Search results for: supply and demand prediction

Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: T. A. Faria, C. C. Toniolo, L. F. Ribeiro

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In order to satisfy the costumer’s needs, many sectors of industry and services has been spending major effort to make its processes more efficient. Facing a situation, when its production cannot cover the demand, the traditional way to achieve the production required involves, mostly, adding shifts, workforce, or even more machines. This paper narrates how lean manufacturing supported a dramatic increase of productivity at a rail welding plant in Brazil in order to meet the demand for the next years.

Keywords: productivity, lean manufacturing, rail welding, value stream mapping

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Nagarjuna Pitty

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It is believed invention “Advanced Method and Process Quick Electric Vehicle Charging” is an Electric Vehicles (EVs) are quickly turning into the heralds of vehicle innovation. This study endeavors to address the inquiries of how module charging process correspondence has been performed between the EV and Electric Vehicle Supply Equipment (EVSE). The energy utilization of gas-powered motors is higher than that of electric engines. An invention is related to an Advanced Method and Process Quick Electric Vehicle Charging. In this research paper, readings on the electric vehicle charging approaches will be checked, and the module charging phases will be described comprehensively.

Keywords: electric, vehicle, charging, notification, IoT, supply, equipment

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Authors: Marimuthu Gurusamy

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In vibration analysis (with accelerometers) of rotating machines with rolling element bearing, the customers are interested to know the failure of the machine well in advance to plan the spare inventory and maintenance. But in real world most of the machines fails before the prediction of vibration analyst or Expert analysis software. Presently the prediction of failure is based on ISO 10816 vibration limits only. But this is not enough to monitor the failure of machines well in advance. Because more than 50% of the machines will fail even the vibration readings are within acceptable zone as per ISO 10816.Hence it requires further detail analysis and different techniques to predict the failure well in advance. In vibration Analysis, the velocity spectrum is used to analyse the root cause of the mechanical problems like unbalance, misalignment and looseness etc. The envelope spectrum are used to analyse the bearing frequency components, hence the failure in inner race, outer race and rolling elements are identified. But so far there is no correlation made between these two concepts. The author used both velocity spectrum and Envelope spectrum to analyse the machine behaviour and bearing condition to correlated the changes in dynamic load (by unbalance, misalignment and looseness etc.) and effect of impact on the bearing. Hence we could able to predict the expected life of the machine and bearings in the rotating equipment (with rolling element bearings). Also we used process parameters like temperature, flow and pressure to correlate with flow induced vibration and load variations, when abnormal vibration occurs due to changes in process parameters. Hence by correlation of velocity spectrum, envelope spectrum and process data with 20 years of experience in vibration analysis, the author could able to predict the rotating Equipment and its component’s deterioration and expected duration for maintenance.

Keywords: vibration analysis, velocity spectrum, envelope spectrum, prediction of deterioration

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: Chiung-Hui Chen

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Traditional service channel is losing its edge due to emerging service technology. To establish interaction with the clients, the service industry is using effective mechanism to give clients direct access to services with emerging technologies. Thus, as service science receives attention, special and unique consumption pattern evolves; henceforth, leading to new market mechanism and influencing attitudes toward life and consumption patterns. The market demand for customized services is thus valued due to the emphasis of personal value, and is gradually changing the demand and supply relationship in the traditional industry. In respect of interior design service, in the process of traditional interior design, a designer converts to a concrete form the concept generated from the ideas and needs dictated by a user (client), by using his/her professional knowledge and drawing tool. The final product is generated through iterations of communication and modification, which is a very time-consuming process. Although this process has been accelerated with the help of computer graphics software today, repeated discussions and confirmations with users are still required to complete the task. In consideration of what is addressed above a space user’s life model is analyzed with visualization technique to create an interaction system modeled after interior design knowledge. The space user document intuitively personal life experience in a model requirement chart, allowing a researcher to analyze interrelation between analysis documents, identify the logic and the substance of data conversion. The repeated data which is documented are then transformed into design information for reuse and sharing. A professional interior designer may sort out the correlation among user’s preference, life pattern and design specification, thus deciding the critical design elements in the process of service design.

