Search results for: binary logistic regression

Authors: Florian Mach, Eric Hund, Malte Stonis

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The dismantling of disused industrial facilities such as nuclear power plants or refineries is an enormous challenge for the planning and control of the logistic processes. Existing control models do not meet the requirements for a proper dismantling of industrial plants. Therefore, the paper presents an approach for the control of dismantling and post-processing processes (e.g. decontamination) in plant decommissioning. In contrast to existing approaches, the dismantling sequence and depth are selected depending on the capacity utilization of required post-processing processes by also considering individual characteristics of respective dismantling tasks (e.g. decontamination success rate, uncertainties regarding the process times). The results can be used in the dismantling of industrial plants (e.g. nuclear power plants) to reduce dismantling time and costs by avoiding bottlenecks such as capacity constraints.

Keywords: dismantling management, logistics planning and control models, nuclear power plant dismantling, reverse logistics

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Authors: Ruben Lopez-Rodriguez

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In a microgrid, diverse energy sources (both renewable and non-renewable) are combined with energy storage units to form a localized power system. Microgrids function as independent entities, capable of meeting the energy needs of specific areas or communities. This paper introduces a Model Predictive Control (MPC) approach tailored for grid-connected microgrids, aiming to optimize their operation. The formulation employs Mixed-Integer Programming (MIP) to find optimal trajectories. This entails the fulfillment of continuous and binary constraints, all while accounting for commutations between various operating conditions such as storage unit charge/discharge, import/export from/towards the main grid, as well as asset connection/disconnection. To validate the proposed approach, a microgrid case study is conducted, and the simulation results are compared with those obtained using a rule-based strategy.

Keywords: microgrids, mixed logical dynamical systems, mixed-integer optimization, model predictive control

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Authors: Raed Alsufyani, Hissam Tawfik, Victor Chang, Muthu Ramachandran

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This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: cloud computing, business intelligence, HOT-fit model, TOE, healthcare and innovation adoption

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Authors: Mahendrapal Singh Pachlaniya, Laxmi Kumre

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The AOI (AND – OR- INVERTER) based design of XOR gate is proposed in this paper with less number of gates. This new XOR gate required four basic gates and basic gate include only AND, OR, Inverter (AOI). Conventional XOR gate required five basic gates. Ripple Carry Adder (RCA) used in parallel addition but propagation delay time is large. RCA replaced with Carry Select Adder (CSLA) to reduce propagation delay time. CSLA design with dual RCA considering carry = ‘0’ and carry = ‘1’, so it is not an area efficient adder. To make area efficient, modified CSLA is designed with single RCA considering carry = ‘0’ and another RCA considering carry = ‘1’ replaced with Binary to Excess 1 Converter (BEC). Now replacement of conventional XOR gate by new design of XOR gate in modified CSLA reduces much area compared to regular CSLA and modified CSLA.

Keywords: CSLA, BEC, XOR gate, area efficient

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Authors: Saheed Olakunle Sanusi, Israel Ajibade Adedeji

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The study analysed the effect of microfinance on the profit level of small and medium scale enterprises in Lagos. The data for the study were obtained by simple random sampling, and total of one hundred and fifty (150) small and medium scale enterprises (SMEs) were sampled for the study. Seventy-five (75) each are microfinance users and non-users. Data were analysed using descriptive statistics, logit model, t-test and ordinary least square (OLS) regression. The mean profit of the enterprises using microfinance is ₦16.8m, while for the non-users of microfinance is ₦5.9m. The mean profit of microfinance users is statistically different from the non-users. The result of the logit model specified for the determinant of access to microfinance showed that three of specified variables- educational status of the enterprise head, credit utilisation and volume of business investment are significant at P < 0.01. Enterprises with many years of experience, highly educated enterprise heads and high volume of business investment have more potential access to microfinance. The OLS regression model indicated that three parameters namely number of school years, the volume of business investment and (dummy) participation in microfinance were found to be significant at P < 0.05. These variables are therefore significant determinants of impacts of microfinance on profit level in the study area. The study, therefore, concludes and recommends that to improve the status of small and medium scale enterprises for an increase in profit, the full benefit of access to microfinance can be enhanced through investment in social infrastructure and human capital development. Also, concerted efforts should be made to encouraged non-users of microfinance among SMEs to use it in order to boost their profit.

