Application of adversarial risk analysis model in pricing strategies with remanufacturing

Purpose: Purpose: This paper mainly focus on the application of adversarial risk analysis (ARA) in pricing strategy with remanufacturing. We hope to obtain more realistic results than classical model. Moreover, we also wish that our research improve the development of ARA in pricing strategy of manufacturing or remanufacturing. Approach: In order to gain more actual research, combining adversarial risk analysis, we explore the pricing strategy with remanufacturing based on Stackelberg model. Especially, we build OEM’s 1-order ARA model and further study on manufacturers and remanufacturers’ pricing strategy. Findings: We find the OEM’s 1-order ARA model for the OEM’s product cost C. Besides, we get according manufacturers and remanufacturers’ pricing strategies. Besides, the pricing strategies based on 1-order ARA model have advantage over than the classical model regardless of OEMs and remanufacturers. Research implications: The research on application of ARA imply that we can get more actual results with this kind of modern risk analysis method and ARA can be extensively in pricing strategies of supply chain. Value: Our research improves the application of ARA in remanufacturing industry. Meanwhile, inspired by this analysis, we can also create different ARA models for different parameters. Furthermore, some results and analysis methods can be applied to other pricing strategies of supply chain.Peer Reviewe

An Universal Image Attractiveness Ranking Framework

We propose a new framework to rank image attractiveness using a novel pairwise deep network trained with a large set of side-by-side multi-labeled image pairs from a web image index. The judges only provide relative ranking between two images without the need to directly assign an absolute score, or rate any predefined image attribute, thus making the rating more intuitive and accurate. We investigate a deep attractiveness rank net (DARN), a combination of deep convolutional neural network and rank net, to directly learn an attractiveness score mean and variance for each image and the underlying criteria the judges use to label each pair. The extension of this model (DARN-V2) is able to adapt to individual judge's personal preference. We also show the attractiveness of search results are significantly improved by using this attractiveness information in a real commercial search engine. We evaluate our model against other state-of-the-art models on our side-by-side web test data and another public aesthetic data set. With much less judgments (1M vs 50M), our model outperforms on side-by-side labeled data, and is comparable on data labeled by absolute score.Comment: Accepted by 2019 Winter Conference on Application of Computer Vision (WACV

STUDY ON OPTIMAL COMBINATION SETTLEMENT PREDICTION MODEL BASED ON LOGISTIC CURVE AND GOMPERTZ CURVE

The Logistic and Gompertz embankment settlement prediction models have poor prediction accuracy for the late settlement of high-filled soil. This study proposes a combination of the two models based on their common characteristics and individuality, and their respective advantages and specific limitations. The minimum logarithmic error square sum of the combined model was used as the objective function to solve the optimal weighting coefficient. The optimal weighted geometric mean combination prediction model was deduced, to improve the confidence of the prediction accuracy of the settlement of high-filled soil. By fitting and analysing the measured settlement data of the engineered high-filled soil with each prediction model, the feasibility of the proposed optimal combination prediction model in the settlement prediction of high-filled soil was tested. It was found that the proposed optimal combination forecasting model was more accurate and adaptable compared to any single model, and was more reliable. Therefore, the proposed combination forecasting model could be used as an effective method to predict the settlement of high-filled soil in the later stages of settlement

Largest parallelotopes contained in simplices

AbstractWe establish in this paper a theorem for the volume of the largest parallelotope contained in a given simplex. Applying this theorem, we prove some inequalities for unions of parallelotopes in a given simplex and some spanning theorems for inscribed simplices

Multi-period Investment Strategies with Transaction Costs Under Cumulative Prospect Theory

This paper focuses on optimal investment strategies under cumulative prospect theory (CPT). Considering transaction costs, we investigate CPT investors multi-period optimal portfolios. Our main contributions relative to previous work are expanding a single-period optimization problem to a multi-period optimization problem and investigating the impact of transaction costs on optimal portfolio selections. In a numerical analysis that applied original data on four stocks from the NASDAQ, we examine the effects of different risks on the optimal portfolio. Moreover, in contrast with the results without transaction costs, we come to conclusion that the optimal strategy with transaction costs is less sensitive to risk

How Does a Portfolio Manager Balance the Relationship Between Money Management and Investment?

