Search results for: optimal setting

Authors: Ssembatya Joseph Mary

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Introduction: Cryptococcal infection in the Central Nervous System (CNS) is frequently seen in human immunodeficiency virus (HIV) patients and others with low immunity as an opportunistic fungal infection. However, CNS cryptococcal granuloma (cryptococcoma) in immunocompetent patients is rare. We present a case of CNS cryptococcoma in an immunocompetent patient and review the literature to illustrate the diagnosis and treatment of such lesions. Case presentation: A 62-year-old, HIV-negative, immunocompetent female patient with no known chronic illness presented with 5 months history of a progressive headache associated with on and off episodic generalized tonic-clonic convulsions. She had been to several hospitals before she was referred to our center with a diagnosis of a brain tumor. Before referral and despite a negative CSF analysis result, she had received treatment for bacterial meningitis with no success. At Mbarara Regional Referral Hospital (MRRH), she had surgery with an excision biopsy which showed features consistent with cryptococcosis on histology. The patient had a successful adjuvant treatment with antifungal drugs following surgery. Conclusion: The diagnosis of a parasitic CNS infection, particularly cryptococcal infection mimicking neoplastic lesions in an immunocompetent patient, was unusual. Surgical management of such lesions from different reports has a bad outcome and management remains totally conservative.

Keywords: Cryptococcal meningitis, immunocompetent patient, Uganda, low resource setting

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Authors: Y. Ghiassi-Farrokhfal

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The number of electric vehicles (EVs) is increasing worldwide. Replacing gas fueled cars with EVs reduces carbon emission. However, the extensive energy consumption of EVs stresses the energy systems, requiring non-green sources of energy (such as gas turbines) to compensate for the new energy demand caused by EVs in the energy systems. To make EVs even a greener solution for the future energy systems, new EV charging stations are equipped with solar PV panels and batteries. This will help serve the energy demand of EVs through the green energy of solar panels. To ensure energy availability, solar panels are combined with batteries. The energy surplus at any point is stored in batteries and is used when there is not enough solar energy to serve the demand. While EV charging stations equipped with solar panels and batteries are green and ecologically optimal, they might not be financially viable solutions, due to battery prices. To make the system viable, we should size the battery economically and operate the system optimally. This is, in general, a challenging problem because of the stochastic nature of the EV arrivals at the charging station, the available solar energy, and the battery operating system. In this work, we provide a mathematical model for this problem and we compute the return on investment (ROI) of such a system, which is designed to be ecologically and financially optimal. We also quantify the minimum required investment in terms of battery and solar panels along with the operating strategy to ensure that a charging station has enough energy to serve its EV demand at any time.

Keywords: solar energy, battery storage, electric vehicle, charging stations

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Authors: Thanny Noeem Taiwo

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The study is an attempt to gauge the way political parties advertised their candidates and parties during the 2015 presidential election. It examined the prominence given to political advertisement by newspapers and their patrons. It also looked at the campaign issues that the candidates emphasized in their campaign and also the appeals used in the advertisements. This research work is examined against the backdrop of the Framing Theory. The agenda setting and framing effects theories are media effects theories that explain how the media affects the people, events and situations in the society. Samples comprising of three daily newspapers were selected to represent the newspapers in the Nigeria. The Punch Newspaper, the Nation and the Vanguard will be used for population of this study. These newspapers were chosen because they are widely read national dailies and also because of its affiliation with some political parties. The study revealed that low prominence was given to political messages because most of the advertisements were in the inside pages of the newspapers. The media or the print medium could be a partner in a political setting as the fourth estate of the realm. While the press was trying hard to be professional, its operations were considerably inhibited by the political adventures of the patrons of the media. There is the need for strict professionalism to the ethics of journalism in the commercialization of political messages that would eventually rub off positively on the way the media is perceived.

Keywords: aAdvertisement, election, framing, newspaper

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Authors: Shunsuke Saito, Kazuho Yoshimoto, Shunichi Ohmori, Sirawadee Arunyanart

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In this paper, we suggest a mechanism of assessment that rater and Ratee (or employees) to convince. There are many problems exist in the personnel assessment. In particular, we were focusing on the three. (1) Raters are not sufficiently recognized assessment point. (2) Ratee are not convinced by the mechanism of assessment. (3) Raters (or Evaluators) and ratees have empathy. We suggest 1: Setting of "understanding of the assessment points." 2: Setting of "relative assessment ability." 3: Proposal of two-way assessment mechanism to solve these problems. As a prerequisite, it is assumed that there are multiple raters. This is because has been a growing importance of multi-faceted assessment. In this model, it determines the weight of each assessment point evaluators by the degree of understanding and assessment ability of raters and ratee. We used the ANP (Analytic Network Process) is a theory that an extension of the decision-making technique AHP (Analytic Hierarchy Process). ANP can be to address the problem of forming a network and assessment of Two-Way is possible. We apply this technique personnel assessment, the weights of rater of each point can be reasonably determined. We suggest absolute assessment for Two-Way assessment by ANP. We have verified that the consent of the two approaches is higher than conventional mechanism. Also, human resources consultant we got a comment about the application of the practice.

Keywords: personnel evaluation, pairwise comparison, analytic network process (ANP), two-ways

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Authors: Aigul Manapova

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We consider an optimal control problem in the higher coefficient of nonlinear equations with a divergent elliptic operator and unbounded nonlinearity, and the Dirichlet boundary condition. The conditions imposed on the coefficients of the state equation are assumed to hold only in a small neighborhood of the exact solution to the original problem. This assumption suggests that the state equation involves nonlinearities of unlimited growth and considerably expands the class of admissible functions as solutions of the state equation. We obtain formulas for the first partial derivatives of the objective functional with respect to the control functions. To calculate the gradients the numerical solutions of the state and adjoint problems are used. We also prove that the gradient of the cost function is Lipchitz continuous.

Keywords: cost functional, differentiability, divergent elliptic operator, optimal control, unbounded nonlinearity

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Authors: Maha Ibrahim Mohamed Khalifa

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Background: Dental fear and anxiety lead to avoidance of dental treatment and deterioration of oral health. Aim of the study: To assess the levels of fear and anxiety among school-age children undergoing dental treatment. Setting: The study was conducted in Outpatient Dental Clinics at Benha Teaching Hospital. Research design: A descriptive research design was utilized to conduct the study. Sample: A purposive sample of 60 school-age children and their mothers attending at the previously mentioned setting was included. Tools: Three tools were used: Tool one: A structured interviewing questionnaire for Personal characteristics of children and their mothers. Tool two: Dental Subscale of Children's Fear Survey Schedule (CFSS-DS). Tool three: Modified Dental Anxiety Scale (MDAS). Results: It was illustrated that more than two-fifths (43.3%) of children had maximum fear and more than half of children (53.3%) had maximal anxiety. Conclusion: Many school-age children undergoing dental treatment suffer from high levels of fear and anxiety. Recommendations: The study recommended further research should be conducted to assess levels of fear and anxiety among children undergoing dental treatments and preferable nursing interventions for reducing their fears and anxieties.

Keywords: fear, anxiety, children, dental treatment

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Authors: Radoslaw Pytlak, Damian Suski

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The aim of the paper is to provide a computational tool for planning a haemodialysis process. It is shown that optimization methods can be used to obtain the most effective treatment focused on removing both urea and phosphorus during the process. In order to achieve that, the IV–compartment model of phosphorus kinetics is applied. This kinetics model takes into account a rebound phenomenon that can occur during haemodialysis and results in a hybrid model of the process. Furthermore, vector fields associated with the model equations are such that it is very likely that using the most intuitive objective functions in the planning problem could lead to solutions which include sliding motions. Therefore, building computational tools for solving the problem of planning a haemodialysis process has required constructing numerical algorithms for solving optimal control problems with hybrid systems. The paper concentrates on minimum time control of hybrid systems since this control objective is the most suitable for the haemodialysis process considered in the paper. The presented approach to optimal control problems with hybrid systems is different from the others in several aspects. First of all, it is assumed that a hybrid system can exhibit sliding modes. Secondly, the system’s motion on the switching surface is described by index 2 differential–algebraic equations, and that guarantees accurate tracking of the sliding motion surface. Thirdly, the gradients of the problem’s functionals are evaluated with the help of adjoint equations. The adjoint equations presented in the paper take into account sliding motion and exhibit jump conditions at transition times. The optimality conditions in the form of the weak maximum principle for optimal control problems with hybrid systems exhibiting sliding modes and with piecewise constant controls are stated. The presented sensitivity analysis can be used to construct globally convergent algorithms for solving considered problems. The paper presents numerical results of solving the haemodialysis planning problem.