Keywords: credit utilisation, logit model, microfinance, small and medium enterprises

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Authors: Maria Anjelica P. Ancheta, Roberto S. Soriano, Erickson L. Llaguno

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The main purpose of this study is to examine whether there is a significant relationship between socio-economic class and household water supply demand, through determining or verifying the factors governing water use consumption patterns of households from a sampling from different socio-economic classes in Metro Manila, the national capital region of the Philippines. This study is also an opportunity to augment the lack of local academic literature due to the very few publications on urban household water demand after 1999. In over 600 Metro Manila households, a rapid survey was conducted on their average monthly water consumption and habits on household water usage. The questions in the rapid survey were based on an extensive review of literature on urban household water demand. Sample households were divided into socio-economic classes A-B and C-D. Cluster analysis, dummy coding and outlier tests were done to prepare the data for regression analysis. Subsequently, backward stepwise regression analysis was used in order to determine different statistical models to describe the determinants of water consumption. The key finding of this study is that the socio-economic class of a household in Metro Manila is a significant factor in water consumption. A-B households consume more water in contrast to C-D families based on the mean average water consumption for A-B and C-D households are 36.75 m3 and 18.92 m3, respectively. The most significant proxy factors of socio-economic class that were related to household water consumption were examined in order to suggest improvements in policy formulation and household water demand management.

Keywords: household water uses, socio-economic classes, urban planning, urban water demand management

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Authors: Jamil Ahmed, Li P. Wong, Yan P. Chua

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The United Nation's sustainable development goals include the target to ensure access to water and sanitation for all; however, very few studies have assessed school-based drinking water in Pakistan. The purpose of this study was to characterize water quality in primary schools of Pakistan and to characterize how recent WASH interventions were associated with school water quality. We conducted a representative cross-sectional study of primary schools in the Sindh province of Pakistan. We used structured observations and structured interviews to ascertain the school’s WASH conditions. Our primary exposures of interest were the implementation of previous WASH interventions in the school and the water source type. Outcomes of interest included water quality (measured by various chemical and microbiological indicators) and water availability at the school’s primary drinking water source. We used log-binomial regression to characterize how WASH exposures were associated with water quality outcomes. We collected data from 256 schools. Groundwater was the primary drinking water source at most schools (87%). Water testing showed that 14% of the school’s water had arsenic above the WHO recommendations, and over 50% of the water samples exceeded recommendations for both lead and cadmium. A majority of the water sources (52%) had fecal coliform contamination. None of the schools had nitrate contamination (0%), and few had fluoride contamination (5%). Regression results indicated that having a recent WASH intervention at the school was not associated with either arsenic contamination (prevalence ratio=0.97; 95% CI: 0.46-2.1) or with fecal coliform contamination (PR=0.88; 95% CI: 0.67-1.17). Our assessment unveiled several water quality gaps that exist, including high heavy metal and fecal contamination. Our findings will help various stakeholders to take suitable action to improve water quality in Pakistani schools.

Keywords: WASH interventions, water quality, primary school children, heavy metals

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Authors: Hossam M. Moftah, Aboul Ella Hassanien

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In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach.

Keywords: traffic counting, traffic management, image processing, object detection, computer vision

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Authors: Shorena Pharjiani

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The Present empirical paper investigates the relationship between FDI and economic growth by 10 selected industries in 10 Central and Eastern European countries from the period 1995 to 2012. Different estimation approaches were used to explore the connection between FDI and economic growth, for example OLS, RE, FE with and without time dummies. Obtained empirical results leads to some main consequences: First, the Central and East European countries (CEEC) attracted foreign direct investment, which raised the productivity of industries they entered in. It should be concluded that the linkage between FDI and output growth by industries is positive and significant enough to suggest that foreign firm’s participation enhanced the productivity of the industries they occupied. There had been an endogeneity problem in the regression and fixed effects estimation approach was used which partially corrected the regression analysis in order to make the results less biased. Second, it should be stressed that the results show that time has an important role in making FDI operational for enhancing output growth by industries via total factor productivity. Third, R&D positively affected economic growth and at the same time, it should take some time for research and development to influence economic growth. Fourth, the general trends masked crucial differences at the country level: over the last 20 years, the analysis of the tables and figures at the country level show that the main recipients of FDI of the 11 Central and Eastern European countries were Hungary, Poland and the Czech Republic. The main reason was that these countries had more open door policies for attracting the FDI. Fifth, according to the graphical analysis, while Hungary had the highest FDI inflow in this region, it was not reflected in the GDP growth as much as in other Central and Eastern European countries.

