Search results for: distributed estimation

Authors: Elke Mackensen

Abstract:

Systems Engineering plays a key role during industrial product development of complex technical systems. The need for systems engineers in industry is growing. However, there is a gap between the industrial need and the academic education. Normally the academic education is focused on the domain specific design, implementation and testing of technical systems. Necessary systems engineering expertise like knowledge about requirements analysis, product cost estimation, management or social skills are poorly taught. Thus, there is the need of new academic concepts for teaching systems engineering skills. This paper presents a project-orientated training concept to prepare students from different technical degree programs for systems engineering activities. The training concept has been initially implemented and applied in the industrial engineering master program of the University of Applied Sciences Offenburg.

Keywords: educational systems engineering training, requirements analysis, system modelling, SysML

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Authors: Maryam Meftahi, Yashar Hamidzadeh

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In recent decades, the amount of solid waste production has been rising. In the meantime, plastic waste is one of the major parts of urban solid waste, so, recycling plastic waste from water bottles has become a serious challenge in the whole world. The experimental program includes the study of the effect of waste plastic fibers on maximum dry density (MDD), optimum moisture content (OMC) with different sizes and contents. Also, one dimensional consolidation tests were carried out to evaluate the benefit of utilizing randomly distributed waste plastics fiber to improve the engineering behavior of a tested soils. Silty soil specimens were prepared and tested at five different percentages of plastic waste content (i.e. 0.25%, 0.50%, 0.75%, 1% and 1.25% by weight of the parent soil). The size of plastic chips used, are 4 mm, 8 mm and 12 mm long and 4 mm in width. The results show that with the addition of waste plastic fibers, the MDD and OMC and also the compressibility of soil decrease significantly.

Keywords: silty soil, waste plastic, compaction, consolidation, reinforcement

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Authors: Marwa I. Shabayek, Ola A. Said, Hanan A. Attaia, Heba A. Awida

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Bladder carcinoma is an important worldwide health problem. Both cystoscopy and urine cytology used in detecting bladder cancer suffer from drawbacks where cystoscopy is an invasive method and urine cytology shows low sensitivity in low grade tumors. This study validates easier and less time-consuming techniques to evaluate the value of combined use of angiogenin and clusterin in comparison and combination with voided urine cytology in the detection of bladder cancer patients. This study includes malignant (bladder cancer patients, n= 50), benign (n=20), and healthy (n=20) groups. The studied groups were subjected to cystoscopic examination, detection of bilharzial antibodies, urine cytology, and estimation of urinary angiogenin and clusterin by ELISA. The overall sensitivity and specifcity were 66% and 75% for angiogenin, 70% and 82.5% for clusterin and 46% and 80% for voided urine cytology. Combined sensitivity of angiogenin and clusterin with urine cytology increased from 82 to 88%.

Keywords: angiogenin, bladder cancer, clusterin, cytology

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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: 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: Joselito Medina-Marin, Alexandr Karelin, Ana Tarasenko, Juan Carlos Seck-Tuoh-Mora, Norberto Hernandez-Romero, Eva Selene Hernandez-Gress

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A production line designer faces with several challenges in manufacturing system design. One of them is the assignment of buffer slots in between every machine of the production line in order to maximize the throughput of the whole line, which is known as the Buffer Allocation Problem (BAP). The BAP is a combinatorial problem that depends on the number of machines and the total number of slots to be distributed on the production line. In this paper, we are proposing a Petri Net (PN) Model to obtain the throughput in unreliable production lines, based on PN mathematical tools and the decomposition method. The results obtained by this methodology are similar to those presented in previous works, and the number of machines is not a hard restriction.

Keywords: buffer allocation problem, Petri Nets, throughput, production lines

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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: 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: Khaled Khleifat

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This study detects the bacterial species that responsible for UTI in both diabetic patients and non-diabetic patients, Jordan. 116 urine samples were investigated in order to determine UTI-causing bacteria. These samples distributed unequally between diabetic male (12) and diabetic female (25) and also non-diabetic male (13) and non-diabetic female (66). The results represent that E.coli is responsible for UTI in both diabetic and non-diabetic patients (15.5% and 29.3% respectively) with large proportion (44.8%). This study showed that not all bacterial species that isolated from the non-diabetic sample could be isolated from diabetic samples. E. coli (15.5%), P. aeruginosa (4.3%), K. pneumonia (1.7%), P. mirabilis (2.6%), S. marcescens (0.9%), S. aureus (1.7%), S. pyogenes (1.7%), E. faecalis (0.9%), S. epidermidis (1.7%) and S. saprophyticus (0.9%). But E. aerogenes, E. cloacae, C. freundii, A. baumannii and B. subtilis are five bacterial species that can’t isolate from all diabetic samples. This study shows that for the treatment of UTI in both diabetic and non-diabetic patients, Chloramphenicol (30 μg), Ciprofloxacin (5 μg) and Vancomycin (30 μg) are more favorable than other antibiotics. In the same time, Cephalothin (30μg) is not recommended.

Keywords: urinary tract infections, diabetes mellitus, bacterial species, infections

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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: Nurul Liyana Abdul Aziz, Nur Aidya Hanum Aizam

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Course timetabling problems occur every semester in a university which includes the allocation of resources (subjects, lecturers and students) to a number of fixed rooms and timeslots. The assignment is carried out in a way such that there are no conflicts within rooms, students and lecturers, as well as fulfilling a range of constraints. The constraints consist of rules and policies set up by the universities as well as lecturers’ and students’ preferences of courses to be allocated in specific timeslots. This paper specifically focuses on the preferences of the course timetabling problem in one of the public universities in Malaysia. The demands will be considered into our existing mathematical model to make it more generalized and can be used widely. We have distributed questionnaires to a number of lecturers and students of the university to investigate their demands and preferences for their desired course timetable. We classify the preferences thus converting them to construct one mathematical model that can produce such timetable.

Keywords: university course timetabling problem, integer programming, preferences, constraints

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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: 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: Preethy Mary Donald, Renjith George Pallivathukal

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Dens evaginatus (DE) is a rare developmental anomaly characterized by a slender enamel-covered tubercle which projects from the occlusal surface of an otherwise normal premolar. DE can often interfere normal occlusion and can lead to complications like sensitivity, pulpal exposure and temporo mandibular joint problems. The orthopantomographs (OPGs) and dental records of patients under the age of 20 who attended the faculty of dentistry, Melaka-Manipal Medical College were examined for DE. Results: The prevalence of DE was 23% among the study group. Males presented with a higher prevalence of 67% and females with 33%. The prevalence of Dens evaginatus was distributed as 28% in maxillary central incisor, 52% in maxillary lateral incisors, 12% in mandibular second premolars. Prevalence in permanent dentitions appeared to be higher than deciduous dentition. The bilateral occurrence of Dens evaginatus is an interesting phenomenon. 57% of the cases of the DE were bilateral.

Keywords: deciduous dentition, dens evaginatus, permanent dentition, prevalence

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Authors: Mohammad Reza Ebrahimi, Behnaz Mahdaviani

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Increasing renewable sources such photovoltaic are the reason of environmental pollution. Because photovoltaic generates power in low voltage, first, generated power should increase. Usually, distributed generation injects their power to AC-Grid, hence after voltage increasing an inverter is needed to convert DC power to AC power. This results in utilization two series converter that grows cost, complexity, and low efficiency. In this paper a single stage inverter is utilized to boost and invert in one stage. Control of this scheme is easier, and its initial cost decreases comparing to conventional double stage inverters. A simple control scheme is used to control active power as well as minimum total harmonic distortion (THD) in injected current. Simulations in MATLAB demonstrate better outputs comparing with conventional approaches.

Keywords: maximum power point tracking, boost inverter, control strategy, three phase inverter

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Authors: Zuliziana Suif, Nordila Ahmad, Sengheng Hul

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

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

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Authors: E. Krishna

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An Extended form of exponential distribution using Marshall and Olkin method is introduced.The location scale family of these distributions is considered. For location scale free family, exact expressions for single and product moments of upper record statistics are derived. The mean, variance and covariance of record values are computed for various values of the shape parameter. Using these the BLUE's of location and scale parameters are derived.The variances and covariance of estimates are obtained.Through Monte Carlo simulation the condence intervals for location and scale parameters are constructed.The Best liner unbiased Predictor (BLUP) of future records are also discussed.

Keywords: BLUE, BLUP, condence interval, Marshall-Olkin distribution, Monte Carlo simulation, prediction of future records, record statistics

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Authors: Ching-Yu Lin

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The number of patients with aphasia has been gradually increasing; however, public awareness of aphasia is still limited. Moreover, surveys about public awareness of aphasia have been conducted in several countries, but there is no research about public awareness of aphasia in Taiwan so far. Therefore, this study aims at the investigation of public awareness of aphasia in Taiwan. In this pilot study, the original English-version questionnaire will be translated into Mandarin Chinese by a speech therapist (the author), and 100 Taiwanese over 18 years old will be recruited to finish the questionnaire. People with an occupation about health or medical will be excluded. In order to reach more people, the questionnaire will be an Internet survey by Google Forms, and the URL of the survey will be distributed by messaging, i.e. e-mail, Facebook Messenger, Instagram DM, or Line. Data will be analyzed via PASW Statistic 18. Descriptive statistics will be used to summarize what proportion of the public have heard of aphasia and what proportion of the public have basic knowledge of aphasia in Taiwan. The sources of information about aphasia will also be investigated. Further, differences in awareness of aphasia due to age, gender, and education level will be discussed.

Keywords: aphasia, public awareness, public knowledge, taiwan

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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: S. Levitsky, R. Bergman

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Fluid rheology may have essential impact on sound propagation in a liquid-filled pipe, especially, in a low frequency range. Rheological parameters of liquid are temperature-sensitive, which ultimately results in a temperature dependence of the wave speed and attenuation in the waveguide. The study is devoted to modeling of this effect at sound propagation in an elastic pipe with polymeric liquid, described by generalized Maxwell model with non-zero high-frequency viscosity. It is assumed that relaxation spectrum is distributed according to the Spriggs law; temperature impact on the liquid rheology is described on the basis of the temperature-superposition principle and activation theory. The dispersion equation for the waveguide, considered as a thin-walled tube with polymeric solution, is obtained within a quasi-one-dimensional formulation. Results of the study illustrate the influence of temperature on sound propagation in the system.

Keywords: elastic tube, sound propagation, temperature effect, viscoelastic liquid

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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: Gansukh Erdenedelger, Doljinsuren Sukhbaatar, Trung Dung Dao, Byeong-Kyu Lee, Han Mo Jeong

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Two kinds of carboxylated thermally reduced graphenes (C-TRGs) having different lateral sizes are examined as a Pickering stabilizer in the suspension polymerization of methyl methacrylate. The size and the shape of the prepared composite particles are irregular due to agglomeration, more evidently when the larger C-TRG is used. In addition, C-TRG is distributed not only on the surface but also inside the composite particles. It indicates that the C-TRG alone is not a stable Pickering agent. However, a very small dosage of acrylic acid remedies all these issues, because acrylic acid interacts with C-TRG and synergizes the stabilizing effect. The compression molded composite of the core/shell poly(methyl methacrylate)/C-TRG particles exhibits a very low percolation threshold of electrical conductivity of 0.03 vol%. It demonstrates that the C-TRG shells of the composite particles effectively form a segregated conductive network throughout the composite.

Keywords: pickering, graphene, polymerization, PMMA

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Authors: Romisaa Ali

Abstract:

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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