Search results for: bearing defect diagnosis.

Authors: Jheng-Long Wu, Pei-Chann Chang, Shih-Ling Chang, Liang-Chih Yu, Jui-Feng Yeh, Chin-Sheng Yang

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The Major Depressive Disorder has been a burden of medical expense in Taiwan as well as the situation around the world. Major Depressive Disorder can be defined into different categories by previous human activities. According to machine learning, we can classify emotion in correct textual language in advance. It can help medical diagnosis to recognize the variance in Major Depressive Disorder automatically. Association language incremental is the characteristic and relationship that can discovery words in sentence. There is an overlapping-category problem for classification. In this paper, we would like to improve the performance in classification in principle of no overlapping-category problems. We present an approach that to discovery words in sentence and it can find in high frequency in the same time and can-t overlap in each category, called Association Language Features by its Category (ALFC). Experimental results show that ALFC distinguish well in Major Depressive Disorder and have better performance. We also compare the approach with baseline and mutual information that use single words alone or correlation measure.

Keywords: Association language features, Emotion Classification, Overlap-Category Feature, Nature Language Processing.

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Authors: S. M. Mahbobur Rahman

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Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.

Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model.

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Authors: V.Manikandan, N.Devarajan

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The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.

Keywords: Artificial neural network, Fault Diagnosis, Identification, Markov parameters.

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Authors: Agnieszka Szczudlińska-Kanoś

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Socio-economic development, which is seen around the world today, has contributed to the emergence of new problems of a social nature. Different political, historical, geographical or economic conditions cause that, in addition to global issues of social policy such as an aging population, unemployment, migration, countries, regions, there are also specific new problems that require diagnosis, individualized approach and efficient, planned solutions. These should include, among others, digital addiction, peer violence, obesity among children, the problem of ‘legal highs’, stress, depression, diseases associated with environmental pollution etc. The central authorities, selected most often with the tools specific to representative democracy, that is, the general election, for many reasons, inter alia, organizational, communication, are not able to effectively diagnose their intensity, territorial distribution, and thus to effectively fight them. This article aims to show how in Poland, citizens influence solving problems related to the broader social policy implemented at the local government level and indicates the possibilities of improving those solutions. The conclusions of theoretical analysis have been supported by empirical studies, which tested the use of instruments of participatory democracy in the planning and creation of communal strategies for solving social problems in one of the Polish voivodeships.

Keywords: Commune, democracy, participation, social policy, social problems.

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Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

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Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, stereotypical motor movements, repetitive gesture, kinect, artificial neural network.

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Authors: Mohammad Aminnia, Mahmood Hosseini

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Environmental and functional conditions, sometimes, necessitate the architectural plan of the building to be asymmetric, and this result in an asymmetric structure. In such cases finding an optimal pattern for locating the components of lateral load bearing system, including shear walls, in the building’s plan is desired. In case of shear wall in addition to the location the shape of the wall cross-section is also an effective factor. Various types of shear walls and their proper layout might come effective in better stiffness distribution and more appropriate seismic response of the building. Several studies have been conducted in the context of analysis and design of shear walls; however, few studies have been performed on making decisions for the location and form of shear walls in multistory buildings, especially those with irregular plan. In this study, an attempt has been made to obtain the most reliable seismic behavior of multi-story reinforced concrete vertically chamfered buildings by using more appropriate shear walls form and arrangement in 7-, 10-, 12-, and 15-stoy buildings. The considered forms and arrangements include common rectangular walls and L-, T-, U- and Z-shaped plan, located as the core or in the outer frames of the building structure. Comparison of seismic behaviors of the buildings, including maximum roof displacement and particularly formation of plastic hinges and their distribution in the buildings’ structures, have been done based on the results of a series of nonlinear time history analyses, by using a set of selected earthquake records. Results show that shear walls with U-shaped cross-section, placed as the building central core, and also walls with Z-shaped cross-section, placed at the corners give the building more reliable seismic behavior.

Keywords: Vertically chamfered buildings, non-linear time history analyses, L-, T-, U- and Z-shaped plan walls.

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Authors: Kasthurirangan Gopalakrishnan, Siddhartha Khaitan, Anshu Manik

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Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.

Keywords: Artificial neural networks, cluster analysis, Kohonen maps, wine recognition.

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Authors: M. Abdeshahian, M. Nabipour, M. Meskarbashee

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To investigate effect of salt stress on Chlorophyll fluorescence four cultivars (fong,star,chamran and kharchia) of wheat (Triticum aestivum) plants subjected to salinity levels ( control,8,12 and 16 dsm-1 ) from one week after emergence to the end of stem elongation under greenhouse condition . results showed that quantum yield of photosystem II from light adopted leaves (ΦPSII), Photochemical quenching (qP) ,quantum yield of dark adopted leaves (fv/fm) and non photochemical quenching (NPq) were affected by salt stress . Salinity levels affected photosynthetic rate. Star and fong cultivars showed minimum and maximum levels of photosynthetic rate in respectively. Minimum photosynthetic rate differences between levels of salinity were shown in Kharchia. Shoot dry matter of all cultivars decreased by increasing salinity levels. Results showed that non photochemical quenching by salinity levels attribute to the decreases in shoot dry matter.

Keywords: salt stress, wheat, chlorophyll fluorescence, photosynthesis , shoot dry matter .

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Authors: T. Denkçeken, M. Canpolat, Đ. Bassorgun, S. Yücel, M.A. Çiftçioğlu, M. Baykara Murat Canpolat , Tuba Denkçeken , Đbrahim Bassorgun , Selçuk Yücel , M. Akif Çiftçioğlu , Mehmet Baykara

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Elastic light single-scattering spectroscopy system with a single optical fiber probe was employed to differentiate cancerous prostate tissue from non-cancerous prostate tissue ex-vivo just after radical prostatectomy. First, ELSSS spectra were acquired from cancerous prostate tissue to define its spectral features. Then, spectra were acquired from normal prostate tissue to define difference in spectral features between the cancerous and normal prostate tissues. Of the total 66 tissue samples were evaluated from nine patients by ELSSS system. Comparing of histopathology results and ELSSS measurements revealed that sign of the spectral slopes of cancerous prostate tissue is negative and non-cancerous tissue is positive in the wavelength range from 450 to 750 nm. Based on the correlation between histopathology results and sign of the spectral slopes, ELSSS system differentiates cancerous prostate tissue from non- cancerous with a sensitivity of 0.95 and a specificity of 0.94.

Keywords: Diagnosis, prostatic neoplasm, prostatectomy, spectrum analysis

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Authors: Fırat Matur, Yekta Ülgen

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Cole-Cole parameters of 40 post-menopausal women are compared with their DEXA bone mineral density measurements. Impedance characteristics of four extremities are compared; left and right extremities are statistically same, but lower extremities are statistically different than upper ones due to their different fat content. The correlation of Cole-Cole impedance parameters to bone mineral density (BMD) is observed to be higher for dominant arm. With the post-menopausal population, ANOVA tests of the dominant arm characteristic frequency, as a predictor for DEXA classified osteopenic and osteoporic population around lumbar spine, is statistically very significant. When used for total lumbar spine osteoporosis diagnosis, the area under the Receiver Operating Curve of the characteristic frequency is 0.830, suggesting that the Cole-Cole plot characteristic frequency could be a useful diagnostic parameter when integrated into standard screening methods for osteoporosis. Moreover, the characteristic frequency can be directly measured by monitoring frequency driven angular behavior of the dominant arm without performing any complex calculation.

Keywords: Bio-impedance spectroscopy, bone mineral density, characteristic frequency, osteoporosis, receiver operating curve.

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Authors: Moses C. Siame, Kazutoshi Haga, Atsushi Shibayama

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This study investigates the removal of silica, alumina and phosphorus as impurities from Sanje iron ore using wet high-intensity magnetic separation (WHIMS). Sanje iron ore contains low-grade hematite ore found in Nampundwe area of Zambia from which iron is to be used as the feed in the steelmaking process. The chemical composition analysis using X-ray Florence spectrometer showed that Sanje low-grade ore contains 48.90 mass% of hematite (Fe₂O₃) with 34.18 mass% as an iron grade. The ore also contains silica (SiO₂) and alumina (Al₂O₃) of 31.10 mass% and 7.65 mass% respectively. The mineralogical analysis using X-ray diffraction spectrometer showed hematite and silica as the major mineral components of the ore while magnetite and alumina exist as minor mineral components. Mineral particle distribution analysis was done using scanning electron microscope with an X-ray energy dispersion spectrometry (SEM-EDS) and images showed that the average mineral size distribution of alumina-silicate gangue particles is in order of 100 μm and exists as iron-bearing interlocked particles. Magnetic separation was done using series L model 4 Magnetic Separator. The effect of various magnetic separation parameters such as magnetic flux density, particle size, and pulp density of the feed was studied during magnetic separation experiments. The ore with average particle size of 25 µm and pulp density of 2.5% was concentrated using pulp flow of 7 L/min. The results showed that 10 T was optimal magnetic flux density which enhanced the recovery of 93.08% of iron with 53.22 mass% grade. The gangue mineral particles containing 12 mass% silica and 3.94 mass% alumna remained in the concentrate, therefore the concentrate was further treated in the second stage WHIMS using the same parameters from the first stage. The second stage process recovered 83.41% of iron with 67.07 mass% grade. Silica was reduced to 2.14 mass% and alumina to 1.30 mass%. Accordingly, phosphorus was also reduced to 0.02 mass%. Therefore, the two stage magnetic separation process was established using these results.

Keywords: Sanje iron ore, magnetic separation, silica, alumina, recovery.

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Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

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Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: Ocular diseases, retinal fundus image, optic disc detection and segmentation, fully convolutional network, overlap measure.

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Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill

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Superstructures like offshore platforms, tall buildings, transition towers, skyscrapers and bridges are normally designed to resist compression, uplift and lateral forces from wind waves, negative skin friction, ship impact and other applied loads. Better understanding and the precise simulation of the response of batter piles under the action of independent uplift loads is a vital topic and an area of active research in the field of geotechnical engineering. This paper investigates the use of finite element code (FEC) to examine the behaviour of model batter piles penetrated in dense sand, subjected to pull-out pressure by means of numerical modelling. The concept of the Winkler Model (beam on elastic foundation) has been used in which the interaction between the pile embedded depth and adjacent soil in the bearing zone is simulated by nonlinear p-y curves. The analysis was conducted on different pile slenderness ratios (lc⁄d) ranging from 7.5, 15.22 and 30 respectively. In addition, the optimum batter angle for a model steel pile penetrated in dense sand has been chosen to be 20° as this is the best angle for this simulation as demonstrated by other researcher published in literature. In this numerical analysis, the soil response is idealized as elasto-plastic and the model piles are described as elastic materials for the purpose of simulation. The results revealed that the applied loads affect the pullout pile capacity as well as the lateral pile response for dense sand together with varying shear strength parameters linked to the pile critical depth. Furthermore, the pile pull-out capacity increases with increasing the pile aspect ratios.

Keywords: Slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), pull-out capacity.

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Authors: Alexander Dao, Oscar Wambuguh

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Irritable Bowel Syndrome (IBS) is a heterogeneous functional bowel disease that is characterized by chronic visceral abdominal pain and abnormal bowel function and habits. Its multifactorial pathophysiology and mechanisms are still largely a mystery to the contemporary biomedical community, although there are many hypotheses to try to explain IBS’s presumed physiological, psychosocial, genetic, and environmental etiologies. IBS’s symptomatic presentation is varied and divided into four major subtypes: IBS-C, IBS-D, IBS-M, and IBS-U. Given its diverse presentation and unclear mechanisms, diagnosis is done through a combination of positive identification utilizing the “Rome IV Irritable Bowel Syndrome Criteria'' (Rome IV) diagnostic criteria while also excluding other potential conditions with similar symptoms. Treatment of IBS is focused on the management of symptoms using an assortment of pharmaceuticals, lifestyle changes, and dietary changes, with future potential in microbial treatment and psychotherapy as other therapy methods. Its chronic, heterogeneous nature and disruptive gastrointestinal (GI) symptoms are negatively impactful on patients’ daily lives, health systems, and society. However, with a better understanding of the gaps in knowledge and technological advances in IBS’s pathophysiology, management, and treatment options, there is optimism for the millions of people worldwide who are suffering from the debilitating effects of IBS.

Keywords: Irritable bowel syndrome, lifestyle, diet, functional gastrointestinal disorder.

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Authors: B. Stroetmann, A. Aisenbrey

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The Siemens Healthcare Sector is one of the world's largest suppliers to the healthcare industry and a trendsetter in medical imaging and therapy, laboratory diagnostics, medical information technology, and hearing aids. Siemens offers its customers products and solutions for the entire range of patient care from a single source – from prevention and early detection to diagnosis, and on to treatment and aftercare. By optimizing clinical workflows for the most common diseases, Siemens also makes healthcare faster, better, and more cost effective. The optimization of clinical workflows requires a multidisciplinary focus and a collaborative approach of e.g. medical advisors, researchers and scientists as well as healthcare economists. This new form of collaboration brings together experts with deep technical experience, physicians with specialized medical knowledge as well as people with comprehensive knowledge about health economics. As Charles Darwin is often quoted as saying, “It is neither the strongest of the species that survive, nor the most intelligent, but the one most responsive to change," We believe that those who can successfully manage this change will emerge as winners, with valuable competitive advantage. Current medical information and knowledge are some of the core assets in the healthcare industry. The main issue is to connect knowledge holders and knowledge recipients from various disciplines efficiently in order to spread and distribute knowledge.

Keywords: Business Excellence, Clinical Knowledge, Knowledge Management, Knowledge Services, Learning Organizations, Trust.

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Authors: Mohammed Tahir, Jonas Lagergren

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Vancron 40, a nitrided powder metallurgical tool Steel, is used in cold work applications where the predominant failure mechanisms are adhesive wear or galling. Typical applications of Vancron 40 are among others fine blanking, cold extrusion, deep drawing and cold work rolls for cluster mills. Vancron 40 positive results for cold work rolls for cluster mills and as a tool for some severe metal forming process makes it competitive compared to other type of work rolls that require higher precision, among others in cold rolling of thin stainless steel, which required high surface finish quality. In this project, three roll materials for cold rolling of stainless steel strip was examined, Vancron 40, Narva 12B (a high-carbon, high-chromium tool steel alloyed with tungsten) and Supra 3 (a Chromium-molybdenum tungsten-vanadium alloyed high speed steel). The purpose of this project was to study the depth profiles of the ironed stainless steel strips, emergence of galling and to study the lubrication performance used by steel industries. Laboratory experiments were conducted to examine scratch of the strip, galling and surface roughness of the roll materials under severe tribological conditions. The critical sliding length for onset of galling was estimated for stainless steel with four different lubricants. Laboratory experiments result of performance evaluation of resistance capability of rolls toward adhesive wear under severe conditions for low and high reductions. Vancron 40 in combination with cold rolling lubricant gave good surface quality, prevents galling of metal surfaces and good bearing capacity.

Keywords: Adhesive wear, Cold rolling, Lubricant, Stainless steel, Surface finish, Vancron 40.

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Authors: Gabi Nehme

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The relationship between different types of Molybdenum disulfide greases under extreme pressure loading and different speed situations have been studied using Design of Experiment (DOE) under 1200rpm steady state rotational speed and cyclic frequencies between 2400 and 1200rpm using a Plint machine software to set up the different rotational speed situations.  Research described here is aimed at providing good friction and wear performance while optimizing cyclic frequencies and MoS2 concentration due to the recent concern about grease behavior in extreme pressure applications. Extreme load of 785 Newton was used in conjunction with different cyclic frequencies (2400rpm -3.75min, 1200rpm -7.5min, 2400rpm -3.75min, 1200rpm -7.5min), to examine lithium based grease with and without MoS2 for equal number of revolutions, and a total run of 36000 revolutions; then compared to 1200rpm steady speed for the same total number of revolutions. 4 Ball wear tester was utilized to run large number of experiments randomly selected by the DOE software. The grease was combined with fine grade MoS2 or technical grade then heated to 750C and the wear scar width was collected at the end of each test. DOE model validation results verify that the data were very significant and can be applied to a wide range of extreme pressure applications. Based on simulation results and Scanning Electron images (SEM), it has been found that wear was largely dependent on the cyclic frequency condition. It is believed that technical grade MoS2 greases under faster cyclic speeds perform better and provides antiwear film that can resist extreme pressure loadings. Figures showed reduced wear scars width and improved frictional values.

 

Keywords: MoS2 grease, wear, friction, extreme load, cyclic frequencies, aircraft grade bearing.

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Authors: Leslie Beale

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Being a patient with a chronic disease is both a physical and emotional experience. The ability to recognize a patient’s emotional health is an important part of a health care provider’s skills. For the purposes of this paper, emotional health is viewed as the way that we feel, and the way that our feelings affect us. Understanding the patient’s emotional health leads to improved provider-patient relationships and health outcomes. For example, when a patient first hears his or her diagnosis from a provider, they might find it difficult to cope with their emotions. Struggling to cope with emotions interferes with the patient’s ability to read, understand, and act on health information and services. As a result, the patient becomes more frustrated and confused, creating barriers to accessing healthcare services. These barriers are challenging for both the patient and their healthcare providers. There are five basic emotions that are part of who we are and are always with us: fear, anger, sadness, joy, and compassion. Living with a chronic disease however can cause a patient to experience and express these emotions in new and unique ways. Within the provider-patient relationship, there needs to be an understanding that each patient experiences these five emotions and, experiences them at different times. In response to this need, the paper highlights a health promotion framework for patients with chronic disease. This framework emphasizes the emotional health of patients.

Keywords: Health promotion, emotional health, patients with chronic disease, patient-centered care.

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.

Keywords: Resilient Machine Learning, attacks, defences, nuclear industry, crack detection.

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Authors: M. Naserian, M. Jooshaki, M. Fotuhi-Firuzabad, M. Hossein Mohammadi Sanjani, A. Oraee

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In recent years, the development of communication infrastructure and smart meters have facilitated the utilization of demand-side resources which can enhance stability and economic efficiency of power systems. Direct load control programs can play an important role in the utilization of demand-side resources in the residential sector. However, investments required for installing control equipment can be a limiting factor in the development of such demand response programs. Thus, selection of consumers with higher potentials is crucial to the success of a direct load control program. Heating, ventilation, and air conditioning (HVAC) systems, which due to the heat capacity of buildings feature relatively high flexibility, make up a major part of household consumption. Considering that the consumption of HVAC systems depends highly on the ambient temperature and bearing in mind the high investments required for control systems enabling direct load control demand response programs, in this paper, a solution is presented to uncover consumers with high air conditioner demand among a large number of consumers and to measure the demand response potential of such consumers. This can pave the way for estimating the investments needed for the implementation of direct load control programs for residential HVAC systems and for estimating the demand response potentials in a distribution system. In doing so, we first cluster consumers into several groups based on the correlation coefficients between hourly consumption data and hourly temperature data using K-means algorithm. Then, by applying a recent algorithm to the hourly consumption and temperature data, consumers with high air conditioner consumption are identified. Finally, demand response potential of such consumers is estimated based on the equivalent desired temperature setpoint changes.

Keywords: Data-driven analysis, demand response, direct load control, HVAC system.

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Authors: Grygorii Kostromin, Vladyslav Povoroznyuk

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Chronic venous diseases (CVD) are one of the common, though controversial problems in medicine. It is generally accepted that this pathology predominantly occurs in women. The issue of excessive weight as a risk factor for CVD is still considered debatable. To the author's best knowledge, today in Ukraine, there are barely any studies that describe the relationship between CVD and obesity. Our study aims to determine the association between age, body composition, obesity and CVD in postmenopausal women. The study was conducted in D. F. Chebotarev Institute of Gerontology, National Academy of Medical Sciences of Ukraine. We have examined 96 postmenopausal women aged 46-85 years (mean age – 66.19 ± 0.96 years), who were divided into two groups depending on the presence of CVD. The women were examined by vascular surgeons. For the diagnosis of CVD, we used clinical, anatomic and pathophysiologic classifications. We also performed clinical, ultrasound and densitometry examinations. We found that the CVD frequency in postmenopausal women increased with age (from 72% in those aged 45-59 years to 84% in those aged 75-89 years). A significant correlation between the total fat mass and age was determined in postmenopausal women with CVD. We also observed a significant correlation between the lower extremities’ fat mass and age in both examined groups. A significant correlation between body mass index and age was determined only in postmenopausal women without CVD.

Keywords: Chronic venous disease, risk factors, age, obesity, postmenopausal women.

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Authors: M. G. Murtaza, E. E. Tzirtzilakis, M. Ferdows

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Biomagnetic fluid dynamics is an interdisciplinary field comprising engineering, medicine, and biology. Bio fluid dynamics is directed towards finding and developing the solutions to some of the human body related diseases and disorders. This article describes the flow and heat transfer of two dimensional, steady, laminar, viscous and incompressible biomagnetic fluid over a non-linear stretching sheet in the presence of magnetic dipole. Our model is consistent with blood fluid namely biomagnetic fluid dynamics (BFD). This model based on the principles of ferrohydrodynamic (FHD). The temperature at the stretching surface is assumed to follow a power law variation, and stretching velocity is assumed to have a nonlinear form with signum function or sign function. The governing boundary layer equations with boundary conditions are simplified to couple higher order equations using usual transformations. Numerical solutions for the governing momentum and energy equations are obtained by efficient numerical techniques based on the common finite difference method with central differencing, on a tridiagonal matrix manipulation and on an iterative procedure. Computations are performed for a wide range of the governing parameters such as magnetic field parameter, power law exponent temperature parameter, and other involved parameters and the effect of these parameters on the velocity and temperature field is presented. It is observed that for different values of the magnetic parameter, the velocity distribution decreases while temperature distribution increases. Besides, the finite difference solutions results for skin-friction coefficient and rate of heat transfer are discussed. This study will have an important bearing on a high targeting efficiency, a high magnetic field is required in the targeted body compartment.

Keywords: Biomagnetic fluid, FHD, nonlinear stretching sheet, slip parameter.

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Authors: Ali Pashaee, Parisa Shooshtari, Gholamreza Atae, Nasser Fatouraee

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Phase-Contrast MR imaging methods are widely used for measurement of blood flow velocity components. Also there are some other tools such as CT and Ultrasound for velocity map detection in intravascular studies. These data are used in deriving flow characteristics. Some clinical applications are investigated which use pressure distribution in diagnosis of intravascular disorders such as vascular stenosis. In this paper an approach to the problem of measurement of intravascular pressure field by using velocity field obtained from flow images is proposed. The method presented in this paper uses an algorithm to calculate nonlinear equations of Navier- Stokes, assuming blood as an incompressible and Newtonian fluid. Flow images usually suffer the lack of spatial resolution. Our attempt is to consider the effect of spatial resolution on the pressure distribution estimated from this method. In order to achieve this aim, velocity map of a numerical phantom is derived at six different spatial resolutions. To determine the effects of vascular stenoses on pressure distribution, a stenotic phantom geometry is considered. A comparison between the pressure distribution obtained from the phantom and the pressure resulted from the algorithm is presented. In this regard we also compared the effects of collocated and staggered computational grids on the pressure distribution resulted from this algorithm.

Keywords: Flow imaging, pressure distribution estimation, phantom, resolution.

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Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

Abstract:

Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: Fuzzy logic, dissolved gas-in-oil analysis, DGA, prediction, power transformer.

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Authors: Sudip Kumar Kundu, Charu Singh

