Search results for: interval estimation

Authors: Africa Makasi

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The research explored factors influencing the adoption of disruptive technologies in the mobile telecommunications industry in Zimbabwe. Data was gathered from the second biggest competitor in the industry with over 3 million subscribers as the main case of study. The survey was conducted by purposively selecting 70 respondents from a population of 3,000,000 (three million) active subscribers from the company’s database. A skip interval of 42,857 was used to randomly select the sample. Customer representatives were selected from the company’s five regional offices using a two-stage cluster sampling technique. Employee participants were purposively selected from the company’s head office. Self-administered questionnaires were used in the research. A pilot test was conducted and the assessment of the reliability of the research instruments used in the research performed. Results of the pilot study were analyzed to test for reliability using SPSS. The results confirmed that the style of leadership and its thrust may help speed up or reduce the adoption of disruptive technologies. This was reflected by a p–value of 0.01 which is less than 0.05. The null hypothesis was thus rejected and the strong relationship between leadership and adoption of disruptive technology is confirmed. Similar results were also obtained with respect to staff competence, availability of funding and the type of infrastructure available Future research should look at organizational ambidexterity as well as exploitation and exploration paradigms in organizations in the telecommunications industry and their impact on the adoption of disruptive technologies.

Keywords: disruptive innovation, adoption, mobile telecommunication industry, exploration and exploitation

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Authors: Sarah Odofin, Zhiwei Gao

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Operations and maintenance of wind turbine have received much attention by researcher due to rapid expansion of wind farms. This paper explores a novel fault diagnosis that is designed and optimized to be very sensitive to faults and robust to disturbances. The faults considered are the sensor faults of which the augmented observer is considered to enlarge faults and to be robust to disturbance. A qualitative model based analysis is proposed for early fault diagnosis to minimize downtime mostly caused by components breakdown and exploit productivity. Simulation results are computed validating the models provided which demonstrates system performance using practical application of fault type examples. The results demonstrate the effectiveness of the developed techniques investigated in a Matlab/Simulink environment.

Keywords: wind turbine, condition monitoring, genetic algorithm, fault diagnosis, augmented observer, disturbance robustness, fault estimation, sensor monitoring

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Authors: Nougrara Zineb

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The unique structure of the blood vessels in the retina has been used for biometric identification. The retina blood vessel pattern is a unique pattern in each individual and it is almost impossible to forge that pattern in a false individual. The retina biometrics’ advantages include high distinctiveness, universality, and stability overtime of the blood vessel pattern. Once the creases have been extracted from the images, a registration stage is necessary, since the position of the retinal vessel structure could change between acquisitions due to the movements of the eye. Image registration consists of following steps: Feature detection, feature matching, transform model estimation and image resembling and transformation. In this paper, we present an algorithm of registration; it is based on the characterization of retinal feature points. For experiments, retinal images from the DRIVE database have been tested. The proposed methodology achieves good results for registration in general.

Keywords: fovea, optic disc, registration, retinal images

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Authors: C. Castro-Egler, A. Durán-Escalante, A. García-González

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This paper presents a method to generate a finite element model of the human auditory inner ear system. The geometric model has been realized using 2D images from a virtual model of temporal bones. A point cloud has been gotten manually from those images to construct a whole mesh with hexahedral elements. The main difference with the predecessor models is the spiral shape of the cochlea with its three scales completely defined: scala tympani, scala media and scala vestibuli; which are separate by basilar membrane and Reissner membrane. To validate this model, numerical simulations have been realised with two models: an isolated inner ear and a whole model of human auditory system. Ideal conditions of displacement are applied over the oval window in the isolated Inner Ear model. The whole model is made up of the outer auditory channel, the tympani, the ossicular chain, and the inner ear. The boundary condition for the whole model is 1Pa over the auditory channel entrance. The numerical simulations by FEM have been done using a harmonic analysis with a frequency range between 100-10.000 Hz with an interval of 100Hz. The following results have been carried out: basilar membrane displacement; the scala media pressure according to the cochlea length and the transfer function of the middle ear normalized with the pressure in the tympanic membrane. The basilar membrane displacements and the pressure in the scala media make it possible to validate the response in frequency of the basilar membrane.

