Public electricity supplies became available in Calcutta as early as the 1890s and by the 1940s had developed to become the major source of power and lighting both for industries and private use. Could the electrification of Calcutta act as an agent of industrial development? What impact did it have on the socio-economic condition of the city? The twentieth century was to be the age of electricity. Apart from industrial use of this new technology, to realise its full practical capacities a new discipline emerged-electrical engineering. The Bengali electrical engineering community, alumni of Sibpur and Jadavpur Engineering Colleges, gradually oriented themselves towards ideas of industrial development based on Indian realities. Few trained electrical engineers entered in the business sector and became very successful. High level of technological innovation such as that of hydroelectricity was also exploited by the Bengali entrepreneurs. The credentials of the Bengali erudite and ordinary people in the field of electricity, which this article tries to explore, offer an interesting example of Indian response to the new knowledge.Modern machine helps to boost the productivity of any industry. We would request the young men of our country to learn the art of electricity, because future is here. The current industrial trend indicates the acceptance of electricity as the main motive power over other sources of energy.

Background:Maternal mortality reflects the quality of obstetric services given to pregnant women in the community.Objectives:The objectives of this study were to calculate the maternal mortality rate in a teaching institution, to assess the epidemiological aspects of maternal mortality, and to assess the different causes of maternal mortality.Materials and Methods:This was a retrospective study where individual records of all maternal deaths occurring in our hospital during the past 6 years from 2009 to 2014 were studied. The cause of death and the factors which led to death in each individual case were analyzed.Results:A total of 105 maternal deaths occurred during the study period. The mean maternal mortality ratio in the study period was 233/100,000 live births. Most maternal deaths (37.14%) occurred in the age group of 20–24 years, multiparous women (74.28%) and in women from rural areas (70.47%). Most of the women were unbooked or unregistered patients (64.76%), and 40.95% cases were referred cases. Direct causes accounted for 90.47% of maternal deaths whereas 9.52% of maternal deaths were due to indirect causes. Hemorrhage (26.6%) and eclampsia (27.6%) were the major direct causes of maternal deaths.Conclusion:There is scope for improvement as a large proportion of the observed deaths are preventable. Improving the rural health centers, upgrading the referral centers, and proper transport system is the need of the hour.

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

Introduction: The Coronavirus Disease 2019 (COVID-19) pandemic has brought about a paramount change in the life. This has lead to a reduction in the number of routine patients visiting the Outpatient Department (OPD) of various hospitals and this department was no exception. Aim: To compare the attendance of patient in Antenatal Care (ANC) and Gynaecology Out Patient Department (GOPD) between pre-lockdown and lockdown period due to COVID-19 pandemic. Materials and Methods: The study was conducted among patients attending the OPD in ANC and Gynaecology for 70 days lockdown from 23rd March to 31st May and 70 days immediate pre-lockdown period from 12th January to 22nd March 2020. Daily attendance was noted and types of patient attending in different sub clinics in Gynaecology OPD compared. Enrolment of new ANC patient and old booked cases was compared during both periods. Descriptive statistics were used and displayed as percentages. Results: There was a significant reduction in number of patients attending OPD in lockdown period. There was a total of 6088 (87.3%) reduction in number of patients in Gynaecology OPD and 2235 (69.6%) reduction of patients in ANC OPD which was found to be significant with p-value <0.001. Reduction of patient in lockdown days among new ANC was 574 while it was 1661 in case of old patients. The different sub clinics of GOPD like infertility (704), endocrine (1450), uro-gynaecology (656), STD/PID (732), postpartum (597), cancer detection (316), abortion and medical termination of pregnancy (MTP) (330), others (1303) also witnessed a diminution of attendance. Conclusion: COVID-19 caused a significant decrease in footfall of patients in outpatient department due to lockdown, though the percentage of types of patient attending Gynaecology OPD was almost same.

Background Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis. Objective Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors. Our study is to develop a computer driven diagnostic system which will enable the clinicians to improve the accuracy in detecting various types of breast tumors. Methods In this article, we proposed a breast cancer classification model based on the hybridization of machine learning approaches for classifying triple-negative breast cancer and non-triple negative breast cancer patients with clinicopathological features collected from multiple tertiary care hospitals/centers. Results The results of genetic algorithm and support vector machine (GA-SVM) hybrid model was compared with classics feature selection SVM hybrid models like support vector machine-recursive feature elimination (SVM-RFE), LASSO-SVM, Grid-SVM, and linear SVM. The classification results obtained from GA-SVM hybrid model outperformed the other compared models when applied on two distinct hospital-based datasets of patients investigated with breast cancer in North West of African subcontinent. To validate the predictive model accuracy, 10-fold cross-validation method was applied on all models with the same multicentered datasets. The model performance was evaluated with well-known metrics like mean squared error, logarithmic loss, F1-score, area under the ROC curve, and the precision–recall curve. Conclusion The hybrid machine learning model can be employed for breast cancer subtypes classification that could help the medical practitioners in better treatment planning and disease outcome.