Search results for: Finite Mixture Models

Authors: Burcu Kaya, Jan-Martin Kaiser, Karl-Friedrich Becker, Tanja Braun, Klaus-Dieter Lang

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Quality of the molded packages is strongly influenced by the process parameters of the transfer molding. To achieve a better package quality and a stable transfer molding process, it is necessary to understand the influence of the process parameters on the package quality. This work aims to comprehend the relationship between the process parameters, and to identify the optimum process parameters for the transfer molding process in order to achieve less voids and wire sweep. To achieve this, a DoE is executed for process optimization and a regression analysis is carried out. A systematic approach is represented to generate models which enable an estimation of the number of voids and wire sweep. Validation experiments are conducted to verify the model and the results are presented.

Keywords: dielectric analysis, electronic packages, epoxy molding compounds, transfer molding process

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Kizito Ugochukwu Nwajeri

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This paper explores the application of hybrid block methods for solving boundary value problems (BVPs), which are prevalent in various fields such as science, engineering, and applied mathematics. Traditionally, numerical approaches such as finite difference and shooting methods, often encounter challenges related to stability and convergence, particularly in the context of complex and nonlinear BVPs. To address these challenges, we propose a hybrid block method that integrates features from both single-step and multi-step techniques. This method allows for the simultaneous computation of multiple solution points while maintaining high accuracy. Specifically, we employ a combination of polynomial interpolation and collocation strategies to derive a system of equations that captures the behavior of the solution across the entire domain. By directly incorporating boundary conditions into the formulation, we enhance the stability and convergence properties of the numerical solution. Furthermore, we introduce an adaptive step-size mechanism to optimize performance based on the local behavior of the solution. This adjustment allows the method to respond effectively to variations in solution behavior, improving both accuracy and computational efficiency. Numerical tests on a variety of boundary value problems demonstrate the effectiveness of the hybrid block methods. These tests showcase significant improvements in accuracy and computational efficiency compared to conventional methods, indicating that our approach is robust and versatile. The results suggest that this hybrid block method is suitable for a wide range of applications in real-world problems, offering a promising alternative to existing numerical techniques.

Keywords: hybrid block methods, boundary value problem, polynomial interpolation, adaptive step-size control, collocation methods

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Authors: Roberta Martino, Viviana Ventre

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Intertemporal choices are choices under conditions of uncertainty in which the consequences are distributed over time. The Discounted Utility Model is the essential reference for describing the individual in the context of intertemporal choice. The model is based on the idea that the individual selects the alternative with the highest utility, which is calculated by multiplying the cardinal utility of the outcome, as if the reception were instantaneous, by the discount function that determines a decrease in the utility value according to how the actual reception of the outcome is far away from the moment the choice is made. Initially, the discount function was assumed to have an exponential trend, whose decrease over time is constant, in line with a profile of a rational investor described by classical economics. Instead, empirical evidence called for the formulation of alternative, hyperbolic models that better represented the actual actions of the investor. Attitudes that do not comply with the principles of classical rationality are termed anomalous, i.e., difficult to rationalize and describe through normative models. The development of behavioral finance, which describes investor behavior through cognitive psychology, has shown that deviations from rationality are due to the limited rationality condition of human beings. What this means is that when a choice is made in a very difficult and information-rich environment, the brain does a compromise job between the cognitive effort required and the selection of an alternative. Moreover, the evaluation and selection phase of the alternative, the collection and processing of information, are dynamics conditioned by systematic distortions of the decision-making process that are the behavioral biases involving the individual's emotional and cognitive system. In this paper we present an original decomposition of the discount function to investigate the psychological principles of hyperbolic discounting. It is possible to decompose the curve into two components: the first component is responsible for the smaller decrease in the outcome as time increases and is related to the individual's impatience; the second component relates to the change in the direction of the tangent vector to the curve and indicates how much the individual perceives the indeterminacy of the future indicating his or her aversion to uncertainty. This decomposition allows interesting conclusions to be drawn with respect to the concept of impatience and the emotional drives involved in decision-making. The contribution that neuroscience can make to decision theory and inter-temporal choice theory is vast as it would allow the description of the decision-making process as the relationship between the individual's emotional and cognitive factors. Neurofinance is a discipline that uses a multidisciplinary approach to investigate how the brain influences decision-making. Indeed, considering that the decision-making process is linked to the activity of the prefrontal cortex and amygdala, neurofinance can help determine the extent to which abnormal attitudes respect the principles of rationality.

Keywords: impatience, intertemporal choice, neurofinance, rationality, uncertainty

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Authors: Michael Wright, Tiago Costa

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The construction industry in SE Asia has been relying on S355 MPa “as rolled” H-beams for many years now. It is an easily sourced, metallurgically simple, reliable product that all designers, fabricators and constructors are familiar with. However, as the Global demand to better use our finite resources gets stronger, the need for an as-rolled S460 MPa H-Beam is becoming more apparent. The Financial benefits of an “as-rolled” S460 MPa H-beam are obvious. The S460 MPa beam which is currently available and used is fabricated from rolled strip. However, making H-beam from 3 x 460 MPa strips requires costly equipment, valuable welding skills & production time, all of which can be in short supply or better used for other purposes. The Metallurgical benefits of an “as-rolled” S460 MPa H-beam are consistency in the product. Fabricated H-beams have inhomogeneous areas where the strips have been welded together - parent metal, heat affected zone and weld metal all in the one body. They also rely heavily on the skill of the welder to guarantee a perfect, defect free weld. If this does not occur, the beam is intrinsically flawed and could lead to failure in service. An as-rolled beam is a relatively homogenous product, with the optimum strength and ductility produced by delivering steel with as fine as possible uniform cross sectional grain size. This is done by cost effective alloy design coupled with proper metallurgical process control implemented into an existing mill’s equipment capability and layout. This paper is designed to highlight the benefits of bring an “as-rolled” S460 MPa H-beam to the construction market place in SE Asia, and hopefully encourage the current “as-rolled” H-beam producers to rise to the challenge and produce an innovative high quality product for the local market.

Keywords: fine grained, As-rolled, long products, process control, metallurgy

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Authors: Reetinder Kaur, R. K. Pathak

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Mental health is recognised as an important global health concern. World Health Organisation in 2004 estimated that neuropsychiatric illnesses in India account for 10.8 percent of the global burden. The prevalence of serious mental illnesses is estimated as 6.5 percent by National Commission of Macroeconomics and Health in 2005. India spends only 0.06 percent of its health budget on mental health. One of the major problems that exist in Indian mental health care is the treatment gap due to scarcity of manpower, inadequate infrastructure and deficiencies in policy initiatives. As a result, traditional healing is a popular resource for mentally ill individuals and their families. The various traditional healing resources include faith healers, healers at temples and Dargahs. Chandigarh is a Union Territory located in North India. It has surplus manpower and infrastructure available for mental health care. Inspite of availability of mental health care services, mentally ill individuals and their families seek help from traditional healers at various Dargahs within or outside Chandigarh. For the present study, the data was collected from four dargahs. A total of thirty patients medically diagnosed with various mental illnesses, their family members who accompanied them and healers were part of this study. The aim of the study was to: Understand the interactions between healer, patient and family members during the course of treatment, understand explanations of mental illnesses and analyse the healing practices in context of culture. The interviews were conducted using an interview guide for the three sets of informants: Healers, patients and family members. The interview guide for healer focussed on the healing process, healer’s understanding of patient’s explanatory models, healer’s knowledge about mental illnesses and types of these illnesses cured by the healer. The interview guide for patients and family members focussed on their understanding of the symptoms, explanations for illness and help-seeking behaviour. The patients were observed over the weeks (every Thursday, the day of pir and healing) during their visits to the healer. Detailed discussions were made with the healer regarding the healing process and benefits of healing. The data was analysed thematically and the themes: The role of sacred, holistic healing, healer’s understanding of patient’s explanatory models of mental illness, the patient’s, and family’s understanding of mental illnesses, healer’s knowledge about mental illnesses, types of mental illnesses cured by the healer, bad dreams and their interpretation emerged. From the analysis of data, it was found that the healers concentrate their interventions in the social arena, ‘curing’ distressed patients by bringing significant changes in their social environment. It is suggested that in order to make the mental health care services effective in India, the collaboration between healers and psychiatrist is essential. However, certain specifications need to be made to make this kind of collaboration successful and beneficial for the stakeholders.

Keywords: Dargah, mental illness, traditional healing, policy

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Authors: Filiz Erden, E. Elhan Özus, Melek Tufan

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If the history of clothing is examined, it is seen that clothing has gone through many stages from ancient times to present. Each nation has shaped its clothing according to its own traditions, customs, beliefs, living conditions. While clothes are being prepared, attributing different meanings to colors and patterns of the fabrics has become a common characteristic of many cultures. It is known that cloths worn in special days such as mourning, weddings, engagements, festivals and business vary according to their models, fabrics, colors and patterns. We witness use of cloth to differentiate people belonging to certain classes from nobles throughout the history. Striped fabric has carried many different meanings and uses throughout the history. In this study, place has been given to the important periods related to the history of striped fabric by examining current meaning of the striped fabric and dimensions of its meanings in the past. Also, evening dresses have been designed by using striped fabrics in order to reveal how striped fabric is liked and demanded after it coped with difficulties and being despised in its history.

Keywords: striped fabric, design, clothing, fasion

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Authors: Kashif Laeeq, Zubair A. Shaikh

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The paradigm of education is drastically changing from conventional to e-learning model. Due to ease of learning with various other benefits, several educational institutions are adopting the e-learning models. Some institutions are still willing to transform their educational system on to e-learning, but due to limited resources, they are still compromising on the old traditional system. The cloud computing could be one of the best solutions to overcome this problem by providing hardware, software, and infrastructure resources with cost efficient manner. The adoption of cloud computing in education will bring revolution in this paradigm. This paper introduces various positive features of e-learning and presents a way how cloud computing technology can be provisioned e-learning model. This paper also investigates the numerous challenges and opportunities that would be observed in cloud computing adoption in e-learning domain. The concept and knowledge present in this paper may create a new direction of research in the domain of cloud-based e-learning.

Keywords: cloud-based e-learning, e-learning, cloud computing application, smart learning

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Authors: Elif Tugce Aksun Tumerkan, Umran Cansu, Gokhan Boran, Fatih Ozogul

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Gelatin is a mixture of peptides obtained from collagen by partial thermal hydrolysis. It is an important and useful biopolymer that is used in the food, pharmacy, and photography products. Generally, gelatins are sourced from pig skin and bones, beef bone and hide, but within the last decade, using alternative gelatin resources has attracted some interest. In this study, functional properties of gelatin extracted from seafood and poultry by-products were evaluated. For this purpose, skins of skipjack tuna (Katsuwonus pelamis) and frog (Rana esculata) were used as seafood by-products and chicken skin as poultry by-product as raw material for gelatin extraction. Following the extraction of gelatin, all samples were lyophilized and stored in plastic bags at room temperature. For comparing gelatins obtained; chemical composition, common quality parameters including bloom value, gel strength, and viscosity in addition to some others like melting and gelling temperatures, hydroxyproline content, and colorimetric parameters were determined. The results showed that the highest protein content obtained in frog gelatin with 90.1% and the highest hydroxyproline content was in chicken gelatin with 7.6% value. Frog gelatin showed a significantly higher (P < 0.05) melting point (42.7°C) compared to that of fish (29.7°C) and chicken (29.7°C) gelatins. The bloom value of gelatin from frog skin was found higher (363 g) than chicken and fish gelatins (352 and 336 g, respectively) (P < 0.05). While fish gelatin had higher lightness (L*) value (92.64) compared to chicken and frog gelatins, redness/greenness (a*) value was significantly higher in frog skin gelatin. Based on the results obtained, it can be concluded that skins of different animals with high commercial value may be utilized as alternative sources to produce gelatin with high yield and desirable functional properties. Functional and quality analysis of gelatin from frog, chicken, and tuna skin showed by-product of poultry and seafood can be used as an alternative gelatine source to mammalian gelatine. The functional properties, including bloom strength, melting points, and viscosity of gelatin from frog skin were more admirable than that of the chicken and tuna skin. Among gelatin groups, significant characteristic differences such as gel strength and physicochemical properties were observed based on not only raw material but also the extraction method.