Keywords: finite elements method, human auditory system model, numerical analysis, 3D modelling cochlea

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Martin Arguelles Perez, Woosoon Yim, Alexander Barzilov

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High-fidelity radiation monitoring is an essential component in the enhancement of the situational awareness capabilities of the Department of Energy’s Office of Environmental Management (DOE-EM) personnel. In this paper, multiple units of unmanned aerial vehicles (UAVs) each equipped with a cadmium zinc telluride (CZT) gamma-ray sensor are used for radiation source localization, which can provide vital real-time data for the EM tasks. To achieve this goal, a fully autonomous system of multicopter-based UAV swarm in 3D tetrahedron formation is used for surveying the area of interest and performing radiation source localization. The CZT sensor used in this study is suitable for small-size multicopter UAVs due to its small size and ease of interfacing with the UAV’s onboard electronics for high-resolution gamma spectroscopy enabling the characterization of radiation hazards. The multicopter platform with a fully autonomous flight feature is suitable for low-altitude applications such as radiation contamination sites. The conventional approach uses a single UAV mapping in a predefined waypoint path to predict the relative location and strength of the source, which can be time-consuming for radiation localization tasks. The proposed UAV swarm-based approach can significantly improve its ability to search for and track radiation sources. In this paper, two approaches are developed using (a) 2D planar circular (3 UAVs) and (b) 3D tetrahedron formation (4 UAVs). In both approaches, accurate estimation of the gradient vector is crucial for heading angle calculation. Each UAV carries the CZT sensor; the real-time radiation data are used for the calculation of a bulk heading vector for the swarm to achieve a UAV swarm’s source-seeking behavior. Also, a spinning formation is studied for both cases to improve gradient estimation near a radiation source. In the 3D tetrahedron formation, a UAV located closest to the source is designated as a lead unit to maintain the tetrahedron formation in space. Such a formation demonstrated a collective and coordinated movement for estimating a gradient vector for the radiation source and determining an optimal heading direction of the swarm. The proposed radiation localization technique is studied by computer simulation and validated experimentally in the indoor flight testbed using gamma sources. The technology presented in this paper provides the capability to readily add/replace radiation sensors to the UAV platforms in the field conditions enabling extensive condition measurement and greatly improving situational awareness and event management. Furthermore, the proposed radiation localization approach allows long-term measurements to be efficiently performed at wide areas of interest to prevent disasters and reduce dose risks to people and infrastructure.

Keywords: radiation, unmanned aerial system(UAV), source localization, UAV swarm, tetrahedron formation

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Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

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A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: forecasting model, steel demand uncertainty, hierarchical Bayesian framework, exponential smoothing method

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Authors: Ali Akbar Heydari

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Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values.

Keywords: control chart, markov chain approach, statistical design, synthetic, variable parameter

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Authors: Khadija Mushtaq, Mian Saqib Mehmood

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This paper attempts to determine the impact of leadership style and learning orientation on organizational performance in Pakistan. A sample of 158 middle managers selected from sports and surgical factories from Sialkot. The empirical estimation is based on a multiple linear regression analysis of the relationship between leadership style, learning orientation and organizational performance. Leadership style is measure through transformational leadership and transactional leadership. The transformational leadership has insignificant impact on organizational performance. The transactional leadership has positive and significant relation with organizational performance. Learning orientation also has positive and significant relation with organizational performance. Linear regression used to estimate the relation between dependent and independent variables. This study suggests top manger should prefer continuous process for improvement for any change in system rather radical change.

