The waning effectiveness of the coronavirus disease-2019 (COVID-19) vaccines and the emergence of new variants have given rise to the possibility of future outbreaks of the infection. COVID-19 has caused more than 43 million reported cases and 526,000 deaths in India so far, and the disease spread is active again despite mass vaccinations. In this article, we present a compartmental epidemiological model incorporating vaccinations with dose-dependent effectiveness. We study a possible sudden outbreak of SARS-CoV2 variants in India, bring out the associated predictions for various vaccination rates, and point out optimum control measures. Our model simulation numbers for the total infected are close to the seroprevalance data in August 2021, and our results show that second dose vaccine effectiveness is the most sensitive parameter in the future evolution of the disease. A combination of vaccination and social distancing is the key to tackling the current situation and for the coming few months. Our simulation shows that social distancing measures show better control over disease spread than higher vaccination rates, and disease spread does not appear to rise sharply in the near future unless a new variant emerges.

MOTIVATION: Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). RESULTS: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. AVAILABILITY AND IMPLEMENTATION: The software is implemented in Matlab, and is provided as supplementary information . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Power Integrity is maintained in a high speed system by designing an efficient decoupling network. This paper provides a generic formulation for decoupling capacitor selection and placement problem which is solved by mixed-integer programming. A real-world example is presented for the same. The minimum number of capacitors that could achieve the target impedance over the desired frequency range are found along with their optimal locations. In order to solve an industrial problem, the s-parameters data of power plane geometry and capacitors are used for the accurate analysis including bulk capacitors and VRM.

In the era of petascale supercomputing, the importance of load balancing is crucial. Although dynamic load balancing is widespread, it is increasingly difficult to implement effectively with thousands of processors or more, prompting a second look at static load-balancing techniques even though the optimal allocation of tasks to processors is an NP-hard problem. We propose a heuristic static load-balancing algorithm, employing fitted benchmarking data, as an alternative to dynamic load balancing. The problem of allocating CPU cores to tasks is formulated as a mixed-integer nonlinear optimization problem, which is solved by using an optimization solver. On 163,840 cores of Blue Gene/P, we achieved a parallel efficiency of 80% for an execution of the fragment molecular orbital method applied to model protein-ligand complexes quantum-mechanically. The obtained allocation is shown to outperform dynamic load balancing by at least a factor of 2, thus motivating the use of this approach on other coarse-grained applications.