Keywords: chicken skin, fish skin, food industry, frog skin, gel strength

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Mohamed Jama Madar, Oso Wilis

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E-learning is the application of information technology in the teaching and learning process. This paper presents the Funnel model as a solution for the problems of implementation of e-learning in tertiary education institutions. While existing models such as TAM, theory-based e-learning and pedagogical model have been used over time, they have generally been found to be inadequate because of their tendencies to treat materials development, instructional design, technology, delivery and governance as separate and isolated entities. Yet it is matching components that bring framework of e-learning strategic implementation. The Funnel model enhances all these into one and applies synchronously and asynchronously to e-learning implementation where the only difference is modalities. Such a model for e-learning implementation has been lacking. The proposed Funnel model avoids ad-ad-hoc approach which has made other systems unused or inefficient, and compromised educational quality. Therefore, the proposed Funnel model should help tertiary education institutions adopt and develop effective and efficient e-learning system which meets users’ requirements.

Keywords: e-learning, pedagogical, technology, strategy

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Authors: Sergio Rapagnà, Alessandro Antonio Papa, Armando Vitale, Andre Di Carlo

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In recent years the world population has enormously increased the use of plastic products for their living needs, in particular for transporting and storing consumer goods such as food and beverage. Plastics are widely used in the automotive industry, in construction of electronic equipment, clothing and home furnishings. Over the last 70 years, the annual production of plastic products has increased from 2 million tons to 460 million tons. About 20% of the last quantity is mismanaged as waste. The consequence of this mismanagement is the release of plastic waste into the terrestrial and marine environments which represents a danger to human health and the ecosystem. Recycling all plastics is difficult because they are often made with mixtures of polymers that are incompatible with each other and contain different additives. The products obtained are always of lower quality and after two/three recycling cycles they must be eliminated either by thermal treatment to produce heat or disposed of in landfill. An alternative to these current solutions is to obtain a mixture of gases rich in H₂, CO and CO₂ suitable for being profitably used for the production of chemicals with consequent savings fossil sources. Obtaining a hydrogen-rich syngas can be achieved by gasification process using the fluidized bed reactor, in presence of steam as the fluidization medium. The fluidized bed reactor allows the gasification process of plastics to be carried out at a constant temperature and allows the use of different plastics with different compositions and different grain sizes. Furthermore, during the gasification process the use of steam increase the gasification of char produced by the first pyrolysis/devolatilization process of the plastic particles. The bed inventory can be made with particles having catalytic properties such as olivine, capable to catalyse the steam reforming reactions of heavy hydrocarbons normally called tars, with a consequent increase in the quantity of gases produced. The plant is composed of a fluidized bed reactor made of AISI 310 steel, having an internal diameter of 0.1 m, containing 3 kg of olivine particles as a bed inventory. The reactor is externally heated by an oven up to 1000 °C. The hot producer gases that exit the reactor, after being cooled, are quantified using a mass flow meter. Gas analyzers are present to measure instantly the volumetric composition of H₂, CO, CO₂, CH₄ and NH₃. At the conference, the results obtained from the continuous gasification of polypropylene (PP) particles in a steam atmosphere at temperatures of 840-860 °C will be presented.

Keywords: gasification, fluidized bed, hydrogen, olivine, polypropyle

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Authors: Halkiopoulos Constantinos, Koutsopoulou Ioanna, Gkintoni Evgenia, Antonopoulou Hera

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Immunization appears to be an essential part of health care service in times of pandemics such as covid-19 and aims not only to protect the health of the population but also the health and sustainability of the economies of the countries affected. It is reported that more than 3.44 billion doses have been administered so far, which accounts for 45 doses for 100 people. Vaccination programs in various countries have been promoted and accepted by people differently and therefore they proceeded in different ways and speed; most countries directing them towards people with vulnerable chronic or recent health statuses. Large scale restriction measures or lockdown, personal protection measures such as masks and gloves and a decrease in leisure and sports activities were also implemented around the world as part of the protection health strategies against the covid-19 pandemic. This research aims to present an analysis based on variations on people’s attitudes towards vaccination based on demographic, social and epidemiological characteristics, and health status on the one hand and perception of health, health satisfaction, pain, and quality of life on the other hand. 1500 Greek e-consumers participated in the research, mainly through social media who took part in an online-based survey voluntarily. The questionnaires included demographic, social and medical characteristics of the participants, and questions asking people’s willingness to be vaccinated and their opinion on whether there should be a vaccine against covid-19. Other stressor factors were also reported in the questionnaires and participants’ loss of someone close due to covid-19, or staying at home quarantine due to being infected from covid-19. WHOQUOL-BREF and GLOBAL PSYCHOTRAUMA SCREEN- GPS were used with kind permission from WHO and from the International Society for Traumatic Stress Studies in this study. Attitudes towards vaccination varied significantly related to aging, level of education, health status and consumer behavior. Health professionals’ attitudes also varied in relation to age, level of education, profession, health status and consumer needs. Vaccines have been the most common technological aid of human civilization so far in the fight against viruses. The results of this study can be used for health managers and digital marketers of pharmaceutical companies and also other staff involved in vaccination programs and for designing health policy immunization strategies during pandemics in order to achieve positive attitudes towards vaccination and larger populations being vaccinated in shorter periods of time after the break out of pandemic. Health staff needs to be trained, aided and supervised to go through with vaccination programs and to be protected through vaccination programs themselves. Feedback in each country’s vaccination program, short backs, deficiencies and delays should be addressed and worked out.

Keywords: consumer behavior, cognitive models, vaccination policy, pandemic, Covid-19, Greece

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Authors: Muhammad Farooq, Ahtasham Gul

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To meet the desired standard, it is important to monitor and analyze different engineering processes to get desired output. The bivariate distributions got a lot of attention in recent years to describe the randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed using two independent models developed by the nesting approach to study the effect of each component on reliability for better understanding. Further, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution, like mean median and quantile function, are discussed. We use inverse Gamma prior to study its frequentist properties by conducting Monte Carlo Markov Chain (MCMC) sampling scheme.

Keywords: reliability, system availability Weibull, inverse Lomax, Monte Carlo Markov Chain, Bayesian

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Authors: Muteeb Ulhaq, Rizwan Latif, Sayed Adnan Qasim, Imran Shafi

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Diesel engines are most efficient and reliable in terms of efficiency, reliability and adaptability. Most of the research and development up till now have been directed towards High-Speed Diesel Engine, for Commercial use. In these engines objective is to optimize maximum acceleration by reducing exhaust emission to meet international standards. In high torque low-speed engines the requirement is altogether different. These types of Engines are mostly used in Maritime Industry, Agriculture industry, Static Engines Compressors Engines etc. Unfortunately due to lack of research and development, these engines have low efficiency and high soot emissions and one of the most effective way to overcome these issues is by efficient combustion in an engine cylinder, the fuel spray atomization process plays a vital role in defining mixture formation, fuel consumption, combustion efficiency and soot emissions. Therefore, a comprehensive understanding of the fuel spray characteristics and atomization process is of a great importance. In this research, we will examine the effects of primary breakup modeling on the spray characteristics under diesel engine conditions. KH-ACT model is applied to cater the effect of aerodynamics in an engine cylinder and also cavitations and turbulence generated inside the injector. It is a modified form of most commonly used KH model, which considers only the aerodynamically induced breakup based on the Kelvin–Helmholtz instability. Our model is extensively evaluated by performing 3-D time-dependent simulations on Open FOAM, which is an open source flow solver. Spray characteristics like Spray Penetration, Liquid length, Spray cone angle and Souter mean diameter (SMD) were validated by comparing the results of Open Foam and Matlab. Including the effects of cavitation and turbulence enhances primary breakup, leading to smaller droplet sizes, decrease in liquid penetration, and increase in the radial dispersion of spray. All these properties favor early evaporation of fuel which enhances Engine efficiency.

