Manufacturer-remanufacturing vs supplier-remanufacturing in a closed-loop supply chain

AbstractRemanufacturing at the component level could be performed by either a manufacturer or a supplier. In this paper, we analyze the performance of manufacturer-remanufacturing and supplier-remanufacturing in a decentralized closed-loop supply chain, and examine their desirability from different stakeholder perspectives. We find that the manufacturer may engage in remanufacturing of used components even if remanufacturing is costlier than traditional manufacturing; given remanufacturing is costlier, the manufacturer may forgo remanufacturing due to a marginal increase in consumer willingness-to-pay for the remanufactured product. If the unit remanufacturing cost is high enough, the manufacturer and consumers prefer manufacturer-remanufacturing, while the supplier and the environment prefer supplier-remanufacturing; otherwise, the manufacturer, the supplier, and consumers prefer supplier-remanufacturing, while the environment׳s preference is contingent on the environmental impact discount for the remanufactured product. Finally, the key findings are distilled into a roadmap to guide the development of remanufacturing

Human Mobility Trends during the COVID-19 Pandemic in the United States

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.Comment: 11 pages, 9 figure

Pathway Bridge Based Multiobjective Optimization Approach for Lurking Pathway Prediction

Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1- TGFβR1- SMAD2/3- SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβ signal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis

Tunnel Settlement Prediction by Transfer Learning

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction

Blockchain-Based Applications for Smart Grids: An Umbrella Review

This article presents an umbrella review of blockchain-based smart grid applications. By umbrella review, we mean that our review is based on systematic reviews of this topic. We aim to synthesize the findings from these systematic reviews and gain deeper insights into this discipline. After studying the systematic reviews, we find it imperative to provide a concise and authoritative description of blockchain technology because many technical inaccuracies permeate many of these papers. This umbrella review is guided by five research questions. The first research question concerns the types of blockchain-based smart grid applications. Existing systematic reviews rarely used a systematic method to classify these applications. To address this issue, we propose a taxonomy of these applications, first by differentiating them based on whether the application is focusing on functional or non-functional aspects of smart grid operations, and then by the specific functions or perspectives that the application aims to implement or enhance. The second research question concerns the roles that blockchain technology plays in smart grid applications. We synthesize the findings by identifying the most prominent benefits that blockchain technology could bring to these applications. We also take the opportunity to point out several common technical mistakes that pervade the blockchain literature, such as equating all forms of blockchains to data immutability. The third research question concerns the guidelines for deciding whether a blockchain-based solution would be useful to address the needs of smart grids. We synthesize the findings by proposing benefit- based guidelines. The fourth research question concerns the maturity levels of blockchain-based smart grid applications. We differentiate between academic-led and industry-led projects. We propose a five-level scale to evaluate the maturity levels. The ranking of the industry-led projects is performed through our own investigation. Our investigation shows that more than half of the industry-led projects mentioned in the systematic reviews are no longer active. Furthermore, although there are numerous news reports and a large number of academic papers published on blockchain-based smart grid applications, very few have been successfully embraced by the industry. The fifth research question concerns the open research issues in the development of blockchain-based smart grid applications. We synthesize the findings and provide our own analysis