Search results for: robust diagnosis

Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

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Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm

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Authors: J. J. Mathew

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Pulmonary tuberculosis is highly prevalent in the Indian subcontinent. Most cases of pulmonary tuberculosis are diagnosed with sputum examinations and the vast majority of these are undertaken by the government run establishments. However, mycobacterial cultures are not routinely done, unless drug resistance is detected based on clinical response. Modern diagnostic tests like bronchoscopy and Genexpert are not routinely employed in the government institutions for the diagnosis of pulmonary tuberculosis, but have been accepted widely by good private institutions. The utility of these investigations in the private sector is not yet well recognized. This retrospective study aims to assess the usefulness of bronchoscopy and Genexpert in the diagnosis of pulmonary tuberculosis in quaternary care private hospital in India. 30 patients with respiratory symptoms raising the possibility of tuberculosis based on clinical and radiological features, but without any significant sputum production, were subject to bronchoscopy and BAL samples taken for microbiological studies, including Genexpert. 6 out of the 30 patients were found to be Genexpert positive and none of them showed Rifampicin resistance. All the 6 cases had upper zone predominant disease. One of the 6 cases of tuberculosis had another co-existent bacterial infection according to the routine culture studies. 6 other cases were proven to be due to other bacterial infections alone, 2 had a malignant diagnosis and the remaining cases were thought to be non-infective pathologies. The Genexpert results were made available within 48 hours in the 6 positive cases. All of them were commenced on standard anti-tuberculous regimen with excellent clinical response. The other infective cases were also managed successfully based on the drug susceptibilities. The study has shown the usefulness of these investigations as early intervention enabled diagnosis facilitating treatment and prevention of any clinical deterioration. The study lends support to early bronchoscopy and Genexpert testing in suspected cases of pulmonary tuberculosis without significant sputum production, in a high prevalence country which normally relies on sputum examination for the diagnosis of pulmonary tuberculosis.

Keywords: pulmonary, tuberculosis, bronchoscopy, genexpert

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Authors: Huong M. T. Nguyen, Hoa A. P. Nguyen, Mai P. T. Nguyen, Ngoc D. Ngo, Van T. Ta, Hai T. Le, Chi V. Phan

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Wilson’s disease (WD) is an autosomal recessive disorder of the copper metabolism, which is caused by a mutation in the copper-transporting P-type ATPase (ATP7B). The mechanism of this disease is the failure of hepatic excretion of copper to bile, and leads to copper deposits in the liver and other organs. The ATP7B gene is located on the long arm of chromosome 13 (13q14.3). This study aimed to investigate the gene mutation in the Vietnamese patients with WD, and make a presymptomatic diagnosis for their familial members. Forty-three WD patients and their 65 siblings were identified as having ATP7B gene mutations. Genomic DNA was extracted from peripheral blood samples; 21 exons and exon-intron boundaries of the ATP7B gene were analyzed by direct sequencing. We recognized four mutations ([R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G) in the sum of 20 detectable mutations, accounting for 87.2% of the total. Mutation S105* was determined to have a high rate (32.6%) in this study. The hotspot regions of ATP7B were found at exons 2, 16, and 8, and intron 14, in 39.6 %, 11.6 %, 9.3%, and 7 % of patients, respectively. Among nine homozygote/compound heterozygote siblings of the patients with WD, three individuals were determined as asymptomatic by screening mutations of the probands. They would begin treatment after diagnosis. In conclusion, 20 different mutations were detected in 43 WD patients. Of this number, four novel mutations were explored, including [R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G. The mutation S105* is the most prevalent and has been considered as a biomarker that can be used in a rapid detection assay for diagnosis of WD patients. Exons 2, 8, and 16, and intron 14 should be screened initially for WD patients in Vietnam. Based on risk profile for WD, genetic testing for presymptomatic patients is also useful in diagnosis and treatment.

Keywords: ATP7B gene, mutation detection, presymptomatic diagnosis, Vietnamese Wilson’s disease

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Authors: Roseline Oghogho Osaseri, Osaseri E. I.

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Lassa fever is an epidemic hemorrhagic fever caused by the Lassa virus, an extremely virulent arena virus. This highly fatal disorder kills 10% to 50% of its victims, but those who survive its early stages usually recover and acquire immunity to secondary attacks. One of the major challenges in giving proper treatment is lack of fast and accurate diagnosis of the disease due to multiplicity of symptoms associated with the disease which could be similar to other clinical conditions and makes it difficult to diagnose early. This paper proposed an Adaptive Neuro Fuzzy Inference System (ANFIS) for the prediction of Lass Fever. In the design of the diagnostic system, four main attributes were considered as the input parameters and one output parameter for the system. The input parameters are Temperature on admission (TA), White Blood Count (WBC), Proteinuria (P) and Abdominal Pain (AP). Sixty-one percent of the datasets were used in training the system while fifty-nine used in testing. Experimental results from this study gave a reliable and accurate prediction of Lassa fever when compared with clinically confirmed cases. In this study, we have proposed Lassa fever diagnostic system to aid surgeons and medical healthcare practictionals in health care facilities who do not have ready access to Polymerase Chain Reaction (PCR) diagnosis to predict possible Lassa fever infection.

Keywords: anfis, lassa fever, medical diagnosis, soft computing

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Authors: Piyush V. Surjagade, Jiamei Deng, Paul Doney, S. R. Shimjith, A. John Arul

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In sophisticated and complex systems such as nuclear power plants, maintaining the system's stability in the presence of uncertainties and disturbances and obtaining a fast dynamic response are the most challenging problems. Thus, to ensure the satisfactory and safe operation of nuclear power plants, this work proposes a new fast, robust optimal switching control strategy for pressurized water reactor-type nuclear power plants. The proposed control strategy guarantees a substantial degree of robustness, fast dynamic response over the entire operational envelope, and optimal performance during the nominal operation of the plant. To improve the robustness, obtain a fast dynamic response, and make the system optimal, a bank of controllers is designed. Various controllers, like a baseline proportional-integral-derivative controller, an optimal linear quadratic Gaussian controller, and a robust adaptive L1 controller, are designed to perform distinct tasks in a specific situation. At any instant of time, the most suitable controller from the bank of controllers is selected using the switching logic unit that designates the controller by monitoring the health of the nuclear power plant or transients. The proposed switching control strategy optimizes the overall performance and increases operational safety and efficiency. Simulation studies have been performed considering various uncertainties and disturbances that demonstrate the applicability and effectiveness of the proposed switching control strategy over some conventional control techniques.

Keywords: switching control, robust control, optimal control, nuclear power control

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Authors: Ajay Kumar Yadav, Masum Paudel, Ritesh Chaudhary

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Background: Understanding the diagnosis and the treatment plan is very important for the patient which reflects the effectiveness of the patient care as well as counseling. Large groups of patients do not understand their emergency care plan or their discharge instructions. With only a little more than 2/3ʳᵈ of the adult population is literate and poorly distributed health service institutions in Nepal, exploring the current status of patient understanding of their diagnosis and treatment would help identify interventions to improve patient compliance with the provided care and the treatment outcomes. Objectives: This study was conducted to identify and describe the areas of patients’ understanding and confusion regarding emergency care and discharge instructions at the Emergency ward of B. P. Koirala Institute of Health Sciences teaching hospital, Dharan, Nepal. Methods: A cross-sectional study was conducted among 426 patients discharged from the emergency unit of BPKIHS. Cases who are leaving against medical advice absconded cases and those patients who came just for vaccination are excluded from the study. Patients’ understanding of the treatment plan and diagnosis was measured. Results: There were 60% men in this study. More than half of the participants reported not being able to read English. More than 90% of the respondents reported they could not read their prescription at all. While patient could point out their understanding of their diagnosis at discharge, most of them could not tell the names and the dosage of all the drugs prescribed to them at discharge. More than 95% of the patients could not tell the most common side effects of the drugs that they are prescribed. Conclusions: There is a need to further explore the factors influencing the understanding of the patients regarding their treatment plan. Interventions to understand the health literacy needs and ways to improve the health literacy of the patients are needed.

Keywords: discharge instruction, emergency ward, health literacy, treatment plan

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Authors: Kelechi Emmanuel, Kyaw Thein Aung, William Burch

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Introduction: Although rewarding in more ways than one, rural medicine can be challenging. The factors that lead to the challenges experienced in rural medicine include but are not limited to scarcity of resources, poor patient education inadequately trained professionals. This is the first single center study done on the challenges of and ways to improve diagnosis in rural medicine. Materials and Methods: Questionnaires were given to providers in a single hospital in rural Tennessee USA. In which providers were asked the question ‘In the past six months, what measures have you taken to improve your diagnostic accuracy given limited resources. Results: The questionnaire was passed to ten physicians working in a two hundred and twentyfive hospital bed. Physicians who participated included physicians in hospital medicine, emergency medicine, surgery, cardiology and gastroenterology. The study found that improved physical examination skills, access to specialist especially via telemedicine and affiliation to centers with more experienced professionals improved diagnosis and overall patient outcome in rural medicine. Conclusion: From this single center study, there is evidence to show that in addition to honing physical examination skills and having access to immediate results of testing done; hospital collaborations and access to highly trained specialist via telemedicine does improve diagnosis in rural medicine.

Keywords: rural medicine, diagnostic accuracy, diagnosis, telemedicine

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Authors: Mitakshara Sharma, Sonal Sharma

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Vesiculobullous diseases are heterogeneous group of dermatological disorders with protean manifestations. The most important technique for the patients with vesiculobullous diseases is conventional histopathology and confirmatory tests like direct immunofluorescence (DIF) and indirect immunofluorescence (IIF). DIF has been used for decades to investigate pathophysiology and in the diagnosis. It detects molecules such as immunoglobulins and complement components. It is done on the perilesional skin. Diagnosis of DIF test depends on features like primary site of the immune deposits, class of immunoglobulin, number of immune deposits and deposition at other sites. The aim of the study is to correlate DIF with clinical and histopathological findings and to analyze the utility of DIF in the diagnosis of these disorders. It is a retrospective descriptive study conducted for 2 years from 2015 to 2017 in Department of Pathology, GTB Hospital on perilesional punch biopsies of vesiculobullous lesions. Biopsies were sent in Michael’s medium. The specimens were washed, frozen and incubated with fluorescein isothiocyanate (FITC) tagged antihuman antibodies IgA, IgG, IgM, C3 & F and were viewed under fluorescent microscope. Out of 401 skin biopsies submitted for DIF, 285 were vesiculobullous diseases, in which the most common was Pemphigus vulgaris (34%) followed by Bullous pemphigoid (21.5%), Dermatitis herpetiformis (16%), Pemphigus foliaceus (11.9%), Linear IgA disease (11.9%), Epidermolysisbullosa (2.39%) and Pemphigus herpetiformis (1.7%). We will be presenting the DIF findings in the all these vesiculobullous diseases. DIF in conjugation with histopathology gives the best diagnostic yield in these lesions. It also helps in the diagnosis whenever there is a clinical and histopathological overlap.

Keywords: antibodies, direct immunofluorescence, pemphigus, vesiculobullous

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Authors: Anas H. Aljemely, Jianping Xuan

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Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed.

Keywords: bearing fault prognostics, developed CNN model, multiple-scale evaluation, deep learning features

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Authors: B. Bouadjemi, S. Bentata, W. Benstaali, A. Abbad, T. Lantri, A. Zitouni

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The purpose of this study was to investigate the structural,electronic and magnetic properties of the cubic praseodymium oxides perovskites PrMnO3. It includes our calculations based on the use of the density functional theory (DFT) with both generalized gradient approximation (GGA) and GGA+U approaches, The spin polarized electronic band structures and densities of states aswellas the integer value of the magnetic moment of the unit cell (6 μB) illustrate that PrMnO3 is half-metallic ferromagnetic. The study shows that the robust half-metallicity makes the cubic PrMnO3 a promising candidate for application in spintronics.

Keywords: Perovskite, DFT, electronic properties, Magnetic moment, half-metallic

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: Maria Ahmadnezhad, Seyedgharani Ghoreishi ‎

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Electrohydraulic servo system have been used in industry in a wide ‎number of applications. Its ‎dynamics are highly nonlinear and also ‎have large extent of model uncertainties and external ‎disturbances. ‎In this thesis, a robust back-stepping control (RBSC) scheme is ‎proposed to overcome ‎the problem of disturbances and system ‎uncertainties effectively and to improve the set-point ‎performance ‎of EHS systems. In order to implement the proposed control ‎scheme, the system ‎uncertainties in EHS systems are considered as ‎total leakage coefficient and effective oil volume. In ‎addition, in ‎order to obtain the virtual controls for stabilizing system, the ‎update rule for the ‎system uncertainty term is induced by the ‎Lyapunov control function (LCF). To verify the ‎performance and ‎robustness of the proposed control system, computer simulation of ‎the ‎proposed control system using Matlab/Simulink Software is ‎executed. From the computer ‎simulation, it was found that the ‎RBSC system produces the desired set-point performance and ‎has ‎robustness to the disturbances and system uncertainties of ‎EHS systems.‎

Keywords: electro hydraulic servo system, back-stepping control, robust back-‎stepping control, Lyapunov redesign‎

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Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

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Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

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Authors: Yingxiao Ye, Christopher Doehring, Angelos Georghiou, Hugh Robinson, Phebe Vayanos

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Human development is a threat to biodiversity, and conservation organizations (COs) are purchasing land to protect areas for biodiversity preservation. However, COs have limited budgets and thus face hard prioritization decisions that are confounded by uncertainty in future human land use. This research proposes a data-driven sequential planning model to help COs choose land parcels that minimize the uncertain human impact on biodiversity. The proposed model is robust to uncertain development, and the sequential decision-making process is adaptive, allowing land purchase decisions to adapt to human land use as it unfolds. The cellular automata model is leveraged to simulate land use development based on climate data, land characteristics, and development threat index from NASA Socioeconomic Data and Applications Center. This simulation is used to model uncertainty in the problem. This research leverages state-of-the-art techniques in the robust optimization literature to propose a computationally tractable reformulation of the model, which can be solved routinely by off-the-shelf solvers like Gurobi or CPLEX. Numerical results based on real data from the Jaguar in Central and South America show that the proposed method reduces conservation loss by 19.46% on average compared to standard approaches such as MARXAN used in practice for biodiversity conservation. Our method may better help guide the decision process in land acquisition and thereby allow conservation organizations to maximize the impact of limited resources.

Keywords: data-driven robust optimization, biodiversity conservation, uncertainty simulation, adaptive sequential planning

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Authors: Maria Ahmadnezhad, Mohammad Reza Soltanpour

Abstract:

Electrohydraulic servo systems have been used in industry in a wide number of applications. Its dynamics ‎are highly nonlinear and also have large extent of model uncertainties and external disturbances. In this ‎thesis, a robust back-stepping control (RBSC) scheme is proposed to overcome the problem of ‎disturbances and system uncertainties effectively and to improve the tracking performance of EHS ‎systems. In order to implement the proposed control scheme, the system uncertainties in EHS systems ‎are considered as total leakage coefficient and effective oil volume. In addition, in order to obtain the ‎virtual controls for stabilizing system, the update rule for the system uncertainty term is induced by the ‎Lyapunov control function (LCF). To verify the performance and robustness of the proposed control ‎system, computer simulation of the proposed control system using Matlab/Simulink Software is ‎executed. From the computer simulation, it was found that the RBSC system produces the desired ‎tracking performance and has robustness to the disturbances and system uncertainties of EHS systems.‎

Keywords: electro hydraulic servo system, back-stepping control, robust back-stepping control, Lyapunov redesign

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Authors: Samra Naz, Aneeqa Ahmed, Qurat-ul-ain Mubarak, Irum Nausheen

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Driver safety systems protect driver from accidents by sensing signs of drowsiness. The paper proposes a technique which can detect the signs of drowsiness and make corresponding decisions to make the driver alert. This paper presents a technique in which the driver will be continuously monitored by a camera and his eyes, head and mouth movements will be observed. If the drowsiness signs are detected on the basis of these three movements under the predefined criteria, driver will be declared as sleepy and he will get alert with the help of alarms. Three robust techniques of drowsiness detection are combined together to make a robust system that can prevent form accident.

Keywords: drowsiness, eye closure, fatigue detection, yawn detection

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

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This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Peri Harish Kumar, Sai Sharan Dwarka, Tajbinder Singh Bains, Suneet John Joseph, Chaitanya Kiran, Sambhu Dutta, Sarah Makram, Mohamed Sayed Zaazouee, Alaa Ahmed Elshanbary

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Objective: To identify cancer and non-cancer causes of death in hepatocellular carcinoma (HCC) patients over different time periods after diagnosis and to compare the mortality risk of each cause in HCC patients with the general population. Methods: In this retrospective cohort study, data of 67,637 HCC patients from 1975 to 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. We investigated the association between different causes of death and the following variables: age, race, tumor stage at diagnosis, and treatment (surgery, chemotherapy, and radiotherapy); each according to the periods of <1 year, 1-5 years, 5-10 years, and >10 years following the diagnosis. Standardized mortality ratios (SMRs) and their 95% confidence intervals (CIs) were calculated for cancer and non-cancer deaths in each of the mentioned periods following diagnosis. Results: Data of 67,637 patients, of whom 50,571 patients died during the follow-up period, were analyzed. Most deaths were due to HCC itself (35,535, 70.3%), followed by other cancers (3,983, 7.9%). Common causes of non-cancer mortality included infectious and parasitic diseases including HIV (2,823 patients, SMR=105.68, 95% CI: 101.82-109.65), chronic liver disease (2,719 patients, SMR=76.56, 95% CI: 73.71,79.5), and heart diseases (1,265 patients, SMR=2.26, 95% CI: 2.14-2.39), with higher mortality risk in HCC patients than in the general population. Conclusion: Cancers stand for most deaths in patients with HCC. Besides, infectious, and parasitic diseases including HIV represent the commonest non-cancer cause of mortality.

Keywords: hepatocellular carcinoma, seer, causes of death, mortality

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Authors: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: Hazem M. Al-Mofleh, John E. Daniels, Joseph W. McKean

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In this paper numerous robust fitting procedures are considered in estimating spatial variograms. In spatial statistics, the conventional variogram fitting procedure (non-linear weighted least squares) suffers from the same outlier problem that has plagued this method from its inception. Even a 3-parameter model, like the variogram, can be adversely affected by a single outlier. This paper uses the Hogg-Type adaptive procedures to select an optimal score function for a rank-based estimator for these non-linear models. Numeric examples and simulation studies will demonstrate the robustness, utility, efficiency, and validity of these estimates.

Keywords: asymptotic relative efficiency, non-linear rank-based, rank estimates, variogram

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Authors: Hanan Alsaeid Alshenawy

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Background: Cervical dysplasia, which is potentially precancerous, has increased in young women. Detection of cervical is important for reducing morbidity and mortality in cervical cancer. This study analyzes the immunohistochemical expression of p16, HPV L1 capsid protein and Ki67 in cervical intraepithelial lesions and correlates them with lesion grade to develop a set of markers for diagnosis and detect the prognosis of cervical cancer precursors. Methods: 75 specimens were analyzed including 15 cases CIN 1, 28 CIN 2, 20 CIN 3, and 12 cervical squamous carcinoma, besides 10 normal cervical tissues. They were stained for p16, HPV L1 and Ki-67. Sensitivity, specificity, predictive values and accuracy were evaluated for each marker. Results: p16 expression increased during the progression from CIN 1 to carcinoma. HPV L1 positivity was detected in CIN 2 and decreased gradually as the CIN grade increased but disappear in carcinoma. Strong Ki-67 expression was observed with high grades CIN and carcinoma. p16, HPV L1 and Ki67 were sensitive but with variable specificity in detecting CIN lesions. Conclusions: p16, HPV L1 and Ki67 are useful set of markers in establishing the risk of high-grade CIN. They complete each other to reach accurate diagnosis and prognosis.

