Varied clinical presentations of scar endometriosis: case reports

Endometriosis is the second most prevalent benign gynaecological condition, in reproductive age women, defined as the presence of functioning endometrial tissue outside uterine cavity. Scar endometriosis is a rare entity with an incidence of 0.3-1% among reproductive age women following obstetric and gynaecologic surgeries. Complaints of pain, tender mass, cyclical association with menstrual cycle is almost pathognomonic. Here we present two case reports of scar endometriosis recently encountered at our institute – caesarean scar endometriosis and episiotomy scar endometriosis. The quoted episiotomy scar endometriosis incidence is about 0.06 - 0.07% while that of caesarean scar endometriosis is 0.03-0.4%

RESPIRATORY FUNCTIONS IN TEXTILE MILL WORKERS: ROLE OF PEAK EXPIRATORY FLOW RATE

Background: Textile industries play a significant role in economical growth of the country. Health concern of these textile mill workers is the biggest challenge. Respiratory alterations were found in these workers with long term exposure of cotton dust. Effect of cotton dust on peak expiratory flow rate is not very well documented in the past.Aim/objective: The main objective of this study was the assessment of respiratory functions in textile mill workers along with the effect on peak expiratory flow rate in these workers.Material & Methods: 130 male textile mill workers were recruited for this study from the different sections of a textile mill. In which, 80 workers were present from exposed and remaining 50 were from non exposed area of a textile mill. 30 to 40 year of age workers with the working history of not more than 5 years, were included for the part of the study. Their Body mass index and respiratory functions were evaluated. Body mass index was calculated by using weight and height of an individual. Respiratory functions including Forced vital capacity and forced expiratory volume in 1 second was estimated by using spirometer and peak expiratory flow rate was measured by means of Wright's peak flow meter.Results: the mean values of FVC, FEV1 and PEFR were found to be significantly (<0.01) lower in exposed group of textile mill when compared non exposed groups. When these values were compared on the basis of BMI, workers with BMI>25kg/m2 were found with lower mean values of FVC, FEV1 and PEFR when compared with workers with BMI<25kg/m2. Also there was significant (<0.05) negative correlation between BMI and PEFR in both exposed group and non exposed group of a textile mill.Conclusion: Workers in textile mill, with short term exposure of cotton dust may also present with respiratory alterations which may increase with long term exposure. Safety controls such as ventilation, etc and routine health check are very much required in textile industries.Keywords: Peak expiratory flow rate, Body mass index, Textile mill.  Â

An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits

The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find great application in neuromorphic circuits as it is possible to couple memory and processing, compared to traditional Von-Neumann digital architectures where memory and processing are separate. Neural networks have a layered structure where information passes from one layer to another and each of these layers have the possibility of a high degree of parallelism. CMOS-Memristor based neural network accelerators provide a method of speeding up neural networks by making use of this parallelism and analog computation. In this project we have conducted an initial investigation into the current state of the art implementation of memristor based programming circuits. Various memristor programming circuits and basic neuromorphic circuits have been simulated. The next phase of our project revolved around designing basic building blocks which can be used to design neural networks. A memristor bridge based synaptic weighting block, a operational transconductor based summing block were initially designed. We then designed activation function blocks which are used to introduce controlled non-linearity. Blocks for a basic rectified linear unit and a novel implementation for tan-hyperbolic function have been proposed. An artificial neural network has been designed using these blocks to validate and test their performance. We have also used these fundamental blocks to design basic layers of Convolutional Neural Networks. Convolutional Neural Networks are heavily used in image processing applications. The core convolutional block has been designed and it has been used as an image processing kernel to test its performance.Comment: Bachelor's thesi

YOGA PRACTICE AND BIOCHEMICAL AND PHYSIOLOGICAL ALTERATIONS IN NORMAL SUBJECTS

ABSTRACTObjective: To assess the effect of 45 minute yogic kriya (Surya Namaskar and Kapalbhati) for 30 days on various physiological and biochemicalparameters.Methods: About 20 Nursing College students of the Santosh Medical University, Ghaziabad, between the age group 17 and 21 years volunteered toparticipate in the study. They were divided into two Groups A and B. Group A students including 10 students in each group were subjected to 30 daysyoge kriya for 45 minutes for 6 days in a week. Statistical analysis: A student's t-test was used for comparing the means of pre- and post-yoga resultsof various parameters.Results: No significant difference was found in systolic blood pressure, pulse, body mass index, hemoglobin except for fasting blood sugar and diastolicblood pressure (p<0.001) among the yoga subject while comparing with baseline values and control.Keywords: Yoga, Biochemical alterations, Physiological alterations, Surya Namaskar and Kapal Bhati

TEC Response and Subsequent GPS Error Caused by the Most severe Geomagnetic Storm of Solar Cycle 24 at India

339-348This paper presents the response of low-latitude and mid-latitude ionosphere to a severe geomagnetic storm that occurred on 17 March 2015 at 0445 UT, and the subsequent effect of this storm on GPS error in the East-West (E-W) and North-South (N-S) directions. The Vertical Total Electron Content (VTEC) data has been analysed from three dual frequency GPS receivers, which were installed under the framework of the International GNSS Service (IGS). For each day of the year, the data is downloadable as a single file in the Receiver Independent Exchange Format (RINEX) from the IGS data portal. The VTEC values from the IGS are obtained at one minute intervals. Results show the variations in GPS derived VTEC during the severe geomagnetic storm. Negative ionospheric storms caused by composition changes are observed at mid-latitude region of Lucknow, while positive ionospheric storms caused by magnetospheric convection and Equatorial Ionospheric Anomaly (EIA) are prominent at low-latitude regions of Bangalore and Hyderabad. The maximum depletion in VTEC peak at mid-latitude region of Lucknow when compared to the quiet day mean VTEC was 61 percent during a negative ionospheric storm that occurred on 18 March 2015, and maximum enhancement in VTEC peak at low-latitude region of Bangalore and Hyderabad when compared to the quiet day mean VTEC was 26 percent and 21 percent respectively during an early positive ionospheric storm on 18 March 2015. Positive ionospheric storms caused by enhanced EIA and Prompt Penetration Electric Fields (PPEF) are prominent at low-latitudes. The highest GPS error during storm time was +7.2m and +11.3m in E-W and N-S directions respectively at Lucknow. The average GPS error in E-W and N-S directions during storm time was higher at the mid-latitude station of Lucknow

TEC Response and Subsequent GPS Error Caused by the Most severe Geomagnetic Storm of Solar Cycle 24 at India

This paper presents the response of low-latitude and mid-latitude ionosphere to a severe geomagnetic storm that occurred on 17 March 2015 at 0445 UT, and the subsequent effect of this storm on GPS error in the East-West (E-W) and North-South (N-S) directions. The Vertical Total Electron Content (VTEC) data has been analysed from three dual frequency GPS receivers, which were installed under the framework of the International GNSS Service (IGS). For each day of the year, the data is downloadable as a single file in the Receiver Independent Exchange Format (RINEX) from the IGS data portal. The VTEC values from the IGS are obtained at one minute intervals. Results   show the variations in GPS derived VTEC during the severe geomagnetic storm. Negative ionospheric storms caused by composition changes are observed at mid-latitude region of Lucknow, while positive ionospheric storms caused by magnetospheric convection and Equatorial Ionospheric Anomaly (EIA) are prominent at low-latitude regions of Bangalore and Hyderabad. The maximum depletion in VTEC peak at mid-latitude region of Lucknow when compared to the quiet day mean VTEC was 61 percent during a negative ionospheric storm that occurred on 18 March 2015, and maximum enhancement in VTEC peak at low-latitude region of Bangalore and Hyderabad when compared to the quiet day mean VTEC was 26 percent and 21 percent respectively during an early positive ionospheric storm on 18 March 2015. Positive ionospheric storms caused by enhanced EIA and Prompt Penetration Electric Fields (PPEF) are prominent at low-latitudes. The highest GPS error during storm time was +7.2 m and +11.3 m in E-W and N-S directions respectively at Lucknow. The average GPS error in E-W and N-S directions during storm time was higher at the mid-latitude station of Lucknow

A Parallel Military Dog based Algorithm for Clustering Big data in Cognitive Industrial Internet of Things

With the advancement of wireless communication, internet of things, and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big data based problems, since it does not require labeled datasets. Recently, meta-heuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big data sets produced from IoT devices, these algorithm fail to respond within desired time due to high computation cost. This paper presents a new meta-heuristic based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big data sets. The optimization efficacy the proposed method is validated against 17 benchmark functions and the results are compared with 5 other recent algorithms namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Further, a parallel version of the proposed method is introduced using MapReduce (MR-MDBO) for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on 2 benchmark UCI datasets and 3 real IoT based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the 6 other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times

Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winter. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA + LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public.</p

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis