In-situ formation characteristic, tribological characterization and anti-corrosion properties of quaternary composites films

Improvements of wear and corrosion properties are essential characteristic in engineering application. A study was made on the structure, electro-oxidation and properties of fabricated Z

Advancing chronic pain relief cloud-based remote management with machine learning in healthcare

Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system’s data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM’s powerful classification skills make it possible to classify patients’ risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient’s preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system’s ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses

In vitro Assessment of Neonicotinoids and Pyrethroids against Tea Mosquito Bug, Helopeltis antonii Sign. (Hemiptera: Miridae) on Guava

The tea mosquito bug (TMB), Helopeltis antonii, is an emerging pest of horticultural crops, specially on guava and moringa. Insecticides are indispensable component for the management of insect pests. Exploration of new molecules with shortest waiting period may pave way for managing TMB in fruit and vegetable crops with nil/low residue. Until now there are no recommended insecticides available under Central Insecticides Board & Registration Committee (CIB&RC) against TMB on guava. In view of the above facts, new molecules with a low waiting period and are recommended by CIB&RC on tea, viz., Clothianidin 50% WDG, Thiacloprid 21.7% SC, Bifenthrin 10% EC, and Thiamethoxam 12.60% + Lambda-Cyhalothrin 9.5% ZC, were chosen and evaluated against TMB under in vitro condition. Clothianidin 50% WDG recorded the highest mortality of 100.00 per cent at 72 hours after treatment (HAT), and the lowest LC50 value (0.328 ppm, fiducial limits: 0.144-0.515 ppm) and LT50 value (10.49 h, fiducial limits: 5.444-14.551 h), followed by Thiamethoxam 12.60% + Lambda-Cyhalothrin 9.5% ZC, Thiacloprid 21.7% SC, and Bifenthrin 10% EC. The results showed that the Clothianidin 50% WDG and Thiamethoxam 12.60% + Lambda-Cyhalothrin 9.5% ZC, were highly effective, with the lowest LC50 and LT50 values. Since TMB occurs from new flushing to fruiting stage of guava, a minimum of two sprays are mandatory to have quality fruit yield. Hence, application of Clothianidin 50% WDG followed by Thiamethoxam 12.60% + Lambda-Cyhalothrin 9.5% ZC on need basis will help to reduce the impact of TMB on guava

Deep learning for infectious disease surveillance integrating internet of things for rapid response

Particularly in the case of emerging infectious diseases and worldwide pandemics, infectious disease monitoring is essential for quick identification and efficient response to epidemics. Improving surveillance systems for quick reaction might be possible with the help of new deep learning and internet of things (IoT) technologies. This paper introduces an infectious disease monitoring architecture based on deep learning coupled with IoT devices to facilitate early diagnosis and proactive intervention measures. This approach uses recurrent neural networks (RNNs) to identify temporal patterns suggestive of infectious disease outbreaks by analyzing sequential data retrieved from IoT devices like smart thermometers and wearable sensors. To identify small changes in health markers and forecast the development of diseases, RNN architectures with long short-term memory (LSTM) networks are used to capture long-range relationships in the data. Spatial analysis permits the integration of geographic data from IoT devices, allowing for the identification of infection hotspots and the tracking of afflicted persons' movements. Quick action steps like focused testing, contact tracing, and medical resource deployment are prompted by abnormalities detected early by real-time monitoring and analysis. Preventing or lessening the severity of infectious disease outbreaks is the goal of the planned monitoring system, which would enhance public health readiness and response capacities

Genetic variability of the bollworm, Helicoverpa armigera, occurring on different host plants

The bollworm, Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) is a polyphagous pest of worldwide occurrence inflicting annual crop damage in India worth US$ 1billion. In India this insect occurs as a major pest in many economically important crops, including cotton, pigeonpea, chickpea, tomato, okra, and blackgram. Understanding the genetic variation among the H. armigera populations occurring on host plants has become essential to understand the variation in their susceptibility to different insecticides, including Bacillus thuringiensis. This preliminary study uses 10 microsatellite simple sequence repeat (SSR) markers, to provide insight into the genetic variability of H. armigera populations from six different host plants. Nine of the SSR primers indicated high variability across the different host associated populations with polymorphism ranging from 75 to 100 per cent. Using the un-weighted pair-group method analysis, H. armigera collected and reared from cotton stood out as unique in one cluster while the insects collected and reared on all other hosts grouped separately