D-Methionine [BSA]

Companies are preferring a sustainable production system that meets the rigorous environmental regulations and fulfills the demands of customers with a qualitative product. The design for remanufacturing (DfRem) is one favorable strategy that facilitates protecting the latent energy and power in salvaged products/components. This paper presents a mathematical model, which describes DfRem as an integral process in developing a new product under three different scenarios as: i) when only new components are used, ii) when a mix of old and new spare part is used, and iii) when only old components are considered. The paper defines a strategy in choosing the core acquisition mathematically that follows an exponential or delayed S-shaped distribution along with the cost model formulations. Using the non-linear optimization modeling, the proposed framework has been validated on the sales data related to products which belong to altered industries such as automobile, telecommunication, and electronics. Results have shown that a product developed using the combination of old and new components is better as compared to using all new components or all old components. Sensitivity analysis has been carried out to examine the impact of the various parameters on the decision variables. The significance of this study is to provide a solution of procuring components in different manners and producing remanufacturable products using either new or used (old) components to the manufacturers. (C) 2020 Elsevier Ltd. All rights reserved.

Water is one of the basic resources for human survival. Water pollution monitoring and protection have been becoming a major problem for many countries all over the world. Most traditional water quality monitoring systems, however, generally focus only on water quality data collection, ignoring data analysis and data mining In addition, some dirty data and data loss may occur due to power failures or transmission failures, further affecting data analysis and its application. In order to meet these needs, by using Internet of things, cloud computing, and big data technologies, we designed and implemented a water quality monitoring data intelligent service platform in C# and PHP language. The platform includes monitoring point addition, monitoring point map labeling, monitoring data uploading, monitoring data processing, early warning of exceeding the standard of monitoring indicators, and other functions modules. Using this platform, we can realize the automatic collection of water quality monitoring data, data cleaning, data analysis, intelligent early warning and early warning information push, and other functions. For better security and convenience, we deployed the system in the Tencent Cloud and tested it. The testing results showed that the data analysis platform could run well and will provide decision support for water resource protection.