Laparoscopic ovarian drilling in clomiphene resistant polycystic ovarian syndrome: clinical response and outcome

Background: Laparoscopic ovarian drilling (LOD) is an alternative method for ovulation induction in clomiphene citrate (CC) resistant polycystic ovary syndrome (PCOS) patients instead of gonadotropins. Objective were to identify the changes in clinical and biochemical profiles and the ovulation and pregnancy rate following LOD in CC resistant PCOS patients.Methods: It was an interventional study infertility unit, department of obstetrics and gynecology, Bangabandhu Sheikh Mujib medical university, Dhaka, between from July 2014 to June 2015. Changes of the above-mentioned parameters were recorded during follow up of patients after LOD. The information is collected and recorded in the preset questionnaire.Results: The characteristics of study population were same before LOD and following LOD. Before LOD, infrequent menstruation was present in 83.3% patients whereas regular menstruation was found in 58.3% patients after 6 months following LOD. Endometrial thickness ≤8 was found in 100.0% in before LOD and endometrial thickness >8 was found in 70.0% after 6 month following LOD. Ovulation was found in 25.0% in after 3-month LOD and was found in 70.0% after 6 months following LOD. Pregnancy was found in 20.0% after 3 months following LOD and 50.0% in after 6 months following LOD. Ovulation and pregnancy outcome was significantly higher in after 6 months following LOD.Conclusions: LOD produces long-term improvement in menstrual regularity and reproductive performance. A sustained improvement observed in acne and BMI. Ovulation and pregnancy were found in 70% and 50.0% respectively after 6 months following LOD

The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research

Rational SOFC material design: new advances and tools

Solid oxide fuel cells (SOFCs) offer great prospects for the most efficient and cost-effective utilization of a wide variety of fuels. However, their commercialization hinges on the rational design of low cost materials with exceptional functionalities. This article highlights some recent progress in probing and mapping surface species and incipient phases relevant to electrode reactions using in situ Raman spectroscopy, synchrotron based x-ray analysis, and multi-scale modeling of charge and mass transport. The combination of in situ characterization and multi-scale modeling is imperative to unraveling the mechanisms of chemical and energy transformation: a vital step for the rational design of next generation SOFC materials.open443

Enhanced Classification Accuracy on Naive Bayes Data Mining Models

A classification paradigm is a data mining framework containing all the concepts extracted from the training dataset to differentiate one class from other classes existed in data. The primary goal of the classification frameworks is to provide a better result in terms of accuracy. However, in most of the cases we can not get better accuracy particularly for huge dataset and dataset with several groups of data . When a classification framework considers whole dataset for training then the algorithm may become unusuable because dataset consisits of several group of data. The alternative way of making classification useable is to identify a similar group of data from the whole training data set and then training each group of similar data. In our paper, we first split the training data using k-means clustering and then train each group with Naive Bayes Classification algorithm. In addition, we saved each model to classify sample or unknown or test data. For unknown data, we classify with the best match group/model and attain higher accuracy rate than the conventional Naive Bayes classifier

Nanostructured electrodes for lithium-ion and lithium-air batteries: the latest developments, challenges, and perspectives

The urgency for clean and secure energy has stimulated a global resurgence in searching for advanced electrical energy storage systems. For now and the foreseeable future, batteries remain the most promising electrical energy storage systems for many applications, from portable electronics to emerging technologies such as electric vehicles and smart grids, by potentially offering significantly improved performance, energy efficiencies, reliability, and energy security while also permitting a drastic reduction in fuel consumption and emissions. The energy and power storage characteristics of batteries critically impact the commercial viability of these emerging technologies. For example, the realization of electric vehicles hinges on the availability of batteries with significantly improved energy and power density, durability, and reduced cost. Further, the design, performance, portability, and innovation of many portable electronics are limited severely by the size, power, and cycle life of the existing batteries. Creation of nanostructured electrode materials represents one of the most attractive strategies to dramatically enhance battery performance, including capacity, rate capability, cycling life, and safety. This review aims at providing the reader with an understanding of the critical scientific challenges facing the development of advanced batteries, various unique attributes of nanostructures or nano-architectures applicable to lithium-ion and lithium-air batteries, the latest developments in novel synthesis and fabrication procedures, the unique capabilities of some powerful, in situ characterization techniques vital to unraveling the mechanisms of charge and mass transport processes associated with battery performance, and the outlook for future-generation batteries that exploit nanoscale materials for significantly improved performance to meet the ever-increasing demands of emerging technologies.close15813

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19