Search results for: forecast methods

Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: Olivia Rybak-Karkosz

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This paper aims to present the results of scientific research on methods of forging art prints and their elements, such as signature or provenance and forensic science methods that might be used to verify their authenticity. In the last decades, the art market has observed significant interest in purchasing prints. They are considered an economical alternative to paintings and a considerable investment. However, the authenticity of an art print is difficult to establish as similar visual effects might be achieved with drawings or xerox. The latter is easy to make using a home printer. They are then offered on flea markets or internet auctions as genuine prints. This probable ease of forgery and, at the same time, the difficulty of distinguishing art print techniques were the main reasons why this research was undertaken. A lack of scientific methods dedicated to disclosing a forgery encouraged the author to verify the possibility of using forensic science's methods known and used in other fields of expertise. This research methodology consisted of completing representative forgery samples collected in selected museums based in Poland and a few in Germany and Austria. That allowed the author to present a typology of methods used to forge art prints. Given that one of the most famous graphic design examples is bills and securities, it seems only appropriate to propose in print verification the usage of methods of detecting counterfeit currency. These methods contain an examination of ink, paper, and watermarks. On prints, additionally, signatures and imprints of stamps, etc., are forged as well. So the examination should be completed with handwriting examination and forensic sphragistics. The paper contains a stipulation to conduct a complex analysis of authenticity with the participation of an art restorer, art historian, and forensic expert as head of this team.

Keywords: art forgery, examination of an artwork, handwriting analysis, prints

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Authors: Won Kyung Ham, Kang Hoon Cho, Sang C. Park

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Presented in this paper is a framework of a software ‘WEMax’. The WEMax is invented for analysis and simulation for manned assembly lines to sustain and improve performance of manufacturing systems. In a manufacturing system, performance, such as productivity, is a key of competitiveness for output products. However, the manned assembly lines are difficult to forecast performance, because human labors are not expectable factors by computer simulation models or mathematical models. Existing approaches to performance forecasting of the manned assembly lines are limited to matters of the human itself, such as ergonomic and workload design, and non-human-factor-relevant simulation. Consequently, an approach for the forecasting and improvement of manned assembly line performance is needed to research. As a solution of the current problem, this study proposes a framework that is for generation and simulation of virtual manned assembly lines, and the framework has been implemented as a software.

Keywords: performance forecasting, simulation, virtual manned assembly line, WEMax

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Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

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Authors: Y. A. Yahaya, Ahmad Tijjani Asabe

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This paper focuses on the formulation of 3-step hybrid Adams type method for the solution of first order differential equation (ODE). The methods which was derived on both grid and off grid points using multistep collocation schemes and also evaluated at some points to produced Block Adams type method and Adams moulton method respectively. The method with the highest order was selected to serve as the corrector. The convergence was valid and efficient. The numerical experiments were carried out and reveal that hybrid Adams type methods performed better than the conventional Adams moulton method.

Keywords: adam-moulton type (amt), corrector method, off-grid, block method, convergence analysis

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Saheed A. Gbadegeshin

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Commercialization method is a means to make inventions available at the market for final consumption. It is described as an important tool for keeping business enterprises sustainable and improving national economic growth. Thus, there are several scholarly publications on it, either presenting or testing different methods for commercialization. However, young entrepreneurs, technologists and scientists would like to know the best method to commercialize their innovations. Then, this question arises: What is the best commercialization method? To answer the question, a systematic literature review was conducted, and practitioners were interviewed. The literary results revealed that there are many methods but new methods are needed to improve commercialization especially during these times of economic crisis and political uncertainty. Similarly, the empirical results showed there are several methods, but the best method is the one that reduces costs, reduces the risks associated with uncertainty, and improves customer participation and acceptability. Therefore, it was concluded that new commercialization method is essential for today's high technologies and a method was presented.

Keywords: commercialization method, technology, knowledge, intellectual property, innovation, invention

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Authors: Hamzat Shittu Atunnise

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This study on Assessment of the Implementation of Recommended Teaching and Evaluation Methods of the Nigeria Certificate in Education (NCE) Arabic Language Curriculum in Colleges of Education in North Western Nigeria was conducted with four objectives, four research questions and four null hypotheses. Descriptive survey design was used and the multistage sampling procedure adopted. Frequency count and percentage were used to answer research questions and chi-square was used to test all the null hypotheses at an Alpha 0.05 level of significance. Two hundred and ninety one subjects were drawn as sample. Questionnaires were used for data collection. The Context, Input, Process and Product (CIPP) model of evaluation was employed. The study findings indicated that: there were no significant difference in the perceptions of lecturers and students from Federal and State Colleges of Education on the following: extent of which lecturers employ appropriate methods in teaching the language and extent of which recommended evaluation methods are utilized for the implementation of Arabic Curriculum. Based on these findings, it was recommended among other things that: lecturers should adopt teaching methodologies that promote interactive learning; Governments should ensure that information and communication technology facilities are made available and usable in all Colleges of Education; Lecturers should vary their evaluation methods because other methods of evaluation can meet and surpass the level of learning and understanding which essay type questions are believed to create and that language labs should be used in teaching Arabic in Colleges of Education because comprehensive language learning is possible through both classroom and language lab teaching.

Keywords: assessment, arabic language, curriculum, methods of teaching, evaluation methods, NCE

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Authors: Sofia Mylona, Dale Hume

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The main scope of this work is to compare the experimental thermal conductivity results of fluids between devices using transient techniques. A range of different liquids within a range of viscosities was measured with two or more devices, and the results were compared between the different methods and the reference equations wherever it was available. The liquids selected are the most commonly used in academic or industrial laboratories to calibrate their thermal conductivity instruments having a variety of thermal conductivity, viscosity, and density. Three transient methods (Transient Hot Wire, Transient Plane Source, and Transient Line Source) were compared for the thermal conductivity measurements taken by using them. These methods have been chosen as the most accurate and because they all follow the same idea; as a function of the logarithm of time, the thermal conductivity is calculated from the slope of a plot of sensor temperature rise. For all measurements, the selected temperature range was at the atmospheric level from 10 to 40 ° C. Our results are coming with an agreement with the objections of several scientists over the reliability of the results of a few popular devices. The observation was surprising that the device used in many laboratories for fast measurements of liquid thermal conductivity display deviations of 500 percent which can be very poorly reproduced.

Keywords: accurate data, liquids, thermal conductivity, transient methods.

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Authors: Marjan Heidarzadeh

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High quality and purity of DNA isolated from canned tuna is essential for species identification. In this study, the efficiency of five different methods for DNA extraction was compared. Method of national standard in Iran, the CTAB precipitation method, Wizard DNA Clean Up system, Nucleospin and GenomicPrep were employed. DNA was extracted from two different canned tuna in brine and oil of the same tuna species. Three samples of each type of product were analyzed with the different methods. The quantity and quality of DNA extracted was evaluated using the 260 nm absorbance and ratio A260/A280 by spectrophotometer picodrop. Results showed that the DNA extraction from canned tuna preserved in different liquid media could be optimized by employing a specific DNA extraction method in each case. Best results were obtained with CTAB method for canned tuna in oil and with Wizard method for canned tuna in brine.

Keywords: canned tuna PCR, DNA, DNA extraction methods, species identification

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Authors: Md. Nazmul Haque, Mahinur Rahman

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The objective of the research is to determine the quantity of household waste generated and forecast the future condition of Ward No 24 (Nirala). For performing that, three core issues are focused: (i) the capacity and service area of the dumping stations; (ii) the present waste generation amount per capita per day; (iii) the responsibility of the local authority in the household waste collection. This research relied on field survey-based data collection from all stakeholders and GIS-based secondary analysis of waste collection points and their coverage. However, these studies are mostly based on the inherent forecasting approaches, cannot predict the amount of waste correctly. The findings of this study suggest that Nirala is a formal residential area introducing a better approach to the waste collection - self-controlled and collection system. Here, a forecasting model proposed for waste generation as Y = -2250387 + 1146.1 * X, where X = year.

Keywords: eco-friendly environment, household waste, linear regression, waste management

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Authors: Loreta Axhami

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Media literacy in the digital age emerges not only as a set of skills to generate true knowledge and information but also as a pedagogy methodology, as a kind of educational philosophy. In addition to such innovations as information integration and communication technologies, media infrastructures, and web usage in the educational system, media literacy enables the change in the learning methods, pedagogy, teaching programs, and school curriculum itself. In this framework, this study focuses on ICT's impact on teaching and learning methods and the degree they are reflected in the Albanian education system. The study is based on a combination of quantitative and qualitative methods of scientific research. Referring to the study findings, it results that student’s limited access to the internet in school, focus on the hardcopy textbooks and the role of the teacher as the only or main source of knowledge and information are some of the main factors contributing to the implementation of authoritarian pedagogical methods in the Albanian education system. In these circumstances, the implementation of media literacy is recommended as an apt educational process for the 21st century, which requires a reconceptualization of textbooks as well as the application of modern teaching and learning methods by integrating information and communication technologies.

Keywords: authoritarian pedagogic model, education system, ICT, media literacy

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Authors: Emhimed Saffor

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In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications.

Keywords: CT images, Matlab, medical images, edge detection

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Authors: Yuri B. Cesarino, Vinícius R. Domingues, Darym J. F. Campos

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Developing countries have some restrains towards the adoption of new technologies and construction methods. Some nations, such as Brazil, still use conventional construction methodologies, knowing its lesser cost-effectiveness. This research has been conducted to demonstrate how industrialized construction methods should be implemented in Brazil, especially in times of need. Using the common sense among different authors with different perspectives, it is clear that the second method is more suitable for construction development because of its great advantages. However, it is unlikely for this process to be adopted in the country as a result of several social-economic restraints. Nonetheless, Brazilian engineers have a major challenge ahead of them, and it will take more than creativity to solve such an issue.

Keywords: Brazilian engineers, construction methods, industrialized construction, infrastructure

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Authors: Fereshteh Ahmadi, Önver Cetrez, Said Zandi, Sharareh Akhavan

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In this paper, we have mapped the coping methods used to address the coronavirus pandemic by members of the academic community. We conducted an anonymous survey of a convenient sample of 674 faculty/staff members and students from September to December 2020. A modified version of the RCOPE scale was used for data collection. The results indicate that both religious and existential coping methods were used by respondents. The study also indicates that even though 71% of in-formants believed in God or another religious figure, 61% reported that they had tried to gain control of the situation directly without the help of God or another religious figure. The ranking of the coping strategies used indicates that the first five methods used by informants were all non-religious coping methods (i.e., secular existential coping methods): regarding life as a part of a greater whole, regarding nature as an important resource, listening to the sound of surrounding nature, being alone and con-templating, and walking/engaging in any activities outdoors giving a spiritual feeling. Our results contribute to the new area of research on academic community’s coping with pandemic-related stress and challenges.

Keywords: academic staff, academics, coping strategies, coronavirus epidemic, higher education.

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Authors: Chih-Hsiang Chang, Pei-Shan Kao

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This paper discusses the reaction of investors in the Taiwan stock market to the most probable unknown earnings-related information and the most probable known earnings-related information. As compared with the previous literature regarding the effect of an official announcement of earnings forecast revision, this paper further analyzes investors’ cognitive bias toward the unknown and known earnings-related information, and the role of media during the investors' reactions to the foresaid information shocks. The empirical results show that both the unknown and known earnings-related information provides useful information content for a stock market. In addition, cognitive bias and disposition effect are the behavioral pitfalls that commonly occur in the process of the investors' reactions to the earnings-related information. Finally, media coverage has a remarkable influence upon the investors' trading decisions.

Keywords: cognitive bias, role of media, disposition effect, earnings-related information, behavioral pitfall

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Authors: Nadia Nisar

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Background: In the temperate regions Influenza activities occur sporadically all year round with peaks coinciding during cold months. Meteorological and environmental conditions play significant role in the transmission of influenza globally. In this study, we assessed the relationship between meteorological parameters and influenza activity in two geographical areas of Pakistan. Methods: Influenza data were collected from Islamabad (north) and Multan (south) regions of national influenza surveillance system during 2010-2015. Meteorological database was obtained from National Climatic Data Center (Pakistan). Logistic regression model with a stepwise approach was used to explore the relationship between meteorological parameters with influenza peaks. In statistical model, we used the weekly proportion of laboratory-confirmed influenza positive samples to represent Influenza activity with metrological parameters as the covariates (temperature, humidity and precipitation). We also evaluate the link between environmental conditions associated with seasonal influenza epidemics: 'cold-dry' and 'humid-rainy'. Results: We found that temperature and humidity was positively associated with influenza in north and south both locations (OR = 0.927 (0.88-0.97)) & (OR = 0.1.078 (1.027-1.132)) and (OR = 1.023 (1.008-1.037)) & (OR = 0.978 (0.964-0.992)) respectively, whilst precipitation was negatively associated with influenza (OR = 1.054 (1.039-1.070)) & (OR = 0.949 (0.935-0.963)). In both regions, temperature and humidity had the highest contribution to the model as compared to the precipitation. We revealed that the p-value for all of climate parameters is <0.05 by Independent-sample t-test. These results demonstrate that there were significant relationships between climate factors and influenza infection with correlation coefficients: 0.52-0.90. The total contribution of these three climatic variables accounted for 89.04%. The reported number of influenza cases increased sharply during the cold-dry season (i.e., winter) when humidity and temperature are at minimal levels. Conclusion: Our findings showed that measures of temperature, humidity and cold-dry season (winter) can be used as indicators to forecast influenza infections. Therefore integrating meteorological parameters for influenza forecasting in the surveillance system may benefit the public health efforts in reducing the burden of seasonal influenza. More studies are necessary to understand the role of these parameters in the viral transmission and host susceptibility process.

Keywords: influenza, climate, metrological, environmental

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Authors: Panudet Saengseedam, Nanthachai Kantanantha

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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.

Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

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Authors: Ahmed Kadry, Gwyn Dodd

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There has been a growing call globally for police forces to embed evidence-based policing research methods into police interventions in order to better understand and evaluate their impact. This research study highlights the success and challenges that police forces may encounter when trying to embed evidence-based research methods within their organisation. 10 in-depth qualitative interviews were conducted with police officers and staff at Greater Manchester Police (GMP) who were tasked with integrating evidence-based research methods into their operational interventions. The findings of the study indicate that with adequate resources and individual expertise, evidence-based research methods can be applied to operational work, including the testing of initiatives with strict controls in order to fully evaluate the impact of an intervention. However, the findings also indicate that this may only be possible where an operational intervention is heavily resourced with police officers and staff who have a strong understanding of evidence-based policing research methods, attained for example through their own graduate studies. In addition, the findings reveal that ample planning time was needed to trial operational interventions that would require strict parameters for what would be tested and how it would be evaluated. In contrast, interviewees underscored that operational interventions with the need for a speedy implementation were less likely to have evidence-based research methods applied. The study contributes to the wider literature on evidence-based policing by providing considerations for police forces globally wishing to apply evidence-based research methods to more of their operational work in order to understand their impact. The study also provides considerations for academics who work closely with police forces in assisting them to embed evidence-based policing. This includes how academics can provide their expertise to police decision makers wanting to underpin their work through evidence-based research methods, such as providing guidance on how to evaluate the impact of their work with varying research methods that they may otherwise be unaware of.  

Keywords: evidence based policing, evidence-based practice, operational policing, organisational change

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

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Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Ahmad Aslizadeh, Farid Ghaderi

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Mapping organizational knowledge is an innovative concept and useful instrument of representation, capturing and visualization of implicit and explicit knowledge. There are a diversity of methods, instruments and techniques presented by different researchers following mapping organizational knowledge to reach determined goals. Implicating of these methods, it is necessary to know their exigencies and conditions in which those can be used. Integrating identified methods of knowledge mapping and comparing them would help knowledge managers to select the appropriate methods. This research conducted to presenting a model and framework to map organizational knowledge. At first, knowledge maps, their applications and necessity are introduced because of extracting comparative framework and detection of their structure. At the next step techniques of researchers such as Eppler, Kim, Egbu, Tandukar and Ebner as knowledge mapping models are presented and surveyed. Finally, they compare and a superior model would be introduced.

Keywords: knowledge mapping, knowledge management, comparative study, business and management

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Authors: Nadaniela Egidi, Pierluigi Maponi

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The approximate solution of a time-harmonic electromagnetic scattering problem for inhomogeneous media is required in several application contexts, and its two-dimensional formulation is a Fredholm integral equation of the second kind. This integral equation provides a formulation for the direct scattering problem, but it has to be solved several times also in the numerical solution of the corresponding inverse scattering problem. The discretization of this Fredholm equation produces large and dense linear systems that are usually solved by iterative methods. In order to improve the efficiency of these iterative methods, we use the Symmetric SOR preconditioning, and we propose an algorithm for the evaluation of the associated relaxation parameter. We show the efficiency of the proposed algorithm by several numerical experiments, where we use two Krylov subspace methods, i.e., Bi-CGSTAB and GMRES.

Keywords: Fredholm integral equation, iterative method, preconditioning, scattering problem

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Authors: Assem M. F. Sallam, Ah. El-S. Makled

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In this paper, there is an implementation, verification, and graphical demonstration of a software application, which can be used swiftly over different preliminary orbit determination methods. A passive orbit determination method is used in this study to determine the location of a satellite or a flying body. It is named a passive orbit determination because it depends on observation without the use of any aids (radio and laser) installed on satellite. In order to understand how these methods work and how their output is accurate when compared with available verification data, the built models help in knowing the different inputs used with each method. Output from the different orbit determination methods (Gibbs, Lambert, and Gauss) will be compared with each other and verified by the data obtained from Satellite Tool Kit (STK) application. A modified model including all of the orbit determination methods using the same input will be introduced to investigate different models output (orbital parameters) for the same input (azimuth, elevation, and time). Simulation software is implemented using MATLAB. A Graphical User Interface (GUI) application named OrDet is produced using the GUI of MATLAB. It includes all the available used inputs and it outputs the current Classical Orbital Elements (COE) of satellite under observation. Produced COE are then used to propagate for a complete revolution and plotted on a 3-D view. Modified model which uses an adapter to allow same input parameters, passes these parameters to the preliminary orbit determination methods under study. Result from all orbit determination methods yield exactly the same COE output, which shows the equality of concept in determination of satellite’s location, but with different numerical methods.

Keywords: orbit determination, STK, Matlab-GUI, satellite tracking

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Authors: M. Barański

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This paper presents a several diagnostic methods designed to electrical machines especially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives. Those methods are preferred by the author in periodic diagnostic of electrical machines. The special attention should be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methods were created in Institute of Electrical Drives and Machines Komel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to-traction drive, turn insulation, vibrations

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Authors: Sumachaya Harnsukworapanich, Tetsuo Ichimori

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The apportionment method is used by many countries, to calculate the distribution of seats in political bodies. For example, this method is used in the United States (U.S.) to distribute house seats proportionally based on the population of the electoral district. Famous apportionment methods include the divisor methods called the Adams Method, Dean Method, Hill Method, Jefferson Method and Webster Method. Sometimes the results from the implementation of these divisor methods are unfair and include errors. Therefore, it is important to examine the optimization of this method by using a bias measurement to figure out precise and fair results. In this research we investigate the bias of divisor methods in the U.S. Houses of Representatives toward large and small states by applying the Stolarsky Mean Method. We compare the bias of the apportionment method by using two famous bias measurements: The Balinski and Young measurement and the Ernst measurement. Both measurements have a formula for large and small states. The Third measurement however, which was created by the researchers, did not factor in the element of large and small states into the formula. All three measurements are compared and the results show that our measurement produces similar results to the other two famous measurements.