Keywords: transformational leadership, transactional leadership, learning orientation, organizational performance, Pakistan

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Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: R. Gupta, P. Jain, S. Das

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One way to approach construction scheduling optimization problem is to focus on the individual aspects of planning, which can be broadly classified as time scheduling, crew and resource management, and cost control. During the last four decades, construction planning has seen a lot of research, but to date, no paper had attempted to summarize the literature available under important heads. This paper addresses each of aspects separately, and presents the findings of an in-depth literature of the various planning techniques. For techniques dealing with time scheduling, the authors have adopted a rough chronological documentation. For crew and resource management, classification has been done on the basis of the different steps involved in the resource planning process. For cost control, techniques dealing with both estimation of costs and the subsequent optimization of costs have been dealt with separately.

Keywords: construction planning techniques, time scheduling, resource planning, cost control

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Authors: Rahma Fitriani, Zerlita Fahdha Pusdiktasari, Herman Cahyo Diartho

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Spatial panel model is commonly used to specify more complicated behavior of economic agent distributed in space at an individual-spatial unit level. There are several spatial panel models which can be adapted based on certain assumptions. A package called splm in R has several functions, ranging from the estimation procedure, specification tests, and model selection tests. In the absence of prior assumptions, a comprehensive procedure which utilizes the available functions in splm must be formed, which is the objective of this study. In this way, the best specification and model can be fitted based on data. The implementation of the procedure works well. It specifies SARAR-FE as the best model for agricultural productivity growth of the 38 East Java’s Regencies/Municipalities.

Keywords: spatial panel, specification, splm, agricultural productivity growth

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Authors: Vera Jancurova

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People are, in general, exposed to behavioral biases, however, the degree and impact are affected by experience, knowledge, and other characteristics. The purpose of this article is to study two of defined behavioral biases, the overconfidence and self-attribution bias, and its impact on economic and non-economic students at different stage of the study. The research method used for the purpose of this study is a controlled field study that contains questions on perception of own confidence and self-attribution and estimation of limits to analyse actual abilities. The results of the research show that economic students seem to be more overconfident than their non–economic colleagues, which seems to be caused by the fact the questionnaire was asking for predicting economic indexes and own knowledge and abilities in financial environment. Surprisingly, the most overconfidence was detected by the students at the beginning of their study (1st-semester students). However, the estimations of real numbers do not point out, that economic students have better results by the prediction itself. The study confirmed the presence of self-attribution bias at all of the respondents.

Keywords: behavioral finance, overconfidence, self-attribution, heuristics and biases

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Authors: Muhammad Randhawa, Muhammad Nadeem

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Guava (Psidium guajava L.) is a vital source of minerals, vitamins, dietary fiber and antioxidants. Owing to highly perishable nature and proning towards chilling injury, diseases, insect-pests and physical damage the main drawbacks of guava after harvesting, present study was designed. Due to its delicacy in physiology, economic importance, effects of pre and postharvest factors and maturity indices, guava fruits should be given prime importance for good quality attributes. In this study guava fruits were stored at 10°C with 80% relative humidity after treating with different levels of sulphate salt of magnesium followed by dipping in cellulose based edible coating hydroxy propyl methyl cellulose (HPMC). The main objective of this coating was to enhance the shelf life of guava by inhibiting the respiration and also by binding the dissolved solids with salt application. Characterization for quality attributes including physical, physiological and bio chemical analysis was performed after every 7 days interval till the fruit remains edible during the storage period of 4 weeks. Finally, data obtained was subjected to statistical analysis. It was concluded on statistical basis that Surahi variety (treated with 5% MgSO4) showed best storage stability and kept its original quality up to almost 23 days during storage.

Keywords: edible coating, guava cultivars, physicochemical attributes, storage

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Authors: Kadri-Ann Kertsmik, Martin Talvik, Kimmo Lylykangas, Simo Ilomets, Targo Kalamees

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This study compares the building carbon footprint (CF) values for a case study of a private house located in a cold climate, using the Level(s) methodology. It provides a framework for measuring the environmental performance of buildings throughout their life cycle, taking into account various factors. The study presents the results of the three scenarios, comparing their carbon emissions and highlighting the benefits of circular material usage. The construction process was thoroughly documented, and all materials and components (including minuscule mechanical fasteners, each meter of cable, a kilogram of mortar, and the component of HVAC systems, among other things) delivered to the construction site were noted. Transportation distances of each delivery, the fuel consumption of construction machines, and electricity consumption for temporary heating and electrical tools were also monitored. Using the detailed data on material and energy resources, the CF was calculated for two scenarios: one where circular material usage was applied and another where virgin materials were used instead of reused ones. The results were compared with the CF calculated based on the building permit design model using the Level(s) methodology. To study the range of possible results in the early stage of CF assessment, the same building permit design was given to several experts. Results showed that embodied carbon values for a built scenario were significantly lower than the values predicted by the building permit stage as a result of more precise material quantities, as the calculation methodology is designed to overestimate the CF. Moreover, designers made an effort to reduce the building's CF by reusing certain materials such as ceramic tiles, lightweight concrete blocks, and timber during the construction process. However, in a cold climate context where operational energy (B6) continues to dominate, the total building CF value changes between the three scenarios were less significant. The calculation for the building permit project was performed by several experts, and CF results were in the same range. It alludes that, for the first estimation of preliminary building CF, using average values proves to be an appropriate method for the Estonian national carbon footprint estimation phase during building permit application. The study also identified several opportunities for reducing the carbon footprint of the building, such as reusing materials from other construction sites, preferring local material producers, and reducing wastage on site. The findings suggest that using circular materials can significantly reduce the carbon footprint of buildings. Overall, the study highlights the importance of using a comprehensive approach to measure the environmental performance of buildings, taking into account both the project and the actually built house. It also emphasises the need for ongoing monitoring for designing the building and construction site waste. The study also gives some examples of how to enable future circularity of building components and materials, e.g., building in layers, using wood as untreated, etc.

Keywords: carbon footprint, circular economy, sustainable construction, level(s) methodology

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Authors: Marian Dragomir, Alin Dragomir

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In the paper, two fault location algorithms are presented for transmission lines which use the line parameters to estimate the distance to the fault. The first algorithm uses only the measurements from one end of the line and the positive and zero sequence parameters of the line, while the second one uses the measurements from both ends of the line and only the positive sequence parameters of the line. The algorithms were tested using a transmission grid transposed in MATLAB. In a first stage it was established a fault location base line, where the algorithms mentioned above estimate the fault locations using the exact line parameters. After that, the positive and zero sequence resistance and reactance of the line were calculated again for different ground resistivity values and then the fault locations were estimated again in order to compare the results with the base line results. The results show that the algorithm which uses the zero sequence impedance of the line is the most sensitive to the line parameters modifications. The other algorithm is less sensitive to the line parameters modification.

Keywords: estimation algorithms, fault location, line parameters, simulation tool

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Authors: Hichem Kmimech, Achref Jabeur Telmoudi, Lotfi Nabli

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The estimation of the initial marking minimum (MIM) is a crucial problem in labeled Petri nets. In the case of multiple choices, the search for the initial marking leads to a problem of optimization of the minimum allocation of resources with two constraints. The first concerns the firing sequence that could be legal on the initial marking with respect to the firing vector. The second deals with the total number of tokens that can be minimal. In this article, the MIM problem is solved by the meta-heuristic particle swarm optimization (PSO). The proposed approach presents the advantages of PSO to satisfy the two previous constraints and find all possible combinations of minimum initial marking with the best computing time. This method, more efficient than conventional ones, has an excellent impact on the resolution of the MIM problem. We prove through a set of definitions, lemmas, and examples, the effectiveness of our approach.

Keywords: marking, production system, labeled Petri nets, particle swarm optimization

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Authors: Rizki Nor Amelia, Farida A. Setiawati

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This study aimed to: (1) describe the characteristics of teacher-made test instrument used to measure the ability of students’chemistry, and (2) identify the presence of the compability difficulty level set by teachers to difficulty level by empirical results. Based on these objectives, this study was a descriptive research. The analysis in this study used the Rasch model and Chi-square statistics. Analysis using Rasch Model was based on the response patterns of high school students to the teacher-made test instrument on chemistry subject Academic Year 2015/2016 in the Yogyakarta. The sample of this research were 358 students taken by cluster random sampling technique. The analysis showed that: (1) a teacher-made tests instrument has a medium on the mean difficulty level. This instrument is capable to measure the ability on the interval of -0,259 ≤ θ ≤ 0,659 logit. Maximum Test Information Function obtained at 18.187 on the ability +0,2 logit; (2) 100% items categorized either as easy or difficult by rasch model is match with the teachers’ judgment; while 37 items are categorized according to rasch model which 8.10% and 10.81% categorized as easy and difficult items respectively according to the teachers, the others are medium categorized. Overall, the distribution of the level of difficulty formulated by the teachers has the distinction (not match) to the level of difficulty based on the empirical results.

Keywords: chemistry, items parameter fitness, Rasch model, teacher-made test

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Authors: Jaime E. Munoz, Jose C. Arcos, Oscar E. Bautista, Ivan E. Campos

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The influence of slippage phenomenon over the destabilization and atomization mechanisms induced via surface acoustic waves on a Newtonian, millimeter-sized, drop deposited on a hydrophilic substrate is studied theoretically. By implementing the Navier-slip model and a lubrication-type approach into the equations which govern the dynamic response of a drop exposed to acoustic stress, a highly nonlinear evolution equation for the air-liquid interface is derived in terms of the acoustic capillary number and the slip coefficient. By numerically solving such an evolution equation, the Spatio-temporal deformation of the drop's free surface is obtained; in this context, atomization of the initial drop into micron-sized droplets is predicted at our numerical model once the acoustically-driven capillary waves reach a critical value: the instability length. Our results show slippage phenomenon at systems with partial and complete wetting favors the formation of capillary waves at the free surface, which traduces in a major volume of liquid being atomized in comparison to the no-slip case for a given time interval. In consequence, slippage at the wall possesses the capability to affect and improve the atomization rate for a drop exposed to a high-frequency acoustic field.

Keywords: capillary instability, lubrication theory, navier-slip condition, SAW atomization

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Authors: Sara Lamari, Toufik Madani

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This study aimed to explore the relationship between reproduction performances and coat characteristics of Montbéliarde cows born in Algeria or imported from Europe during the hot season in Algeria. Hair coat traits (hair coat color, Hair Weight, hair length, the number of hair per unit area, total hair diameters and hair medulla diameters) were estimated in 18 imported cattle and 49 locally born cows. These traits were measured in an area of 20cm below the dorsal line in the center of the thorax. Results showed that hair coats were significantly different between locally born and imported cows. Imported cows had whiter coats when compared to locally born cows for Montbéliarde cows. A significant effect of total hair diameter was observed on the interval from calving to conception (IC) for imported Montbéliarde cows, suggesting less incidence of heat stress on reproduction efficiency of cows with thin diameter hair coats. Montbéliarde cows with short hair coat registered significantly more number of mating per conception (2, 28±1, 93 Vs. 1,67±0,92) and IC (98,04±78,81Vs 74.53 ± 35.60 days) when compared to cows with long hairs. Hair works as a temperature regulator in association with muscles in the skin and may affect reproduction performances during hit stress season. It can be assumed that the length and a total diameter of hairs for the Montbeliarde breed appears to be related to their reproductive efficiency.

