Keywords Tissue P System; Wireless Sensor Network; Multi-Objective problem; Task Assignment; Decision Support System; Parallel computing; Sustainable computing Abstract The contemporary wireless sensor applications employ a Heterogeneous Wireless Sensor Network (HeWSN) to achieve its multi-objective missions. Modern wireless nodes constituting the HeWSN are more versatile in terms of its capabilities, functionalities, and applications. Assigning tasks in a dynamic HeWSN environment are challenging due to its inherent heterogeneous properties and capabilities. The investigation of existing task assignment algorithms reveals (i) the majority of the existing task assignment algorithms were designed for the homogeneous environment, (ii) most of the nature-inspired algorithms were built for centralized architecture. Scheduling tasks by existing task assignment algorithms lead to underutilization of resources as well as to the rapid depletion of network resources. To this end, a novel, distributed, heterogeneous task assignment algorithm adhering the modern sensors capabilities, functionalities and sensor application to attain sustainable computing is required. Based on the investigation, Tissue P-System inspired task assignment algorithm for the distributed heterogeneous WSN has been modelled. The experimental analyses of the proposed method have been self-evaluated as well as compared with the corresponding recent benchmark algorithms under various conditions and its performance metrics are analysed. Author Affiliation: Karunya Institute of Technology & Sciences, Coimbatore, Tamil Nadu 641 114, India * Corresponding author. Article History: Received 18 November 2019; Revised 11 June 2020; Accepted 21 June 2020 (footnote) Peer review under responsibility of King Saud University. Byline: Titus Issac [firstname.lastname@example.org] (*), Salaja Silas, Elijah Blessing Rajsingh
De Santis M, Sorbelli D, Vallet V, Gomes ASP, Storchi L, and Belpassi L
Journal of chemical theory and computation [J Chem Theory Comput] 2022 Sep 29. Date of Electronic Publication: 2022 Sep 29.
Frozen density embedding (FDE) represents an embedding scheme in which environmental effects are included from first-principles calculations by considering the surrounding system explicitly by means of its electron density. In the present paper, we extend the full four-component relativistic Dirac-Kohn-Sham (DKS) method, as implemented in the BERTHA code, to include environmental and confinement effects with the FDE scheme (DKS-in-DFT FDE). The implementation, based on the auxiliary density fitting techniques, has been enormously facilitated by BERTHA's python API (PyBERTHA), which facilitates the interoperability with other FDE implementations available through the PyADF framework. The accuracy and numerical stability of this new implementation, also using different auxiliary fitting basis sets, has been demonstrated on the simple NH 3 -H 2 O system, in comparison with a reference nonrelativistic implementation. The computational performance has been evaluated on a series of gold clusters (Au n , with n = 2, 4, 8) embedded into an increasing number of water molecules (5, 10, 20, 40, and 80 water molecules). We found that the procedure scales approximately linearly both with the size of the frozen surrounding environment (consistent with the underpinnings of the FDE approach) and with the size of the active system (in line with the use of density fitting). Finally, we applied the code to a series of heavy (Rn) and super-heavy elements (Cn, Fl, Og) embedded in a C 60 cage to explore the confinement effect induced by C 60 on their electronic structure. We compare the results from our simulations, with respect to more-approximate models employed in the atomic physics literature. Our results indicate that the specific interactions described by FDE are able to improve upon the cruder approximations currently employed, and, thus, they provide a basis from which to generate more-realistic radial potentials for confined atoms.