EPCglobal Network Integrated Dynamic Carbon Footprints on Mobile Phones

Dynamic carbon footprint reflects potential differences between instances of the same product. To deliver dynamic carbon footprint to end consumers, we should adopt a new and innovative style as it cannot be implemented by common physical labels. This paper presents how EPCglobal network can be used to track carbon emissions, which then are the input of Sourcemap to create a lifecycle map. What’s more, the lifecycle map is displayed on mobile phones to be a convincing persuasive technology

Research Of E-Commerce Enterprises Capability Maturity Theory And Initial Model Construction

With the constant development and evolution of “Internet+” strategic thinking, the electronic commerce enterprises have obtained the unprecedented growth, but also faced with great survival pressure and challenges. This research is based on the review and combing the historical development of capability maturity and in the light of the characteristics of e-commerce enterprises building a capability maturity model which contains five levels: the initial level, the repeatable level, the standard level, the managed level and the optimal level and five dimensions: strategy, organization, process, personnel and technical support. The capability maturity initial model of e-commerce enterprises establishes basic demand are obtained earnings, controlling risk and optimizing resources and with different stages of target the capabilities the electronic commerce enterprises should owned, at last this model generalizes a clear direction and standard for the e-commerce enterprises management

Replenishment Modeling for Complex Automatic Picking System

The replenishment plan of complex automatic picking system(CAPS) determines the efficiency of picking and whether the picking can be operated smoothl(1)y, which is not only highly related to the pallet storage area and picking buffer but also the operation and operating efficiency of Horizontal Dispenser (HD) and Channel Dispenser(CD). The questions here can be summarized as when replenishment should be required and how many cartons of cigarettes should be dedicated to picking buffer. To answer these questions, we first present a comprehensive description for the general multi-tier, multi-model inventory system of CAPS and analyse the stocking and picking activities of HD and CD. Then we consider replenishment in two cases. One is single replenishing request from one picking flue, the other is multiple replenishing requests from more than one picking flues at the same time which is more complex. For the latter case, we solve the problem based-on optimal scheduling policy. Our mathematical model provides a viable solution to optimize replenishment operation for CAPS

A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand

In remanufacturing research, most researchers predominantly emphasised on the recovery of whole product (core) rather than at the component level due to its complexity. In contrast, this paper addresses the challenges to focus on remanufacturing through component recovery, so as to solve production planning problems of hybrid remanufacturing and manufacturing systems. To deal with the uncertainties of quality and quantity of product returns, the processing time of remanufacturing, remanufacturing costs, as well as market demands, a robust optimisation model was developed in this research and a case study was used to evaluate its effectiveness and efficiency. To strengthen this research, a sensitivity analysis of the uncertain parameters and the original equipment manufacturer’s (OEM’s) pricing strategy was also conducted. The research finding shows that the market demand volatility leads to a significant increase in the under fulfilment and a reduction in OEM’s profit. On the other hand, recovery cost reduction, as endogenous cost saving, encourages the OEM to produce more remanufactured products with the increase in market demand. Furthermore, the OEM may risk profit loss if they raise the price of new products, and inversely, they could gain more if the price of remanufactured products is raised

Tamper detection in RFID-enabled supply chains using fragile watermarking

While mainstream RFID research has been focused on solving privacy issues, security in general and data tampering in specific is still an open question. This paper analyzes potential security threats especially data tampering in RFID-enabled supply chains and proposes solutions how these threats might be addressed using fragile watermarking technologies. We first survey RFID system and its security problems, and then explain the importance of fragile watermarking schemes for RFID systems and possible applications using fragile watermarking to detect and locate any modification in RFID systems. Finally we suggest possible solutions using fragile watermarking for RFID-enabled supply chain

Tamper Detection in the EPC Network Using Digital Watermarking

National Science Foundation of China [70971112, 70902042

A Survey on Deep Learning in COVID-19 Diagnosis

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research