Search results for: across-through variables

Authors: Javad Sarvestan, Zdenek Svoboda

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Force-time (F-T) curvature characteristics are globally referenced as the main indicators of athletic jump performance. Nevertheless, to the best of authors’ knowledge, no investigation tried to deeply study the relationship between F-T curve variables and real-game jump performance among elite volleyball players. To this end, this study was designated to investigate the association between F-T curve variables, including movement timings, force, velocity, power, rate of force development (RFD), modified reactive strength index (RSImod), and net impulse with spike jump height during real-game circumstances. Twelve young elite volleyball players performed 3 countermovement jump (CMJ) and 3 spike jump in real-game circumstances with 1-minute rest intervals to prevent fatigue. Shapiro-Wilk statistical test illustrated the normality of data distribution, and Pearson’s product correlation test portrayed a significant correlation between CMJ height and peak RFD (0.85), average RFD (r=0.81), RSImod (r=0.88) and concentric net impulse (r=0.98), and also significant correlation between spike jump height and peak RFD (0.73), average RFD (r=0.80), RSImod (r=0.62) and concentric net impulse (r=0.71). Multiple regression analysis also reported that these factors have a strong contribution in predicting of CMJ (98%) and spike jump (77%) heights. Outcomes of this study confirm that the RFD, concentric net impulse, and RSImod values could precisely monitor and track the volleyball attackers’ explosive strength, muscular stretch-shortening cycle function efficiency, and ultimate spike jump height. To this effect, volleyball coaches and trainers are advised to have an in-depth focus on their athletes’ progression or the impacts of strength trainings by observing and chasing the F-T curve variables such as RFD, net impulse, and RSImod.

Keywords: net impulse, reactive strength index, rate of force development, stretch-shortening cycle

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Authors: Khatesiree Sripoothon, Usanee Sengpanich, Rattana Sittioum

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The objective of this study is to examine the factors influencing consumer purchasing decisions about healthy food. This model consists of two latent variables: Consumer Perception relating to NCDs and Consumer Perceived Product Value. The study was conducted in the northern provinces of Thailand, which are popular with tourists and have received support from the government for health tourism. A survey was used as the data collection method, and the questionnaire was applied to 385 tourists. An accidental sampling method was used to identify the sample. The statistics of frequency, percentage, mean, and structural equation model were used to analyze the data obtained. Additionally, all factors had a significant positive influence on healthy food purchasing decisions (p<0.01) and were predictive of healthy food purchasing decisions at 46.20 (R2=0.462). Also, these findings seem to underline a supposition that consumer perceptions of NCDs and perceived product value are key variables that strengthens the competitive effects of a healthy-friendly business entrepreneur. Moreover, reduce the country's public health costs for treating patients with the disease of NCDs in Thailand.

Keywords: healthy food, perceived product value, perception of non-communicable diseases, purchasing decisions

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Authors: Rashmi Uchil, A. H. Sequeira

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Post-liberalization era in India has seen an unprecedented growth of mergers, both domestic as well as cross-border deals. Indian organizations have slowly begun appreciating this inorganic method of growth. However, all is not well as is evidenced in the lowering value creation of organizations after mergers. Several studies have identified that organizational culture is one of the key factors that affects the success of mergers. But very few studies have been attempted in this realm in India. The current study attempts to identify the factors in the organizational culture variable that may be unique to India. It also focuses on the difference in the impact of organizational culture on merger of organizations in the manufacturing and service sectors in India. The study uses a mixed research approach. An exploratory research approach is adopted to identify the variables that constitute organizational culture specifically in the Indian scenario. A few hypotheses were developed from the identified variables and tested to arrive at the Grounded Theory. The Grounded Theory approach used in the study, attempts to integrate the variables related to organizational culture. Descriptive approach is used to validate the developed grounded theory with a new empirical data set and thus test the relationship between the organizational culture variables and the success of mergers. Empirical data is captured from merged organizations situated in major cities of India. These organizations represent significant proportions of the total number of organizations which have adopted mergers. The mix of industries included software, banking, manufacturing, pharmaceutical and financial services. Mixed sampling approach was adopted for this study. The first phase of sampling was conducted using the probability method of stratified random sampling. The study further used the non-probability method of judgmental sampling. Adequate sample size was identified for the study which represents the top, middle and junior management levels of the organizations that had adopted mergers. Validity and reliability of the research instrument was ensured with appropriate tests. Statistical tools like regression analysis, correlation analysis and factor analysis were used for data analysis. The results of the study revealed a strong relationship between organizational culture and its impact on the success of mergers. The study also revealed that the results were unique to the extent that they highlighted a marked difference in the manner of internalization of change of organizational culture after merger by the organizations in the manufacturing sector. Further, the study reveals that the organizations in the service sector internalized the changes at a slower rate. The study also portrays the industries in the manufacturing sector as more proactive and can contribute to a change in the perception of the said organizations.

Keywords: manufacturing industries, mergers, organizational culture, service industries

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Authors: Olivier Mesly

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This article examines the likelihood of deception on the part of borrowers wishing to obtain credit from institutional or private lenders. In our first study, we identify five explanatory variables that account for nearly forty percent of the propensity to act deceitfully: a poor credit history, debt, risky behavior, and to a much lesser degree, irrational behavior and disconnection from the bundle of needs, goals, and preferences. For the second study, we remodeled the initial questionnaire to adapt it to the needs of institutional bankers and borrowers, especially those that engage in money on-line peer-to-peer lending, a growing business fueled by the COVID pandemic. We find that the three key psychological variables that help to indirectly predict the likelihood of deceitful behaviors and possible default on loan reimbursement, i.e., risky behaviors, ir-rationality, and dis-connection, interact with each other to form a loop. This study presents two benefits: first, we provide evidence that it is to some degree possible to tighten control over lending practices. Second, we offer a pragmatic tool: a questionnaire, that lenders can use or adapt to gauge potential borrowers’ deceit, notably by combining their results with standard hard-data measures of risk.

Keywords: bundle of needs, default, debt, deception, risk, peer-to-peer lending

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Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Mohammad Anwar, Shah Waliullah

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This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models.

Keywords: Bayesian approach, common effect, fixed effect, random effect, Dynamic Random Effect Model

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Authors: Masoud Sheidaei, Fahimeh Koohdar

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Date palms are economically important tree plants with high nutrition and medicinal values. More than 400 date palm cultivars are cultivated in many regions of Iran, but no report is available on landscape genetics and species distribution modeling of these trees from the country. Therefore, the present study provides a detailed insight into the genetic diversity and structure of date palm populations in Iran and investigates the effects of geographical and climatic variables on the structuring of genetic diversity in them. We used different computational methods in the study like, spatial principal components analysis (sPCA), redundancy analysis (RDA), latent factor mixed model (LFMM), and Maxent and Dismo models of species distribution modeling. We used a combination of different molecular markers for this study. The results showed that both global and local spatial features play an important role in the genetic structuring of date palms, and the genetic regions associated with local adaptation and climatic variables were identified. The effects of climatic change on the distribution of these taxa and the genetic regions adaptive to these changes will be discussed.

Keywords: adaptive genetic regions, genetic diversity, isolation by distance, populations divergence

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Authors: Waheed O. Majekodunmi

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Climate change is a real global phenomenon and an unquestionable threat to our quest for a healthy and livable planet. It is now regarded as potentially the most monumental environmental challenge people and the planet will be confronted with over the next centuries. Expectedly, climate change mitigation was one of the central themes of COP 28. Despite contributing the least to climate change, Africa is and remains the hardest hit by the negative consequences of climate change including poor growth performance. Currently, it is being hypothesized that the high level of vulnerability and exposure to climate-related disasters, low adaptive capacity against global warming and high mitigation costs of climate change across the continent could be linked to the recent abysmal economic performance of African countries, especially in oil-producing countries where greenhouse gas emissions, is potentially more prevalent. This paper examines the impact of climate change on the economic performance of selected oil-producing countries in Africa using evidence from Nigeria, Algeria and Angola. The objective of the study is to determine whether or not climate change influences the economic performance of oil-producing countries in Africa by examining the nexus between economic growth and climate-related variables. The study seeks to investigate the effect of climate change on the pace of economic growth in African oil-producing countries. To achieve the research objectives, this study utilizes a quantitative approach by using historical and current secondary data sets to determine the relationship between climate-related variables and economic growth variables in the selected countries. The study employed numbers, percentages, tables and trend graphs to explain the trends or common patterns between climate change, economic growth and determinants of economic growth: governance effectiveness, infrastructure, macroeconomic stability and regulatory efficiency. Results from the empirical analysis of data show that the trends of economic growth and climate-related variables in the selected oil-producing countries are in the opposite directions as the increasing share of renewable energy sources in total energy consumption and the reduction in greenhouse gas emissions per capita in the oil-producing countries did not translate to higher economic growth. Further findings show that annual surface temperatures in the selected countries do not share similar trends with the food imports ratio and GDP per capita annual growth rate suggesting that climate change does not impact significantly agricultural productivity and economic growth in oil-producing countries in Africa. Annual surface temperature was also found to not share a similar pattern with governance effectiveness, macroeconomic stability and regulatory efficiency reinforcing the claim that some economic growth variables are independent of climate change. The policy implication of this research is that oil-producing African countries need to focus more on improving the macroeconomic environment and streamlining governance and institutional processes to boost their economic performance before considering the adoption of climate change adaptation and mitigation strategies.

