraic and geometric properties of the extracted anatomical points. Specifically, the second order polynomial regression is employed to a subset of anatomical points to produce the regression coefficients which are to be utilized as a part of feature vector in scoring process. In addition, the angles at thurl, pin, tail head and hook bone area are computed to extend the feature vector. Finally, in the third module, the extracted feature vectors are trained by using Markov Classification process to assign BCS for individual cows. Then the assigned BCS are revised by using multiple regression method to produce the final BCS score for dairy cows. In order to confirm the validity of proposed method, a monitoring video camera is set up at the milk rotary parlor to take top view images of cows. The proposed method extracts the key anatomical points and the corresponding feature vectors for each individual cows. Then the multiple regression calculator and Markov Chain Classification process are utilized to produce the estimated body condition score for each cow. The experimental results tested on 100 dairy cows from self-collected dataset and public bench mark dataset show very promising with accuracy of 98%.

Keywords: algebraic geometric imaging approach, body condition score, Markov classification, polynomial regression

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Authors: Jui-Chen Huang

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This research used artificial intelligence method to explore elderly’s opinions on the reuse of telecare, its effect on their service quality, satisfaction and the relationship between customer perceived value and intention to reuse. This study conducted a questionnaire survey on the elderly. A total of 124 valid copies of a questionnaire were obtained. It adopted Backpropagation Network (BPN) to propose an effective and feasible analysis method, which is different from the traditional method. Two third of the total samples (82 samples) were taken as the training data, and the one third of the samples (42 samples) were taken as the testing data. The training and testing data RMSE (root mean square error) are 0.022 and 0.009 in the BPN, respectively. As shown, the errors are acceptable. On the other hand, the training and testing data RMSE are 0.100 and 0.099 in the regression model, respectively. In addition, the results showed the service quality has the greatest effects on the intention to reuse, followed by the satisfaction, and perceived value. This result of the Backpropagation Network method is better than the regression analysis. This result can be used as a reference for future research.

Keywords: artificial intelligence, backpropagation network (BPN), elderly, reuse, telecare

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Htet Khaing Lin

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This study explores the relationship between a diverse set of variables, encompassing both environmental and socio-economic factors, and poverty levels in the Philippines for the years 2012, 2015, and 2018. Employing Ordinary Least Squares (OLS), Spatial Lag Models (SLM), and Spatial Error Models (SEM), this study delves into the dynamics of key indicators, including daytime and nighttime land surface temperature, cropland surface, urban land surface, rainfall, population size, normalized difference water, vegetation, and drought indices. The findings reveal consistent patterns and unexpected correlations, highlighting the need for nuanced policies that address the multifaceted challenges arising from the interplay of environmental and socio-economic factors.

Keywords: poverty analysis, OLS, spatial lag models, spatial error models, Philippines, google earth engine, satellite data, environmental dynamics, socio-economic factors

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Authors: Passant Youssef, Ahmed El-Tair, Amr El-Nemr

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Lately, with the environmental changes, enthusiasts trigger to stop the contamination of environment. Thus, various efforts were exerted for innovating environmental friendly concrete to sustain as a ‘Green Building’ material. Green building materials consider the cement industry as one of the most sources of air pollutant with high rate of carbon dioxide (CO₂) emissions. Several methods were developed to extensively reduce the influence of cement industry on environment. These methods such as using supplementary cementitious material or improving the cement manufacturing process are still under investigation. However, with the presence of recycled wastes from construction and finishing materials, the use of supplementary cementitious materials seems to provide an economic solution. Furthermore, it improves the mechanical properties of cement paste, in addition to; it modulates the workability and durability of concrete. In this paper, the glass powder was considered to be used as partial replacement of cement. This study provided the mechanical influence for using the glass powder as partial replacement of cement. In addition, it examines the microstructure of cement mortar using scanning electron microscope and X-ray diffraction. The cement in concrete is replaced by waste glass powder in steps of 5%, 10%, 15%, 20% and 25% by weight of cement and its effects on compressive and flexure strength were determined after 7 and 28 days. It was found that the 5% glass powder replacement increased the 7 days compressive strength by 20.5%, however, there was no increase in compressive strength after 28 days; which means that the glass powder did not react in the cement mortar due to its amorphous nature on the long run, and it can act as fine aggregate better that cement replacement. As well as, the 5% and 10% glass powder replacement increased the 28 days flexural strength by 46.9%. SEM micrographs showed very dense matrix for the optimum specimen compared to control specimen as well; some glass particles were clearly observed. High counts of silica were optimized from XRD while amorphous materials such as calcium silicate cannot be directly detected.

Keywords: supplementary materials, glass powder, concrete, cementitious materials

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Authors: Ikpechukwu Njoku

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The study examined appraisal of shipping trade influence on the economic growth in Nigeria from 1981-2016 by the use of secondary data collected from the Central Bank of Nigeria. The main objectives are to examine the trend of shipping trade in Nigeria as well as determine the influence of economic growth on gross domestic product (GDP). The study employed both descriptive and influential tools. The study adopted cointegration regression method for the analysis of each of the variables (shipping trade, external reserves and external debts). The results show that there is a statistically significant relationship between GDP and external reserves with p-value 0.0190. Also the result revealed that there is a statistically significant relationship between GDP and shipping trade with p-value 0.000. However, shipping trade and external reserves contributed positively at 1% and 5% level of significance respectively while external debts impacted negatively to GDP at 5% level of significance with a long run variance of cointegration regression. Therefore, the study suggests that government should do all it can to curtail foreign dominance and repatriation of profit for a more sustainable economy as well as upgrade port facilities, prevent unnecessary delays and encourage exportable goods for maximum deployment of ships.

Keywords: external debts, external reserve, GDP, shipping trade

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Authors: Kaycee Bills

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This study will determine if students with disabilities have higher student loan debt payments than other student populations. The hypothesis was that students with disabilities would have significantly higher student loan debt payments than other students due to the length of time they spend in school. Using the Bachelorette and Beyond Study Wave 2015/017 dataset, quantitative methods were employed. These data analysis methods included linear regression and a correlation matrix. Due to the exploratory nature of the study, the significance levels for the overall model and each variable were set at .05. The correlation matrix demonstrated that students with certain types of disabilities are more likely to fall under higher student loan payment brackets than students without disabilities. These results also varied among the different types of disabilities. The result of the overall linear regression model was statistically significant (p = .04). Despite the overall model being statistically significant, the majority of the significance values for the different types of disabilities were null. However, several other variables had statistically significant results, such as veterans, people of minority races, and people who attended private schools. Implications for how this impacts the economy, capitalism, and financial wellbeing of various students are discussed.

Keywords: disability, student loan debt, higher education, social work

Procedia PDF Downloads 168