Keywords: Kelvin–Helmholtz instability, open foam, primary breakup, souter mean diameter, turbulence

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Authors: Rony Tawk, Boutros Ghannam, Maroun Nemer

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Topology optimization technique presents a potential tool for the design and optimization of structures involved in mass and heat transfer. The method starts with an initial intermediate domain and should be able to progressively distribute the solid and the two fluids exchanging heat. The multi-objective function of the problem takes into account minimization of total pressure loss and maximization of heat transfer between solid and fluid subdomains. Existing methods account for the presence of only one fluid, while the actual work extends optimization distribution of solid and two different fluids. This requires to separate the channels of both fluids and to ensure a minimum solid thickness between them. This is done by adding a third objective function to the multi-objective optimization problem. This article uses density approach where each cell holds two local design parameters ranging from 0 to 1, where the combination of their extremums defines the presence of solid, cold fluid or hot fluid in this cell. Finite volume method is used for direct solver coupled with a discrete adjoint approach for sensitivity analysis and method of moving asymptotes for numerical optimization. Several examples are presented to show the ability of the method to find a trade-off between minimization of power dissipation and maximization of heat transfer while ensuring the separation and continuity of the channel of each fluid without crossing or mixing the fluids. The main conclusion is the possibility to find an optimal bi-fluid domain using topology optimization, defining a fluid to fluid heat exchanger device.

Keywords: topology optimization, density approach, bi-fluid domain, laminar steady state regime, fluid-to-fluid heat exchanger

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Authors: Wen Liang Chang

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In this study, the optimal replacement time and number of preventive maintenance (PM) actions were investigated for a second-hand device. Suppose that a user intends to use a second-hand device for manufacturing products, and that the device is replaced with a new one. Any device failure is rectified through minimal repair, thereby incurring a fixed repair cost to the user. If the new device fails within the FRW period, minimal repair is performed at no cost to the user. After the FRW expires, a failed device is repaired and the cost of repair is incurred by the user. In this study, two profit models were developed, and the optimal replacement time and number of PM actions were determined to maximize profits. Finally, the influence of the optimal replacement time and number of PM actions were elaborated on, using numerical examples.

Keywords: second-hand device, preventive maintenance, replacement time, device failure

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Authors: Dalvinder Kaur Mangal

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For the past decade, noise corrupted output measurements have been a fundamental research problem to be investigated. On the other hand, the estimation of the parameters for linear dynamic systems when also the input is affected by noise is recognized as more difficult problem which only recently has received increasing attention. Representations where errors or measurement noises/disturbances are present on both the inputs and outputs are usually called errors-in-variables (EIV) models. These disturbances may also have additive effects which are also considered in this paper. Parameter estimation of false EIV problem using equation error, output error and iterative prefiltering identification schemes with and without additive uncertainty, when only the output observation is corrupted by noise has been dealt in this paper. The comparative study of these three schemes has also been carried out.

Keywords: errors-in-variable (EIV), false EIV, equation error, output error, iterative prefiltering, Gaussian noise

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Authors: Qian Wang, Haibo Gu