Keywords: climate change, climate vulnerability, economic growth, greenhouse gas emissions per capita, oil-producing countries, share of renewable energy in total energy consumption

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Authors: Khalid Ahmed Abdoun, Mohamed Abdulwahid Alsoufi, Ibrahim Abdullah Alhidary

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The intention of this study was to evaluate the effect of dietary Cr supplementation on haematological parameters in camel calves reared under hot summer conditions. Fifteen male camel calves (5 – 6 months old) were randomly allotted to three dietary treatments (n = 5) for a period of 84 days. Camel calves were fed ad libitum on basal diet without Cr supplementation (control), basal diet supplemented with 0.5 mg Cr/kg DM (Cr 0.5) or basal diet supplemented with 1.0 mg Cr/kg DM (Cr 1.0). During this, blood samples were collected every four weeks for hematological examination. The obtained results revealed that dietary Cr supplementation to camel calves reared under hot summer did not show significant effects (P> 0.05) on hematological variables. However, the neutrophil to lymphocytes ratio (N: L ratio) was significantly (P < 0.05) reduced in camel calves fed on diets supplemented with chromium. In conclusion, Chromium supplementation to the diet of camel calves did not show any significant effects on hematological variables. Whereas, the neutrophil to lymphocytes ratio (N: L ratio) was reduced in camel calves fed diets supplemented with chromium.

Keywords: camel calves, chromium, haematological, immune response

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Authors: Münevver Başman, Ezel Tavşancıl

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Making the right decisions about students depends on teachers’ use of the assessment and evaluation techniques effectively. In order to do that, teachers should have positive attitudes and adequate self-efficacy perception towards assessment and evaluation. The purpose of this study is to investigate relationship between self-efficacy perception and the attitudes of prospective teachers towards assessment and evaluation and what kind of differences these issues have in terms of a variety of demographic variables. The study group consisted of 277 prospective teachers who have been studying in different departments of Marmara University, Faculty of Education. In this study, ‘Personal Information Form’, ‘A Perceptual Scale for Measurement and Evaluation of Prospective Teachers Self-Efficacy in Education’ and ‘Attitudes toward Educational Measurement Inventory’ are applied. As a result, positive correlation was found between self-efficacy perceptions and the attitudes of prospective teachers towards assessment and evaluation. Considering different departments, there is a significant difference between the mean score of attitudes of prospective teachers and between the mean score of self-efficacy perceptions of them. However, considering variables of attending statistics class and the class types at the graduated high school, there is no significant difference between the mean score of attitudes of prospective teachers and between the mean score of self-efficacy perceptions of them.

Keywords: attitude, perception, prospective teacher, self-efficacy

Procedia PDF Downloads 279

Authors: Adeola Adenikinju, Olusanya E. Olubusoye, Lateef O. Akinpelu, Dilinna L. Nwobi

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Since the enactment of the Fiscal Responsibility Act (2007), the Federal Government of Nigeria (FGN) has made public its fiscal budget and the subsequent implementation report. A critical review of these documents shows significant variations in the five macroeconomic variables which are inputs in each Presidential budget; oil Production target (mbpd), oil price ($), Foreign exchange rate(N/$), and Gross Domestic Product growth rate (%) and inflation rate (%). This results in underperformance of the Federal budget expected output in terms of non-oil and oil revenue aggregates. This paper evaluates first the existing variance between budgeted and actuals, then the relationship and causality between the determinants of Federal fiscal budget assumptions, and finally the determinants of FGN’s Gross Oil Revenue. The paper employed the use of descriptive statistics, the Autoregressive distributed lag (ARDL) model, and a Profit oil probabilistic model to achieve these objectives. This model permits for both the static and dynamic effect(s) of the independent variable(s) on the dependent variable, unlike a static model that accounts for static or fixed effect(s) only. It offers a technique for checking the existence of a long-run relationship between variables, unlike other tests of cointegration, such as the Engle-Granger and Johansen tests, which consider only non-stationary series that are integrated of the same order. Finally, even with small sample size, the ARDL model is known to generate a valid result, for it is the dependent variable and is the explanatory variable. The results showed that there is a long-run relationship between oil revenue as a proxy for budget performance and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a short-run relationship between oil revenue and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a long-run relationship between non-oil revenue and its determinants; inflation rate, GDP growth rate, and foreign exchange rate. The grangers’ causality test results show that there is a mono-directional causality between oil revenue and its determinants. The Federal budget assumptions only explain 68% of oil revenue and 62% of non-oil revenue. There is a mono-directional causality between non-oil revenue and its determinants. The Profit oil Model describes production sharing contracts, joint ventures, and modified carrying arrangements as the greatest contributors to FGN’s gross oil revenue. This provides empirical justification for the selected macroeconomic variables used in the Federal budget design and performance evaluation. The research recommends other variables, debt and money supply, be included in the Federal budget design to explain the Federal budget revenue performance further.