Keywords: central and East European countries (CEEC), economic growth, FDI, panel data

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Authors: Sizar Abed Mohammed, Zahraa Ghazi Sadeeq

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The use of the classic method, least squares (OLS) to estimate the linear regression parameters, when they are available assumptions, and capabilities that have good characteristics, such as impartiality, minimum variance, consistency, and so on. The development of alternative statistical techniques to estimate the parameters, when the data are contaminated with outliers. These are powerful methods (or resistance). In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from Duhok company for manufacturing furniture, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). Important conclusions that this study came up with are: a number of typical values detected by using four methods in the furniture line and very close to the data. This refers to the fact that close to the normal distribution of standard errors, but typical values in the doors line data, using OLS less than that detected by the powerful ways. This means that the standard errors of the distribution are far from normal departure. Another important conclusion is that the estimated values of the parameters by using the lifeline is very far from the estimated values using powerful methods for line doors, gave LTS- destined better results using standard MSE, and gave the M- estimator better results using standard MAPE. Moreover, we noticed that using standard MSAE, and MM- estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data.

Keywords: Robest, LTS, M estimate, MSE

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Authors: O.O. Ehinmowo, A.I. Fatuase, D.F. Oke

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Nigeria is endowed with resources that could boost the economy as well as generate income and provide jobs to the teaming populace. One of the keys of attaining this is by making the environment conducive for the entrepreneurs to excel in their respective enterprises so that more income could be accrued to the entrepreneurs. This study therefore examines income inequality among selected entrepreneurs in Ondo State, Nigeria using primary data. A multistage sampling technique was used to select 200 respondents for the study with the aid of structured questionnaire and personal interview. The data collected were subjected to descriptive statistics, Lorenz curve, Gini coefficient and Double - Log regression model. Results revealed that majority of the entrepreneurs (63%) were males and 90% were married with an average age of 44 years. About 40% of the respondents spent at most 12 years in school with 81% of the respondents had 4-6 members per household, while hair dressing (43.5%) and fashion designing (31.5%) were the most common enterprises among the sampled respondents. The findings also showed that majority of the entrepreneurs in hairdressing, fashion designing and laundry service earned below N200,000 per annum while the majority of those in restaurant and food vending earned between N400,000 – N600,000 followed by the entrepreneurs in pure water enterprise where majority earned N800,000 and above per annum. The result of the Gini coefficient (0.58) indicated that there was presence of inequality among the entrepreneurs which was also affirmed by the Lorenz curve. The Regression results showed that gender, household size and number of employees significantly affected the income of the entrepreneurs in the study area. Therefore, more female households should be encouraged into entrepreneurial businesses and government should give incentive cum conductive environment that could bridge the disparity in the income of the entrepreneurs in their various enterprises.

Keywords: entrepreneurs, Gini coefficient, income inequality, Lorenz curve

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Gurbir Kaur, Surinder Pal Singh

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The present study is a part of the research work on the effect of limestone powder (LP), silica fume (SF) and metakaolin (MK), on the flexural fatigue performance of steel fibre reinforced concrete (SFRC). Corrugated rectangular steel fibres of size 0.6x2.0x35 mm at a constant volume fraction of 1.0% have been incorporated in all mix combinations as the reinforcing material. Three mix combinations were prepared by replacing 30% of ordinary Portland cement (OPC) by weight with these cement additives in binary and ternary fashion to demonstrate their contribution. An experimental programme was conducted to obtain the fatigue lives of all mix combinations at various stress levels. The fatigue life data have been analysed as an attempt to determine the relationship between stress level ‘S’, number of cycles to failure ‘N’ and probability of failure ‘Pf’ for all mix combinations. The experimental coefficients of the fatigue equation have also been obtained from the fatigue data to represent the S-N-Pf curves analytically.

