Search results for: vector borne diseases

Authors: Maryam M. Bashir, Luai A. Ahmed, Meera R. Alshamsi, Sara Almahrooqi, Taif Alyammahi, Shooq A. Alshehhi, Waad I. Alhammadi, Fatima H. Alhammadi, Hind A. Alhosani, Rami H. Al-Rifai, Fatma Al-Maskari

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Obesity in the Middle East and North Africa (MENA) region has exponentially increased over the past five decades due to rapid urbanization and unhealthy lifestyle changes. It has been well established that overweight and obesity increase the risk of non-communicable diseases (NCDs) and are the leading cause of mortality and economic burden locally, and globally. In the United Arab Emirates (UAE), there is a growing epidemic of obesity and other chronic diseases like type 2 diabetes mellitus and cardiovascular diseases. Prevalence of overweight and obesity in UAE range up to 70% depending on the group being studied. Hence, there is a need to explore their patterns in the country for more targeted and responsive interventions. Our study aimed to explore the patterns of overweight and obesity and some self-reported chronic diseases among university students in Abu Dhabi, the capital city of UAE. A validated online self-administered questionnaire was used to collect data from UAE University (UAEU) students, 18years and above, from August to September 2021. Students’ characteristics were summarized using appropriate descriptive statistics. Overweight, obesity and self-reported chronic diseases were described and compared between male and female students using chi-square and t tests. Other associated factors were also explored in relation to overweight and obesity. All analyses were conducted using STATA statistical software version 16.1 (StataCorp LLC, College Station, TX, USA). 902 students participated in the study. 79.8% were females and mean age was 21.90 ± 5.19 years. Majority of the respondents were undergraduate students (80.71%). The prevalence of self-reported chronic diseases was 22.95%. Obesity (BMI≥30kg/m2), Diabetes Mellitus, and Asthma/Allergies were the commonest diseases (12.48%, 4.21% & 3.22%, respectively). Approximately 5% of the students reported more than one chronic disease. Out of the 833 participating students who had complete weight and height data, prevalence of overweight and obesity was 34.81% (22.33% and 12.48%, respectively). More than half of the male students (54.36%) were overweight or obese. This is significantly higher than in female students (30.56%, p=0.001). Overweight/obesity when compared to normal weight is associated with increasing mean age [23.40 vs 21.01, respectively (p=0.001)]. In addition to gender and age, being married [57.63% vs 31.05% (p=0.001)], being a postgraduate student [51.59% vs 30.92% (p=0.001)] and having two or more chronic diseases [65.85% vs 33.21% (p=0.001)] were also significantly associated with overweight/obesity. Our study showed that almost a quarter of the participating university students reported at least one chronic disease. Obesity was the commonest and more than 1 in 3 students were either overweight or obese. This shows the need for intensive health promotion and screening programs on obesity and other chronic diseases to meet the health needs of these students. This study is also a basis for further research, especially qualitative, to explore the relevant risk factors and risk groups for more targeted interventions.

Keywords: chronic disease, obesity, overweight, students, United Arab Emirates

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Authors: Gu Pang, Bartosz Gebka

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We forecast the demand of total container throughput at the Indonesia’s largest seaport, Tanjung Priok Port. We propose four univariate forecasting models, including SARIMA, the additive Seasonal Holt-Winters, the multiplicative Seasonal Holt-Winters and the Vector Error Correction Model. Our aim is to provide insights into whether forecasting the total container throughput obtained by historical aggregated port throughput time series is superior to the forecasts of the total throughput obtained by summing up the best individual terminal forecasts. We test the monthly port/individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on Mean Absolute Error and Root Mean Squared Error. Our results show that the multiplicative Seasonal Holt-Winters model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The Vector Error Correction Model provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port's capacity and construction of new container terminals at Tanjung Priok Port.

Keywords: SARIMA, Seasonal Holt-Winters, Vector Error Correction Model, container throughput

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Authors: Deepak Jugran

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The last century has witnessed rapid scientific growth, and social policies mainly targeted to increase the “life expectancy” of the people. As a result of these developments, the aging as well as ailing population, is increasing by every day. Despite an increase in “life expectancy”, we have not recorded compression in morbidity numbers as the age of onset of the majority of health issues has not increased substantially. In recent years, the prevalence of chronic diseases along with the improved treatment has also resulted in the increase of people living with chronic diseases. The last decade has also focused on social policies to increase the life expectancy in the population; however, in recent decades, social policies and biomedical research are gradually shifting on the potential of increasing healthy life or healthspan. In this article, we review the existing framework of lifespan and healthspan and wish to ignite a discussion among social scientists and public health experts to propose a wholistic framework to balance the trade-offs on social policies for “lifespan” and “healthspan”.

Keywords: lifespan, healthspan, chronic diseases, social policies

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Authors: Yixun Shi

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Many interior point methods for convex programming solve an (n+m)x(n+m)linear system in each iteration. Many implementations solve this system in each iteration by considering an equivalent mXm system (4) as listed in the paper, and thus the job is reduced into solving the system (4). However, the system(4) has to be solved exactly since otherwise the error would be entirely passed onto the last m equations of the original system. Often the Cholesky factorization is computed to obtain the exact solution of (4). One Cholesky factorization is to be done in every iteration, resulting in higher computational costs. In this paper, two iterative methods for solving linear systems using vector division are combined together and embedded into interior point methods. Instead of computing one Cholesky factorization in each iteration, it requires only one Cholesky factorization in the entire procedure, thus significantly reduces the amount of computation needed for solving the problem. Based on that, a hybrid algorithm for solving convex programming problems is proposed.

Keywords: convex programming, interior point method, linear systems, vector division

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Authors: Paras Ram, Vikas Kumar

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The transmission of Malaria with seasonal were studied through the use of mathematical models. The data from the annual number of Malaria cases reported to the Division of Epidemiology, Ministry of Public Health, Thailand during the period 1997-2011 were analyzed. The transmission of Malaria with seasonal was studied by formulating a mathematical model which had been modified to describe different situations encountered in the transmission of Malaria. In our model, the population was separated into two groups: the human and vector groups, and then constructed a system of nonlinear differential equations. Each human group was divided into susceptible, infectious in hot season, infectious in rainy season, infectious in cool season and recovered classes. The vector population was separated into two classes only: susceptible and infectious vectors. The analysis of the models was given by the standard dynamical modeling.

Keywords: ferrofluid, magnetic field, porous medium, rotating disk, Neuringer-Rosensweig Model

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Authors: Vahid Anari, Leila Shahmohammadi

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Diagnosing and interpreting manually from a large cohort dataset of immunohistochemically stained tissue of tumors using an optical microscope involves subjectivity and also is tedious for pathologist specialists. Moreover, digital pathology today represents more of an evolution than a revolution in pathology. In this paper, we develop and test an unsupervised algorithm that can automatically enhance the IHC image of a meningioma tumor and classify cells into positive (proliferative) and negative (normal) cells. A dataset including 150 images is used to test the scheme. In addition, a new adaptive color image enhancement method is proposed based on a vector directional filter (VDF) and statistical properties of filtering the window. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: digital pathology, cell segmentation, immunohistochemically, noise reduction

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Authors: Luna Degife, Desalegn Belay, Yoseph Worku, Tigist Tesfaye, Assefa Tufa, Abyot Bekele, Zegeye Hailemariam, Abay Hagos

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Half of the world’s population is at risk of Dengue Fever (DF), a highly under-recognized and underreported mosquito-borne viral disease with high prevalence in the tropical and subtropical regions. Globally, an estimated 50 to 200 million cases and 20, 000 DF deaths occur annually as per the world health organization report. In Ethiopia, the first outbreak occurred in 2013 in Diredawa administration city. Afterward, three outbreaks have been reported from the eastern part of the country. We received a report of the fifth DF outbreak for Ethiopia and the second for Diredawa city on October 4, 2015. We conducted the investigation to confirm the outbreak, identify the risk factors for the repeatedly occurrence of the disease and implement control measures. We conducted un- matched case-control study and defined a suspected DF case as any person with fever of 2-7 days and 2 or more of the following: a headache, arthralgia, myalgia, rash, or bleeding from any part of the body. Controls were residents of Diredawa city without DF symptoms. We interviewed 70 Cases and 140 controls from all health facilities in Diredawa city from October 7 to 15; 2015. Epi Info version 7.1.5.0 was used to analyze the data and multivariable logistic regression was conducted to assess risk factors for DF. Sixty-nine blood samples were collected for Laboratory confirmation.The mean age for cases was 23.7±9.5 standard deviation (SD) and for controls 31.2±13 SD. Close contact with DF patient (Adjusted odds ratio (AOR)=5.36, 95% confidence interval(CI): 2.75-10.44), nonuse of long-lasting insecticidal nets (AOR=2.74, 95% CI: 1.06-7.08) and availability of stagnant water in the village (AOR=3.61, 95% CI:1.31-9.93) were independent risk factors associated with higher rates of the disease. Forty-two samples were tested positive. Endemicity of DF is becoming a concern for Diredawa city after the first outbreak. Therefore, effective vector control activities need to be part of long-term preventive measures.

Keywords: dengue fever, Diredawa, outbreak, risk factors, second

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Authors: Xin Li, Ji-Yang Yu, Yue-Hua Niu, Lu-Yuan Wang

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As an inevitable chain of the space data acquirement system, space-borne storage system based on Nand Flash has gradually been implemented in spacecraft. In face of massive, parallel and varied data on board, efficient data management become an important issue of storage research. Face to the requirements of high-performance and reliability in Nand Flash storage system, a combination of hardware and file system design can drastically increase system dependability, even for missions with a very long duration. More sophisticated flash storage concepts with advanced operating systems have been researched to improve the reliability of Nand Flash storage system on satellites. In this paper, architecture of file system with multi-channel data acquisition and storage on board is proposed, which obtains large-capacity and high-performance with the combine of intra-channel pipeline and device partition in Nand Flash. Multi-channel data in different rate are stored as independent files with parallel-storage system in device partition, which assures the high-effective and reliable throughput of file treatments. For massive and high-speed data storage, an efficiency assessment model is established to calculate the bandwidth formula of intra-channel pipeline. Information tables designed in Magnetoresistive RAM (MRAM) hold the management of bad block in Nand Flash and the arrangement of file system address for the high-reliability of data storage. During the full-load test, the throughput of 3D PLUS Module 160Gb Nand Flash can reach 120Mbps for store and reach 120Mbps for playback, which efficiently satisfies the requirement of multi-channel data acquisition in Satellite. Compared with previous literature, the results of experiments verify the advantages of the proposed system.

Keywords: device partition architecture, intra-channel pipelining, nand flash, parallel storage

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Authors: J. Jarolímek, P. Urban, P. Pavlínek, D. Dzúrová

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The industry constitutes a dominant economic sector in Czechia. The automotive industry represents the most important industrial sector in terms of gross value added and the number of employees. The objective of this study was to analyse the occurrence of occupational diseases (OD) in the automotive industry in Czechia during the 2001-2014 period. Whereas the occurrence of OD in other sectors has generally been decreasing, it has been increasing in the automotive industry, including growing spatial discrepancies. Data on OD cases were retrieved from the National Registry of Occupational Diseases. Further, we conducted a survey in automotive companies with a focus on occupational health services and positions of the companies in global production networks (GPNs). An analysis of OD distribution in the automotive industry was performed (age, gender, company size and its role in GPNs, regional distribution of studied companies, and regional unemployment rate), and was accompanied by an assessment of the quality and range of occupational health services. The employees older than 40 years had nearly 2.5 times higher probability of OD occurrence compared with employees younger than 40 years (OR 2.41; 95% CI: 2.05-2.85). The OD occurrence probability was 3 times higher for women than for men (OR 3.01; 95 % CI: 2.55-3.55). The OD incidence rate was increasing with the size of the company. An association between the OD incidence and the unemployment rate was not confirmed.

Keywords: occupational diseases, automotive industry, health geography, unemployment

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Authors: Rao Muhammad Afzal Khan

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Nano technology is emerging as a useful technology in nearly all areas of Science and Technology. Its role in medical imaging is attracting the researchers towards existing and new imaging modalities and techniques. This presentation gives an overview of the development of the work done throughout the world. Furthermore, it lays an idea into the scope of the future use of this technology for diagnosing different diseases. A comparative analysis has also been discussed with an emphasis to detect diseases, in general, and cancer, in particular.

Keywords: medical imaging, cancer detection, diagnosis, nano-imaging, nanotechnology

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Authors: Qiang Xu, Xingxin Wu, Wenjie Guo, Xingqi Wang, Yang Sun, Renxiang Tan

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The phosphatase SHP-2 is known to exert regulatory activities on cytokine receptor signaling and the dysregulation of SHP-2 has been implicated in the pathogenesis of a variety of diseases. Here we report several small molecule regulators of SHP-2 for the treatment of T cell-mediated diseases. The new cyclodepsipeptide trichomides A, isolated from the fermentation products of Trichothecium roseum, increased the phosphorylation of SHP-2 in activated T cells, and ameliorated contact dermatitis in mice. The trichomides A’s effects were significantly reversed by using the SHP-2-specific inhibitor PHPS1 or T cell-conditional SHP-2 knockout mice. Another compound is a cerebroside Fusaruside isolated from the endophytic fungus Fusarium sp. IFB-121. Fusaruside also triggered the tyrosine phosphorylation of SHP-2, which provided a possible mean of selectively targeting STAT1 for the treatment of Th1 cell-mediated inflammation and led to the discovery of the non-phosphatase-like function of SHP-2. Namely, the Fusaruside-activated pY-SHP-2 selectively sequestrated the cytosolic STAT1 to prevent its recruitment to IFN-R, which contributed to the improvement of experimental colitis in mice. Blocking the pY-SHP-2-STAT1 interaction, with SHP-2 inhibitor NSC-87877 or using T cells from conditional SHP-2 knockout mice, reversed the effects of fusaruside. Furthermore, the fusaruside’s effect is independent of the phosphatase activity of SHP-2, demonstrating a novel role for SHP-2 in regulating STAT1 signaling and Th1-type immune responses.

Keywords: SHP-2, small molecules, T cell, T cell-mediated diseases

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Authors: Birmohan Singh, V.K.Jain

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Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: Reenu Yadav, S. K. Yadav

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Herbal therapy has been used for daily oral health care to prevent, treat or cure oral conditions from halitosis to periodontal diseases. The importance of mouth and teeth cleanliness has been recognized from the earliest days of civilization to the 21st century. In the present study, leaves and seeds of Cordia obliqua and barks and twigs of Beautea monosperma, which is used traditionally for oral diseases was evaluated for its antimicrobial activity. The antimicrobial activity tests indicated that the methanolic extract exhibited stronger activities against the commonly encountered oral bacterial and fungal pathogens. The mouthwash formulation prepared and it is compared with marketed formulation HiOra. The results indicated that the herbal mouthwash could inhibit the growth of oral pathogens and may prevent plaque and other periodontal diseases caused by dental pathogens.

Keywords: herbal mouthwash, bio medicine, life sciences, herbal extracts

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Authors: Natalia D. Vaysfel’d, Zinaida Y. Zhuravlova

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The loaded plane elastic semistrip, the lateral boundaries of which are fixed, is considered. The integral transformations are applied directly to Lame’s equations. It leads to one dimensional boundary value problem in the transformations’ domain which is formulated as a vector one. With the help of the matrix differential calculation’s apparatus and apparatus of Green matrix function the exact solution of a vector problem is constructed. After the satisfying the boundary condition at the semi strip’s edge the problem is reduced to the solving of the integral singular equation with regard of the unknown stress at the semis trip’s edge. The equation is solved with the orthogonal polynomials method that takes into consideration the real singularities of the solution at the ends of integration interval. The normal stress at the edge of the semis trip were calculated and analyzed.