Keywords: p16, HPV L1, Ki67, CIN, cervical carcinoma

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Authors: Shah Abbas

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Rheumatoid arthritis is a systemic, inflammatory autoimmune disease, affecting an overall 1% of the global population. Despite being tremendous efforts by scientists, early diagnosis of RA still has not been achieved. In the current study, a Graphene oxide (GO) based electrochemical sensor has been developed for early diagnosis of RA through Cyclic voltammetry. Chitosan (CHI), a CPnatural polymer has also been incorporated along with GO in order to enhance the biocompatibility and functionalization potential of the biosensor. CCPs are known antigens for Anti Citrullinated Peptide Antibodies (ACPAs) which can be detected in serum even 14 years before the appearance of symptoms, thus they are believed to be an ideal target for the early diagnosis of RA. This study has yielded some promising results regarding the binding and detection of ACPAs through changes in the electrochemical properties of biosensing material. The cyclic voltammogram of this biosensor reflects the binding of ACPAs to the biosensor surface, due to its shifts observed in the current flow (cathodic current) as compared to the when no ACPAs bind as it is absent in RA negative patients.

Keywords: rheumatoid arthritis, peptide sensor, graphene oxide, anti citrullinated peptide antibodies, cyclic voltammetry

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Authors: Marek Dlapa

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The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.

Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value

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Authors: Tayyibah Shah Alam, Joe Thomas, Nayantara Shenoy

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Presenting a case that we would like to share, the answer is straight forward but the path taken to get to the diagnosis is where it gets interesting. A 31-year-old lady presented to the Rheumatology Outpatient department with left-sided chest pain associated with left-sided elbow joint pain intensifying over the last 2 days. She had been having a prolonged history of chest pain with minimal intensity since 2016. The pain is intermittent in nature. Aggravated while exerting, lifting heavy weights and lying down. Relieved while sitting. Her physical examination and laboratory tests were within normal limits. An electrocardiogram (ECG) showed normal sinus rhythm and a chest X-ray with no significant abnormality was noted. The primary suspicion was recurrent costochondritis. Cardiac blood inflammatory markers and Echo were normal, ruling out ACS. CT chest and MRI Thorax contrast showed small ill-defined STIR hyperintensity with thin peripheral enhancement in the anterior mediastinum in the left side posterior to the 5th costal cartilage and anterior to the pericardium suggestive of changes in the fat-focal panniculitis. Confirming the diagnosis as Epicardial fat necrosis. She was started on Colchicine and Nonsteroidal anti-inflammatory drugs for 2-3 weeks, following which a repeat CT showed resolution of the lesion and improvement in her. It is often under-recognized or misdiagnosed. CT scan was collectively used to establish the diagnosis. Making the correct diagnosis prospectively alleviates unnecessary testing in favor of conservative management.

Keywords: EFN, panniculitis, unknown etiology, recurrent chest pain

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Authors: Seema Singh Parmar, Shweta Chauhan

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Aims & Objectives: To evaluate the presence of various psychiatric disorders in patients reporting with a headache as the only symptom. Methodology: 200 patients with the chief complain of a headache who visited the psychiatric OPD of a tertiary care were investigated. Out of them 50 who had pure psychiatric illness without any other neurological disease were investigated, and their diagnosis was made. Independent sample t-tests were applied to generate results. Results: The most common psychiatric diagnosis seen in the sample was Depression (64%) out of which 47% showed features of Depression with anxious distress. Other psychiatric disorders seen were Generalized Anxiety Disorder, Panic Attacks, Somatic Symptom Disorder and Obsessive Compulsive Disorder. For pure psychiatry, headache related illnesses female to male ratio was 1.64. Conclusion: The increasing frequency of psychiatric disorders among patients who only visit the doctor seeking treat a headache shows the need for better identification of psychiatric disorders because proper diagnosis and target of psychiatric treatment shall give complete relief to the patient’s symptomatology.

