ABSTRACT
Production of defective products is a very general phenomenon. But backorder and shortages occur due to this defective product, and it hampers the manufacturer's reputation along with customer satisfaction. That is why, these outsourced products supply, a portion of required products for in-line production. This study develops a flexible production model that reworks repairable defective products and outsources products to prevent backlogging. A percentage of total in-line production is defective products, which is random, and those defective products are repairable. A green investment helps the reworking process, which has a direct impact on the market demand for products. A classical optimization solves the profit maximization model, and a numerical method proves the global optimal solutions. Sensitivity analysis, managerial insights, and discussions provide the highlights and decision-making strategies for the applicability of this model.
Subject(s)
Outsourced ServicesABSTRACT
The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.
ABSTRACT
Using multiplex real-time PCR, 960 fecal samples collected from poultry, cattle, and patients with diarrhea in Bangladesh were screened for diarrheagenic Escherichia coli (DEC). The invasion-related gene virB showed the highest prevalence in human patients (41%) and was shown to be positively correlated first with afaB with regards to diffuse adhesion and second with aggR with regards to aggregative adhesion. These three genes were specific to human patients. In contrast, the Shiga toxin genes stx1 (57%) and stx2 (40%) were prevalent in cattle samples. The eae gene, which is associated with attaching and effacing lesion formation, and the elt and est genes, which are associated with enterotoxins, were detected from all three sample sources. Heat map construction and hierarchical clustering assigned the samples into five different clusters, with the patient samples positive for virB and afaB being placed together in one cluster. Although the detection of virulence genes cannot be a direct indication of the distribution of diarrheagenic organisms, their detection suggests that Shigella spp. or enteroinvasive E. coli are the most prevalent diarrheagenic bacteria in Bangladesh and that diffusely adherent E. coli is concomitantly present with these bacteria. eae-possessing organisms in patients may come from cattle and poultry sources. The small number of stx-positive patients could be explained by the small number of animal samples that were positive for both eae and stx.