Keywords: ARDL, budget performance, oil price, oil quantity, oil revenue

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Authors: Mervat Kassem, Nourhan Fanaki, F. Dabbous, Hamida Abou-Shleib, Y. R. Abdel-Fattah

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With increasing environmental awareness and emphasis on a sustainable society in harmony with the global environment, biosurfactants are gaining prominence and have already taken over for a number of important industrial uses. They are produced by living organisms, for examples Pseudomonas aeruginosa which produces rhamnolipids, Candida (formerly Torulopsis) bombicola, which produces high yields of sophorolipids from vegetable oils and sugars and Bacillus subtilis which produces a lipopeptide called surfactin. The main goal of this work was to optimize biosurfactants production by an environmental Gram positive isolate for large scale production with maximum yield and low cost. After molecular characterization, phylogenetic tree was constructed where it was found to be B. subtilis, which close matches to B. subtilis subsp. subtilis strain CICC 10260. For optimizing its biosurfactants production, sequential statistical design using Plackett-Burman and response surface methodology, was applied where 11 variables were screened. When analyzing the regression coefficients for the 11 variables, pH, glucose, glycerol, yeast extract, ammonium chloride and ammonium nitrate were found to have a positive effect on the biosurfactants production. Ammonium nitrate, pH and glucose were further studied as significant independent variables for Box-Behnken design and their optimal levels were estimated and were found to be 7.328 pH value, 3 g% glucose and 0.21g % ammonium nitrate yielding high biosurfactants concentration that reduced the surface tension of the culture medium from 72 to 18.16 mN/m. Next, kinetics of cell growth and biosurfactants production by the tested B. subtilis isolate, in bioreactor was compared with that of shake flask where the maximum growth and specific growth (µ) in the bioreactor was higher by about 25 and 53%, respectively, than in shake flask experiment, while the biosurfactants production kinetics was almost the same in both shake flask and bioreactor experiments.

Keywords: biosurfactants, B. subtilis, molecular identification, phylogenetic trees, Plackett-Burman design, Box-Behnken design, 16S rRNA

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Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

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Authors: Nazli Akhlaghinia, Ali Rajabzadeh Ghatari

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In today's world, with increasing environmental awareness, firms are facing severe pressure from various stakeholders, including the government and customers, to reduce their harmful effects on the environment. Over the past few decades, the increasing effects of global warming, climate change, waste, and air pollution have increased the global attention of experts to the issue of the green supply chain and led them to the optimal solution for greenery. Green supply chain management (GSCM) plays an important role in motivating the sustainability of the organization. With increasing environmental concerns, the main objective of the research is to use system thinking methodology and Vensim software for designing a dynamic system model for green supply chain and observing behaviors. Using this methodology, we look for the effects of a green supply chain structure on the behavioral dynamics of output variables. We try to simulate the complexity of GSCM in a period of 30 months and observe the complexity of behaviors of variables including sustainability, providing green products, and reducing energy consumption, and consequently reducing sample pollution.

Keywords: supply chain management, green supply chain management, system dynamics, energy consumption

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Authors: Jeremy Attard, Jordan François, Serge Lazzarini, Thierry Masson

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Gauge theories are the natural background for describing geometrically fundamental interactions using principal and associated fiber bundles as dynamical entities. The central notion of these theories is their local gauge symmetry implemented by the local action of a Lie group H. There exist several methods used to reduce the symmetry of a gauge theory, like gauge fixing, bundle reduction theorem or spontaneous symmetry breaking mechanism (SSBM). This paper is a presentation of another method of gauge symmetry reduction, distinct from those three. Given a symmetry group H acting on a fiber bundle and its naturally associated fields (Ehresmann (or Cartan) connection, curvature, matter fields, etc.) there sometimes exists a way to erase (in whole or in part) the H-action by just reconfiguring these fields, i.e. by making a mere change of field variables in order to get new (‘composite‘) fields on which H (in whole or in part) does not act anymore. Two examples: the re-interpretation of the BEHGHK (Higgs) mechanism, on the one hand, and the top-down construction of Tractor and Penrose's Twistor spaces and connections in the framework of conformal Cartan geometry, one the other, will be discussed. They have, of course, nothing to do with each other but the dressing field method can be applied on both to get a new insight. In the first example, it turns out, indeed, that generation of masses in the Standard Model can be separated from the symmetry breaking, the latter being a mere change of field variables, i.e. a dressing. This offers an interpretation in opposition with the one usually found in textbooks. In the second case, the dressing field method applied to the conformal Cartan geometry offer a way of understanding the deep geometric nature of the so-called Tractors and Twistors. The dressing field method, distinct from a gauge transformation (even if it can have apparently the same form), is a systematic way of finding and erasing artificial symmetries of a theory, by a mere change of field variables which redistributes the degrees of freedom of the theories.

Keywords: BEHGHK (Higgs) mechanism, conformal gravity, gauge theory, spontaneous symmetry breaking, symmetry reduction, twistors and tractors

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Authors: Paul Han, Jun-Geol Baek

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In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: Carlos Alfredo Camargo Vila, Fredy Augusto Avellaneda Vargas, Debora Alcida Nabarlatz

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Due to the current need to implement environmentally friendly technologies, the possibility of using renewable raw materials to produce bioproducts such as biofuels, or in this case, to produce biolubricant bases, from residual oils (tallow), originating has been studied of the bovine industry. Therefore, it is hypothesized that through the study and control of the operating variables involved in the reverse transesterification method, a biolubricant base with high performance is obtained on a laboratory scale using animal fats from the bovine industry as raw materials, as an alternative for material recovery and environmental benefit. To implement this process, esterification of the crude tallow oil must be carried out in the first instance, which allows the acidity index to be decreased ( > 1 mg KOH/g oil), this by means of an acid catalysis with sulfuric acid and methanol, molar ratio 7.5:1 methanol: tallow, 1.75% w/w catalyst at 60°C for 150 minutes. Once the conditioning has been completed, the biodiesel is continued to be obtained from the improved sebum, for which an experimental design for the transesterification method is implemented, thus evaluating the effects of the variables involved in the process such as the methanol molar ratio: improved sebum and catalyst percentage (KOH) over methyl ester content (% FAME). Finding that the highest percentage of FAME (92.5%) is given with a 7.5:1 methanol: improved tallow ratio and 0.75% catalyst at 60°C for 120 minutes. And although the% FAME of the biodiesel produced does not make it suitable for commercialization, it does ( > 90%) for its use as a raw material in obtaining biolubricant bases. Finally, once the biodiesel is obtained, an experimental design is carried out to obtain biolubricant bases using the reverse transesterification method, which allows the study of the effects of the biodiesel: TMP (Trimethylolpropane) molar ratio and the percentage of catalyst on viscosity and yield as response variables. As a result, a biolubricant base is obtained that meets the requirements of ISO VG (Classification for industrial lubricants according to ASTM D 2422) 32 (viscosity and viscosity index) for commercial lubricant bases, using a 4:1 biodiesel molar ratio: TMP and 0.51% catalyst at 120°C, at a pressure of 50 mbar for 180 minutes. It is necessary to highlight that the product obtained consists of two phases, a liquid and a solid one, being the first object of study, and leaving the classification and possible application of the second one incognito. Therefore, it is recommended to carry out studies of the greater depth that allows characterizing both phases, as well as improving the method of obtaining by optimizing the variables involved in the process and thus achieving superior results.

Keywords: biolubricant base, bovine tallow, renewable resources, reverse transesterification

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Authors: Sudawan Somjai

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The purposes of this research were to investigate the five important perceptions of service quality from inbound tourists who visited Bangkok, Thailand in the first quarter of 2014. Data were collected from over 10 important tourist destinations in Bangkok. The independent variables of this study included gender, age, levels of education, occupation, income, and country of origin while the dependent variables included their experience, opinion, and comment on the service received during visited tourist destinations. A simple random sampling method was performed to obtain 400 respondents. The respondents were both male and female in the same proportion. However, the majority were between 31-40 years old. Most were married with an undergraduate degree. Most were considered themselves as middle income with an average income of the respondents was between $30,001-40,000 per year. The findings revealed that the majority of respondents came to Bangkok because of low cost and high quality of tourism. The majority came to Bangkok for the first time and spent about 10 days in Thailand. The five important service perceptions that were observed by the inbound tourists in descending order according to mean were reliable of service provider, proper time of service provider, competency of service provider, neat and clean of service provider, and politeness of service provider.

Keywords: experience, inbound tourists, perception, service quality

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Authors: Ignatius Madu

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The study aims at assessing the environmental and socioeconomic determinants of climate change resilience in rural Nigeria. This is necessary because researches and development efforts on building climate change resilience of rural areas in developing countries are usually made without the knowledge of the impacts of the inherent rural characteristics that determine resilient capacities of the households. This has, in many cases, led to costly mistakes, delayed responses, inaccurate outcomes, and other difficulties. Consequently, this assessment becomes crucial not only to policymakers and people living in risk-prone environments in rural areas but also to fill the research gap. To achieve the aim, secondary data were obtained from the Annual Abstract of Statistics 2017, LSMS-Integrated Surveys on Agriculture and General Household Survey Panel 2015/2016, and National Agriculture Sample Survey (NASS), 2010/2011.Resilience was calculated by weighting and adding the adaptive, absorptive and anticipatory measures of households variables aggregated at state levels and then regressed against rural environmental and socioeconomic characteristics influencing it. From the regression, the coefficients of the variables were used to compute the impacts of the variables using the Stochastic Regression of Impacts on Population, Affluence and Technology (STIRPAT) Model. The results showed that the northern States are generally low in resilient indices and are impacted less by the development indicators. The major determining factors are percentage of non-poor, environmental protection, road transport development, landholding, agricultural input, population density, dependency ratio (inverse), household asserts, education and maternal care. The paper concludes that any effort to a successful resilient building in rural areas of the country should first address these key factors that enhance rural development and wellbeing since it is better to take action before shocks take place.

Keywords: climate change resilience; spatial impacts; STIRPAT model; Nigeria

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Authors: Jeremy Ritchey

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Undocumented immigrants in the U.S. face many challenges when looking to progress in society, especially when pursuing post-secondary education. The majority of research done on state-level policy adoption pertaining to undocumented higher-education pursuits, specifically in-state resident tuition and financial aid eligibility policies, have framed the discussion on the potential and actual impacts which implementation can and has achieved. What is missing is a model to view the social, political and demographic landscapes upon which such policies (in their various forms) find a route to legislative enactment. This research looks to address this gap in the field by investigating the correlations and significant state-level variables which can be operationalized to construct a framework for adoption of these specific policies. In the process, analysis will show that past unexamined conceptualizations of how such policies come to fruition may be limited or contradictory when compared to available data. Circling on the principles of Policy Innovation and Policy Diffusion theory, this study looks to use variables collected via Michigan State University’s Correlates of State Policy Project, a collectively and ongoing compiled database project centered around annual variables (1900-2016) collected from all 50 states relevant to policy research. Using established variable groupings (demographic, political, social capital measurements, and educational system measurements) from the time period of 2000 to 2014 (2001 being when such policies began), one can see how this data correlates with the adoption of policies related to undocumented students and in-state college tuition. After regression analysis, the results will illuminate which variables appears significant and to what effect, as to help formulate a model upon which to explain when adoption appears to occur and when it does not. Early results have shown that traditionally held conceptions on conservative and liberal identities of the state, as they relate to the likelihood of such policies being adopted, did not fall in line with the collected data. Democratic and liberally identified states were, overall, less likely to adopt pro-undocumented higher education policies than Republican and conservatively identified states and vis versa. While further analysis is needed as to improve the model’s explanatory power, preliminary findings are showing promise in widening our understanding of policy adoption factors in this realm of policies compared to the gap of such knowledge in the publications of the field as it currently exists. The model also looks to serve as an important tool for policymakers in framing such potential policies in a way that is congruent with the relevant state-level determining factors while being sensitive to the most apparent sources of potential friction. While additional variable groups and individual variables will ultimately need to be added and controlled for, this research has already begun to demonstrate how shallow or unexamined reasoning behind policy adoption in the realm of this topic needs to be addressed or else the risk is erroneous conceptions leaking into the foundation of this growing and ever important field.

Keywords: policy adoption, in-state tuition, higher education, undocumented immigrants

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Authors: Thomas Weber, Nina Strobel, Thomas Kohne, Eberhard Abele

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In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality.

Keywords: CHP, Energy 4.0, energy storage, MILP, optimization, virtual power plant

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Authors: Ismail Seçer

Abstract:

The purpose of this study is to investigate the effects of anxiety sensitivity in adolescents on anxiety disorder and childhood depression. Mood disorders and anxiety disorders in children and adolescents can be given examples of important research topics in recent years. The participants of the study consist of 670 students in Erzurum and Erzincan city centers. The participants of the study were 670 secondary and high school students studying in city centers of Erzurum and Erzincan. The participants were chosen based on convenience sampling. The participants were between the ages of 13 and 18 (M=15.7, Ss= 1.35) and 355 were male and 315 were female. The data were collected through Anxiety Sensitivity Index and Anxiety and Depression Index for Children and Adolescents. For data analysis, Correlation analysis and Structural Equation Model were used. In this study, correlational descriptive survey was used. This model enables the researcher to make predictions related to different variables based on the information obtained from one or more variables. Therefore, the purpose is to make predictions considering anxiety disorder and childhood depression based on anxiety sensitivity. For this purpose, latent variable and structural equation model was used. Structural equation model is an analysis method which enables the identification of direct and indirect effects by determining the relationship between observable and latent variables and testing their effects on a single model. CFI, RMR, RMSEA and SRMR, which are commonly accepted fit indices in structural equation model, were used. The results revealed that anxiety sensitivity impacts anxiety disorder and childhood depression through direct and indirect effects in a positive way. The results are discussed in line with the relevant literature. This finding can be considered that anxiety sensitivity can be a significant risk source in terms of children's and adolescents’ anxiety disorder experience. This finding is consistent with relevant research highlighting that in case the anxiety sensitivity increases then the obsessive compulsive disorder and panic attack increase too. The adolescents’ experience of anxiety can be attributed to anxiety sensitivity.

Keywords: anxiety sensitivity, anxiety, depression, structural equation

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Authors: Asma Hanif, A. I. K. Butt, Shabir Ahmad, Rahim Ud Din, Mustafa Inc

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This paper is devoted to a study of the fuzzy fractional mathematical model reviewing the transmission dynamics of the infectious disease Covid-19. The proposed dynamical model consists of susceptible, exposed, symptomatic, asymptomatic, quarantine, hospitalized and recovered compartments. In this study, we deal with the fuzzy fractional model defined in Caputo’s sense. We show the positivity of state variables that all the state variables that represent different compartments of the model are positive. Using Gronwall inequality, we show that the solution of the model is bounded. Using the notion of the next-generation matrix, we find the basic reproduction number of the model. We demonstrate the local and global stability of the equilibrium point by using the concept of Castillo-Chavez and Lyapunov theory with the Lasalle invariant principle, respectively. We present the results that reveal the existence and uniqueness of the solution of the considered model through the fixed point theorem of Schauder and Banach. Using the fuzzy hybrid Laplace method, we acquire the approximate solution of the proposed model. The results are graphically presented via MATLAB-17.

Keywords: Caputo fractional derivative, existence and uniqueness, gronwall inequality, Lyapunov theory

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Authors: Abdul Rashid, Varsha Rokade

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Service Quality within retail units is pivotal for satisfying customers and retaining them. This study on customer perception towards Service Quality variables in Retail aims to identify the dimensions and their impact on customers. An analytical study of the different retail service quality variables was done to understand the relationship between them. The study tries exploring the factors that attract the customers towards the organised and unorganised retail stores in the capital city of Madhya Pradesh, India. As organised retailers are seen as offering similar products in the outlets, improving service quality is seen as critical to ensuring competitive advantage over unorganised retailers. Data were collected through a structured questionnaire on a five-point Likert scale from existing walk-in customers of selected organised and unorganised retail stores in Bhopal City of Madhya Pradesh, India. The data was then analysed by factor analysis using (SPSS) Statistical Package for the Social Sciences especially Percentage analysis, ANOVA and Chi-Square. This study tries to find interrelationship between various Retail Service Quality dimensions, which will help the retailers to identify the steps needed to improve the overall quality of service. Thus, the findings of the study prove to be helpful in understanding the service quality variables which should be considered by organised and unorganised retail stores in Capital city of Madhya Pradesh, India.Also, findings of this empirical research reiterate the point of view that dimensions of Service Quality in Retail play an important role in enhancing customer satisfaction – a sector with high growth potential and tremendous opportunities in rapidly growing economies like India’s. With the introduction of FDI in multi-brand retailing, a large number of international retail players are expected to enter the Indian market, this intern will bring more competition in the retail sector. For benchmarking themselves with global standards, the Indian retailers will have to improve their service quality.

Keywords: organized retail, unorganised retail, retail service quality, service quality dimension

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Authors: Hee Jae Shin, Lee Ku Kwac, In Pyo Cha, Min Sang Lee, Hyun Kyung Yoon, Hong Gun Kim

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In rapid industrial development has increased the demand for high-strength and lightweight materials. Thus, various CFRP (Carbon Fiber Reinforced Plastics) with composite materials are being used. The design variables of CFRP are its lamination direction, order, and thickness. Thus, the hardness and strength of CFRP depend much on their design variables. In this paper, the lamination direction of CFRP was used to produce a symmetrical ply [0°/0°, -15°/+15°, -30°/+30°, -45°/+45°, -60°/+60°, -75°/+75°, and 90°/90°] and an asymmetrical ply [0°/15°, 0°/30°, 0°/45°, 0°/60° 0°/75°, and 0°/90°]. The bending flexure stress of the CFRP specimen was evaluated through a bending test. Its thermal property was measured using an infrared camera. The symmetrical specimen and the asymmetrical specimen were analyzed. The results showed that the asymmetrical specimen increased the bending loads according to the increase in the orientation angle; and from 0°, the symmetrical specimen showed a tendency opposite the asymmetrical tendency because the tensile force of fiber differs at the vertical direction of its load. Also, the infrared camera showed that the thermal property had a trend similar to that of the mechanical properties.

Keywords: Carbon Fiber Reinforced Plastic (CFRP), bending test, infrared camera, composite

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Authors: Alessandra Marcelletti, Giovanni Trovato

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Corruption in public offices is found to be the reflection of country-specific features, however, the exact magnitude and the statistical significance of its determinants effect has not yet been identified. The paper aims to propose an estimation method to measure the impact of country fundamentals on corruption, showing that covariates could differently affect the extent of corruption across countries. Thus, we exploit a model able to take into account different factors affecting the incentive to ask or to be asked for a bribe, coherently with the use of the Corruption Perception Index. We assume that discordant results achieved in literature may be explained by omitted hidden factors affecting the agents' decision process. Moreover, assuming homogeneous covariates effect may lead to unreliable conclusions since the country-specific environment is not accounted for. We apply a Finite Mixture Model with concomitant variables to 129 countries from 1995 to 2006, accounting for the impact of the initial conditions in the socio-economic structure on the corruption patterns. Our findings confirm the hypothesis of the decision process of accepting or asking for a bribe varies with specific country fundamental features.

Keywords: Corruption, Finite Mixture Models, Concomitant Variables, Countries Classification

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Authors: Tomoaki Hashimoto

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Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization.

Keywords: optimal control, stochastic systems, random dither, quantization

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Authors: Pontsho E. Tseke, Phatu W. Mashela

Abstract:

Climate-smart agriculture dictates that underusilised indigenous plant, which served as food for local marginalized communities, be assessed for introduction into mainstream agriculture. Most of the underutilised indigenous plants had survived adverse conditions in the wild; with limited information on how the interact with most abiotic and biotic factors. Cucumis myriocarpus leafy vegetable has nutritional, pharmacological and industrial applications, with limited information on how it interacts with effective microorganisms. The objective of this study was to determine the effects vesicular arbuscular mycorrhiza (VAM) on the growth of C. myriocarpus indigenous leafy vegetable under greenhouse conditions. Four-weeks-old seedlings of C. myriocarpus were transplanted into 20-cm-diameter plastic pots. Two weeks after transplanting, VAM was applied at 0, 10, 20, 30, 40, 50, 60 and 70 g Biocult-VAM plant. At 56 days after treatments, plant growth variables of C. myriocarpus with increase Biocult-VAM levels exhibited positive quadratic relations. Plant variables and increasing concentrations of salinity exhibited positive quadric relations, with 95 to 99% associations. Inclusion, Biocult-VAM can be used in sustainable production of C. myriocarpus for functional food security.

Keywords: abiotic, biotic, rhizasphere, sustainable agriculture

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Authors: Oluwasanmi Teniola, Abraham Adeleke, Ademola Ibitoye, Moshood Shitu

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To establish an economical and efficient process for the recovery of gold metal from refractory gold ore obtained from Esperando axis of Osun state Nigeria, the adsorption of gold (III) from aqua reqia leached solution of the ore using bagasse nanoparticles has been studied under various experimental variables using batch technique. The extraction percentage of gold (III) on the prepared bagasse nanoparticles was determined from its distribution coefficients as a function of solution pH, contact time, adsorbent, adsorbate concentrations, and temperature. The rate of adsorption of gold (III) on the prepared bagasse nanoparticles is dependent on pH, metal concentration, amount of adsorbate, stirring rate, and temperature. The adsorption data obtained fit into the Langmuir and Freundlich equations. Three different temperatures were used to determine the thermodynamic parameters of the adsorption of gold (III) on bagasse nanoparticles. The heat of adsorption was measured to be a positive value ΔHo = +51.23kJ/mol, which serves as an indication that the adsorption of gold (III) on bagasse nanoparticles is endothermic. Also, the negative value of ΔGo = -0.6205 kJ/mol at 318K shows the spontaneity of the process. As the temperature was increased, the value of ΔGo becomes more negative, indicating that an increase in temperature favors the adsorption process. With the application of optimal adsorption variables, the adsorption capacity of gold was 0.78 mg/g of the adsorbent, out of which 0.70 mg of gold was desorbed with 0.1 % thiourea solution.

Keywords: adsorption, bagasse, extraction, nanoparticles, recovery

Procedia PDF Downloads 132