Keywords: haemodialysis planning process, hybrid systems, optimal control, sliding motion

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Authors: Paolo Russu

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The purpose of the present paper is to highlight some features of an economic growth model with environmental negative externalities, giving rise to a three-dimensional dynamic system. In particular, we show that the economy, which is based on a Perfect-Substitution Technologies function of production, has no neither indeterminacy nor poverty trap. This implies that equilibrium select by economy depends on the history (initial values of state variable) of the economy rather than on expectations of economies agents. Moreover, by contrast, we prove that the basin of attraction of locally equilibrium points may be very large, as they can extend up to the boundary of the system phase space. The infinite-horizon optimal control problem has the purpose of maximizing the representative agent’s instantaneous utility function depending on leisure and consumption.

Keywords: Hopf bifurcation, open-access natural resources, optimal control, perfect-substitution technologies, Poincarè compactification

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Authors: Alhusein Almahjoub, Dongyu Qiu

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This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

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Authors: Marouen Dghim, Mohsen Ferchichi

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The effect of synthetic jet actuation on the roll-up and the development of a wing tip vortex downstream a square-tipped rectangular wing was investigated experimentally using hotwire anemometry. The wing is equipped with a hallow cavity designed to generate a high aspect ratio synthetic jets blowing at an angles with respect to the spanwise direction. The structure of the wing tip vortex under the effect of fluidic actuation was examined at a chord Reynolds number Re_c=8×10^4. An extensive qualitative study on the effect of actuation on the spanwise pressure distribution at c⁄4 was achieved using pressure scanner measurements in order to determine the optimal actuation parameters namely, the blowing momentum coefficient, Cμ, and the non-dimensionalized actuation frequency, F^+. A qualitative study on the effect of actuation parameters on the spanwise pressure distribution showed that optimal actuation frequencies of the synthetic jet were found within the range amplified by both long and short wave instabilities where spanwise pressure coefficients exhibited a considerable decrease by up to 60%. The vortex appeared larger and more diffuse than that of the natural vortex case. Operating the synthetic jet seemed to introduce unsteadiness and turbulence into the vortex core. Based on the ‘a priori’ optimal selected parameters, results of the hotwire wake survey indicated that the actuation achieved a reduction and broadening of the axial velocity deficit. A decrease in the peak tangential velocity associated with an increase in the vortex core radius was reported as a result of the accelerated radial transport of angular momentum. Peak vorticity level near the core was also found to be largely diffused as a direct result of the increased turbulent mixing within the vortex. The wing tip vortex a exhibited a reduced strength and a diffused core as a direct result of increased turbulent mixing due to the presence of turbulent small scale vortices within its core. It is believed that the increased turbulence within the vortex due to the synthetic jet control was the main mechanism associated with the decreased strength and increased size of the wing tip vortex as it evolves downstream. A comparison with a ‘non-optimal’ case was included to demonstrate the effectiveness of selecting the appropriate control parameters. The Synthetic Jet will be operated at various actuation configurations and an extensive parametric study is projected to determine the optimal actuation parameters.

Keywords: flow control, hotwire anemometry, synthetic jet, wing tip vortex

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Authors: L. Belagraa, A. Belguendouz, Y. Rouabah, A. Bouzid, A. Noui, O. Kessal

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The use of cements CRS in aggressive environments showed a lot of benefits as like good mechanical responses and therefore better durability, however, their manufacturing consume a lot of clinker, which leads to the random hazardous deposits, the shortage of natural resources and the gas and the dust emissions mainly; (CO2) with its ecological negative impact on the environment. Technical, economic and environmental benefits by the use of blended cements have been reported and being considered as a research area of great interest. The purpose of this study is to evaluate the influence of the substitution of natural pozzolan on the physico-chemical properties of the new formulated binder and the mechanical behavior of mortar containing this binary cement. Hence, the pozzolan replacement is composed with different proportions (0%, 2.5%, 5%, 7.5% and 10%). The physico-chemical properties of cement resistant to sulfate (CRS) alternative composition were investigated. Further, the behavior of the mortars based on this binder is studied. These characteristics includes chemical composition, density and fineness, consistency, setting time, shrinkage, absorption and the mechanical response. The results obtained showed that the substitution of pozzolan at the optimal ratio of 5% has a positive effect on the resulting cement, greater specific surface area, reduced water demand, accelerating the process of hydration, a better mechanical responses and decreased absorption. Therefore, economic and ecological cement based on mineral addition like pozzolan could be possible as well as advantageous to the formulation of environmental mortars.

Keywords: Cement Resistant to Sulfate (CRS), environmental mortars mechanical response, physico-chemical properties, pozzolan

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Authors: E. Tandis, E. Assareh

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Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed

Keywords: Blade Design, Optimization, Genetic Algorithm, Bees Algorithm, Genetic-Based Bees Algorithm, Large Wind Turbine

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Pius W. Molo Chin

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Real life problems such as the Huxley equation are always modeled as nonlinear differential equations. These problems need accurate and reliable methods for their solutions. In this paper, we propose a nonstandard finite difference method in time and the Galerkin combined with the compactness method in the space variables. This coupled method, is used to analyze a two dimensional Huxley equation for the existence and uniqueness of the continuous solution of the problem in appropriate spaces to be defined. We proceed to design a numerical scheme consisting of the aforementioned method and show that the scheme is stable. We further show that the stable scheme converges with the rate which is optimal in both the L2 as well as the H1-norms. Furthermore, we show that the scheme replicates the decaying qualities of the exact solution. Numerical experiments are presented with the help of an example to justify the validity of the designed scheme.

Keywords: Huxley equations, non-standard finite difference method, Galerkin method, optimal rate of convergence

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Authors: Jiahui Yang, John Quigley, Lesley Walls

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In this paper, the authors develop a stochastic model regarding the investment in supplier delivery performance development from a buyer’s perspective. The authors propose a multivariate model through a Multinomial-Dirichlet distribution within an Empirical Bayesian inference framework, representing both the epistemic and aleatory uncertainties in deliveries. A closed form solution is obtained and the lower and upper bound for both optimal investment level and expected profit under uncertainty are derived. The theoretical properties provide decision makers with useful insights regarding supplier delivery performance improvement problems where multiple delivery statuses are involved. The authors also extend the model from a single supplier investment into a supplier portfolio, using a Lagrangian method to obtain a theoretical expression for an optimal investment level and overall expected profit. The model enables a buyer to know how the marginal expected profit/investment level of each supplier changes with respect to the budget and which supplier should be invested in when additional budget is available. An application of this model is illustrated in a simulation study. Overall, the main contribution of this study is to provide an optimal investment decision making framework for supplier development, taking into account multiple delivery statuses as well as multiple projects.

Keywords: decision making, empirical bayesian, portfolio optimization, supplier development, supply chain management

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Authors: Kevin Henry

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Introduction: This research identifies the main struggles of non-binary people in an organizational setting using the ZMET – method of in-depth interviews. The research was done in Iceland, a country that is repeatedly listed in the top countries for gender equality and found three main categories of non-binary struggles in organizations. These categories can be used to improve organizational non-binary inclusion. Aim: The main questions this paper will answer are: Which unique obstacles are non-binary people facing in their daily organizational life? Which organizational and individual measures help with more inclusion of non-binary people? How can organizational gender equality measures be made more inclusive of non-binary issues? Background: Even though gender equality is a much-researched topic, the struggles of non-binary people are often overlooked in gender equality research. Additionally, non-binary and transgender people are frequently researched together, even though their struggles can be very different. Research focused on non-binary people is, in many cases, done on a more structural or organizational level with quantitative data such as salary or position within an organization. This research focuses on the individual and their struggles with qualitative data to derive measures for non-binary inclusion and equality. Method: An adapted approach of the ZMET-Method (Zaltman Metaphor Elicitation Technique) will be used, during which in-depth interviews are held with individuals, utilizing pictures as a metaphorical starting point to discuss their main thoughts and feelings on being non-binary in an organizational setting. Interviewees prepared five pictures, each representing one key thought or feeling about their organizational life. The interviewer then lets the interviewee describe each picture and asks probing questions to get a deeper understanding of each individual topic. This method helps with a mostly unbiased data collection process by only asking probing questions during the interview and not leading the interviewee in any certain direction. Results: This research has identified three main categories of struggles non-binary people are facing in an organizational setting: internal (personal) struggles, external struggles and structural struggles. Internal struggles refer to struggles that originate from the person themselves (e.g., struggles with their own identity). External struggles refer to struggles from the outside (e.g. harassment from coworkers, exclusion). Structural struggles refer to struggles that are built into the organizational policy or facilities (e.g. restrooms, gendered language). Conclusion: This study shows that there are many struggles for non-binary people in organizations and that even in countries that pride themselves on being progressive and having a high level of gender equality, there is still much to be done for non-binary inclusion. Implications for Organizations: Organizations that strive to improve the inclusion of all genders should pay attention to how their structures are built, how their training is conducted, and how their policies affect people of various genders. Simple changes like making restrooms gender-neutral and using neutral language in company communications are good examples of small structural steps for more inclusion.

Keywords: gender equality, non-binary, organizations, ZMET

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Authors: Tomoaki Hashimoto

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Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition.

Keywords: computational simulations, optimal control, predictive control, stochastic systems, discrete-time systems

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Authors: Tiffany Hollis

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Childhood trauma is well documented in mental health research, yet has received little attention in urban schools. Child trauma affects brain development and impacts cognitive, emotional, and behavioral functioning. When educators understand that some of the behaviors that appear to be aggressive in nature might be the result of a hidden diagnosis of trauma, learning can take place, and the child can thrive in the classroom setting. Traumatized children, however, do not fit neatly into any single ‘box.’ Although many children enter school each day carrying with them the experience of exposure to violence in the home, the symptoms of their trauma can be multifaceted and complex, requiring individualized therapeutic attention. The purpose of this study was to examine how prepared educators are to address the unique challenges facing children who experience trauma. Given the vast number of traumatized children in our society, it is evident that our education system must investigate ways to create an optimal learning environment that accounts for trauma, addresses its impact on cognitive and behavioral development, and facilitates mental and emotional health and well-being. The researcher describes the knowledge, attitudes, dispositions, and skills relating to trauma-informed knowledge of induction level teachers in a diverse middle school. The data for this study were collected through interviews with teachers, who are in the induction phase (the first three years of their teaching career). The study findings paint a clear picture of how ill-prepared educators are to address the needs of students who have experienced trauma and the implications for the development of a professional development workshop or series of workshops that train teachers how to recognize and address and respond to the needs of students. The study shows how teachers often lack skills to meet the needs of students who have experienced trauma. Traumatized children regularly carry a heavy weight on their shoulders. Children who have experienced trauma may feel that the world is filled with unresponsive, threatening adults, and peers. Despite this, supportive interventions can provide traumatized children with places to go that are safe, stimulating, and even fun. Schools offer an environment that potentially meets these requirements by creating safe spaces where students can feel at ease and have fun while also learning via stimulating educational activities. This study highlights the lack of preparedness of educators to address the academic, behavioral, and cognitive needs of students who have experienced trauma. These findings provide implications for the creation of a professional development workshop that addresses how to recognize and address the needs of students who have experienced some type of trauma. They also provide implications for future research with a focus on specific interventions that enable the creation of optimal learning environments where students who have experienced trauma and all students can succeed, regardless of their life experiences.

Keywords: educator preparation, induction educators, professional development, trauma-informed

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Authors: Sarah Mhae Diaz, Erika Anna De Leon, Jacklin Alwil Cartagena, Geordan Caruncong, Micah Riezl Gonzales

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This study examined the intervention of threat and surveillance on the obedience of 100 preschool children through a task variable experiment replicated from the previous studies of Higbee (1979), and Chua, J., Chua, M., & Pico (1983). Nowadays, obedience among Filipino children to authority is disregarded since they are more outspoken and rebel due to social influences. With this, aside from corporal punishment, threat and surveillance became a mean of inducing obedience. Threat, according to the Dissonance Theory, can give attitudinal change. On the other hand, surveillance, according to the Theory of Social Facilitation, can either contribute to the completion or failure to do a task. Through a 2x2 factorial design, results show; (1) threat (F(1,96) = 12.487, p < 0.05) and (2) surveillance (F(1,96)=9.942, p<.05) had a significant main effect on obedience, suggesting that the Dissonance Theory and Theory of Social Facilitation is respectively true in the study. On the other hand, (3) no interaction (F(1,96)=1.303, p > .05) was seen since threat and surveillance both have a main effect that could be positive or negative, or could be because of their complementary property as supported by the post-hoc results. Also, (4) most effective commanding style is threat and surveillance setting (M = 30.04, SD = 7.971) due to the significant main effect of the two variables. With this, in the Filipino Setting, threat and surveillance has proven to be a very effective strategy to discipline and induce obedience from a child.

Keywords: experimental study, obedience, preschool children, surveillance, threat

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Authors: Ons Triki, Fathi Abid

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Along with the increase in the use of leasing contracts in corporate finance, multiple studies aim to model the credit risk of the lease in order to cover the losses of the lessor of the asset if the lessee goes bankrupt. In the current research paper, a convertible lease contract is elaborated in a continuous time stochastic universe aiming to ensure the financial stability of the firm and quickly recover the losses of the counterparties to the lease in case of default. This work examines the term structure of the lease rates taking into account the credit default risk and the capital structure of the firm. The interaction between the lessee's capital structure and the equilibrium lease rate has been assessed by applying the competitive lease market argument developed by Grenadier (1996) and the endogenous structural default model set forward by Leland and Toft (1996). The cumulative probability of default was calculated by referring to Leland and Toft (1996) and Yildirim and Huan (2006). Additionally, the link between lessee credit risk and lease rate was addressed so as to explore the impact of convertible lease financing on the term structure of the lease rate, the optimal leverage ratio, the cumulative default probability, and the optimal firm value by applying an endogenous conversion threshold. The numerical analysis is suggestive that the duration structure of lease rates increases with the increase in the degree of the market price of risk. The maximal value of the firm decreases with the effect of the optimal leverage ratio. The results are indicative that the cumulative probability of default increases with the maturity of the lease contract if the volatility of the asset service flows is significant. Introducing the convertible lease contract will increase the optimal value of the firm as a function of asset volatility for a high initial service flow level and a conversion ratio close to 1.

Keywords: convertible lease contract, lease rate, credit-risk, capital structure, default probability

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Authors: Lana Dalawr Jalal

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This paper addresses the problem of offline path planning for Unmanned Aerial Vehicles (UAVs) in complex three-dimensional environment with obstacles, which is modelled by 3D Cartesian grid system. Path planning for UAVs require the computational intelligence methods to move aerial vehicles along the flight path effectively to target while avoiding obstacles. In this paper Modified Particle Swarm Optimization (MPSO) algorithm is applied to generate the optimal collision free 3D flight path for UAV. The simulations results clearly demonstrate effectiveness of the proposed algorithm in guiding UAV to the final destination by providing optimal feasible path quickly and effectively.

Keywords: obstacle avoidance, particle swarm optimization, three-dimensional path planning unmanned aerial vehicles

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Authors: Chia-Chen Wei, Pack Hsieh, Jeffrey Chen

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Surface Mount Technology (SMT) is widely used in the area of the electronic assembly in which the electronic components are mounted to the surface of the printed circuit board (PCB). Most of the defects in the SMT process are mainly related to the quality of solder paste printing. These defects lead to considerable manufacturing costs in the electronics assembly industry. Therefore, the solder paste inspection (SPI) machine for controlling and monitoring the amount of solder paste printing has become an important part of the production process. So far, the setting of the SPI threshold is based on statistical analysis and experts’ experiences to determine the appropriate threshold settings. Because the production data are not normal distribution and there are various variations in the production processes, defects related to solder paste printing still occur. In order to solve this problem, this paper proposes an online machine learning algorithm, called the automatic threshold adjustment (ATA) algorithm, and closed-loop architecture in the SMT process to determine the best threshold settings. Simulation experiments prove that our proposed threshold settings improve the accuracy from 99.85% to 100%.

Keywords: big data analytics, Industry 4.0, SPI threshold setting, surface mount technology

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Authors: Zhong-Dan Tu, Wang-Xi Fan, Yi-Chen Huang, Jen-Taut Yeh