Keywords: cement additives, fatigue life, probability of failure, steel fibre reinforced concrete

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Authors: Solmaz Jahed Shiran, John Hearne, Tayebeh Saghapour

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Daily travel behavior is strongly influenced by the location of the places of residence, education, and employment. Hence a change in those locations due to a move or changes in an occupation leads to a change in travel behavior. Given the interventions of housing mobility and travel behaviors, the hypothesis is that a mobile housing market allows households to move as a result of any change in their life course, allowing them to be closer to central services, public transport facilities and workplace and hence reducing the time spent by individuals on daily travel. Conversely, household’s immobility may lead to longer commutes of residents, for example, after a change of a job or a need for new services such as schools for children who have reached their school age. This paper aims to investigate the association between residential mobility and travel behavior. The Victorian Integrated Survey of Travel and Activity (VISTA) data is used for the empirical analysis. Car ownership and journey to work time and distance of employed people are used as indicators of travel behavior. Change of usual residence within the last five years used to identify movers and non-movers. Statistical analysis, including regression models, is used to compare the travel behavior of movers and non-movers. The results show travel time, and the distance does not differ for movers and non-movers. However, this is not the case when taking into account the residence tenure-type. In addition, car ownership rate and number found to be significantly higher for non-movers. It is hoped that the results from this study will contribute to a better understanding of factors other than common socioeconomic and built environment features influencing travel behavior.

Keywords: journey to work, regression models, residential mobility, commute mode, car ownership

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Authors: Anupama Singh, Papia Raj

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Though Municipal Solid Waste (MSW) is a worldwide problem, yet its implications are enormous in developing countries, as they are unable to provide proper Municipal Solid Waste Management (MSWM) for the large volume of MSW. As a result, the collected wastes are dumped in open dumping at landfilling sites while the uncollected wastes remain strewn on the roadside, many-a-time clogging drainage. Such unsafe and inadequate management of MSW causes various public health hazards. For example, MSW directly on contact or by leachate contaminate the soil, surface water, and ground water; open burning causes air pollution; anaerobic digestion between the piles of MSW enhance the greenhouse gases i.e., carbon dioxide and methane (CO2 and CH4) into the atmosphere. Moreover, open dumping can cause spread of vector borne disease like cholera, typhoid, dysentery, and so on. Patna, the capital city of Bihar, one of the most underdeveloped provinces in India, is a unique representation of this situation. Patna has been identified as the ‘garbage city’. Over the last decade there has been an exponential increase in the quantity of MSW generation in Patna. Though a large proportion of such MSW is recyclable in nature, only a negligible portion is recycled. Plastic constitutes the major chunk of the recyclable waste. The chemical composition of plastic is versatile consisting of toxic compounds, such as, plasticizers, like adipates and phthalates. Pigmented plastic is highly toxic and it contains harmful metals such as copper, lead, chromium, cobalt, selenium, and cadmium. Human population becomes vulnerable to an array of health problems as they are exposed to these toxic chemicals multiple times a day through air, water, dust, and food. Based on analysis of health data it can be emphasized that in Patna there has been an increase in the incidence of specific diseases, such as, diarrhoea, dysentry, acute respiratory infection (ARI), asthma, and other chronic respiratory diseases (CRD). This trend can be attributed to improper MSWM. The results were reiterated through a survey (N=127) conducted during 2014-15 in selected areas of Patna. Random sampling method of data collection was used to better understand the relationship between different variables affecting public health due to exposure to MSW and lack of MSWM. The results derived through bivariate and logistic regression analysis of the survey data indicate that segregation of wastes at source, segregation behavior, collection bins in the area, distance of collection bins from residential area, and transportation of MSW are the major determinants of public health issues. Sustainable recycling is a robust method for MSWM with its pioneer concerns being environment, society, and economy. It thus ensures minimal threat to environment and ecology consequently improving public health conditions. Hence, this paper concludes that sustainable recycling would be the most viable approach to manage MSW in Patna and would eventually reduce public health hazards.

