Search

In today's environment, organizations utilise Machine Learning based-models to keep stocks depending on the demand for a particular type of product. This article develops an inventory model considering the imperfect deteriorating product in a fuzzy environment. The shortages are allowed and partially backlogged. Since the deterioration rate and defective percentage in quantity in the received lot may not be predicted precisely because it depends on many uncertain situations, therefore both are considered fuzzy variables. This study aims to determine the optimal ordering quantity and replenishment period to optimize (minimize) the average overall cost with carbon emission cost. The defuzzification process is done using the sign distance approach method. A methodology based on Machine Learning is used to demand forecast seasonally. Some numerical examples are taken to validate the proposed mathematical model. The findings demonstrate the generation of direct month-wise predicted demand for deteriorating products based on the input of the month value, enabling organizations to optimize their inventory management according to forecasted demand. A comparative analysis is conducted between fixed and month-wise forecasted demand by highlighting the advantages of machine learning-based forecasting approaches. Sensitivity analysis performs to examine the behaviour of several parameters on an optimal solution and provides some managerial insights. [ABSTRACT FROM AUTHOR]

Uranium is a highly hazardous heavy metal and radionuclide. The distribution of uranium in rock, soil, fruit, and water has been measured in the Balod area. Samples were collected using one square kilometer griding technique from Jhalmala Paragaon, Parsoda, Dewerbatt, Pakurbatt, and Jagarata Deur tarai. The distribution of uranium is quantified within the ranges of 0.7–3.6 (ppb) in soil, 0.5–4.4 ppb in rock, 0.2–52.6 ppb in groundwater, and 0.2–0.7 ppb in fruit.The presence and movement of uranium has been verified through a systematic statistical investigation, and its mobility in environmental components has been established. [ABSTRACT FROM AUTHOR]

Salmonella is a well-known food-borne pathogen causing disease in humans and animals worldwide. Peptidyl-prolyl isomerases (PPIases) catalyse the cis–trans isomerisation of prolyl bound, which is a slow and rate-limiting step of protein folding. Here, we present the biochemical and molecular characterisation of a novel multi-domain parvulin-type, PPIases-C from the pathogenic bacteria Salmonella Typhimurium, annotated as rPpiC. The recombinant plasmid PpiC_pET28c was used for protein induction using 1.5 mM concentration of isopropyl-β-D-thiogalactopyranoside at 30 °C. Subsequently, the protein was identified by using the LC–MS technique showing high match score and sequence coverage with available PPIases-C proteins database. Using the succinyl-ala-phe-pro-phe-p nitroanilide as a substrate, Vmax of the enzyme was found to be 0.8187 ± 0.1352 µmoles/min and Km = 1.6014 ± 0.8449 µM, respectively. With this, we conclude that rPpiC protein is an active form of protein from Salmonella Typhimurium and plays an important role in protein folding. [ABSTRACT FROM AUTHOR]