Efficient k-means clustering and greedy selection-based reduction of nodal search space for optimization of sensor placement in the water distribution networks.

Monitoring of water distribution network (WDN) requires placement of sensors at strategic locations to detect maximum contamination events at the earliest. The multi-objective optimization (MOO) of sensor placement is a complicated problem owing to its combinatorial nature, interconnected and large WDN sizes, and temporal flows producing complex outcomes for a given set of contamination events. In this study, a new method is proposed to reduce the complexity of the problem by condensing the nodal search space. This method first segregates the nodes based on intrusion events detected, using k-means clustering, followed by selecting nodes from each group based on the improvement observed in the objectives, namely, contamination event detection, expected detection time, and affected population. The selected nodes formed the decision variable space for the MOO study. The developed strategy was tested on two benchmark networks: BWSN Network1 and C-town network, and its performance is compared with the traditional method in terms of hypervolume contribution rate (CR) indicator and the number of Pareto points. The optimal subset of nodes generated twice the number of Pareto points than the complete set of nodes set for placing 20 sensors and had 10% more than CR indicator than the traditional method. For the placement of 5 sensors, the proposed solutions were better at the higher detection likelihood values, which is required to achieve maximum detection. The proposed sensor placement algorithm can be easily scaled to large WDNs. It is expected to provide a better optimal sensor placement solution irrespective of network size as compared to the traditional approach.

Emerging spectrum of post-COVID-19 syndrome.

'Post-COVID-19 syndrome' refers to symptoms in the convalescent phase following initial COVID-19 infection. This term encompasses a wide array of presentation involving lungs, heart and the neuromuscular system. Pulmonary manifestations include post-COVID-19 fibrosis, which is akin to post acute respiratory distress syndrome fibrosis and may reflect the permanent damage to the lungs following an initial bout of infection. Cardiovascular system is often involved, and the presentation can be in terms of acute coronary syndrome, myocarditis and heart failure. Clinical manifestations are often varied and non-specific, which entails a detailed workup and a multidisciplinary approach. Post-COVID-19 syndrome adds to the overall disease morbidity and leads to a prolonged hospital stay, greater healthcare utilisation and loss of productivity marring the country's dwindling economy. Thus, it is imperative that post-COVID-19 syndrome be prevented and identified early followed by a prompt treatment.