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Novel ultrahigh molecular weight polyethylene (UHMWPE)/cellulose nanofiber (CNF) (F100CNFy) and UHMWPE/modified cellulose nanofiber (MCNF) (F100MCNFxy) as-prepared nanocomposite fibers were prepared by spinning F100CNFy and F100MCNFxy gel solutions, respectively. Cellulose nanofibers were successfully prepared by proper acid treatment of cotton fibers using sulfuric acid solutions. The best prepared CNF is with specific surface areas around 120 m2/g and a nanofiber diameter of 20 nm. Modified cellulose nanofiber was prepared by grafting maleic anhydride grafted polyethylene (PE-g-MAH) onto cellulose nanofibers. The achievable draw ratio (Dra) values of each F100MCNFxy as-prepared fiber series specimens approached a maximal value as their MCNF contents reached the optimal value at 0.05 phr. In which, the maximum Dra value obtained for F100MCNFx0.05 as-prepared fiber specimen prepared at the optimal MCNF content reached another maximum value as the weight ratio of PE-g-MAH to CNF approach an optimal value at 6. Similar to those found for the achievable drawing properties of the as-prepared fibers, the orientation factor, tensile strength (σ f) and initial modulus (E) values of drawn F100MCNF6y fiber series specimens with a fixed draw ratio reach a maximal value as their MCNF contents approach the optimal value, wherein the σ f and E values of the drawn F100MCNFxy fiber specimens are significantly higher than those of the drawn F100 fiber specimens and corresponding drawn F100CNFy fiber specimens prepared at the same draw ratios and CNF contents but without modification. To understand the interesting ultradrawing, thermal, orientation and tensile properties of F100CNFy and F100MCNFxy fiber specimens, Fourier transform infra-red, specific surface areas, and transmission electron microcopic analyses of the original and modified CNF nanofillers were performed in this study.

Keywords: ultradrawing, cellulose nanofibers, ultrahigh molecular weight polyethylene, nanocomposite fibers

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Authors: Jovina Concepcion Bachynski, Lenora Duhn, Idevania G. Costa, Pilar Camargo-Plazas

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Kidney failure is a life-limiting condition for which treatment, such as dialysis (hemodialysis and peritoneal dialysis), can exact a tremendously high physical and psychosocial symptom burden. Kidney failure can be severe enough to require a palliative approach to care. The term supportive care can be used in lieu of palliative care to avoid the misunderstanding that palliative care is synonymous with end-of-life or hospice care. Kidney supportive care, encompassing advance care planning, is an approach to care that improves the quality of life for people receiving dialysis through early identification and treatment of symptoms throughout the disease trajectory. Advanced care planning involves ongoing conversations about the values, goals, and preferences for future care between individuals and their healthcare teams. Kidney supportive care is underutilized and often initiated late in this population. There is evidence to indicate nurses are not providing the necessary elements of supportive kidney care. Dialysis nurses’ delay or lack of engagement in supportive care until close to the end of life may result in people dying without receiving optimal palliative care services. Using Charmaz’s constructivist grounded theory, the purpose of this doctoral study is to develop a substantive theory that explains the process of engagement in supportive care by nurses working in dialysis settings in Canada. Through initial purposeful and subsequent theoretical sampling, 23 nurses with current or recent work experience in outpatient hemodialysis, home hemodialysis, and peritoneal dialysis settings drawn from across Canada were recruited to participate in two intensive interviews using the Zoom© teleconferencing platform. Concurrent data collection and data analysis, constant comparative analysis of initial and focused codes until the attainment of theoretical saturation, and memo-writing, as well as researcher reflexivity, have been undertaken to aid the emergence of concepts, categories, and, ultimately, the constructed theory. At the time of abstract submission, data analysis is currently at the second level of coding (i.e., focused coding stage) of the research study. Preliminary categories include: (a) focusing on biomedical care; (b) multi-dimensional challenges to having the conversation; (c) connecting and setting boundaries with patients; (d) difficulty articulating kidney-supportive care; and (e) unwittingly practising kidney-supportive care. For the conference, the resulting theory will be presented. Nurses working in dialysis are well-positioned to ensure the delivery of quality kidney-supportive care. This study will help to determine the process and the factors enabling and impeding nurse engagement in supportive care in dialysis to effect change for normalizing advance care planning conversations in the clinical setting. This improved practice will have substantive beneficial implications for the many individuals living with kidney failure and their supporting loved ones.