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As a great physiographic divide, the Himalayas affecting a large system of water and air circulation which helps to determine the climatic condition in the Indian subcontinent to the south and mid-Asian highlands to the north. It creates obstacles by defending chill continental air from north side into India in winter and also defends rain-bearing southwesterly monsoon to give up maximum precipitation in that area in monsoon season. Nowadays extreme weather conditions such as heavy precipitation, cloudburst, flash flood, landslide and extreme avalanches are the regular happening incidents in the region of North Western Himalayan (NWH). The present study has been planned to investigate the suitable model(s) to find out the rainfall pattern over that region. For this investigation, selected models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and Coupled Model Intercomparison Project Phase 5 (CMIP5) has been utilized in a consistent framework for the period of 1976 to 2000 (historical). The ability of these driving models from CORDEX domain and CMIP5 has been examined according to their capability of the spatial distribution as well as time series plot of rainfall over NWH in the rainy season and compared with the ground-based Indian Meteorological Department (IMD) gridded rainfall data set. It is noted from the analysis that the models like MIROC5 and MPI-ESM-LR from the both CORDEX and CMIP5 provide the best spatial distribution of rainfall over NWH region. But the driving models from CORDEX underestimates the daily rainfall amount as compared to CMIP5 driving models as it is unable to capture daily rainfall data properly when it has been plotted for time series (TS) individually for the state of Uttarakhand (UK) and Himachal Pradesh (HP). So finally it can be said that the driving models from CMIP5 are better than CORDEX domain models to investigate the rainfall pattern over NWH region.

Keywords: Global warming, rainfall, CMIP5, CORDEX, North Western Himalayan region.

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Authors: Miroslav Byrtus

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Large rotating systems, especially gear drives and gearboxes, occur as parts of many mechanical devices transmitting the torque with relatively small loss of power. With the increased demand for high speed machinery, mathematical modeling and dynamic analysis of gear drives gained importance. Mathematical description of such mechanical systems is a complex task evolving for several decades. In gear drive dynamic models, which include flexible shafts, bearings and gearing and use the finite elements, nonlinear effects due to gear mesh and bearings are usually ignored, for such models have large number of degrees of freedom (DOF) and it is computationally expensive to analyze nonlinear systems with large number of DOF. Therefore, these models are not suitable for simulation of nonlinear behavior with amplitude jumps in frequency response. The contribution uses a methodology of nonlinear large rotating system modeling which is based on degrees of freedom (DOF) number reduction using modal synthesis method (MSM). The MSM enables significant DOF number reduction while keeping the nonlinear behavior of the system in a specific frequency range. Further, the MSM with DOF number reduction is suitable for including detail models of nonlinear couplings (mainly gear and bearing couplings) into the complete gear drive models. Since each subsystem is modeled separately using different FEM systems, it is advantageous to parameterize models of subsystems and to use the parameterization for optimization of chosen design parameters. Final complex model of gear drive is assembled in MATLAB and MATLAB tools are used for dynamical analysis of the nonlinear system. The contribution is further focused on developing of a methodology for investigation of behavior of the system by Nonlinear Normal Modes with combination of the MSM using numerical continuation method. The proposed methodology will be tested using a two-stage gearbox including its housing.

Keywords: Vibro-impact system, rotating system, gear drive, modal synthesis method, numerical continuation method, periodic solution.

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Authors: C. Gunavathi, K. Premalatha

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Microarray technology is universally used in the study of disease diagnosis using gene expression levels. The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Abundant methods and techniques have been proposed for tumor classification using microarray gene expression data. Feature or gene selection methods can be used to mine the genes that directly involve in the classification and to eliminate irrelevant genes. In this paper statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR) and F-Statistics are used to rank the genes. The ranked genes are used for further classification. Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping (SFL) algorithm are used to find the significant genes from the top-m ranked genes. The Naïve Bayes Classifier (NBC) is used to classify the samples based on the significant genes. The proposed work is applied on Lung and Ovarian datasets. The experimental results show that the proposed method achieves 100% accuracy in all the three datasets and the results are compared with previous works.

Keywords: Microarray, T-Statistics, Signal-to-Noise Ratio, FStatistics, Particle Swarm Optimization, Shuffled Frog Leaping, Naïve Bayes Classifier.

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: Diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography.

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Authors: Chintanu K. Sarmah, Sandhya Samarasinghe, Don Kulasiri, Daniel Catchpoole

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Gene expression profiling is rapidly evolving into a powerful technique for investigating tumor malignancies. The researchers are overwhelmed with the microarray-based platforms and methods that confer them the freedom to conduct large-scale gene expression profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data.

Keywords: Gene expression profiling, microarray, cDNA, Affymetrix, childhood leukaemia.

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