Keywords: information design, life model-based, aesthetic computing, communication

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Authors: Swati Sundararajan, , Asit B. Samui, Prashant S. Kulkarni

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The global energy crisis has led to extensive research on alternative sources of energy. The gap between energy supply and demand can be met by thermal energy storage techniques, of which latent heat storage is most effective in the form of phase change materials (PCMs). Phase change materials utilize latent heat absorbed or released over a narrow temperature range of the material undergoing phase transformation, to store energy. The latent heat can be utilized for heating or cooling purposes. It can also be used for converting to electricity. All these actions amount to minimizing the load on electricity demand. These materials retain this property over repeated number of cycles. Different PCMs differ in the phase change temperature and the heat storage capacities. Poly(ethylene glycol) (PEG) was cross-linked to hydroxyl-terminated poly(dimethyl siloxane) (PDMS) in the presence of cross-linker, tetraethyl orthosilicate (TEOS) and catalyst, dibutyltin dilaurate. Four different ratios of PEG and PDMS were reacted together, and the composition with the lowest PEG concentration resulted in the formation of a flexible solid-solid phase change membrane. The other compositions are obtained in powder form. The enthalpy values of the prepared PCMs were studied by using differential scanning calorimetry and the crystallization properties were analyzed by using X-ray diffraction and polarized optical microscopy. The incorporation of silicone moiety was expected to reduce the hydrophilic character of PEG, which was evaluated by measurement of contact angle. The membrane forming ability of this crosslinked polymer can be extended to several smart packaging, building and textile applications. The detailed synthesis, characterization and performance evaluation of the crosslinked polymer blend will be incorporated in the presentation.

Keywords: phase change materials, poly(ethylene glycol), poly(dimethyl siloxane), thermal energy storage

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Authors: T. T. Tham

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The aim of this study is to propose an integrated approach to determine the most suitable programs, based on Quality Function Deployment (QFD), Sensitivity Analysis (SA) and Multi-Objective Linear Programming model (MOLP). Firstly, QFD is used to determine business requirements and transform them into business and supply chain programs. From the QFD, technical scores of all programs are obtained. All programs are then evaluated through five criteria (productivity, quality, cost, technical score, and feasibility). Sets of weight of these criteria are built using Sensitivity Analysis. Multi-Objective Linear Programming model is applied to select suitable programs according to multiple conflicting objectives under a budget constraint. A case study from the Sai Gon-Mien Tay Beer Company is given to illustrate the proposed methodology. The outcome of the study provides a comprehensive picture for companies to select suitable programs to obtain the optimal solution according to their preference.

Keywords: business program, multi-objective linear programming model, quality function deployment, sensitivity analysis, supply chain management

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Authors: Siddhant Sahu, P. James Daniel Paul

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Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.

Keywords: technical analysis, expert system, law of demand, stocks, portfolio analysis, Indian automotive sector

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Authors: Elda Maraj, Shkelqim Kuka

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Coronary heart disease causes many deaths in the world. Unfortunately, this problem will continue to increase in the future. In this paper, a fuzzy logic model to predict coronary heart disease is presented. This model has been developed with seven input variables and one output variable that was implemented for 30 patients in Albania. Here fuzzy logic toolbox of MATLAB is used. Fuzzy model inputs are considered as cholesterol, blood pressure, physical activity, age, BMI, smoking, and diabetes, whereas the output is the disease classification. The fuzzy sets and membership functions are chosen in an appropriate manner. Centroid method is used for defuzzification. The database is taken from University Hospital Center "Mother Teresa" in Tirana, Albania.

Keywords: coronary heart disease, fuzzy logic toolbox, membership function, prediction model

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Authors: Abdullah Al Faruque, Ram Balachandar

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Series of laboratory tests were carried out to study the extent of scour caused by a three-dimensional wall jets exiting from a square cross-section nozzle and into a non-cohesive sand beds. Previous observations have indicated that the effect of the tailwater depth was significant for densimetric Froude number greater than ten. However, the present results indicate that the cut off value could be lower depending on the value of grain size-to-nozzle width ratio. Numbers of equations are drawn out for a better scaling of numerous scour parameters. Also suggested the empirical prediction of scour to predict the scour centre line profile and plan view of scour profile at any particular time.

Keywords: densimetric froude number, jets, nozzle, sand, scour, tailwater, time

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Authors: Arnaud Deshais

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The supply chain industry has integrated the need for supplier Diversity and Inclusion (D&I), mostly from an ethical and moral argument. In addition, in some countries, it is also a legal requirement for companies reaching a certain size. As a matter of fact, a lot of successful companies have developed a Corporate Social Responsibility Program that encourages diversity and inclusion in the supply chain, such as building strong relationships with minority owned businesses (women, LGBT, veterans, etc.). Outside ethical and legal perspectives, it is also worth researching the economic and financial benefits of pursuing such efforts. Through surveys of purchasing and supply chain managers in their current roles as well as review of some case studies on supplier based D&I programs, it becomes apparent that a financial return on investment is to be expected as well for companies who make a concerted effort to grow their D&I programs. The study explores the levers to increase shareholder value and business efficiencies. Finally, the research highlights the competitive advantage related to a broad minority based supplier network. The benefits manifest themselves in the areas of competitiveness, innovation, and collaboration. The economic reward ends up being at the forefront of those programs while being an opportunity for organizations to become 'a good citizen'.

Keywords: diversity, inclusion, purchasing, supplier

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Authors: Khaled S. Abdallah, Abdel-Aziz M. Mohamed

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Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.

Keywords: modeling, truck rental, supply chains management.

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Authors: Daniel Mokatsanyane, Johnny Jansen Van Rensburg

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The integral role oil-crushing companies play in sunflower oil production is often overlooked to offer high-quality oil to refineries and end consumers. Sunflower oil crushing companies in South Africa are exposed to price fluctuations resulting from the local and international markets. Hedging instruments enable these companies to hedge themselves against unexpected prices spikes and to ensure sustained profitability. A crushing company is a necessary middleman, and as such, these companies have exposure to the purchasing and selling sides of sunflower. Sunflower oil crushing companies purchase sunflower seeds from farmers or agricultural companies that provide storage facilities. The purchasing price is determined by the supply and demand of sunflower seed, both national and international. When the price of sunflower seeds in South Africa is high but still below import parity, then the crush margins realised by these companies are reduced or even negative at times. There are three main products made by sunflower oil crushing companies, oil, meal, and shells. Profits are realised from selling three products, namely, sunflower oil, meal and shells. However, when selling sunflower oil to refineries, sunflower oil crushing companies needs to hedge themselves against a reduction in vegetable oil prices. Hedging oil prices is often done via futures and is subject to specific volume commitments before a hedge position can be taken in. Furthermore, South African oil-crushing companies hedge sunflower oil with international, Over-the-counter contracts as South Africa is a price taker of sunflower oil and not a price maker. As such, South Africa provides a fraction of the world’s sunflower oil supply and, therefore, has minimal influence on price changes. The advantage of hedging using futures ensures that the sunflower crushing company will know the profits they will realise, but the downside is that they can no longer benefit from a price increase. Alternative hedging instruments like options might pose a solution to the opportunity cost does not go missing and that profit margins are locked in at the best possible prices for the oil crushing company. This paper aims to investigate the possibility of employing options alongside futures to simulate different scenarios to determine if options can bridge the opportunity cost gap.

