Modified ghee/clarified butter composition for food technology

Breeds of cows contribute to increase solid and fats in milk. Feed,veterinary knowledge and animal welfare were analyzed for ghee production. Different stages of prototype are built with different configurations and state. The best state of ghee are identified for commercialization. Product has been sold to marke

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniquesthat incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy

Analysis of Correlation of Induced Frequency and Cream Skimming Efficiency through Ultrasonic Technology

Ultrasonic cream separator is a very new technology especially in the dairy industry. Ultrasonic separator machine is an eco-friendly technology that could boost the separation process and it could act as supplement to heat-based technology. However, ultrasonic separators are well developed in other industries such as sludge separation, emulsion breaking, de-oiling and sewage disposal. This technology has been implemented in the food industries recently too. The primary function of an ultrasonic cream separator machine is to separate the milk into two products i.e., cream, and skimmed milk. It is an instantaneous process which saves up workload, manpower, time, and cost. This machine intends to coagulate the fat particles with one another once a certain frequency is induced in the milk. Cream, which is lighter molecule will coagulate and float, whereas the heavier molecules of milk such as protein and minerals will sink. Upon separation, the fat molecules can be scooped out or separated through flushing from below. This machine is at a very agile stage that leads to inefficient cream skimming which leads to fluctuation of fat particles coagulation that leads to inefficient cream skimming. The intention of this study is to evaluate the factors that have a direct relationship with low performance of cream skimming via ultrasonic cream separation and come up with an ideal possible solution to enhance the process of cream separation

FTIR analysis of plant-based cellulose as adsorbents for water remediation

Finding an effective, green adsorbent for removal of heavy metals is one of the main problems in water purification field. Cellulose has gain tremendous attention for its variability of purposes including heavy metal removal via adsorption. As a preliminary material study, Fourier Transform Infrared Spectroscopy (FTIR) would be a good step in analyzing the removal potential of an adsorbent. In this study, cellulose-based adsorbent extracted from Pandan leaves was subjected to acid hydrolysis after being pre-treated with alkali and bleaching treatment. The output material was then analyzed in this research using FTIR. The result showed that some components were removed after the treatments and the material has potential for future development as adsorbent for heavy metal removal due to presence of carboxyl group in the backbone

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication