Search results for: pregnancy diagnosis

Authors: K. Yahia, A. Ghoggal, A. Titaouine, S. E. Zouzou, F. Benchabane

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This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method.

Keywords: Induction Motors (IMs), Inter-turn Short-Circuits Diagnosis, Discrete Wavelet Transform (DWT), Current Park’s Vector Modulus (CPVM)

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Authors: Buse Bahitli, Samiye Mete

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The aim of the study was to evaluate the quality of sexual life of women with diagnosed gynecological cancer and receive treatment. The study was a descriptive and cross-sectional type, and it was carried out with 276 women. Information Form and Sexual Quality of Life Scale-Female (SQOL) form was used in the study. The data was evaluated using Mann-Whitney U and Kruskal-Wallis test. In the study, Sexual Quality of Life Scale-Female average score was 68.83 ± 21.17. The %43.1 of women was endometrial cancer, %30.8 was cervical cancer, %24.6 was ovarian cancer, and %1.4 was vulvar cancer. The average time to diagnosis of patients is 41.80 ± 47.64 months. There was no significant difference mean SQOL according to individual/sociodemographic characteristics like age, education. Gynecological cancer-related characteristics like gynaecological cancer type, treatment type, surgery type were found not to affect the mean score of SQOL. However, it was found that the difference was due to the higher SQOL score in the group with a diagnosis time of 25 months and over (X²KW= 6.356, p= 0.046). The reason of significant difference means SQOL according to diagnosis over time might be that women adapted to cancer diagnosis. While women with gynaecologic cancer are evaluating their sexual lives, it is necessary to evaluate them with good evaluation tools.

Keywords: gynecological cancers, sexuality, quality of sexual life, SQOL

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Authors: Z. Nurzaireena, K. Azalea, T. Azirawaty, S. Jameela, G. Muralitharan

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The aim of this study is the review of the management practices of sickle cell disease patients during pregnancy, as well as the maternal and neonatal outcome at Ampang Hospital, Selangor. The study consisted of a review of pregnant patients with sickle cell disease under follow up at the Hematology Clinic, Ampang Hospital over the last seven years to assess their management and maternal-fetal outcome. The results of the review show that Ampang Hospital is considered the public hematology centre for sickle cell disease and had successfully managed three pregnancies throughout the last seven years. Patients’ presentations, managements and maternal-fetal outcome were compared and reviewed for academic improvements. All three patients were seen very early in their pregnancy and had been given a regime of folic acid, antibiotics and thrombo-prophylactic drugs. Close monitoring of maternal and fetal well being was done by the hematologists and obstetricians. Among the patients, there were multiple admissions during the pregnancy for either a painful sickle cell bone crisis, haemolysis following an infection and anemia requiring phenotype- matched blood and exchange transfusions. Broad spectrum antibiotics coverage during and infection, hydration, pain management and venous-thrombolism prophylaxis were mandatory. The pregnancies managed to reach near term in the third trimester but all required emergency caesarean section for obstetric indications. All pregnancies resulted in live births with good fetal outcome. During post partum all were nursed closely in the high dependency units for further complications and were discharged well. Post partum follow up and contraception counseling was comprehensively given for future pregnancies. Sickle cell disease is uncommonly seen in the East, especially in the South East Asian region, yet more cases are seen in the current decade due to improved medical expertise and advance medical laboratory technologies. Pregnancy itself is a risk factor for sickle cell patients as increased thrombosis event and risk of infections can lead to multiple crisis, haemolysis, anemia and vaso-occlusive complications including eclampsia, cerebrovasular accidents and acute bone pain. Patients mostly require multiple blood product transfusions thus phenotype-matched blood is required to reduce the risk of alloimmunozation. Emphasizing the risks and complications in preconception counseling and establishing an ultimate pregnancy plan would probably reduce the risk of morbidity and mortality to the mother and unborn child. Early management for risk of infection, thromboembolic events and adequate hydration is mandatory. A holistic approach involving multidisciplinary team care between the hematologist, obstetricians, anesthetist, neonatologist and close nursing care for both mother and baby would ensure the best outcome. In conclusion, sickle cell disease by itself is a high risk medical condition and pregnancy would further amplify the risk. Thus, close monitoring with combine multidisciplinary care, counseling and educating the patients are crucial in achieving the safe outcome.

