SummaryUAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV‐IoT collaborative system has become a current hot research topic. However, other concerns require in‐depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision‐making, energy harvesting, and so forth. Here, we suggest a task‐scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA‐WS in terms of energy use (67%) and delays (36%).
Tuberculosis (TB) is a communicable disease that is a major cause of ill health and one of the leading causes of death worldwide. Until the coronavirus (COVID-19) pandemic, TB was the leading cause of death from a single infectious agent, ranking above HIV/AIDS. Multidrug- resistant TB (MDR-TB) remains a public health crisis and a health security threat. Only about one in three people with drug resistant TB accessed treatment in 2020. It was a hospital based, non-randomized and without control group observational and prospectiveMaterial And Methods study, in cohort of DRTB patients conducted at Nodal DRTB Centre and Department of Tuberculosis and Respiratory Diseases S. N. Medical College Agra, Uttar Pradesh, India 53.89% male and 46.11 % females were affected in the study. 65% patients and 35% patients belong toResult rural and urban populations areas respectively. Cough was present in 100% patients followed by the fever which was present in 95.56% patients. Cough with expectorations was present in 94.44% patients and loss of appetite in 78.89% patients.CONCLUSION: Early detection will be helpful in not only modifying the disease course but also delaying and preventing fatal complication hence patient may be treated earliest by various measures.
COPD is the third leading cause of death worldwide and the second leading cause of death in India. There are so many factors that contribute to the development of COPD and determine its progression and severity. This study was done to know the Sociodemographic and clinical proles of smoker and non-smoker COPD patients attending a tertiary care centre in North India. This was a hospital-based prospective andMaterial and Method: observational study conducted in the Department of TB and Respiratory Diseases, S.N. Medical College, Agra. Patients were recruited for the study based on GOLD criteria (Post Bronchodilator FEV1/FVC < 0.7). Patients were evaluated based on demographic characteristics and clinical features. Results: Among the total eligible patient's prevalence of smoker COPD patients was 62.96% and the prevalence of non-smoker patients was 37.04%. Among the eligible 108 cohort patients, 70 (64.81%) were males and 38 (35.18%) were females. Female patients were higher in the non-smoker group (75%). The higher number of patients enrolled were from a rural background (60.18%). Mean age of the population was 48.8 years with a majority of patients lying in the younger age group and were malnourished (BMI<18.5). Breathlessness was the most common symptom reported (49%) and smoker COPD patients were having severe dyspnoea (46.29%). 37.04% prevalence of non-smoker COPD was noted. Early detectionConclusion: with the help of spirometry will be helpful in not only modifying the disease course but also delaying and preventing fatal complications. Hence, patients may be treated earliest by various measures like lifestyle modication, smoking cessation, etc.
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