Keywords: semi strip, Green's Matrix, fourier transformation, orthogonal polynomials method

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Authors: Asrat M.Belachew, Tiago Pereira, Institute of Mathematics, Computer Sciences, Avenida Trabalhador São Carlense, 400, São Carlos, 13566-590, Brazil

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Infectious diseases are among the most prominent threats to human beings. They cause morbidity and mortality to an individual and collapse the social, economic, and political systems of the whole world collectively. Mathematical models are fundamental tools and provide a comprehensive understanding of how infectious diseases spread and designing the control strategy to mitigate infectious diseases from the host population. Modeling the spread of infectious diseases using a compartmental model of inhomogeneous populations is good in terms of complexity. However, in the real world, there is a situation that accounts for heterogeneity, such as ages, locations, and contact patterns of the population which are ignored in a homogeneous setting. In this work, we study how classical an SEIR infectious disease spreading of the compartmental model can be extended by incorporating the mobility of population between heterogeneous cities during an outbreak of infectious disease. We have formulated an SEIR multi-cities epidemic spreading model using a system of 4k ordinary differential equations to describe the disease transmission dynamics in k-cities during the day and night. We have shownthat the model is epidemiologically (i.e., variables have biological interpretation) and mathematically (i.e., a unique bounded solution exists all the time) well-posed. We constructed the next-generation matrix (NGM) for the model and calculated the basic reproduction number R0for SEIR-epidemic spreading model with cities mobility. R0of the disease depends on the spectral radius mobility operator, and it is a threshold between asymptotic stability of the disease-free equilibrium and disease persistence. Using the eigenvalue perturbation theorem, we showed that sending a fraction of the population between cities decreases the reproduction number of diseases in interconnected cities. As a result, disease transmissiondecreases in the population.

Keywords: SEIR-model, mathematical model, city mobility, epidemic spreading

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Authors: Nadia Ben Si Ali, Nadia Benalia, Nora Zerzouri

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Wind energy generation has attracted great interests in recent years. Doubly Fed Induction Generator (DFIG) for wind turbines are largely deployed because variable-speed wind turbines have many advantages over fixed-speed generation such as increased energy capture, operation at maximum power point, improved efficiency, and power quality. This paper presents the operation and vector control of a Doubly-fed Induction Generator (DFIG) system where the stator is connected directly to a stiff grid and the rotor is connected to the grid through bidirectional back-to-back AC-DC-AC converter. The basic operational characteristics, mathematical model of the aerodynamic system and vector control technique which is used to obtain decoupled control of powers are investigated using the software Mathlab/Simulink.

Keywords: wind turbine, Doubly Fed Induction Generator, wind speed controller, power system stability

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Authors: Karthikeyan Kalirajan, Ashok Joshi

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An optimization problem is to setup to maximize the terminal kinetic energy of a maneuverable reentry vehicle (MaRV). The target location, the impact angle is given as constraints. The MaRV uses an explicit guidance law called Vector guidance. This law has two gains which are taken as decision variables. The problem is to find the optimal value of these gains which will result in minimum miss distance and impact angle error. Using a simple 3DOF non-rotating flat earth model and Lockheed martin HP-MARV as the reentry vehicle, the nature of solutions of the optimization problem is studied. This is achieved by carrying out a parametric study for a range of closed loop gain values and the corresponding impact angle error and the miss distance values are generated. The results show that there are well defined lower and upper bounds on the gains that result in near optimal terminal guidance solution. It is found from this study, that there exist common permissible regions (values of gains) where all constraints are met. Moreover, the permissible region lies between flat regions and hence the optimization algorithm has to be chosen carefully. It is also found that, only one of the gain values is independent and that the other dependent gain value is related through a simple straight-line expression. Moreover, to reduce the computational burden of finding the optimal value of two gains, a guidance law called Diveline guidance is discussed, which uses single gain. The derivation of the Diveline guidance law from Vector guidance law is discussed in this paper.

Keywords: Marv guidance, reentry trajectory, trajectory optimization, guidance gain selection

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Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: binary vector quantization (BVQ), DCT coefficients, face recognition, local binary patterns (LBP)

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Authors: Sumit Kumar Das, Laishram Ladusingh