Keywords: anaemia, haemoglobinopathies, pregnancy, sickle cell disease

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Authors: Jalel Khelil

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In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Warda Fatima, Hasnain Javed

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The emerging trend of Drug resistance in childhood Tuberculosis is increasing worldwide and now becoming a priority challenge for National TB Control Programs of the world. Childhood TB accounts for 10-15% of total TB burden across the globe and same proportion is quantified in case of drug resistant TB. One third population suffering from MDR TB dies annually because of non-diagnosis and unavailability of appropriate treatment. However, true Childhood MDR TB cannot be estimated due to non-confirmation. Diagnosis of Pediatric TB by sputum Smear Microscopy and Culture inoculation are limited due to paucibacillary nature and difficulties in obtaining adequate sputum specimens. Diagnosis becomes more difficult when it comes to HIV infected child. New molecular advancements for early case detection of TB and MDR TB in adults have not been endorsed in children. Multi centered trials are needed to design better diagnostic approaches and efficient and safer treatments for DR TB in high burden countries. The aim of the present study is to sketch out the current situation of the childhood Drug resistant TB especially in the developing world and to highlight the classic and novel methods that are to be implemented in high-burden resource-limited locations.

Keywords: drug resistant TB, childhood, diagnosis, novel methods

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Authors: Frederick Huie, Lauren Fusfeld, William Burchenal, Scott Howell, Alyssa McVey, Thomas F. Goss

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Alzheimer’s Disease (AD) is a degenerative brain disease characterized by memory loss and cognitive decline that presents a substantial economic burden for patients and health insurers in the US. This study evaluates the payer budget impact of the DISCERN™ test in the diagnosis and management of patients with symptoms of dementia evaluated for AD. DISCERN™ comprises three assays that assess critical factors related to AD that regulate memory, formation of synaptic connections among neurons, and levels of amyloid plaques and neurofibrillary tangles in the brain and can provide a quicker, more accurate diagnosis than tests in the current diagnostic pathway (CDP). An Excel-based model with a three-year horizon was developed to assess the budget impact of DISCERN™ compared with CDP in a Medicare Advantage plan with 1M beneficiaries. Model parameters were identified through a literature review and were verified through consultation with clinicians experienced in diagnosis and management of AD. The model assesses direct medical costs/savings for patients based on the following categories: •Diagnosis: costs of diagnosis using DISCERN™ and CDP. •False Negative (FN) diagnosis: incremental cost of care avoidable with a correct AD diagnosis and appropriately directed medication. •True Positive (TP) diagnosis: AD medication costs; cost from a later TP diagnosis with the CDP versus DISCERN™ in the year of diagnosis, and savings from the delay in AD progression due to appropriate AD medication in patients who are correctly diagnosed after a FN diagnosis.•False Positive (FP) diagnosis: cost of AD medication for patients who do not have AD. A one-way sensitivity analysis was conducted to assess the effect of varying key clinical and cost parameters ±10%. An additional scenario analysis was developed to evaluate the impact of individual inputs. In the base scenario, DISCERN™ is estimated to decrease costs by $4.75M over three years, equating to approximately $63.11 saved per test per year for a cohort followed over three years. While the diagnosis cost is higher with DISCERN™ than with CDP modalities, this cost is offset by the higher overall costs associated with CDP due to the longer time needed to receive a TP diagnosis and the larger number of patients who receive a FN diagnosis and progress more rapidly than if they had received appropriate AD medication. The sensitivity analysis shows that the three parameters with the greatest impact on savings are: reduced sensitivity of DISCERN™, improved sensitivity of the CDP, and a reduction in the percentage of disease progression that is avoided with appropriate AD medication. A scenario analysis in which DISCERN™ reduces the utilization for patients of computed tomography from 21% in the base case to 16%, magnetic resonance imaging from 37% to 27% and cerebrospinal fluid biomarker testing, positive emission tomography, electroencephalograms, and polysomnography testing from 4%, 5%, 10%, and 8%, respectively, in the base case to 0%, results in an overall three-year net savings of $14.5M. DISCERN™ improves the rate of accurate, definitive diagnosis of AD earlier in the disease and may generate savings for Medicare Advantage plans.

