Tracking of invader drone using hybrid unscented Kalman-Continuous Ant Colony Filter (HUK-CACF).

Splendid Unmanned Aerial Vehicle (UAV) applications upshot its enormous use in densely inhabited areas, which is a matter of concern. In such areas, a proper tracking system is required to track an unauthorized/invader drone to ensure safety. With the flexibility of reaching inaccessible places, an Unmanned Aerial Vehicle Mounted Adaptable Radar Antenna Array (UAVMARAA) could be used. In this regard, a Hybrid Unscented Kalman-Continuous Ant Colony Filter (HUK-CACF) is proposed to estimate the position of the invader drone efficiently. Simulation results demonstrate the efficiency and robustness of the proposed filter for tracking system compared to the existing filters in terms of success rate. Further, for various Adaptable Radar Antenna Array (ARAA) patterns such as Uniform Linear Array (ULA), Uniform Rectangular Array (URA), and Uniform Circular Array (UCA), analysis is done for pertaining actual tracking effect for various parameters such as bearing, Doppler shift, ranging, and Radar Cross Section (RCS) by considering wobbling and mutual coupling (MC) effect. The result shows that the proposed filter outperforms in all the scenarios. Among the various ARAA, URA performs better than the other configurations.

Air pollution prediction system using XRSTH-LSTM algorithm.

Globally, there are significant worries about the rise in air pollution (AP) from substances that are harmful to human health, different living forms, and unfavorable environmental imbalances. To overcome the problem, AI-based prediction model is the need of the hour. Therefore, an attempt was made to develop a novel AP prediction system based on Xavier Reptile Switan-h-based Long-Short Term Memory (XRSTH-LSTM), which undergoes fine-tuning at various steps such as pre-processing, attribute extraction, and air-quality index prediction, in order to reduce computational cost and also to increase accuracy as well as precision. The dataset used to train the proposed methodology is Air Quality Data in India (2015-2020), taken from publically available sources Kaggle. The dataset includes information on the AQI and air quality at different stations in numerous Indian cities at hourly and daily intervals. The accuracy has been calculated using MSE, MAPE, RMSE, precision, recall, and F-measure. The robustness of the proposed model is tested using parameters such as negative predicted value and Mathew correlation coefficient. The proposed model is found to efficiently process air quality with an improved accuracy of 98.52% and precision of 99.79%, which is 0.74% higher than the existing state-of-the-art model. The testing findings showed that the proposed approach worked better than the current models and offered a higher rate of accuracy in predicting air pollution.

ABTCN: an efficient hybrid deep learning approach for atmospheric temperature prediction.

Temperature prediction is an important and significant step for monitoring global warming and the environment to save and protect human lives. The climatology parameters such as temperature, pressure, and wind speed are time-series data and are well predicted with data driven models. However, data-driven models have certain constraints, due to which these models are unable to predict the missing values and erroneous data caused by factors like sensor failure and natural disasters. In order to solve this issue, an efficient hybrid model, i.e., attention-based bidirectional long short term memory temporal convolution network (ABTCN) architecture is proposed. ABTCN uses k-nearest neighbor (KNN) imputation method for handling the missing data. A bidirectional long short term memory (Bi-LSTM) network with self-attention mechanism and temporal convolutional network (TCN) model that aids in the extraction of features from complex data and prediction of long data sequence. The performance of the proposed model is evaluated in comparison to various state-of-the-art deep learning models using error metrics such as MAE, MSE, RMSE, and R2 score. It is observed that our proposed model is superior over other models with high accuracy.

Digital Health Care in Public Private Partnership Mode.

Background: Health care is provided in developing countries, in a milieu of acute shortages of health care infrastructure and personnel. Governments are realizing that digital health through public private partnerships (PPPs) could address this issue. Literature review did not reveal reports on primary use of telemedicine or Technology-enabled Remote Health care (TeRH) in a PPP mode. Materials and Methods: The authors report using digital health in a mega PPP project in nine districts in Andhra Pradesh, a state in South India, where millions are benefiting from TeRH. Strategies deployed to address operational, technical, and clinical challenges in virtually reaching the unreached deploying technology are described. A detailed analysis was made of services provided in 183 Urban Primary Health Centres (UPHCs) over 47 months. Results: 2,648,322 unique patients had quality digital health care. Of 11,055,936 consultations, 1,013,996 were specialist teleconsultations, including cardiology, endocrinology, general medicine, orthopedics and OB/Gynecology. 7,408,283 laboratory tests were done. Costs for laboratory tests was 28.84% of that in private laboratories. Cost per specialist teleconsultation was [Formula: see text]165 (Rupees). Quality control of laboratories was ensured through remote monitoring. Discussion: Implementing digital health in PPP projects requires expertise across clinical, technology, contract management, financing, data standards, information security, project planning, and cost-effective implementation. Conclusions: This successful mega project has confirmed that given a dedicated cooperative team e-health in a PPP mode in a developing country is eminently doable. Digital health care records were introduced and maintained for 100% of the beneficiaries (2.6 million in this study). TeRH can now bridge the health care divide.

Stereology of the myocardium in human foetuses.

The rate of cellular proliferation and hypertrophy of the cardiac myocytes in the human perinatal period is still controversial. This work uses stereology to evaluate the prenatal quantitative changes of the myocardium. The hearts of 36 human foetuses, ranging from the 2nd trimester to the 3rd trimester, were studied. Fifteen random microscopic fields were analyzed in each heart. The following stereological parameters were determined: Vv[myocyte] and Vv[interstitium] (the volume densities of the cardiac myocyte and interstitium, respectively) and the Nv[myocyte] (the numerical density of the cardiac myocytes). The total number of myocytes (N[myocyte]) and the mean myocyte volume (V[myocyte]) were also determined. All differences between the second and the third trimester of gestation, tested with the Mann-Whitney test, were statistically significant (P < 0.05). The Vv[myocyte] decreased 8.69% and the Vv[interstitium] increased 49.83% in this period. Simultaneously, the Nv[myocyte] decreased 16.64%, the V[myocyte] increased 16.39%, the cardiac weight increased 366.67% and the N[myocyte] increased 272.06%. In conclusion, during the last two gestational trimesters the human heart increases in weight more than 4.5 times, the volume density of myocytes decreases while the volume density of the cardiac interstitium increases. The numerical density of myocytes per myocardium volume decreases but the myocytes became greater in mean volume (more than 16%).