Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Performance study of chromium (VI) removal in presence of phenol in a continuous packed bed reactor by Escherichia coli isolated from East Calcutta Wetlands.

Abstract: Organic pollutants, like phenol, along with heavy metals, like chromium, are present in various industrial effluents that pose serious health hazard to humans. The present study looked at removal of chromium (VI) in presence of phenol in a counter-current continuous packed bed reactor packed with E. coli cells immobilized on clay chips. The cells removed 85% of 500 mg/L of chromium (VI) from MS media containing glucose. Glucose was then replaced by 500 mg/L phenol. Temperature and pH of the MS media prior to addition of phenol were 30°C and 7, respectively. Hydraulic retention times of phenol- and chromium (VI)-containing synthetic media and air flow rates were varied to study the removal efficiency of the reactor system. Then temperature conditions of the reactor system were varied from 10°C to 50°C, the optimum being 30°C. The pH of the media was varied from pH 1 to pH 12, and the optimum pH was found to be 7. The maximum removal efficiency of 77.7% was achieved for synthetic media containing phenol and chromium (VI) in the continuous reactor system at optimized conditions, namely, hydraulic retention time at 4.44 hr, air flow rate at 2.5 lpm, temperature at 30°C, and pH at 7.

A multi-objective multi-item solid transportation problem with vehicle cost, volume and weight capacity under fuzzy environment

Abstract: Generally, in transportation problem, full vehicles (e.g., light commercial vehicles, medium duty and heavy duty trucks, etc.) are to be booked, and transportation cost of a vehicle has to be paid irrespective of the fulfilment of the capacity of the vehicle. Besides the transportation cost, total time that includes travel time of a vehicle, loading and unloading times of products is also an important issue. Also, instead of a single item, different types of items may need to be transported from some sources to destinations through different types of conveyances. The optimal transportation policy may be affected by many other issues like volume and weight of per unit of product, unavailability of sufficient number of certain types of vehicles, etc. In this paper, we formulate a multi-objective multi-item solid transportation problem by addressing all these issues. The problem is formulated with the transportation cost and time parameters as fuzzy variables. Using credibility theory of fuzzy variables, a chance-constraint programming model is formulated, and is then transformed into the corresponding deterministic form. Finally numerical example is provided to illustrate the problem.

GAN-Based Novel Approach for Data Augmentation with Improved Disease Classification

Abstract: Deep learning, via Convolutional Neural Network (CNN) models, has had significant breakthroughs and achievements in image classification tasks where there is a sufficient amount of annotated data. Generally, medical image datasets are highly imbalanced, and training a convolutional neural network model to classify diseases across classes does not give the desired performance. To combat this, data augmentation is required, and in this chapter, we propose a new strategy to improve the generalization performance of CNN model using a novel online data augmentation strategy with Deep Convolutional Generative Adversarial Network (DCGAN). This helps in regularizing the training and gives better performance across classes, as it prevents the model from overfitting to the majority class. We performed our experiment on NIH chest X-ray image dataset, available openly, considering three classes: Infiltration, Atelectasis and No Findings. The test accuracy of the CNN model is 60.3% compared to the 65.3% test accuracy of the online GAN-augmented CNN model.

Performance study and kinetic modeling of hybrid bioreactor for treatment of bi-substrate mixture of phenol-m-cresol in wastewater: process optimization with response surface methodology.

Abstract: Performance of a hybrid reactor comprising of trickling filter (TF) and aeration tank (AT) unit was studied for biological treatment of wastewater containing mixture of phenol and m -cresol, using mixed microbial culture. The reactor was operated with hydraulic loading rates (HLR) and phenolics loading rates (PLR) between 0.222–1.078 m 3 /(m 2 ·day) and 0.900–3.456 kg/(m 3 ·day), respectively. The efficiency of substrate removal varied between 71%–100% for the range of HLR and PLR studied. The fixed film unit showed better substrate removal efficiency than the aeration tank and was more resistant to substrate inhibition. The kinetic parameters related to both units of the reactor were evaluated and their variation with HLR and PLR were monitored. It revealed the presence of substrate inhibition at high PLR both in TF and AT unit. The biofilm model established the substrate concentration profile within the film by solving differential equation of substrate mass transfer using boundary problem solver tool ‘bvp4c’ of MATLAB 7.1©software. Response surface methodology was used to design and optimize the biodegradation process using Design Expert 8 software, where phenol and m -cresol concentrations, residence time were chosen as input variables and percentage of removal was the response. The design of experiment showed that a quadratic model could be fitted best for the present experimental study. Significant interaction of the residence time with the substrate concentrations was observed. The optimized condition for operating the reactor as predicted by the model was 230 mg/L of phenol, 190 mg/L of m -cresol with residence time of 24.82 hr to achieve 99.92% substrate removal.

Impact of COVID-19 pandemic on viral hepatitis in Africa: Challenges and way forward.

Abstract: With the overwhelming COVID-19 pandemic in Africa, many other severe epidemics have been given low priority, such as viral hepatitis. Patient mortality due to viral hepatitis has raised concern to COVID-19 patients due to compromise with undiagnosed hepatitis in Africa. The pandemic has worsened the control of the viral hepatitis epidemic as healthcare control facilities have moved their focus towards curbing COVID-19 infections. However, different challenges have arisen to viral hepatitis patients because of low health attention that declines the progress of already diagnosed hepatitis patients. Follow-up plans, routine testing and treatment plans for viral hepatitis are no longer as strict with the human resources transferred towards combating the pandemic. Thus, a global effort is required to abide by renewed recommendations to eradicate viral hepatitis in Africa that also fit the current picture of the COVID-19 pandemic. The article discusses the current challenges viral hepatitis patients faced during the COVID-19 pandemic and important recommendations that can see through these challenges in Africa.