Keywords: Alzheimer’s disease, budget, dementia, diagnosis.

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Authors: Bonnie R. Brammall, Rhonda M. Garad, Helena J. Teede, Cheryce L. Harrison

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Introduction: Reproductive aged women are at high-risk for accelerated weight gain and obesity development, with pregnancy recognised as a critical contributory life phase. Healthy lifestyle interventions during the preconception and antenatal period improve maternal and infant health outcomes. Yet, interventions from preconception through to postpartum and translation and implementation into real-world healthcare settings remain limited. OptimalMe is a randomised, hybrid implementation effectiveness study of evidence-based healthy lifestyle intervention. Here, we report engagement, acceptability of the intervention during preconception, and self-reported behaviour change outcomes as a result of the preconception phase of the intervention. Methods: Reproductive aged women who upgraded their private health insurance to include pregnancy and birth cover, signalling a pregnancy intention, were invited to participate. Women received access to an online portal with preconception health and lifestyle modules, goal-setting and behaviour change tools, monthly SMS messages, and two coaching sessions (randomised to video or phone) prior to pregnancy. Results: Overall n=527 expressed interest in participating. Of these, n=33 did not meet inclusion criteria, n=8 were not contactable for eligibility screening, and n=177 failed to engage after the screening, leaving n=309 who were enrolled in OptimalMe and randomised to intervention delivery method. Engagement with coaching sessions dropped by 25% for session two, with no difference between intervention groups. Women had a mean (SD) age of 31.7 (4.3) years and, at baseline, a self-reported mean BMI of 25.7 (6.1) kg/m², with 55.8% (n=172) of a healthy BMI. Behaviour was sub-optimal with infrequent self-weighing (38.1%), alcohol consumption prevalent (57.1%), sub-optimal pre-pregnancy supplementation (61.5%), and incomplete medical screening. Post-intervention 73.2% of women reported engagement with a GP for preconception care and improved lifestyle behaviour (85.5%), since starting OptimalMe. Direct pre-and-post comparison of individual participant data showed that of 322 points of potential change (up-to-date cervical screening, elimination of high-risk behaviours [alcohol, drugs, smoking], uptake of preconception supplements and improved weighing habits) 158 (49.1%) points of change were achieved. Health coaching sessions were found to improve accountability and confidence, yet further personalisation and support were desired. Engagement with video and phone sessions was comparable, having similar impacts on behaviour change, and both methods were well accepted and increased women's accountability. Conclusion: A low-intensity digital health and lifestyle program with embedded health coaching can improve the uptake of preconception care and lead to self-reported behaviour change. This is the first program of its kind to reach an otherwise healthy population of women planning a pregnancy. Women who were otherwise healthy showed divergence from preconception health and lifestyle objectives and benefited from the intervention. OptimalMe shows promising results for population-based behaviour change interventions that can improve preconception lifestyle habits and increase engagement with clinical health care for pregnancy preparation.

Keywords: preconception, pregnancy, preventative health, weight gain prevention, self-management, behaviour change, digital health, telehealth, intervention, women's health

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Authors: S Eli, DGB Kalio, BOA Altraide, P Kua, DA MacPepple, FE Okonofua

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Background: Anaemia in pregnancy is worrisome morbidity encountered by obstetricians and gynaecologist in the developing countries of the world. It is an indirect cause of maternal mortality and also a cause of perinatal mortality. Aim: The study aimed to ascertain the prevalence of anaemia amongst antenatal clinic (ANC) attendees at booking at The Rivers State University Teaching Hospital (RSUTH), Port Harcourt, Rivers State, Nigeria. Method: The method was a cross-sectional study of ANC attendees at booking at RSUTH. The cut-off for anaemia by the WHO used for this study was packed cell volume (PCV) less than 33%. Simple randomized sampling method was used. Information was analyzed using SPSS version 25. Result: A total of 500 questionnaires were distributed, and 488 questionnaires retrieved. The mean age was of the ANC attendees was 31.44 years, and the modal parity was 0. Three hundred and fifty-seven (73.2%) of the respondents had a tertiary level of education, 126(25.8%) had a secondary level of education while 5 (1%) of the respondents had a primary level of education. Five (1%) of the respondents did not volunteer their educational status. The modal packed cell volume was 32%. Three hundred and eighty-two (78.3%) of the ANC attendees had PCV level less than 33% compared to 106 (21.7%) who had PCV equal or greater than 33%. Conclusion: The study revealed that the prevalence of anaemia in pregnancy amongst ANC attendees at the RSUTH was high, representing 73.3% of the subjects. Anaemia was common amongst multiparas (38.5%). Malaria prophylaxis, as well as encouraging pregnant women to be compliant with their routine antenatal drugs as well as counseling on the right diet, cannot be overemphasized during pregnancy. In addition, women should use family planning for child spacing for them to recover from previous pregnancies.

Keywords: anaemia, ANC attendees, Nigeria, prevalence

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Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

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Authors: Ibrahim Aly, Rabab Zalat, Bahaa EL Deen W. El Aswad, Ismail M. Moharm, Basam M. Masoud, Tarek Diab

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The objective of the present study was to evaluate the novel nanomagnetic beads based–latex agglutination assay (NMB-LAT) as a simple test for diagnosis of S. haematobium as well as standardize the novel nanomagnetic beads based –ELISA (NMB-ELISA). According to urine examination this study included 85 S. haematobium infected patients, 30 other parasites infected patients and 25 negative control samples. The sensitivity of novel NMB-LAT was 82.4% versus 96.5% and 88.2% for NMB-ELISA and currently used sandwich ELISA respectively. The specificity of NMB-LAT was 83.6% versus 96.3% and 87.3% for NMB-ELISA and currently used sandwich ELISA respectively. In conclusion, the novel NMB-ELISA is a valuable applicable diagnostic technique for diagnosis of human schistosomiasis haematobium. The novel NMB-ELISA assay is a suitable applicable diagnostic method in field survey especially when followed by ELISA as a confirmatory test in query false negative results. Trials are required to increase the sensitivity and specificity of NMB-ELISA assay.

Keywords: diagnosis, iatex agglutination, nanomagnetic beads, sandwich ELISA

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Frazer Kirk, Rohen Skiba, Pankaj Saxena

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The natural history of the QAV is poorly understood due to the exceeding rarity of the condition. Incidence rates vary between 0.00028-1%. Classically patients present with Aortic Regurgitation (AR) between 40-60 years of age experiencing palpitations, chest pain, or heart failure. (1, 2) Echocardiography is the mainstay of diagnosis for this condition; however, given the rarity of this condition, it can easily be overlooked, as demonstrated here. The case report that follows serves as a reminder of the condition to reduce the innate cognitive bias to overlook the diagnosis due to the availability heuristic. Intraoperative photography, echocardiographic and magnetic resonance imaging from this case for reference to demonstrate that while the diagnosis of Aortic regurgitation was recognized early, the valve morphology was underappreciated.

Keywords: quadricuspid aortic valve, cardiac surgery, echocardiography, congenital

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Authors: Nermeen Elbeltagy, Helmy abd Elsatar, Sara Hassan, Mohamed Darwish

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Introduction: with the inial appearance in Wuhan, China, in December 2019, the coronavirus disease-related respiratory infection (COVID-19) has rapidly spread among people all over the world. The WHO considered it a pandemic in March 2020. The severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreaks have proved that pregnant females as well as their fetuses are exposed to adverse outcomes, including high rates of intensive care unit (ICU) admission and case fatality. Physiological changes occurring during pregnancy such as the increased transverse diameter of the thoracic cage as well as the elevation of the diaphragm can expose the mother to severe infections because of her decreased tolerance for hypoxia. Furthermore, vasodilation and changes in lung capacity can cause mucosal edema and an increase in upper respiratory tract secretions. In addition, the increased susceptibility to infection is enhanced by changes in cellmediated immunity. Aim of the work: to study the effect of COVID-19 on pregnant females admitted to El-Shatby Maternity University Hospital regarding maternal antepartum, intrapartum and postpartum adverse effects on the mothers and their neonates. Method: A retrospective cohort study was done between October 2020 and October 2022. Maternal characteristics and associated health conditions of COVID-19 positive parents were investigated. Also, the severity of their conditions and me of infection (first or second or third trimester)were explored. Cases were diagnosed based on presence of symptoms suggestive of COVID-19, laboratory tests (other than PCR) and radiological findings.all cases were confirmed by positive PCR test results. Results: The most common adverse maternal outcomes were pre-term labor (11.6%) followed by premature rupture of membranes (5.7%), post-partum hemorrhage (5.4%), preeclampsia (5.0%) and placental abrupon (4.3%). One sixth of the neonates of the studied paents were admied to NICUs and 6.5% of them had respiratory distress with no neonatal deaths. The majority of neonates (85.4%) had a birth weight of 2500- 4000g (normal range). Most of the neonates (77.9%) had an APGAR score of equal or more than 7 in 5 minutes. Conclusion: the most common comorbidity that might increase the incidence of COVID-19 before pregnancy were diabetes, cardiac disorders/ chronic hypertension and chronic obstructive lung diseases (non-asthma). During pregnancy, anemia followed by gestational diabetes and pre-eclampsia/gestational hypertension were the most prevalent comorbidity. So, severity of infection can be reduced by good antenatal care.

Keywords: COVID-19, pregnancy outcome, complicated pregnancy., COVID in Egypt

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Authors: Nabaweesi Josephine, Namwanje Regina Germina

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Antenatal care is referred to as the totality of care given to pregnant women from conception to delivery from a certified health care setting. A number of 8 contacts is recommended throughout pregnancy, according to WHO, 2016. Antenatal services are free in Uganda courtesy of the government of Uganda, though attendance is still very low, which has continued to cause maternal and infant mortality and morbidity from preventable causes. Early booking has an advantage for proper pregnancy information sharing and pregnancy monitoring. The purpose of this study was to determine pregnant women's knowledge, attitudes, and practices towards attendance of antenatal care at Mukono General Hospital. A sample of 60 pregnant women was used, and a descriptive quantitative design was employed. Data was collected using a structured questionnaire consisting of questions about socio-demographic factors, knowledge, attitude, and practice, and this was affected using the structured interview method. Pregnant women had good practice at 90.2%, a positive attitude of 94.6%, and slightly less knowledge of 66.7%. Only 12% were knowledgeable about the number of antenatal care visits recommended, 45% had knowledge about when to initiate first antenatal care visit, and 79% had a positive attitude towards the early booking. We recommend that pregnant women are given all the necessary information regarding antenatal care with special emphasis on the recommended number of visits and when to initiate their first visit and encourage early booking in order to achieve the 8 contacts WHO policy for antenatal care since when we increase knowledge, we increase antenatal care utilization according to Anderson's behavioral model.

Keywords: ANC- antenatal care, contacts, mortality, morbidity

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Ilona Krone

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Maternal smoking during pregnancy increases the risk of perinatal/postnatal negative health outcomes; however, only 1 in 5 pregnant smokers quit smoking. That is a clinical and public health problem. Pregnant smokers have negative paternal support, and higher levels of perceived stress than non-smokers and quitters return to smoking in a stressful situation. A crisis like the COVID-19 outbreak causes significant uncertainty and stress. For pregnant women, additional stress may increase due to concerns for their fetus. Strategies targeting maternal stress and isolation may be particularly useful to prevent negative outcomes for women and their fetuses. Within the post-doctoral study, cooperating with leading specialists, an innovative program for pregnant smokers will be developed. Feasibility for reducing craving, distress intolerance, Covid 19 related stress, and fear in pregnant women in Latvia will be assessed.

