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Introduction: Anopheles stephensi is a major urban malaria vector in Rajasthan, India, and is responsible for spreading persistent malaria throughout the year. In Rajasthan, An. stephensi is invariably distributed and has three bioforms discriminated based on the number of the ridge on the eggs viz; Type, Mysorensis, and Intermediate. The present study aimed to understand the distribution pattern of these bioforms in rural and urban setups as they also have differences in their malaria transmission capacity. Methods: Gravid mosquitoes and the larvae were collected from different habitats of districts of Rajasthan. The gravid females An. stephensi were allowed to lay eggs. These eggs then were subjected to morphometric analysis and counted for the number of ridges for bioform identification. Results: About 15000 ± 200 eggs were obtained from ~190 gravid An. stephensi collected from 45 localities (11 Urban & 34 Rural) of eight districts of Rajasthan. Out of which 3569 eggs were subjected to morphometric analysis. Mysorensis bioform (49.7%) was observed to have higher percent over Intermediate (25.5%) and Type (24.6%) bioforms. Mysorensis and Intermediate were found more in rural areas whereas the Type bioform dominated higher in urban areas. Discussion: The Mysorensis bioform was found to be dominant throughout the year in all seasons in rural areas. Type bioform preferred indoor breeding places like underground tanks, cement tanks whereas other bioforms preferred outdoor breeding places. Egg size was found to be directly proportional to the number of ridges on the eggs (r = 0.55). No reproductive isolation was observed among these bioforms. Conclusions: The Mysorensis bioform is more prominent than other bioforms. Subspecies level understanding helps to plan effective control measures according to the breeding site selection majorly by Type bioform, an efficient vector in this region. [ABSTRACT FROM AUTHOR]

The majority of the power distribution problems are addressed in this study by outlining a scheduling and allocation system that is based on rules. The smart home environment incorporates the suggested model as the intermediate layer. In a smart home, managing overload and power failure is the primary goal of the suggested max-max-based demand response scheduling method. The proposed model is an extension of the demand response measure while considering the load and failure rate analysis. This model is applied in a real-time environment that processes the historical information of power usage in the environment. This model captures the information available by the resources, centralized database information, and the current request parameters. The control and configuration unit are defined to process the load, history, and demand of the users. In this model, effective resource allocation and scheduling are provided. The proposed model is compared against conventional first come-first serve (FCFS), shortest job first (SJF), longest job first (LJF), demand response, and fuzzybased demand response methods. The comparative evaluation is done on average delay, failure rate, and task-switching parameters. The analysis results obtained against these parameters confirm that the presented maxmax-based parametric demand response scheduling and resource allocation method enhanced the reliability and effectiveness of the smart home environment. [ABSTRACT FROM AUTHOR]

It is important to study the recent malaria incidence trends in urban areas resulting from rapid urbanization that can lead to changes in environmental conditions for malaria. This retrospective study assessed trends in malaria patients, their distribution according to parasite species, patient demographics, and weather data for the past 8 years at a malaria clinic in the National Institute of Malaria Research, New Delhi, India. We overlaid the effects of environmental factors such as rainfall, relative humidity, and temperature on malaria incidence. The malaria data were digitized for a period spanning 2012 to 2019, during which 36,892 patients with fever attended the clinic. Of these, 865 (2.3%) were diagnosed with malaria microscopically. Plasmodium vivax was predominant (96.2%), and very few patients were of Plasmodium falciparum (3.5%) or mixed infections (0.3%). The patients with malaria were within a 10-km radius of the clinic. Males (70.9%) were more commonly affected than females (29.1%). Of the total malaria patients, a majority (∼78%) belonged to the > 15-year age group. A total of 593 malaria patients (68.6%) received primaquine. These patients were most commonly diagnosed in April through October. Furthermore, there was a lag of 1 month between the rainfall peak and the malaria case peak. The peak in malaria cases corresponded to a mean temperature of 25 to 30°C and a relative humidity of 60% to 80%. This analysis will be useful for policymakers in evaluating current interventions and in accelerating malaria control further in urban areas of India.

It is important for malaria-endemic countries to address malaria control across international borders, and in particular to prioritize appropriate rapid diagnosis, treatment and surveillance. Bhutan and India aim to achieve malaria elimination by 2023 and 2030 respectively. Malaria elimination along the Indo-Bhutan border is of common concern. We delineated malaria epidemiology along the border to provide a blueprint for focusing malaria control efforts in key foci within this region. Epidemiological data from 2015 to 2019 were analyzed, as the most drastic reductions in malaria burden across most parts of India were witnessed in this time frame. Several areas of concern include low surveillance in most border districts, favorable climatic conditions for perennial malaria transmission, and movement of potential parasite carriers because of the porous borders. India and Bhutan need to control the importation/exportation of malaria cases. We highlight the foci of concern for which implementing tailor-made malaria control strategies may benefit both countries.