Keywords: anxiety disorders, depression, headache, panic attacks

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

Abstract:

Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

Procedia PDF Downloads 175

Authors: James Boag

Abstract:

The needs of people with a diagnosis of dementia and their carers and families are physical, psychosocial, and psychological and begin at the time of diagnosis. There is frequently a lack of emotional support and counselling. Care- giving support is required from the presentation of the first symptoms of dementia until death. Alzheimer's disease begins decades before the clinical symptoms begin to appear, and in many cases, it remains undiagnosed, or diagnosed too late for any possible interventions to have any effect. However, if an incorrect diagnosis is given, it may result in a person being treated, without effect, for a type of dementia they do not have and delaying the interventions they should have received. Being diagnosed with dementia can cause emotional distress to the person, and physical and emotional support is needed, which will become more important as the disease progresses. The severity of the patient's dementia and their symptoms has a bearing of the impact on the carer and the support needed. A lack of insight and /or a denial of the diagnosis, grief, reacting to anticipated future losses, and coping methods to maximise the disease outcome, are things that should be addressed. Because of the stigma, it is important for carers not to lose contact with family and others because social isolation leads to depression and burnout. The impact on a carer's well- being and quality of life can be influenced by the severity of the illness, its type of dementia, its symptoms, healthcare support, financial and social status, career, age, health, residential setting, and relationship to the patient. Carer burnout due to lack of support leads to people diagnosed with dementia being put into residential care prematurely. Often dementia is not recognised as a terminal illness, limiting the ability of the person diagnosed with dementia and their carers to work on advance care planning and getting access to palliative and other support. Many carers have been satisfied with the physical support they were given in their everyday life, however, it was agreed that there was an immense unmet need for psychosocial support, especially after diagnosis and approaching end of life. Providing continuity and coordination of care is important. Training is necessary for providers to understand that every case is different, and they should understand the complexities. Grief, the emotional response to loss, is suffered during the progression of the disease and long afterwards, and carers should continue to be supported after the death of the person they were caring for.

Keywords: dementia, caring, challenges, needs

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Authors: Chih-Chieh Yang, Chung-Neng Huang

Abstract:

In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources.

Keywords: ANFIS, fault diagnosis, power system, smart meter

Procedia PDF Downloads 113

Authors: Walied Hanora

Abstract:

This paper presents the problem of robust state feedback H∞ for discrete time nonlinear system represented by Takagi-Sugeno fuzzy systems. Based on fuzzy lyapunov function, the condition ,which is represented in the form of Liner Matrix Inequalities (LMI), guarantees the H∞ performance of the T-S fuzzy system with uncertainties. By comparison with recent literature, this approach will be more relaxed condition. Finally, an example is given to illustrate the proposed result.

Keywords: fuzzy lyapunov function, H∞ control , linear matrix inequalities, state feedback, T-S fuzzy systems

Procedia PDF Downloads 253

Authors: Siwar Fadhel, Imen Bahri, Man Zhang

Abstract:

The switched reluctance machine (SRM) has undeniable qualities in terms of low cost and mechanical robustness. However, its highly nonlinear character and its uncertain parameters justify the development of complicated controls. In this paper, authors present the design of a robust H-infinity current controller for an 8/6 SRM with taking into account the nonlinearity of the SRM and with rejection of disturbances. The electromagnetic torque is indirectly regulated through the current controller. To show the performances of this control, a robustness analysis is performed by comparing the H-infinity and PI controller simulation results. This comparison demonstrates better performances for the presented controller. The effectiveness and robustness of the presented controller are also demonstrated by experimental tests.

Keywords: current regulation, experimentation, robust H-infinity control, switched reluctance machine

Procedia PDF Downloads 278