Keywords: dialysis, kidney failure, nursing, supportive care

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Jolanta Jonak, Sylvia Tolczyk

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Educational systems are tasked with preparing students for future success in academic or work environments. Schools strive to achieve this goal, but often it is challenging as conventional teaching approaches are often ineffective in increasingly diverse educational systems. In today’s ever-increasing global society, educational systems become increasingly diverse in terms of cultural and linguistic differences, learning preferences and styles, ability and disability. Through increased understanding of disabilities and improved identification processes, students having some form of disabilities tend to be identified earlier than in the past, meaning that more students with identified disabilities are being supported in our classrooms. Also, a large majority of students with disabilities are educated in general education environments. Due to cognitive makeup and life experiences, students have varying learning styles and preferences impacting how they receive and express what they are learning. Many students come from bi or multilingual households and with varying proficiencies in the English language, further impacting their learning. All these factors need to be seriously considered when developing learning opportunities for student's. Educators try to adjust their teaching practices as they discover that conventional methods are often ineffective in reaching each student’s potential. Many teachers do not have the necessary educational background or training to know how to teach students whose learning needs are more unique and may vary from the norm. This is further complicated by the fact that many classrooms lack consistent access to interventionists/coaches that are adequately trained in evidence-based approaches to meet the needs of all students, regardless of what their academic needs may be. One evidence-based way for providing successful education for all students is by incorporating cognitive approaches and strategies that tap into affective, recognition, and strategic networks in the student's brain. This can be done through Differentiated Instruction (DI). Differentiated Instruction is increasingly recognized model that is established on the basic principles of Universal Design for Learning. This form of support ensures that regardless of the students’ learning preferences and cognitive learning profiles, they have opportunities to learn through approaches that are suitable to their needs. This approach improves the educational outcomes of students with special needs and it benefits other students as it accommodates learning styles as well as the scope of unique learning needs that are evident in the typical classroom setting. Differentiated Instruction also is recognized as an evidence-based best practice in education and is highly effective when it is implemented within the tiered system of the Response to Intervention (RTI) model. Recognition of DI becomes more common; however, there is still limited understanding of the effective implementation and use of strategies that can create unique learning environments for each student within the same setting. Through employing knowledge of a variety of instructional strategies, general and special education teachers can facilitate optimal learning for all students, with and without a disability. A desired byproduct of DI is that it can eliminate inaccurate perceptions about the students’ learning abilities, unnecessary referrals for special education evaluations, and inaccurate decisions about the presence of a disability.

Keywords: differentiated instruction, universal design for learning, special education, diversity

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Authors: Habib Ur Rahman Habib

Abstract:

Renewable energy resources are being realized as suitable options in hybrid energy planning for on-grid and micro grid. In this paper, operation, planning and optimal design of on-grid distributed energy resources based hybrid system are investigated. The aim is to minimize the cost of the overall energy system keeping in view the environmental emission and minimum penetration of conventional energy resources. Seven grid connected different case studies including diesel only, diesel-renewable based, and renewable based only are designed to perform economic analysis, operational planning and emission. Sensitivity analysis is implemented to investigate the impact of different parameters on the performance of energy resources.

Keywords: data management, renewable energy, distributed energy, smart grid, micro-grid, modeling, energy planning, design optimization

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Authors: Faisal Algosair

Abstract:

We examine the role of monetary policy in the presence of commodity price shocks using a Dynamic stochastic general equilibrium (DSGE) model with price and wage rigidities. The model characterizes a commodity exporter by its degree of export diversification, and explores the following monetary regimes: flexible domestic inflation targeting; flexible Consumer Price Index inflation targeting; exchange rate peg; and optimal rule. An increase in the degree of diversification is found to mitigate responses to commodity shocks. The welfare comparison suggests that a flexible exchange rate regime under the optimal rule is preferred to an exchange rate peg. However, monetary policy provides limited stabilization effects in an economy with low degree of export diversification.

Keywords: business cycle, commodity price, exchange rate, global financial cycle

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Authors: Min-Tsai Lai, Taqwa Hariguna

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This paper presents a periodical replacement model for a system, considering the concept of single and cumulative repair cost limits simultaneously. The failures are divided into two types. Minor failure can be corrected by minimal repair and serious failure makes the system breakdown completely. When a minor failure occurs, if the repair cost is less than a single repair cost limit L1 and the accumulated repair cost is less than a cumulative repair cost limit L2, then minimal repair is executed, otherwise, the system is preventively replaced. The system is also replaced at time T or at serious failure. The optimal period T minimizing the long-run expected cost per unit time is verified to be finite and unique under some specific conditions.

Keywords: repair-cost limit, cumulative repair-cost limit, minimal repair, periodical replacement policy

Procedia PDF Downloads 342