Keywords: apportionment, bias, divisor, fair, measurement

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Authors: Selin Guney, Andres Riquelme

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The traditional bio-economic method for fisheries modeling uses some estimate of the growth parameters and the system carrying capacity from a biological model for the population dynamics (usually a logistic population growth model) which is then analyzed as a traditional production function. The stock dynamic is transformed into a revenue function and then compared with the extraction costs to estimate the maximum economic yield. In this paper, the logistic population growth model for the population is combined with a forecast of the abundance and location of the stock by using a generalized additive model approach. The paper focuses on the Chilean hake population. This method allows for the incorporation of climatic variables and the interaction with other marine species, which in turn will increase the reliability of the estimates and generate better extraction paths for different conservation objectives, such as the maximum biological yield or the maximum economic yield.

Keywords: bio-economic, fisheries, GAM, production

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Authors: Uliana N. Tumanova, Viacheslav M. Lyapin, Vladimir G. Bychenko, Alexandr I. Shchegolev, Gennady T. Sukhikh

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All cases of stillbirth undoubtedly subject to postmortem examination, since it is necessary to find out the cause of the stillbirths, as well as a forecast of future pregnancies and their outcomes. Determination of the time of death is an important issue which is addressed during the examination of the body of a stillborn. It is mean the period from the time of death until the birth of the fetus. The time for fetal deaths determination is based on the assessment of the severity of the processes of maceration. To study the possibilities of postmortem magnetic resonance imaging (MRI) for determining the time of intrauterine fetal death based on the evaluation of maceration in the kidney. We have conducted MRI morphological comparisons of 7 dead fetuses (18-21 gestational weeks) and 26 stillbirths (22-39 gestational weeks), and 15 bodies of died newborns at the age of 2 hours – 36 days. Postmortem MRI 3T was performed before the autopsy. The signal intensity of the kidney tissue (SIK), pleural fluid (SIF), external air (SIA) was determined on T1-WI and T2-WI. Macroscopic and histological signs of maceration severity and time of death were evaluated in the autopsy. Based on the results of the morphological study, the degree of maceration varied from 0 to 4. In 13 cases, the time of intrauterine death was up to 6 hours, in 2 cases - 6-12 hours, in 4 -12-24 hours, in 9 -2-3 days, in 3 -1 week, in 2 -1,5-2 weeks. At 15 dead newborns, signs of maceration were absent, naturally. Based on the data from SIK, SIF, SIA on MR-tomograms, we calculated the coefficient of MR-maceration (M). The calculation of the time of intrauterine death (MP-t) (hours) was performed by our formula: МR-t = 16,87+95,38×М²-75,32×М. A direct positive correlation of MR-t and autopsy data from the dead at the gestational ages 22-40 weeks, with a dead time, not more than 1 week, was received. The maceration at the antenatal fetal death is characterized by changes in T1-WI and T2-WI signals at postmortem MRI. The calculation of MP-t allows defining accurately the time of intrauterine death within one week at the stillbirths who died on 22-40 gestational weeks. Thus, our study convincingly demonstrates that radiological methods can be used for postmortem study of the bodies, in particular, the bodies of stillborn to determine the time of intrauterine death. Postmortem MRI allows for an objective and sufficiently accurate analysis of pathological processes with the possibility of their documentation, storage, and analysis after the burial of the body.

Keywords: intrauterine death, maceration, postmortem MRI, stillborn

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Authors: W. H. Wan Zakiyatussariroh, A. A. Nasuhar, W. Y. Wan Fairos, Z. A. Nazatul Shahreen

Abstract:

Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation.

Keywords: time series modeling, Box-Jenkins, SARIMA, forecasting

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