An event triggered control scheme for enhanced production of Escherichia coli and biomass concentration during fed-batch cultivation.

Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7 %, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption.

A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.

A realistic perspective for CO2 triggered tuning of electrical conductivity.

To seek sustainable CO2 sequestration and conversion, an electrochemical cell has been investigated for carbon capture and utilization strategy (CCU). In this cell, atmospheric CO2 is captured under ambient conditions and incorporated into power generation using zinc nanopowder as the catalyst. As a result, a method was developed to tune the electronic property of zinc by passing CO2. It was observed that nearly three orders of magnitude of conductivity could be changed along with achieving a carbon capture strategy. The system also exhibited good stability. In this process, it was observed that efficient current generation could be achieved due to zinc's active participation as a catalyst. The detailed physicochemical characterizations of catalysts were also examined. XRD, FTIR and TEM analysis perform the structural and morphological characterization. The system performance was further investigated using different criteria.

Highly sensitive and selective colorimetric sensing of CO2 for biomedical applications.

The concentration of carbon dioxide (CO2) in unhealthy people differs greatly from healthy people. High-precision CO2 detection with a quick response time is essential for many biomedical applications. A major focus of this research is on the detection of CO2, one of the most important health biomarkers. We investigated a low-cost, flexible, and reliable strategy by using dyes for colorimetric CO2 sensing in this study. The impacts of temperature, pH, reaction time, reusability, concentration, and dye selectivity were studied thoroughly. This study described real-time CO2 analysis. Using this multi-dye method, we got an average detection limit of 1.98 ppm for CO2, in the range of 50-120 ppm. A portable colorimetric instrument with a smartphone-assisted unit was constructed to determine the relative red/green/blue values for real-time and practical applications within 15 s of interaction and the readings are very similar to those of an optical fiber probe. Environmental and biological chemistry applications are likely to benefit greatly from this unique approach.

A comparative energy and exergy efficiency study of hemispherical and single-slope solar stills.

In this experimental work, a comparative energy and exergy efficiency study of hemispherical solar still and a single-slope solar still has been carried out. The experiments were conducted in southeast Algeria on 25-5-2020 and 3-6-2020 in the natural climatic environment, and daily accumulation of distilled water produced for both distilleries was measured. The maximum obtained cumulative yield of distilled products is equal to 5.38 kg/m2/day for the hemispherical solar still, and 3.64 kg/m2/day for the single-slope solar still. The overall daily productivity was improved by 47.96% for the hemispherical solar still compared to the single-slope solar still. The maximum daily energy efficiency of the single-slope solar still is 25.81%, and hemispherical solar still is 38.61%. Similarly, the maximum daily exergy efficiency of single-slope solar still is 1.8%, and hemispherical solar still is 3.1%. The main conclusion from the study is the hemispherical distillery greatly enhances productivity as compared to the single-slope distillate and gives more efficiency. Thus, the hemispherical solar still is recommended to be used to provide safe drinking water from salty water.