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Abstract In India, over 25,000 people have died from cardiovascular annually over the past 4 years , and over 28,000 in the previous 3 years. Most of the deaths nowadays are mainly due to cardiovascular diseases (CVD). Arrhythmia is the leading cause of cardiovascular mortality. Arrhythmia is a condition in which the heartbeat is abnormally fast or slow. The current detection method for diseases is analyzing by the electrocardiogram (ECG), a medical monitoring technique that records heart activity. Since actuations in ECG signals are so slight that they cannot be seen by the human eye, the identification of cardiac arrhythmias is one of the most difficult undertakings. Unfortunately, it takes a lot of medical time and money to find professionals to examine a large amount of ECG data . As a result, machine learning-based methods have become increasingly prevalent for recognizing ECG features. In this work, we classify five different heartbeats using the MIT-BIH arrhythmia database . Wavelet self-adaptive thresholding methods are used to first denoise the ECG signal. Then, an efficient 12-layer deep 1D Convolutional Neural Network (CNN) is introduced for better features extraction, and finally, SoftMax and machine learning classifiers are applied to classify the heartbeats. The proposed method achieved an average accuracy of 99.40%, precision of 98.78%, recall of 98.78%, and F1 score of 98.74%, which clearly show that it outperforms with the exiting model . Architecture of proposed work is simple but effective in remote cardiac diagnosis paradigm that can be implemented on e-health devices.

Introduction Vitamin D status may be an important determinant of multidrug-resistant tuberculosis (MDR-TB) infection, progression to disease and treatment outcomes. Novel and potentially cost-effective therapies such as vitamin D supplementation are needed to stem the tide of TB and MDR-TB globally, particularly in India, a country that accounts for the largest fraction of the world’s TB incidence and MDR-TB incidence, and where vitamin D deficiency is endemic. While vitamin D has shown some promise in the treatment of MDR-TB, its role in the context of MDR-TB infection and progression to disease is largely unknown.Methods and analysis Through a case–control study in Mumbai, India, we aim to examine associations between vitamin D status and active MDR-TB and to investigate vitamin D status and TB infection among controls. Cases are adult outpatient pulmonary patients with MDR-TB recruited from two public TB clinics. Controls are recruited from the cases’ household contacts and from non-respiratory departments of the facilities where cases were recruited. Cases and controls are assessed for serum 25-hydroxyvitamin D concentration, nutrient intake, diet quality, anthropometry and other relevant clinical and sociodemographic parameters. Controls undergo additional clinical assessments to rule out active TB and laboratory assessments to determine presence of TB infection. Statistical analysis investigates associations between vitamin D status and active MDR-TB and between vitamin D status and TB infection among controls, accounting for potential confounding effects of diet, anthropometry and other covariates.Ethics and dissemination This study has been approved by Harvard T.H. Chan School of Public Health Institutional Review Board; Foundation for Medical Research Institutional Research Ethics Committee and Health Ministry’s Screening Committee of the Indian Council for Medical Research. Permission was granted by the Municipal Corporation of Greater Mumbai, India, a collaborating partner on this research. Outcomes will be disseminated through publication and scientific presentation.Trial registration number NCT04342598.

This study proposes a novel user-centric cyber-physical framework to achieve distributed demand response (DDR) in a distribution system where local schedulers (LS) present at individual buses program both local and non-local loads to reduce the maximum load within a sparse communication setting. A COnjoint Methodology for communication and controller DESign (COMDES) has been proposed to design the topology of the communication network while considering various cyber and physical properties of the smart grid like bandwidth, load profile and voltage controller stability margin. The designed communication network provides a unified pathway for exchanging multiple types of data among different points in the grid to consummate the advanced ideas of DDR and multi-input multi-output voltage control concurrently. Concepts of Lyapunov stability and linear matrix inequalities are employed to design optimisation frameworks for tuning the values of voltage controllers which, in turn, play a vital role in pooling the loads to be scheduled by the individual LS for execution. Established algorithms like earliest deadline first, least laxity first and dynamic rate priority are used to generate switching schedules for redistributed loads and achieve peak shaving. IEEE 4 bus and 14 bus models have been used to demonstrate the efficacy of the proposed framework.

Filtration steps are ubiquitous in biotech processes due to the simplicity of operation, ease of scalability and the myriad of operations that they can be used for. Microfiltration, depth filtration, ultrafiltration and diafiltration are some of the most commonly used biotech unit operations. For clean feed streams, when fouling is minimal, scaling of these unit operations is performed linearly based on the filter area per unit volume of feed stream. However, for cases when considerable fouling occurs, such as the case of harvesting a therapeutic product expressed in Pichia pastoris, linear scaling may not be possible and current industrial practices involve use of 20–30% excess filter area over and above the calculated filter area to account for the uncertainty in scaling. In view of the fact that filters used for harvest are likely to have a very limited lifetime, this oversizing of the filters can add considerable cost of goods for the manufacturer. Modeling offers a way out of this conundrum. In this paper, we examine feasibility of using the various proposed models for filtration of a therapeutic product expressed in Pichia pastoris at constant pressure. It is observed that none of the individual models yield a satisfactory fit of the data, thus indicating that more than one fouling mechanism is at work. Filters with smaller pores were found to undergo fouling via complete pore blocking followed by cake filtration. On the other hand, filters with larger pores were found to undergo fouling via intermediate pore blocking followed by cake filtration. The proposed approach can be used for more accurate sizing of microfilters and depth filters.

Cyber physical systems like smart grid are largely migrating towards distributed control philosophy to achieve high reliability. The design of communication network between various sensors and controllers plays an important role in control of these systems. The design process involves examining a number of topological combinations, which increase exponentially with the number of nodes in the considered system. Moreover, for a practical system, the different characteristics and availability of various physical and communication resources in the network pose multiple constraints on this design. In this work, a generalised constraint-based sensor controller connection design methodology has been developed, which effectively reduces the number of combinations, to design more stable cyber-physical controllers. To handle variations in multiple parameters in physical and communication domain, different controllers have been developed for different operating conditions that are scheduled as per requirement. The methodology has been shown to stabilise bus voltages in a smart grid scenario under variations in load, communication delays and loss of communication links.