Study of Disintegration of a High Speed Liquid Jet Using VOF Method

Numerical simulations are carried out to look at the primary atomization of a 2-D planar liquid jet. A finite volume method based solver is developed and interface capturing is done by volume of fluid (VOF) method. The solver uses a projection algorithm to solve the governing equations. Preconditioned conjugate gradient method is used to solve the pressure poisson equation. This part of the solver is ported on to graphics processing unit (GPU) to meet the computational demand required. The solver is validated against standard benchmark test cases. Initially the parallelized version on GPU is compared with the serial version on single CPU core to estimate the speed up achieved. Effect of liquid inlet velocity on jet disintegration is studied

The Convention on Animal Protection: The Missing Link in a One Health Global Strategy for Pandemic Prevention

As the COVID-19 pandemic demonstrates, society’s failure to address animal well-being has had grave consequences not just for animals but also for humans. The emergence of zoonotic diseases is largely a result of high-risk contact with and mistreatment of animals, and it obligates states to assess the risks and mitigate, if not prevent, the underlying harms to animals that ensue. In keeping with the One Health approach, the proposed Convention on Animal Protection for Public Health, Animal Welfare, and the Environment (CAP) lays the groundwork for a comprehensive global strategy to address the missing link in other approaches to the pandemic—specifically by recognizing explicitly that the protection of animal well-being is good for animals, for people, and for the planet. This Article sets CAP in its historical context, capturing how previous international agreements have been reached to preserve the exploitation of animals as living resources but have not ventured much further than that. The Article looks at how high-risk contact with and mistreatment of animals led to the emergence of COVID-19 and highlights how existing legal frameworks are ill-equipped to prevent similar pandemics. The Article then turns to a discussion of CAP—its origins with the adoption of an American Bar Association (ABA) policy urging the negotiation of a treaty to prevent pandemics by advancing animal protection and welfare, as well as its structure and provisions as framed by its first draft—and distinguishes CAP from other treaty proposals. In conclusion, the Article underscores the opportunity CAP presents not just to help prevent future pandemics but also to advance animals’ intrinsic interests, which are inextricably interwoven with our own

Analgesic and anti-inflammatory activity of hydroalcoholic extract of Piper betle leaves in experimental animals

Background: Piper betle leaf, commonly known as ‘paan’ has long been known for its various medicinal properties in traditional medicine but certain properties have remained less explored. We tried to assess the analgesic and anti-inflammatory activities of Piper betle leaves.Methods: Hydroalcoholic extract of Piper betle leaves (HEPBL) was extracted using soxhlet apparatus and its phytochemical analysis was performed. Wistar rats and Albino mice were used for all the experiments. Acute toxicity study was also done according to OECD guideline no.425 and the test doses were decided accordingly. The experimental models of tail-flick method and acetic acid induced writhing were used to study the analgesic activity whereas carrageenan induced paw edema and cotton pellet granuloma models were used for anti-inflammatory action. Statistical analysis was performed using one-way analysis of variance (ANOVA) followed by Dunnett's test.Results: HEPBL showed significant analgesic activity at the doses of 100 mg/kg and 200 mg/kg, and showed significant anti-inflammatory activity at the doses of 50 mg/kg, 100 mg/kg and 200 mg/kg. The sub-therapeutic dose of HEPBL at 50 mg/kg also potentiated the analgesic effect of sub-therapeutic doses of standard analgesics. The analgesic and anti-inflammatory activity of P.betle may be attributed to the presence of various phyto constituents’ viz. flavonoids, tannins, phenols and glycosides.Conclusions: HEPBL has significant analgesic and anti-inflammatory activity in experimental animals in our study

Heterologous expression of phytase in Schizochytrium sp. as a fortified feed additive for the Livestock industry

Phytates present in plant-derived feed can chelate nutrients and reduce their bioavailability for monogastric animals such as poultry and swine. The addition of hydrolase, phytase can alleviate this problem but is hindered by its cost. The goal of the current study is to clone, express and purify the phytase gene from Bacillus sp. (DS11) into Schizochytrium sp. ATCC 20888 is also a good producer of Docosahexaenoic acid (DHA). This is expected to enhance animal nutrition and reduce phosphate pollution. The DNA sequence analysis using multiple sequence alignments showed significant similarity to the phytase gene from Bacillus sp. (DS11). Subsequently, specific primers were designed based on the consensus sequence of the Bacillus phytase gene obtained from sequencing. The coding DNA sequence was determined to have a length of 1152 base pairs. Phytase gene was successfully cloned into the pRI201-AN DNA vector and transformed into Schyzochytrium sp. Screening on G418 plates showed 53 resistant colonies and from this 11 prominent colonies were chosen for further testing. Out of this, 8 colonies tested positive, with colony PCR having 1.5 kb with a phytase activity of 1.77 U/ml of crude lysate. Further purification with Ni-NTA affinity chromatography provided a specific activity of 15.59 U/mg. This appears to be the first ever reported recombinant phytase produced in Schizochytrium sp. The phytase recommendations are 250U/Kg of feed preparation for broiler & swine diets  . It was also determined that 72.64 U/5.2 gm of wet biomass and 1.80% of w/w microalgae would fulfil these requirements per kg of feed preparation

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, identifying machine faults at an early stage is critical. With the rapid advancement in artificial intelligence-based methods, developing automated systems that can diagnose machinery faults is necessary and challenging. This paper proposes a multi-channel time-frequency domain deep convolutional neural network (CNN)-based approach for machinery fault diagnosis using multivariate time-series data from multisensors (tachometer, microphone, underhang bearing accelerometer, and overhand bearing accelerometer). The wavelet synchro-squeezed transform (WSST) based technique is used to evaluate the time-frequency images from the multivariate time-series data. The time-frequency images are fed into the multi-channel deep CNN model for automated fault detection. The proposed multi-channel deep CNN model is multi-headed, considering the time-frequency domain information of each channel time-series data for automated fault detection. The proposed model’s performance is compared to benchmark models regarding testing accuracy, total parameters, and model size. Experiments have shown that the proposed model outperforms benchmark models regarding classification accuracy. The proposed multi-channel CNN model has obtained the accuracy and F1-score values of 99.48% and 99% for fault classification using time-frequency images of multi-sensor data. Finally, the proposed model’s performance is measured regarding inference time when deployed on edge computing devices such as the Raspberry Pi and the Nvidia Jetson AGX Xavier.publishedVersio

Data as a Service (Daas) in Cloud Computing

Data has become the enabling technology for many of the recent innovations More data trumps smarter algorithms has been the mantra behind this revolution in computing Given the rate at which the data is produced there is need for scalable solutions to extract information out of them Allowing the data to be stored in the cloud and be accessed without geographical and scalability limitations will remove many bottlenecks in bringing data-oriented innovations Current cloud architecture solves the issues of accessibility and scalability but poses several new challenges such as automatic management of the service pricing the data and security of the data This talk will include several techniques to address these challenges using automatic physical design service-based pricing and cryptographic mechanisms Data Information Knowledge Intelligenc