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To drive practical interpretations and applications of various policies in building the transnational education joint-ventures, middle managers learn to navigate through uncertainties and ambiguities. However, the current literature views very little about those middle managers’ experiences, perceptions, and practices. This paper takes the empirical approach and aims to uncover the middle managers’ experiences by conducting interviews, campus visits, and document analysis. Following the qualitative research method approach, the researchers gathered information from a mixture of fourteen foreign and Chinese managers. Their perceptions of the China-foreign cooperation in higher education and their perceived roles have offered important, valuable insights to this group of people’s attitudes and management performances. The diverse cultural and demographic backgrounds contributed to the significance of the study. There are four key findings. One, middle managers’ immediate micro-contexts and individual attitudes are the top two influential factors in managers’ performances. Two, the foreign middle managers showed a stronger sense of self-identity in risk-taking. Three, the Chinese middle managers preferred to see difficulties as part of their assigned responsibilities. Four, middle managers in independent universities demonstrated a stronger sense of belonging and fewer frustrations than middle managers in secondary institutes. The researchers propose that training for managers in a transnational educational setting should consider these discoveries when select fitting topics and content. In particular, middle managers should be better prepared to anticipate their everyday jobs in the micro-environment; hence, information concerning sponsor organizations’ working culture is as essential as knowing the national and local regulations, and socio-culture. Different case studies can help the managers to recognize and celebrate the diversity in transnational education. Situational stories can help them to become aware of the diverse and wide range of work contexts so that they will not feel to be left alone when facing challenges without relevant previous experience or training. Though this research is a case study based in the Chinese transnational higher education setting, the implications could be relevant and comparable to other transnational higher education situations and help to continue expanding the potential applications in this field.

Keywords: educational management, middle manager performance, transnational higher education

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Authors: Zuliziana Suif, Nordila Ahmad, Sengheng Hul

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This paper evaluates the impact of climate change on suspended sediment changes in the Mekong River Basin. In this study, the distributed process-based sediment transport model is used to examine the potential impact of future climate on suspended sediment dynamic changes in the Mekong River Basin. To this end, climate scenarios from two General Circulation Model (GCMs) were considered in the scenario analysis. The simulation results show that the sediment load and concentration shows 0.64% to 69% increase in the near future (2041-2050) and 2.5% to 95% in the far future (2090- 2099). As the projected climate change impact on sediment varies remarkably between the different climate models, the uncertainty should be taken into account in sediment management. Overall, the changes in sediment load and concentration can have a great implication for related sediment management.

Keywords: climate change, suspended sediment, Mekong River Basin, GCMs

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Authors: Rana Rimawi, Ayman Baklizi

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Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given.

Keywords: point estimation, type II generalized logistic distribution, progressive censoring, maximum likelihood estimation

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Jinghua Zhang

Abstract:

Shaft-tunnel junction is a typical case of the structural nonuniformity in underground structures. The shaft and the tunnel possess greatly different structural features. Even under uniform excitations, they tend to behave discrepantly. Studies on shaft-tunnel junctions are mainly performed numerically. Shaking table tests are also conducted. Although many numerical and experimental data are obtained, an analytical solution still has great merits of gaining more insights into the shaft-tunnel problem. This paper will try to remedy the situation. Since the seismic responses of shaft-tunnel junctions are very related to directions of the excitations, they are studied in two scenarios: the longitudinal-excitation scenario and the transverse-excitation scenario. The former scenario will be addressed in this paper. Given that responses of the tunnel are highly dependent on the shaft, the analytical solutions would be developed firstly for the vertical shaft. Then, the seismic responses of the tunnel would be discussed. Since vertical shafts bear a resemblance to rigid caissons, the solution proposed in this paper is derived by introducing terms of shaft-tunnel and soil-tunnel interactions into equations originally developed for rigid caissons. The validity of the solution is examined by a validation model computed by finite element method. The mutual influence between the shaft and the tunnel is introduced. The soil-structure interactions are discussed parametrically based on the proposed equations. The shaft-tunnel relative displacement and the soil-tunnel relative stiffness are found to be the most important parameters affecting the magnitudes and distributions of the internal forces of the tunnel. A hinge-joint at the shaft-tunnel junction could significantly reduce the degree of stress concentration compared with a rigid joint.

Keywords: analytical solution, longitudinal excitation, numerical validation , shaft-tunnel junction

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong

Abstract:

Earthquake is one of the main loads threatening dam safety. Once the dam is damaged, it will bring huge losses of life and property to the country and people. Therefore, it is very important to research the seismic safety of the dam. Due to the complex foundation conditions, high fortification intensity, and high scientific and technological content, it is necessary to adopt reasonable methods to evaluate the seismic safety performance of concrete arch dams built and under construction in strong earthquake areas. Structural seismic vulnerability analysis can predict the probability of structural failure at all levels under different intensity earthquakes, which can provide a scientific basis for reasonable seismic safety evaluation and decision-making. In this paper, the response surface method (RSM) is applied to the seismic vulnerability analysis of arch dams, which improves the efficiency of vulnerability analysis. Based on the central composite test design method, the material-seismic intensity samples are established. The response surface model (RSM) with arch crown displacement as performance index is obtained by finite element (FE) calculation of the samples, and then the accuracy of the response surface model (RSM) is verified. To obtain the seismic vulnerability curves, the seismic intensity measure ??(?1) is chosen to be 0.1~1.2g, with an interval of 0.1g and a total of 12 intensity levels. For each seismic intensity level, the arch crown displacement corresponding to 100 sets of different material samples can be calculated by algebraic operation of the response surface model (RSM), which avoids 1200 times of nonlinear dynamic calculation of arch dam; thus, the efficiency of vulnerability analysis is improved greatly.

Keywords: high concrete arch dam, performance index, response surface method, seismic vulnerability analysis, vector-valued intensity measure

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Authors: Apollinaire Barme, Simon Tamayo, Arthur Gaudron

Abstract:

This paper presents a statistical approach to identify explanatory variables linearly related to e-commerce sales. The proposed methodology allows specifying a regression model in order to quantify the relevance between openly available data (economic and demographic) and national e-commerce sales. The proposed methodology consists in collecting data, preselecting input variables, performing regressions for choosing variables and models, testing and validating. The usefulness of the proposed approach is twofold: on the one hand, it allows identifying the variables that influence e- commerce sales with an accessible approach. And on the other hand, it can be used to model future sales from the input variables. Results show that e-commerce is linearly dependent on 11 economic and demographic indicators.

Keywords: e-commerce, statistical modeling, regression, empirical research

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

Abstract:

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

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

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Authors: Tarek A. Mekail, Walid M. A. Elmagid

Abstract:

Many researches have concentrated on improving the aerodynamic performance of wind turbine blade through testing and theoretical studies. A small wind turbine blade is designed, fabricated and tested. The power performance of small horizontal axis wind turbines is simulated in details using Computational Fluid Dynamic (CFD). The three-dimensional CFD models are presented using ANSYS-CFX v13 software for predicting the performance of a small horizontal axis wind turbine. The simulation results are compared with the experimental data measured from a small wind turbine model, which designed according to a vehicle-based test system. The analysis of wake effect and aerodynamic of the blade can be carried out when the rotational effect was simulated. Finally, comparison between experimental, numerical and analytical performance has been done. The comparison is fairly good.

Keywords: small wind turbine, CFD of wind turbine, CFD, performance of wind turbine, test of small wind turbine, wind turbine aerodynamic, 3D model

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Authors: Tadaaki Tomiie, Hiroki Shinkawa

Abstract:

The present study was aimed to examine the structure of children’s adaptation during school transition and to identify a commonality and dissimilarity at the elementary and junior high school. 1,983 students in the 6th grade and 2,051 students in the 7th grade were extracted by stratified two-stage random sampling and completed the ASSESS that evaluated the school adaptation from the view point of ‘general satisfaction’, ‘teachers’ support’, ‘friends’ support’, ‘anti-bullying relationship’, ‘prosocial skills’, and ‘academic adaptation’. The 7th graders tend to be worse adaptation than the 6th graders. A structural equation modeling showed the goodness of fit for each grades. Both models were very similar but the 7th graders’ model showed a lower coefficient at the pass from ‘teachers’ support’ to ‘friends’ support’. The role of ‘teachers’ support’ was decreased to keep a good relation in junior high school. We also discussed how we provide a continuous assistance for prevention of the 7th graders’ gap.

Keywords: school transition, social support, psychological adaptation, K-12

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

Abstract:

Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 254