Keywords: COVID 19, mindfulness, motivation, pregnancy, smoking cessation

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Authors: Hacer Unver, Rukuye Aylaz

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Aim: This study was conducted in order to determine the effects of exercising on quality of life in pregnancy. Material and Method: The population of the study was formed by 580 pregnants who were registered to 10 Family Health Center located in the city center of Malatya. The sample of the study, on the other hand, was formed by 230 pregnants who had minimal sample size according to known population sample size calculation. The data of this descriptive study was collected between October 2013 and September 2014 from the Family Health Centers located in the city center of Malatya. The data were collected using pregnant introductory form, exercise benefit and barrier scale, quality of life scale. Percentage distributions, t-test, Variance Analysis (ANOVA), Kruskal-Wallis, Mann-Whitney U and Pearson Correlation tests were used in the analysis of the data. Result: It was determined that 69.1% of the pregnants participating to the study did not know the benefits of exercising and 89.6% did not exercise. Quality of life mental health scores of those who exercised were determined to be higher and statistically significant (p<0.05). A positive correlation was determined between the exercise benefit scala and physical quality of life scores of the pregnants in this study (0.268, p=0.001). It was also detected that the more exercise performed led to higher total quality of life scores. Conclusion: In consequence, exercising was determined to positively affect the quality of life in pregnants. Therefore, it is recommended that nurses should give education regarding the importance and benefits of exercise during pregnancy in order to increase the quality of life.

Keywords: exercise, midwife, pregnant woman, quality of life

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Authors: O. P. Rahi, Manoj Kumar

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Power transformers are essential and expensive equipments in electrical power system. Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by conventional method is not always an easy task due to variability of gas data and operational variables. In this paper, an extension theory based power transformer fault diagnosis method is presented. Extension theory tries to solve contradictions and incompatibility problems. This paper first briefly introduces the basic concept of matter element theory, establishes the matter element models for three-ratio method, and then briefly discusses extension set theory. Detailed analysis is carried out on the extended relation function (ERF) adopted in this paper for transformer fault diagnosis. The detailed diagnosing steps are offered. Simulation proves that the proposed method can overcome the drawbacks of the conventional three-ratio method, such as no matching and failure to diagnose multi-fault. It enhances diagnosing accuracy.

Keywords: DGA, extension theory, ERF, fault diagnosis power transformers, fault diagnosis, fuzzy logic

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Authors: Tahamina Begum, Wan Nor Azlen Wan Mohamad, Faruque Reza, Wan Rosilawati Wan Rosli

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To assess auditory perception and cognitive function during pregnancy is necessary as the pregnant women need extra effort for attention mainly for their executive function to maintain their quality of life. This study aimed to investigate neural correlates of cognitive and behavioral processing during mid trimester pregnancy. Event-Related Potentials (ERPs) were studied by using 128-sensor net and PAS or COWA (controlled Oral Word Association), WCST (Wisconsin Card Sorting Test), RAVLTIM (Rey Auditory Verbal and Learning Test: immediate or interference recall, delayed recall (RAVLT DR) and total score (RAVLT TS) were tested for neuropsychology assessment. In total 18 subjects were recruited (n= 9 in each group; control and pregnant group). All participants of the pregnant group were within 16-27 (mid trimester) weeks gestation. Age and education matched control healthy subjects were recruited in the control group. Participants were given a standardized test of auditory cognitive function as auditory oddball paradigm during ERP study. In this paradigm, two different auditory stimuli (standard and target stimuli) were used where subjects counted silently only target stimuli with giving attention by ignoring standard stimuli. Mean differences between target and standard stimuli were compared across groups. N100 (auditory sensory ERP component) and P300 (auditory cognitive ERP component) were recorded at T3, T4, T5, T6, Cz and Pz electrode sites. An equal number of electrodes showed non-significantly shorter amplitude of N100 component (except significantly shorter at T3, P= 0.05) and non-significant longer latencies (except significantly longer latency at T5, P= 0.008) of N100 component in pregnant group comparing control. In case of P300 component, maximum electrode sites showed non-significantly higher amplitudes and equal number of sites showed non-significant shorter latencies in pregnant group comparing control. Neuropsychology results revealed the non-significant higher score of PAS, lower score of WCST, lower score of RAVLTIM and RAVLTDR in pregnant group comparing control. The results of N100 component and RAVLT scores concluded that auditory perception is mildly impaired and P300 component proved very mild cognitive dysfunction with good executive functions in second trimester of pregnancy.