A portfolio manager can obtain profits from charging management fees to individual investors for helping them to invest. Moreover, as an insider, the portfolio manager can obtain proportional brokerage charges on the return on investment by investing the individual investors’ money that he manages. How does the manager balance money management and investment to maximize his total profits? This is the problem that we study in this article. We model the relationship between money management fees and the amount invested. In addition, we investigate how to determine money management fees and the amount of investment needed to maximize the manager’s total profits, including from management fees and brokerage charges

New insights in to the ameliorative effects of zinc and iron oxide nanoparticles to arsenic stressed spinach (Spinacia oleracea L.)

Nanotechnology is capturing great interest worldwide due to their stirring applications in various fields and also individual application of iron oxide nanoparticle (FeO−NPs) and zinc oxide nanoparticle (ZnO−NPs) have been studied in many literatures. However, the combined application of FeO and ZnO−NPs is a novel approach and studied in only few studies. For this purpose, a pot experiment was conducted to examine the plant growth and biomass, photosynthetic pigments, gas exchange attributes, oxidative stress and response of antioxidant compounds (enzymatic and nonenzymatic), sugars, nutritional status of the plant, organic acid exudation pattern As accumulation from the different parts of the plants in spinach (Spinacia oleracea L.) under the different As concentrations i.e., 0 (no As), 60 and 120 μM] which were primed with combined application of two levels of FeO−NPs (10 and 20 mg L−1) and ZnO−NPs (20 and 40 mg L−1). Results from the present study showed that the increasing levels of As in the soil significantly (P \u3c 0.05) decreased plant growth and biomass, photosynthetic pigments, gas exchange attributes, sugars, and nutritional contents from the roots and shoots of the plants. In contrast, increasing levels of As in the soil significantly (P \u3c 0.05) increased oxidative stress indicators in term of malondialdehyde, hydrogen peroxide, and electrolyte leakage, and also increased organic acid exudation patter in the roots of S. oleracea. The negative impact of As toxicity can overcome the combined application of ZnO−NPs and FeO-NPs, which ultimately increased plant growth and biomass by capturing the reactive oxygen species, and decreased oxidative stress in S. oleracea by decreasing the As contents in the roots and shoots of the plants. Research findings, therefore, suggest that the combined application of ZnO−NPs and FeO-NPs can ameliorate As toxicity in S. oleracea, resulting in improved plant growth and composition under As stress, as depicted by balanced exudation of organic acids

HDX-guided EPR spectroscopy to interrogate membrane protein dynamics

This project was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) grant (BB/S018069/1) to C.P., who also acknowledges support from the Wellcome Trust (WT) (219999/Z/19/Z) and the Chinese Scholarship Council (CSC) in the form of studentships for B.J.L. and B.W., respectively. A.N.C. is a Sir Henry Dale Fellow jointly funded by the WT and the Royal Society (220628/Z/20/Z). Funding from the BBSRC (BB/M012573/1) enabled the purchase of mass spectrometry equipment.Solvent accessibilities of and distances between protein residues measured by pulsed-EPR approaches provide high-resolution information on dynamic protein motions. We describe protocols for the purification and site-directed spin labeling of integral membrane proteins. In our protocol, peptide-level HDX-MS is used as a precursor to guide single-residue resolution ESEEM accessibility measurements and spin labeling strategies for EPR applications. Exploiting the pentameric MscL channel as a model, we discuss the use of cwEPR, DEER/PELDOR, and ESEEM spectroscopies to interrogate membrane protein dynamics. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).Publisher PDFPeer reviewe