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Aim: The aim of the study is to examine the differential of epidemiological transition across fifteen agro-climatic zones of India and its effect on health-seeking behavior. Data and Methods: Unit level data on consumption expenditure on health of India from three decadal rounds conducted by National Sample Survey Organization are used for the analysis. These three rounds are 52nd (1995-96), 60th (2004-05) and 71st (2014-15). The age-adjusted prevalence rate for communicable diseases and non-communicable diseases are estimated for fifteen agro-climatic zones of India for three time periods. Bivariate analysis is used to find out determinants of health-seeking behavior. Multilevel logistic regression is used to examine factors effecting on household health-seeking behavior. Result: The prevalence of communicable diseases is increasing in most of the zones of India. Every South Indian zones, Gujarat plains, and lower Gangetic plain are facing the severe attack of dual burden of diseases. Demand for medical advice has increased in southern zones, and east zones, reliance on private healthcare facilities are increasing in most of the zone. Demographic characteristics of the household head have a significant impact on health-seeking behavior. Conclusion: Proper program implementation is required considering the disease prevalence and differential in the pattern of health seeking behavior. Along with initiation and strengthening of programs for non-communicable, existing programs for communicable diseases need to monitor and supervised strictly.

Keywords: agro-climatic zone, epidemiological transition, health-seeking behavior, multilevel regression

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Perizat Aitmaganbet, Kerbez Kimatova, Gulmira Umarova

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As a result of medical check-up conducted in the framework of this research study an evaluation of the health status of the population living in the oil-producing regions, namely Sarkul and Kenkiyak villages in Aktobe was examined. With the help of the Spearman correlation, the connection between the level of hazard chemical elements in the atmosphere and the health of population living in the regions of oil and gas industry was estimated. Background & Objective. The oil and gas resource-extraction industries play an important role in improving the economic conditions of the Republic of Kazakhstan, especially for the oil-producing administrative regions. However, environmental problems may adversely affect the health of people living in that area. Thus, the aim of the study is to evaluate the exposure to negative environmental factors of the adult population living in Sarkul and Kenkiyak villages, the oil and gas producing areas in the Aktobe region. Methods. After conducting medical check-up among the population of Sarkul and Kenkiyak villages. A single cross-sectional study was conducted. The population consisted of randomly sampled 372 adults (181 males and 191 females). Also, atmospheric air probes were taken to measure the level of hazardous chemical elements in the air. The nonparametric method of the Spearman correlation analysis was performed between the mean concentration of substances exceeding the Maximum Permissible Concentration and the classes of newly diagnosed diseases. Selection and analysis of air samples were carried out according to the developed research protocol; the qualitative-quantitative analysis was carried out on the Gas analyzer HANK-4 apparatus. Findings. The medical examination of the population identified the following diseases: the first two dominant were diseases of the circulatory and digestive systems, in the 3rd place - diseases of the genitourinary system, and the nervous system and diseases of the ear and mastoid process were on the fourth and fifth places. Moreover, significant pollution of atmospheric air by carbon monoxide (MPC-5,0 mg/m3), benzapyrene (MPC-1mg/m3), dust (MPC-0,5 mg/m3) and phenol (МРС-0,035mg/m3) were identified in places. Correlation dependencies between these pollutants of air and the diseases of the population were established, as a result of diseases of the circulatory system (r = 0,7), ear and mastoid process (r = 0,7), nervous system (r = 0,6) and digestive organs(r = 0,6 ); between the concentration of carbon monoxide and diseases of the circulatory system (r = 0.6), the digestive system(r = 0.6), the genitourinary system (r = 0.6) and the musculoskeletal system; between nitric oxide and diseases of the digestive system (r = 0,7) and the circulatory system (r = 0,6); between benzopyrene and diseases of the digestive system (r = 0,6), the genitourinary system (r = 0,6) and the nervous system (r = 0,4). Conclusion. The positive correlation was found between air pollution and the health of the population living in Sarkul and Kenkiyak villages. To enhance the reliability of the results we are going to continue this study further.

Keywords: atmospheric air, chemical substances, oil and gas, public health

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Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

Abstract:

The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Daohong Yang, Xiang Zhang, Lei Li, Wanting Zhou

Abstract:

Visual Simultaneous Localization and Mapping (VSLAM) is a technology that obtains information in the environment for self-positioning and mapping. It is widely used in computer vision, robotics and other fields. Many visual SLAM systems, such as OBSLAM3, employ a constant-speed motion model that provides the initial pose of the current frame to improve the speed and accuracy of feature matching. However, in actual situations, the constant velocity motion model is often difficult to be satisfied, which may lead to a large deviation between the obtained initial pose and the real value, and may lead to errors in nonlinear optimization results. Therefore, this paper proposed a motion model based on acceleration, which can be applied on most SLAM systems. In order to better describe the acceleration of the camera pose, we decoupled the pose transformation matrix, and calculated the rotation matrix and the translation vector respectively, where the rotation matrix is represented by rotation vector. We assume that, in a short period of time, the changes of rotating angular velocity and translation vector remain the same. Based on this assumption, the initial pose of the current frame is estimated. In addition, the error of constant velocity model was analyzed theoretically. Finally, we applied our proposed approach to the ORBSLAM3 system and evaluated two sets of sequences on the TUM dataset. The results showed that our proposed method had a more accurate initial pose estimation and the accuracy of ORBSLAM3 system is improved by 6.61% and 6.46% respectively on the two test sequences.

Keywords: error estimation, constant acceleration motion model, pose estimation, visual SLAM

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Authors: A. Biran, P. Sobhe Bidari, A. Almazroe, V. Lakshminarayanan, K. Raahemifar

Abstract:

Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.

Keywords: diabetic retinopathy, fundus images, STARE, Gabor filter, support vector machine

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Authors: Omolara Lade, David Oloke

Abstract:

The approach to water management worldwide is currently in transition, with a shift from centralised infrastructures to greater consideration of decentralised technologies, such as rainwater harvesting (RWH). However, in Nigeria, implementation of sustainable water management, such as RWH systems, is inefficient and social, environmental and technical barriers, concerns and knowledge gaps exist, which currently restrict its widespread utilisation. This inefficiency contributes to water scarcity, water-borne diseases, and loss of lives and property due to flooding. Meanwhile, several RWH technologies have been developed to improve SWM through both demand and storm-water management. Such technologies involve the use of reinforced concrete cement (RCC) storage tanks, surface water reservoirs and ground-water recharge pits as storage systems. A framework was developed to assess the significance and extent of water management problems, match the problems with existing RWH-based solutions and develop a robust ready-to-use decision support tool that can quantify the costs and benefits of implementing several RWH-based storage systems. The methodology adopted was the mixed method approach, involving a detailed literature review, followed by a questionnaire survey of household respondents, Nigerian Architects and Civil Engineers and focus group discussion with stakeholders. 18 selection attributes have been defined and three alternatives have been identified in this research. The questionnaires were analysed using SPSS, excel and selected statistical methods to derive weightings of the attributes for the tool. Following this, three case studies were modelled using RainCycle software. From the results, the MDA model chose RCC tank as the most appropriate storage system for RWH.

Keywords: rainwater harvesting, modelling, hydraulic assessment, whole life cost, decision support system

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Authors: Shittu Bisi Agnes

Abstract:

Ebola virus Diseases outbreak in Nigeria caused a lot of concerns considering the mode of transmission and no known cure discovered. Therefore a lot of safety measures were taken which eventually led to the eradication of the virus in Nigeria. This therefore attempted to determine the various safety measures, how socio-economic characteristic of the people affected adherence to safety measures. And provide reasonable recommendations for total eradication of the virus, future outbreak and general environmental safety Data were collected with the aid of well structured questionnaires and administered 180 randomly selected of the state and oral interview was also utilize. Data collected were analysed using both descriptive tools and inferential statistics vis-a-vis regression analysis. Finding showed that 70.5% was strongly adhere to almost all the measures, 15.2% was fairly advent, 3% was poorly observing the selected measures while 1.3% was in different. 65% of the respondents was strongly aware of the advent of ebola virus diseases, 20% was fairly in awareness, 8.5% was poorly in awareness while 6.55% was in aware of any disease outbreak. Safety measures put forwards were; hand washing, use of hand sanitize-rs, no shaking of hands non-consumption of wildlife games(Bush Meat) and general health and environmental safety measures. It was recommended that policy instrument to increase peoples income will accelerate eradication of diseases as this will enable households to pay for monetary safety measures, health and environmental education, in form of talk shop, workshop, lectures could be organised at the political ward levels, schools, market women, religious bodies functional unions and mass media.

Keywords: ebola diseases, pay, safety, outbreak

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Authors: Anita Setianingrum, Oki S. Jaya, Zuherman Rustam

Abstract:

Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy.

Keywords: ANFIS, fuzzy time series, stock forecasting, SVR

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