An energy-efficient pumping system for sustainable cities and society: Optimization, mathematical modeling, and, impact assessment

In this research work, we have focused on one of the reasons called drawdown (difference between static and pumping heads) for getting maximum efficiency. Therefore, various mechanical attachments have been designed and fabricated for performance evaluation. Since pump performance and drawdown are inversely related, the primary goal is to reduce drawdown as much as possible. The effect of various types of mechanical attachments on pump performance is investigated in this research work. Three bowl-type mechanical attachments can be integrated at once and can increase efficiency by up to 58%, which is 8% more than utilizing no attachment. Additionally, the impact of bore well diameter on pump performance has been studied. In addition, the impact of applying mechanical attachment at two pumping sites has been investigated, and a considerable amount of energy savings has been found. The response surface methodology (RSM)-based optimization of the various input parameters has also been examined. It was found that the maximum 62.04 % could be achieved through a head of 66.5 m, a discharge of 0.012 m3/s, an input power of 12,605 W, and a bore well diameter of 0.215054 m, having three bowl-type mechanical attachments at a time. The mathematical modeling was also performed using analysis of variance (ANOVA) and formulated some equations for pumping efficiency with various pumping input parameters. Since there is very little variation between actual and anticipated performance, it can be used to evaluate the pumping system’s performance in relation to various input parameters. As a result, maintaining sustainable cities and societies might greatly benefit from the energy-efficient pumping system

predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance.

Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes