One-Machine Scheduling To Minimize Mean Tardiness With Minimum Number Tardy

In this paper the one-machine scheduling problem with the objective of minimizing the mean tardiness subject to maintaining a prescribed number of tardy jobs is analysed An algorithm for solving this problem is presented. It is proved that the schedule generated by the proposed algorithm is indeed optimal

One-Machine Scheduling To Minimize Mean Tardiness With Minimum Number Tardy

In this paper the one-machine scheduling problem with the objective of minimizing the mean tardiness subject to maintaining a prescribed number of tardy jobs is analysed An algorithm for solving this problem is presented. It is proved that the schedule generated by the proposed algorithm is indeed optimal

One-Machine Scheduling To Minimize Mean Tardiness With Minimum Number Tardy

In this paper the one-machine scheduling problem with the objective of minimizing the mean tardiness subject to maintaining a prescribed number of tardy jobs is analysed An algorithm for solving this problem is presented. It is proved that the schedule generated by the proposed algorithm is indeed optimal

Urban-Tissue Optimization through Evolutionary Computation

The experiments analyzed in this paper focus their research on the use of Evolutionary Computation (EC) applied to a parametrized urban tissue. Through the application of EC, it is possible to develop a design under a single model that addresses multiple conflicting objectives. The experiments presented are based on Cerdà’s master plan in Barcelona, specifically on the iconic Eixample block which is grouped into a 4 × 4 urban Superblock. The proposal aims to reach the existing high density of the city while reclaiming the block relations proposed by Cerdà’s original plan. Generating and ranking multiple individuals in a population through several generations ensures a flexible solution rather than a single “optimal” one. Final results in the Pareto front show a successful and diverse set of solutions that approximate Cerdà’s and the existing Barcelona’s Eixample states. Further analysis proposes different methodologies and considerations to choose appropriate individuals within the front depending on design requirements

Spatiotemporal mapping of malaria incidence in Sudan using routine surveillance data

Malaria is a serious threat to global health, with over [Formula: see text] of the cases reported in 2020 by the World Health Organization in African countries, including Sudan. Sudan is a low-income country with a limited healthcare system and a substantial burden of malaria. The epidemiology of malaria in Sudan is rapidly changing due to factors including the rapidly developing resistance to drugs and insecticides among the parasites and vectors, respectively; the growing population living in humanitarian settings due to political instability; and the recent emergence of Anopheles stephensi in the country. These factors contribute to changes in the distribution of the parasites species as well as malaria vectors in Sudan, and the shifting patterns of malaria epidemiology underscore the need for investment in improved situational awareness, early preparedness, and a national prevention and control strategy that is updated, evidence based, and proactive. A key component of this strategy is accurate, high-resolution endemicity maps of species-specific malaria. Here, we present a spatiotemporal Bayesian model, developed in collaboration with the Sudanese Ministry of Health, that predicts a fine-scale (1 km [Formula: see text] 1 km) clinical incidence and seasonality profiles for Plasmodium falciparum and Plasmodium vivax across the country. We use monthly malaria case counts for both species collected via routine surveillance between January 2017 and December 2019, as well as a suite of high-resolution environmental covariates to inform our predictions. These epidemiological maps provide a useful resource for strategic planning and cost-effective implementation of malaria interventions, thus informing policymakers in Sudan to achieve success in malaria control and elimination

Cosmological Solutions to the Lithium Problem

The abundance of primordial lithium is derived from the observed spectroscopy of metal-poor stars in the galactic halo. However, the observationally inferred abundance remains at about a factor of three below the abundance predicted by standard big bang nucleosynthesis (BBN). The resolution of this dilemma can be either astrophysical (stars destroy lithium after BBN), nuclear (reactions destroy lithium during BBN), or cosmological, i.e. new physics beyond the standard BBN is responsible for destroying lithium. Here, we overview a variety of possible cosmological solutions, and their shortcomings. On the one hand, we examine the possibility of physical processes that modify the velocity distribution of particles from the usually assumed Maxwell-Boltzmann statistics. A physical justification for this is an inhomogeneous spatial distribution of domains of primordial magnetic field strength as a means to reduce the primordial lithium abundance. Another possibility is that scattering with the mildly relativistic electrons in the background plasma alters the baryon distribution to one resembling a Fermi-Dirac distribution. We show that neither of these possibilities can adequately resolve the lithium problem. A number of alternate hybrid models are discussed including a mix of neutrino degeneracy, unified dark matter, axion cooling, and the presence of decaying and/or charged supersymmetric particles.Comment: 6 pages, 0 figures, conference proceeding

Ethylene responsive transcription factor ERF109 retards PCD and improves salt tolerance in plant

Semi-quantitative RT-PCR for tobacco VIGS lines of 13 knocked down TFs induced 2 h post oxalic acid treatment (20 mM) as compared to their WT and VIGS line with empty pTRV2 (V2) plants. Amplicon sizes of different genes and primers used are shown in Additional file 5: Table S3. The Nbactin gene was used as the house-keeping control. Gene codes refer to those indicated in Additional file 3: Table S2. (DOCX 684 kb