Keywords: municipal solid waste, Patna, public health, sustainable recycling

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Authors: Septiany Trisnaningtyas

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Purpose: This research aims to examine the factors affecting the profitability of pharmaceutical company during the Covid-19 Pandemic in Indonesia. A sharp decline in the number of patients coming to the hospital for treatment during the pandemic has an impact on the growth of the pharmaceutical sector and brought major changes in financial position and business performance. Pharmaceutical companies that provide products related to the Covid-19 pandemic can survive and continue to grow. This study investigates the factors affecting the profitability of pharmaceutical company during the Covid-19 Pandemic in Indonesia associated with the number of Covid-19 cases. Design/methodology/approach: This study uses panel-data regression models to evaluate the influence of the number of Covid-19 confirmed cases on profitability of ninelisted pharmaceuticalcompanies in Indonesia. This research is based on four independent variables that were empirically examined for their relationship with profitability. These variables are liquidity (current ratio), growth rate (sales growth), firm size (total sales), and market power (the Lerner index). Covid-19 case is used as moderating variable. Data of nine pharmaceutical companies listed on the Indonesia Stock Exchange covering the period of 2018–2021 were extracted from companies’ quarterly annual reports. Findings: In the period during Covid-19, company growth (sales growth) and market power (lerner index) have a positive and significant relationship to ROA and ROE. Total of confirmed Covid-19 cases has a positive and significant relationship to ROA and is proven to have a moderating effect between company’s growth (sales growth) to ROA and ROE and market power (Lerner index) to ROA. Research limitations/implications: Due to data availability, this study only includes data from nine listed pharmaceutical companies in Indonesian Stock exchange and quarterly annual reportscovering the period of 2018-2021. Originality/value: This study focuses onpharmaceutical companies in Indonesia during Covid-19 pandemic. Previous study analyzes the data from pharmaceutical companies’ annual reports since 2014 and focus on universal health coverage (national health insurance) implementation from the Indonesian government. This study analyzes the data using fixed effect panel-data regression models to evaluate the influence of Covid-19 confirmed cases on profitability. Pooled ordinary least squares regression and fixed effects were used to analyze the data in previous study. This study also investigate the moderating effect of Covid-19 confirmed cases to profitability in relevant with the pandemic situation.

Keywords: profitability, indonesia, pharmaceutical, Covid-19

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Authors: Veronica Sri Lestari, Asmuddin Natsir, Hasmida Karim, Ian Patrick

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The research aimed at investigating the factors affecting consumers’ willingness to pay for chicken meat from biosecure farms. The research was conducted in Makassar City, South Sulawesi Province, Indonesia. Samples were taken using random sampling technique in two supermarkets namely Lotte Mart and Gelael. Total samples were 50 respondents which comprised the chicken meat consumers. To find out the consumers’ willingness to pay for chicken meat from the biosecure farms, the contingent valuation method was utilized. Data were collected through interviews and questionnaires. Probit Logistic was estimated to examine the factors affecting the consumers’ willingness to pay for at the premium price for chicken meat from the biosecure farms. The research indicates that the education and income affect significantly the consumers’ willingness to pay for chicken meat from the biosecure farms (P < 0.05). The results of the study will be beneficial for the policy makers, producers, consumers and those conducting research.

Keywords: biosecure, chicken, farms, consumer, willingness-to-pay

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Authors: Arpine Maghakyan

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

Keywords: audit fees, auditor quality, digitalization, Big4

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity

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Authors: Adewale Oladapo, C. A. Afolami