Keywords: derivatives, hedging, price risk, sunflower, sunflower oil, South Africa

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Authors: Zeliha Sayar

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Central banks all over the world appear to be focusing first and foremost on retail central bank digital currency CBDC), i.e., digital cash/money. This approach is predicated on the belief that the use of cash has decreased, owing primarily to technological advancements and pandemic restrictions, and that a suitable foundation for the transition to a cashless society has been revealed. This study aims to contribute to the debate over whether digital money/CBDC can be a substitute or supplement to physical cash by examining the potential effects on cash demand. For this reason, this paper compares two emerging countries, Turkey, and Thailand, to demystify the impact of e-payment and COVID-19 restrictions on cash demand by employing fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and the canonical cointegrating regression (CCR). The currency in circulation in two emerging countries, Turkey and Thailand, was examined in order to estimate the elasticity of different types of retail payments. The results demonstrate that real internet and mobile, cart, contactless payment, and e-money are long-term determinants of real cash demand in these two developing countries. Furthermore, with the exception of contactless payments in Turkey, there is a positive relationship between the currency in circulation and the various types of retail payments. According to findings, COVID-19 restrictions encourage the demand for cash, resulting in cash hoarding.

Keywords: CCR, DOLS, e-money, FMOLS, real cash

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Authors: L. Hamra, J. F. Demonceau, V. Denoël

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The aim of this paper is to study a beam extracted from a frame and subjected to blast loading. The demand of ductility depends on six dimensionless parameters: two related to the blast loading, two referring to the bending behavior of the beam and two corresponding to the dynamic behavior of the rest of the structure. We develop a full analytical procedure that provides the ductility demand as a function of these six dimensionless parameters.

Keywords: analytical procedure, blast loading, membrane force, P-I diagram

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Authors: Salami Akeem Olanrewaju

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The transportation models or problems are primarily concerned with the optimal (best possible) way in which a product produced at different factories or plants (called supply origins) can be transported to a number of warehouses or customers (called demand destinations). The objective in a transportation problem is to fully satisfy the destination requirements within the operating production capacity constraints at the minimum possible cost. The objective of this study is to determine ways of minimizing transport cost in order to maximum profit. Data were gathered from the records of the Distribution Department of 7-Up Bottling Company Plc. Ilorin, Kwara State, Nigeria. The data were analyzed using SPSS (Statistical Package for Social Sciences) while applying the three methods of solving a transportation problem. The three methods produced the same results; therefore, any of the method can be adopted by the company in transporting its final products to the wholesale dealers in order to minimize total production cost.

Keywords: cost minimization, resources utilization, distribution system, allocation problem

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Authors: Stanzin Gawa, Nalini Ranjan Kumar, Gohar Bilal Wani, Vinay Maruti Hatte, A. Vinay

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Rainbow trout (Oncorhynchus mykiss) and Brown trout (Salmo trutta fario) are the two species of trout which were once introduced by British in waters of Kashmir has well adapted to favorable climatic conditions. Cold water fisheries are one of the emerging sectors in Kashmir valley and trout holds an important place Jammu and Kashmir fisheries. Realizing the immense potential of trout culture in Kashmir region, the state fisheries department started privatizing trout culture under the centrally funded scheme of RKVY in which they provide 80 percent subsidy for raceway construction and supply of feed and seed for the first year since 2009-10 and at present there are 362 private trout farms. To cater the growing demand for trout in the valley, it is important to understand the bottlenecks faced in the propagation of trout culture. Value chain analysis provides a generic framework to understand the various activities and processes, mapping and studying linkages is first step that needs to be done in any value chain analysis. In Kashmir, it is found that trout hatcheries play a crucial role in insuring the continuous supply of trout seed in valley. Feed is most limiting factor in trout culture and the farmer has to incur high cost in payment and in the transportation of feed from the feed mill to farm. Lack of aqua clinic in the Kashmir valley needs to be addressed. Brood stock maintenance, breeding and seed production, technical assistance to private farmer, extension services have to be strengthened and there is need to development healthier environment for new entrepreneurs. It was found that trout farmers do not avail credit facility as there is no well define credit scheme for fisheries in the state. The study showed weak institutional linkages. Research and development should focus more on applied science rather than basic science.

Keywords: trout, Kashmir, value chain, linkages, culture

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Roli Pradhan

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All over the globe, the resources of the government are declining, and the funding requirements in education are on a constant rise. The governments are desperately increasing the budgetary allocation for higher education, the economic plans have been labeling investment in higher education to be immensely vital for development of the nation. Still the fact is that the government of the developing nations like India lacks the potential to fund the rising demands of this sector. In the face of declining government funding for higher education, there are the growing needs and justifiable pressure for direct beneficiaries to bear a reasonable part of the cost of higher education. The supply-demand gap in higher education in India is on the increase. This paper evaluates the Indian National Education Policy over the past three decades, furnishes the need of financing of education by private players. The paper also covers the aspects of incorporating the different forms of financing in education and also focuses on the regulations pertaining to quality maintenance in the education system. The paper also targets to suggest policy imperatives for the future education policy for India.

Keywords: national education policy, privatisation, private financing, government funding

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Authors: Merhawit Fisseha Gebremariam, Yohannes Yebabe Tesfay

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This study focuses on the formulation of a sustainable, effective, and efficient supply chain strategy framework that will enable Ethiopian privatized firms. The study examined the role of privatization in productive sourcing, production, and delivery to Ethiopian firm’s performances. To analyze our hypothesis, the authors applied the concepts of Key Performance Indicator (KPI), strategic outsourcing, purchasing portfolio analysis, and Porter's marketing analysis. The authors selected ten privatized companies and compared their financial, market expansion, and sustainability performances. The Chi-Square Test showed that at the 5% level of significance, privatization and outsourcing activities can assist the business performances of Ethiopian firms in terms of product promotion and new market expansion. At the 5% level of significance, the independent t-test result showed that firms that were privatized by Ethiopian investors showed stronger financial performance than those that were privatized by foreign investors. Furthermore, it is better if Ethiopian firms apply both cost leadership and differentiated strategy to enhance thriving in their business area. Ethiopian firms need to implement the supply chain operations reference (SCOR) model for an exclusive framework that supports communication links the supply chain partners, and enhances productivity. The government of Ethiopia should be aware that the privatization of firms by Ethiopian investors will strengthen the economy. Otherwise, the privatization process will be risky for the country, and therefore, the government of Ethiopia should stop doing those activities.

Keywords: correlation analysis, market strategies, KPIs, privatization, risk and Ethiopia

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Authors: Giang Ngo Tinh Nguyen

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Agriculture plays an important role in the economy of Vietnam and coffee is one of most crucial agricultural commodities for exporting but the current farming methods and processing infrastructure could not keep up with the development of the sector. There are many catastrophic impacts on the environment such as deforestation; soil degradation that leads to a decrease in the quality of coffee beans. Therefore, improving supply chain to develop the cultivation of sustainable coffee is one of the most important strategies to boost the coffee industry and create a competitive advantage for Vietnamese coffee in the worldwide market. If all stakeholders in the supply chain network unite together; the sustainable production of coffee will be scaled up and the future of coffee industry will be firmly secured. Buon Ma Thuot city, Dak Lak province is the principal growing region for Vietnamese coffee which accounted for a third of total coffee area in Vietnam. It plays a strategically crucial role in the development of sustainable Vietnamese coffee. Thus, the research is to improve the supply chain of sustainable Vietnamese coffee production in Buon Ma Thuot city, Dak Lak province, Vietnam for the purpose of increasing the yields and export availability as well as helping coffee farmers to be more flexible in an ever-changing market situation. It will help to affirm Vietnamese coffee brand when entering international market; improve the livelihood of farmers and conserve the environment of this area. Besides, after analyzing the data, a logistic regression model is established to explain the relationship between the dependent variable and independent variables to help sustainable coffee organizations forecast the probability of farmer will be having a sustainable certificate with their current situation and help them choose promising candidates to develop sustainable programs. It investigates opinions of local farmers through quantitative surveys. Qualitative interviews are also used to interview local collectors and staff of Trung Nguyen manufacturing company to have an overview of the situation.

Keywords: supply chain management, sustainable agricultural development, sustainable coffee, Vietnamese coffee

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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Authors: Sumit Rahangdale

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This research paper is a comparative study of two BRTS cities of Madhya Pradesh (INDIA), Bhopal and Indore. Indore is the largest and most populous city of Madhya Pradesh, with heavy traffic, while Bhopal though being the capital of Madhya Pradesh is comparatively less developed and shows less traffic The cities show similarity in case of peri-urban nature, but variation is observed in transportation fare, where Indore has been able to reduce it but Bhopal couldn’t, one of the reason for it is the para-transit services. Indore can be considered as a successful model due to the low fares and can be implemented in other parts of the city. The research paper tries to identify relation of para-transit services with the peri-urban connectivity and provide a solution for the Bhopal case study.

Keywords: demand-supply-fare relationship, mobility and accessibility, paratransit, peri-urban connectivity

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Authors: Prozorkevitch D., Mishurov A., Sokolov K., Karsakov L., Pestrikova L.

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The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future.

Keywords: barents sea ecosystem, abiotic, biotic, data sets, trends, prediction

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Authors: Yasir E. Mohieldeen

Abstract:

Qatar is one of the most water scarce countries in the World. In 2014, the average per capita rainfall was less than 29 m3/y/ca, while the global average is 6,000 m3/y/ca. However, the per capita water consumption in Qatar is among the highest in the World: more than 500 liters per person per day, whereas the global average is 160 liters per person per day. Since the early 2000s, Qatar has been relying heavily on desalinated water from the Arabian Gulf as the main source of fresh water. In 2009, about 99.9% of the total potable water produced was desalinated. Reliance on desalinated water makes Qatar very vulnerable to water related natural disasters, such as the red-tide phenomenon. Qatar’s strategic water reserve lasts for only 7 days. In case of red-tide outbreak, the country would not be able to desalinate water for days, let alone the months that this disaster would bring about (as it clogs the desalination equipment). The 2008-09 red-tide outbreak, for instance, lasted for more than eight months and forced the closure of desalination plants in the region for weeks. This study aims at identifying favorite conditions for red-tide outbreaks, using satellite data along with in-situ measurements. This identification would allow the prediction of these outbreaks and their hotspots. Prediction and monitoring of outbreaks are crucial to water security in the country, as different measures could be put in place in advance to prevent an outbreak and mitigate its impact if it happened. Red-tide outbreaks are detected using different algorithms for chlorophyll concentration in the Gulf waters. Vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used along with Surface Algae Bloom Index (SABI) to detect known outbreaks. MODIS (or Moderate Resolution Imaging Spectroradiometer) bands are used to calculate these indices. A red-tide outbreaks atlas in the Arabian Gulf is being produced. Prediction of red-tide outbreaks ahead of their occurrences would give critical information on possible water-shortage in the country. Detecting known outbreaks in the past few decades and related parameters (e.g. water salinity, water surface temperature, nutrition, sandstorms, … etc) enables the identification of favorite conditions of red-tide outbreak that are key to the prediction of these outbreaks.

Keywords: Arabian Gulf, MODIS, red-tide detection, strategic water reserve, water desalination

Procedia PDF Downloads 97

Authors: Payam Najafi, Behnam Ebrahimi, Hamid Montazerolghaem, Safoura Akbari-Alavijeh, Rasoul Tarkesh Esfahani

Abstract:

The combination of work and study has been recently gained lots of attention due to the crucial demand of industries to skillfully trained youth. Nevertheless, the distance between university and industry makes this combination challenging. According to the OECD (2012), in most countries, there is a limited link between students’ field of study and their area of work while studying. On the other hand, high unemployment rates among the specialized workforce, which is common in developing countries, highlights the need to strengthen this relationship. Innovative Center of Isfahan Chamber of Commerce has defined a project called 'POUYESH', which helps students to find related work opportunities to their field of study as well as supporting industries to supply their needed workforce. The present research is sought to explore the effect of the running project as a model of combining work and study on the university-industry linkage.

Keywords: work and study, university-industry linkage, POUYESH project, field of study

Procedia PDF Downloads 174

Authors: Dariush Jafari, S. Mostafa Nowee

Abstract:

In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

Procedia PDF Downloads 379