Search results for: risk classification

Authors: Boualem El Kechebour, Djilali Benouar

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The objective of this work is to present a expertise on flooding hazard analysis and how to reduce the risk. The analysis concerns the disaster induced by the flood on November 10/11, 2001 in the Bab El Oued district of the city of Algiers.The study begins by an expertise of damages in related with the urban environment and the history of the urban growth of the site. After this phase, the work is focalized on the identification of the existing correlations between the development of the town and its vulnerability. The final step consists to elaborate the interpretations on the interactions between the urban growth, the sewerage network and the vulnerability of the urban system.In conclusion, several recommendations are formulated permitting the mitigation of the risk in the future. The principal recommendations concern the new urban operations and the existing urbanized sites.

Keywords: urban growth, sewerage network, vulnerability of town, flooding risk, mitigation

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Authors: A. Deswal, N. S. Deora, H. N. Mishra

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The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyze spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), Discriminant Factorial Analysis (DFA) and Soft Independent Modelling by Class Analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable countsshowed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20hrs and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Keywords: Electronic-nose, bacteriological, shelf-life, classification.

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Authors: P. Soda, G.Iannello

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In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.

Keywords: Artificial neural networks, computer aided diagnosis, image classification, indirect immuno-fluorescence, pattern recognition.

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Authors: Nora A. Naguib, Ahmed E. Hussein, Hesham A. Keshk, Mohamed I. El-Adawy

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Classification of video sequences based on their contents is a vital process for adaptation techniques. It helps decide which adaptation technique best fits the resource reduction requested by the client. In this paper we used the principal feature analysis algorithm to select a reduced subset of video features. The main idea is to select only one feature from each class based on the similarities between the features within that class. Our results showed that using this feature reduction technique the source video features can be completely omitted from future classification of video sequences.

Keywords: Adaptation, feature selection, H.264, Principal Feature Analysis (PFA)

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Authors: Nebi Gedik

Abstract:

Heart disease threatens the lives of a great number of people every year around the world. Heart issues lead to many of all deaths; therefore, early diagnosis and treatment are critical. The diagnosis of heart disease is complicated due to several factors affecting health such as high blood pressure, raised cholesterol, an irregular pulse rhythm, and more. Artificial intelligence has the potential to assist in the early detection and treatment of diseases. Improving heart failure prediction is one of the primary goals of research on heart disease risk assessment. This study aims to determine the features that provide the most successful classification prediction in detecting cardiovascular disease. The performances of each feature are compared using the K-Nearest Neighbor machine learning method. The feature that gives the most successful performance has been identified.

Keywords: Cardiovascular disease, feature extraction, supervised learning, k-NN.

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Authors: Larbi Omar, Elberrichi Zakaria

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Many research works are carried out on the analysis of traces in a digital learning environment. These studies produce large volumes of usage tracks from the various actions performed by a user. However, to exploit these data, compare and improve performance, several issues are raised. To remedy this, several works deal with this problem seen recently. This research studied a series of questions about format and description of the data to be shared. Our goal is to share thoughts on these issues by presenting our experience in the analysis of trace-based log files, comparing several approaches used in automatic classification applied to e-learning platforms. Finally, the obtained results are discussed.

Keywords: Classification, , e-learning platform, log file, Trace.

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Authors: Kexin Jia, Youxin Lu

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To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).

Keywords: Modulation classification, combined classifier, fuzzy neural network, interclass distance.

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Authors: Serge Provost, Yishan Zang

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Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.

Keywords: Arctangent transformation, change of variables, heavy-tailed distributions, tail classification.

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Authors: Melek Kalkan, Hatice Odacı, Hatice Epli Koç

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The aim of this study is to compare the innovativeness, risk taking, and focusing on opportunity of the nurse managers and nurses. The data are collected from nurse managers and nurses in Ondokuz Mayıs University, Faculty of Medicine Hospital and Karadeniz Technical University, Faculty of Medicine Hospital. The study sample consisted of 151 participants, 76 nurse managers (50.3%) and 75 nurses (49.7%). All participants have been assessed by Participant Information Form and Corporate Entrepreneurship Scale. In data analysis, independent t-test has applied. The results show that there are significant differences between nurse managers and nurses on innovativeness (t = 2.42, p < 0.05), risk taking (t = 3.62, p < 0.01), and focusing on opportunity (t = 2.16, p < 0.05). Consequently, it can be said that nurse managers have more innovativeness than nurses and tend to take more risks and focus more on opportunities. 

