ABSTRACT
This paper focuses on developing a water and energy-saving reliable irrigation system using state-of-the-art computing, communication, and optimal energy management framework. The framework integrates real-time soil moisture and weather forecasting information to decide the time of irrigation and quantity of water required for potato crops, which is made available to the users across a region through the cloud-based irrigation decision support system. This is accomplished through various modules such as data acquisition, soil moisture forecasting, smart irrigation scheduling, and energy management scheme. The main emphasizes is on the electrical segment which demonstrates an energy management scheme for PV-battery based grid-connected system to operate the irrigation system valves and water pump. The proposed scheme is verified through simulation and dSpace-based real-time experiment studies. Overall, the proposed energy management system demonstrates an improvement in the optimal onsite solar power generation and storage capacity to power the solar pump which save the electrical energy as well as the water in order to establish an improved solar-irrigation system. Finally, the proposed system achieved water and energy savings of around 9.24 % for potato crop with full irrigation enhancing the Water-Energy-Food Nexus at field scale.
ABSTRACT
Peptides are used as reagents both for basic research and diagnostic purposes. Therefore, there is a need for novel methods for the design of peptide molecules with a particular specific physiochemical profile. The properties of the peptides are governed by the nature of amino acids constituting the peptide. There is a lack of a web server or tools which could predict all the possible combinations of the peptides generated because of the combinations of amino acids based on the physiochemical properties. We have developed a peptide combination generator (PepCoGen), a web server for generating all the possible combinations of peptides by varying the amino acids having similar physiochemical properties at a particular position. It also predicts other properties of the peptides including molecular weight, charge, solubility, hydrophobic plot, and isoelectric point, and random three-dimensional models for each generated combination.