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The study analyzed the effect of micro credit access on poverty reduction among small scale women entrepreneurs in Ondo state, Nigeria. Primary data were collected in a cross-sectional survey of 100 randomly selected woman entrepreneurs. These were drawn in multistage sampling process covering four local government areas (LGAS). Data collected include socio economics characteristics of respondents, access to micro credit, sources of micro credit, and constraints faced by the entrepreneur in sourcing for micro credit. Data were analyzed using descriptive statistics, Foster, Greer and Thorbecke (FGT) index of poverty measure, Gini coefficients and probit regression analysis. The study found that respondents sampled for the survey were within the age range of 31-40 years with mean age 38.6%. Mostly (56.0%) of the respondents were educated to the tune of primary school. Majority (87.0%) of the respondents were married with fairly large household size of (4-5). The poverty index analysis revealed that most (67%) of the sample respondents were poor. The result of the Probit regression analyzed showed that income was a significant variable in micro credit access, while the result of the Gini coefficient revealed a very high income inequality among the respondents. The study concluded that most of the respondents were poor and return on investment (income) was an important variable that increased the chance of respondents in sourcing for micro-credit loan and recommended that income realized by entrepreneur should be properly documented to facilitate loan accessibility.

Keywords: entrepreneurs, income, micro-credit, poverty

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

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

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

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Authors: Tadele Tesfay Teame, Tsegaye Abrehame, Hágen István Zsombor

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Liquidity is one of the main concerns for banks, and thus achieving the optimum level of liquidity is critical. The main objective of this study is to discover the driving force of selected private commercial banks’ liquidity. In order to achieve the objective explanatory research design and quantitative research approach were used. Data has been collected from a secondary source of the sampled Ethiopian private commercial banks’ financial statements, the National Bank of Ethiopia, and the Minister of Finance, the sample covering the period from 2011 to 2022. Bank-specific and macroeconomic variables were analyzed by using the balanced panel fixed effect regression model. Bank’s liquidity ratio is measured by the total liquid asset to total deposits. The findings of the study revealed that bank size, capital adequacy, loan growth rate, and non-performing loan had a statistically significant impact on private commercial banks’ liquidity, and annual inflation rate and interest rate margin had a statistically significant impact on the liquidity of Ethiopian private commercial banks measured by L1 (bank liquidity). Thus, banks in Ethiopia should not only be concerned about internal structures and policies/procedures, but they must consider both the internal environment and the macroeconomic environment together in developing their strategies to efficiently manage their liquidity position and private commercial banks to maintain their financial proficiency shall have bank liquidity management policy by assimilating both bank-specific and macro-economic variables.

Keywords: liquidity, Ethiopian private commercial banks, liquidity ratio, panel data regression analysis

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Authors: Segen Asayehegn

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Introduction: In Tigray, Ethiopia, next to cervical cancer, breast cancer is one of the most common cancer health problems for women. Objectives: This article is proposed to identify the prospective and potential risk factors affecting the time-to-first-recurrence of breast cancer patients in Tigray, Ethiopia. Methods: The data were taken from the patient’s medical record that registered from January 2010 to January 2020. The study considered a sample size of 1842 breast cancer patients. Powerful non-parametric and parametric shared frailty survival regression models (FSRM) were applied, and model comparisons were performed. Results: Out of 1842 breast cancer patients, about 1290 (70.02%) recovered/cured the disease. The median cure time from breast cancer is found at 12.8 months. The model comparison suggested that the lognormal parametric shared a frailty survival regression model predicted that treatment, stage of breast cancer, smoking habit, and marital status significantly affects the first recurrence of breast cancer. Conclusion: Factors like treatment, stages of cancer, and marital status were improved while smoking habits worsened the time to cure breast cancer. Recommendation: Thus, the authors recommend reducing breast cancer health problems, the regional health sector facilities need to be improved. More importantly, concerned bodies and medical doctors should emphasize the identified factors during treatment. Furthermore, general awareness programs should be given to the community on the identified factors.

Keywords: acceleration factor, breast cancer, Ethiopia, shared frailty survival models, Tigray

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Authors: Sadhana Ghnayem

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

Keywords: conflict, religion, conflict management style, obedience

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Authors: Gideon C. Joseph

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The ability to localize mobile devices is quite important, as some applications may require location information of these devices to operate or deliver better services to the users. Although there are several ways of acquiring location data of mobile devices, the WLAN fingerprinting approach has been considered in this work. This approach uses the Received Signal Strength Indicator (RSSI) measurement as a function of the position of the mobile device. RSSI is a quantitative technique of describing the radio frequency power carried by a signal. RSSI may be used to determine RF link quality and is very useful in dense traffic scenarios where interference is of major concern, for example, indoor environments. This research aims to design a system that can predict the location of a mobile device, when supplied with the mobile’s RSSIs. The developed system takes as input the RSSIs relating to the mobile device, and outputs parameters that describe the location of the device such as the longitude, latitude, floor, and building. The relationship between the Received Signal Strengths (RSSs) of mobile devices and their corresponding locations is meant to be modelled; hence, subsequent locations of mobile devices can be predicted using the developed model. It is obvious that describing mathematical relationships between the RSSIs measurements and localization parameters is one option to modelling the problem, but the complexity of such an approach is a serious turn-off. In contrast, we propose an intelligent system that can learn the mapping of such RSSIs measurements to the localization parameters to be predicted. The system is capable of upgrading its performance as more experiential knowledge is acquired. The most appealing consideration to using such a system for this task is that complicated mathematical analysis and theoretical frameworks are excluded or not needed; the intelligent system on its own learns the underlying relationship in the supplied data (RSSI levels) that corresponds to the localization parameters. These localization parameters to be predicted are of two different tasks: Longitude and latitude of mobile devices are real values (regression problem), while the floor and building of the mobile devices are of integer values or categorical (classification problem). This research work presents artificial neural network based intelligent systems to model the relationship between the RSSIs predictors and the mobile device localization parameters. The designed systems were trained and validated on the collected WLAN fingerprint database. The trained networks were then tested with another supplied database to obtain the performance of trained systems on achieved Mean Absolute Error (MAE) and error rates for the regression and classification tasks involved therein.

Keywords: indoor localization, WLAN fingerprinting, neural networks, classification, regression

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Authors: Mingzhu Lyu, Runlei Ren, Xinyu Dai, Jiaxuan Pi, Kanghua Li

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The study of video game policy in China has been divided into two branches: "pedagogy" and "game industry". The binary perspective of policy reveals the "contradictory" side of policy performance. Based on this suspicion, this paper constructs a three-dimensional sequence of time, content and institutions of game policy, and establishes the "contradictory" aspects of policy performance between 1949 and 2019. A central-level database of game policies, clarifying that our game policies follow a shift from reactive response to proactive guidance, stigmatization and de-stigmatization, the evolutionary logic. The study found that the central government has always maintained a strict requirement and prudent guidance for game policy, and the deep contradictions in game policy stem from the essential conflict between the natural amusement of games and the seriousness of the educational system, and the Chinese government's use of the understanding of the public and private domains and the Managing of the conflict.

Keywords: game industry, gaming policy, public domain, private domain

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Authors: Ranjini Ramanath, Nanjunda P. Swamy

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Construction has existed in our lives since time immemorial. However, unlike any other industry, construction projects have their own unique challenges – project type, purpose and end use of the project, geographical conditions, logistic arrangements, largely unorganized manpower and requirement of diverse skill sets, etc. These unique characteristics bring in their own level of risk and uncertainties to the project, which cause the project to deviate from its planned objectives of time, cost, quality, etc. over the many years, there have been significant developments in the way construction projects are conceptualized, planned, and managed. With the rapid increase in the population, increased rate of urbanization, there is a growing demand for infrastructure development, and it is required that the projects are delivered timely, and efficiently. In an age where ‘Time is Money,' implementation of new techniques of project management is required in leading to successful projects. This paper proposes a different approach to project management, which if applied in construction projects, can help in the accomplishment of the project objectives in a faster manner.

Keywords: critical chain project management methodology, critical chain, project management, construction management

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Authors: Atia Alam

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Growing liberalisation and intense market competition increase firm’s risk exposure and induce corporations to use derivatives extensively as a risk management instrument, which results in decrease in firm’s risk, and increase in value. Present study contributes towards existing literature by providing an in-depth analysis regarding the effect of extent of derivative usage on firm’s risk and value by using panel data models and seemingly unrelated regression technique. New evidence is established in current literature by dividing the sample data based on firm’s Exchange Rate (ER) and Interest Rate (IR) exposure. Analysis is performed for the effect of extent of derivative usage on firm’s risk and value and its variation with respect to the ER and IR exposure. Sample data consists of 166 Pakistani firms listed on Pakistan stock exchange for the period of 2004-2010. Results show that extensive usage of derivative instruments significantly increases firm value and reduces firm’s risk. Furthermore, comprehensive analysis depicts that Pakistani corporations having higher exchange rate exposure, with respect to foreign sales, and higher interest rate exposure, on the basis of industry adjusted leverage, have higher firm value and lower risk. Findings from seemingly unrelated regression also provide robustness to results obtained through panel data analysis. Study also highlights the role of derivative usage as a risk management instrument in high and low ER and IR risk and helps practitioners in understanding how value increasing effect of extent of derivative usage varies with the intensity of firm’s risk exposure.

Keywords: extent of derivative usage, firm value, risk, Pakistan, non-financial firms

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Authors: Noor Hasmini Abdghani, Nik Kamariah Nikmat, Nor Hayati Ahmad

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Rapid growth of the internet technology led to changes in the consumer lifestyles. This involved customer buying behaviour-based internet that create new kind of buying strategy. Hence, it has summoned many of world firms including Malaysia to generate new quality strategy in preparation to face new customer buying lifestyles. Particularly, this study focused on identifying online customer segment of automobile passenger car customers. Secondly, the objective is to understand online customer’s receptiveness towards internet technologies. This study distributed 700 questionnaires whereby 582 were returned representing 83% response rate. The data were analysed using factor and regression analyses. The result from the factor analysis precipitates four online passenger car segmentations in Malaysia, which are: Segment (1)- Automobile Online shopping Preferences, Segment (2)- Automobile Online Brand Comparison, Segment (3)- Automobile Online Information Seeking and Segment (4)- Automobile Offline Shopping Preferences. In understanding the online customer’s receptiveness towards internet, the regression result shows that there is significant relationship between each of four segments of online passenger car customer with attitude towards automobile online shopping. This implies that, for online customers to have receptiveness toward internet technologies, he or she must have preferences toward online shopping or at least prefer to browse any related information online even if the actual purchase is made at the traditional store. With this proposed segmentation strategy, the firms especially the automobile firms will be able to understand their online customer behavior. At least, the proposed segmentation strategy will help the firms to strategize quality management approach for their online customers’ buying decision making.

Keywords: Automobile, Market Segmentation, Online Shopping Attitude, Quality Management Strategy

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Authors: Mohammad Sadegh Allahyari, Sorush Marzban

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The main aim of this study was to investigate the factors affecting farmers' attitude of the Shanderman District in Masal (Guilan Province in the north of Iran), towards the concepts of multifunctional agriculture. The statistical population consisted of all 4908 in Shanderman.The sample of the present study consisted of 209 subjects who were selected from the total population using the Bartlett et al. Table. Questionnaire as the main tool of data collection was divided in two parts. The first part of questionnaire consisted of farmers' profiles regarding individual, technical-agronomic, economic and social characteristics. The second part included items to identify the farmers’ attitudes regarding different aspects of multifunctional agriculture. The validity of the questionnaire was assessed by professors and experts. Cronbach's alpha was used to determine the reliability (α= 0.844), which is considered an acceptable reliability value. Overall, the average scores of attitudes towards multifunctional agriculture show a positive tendency towards multifunctional agriculture, considering farmers' attitudes of the Shanderman district (SD = 0.53, M = 3.81). Results also highlight a significant difference between farmers' income source levels (F = 0.049) and agricultural literature review (F = 0.022) toward farmers' attitudes considering multifunctional agriculture (p < 0.05). Pearson correlations also indicated that there is a positive relationship between positive attitudes and family size (r = 0.154), farmers' experience (r = 0.246), size of land under cultivation (r = 0.186), income (r = 0.227), and social contribution activities (r = 0.224). The results of multiple regression analyses showed that the variation in the dependent variable depended on the farmers' experience in agricultural activities and their social contribution activities. This means that the variables included in the regression analysis are estimated to explain 12 percent of the variation in the dependent variable.

Keywords: multifunctional agriculture, attitude, effective factor, sustainable agriculture

Procedia PDF Downloads 220