Keywords: Focusing on opportunity attitudes, innovativeness, risk taking, nurse.

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Authors: W.S. Lim, M.V.C. Rao

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In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of both networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experiences higher plasticity than PNN. An enhanced version called parallel MRAN (pMRAN) is proposed to solve this problem and is proven to be stable in prediction and also outperformed the original MRAN.

Keywords: Ultrasonic sensing, target classification, minimalresource allocation network (MRAN), probabilistic neural network(PNN), stability-plasticity dilemma.

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Authors: Kh. Ashrafi, Gh. A. Hoshyaripour

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Atmospheric stability plays the most important role in the transport and dispersion of air pollutants. Different methods are used for stability determination with varying degrees of complexity. Most of these methods are based on the relative magnitude of convective and mechanical turbulence in atmospheric motions. Richardson number, Monin-Obukhov length, Pasquill-Gifford stability classification and Pasquill–Turner stability classification, are the most common parameters and methods. The Pasquill–Turner Method (PTM), which is employed in this study, makes use of observations of wind speed, insolation and the time of day to classify atmospheric stability with distinguishable indices. In this study, a model is presented to determination of atmospheric stability conditions using PTM. As a case study, meteorological data of Mehrabad station in Tehran from 2000 to 2005 is applied to model. Here, three different categories are considered to deduce the pattern of stability conditions. First, the total pattern of stability classification is obtained and results show that atmosphere is 38.77%, 27.26%, 33.97%, at stable, neutral and unstable condition, respectively. It is also observed that days are mostly unstable (66.50%) while nights are mostly stable (72.55%). Second, monthly and seasonal patterns are derived and results indicate that relative frequency of stable conditions decrease during January to June and increase during June to December, while results for unstable conditions are exactly in opposite manner. Autumn is the most stable season with relative frequency of 50.69% for stable condition, whilst, it is 42.79%, 34.38% and 27.08% for winter, summer and spring, respectively. Hourly stability pattern is the third category that points out that unstable condition is dominant from approximately 03-15 GTM and 04-12 GTM for warm and cold seasons, respectively. Finally, correlation between atmospheric stability and CO concentration is achieved.

Keywords: Atmospheric stability, Pasquill-Turner classification, convective turbulence, mechanical turbulence, Tehran.

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Authors: Bello Nurudeen Akinsola

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The study analyzed the risk and returns of commercial-property in Southwestern Nigeria and selected stocksmarket investment between 2000 and 2009; compared the inflation hedging characteristics and diversification potentials of investing in commercial-property and selected stock- market investment. Primary data were collected on characteristics, rental and capital values of commercial- properties from their property managers through the use of questionnaire. Secondary data on stock prices and dividends on banking, insurance and conglomerates sectors were sourced from the Nigerian Stock Exchange (2000-2009). The result showed that average return on all the selected stock- investments was higher than that of commercial-property. As regards risk, commercial-property indicated lower risk, compared to stocks. Also the stock-investment had better inflation hedging capacity than commercial-properties; combination of both had diversification potentials. The study concluded that stock-market investment offered attractive higher return than commercial-property although with higher risk and there could be diversification benefits in combining commercial-property with stock- investment.

Keywords: Commercial-Property, Return, Risk, Stock Market

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Authors: Todor Bačkalić, Marinko Maslarić, Milosav Georgijević, Sanja Bojić

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Presented article outlines a rationale, why it is necessary to develop competence about infrastructure risk in water transport. Climate changes are evident and require special attention and global monitoring. Current risk assessment methods for Inland waterway transport just consider some dramatic events. We present a new method for the assessment of risk and vulnerability of inland waterway transport where river depth represents a crucial part. The analysis of water level changes in the lower Danube was done for two significant periods (1965-1979 and 1998-2012).

Keywords: Container ship, draught, probability, the Danube.

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Authors: Martina Kanciruk, Jac W. Andrews, Tyrone Donnon

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The purpose of this study was to determine the significance of maternal smoking for the development of childhood overweight and/or obesity. Accordingly, a systematic literature review of English-language studies published from 1980 to 2012 using the following data bases: MEDLINE, PsychINFO, Cochrane Database of Systematic Reviews, and Dissertation Abstracts International was conducted. The following terms were used in the search: pregnancy, overweight, obesity, smoking, parents, childhood, risk factors. Eighteen studies of maternal smoking during pregnancy and obesity conducted in Europe, Asia, North America, and South America met the inclusion criteria. A meta-analysis of these studies indicated that maternal smoking during pregnancy is a significant risk factor for overweight and obesity; mothers who smoke during pregnancy are at a greater risk for developing obesity or overweight; the quantity of cigarettes consumed by the mother during pregnancy influenced the odds of offspring overweight and/or obesity. In addition, the results from moderator analyses suggest that part of the heterogeneity discovered between the studies can be explained by the region of world that the study occurred in and the age of the child at the time of weight assessment.

Keywords: Childhood obesity, overweight, smoking, parents, risk factors.

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Authors: M. Abdullah-Al-Wadud

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Sensor-based Activity Recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian-based classification, Activity recognition, sensor data, object-usage model.

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Authors: G. Murugeswari, A. Suruliandi

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This paper proposes a rotational invariant texture feature based on the roughness property of the image for psoriasis image analysis. In this work, we have applied this feature for image classification and segmentation. The fuzzy concept is employed to overcome the imprecision of roughness. Since the psoriasis lesion is modeled by a rough surface, the feature is extended for calculating the Psoriasis Area Severity Index value. For classification and segmentation, the Nearest Neighbor algorithm is applied. We have obtained promising results for identifying affected lesions by using the roughness index and severity level estimation.

Keywords: Fuzzy texture feature, psoriasis, roughness feature, skin disease.

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Authors: Yi-Shiang Shiu, Meng-Lung Lin, Kang-Tsung Chang, Tzu-How Chu

Abstract:

Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, ideal for mapping paddy rice fields in East Asia. To map rice fields, this study first determined the transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. The unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index and experimented with object-based pre-classification and post-classification. This paper discusses results of different image classification methods in an attempt to find a precise and automatic solution to mapping paddy rice in Taiwan.

Keywords: paddy rice fields; multi-temporal; FORMOSAT-2images, normalized difference vegetation index, object-basedclassification.

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Authors: Hazem J. Smadi

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FMEA has been used for several years and proved its efficiency for system’s risk analysis due to failures. Risk priority number found in FMEA is used to rank failure modes that may occur in a system. There are some guidelines in the literature to assign the values of FMEA components known as Severity, Occurrence and Detection. This paper propose a method to assign the value for occurrence in more realistic manner representing the state of the system under study rather than depending totally on the experience of the analyst. This method uses the hazard function of a system to determine the value of occurrence depending on the behavior of the hazard being constant, increasing or decreasing.

Keywords: FMEA, Hazard Function, Risk Priority Number.

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: Active Contour, Bayesian, Echocardiographic image, Feature vector.

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Authors: Imen Dhaou

Abstract:

This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets.

Keywords: CVaR, Dow Jones Islamic index, GJR-GARCH-EVT-pair copula, portfolio optimization.

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Authors: A. M. H. Elyasir, K. S. M. Anbananthen

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The internet is growing larger and becoming the most popular platform for the people to share their opinion in different interests. We choose the education domain specifically comparing some Malaysian universities against each other. This comparison produces benchmark based on different criteria shared by the online users in various online resources including Twitter, Facebook and web pages. The comparison is accomplished using opinion mining framework to extract, process the unstructured text and classify the result to positive, negative or neutral (polarity). Hence, we divide our framework to three main stages; opinion collection (extraction), unstructured text processing and polarity classification. The extraction stage includes web crawling, HTML parsing, Sentence segmentation for punctuation classification, Part of Speech (POS) tagging, the second stage processes the unstructured text with stemming and stop words removal and finally prepare the raw text for classification using Named Entity Recognition (NER). Last phase is to classify the polarity and present overall result for the comparison among the Malaysian universities. The final result is useful for those who are interested to study in Malaysia, in which our final output declares clear winners based on the public opinions all over the web.

Keywords: Entity Recognition, Education Domain, Opinion Mining, Unstructured Text.

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: Decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle.

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: Sentiment analysis, term weighting scheme, 1/sigma.

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Authors: Paul W. Murray, Marco Barajas

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Recent changes in supply chains, especially globalization and collaboration, have created new risks for enterprises of all sizes. A variety of complex frameworks, often based on enterprise risk management strategies have been presented under the heading of Supply Chain Risk Management (SCRM). The literature on promotes the benefits of a robust SCRM strategy; however, implementing SCRM is difficult and resource demanding for Large Enterprises (LEs), and essentially out of reach for Small & Medium Enterprises (SMEs). This research debunks the idea that SCRM is necessary for all enterprises and instead proposes a simple and effective Vendor Selection Template (VST). Empirical testing and a survey of supply chain practitioners provide a measure of validation to the VST. The resulting VSTis a valuable contribution because is easy to use, provides practical results, and is sufficiently flexible to be universally applied to SMEs.

Keywords: Multiple Regression Analysis, Supply Chain Management, Risk Assessment, Vendor Selection.

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Authors: P. Beraldi, M. E. Bruni

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Data Envelopment Analysis (DEA) is one of the most widely used technique for evaluating the relative efficiency of a set of homogeneous decision making units. Traditionally, it assumes that input and output variables are known in advance, ignoring the critical issue of data uncertainty. In this paper, we deal with the problem of efficiency evaluation under uncertain conditions by adopting the general framework of the stochastic programming. We assume that output parameters are represented by discretely distributed random variables and we propose two different models defined according to a neutral and risk-averse perspective. The models have been validated by considering a real case study concerning the evaluation of the technical efficiency of a sample of individual firms operating in the Italian leather manufacturing industry. Our findings show the validity of the proposed approach as ex-ante evaluation technique by providing the decision maker with useful insights depending on his risk aversion degree.

Keywords: DEA, Stochastic Programming, Ex-ante evaluation technique, Conditional Value at Risk.

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Authors: Cai Guoqiang, Jia Limin, Zhou Liming, Liang yu, Li xi

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This paper analysis the integrated use of safety monitoring with the domestic and international latest research on rail safety protection system, and focus on the implementation of an organic whole system, with the monitoring and early warning, risk assessment, predictive control and emergency rescue system. The system framework, contents and system structure of Security system is proposed completely. It-s pointed out that the Security system is a negative feedback system composed of by safety monitoring and warning system, risk assessment and emergency rescue system. Safety monitoring and warning system focus on the monitoring target monitoring, early warning, tracking, integration of decision-making, for objective and subjective risks factors. Risk assessment system analysis the occurrence of a major Security risk mechanism, determines the standard of the future short, medium and long term safety conditions, and give prop for development of safety indicators, accident analysis and safety standards. Emergency rescue system is with the goal of rapid and effective rescue work for accident, to minimize casualties and property losses.

Keywords: rail safety protection, monitoring and early warning, risk assessment, emergency rescue.

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Authors: Khlystun Yuliia Igorivna

Abstract:

Monumental church paintings are often perceived either as the interior decoration of the temple or as the "Gospel for the illiterate," as the temple painting often contains scenes from Holy Scripture. In science the painting of the Orthodox Church is mainly the subject of study of art critics, but from the point of view of culturology and semiotics, it is insufficiently studied. The symbolism of monumental church painting is insufficiently revealed. The aim of this paper is to give a description of symbolic signs, to classify them, to give examples for each type of sign from the paintings of modern temples of Eastern Ukraine, on the basis of semiotic analysis of iconographic plots used in monumental church painting. We offer own classification of symbols of monumental church painting, using examples from the murals of modern Orthodox churches in Eastern Ukraine, mainly from the Donetsk region. When analyzing the semantics of symbolic signs, the following methods of the culturological approach were used: semiotic, iconological, iconographic, hermeneutic, culturological, descriptive, comparative-historical, visual-analytical. When interpreting the meanings of symbolic signs, scientific, cultural and theological literature were used. Photos taken by the author have been added to the article.

Keywords: Iconography, painting of Orthodox Church, Orthodox Church, semiotic signs in modern iconography, classification of symbols in painting of Orthodox Church.

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Authors: Chan-Sik Kim, Jong-Seong Kim, Se Won Lee, Hoo-Gon Choi

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In rapidly changing market environment, firms are investing a lot of time and resources into new product development (NPD) projects to make profit and to obtain competitive advantage. However, failure rate of NPD projects is becoming high due to various internal and external risks which hinder successful NPD projects. To reduce the failure rate, it is critical that risks have to be managed effectively and efficiently through good strategy, and treated by optimal responses to minimize risk cost. Four strategies are adopted to handle the risks in this study. The optimal responses are characterized by high reduction of risk costs with high efficiency. This study suggests a framework to decide the optimal responses considering the core risks, risk costs, response efficiency and response costs for successful NPD projects. Both binary particles warm optimization (BPSO) and multi-objective particle swarm optimization (MOPSO) methods are mainly used in the framework. Although several limitations exist in use for real industries, the frame work shows good strength for handling the risks with highly scientific ways through an example.

Keywords: NPD projects, risk cost, strategy, optimal responses, Particle Swarm Optimization.

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Authors: Samit Ari, Goutam Saha

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Artificial Neural Network (ANN) has been extensively used for classification of heart sounds for its discriminative training ability and easy implementation. However, it suffers from overparameterization if the number of nodes is not chosen properly. In such cases, when the dataset has redundancy within it, ANN is trained along with this redundant information that results in poor validation. Also a larger network means more computational expense resulting more hardware and time related cost. Therefore, an optimum design of neural network is needed towards real-time detection of pathological patterns, if any from heart sound signal. The aims of this work are to (i) select a set of input features that are effective for identification of heart sound signals and (ii) make certain optimum selection of nodes in the hidden layer for a more effective ANN structure. Here, we present an optimization technique that involves Singular Value Decomposition (SVD) and QR factorization with column pivoting (QRcp) methodology to optimize empirically chosen over-parameterized ANN structure. Input nodes present in ANN structure is optimized by SVD followed by QRcp while only SVD is required to prune undesirable hidden nodes. The result is presented for classifying 12 common pathological cases and normal heart sound.

Keywords: ANN, Classification of heart diseases, murmurs, optimization, Phonocardiogram, QRcp, SVD.

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Authors: Abiodun M. Lawal, Benjamin O. Olley

Abstract:

Long Distance Truck Drivers (LDTDs) have been found to be a high risk group in the spread of HIV/AIDS globally; perhaps, due to their high Sexual Risk Behaviours (SRBs). Interventions for reducing SRBs in trucking population have not been fully exploited. A quasi-experimental control group pretest-posttest design was used to assess the efficacy of psycho-education and behavioural skills training in reducing SRBs among LDTDs. Sixteen drivers rivers were randomly assigned into either experimental or control groups using balloting technique. Questionnaire was used as an instrument for data collection. Repeated measures t-test and independent t-test were used to test hypotheses. Intervention had significant effect on the SRBs among LDTDs at post-test (t{7}= 6.01, p<.01) and at follow up (t{7} = 6.42, p<.01). No significant difference in sexual risk behaviour of LDTDs at post-test and at follow-up stage. Similarly, intervention had significant effects on sexual risk behaviour at post-test (t {14} = - 4.69, p<.05) and at follow-up (t {14} = -9.56, p<.05) respectively. At post-test and follow-up stages, drivers in experimental group reported reduced SRBs than those in control group. Drivers in experimental group reported lower sexual risk behaviour a week after intervention as well as at three months follow-up than those in control group. It is concluded that HIV/AIDS preventive intervention that provides the necessary informational and behavioural skills content can significantly impact long distance truck drivers’ sexual risk behaviours.

Keywords: HIV/AIDS interventions, Long distance truck drivers, Nigeria, Sexual risk behaviours.

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