Keywords: hair coat, reproduction, Montbeliarde cow, hot season

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Authors: Mudafara Bengleil

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Estimation of the magnitude and causes of poisoning was the objective of the current study. A retrospective study of medical records of all poisoning children admitted to Benghazi Children Hospital in Libya from January 2008 up to December 2010. Number of children admitted was 244; the age ranged from less than one to 13 years old. Most of cases were admitted with mild symptom and the majority of them were boys. Only few cases admitted to intensive care unit and there was no mortality recorded through the period of study. Age group 1 to 3 years (50.8%) had the highest frequency of admission and the peak of admission was during summer. The most common cause of admission was due to ingestion of medication (53.69%), House hold product exposure (26.64%) was the second causes of admission while, 19.67% of admissions were due to Food poisoning. Almost all admitted cases were accidental and medicines were the most consumed substances in addition, improper storage of toxic agents were the first risk factor of poisoning. Present results indicated that, children poisoning seems to be a common pediatric care problem which need to control and prevent.

Keywords: poisoning, children, hospital, medical

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Authors: O. Balamane-Zizi, L. M. Rouidi, A. Boukhrissa, N. Daas, H. Ait-amar

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The objective of this study was to test the possibility of a mixed treatment (physical-chemical and biological) of Ouled Fayet leachates which date of 10 years and has a large fraction of hard COD that can be reduced by coagulation-flocculation. Previous batch tests showed the possibility of applying the physical-chemical and biological treatments separately; the removal efficiencies obtained in this case were not interesting. We propose, therefore, to test the possibility of a combined treatment, in order to improve the quality of the leachates. Estimation of the treatment’s effectiveness was done by analysis of some pollution parameters such as COD, suspended solids, and heavy metals (particularly iron and nickel). The main results obtained after the combination of treatments, show reduction rate of about 63% for COD, 73% for suspended solids and 80% for iron and nickel. We also noted an improvement in the turbidity of treated leachates.

Keywords: landfill leachates, COD, physical-chemical treatment, biological treatment

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Authors: Mike Tsionas

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The paper develops new indices of financial stability based on an explicit model of expected utility maximization by financial institutions subject to the classical technology restrictions of neoclassical production theory. The model can be estimated using standard econometric techniques, like GMM for dynamic panel data and latent factor analysis for the estimation of co-variance matrices. An explicit functional form for the utility function is not needed and we show how measures of risk aversion and prudence (downside risk aversion) can be derived and estimated from the model. The model is estimated using data for Eurozone countries and we focus particularly on (i) the use of the modeling approach as an “early warning mechanism”, (ii) the bank- and country-specific estimates of risk aversion and prudence (downside risk aversion), and (iii) the derivation of a generalized measure of risk that relies on loan-price uncertainty.

Keywords: financial stability, banking, expected utility maximization, sub-prime crisis, financial crisis, eurozone, PIIGS

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Authors: Sallama Resen, Celal Öztürk

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Distance estimation between tow nodes has wide scope of surveillance and tracking applications. This paper suggests a Bluetooth Low Energy (BLE) technology as a media for transceiver and receiver signal in small indoor areas. As an example, BLE communication technologies used in child safety domains. Local network is designed to detect child position in indoor school area consisting Mobile Beacons (MB), Access Points (AP) and Smart Phones (SP) where MBs stuck in children’s shoes as wearable sensors. This paper presents a technique that can detect mobile beacons’ position and help finding children’s location within dynamic environment. By means of bluetooth beacons that are attached to child’s shoes, the distance between the MB and teachers SP is estimated with an accuracy of less than one meter. From the simulation results, it is shown that high accuracy of position coordinates are achieved for multi-mobile beacons in different environments.

Keywords: bluetooth low energy, child safety, mobile beacons, received signal strength

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Authors: Masoud Rabbani, Majede Smizadeh, Hamed Farrokhi-Asl

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We study jointly determining prices and production in a multiple period horizon under a general non-stationary stochastic demand with continuous prices. In some periods we need to increase capacity of production to satisfy demand. This paper presents a model to aid multi-period production capacity planning by quantifying the trade-off between product quality and production cost. The product quality is estimated as the statistical variation from the target performances obtained from the output tolerances of the production machines that manufacture the components. We consider different tolerance for different machines that use to increase capacity. The production cost is estimated as the total cost of owning and operating a production facility during the planning horizon.so capacity planning has cost that impact on price. Pricing products often turns out to be difficult to measure them because customers have a reservation price to pay that impact on price and demand. We decide to determine prices and production for periods after enhance capacity and consider reservation price to determine price. First we use an algorithm base on fuzzy set of the optimal objective function values to determine capacity planning by determine maximize interval from upper bound in minimum objectives and define weight for objectives. Then we try to determine inventory and pricing policies. We can use a lemma to solve a problem in MATLAB and find exact answer.

Keywords: price policy, inventory policy, capacity planning, product quality, epsilon -constraint

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: John Knight, Fuchun Li, Yan Xu

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Based on a new method of the nonparametric estimator of the drift function, we propose a consistent test for the parametric specification of the drift function in the short rate diffusion process using observations from a panel of yields. The test statistic is shown to follow an asymptotic normal distribution under the null hypothesis that the parametric drift function is correctly specified, and converges to infinity under the alternative. Taking the daily 7-day European rates as a proxy of the short rate, we use our test to examine whether the drift of the short rate diffusion process is linear or nonlinear, which is an unresolved important issue in the short rate modeling literature. The testing results indicate that none of the drift functions in this literature adequately captures the dynamics of the drift, but nonlinear specification performs better than the linear specification.

Keywords: diffusion process, nonparametric estimation, derivative security price, drift function and volatility function

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Authors: Jiri Dufek

Abstract:

The national model of the Czech Republic has been modified in a detailed way to get detailed travel demand in the municipality level (cities, villages over 300 inhabitants). As a technique for this detailed modelling, three-dimensional procedure for calibrating gravity models, was used. Besides of zone production and attraction, which is usual in gravity models, the next additional parameter for trip distribution was introduced. Usually it is called by “third dimension”. In the model, this parameter is a demand between regions. The distribution procedure involved calculation of appropriate skim matrices and its multiplication by three coefficients obtained by iterative balancing of production, attraction and third dimension. This type of trip distribution was processed in R-project and the results were used in the Czech Republic transport model, created in PTV Vision. This process generated more precise results in local level od the model (towns, villages)

Keywords: trip distribution, three dimension, transport model, municipalities

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Authors: Naydelis Brito Suárez, Deni Librado Torres Román, Fernando Hermosillo Reynoso

Abstract:

Automatic detection and generation of a dynamic ROI (Region of Interest) in vehicle traffic surveillance videos based on a static camera in Intelligent Transportation Systems is challenging for computer vision-based systems. The dynamic ROI, being a changing ROI, should capture any other moving object located outside of a static ROI. In this work, the video is represented by a Tensor model composed of a Background and a Foreground Tensor, which contains all moving vehicles or objects. The values of each pixel over a time interval are represented by time series, and some pixel rows were selected. This paper proposes a pixel entropy-based algorithm for automatic detection and generation of a dynamic ROI in traffic videos under the assumption of two types of theoretical pixel entropy behaviors: (1) a pixel located at the road shows a high entropy value due to disturbances in this zone by vehicle traffic, (2) a pixel located outside the road shows a relatively low entropy value. To study the statistical behavior of the selected pixels, detecting the entropy changes and consequently moving objects, Shannon, Tsallis, and Approximate entropies were employed. Although Tsallis entropy achieved very high results in real-time, Approximate entropy showed results slightly better but in greater time.

Keywords: convex hull, dynamic ROI detection, pixel entropy, time series, moving objects

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

Abstract:

In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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