Keywords: auditory perception, pregnancy, stimuli, trimester

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Authors: Toumi Farah, Charallah Salima

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In the Algerian Sahara, goat farming is predominant, and it’s associated with other types of breeding, particularly camel and sheep; it also constitutes a significant proportion of breeding exclusively goat. This Saharan goat is a small ruminant with a black dress with white’s spots, hanging ears, and a coat more or less long. It is known for its hardiness and resistance to adverse conditions of arid zones and its perfect ecophysiological adaptation to harsh environmental conditions. However, pregnancy alterations, particularly abortion, degrade its productivity and cause economic losses, having both direct and indirect effects on animal production, like the costs of veterinary interventions and the reconstitution of livestock. The purpose of this work is to study the histological aspect of the liver of goats’ aborted living under nomadic herds in the region of Béni-Abbès (30° 7' N, 2° 10 'O). The organs were collected in physiological serum, rinsed, and then fixed with formaldehyde (37°, diluted at 10%). After that, these samples were processed for a topographic study. The morphometric study of the liver was performed by using an image analysis and processing software "Image J"; the various measurements obtained are intended to specify the supposed stage of development according to the body weight. The histological structure of the liver shows that the hepatic parenchyma consists of vascular conjunctive spaces surrounded by Glisson’s capsule. The sinusoids and hepatic portal vein are full of red blood cells, representing sinusoidal congestion and a thrombosed vein. At high magnification, the blood vessels show the presence of vascular thrombosis and haemorrhage in some areas of the hepatic parenchyma. Morphometric analysis shows that the number of liver parenchymal cells and the diameter of liver vessels vary according to the stage of development. The results obtained will provide details of the anatomical and cellular elements that can be used in the diagnosis of early or late abortion and late embryonic death. It would be interesting to find, by immunohistochemistry, some inflammatory markers useful for monitoring the progress of pregnancy and bioindicators of fetomaternal distress.

Keywords: aborting goat, arid zone, liver, histopathology

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Authors: D. P. Rahardja, A. L. Toleng, M. Yusuf

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The objective of this study was to investigate the life-long effect of in utero nutrition fed at different stages of pregnancy in Bali cows (n = 40): (U1) without in utero nutrition (0 – parturition, negative control); (U2) 0 – 90 d of gestation; (U3) 90 - 180 d of gestation; (U4) 180 d – parturition; and (U5) in utero nutrition along gestation period (0 d to parturition – positive control) on the growth performance of the offspring to weaning age. The results indicated that effect of maternal nutrition on male and female offspring were particularly indicated by the growth performance of both the male and female offspring from birth to weaning.

Keywords: Bali cows, birth weight, maternal nutrition, pre-weaning daily gain, weaning weight

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Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

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In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

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Authors: F. Sallem, B. Dahhou, A. Kamoun

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In this work, the main problem considered is the detection and the isolation of the actuator fault. A new formulation of the linear system is generated to obtain the conditions of the actuator fault diagnosis. The proposed method is based on the representation of the actuator as a subsystem connected with the process system in cascade manner. The designed formulation is generated to obtain the conditions of the actuator fault detection and isolation. Detectability conditions are expressed in terms of the invertibility notions. An example and a comparative analysis with the classic formulation illustrate the performances of such approach for simple actuator fault diagnosis by using the linear model of nuclear reactor.

Keywords: actuator fault, Fault detection, left invertibility, nuclear reactor, observability, parameter intervals, system inversion

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Authors: Andres Frederic

Abstract:

We present the MindFlow system supporting the detection and the diagnosis of stresses. The heart of the system is a knowledge synthesis engine allowing occupational health stakeholders (psychologists, occupational therapists and human resource managers) to formulate queries related to stress and responding to users requests by recommending a pattern of stress if one exists. The stress pattern diagnosis is based on expert knowledge stored in the MindFlow stress ontology including stress feature vector. The query processing may involve direct access to the MindFlow system by occupational health stakeholders, online communication between the MindFlow system and the MindFlow domain experts, or direct dialog between a occupational health stakeholder and a MindFlow domain expert. The MindFlow knowledge model is generic in the sense that it supports the needs of psychologists, occupational therapists and human resource managers. The system presented in this paper is currently under development as part of a Dutch-Japanese project and aims to assist organisation in the quick diagnosis of stress patterns.

Keywords: occupational stress, stress management, physiological measurement, accident prevention

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Authors: Rajamani Doraiswami, Lahouari Cheded

Abstract:

Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system.

Keywords: identification, linear parameter-varying systems, least-squares estimation, fault diagnosis, Kalman filter, emulators

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Authors: Bin Feng, Zhulin Zong

Abstract:

Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: S. Hallal, Z. Chami, A. Hadji-Lehtihet, S. Sokhal-Boudella, A. Berhoune, L. Yargui

Abstract:

Gaucher disease is the most common lysosomal storage in our population, it is due to a deficiency of β –glucosidase acid. The enzyme deficiency causes a pathological accumulation of undegraded substrate in lysosomes. This metabolic overload is responsible for a multisystemic disease with hepatosplenomegaly, anemia, thrombocytopenia, and bone involvement. Neurological involvement is rare. The laboratory diagnosis of Gaucher disease consists of phenotypic diagnosis by determining the enzymatic activity of β - glucosidase by fluorimetric method, a study by genotypic diagnosis in the GBA gene, limiting the search recurrent mutations (N370S, L444P, 84 GG); PCR followed by an enzymatic digestion. Abnormal profiles were verified by sequencing. Monitoring of treated patients is provided by the determination of chitotriosidase. Our experience spaning a period of 6 years (2007-2014) has enabled us to diagnose 78 patients out of a total of 328 requests from the various departments of pediatrics, internal medicine, neurology. Genotypic diagnosis focused on the entire family of 9 children treated at pediatric CHU Mustapha, which help define the clinical form; or 5 of them had type III disease, carrying the L444P mutation in the homozygous state. Three others were composite (N370/L444P) (N370S/other unintended mutation in our study), and only in one family no recurrent mutation has been found. This molecular study permits screening of heterozygous essential for genetic counseling.

Keywords: Gaucher disease, mutations, N370S, L444P

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

Abstract:

Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Toufik Bensana, Slimane Mekhilef, Kamel Tadjine

Abstract:

Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally non stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, condition monitoring, local mean decomposition, rolling element bearing, vibration analysis

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Authors: Agus Sahri, Cahya Putra Dinata, Faishal Andhi Rokhman

Abstract:

Intersection is a critical area on a highway which is a place of conflict points and congestion due to the meeting of two or more roads. Conflicts that occur at the intersection include diverging, merging, weaving, and crossing. To deal with these conflicts, a crossing control system is needed, at a plot of intersection there are two control systems namely signal intersections and non-signalized intersections. The control system at a plot of intersection can affect the intersection performance. In Indonesia there are still many intersections with poor intersection performance. In analyzing the parameters to measure the performance of a plot of intersection in Indonesia, it is guided by the 1997 Indonesian Road Capacity Manual. For this reason, this study aims to develop laboratory diagnostics at plot intersections to analyze parameters that can affect the performance of an intersection. The research method used is research and development. The laboratory diagnosis includes anamnesis, differential diagnosis, inspection, diagnosis, prognosis, specimens, analysis and sample data analysts. It is expected that this research can encourage the development and application of laboratory diagnostics at a plot of intersection in Indonesia so that intersections can function optimally.

Keywords: intersection, the laboratory diagnostic, control systems, Indonesia

Procedia PDF Downloads 180