A big data based cost prediction method for remanufacturing end-of-life products

Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products

Data-driven ecological performance evaluation for remanufacturing process

Remanufacturing has received extensive attention due to its advantages in material and energy saving, emission reduction and is often considered a viable approach for the realization of a circular economy. Remanufacturing ecological performance reflects the ability of an enterprise to balance economic and environmental benefits. Therefore, evaluating the remanufacturing ecological performance is of great significance for leveraging the benefits of remanufacturing and promoting the concept of sustainability and the implementation of a circular economy in the industry. To this end, a set of data-driven techniques, i.e., data envelopment analysis, R clustering and grey relational analysis, are deployed to analyze and evaluate the ecological performance of a remanufacturing process. The effectiveness and feasibility of the proposed method are illustrated via a case study of remanufacturing for hydraulic cylinder and boom cylinder. Furthermore, a number of critical factors, e.g., energy-saving rate, remanufacturing process cost and rate of remanufacturing, for end-of-life products have been identified as the key drivers impacting the remanufacturing ecological performance. So as to improve remanufacturing ecological performance, optimizing production technology, implementing lean remanufacturing and raising public acceptability over remanufacturing products are effective measures. The research results of the present work can provide support for remanufacturing enterprises to guide and improve their ecological performance and formulate better development strategies

An integrated decision-making method for selecting machine tool guideways considering remanufacturability

As one of the most important components of machine tool, guideway has an important driving force to comprehensively improve the remanufacturability of machine tools. To select optimal guideway for machine tool remanufacturing, an integrated multi-criteria decision-making (MCDM) approach that combines improved analytic hierarchy process (AHP) and connection degree-based technique of ranking preferences by similarity to the ideal solution (CD-TOPSIS) method is proposed. The improved AHP is employed to calculate the weights of each criterion and the CD-TOPSIS is adapted to complete the task of sorting; finally, the comprehensive evaluation of the alternatives is carried out. A case study, i.e. eight types of guideways, is illustrated to verify the proposed MCDM method. In addition, comparison with existing methods is performed to validate the effective and reliability for the proposed hybrid approach. Also, sensitivity analysis is provided to evaluate the robustness of the method. The final result shows the method provides reliable decision support for the selection of machine tool guideways for remanufacturing

A data-driven based decomposition?integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products

Application of microbial organic fertilizers promotes the utilization of nutrients and restoration of microbial community structure and function in rhizosphere soils after dazomet fumigation

IntroductionSoil fumigant dazomet is a broad-spectrum nematicide and fungicide that can kill non-target microbes. Fungicides or organic fertilizers are often added after fumigation to improve the recovery of soil microbes. However, the effect of adding microbial organic fertilizers (MOF) after fumigation on the structure and function of rhizosphere soil microbial communities of crops is unclear.MethodsTherefore, we investigated the effects of adding Junweinong and Junlisu MOFs after dazomet fumigation on the structure and function of rhizosphere microbial communities and its relationship with soil properties and enzyme activities.Results and discussionThe results showed that the addition of these two MOFs after dazomet fumigation significantly reduced the rhizosphere soil available phosphorus, available potassium, organic matter content, and urease, alkaline phosphatase, and catalase activities, but increased the soil pH compared with the fumigation treatment. The application of MOFs after fumigation resulted in significant enrichment of bacteria such as Gaiella, norank_f_Vicinamibacteraceae, and Flavisolibacter and fungi such as Peroneutypa, Olpidium, and Microascus in the rhizosphere soil of the crop and increased the relative abundance of functional genes of 13 kinds of amino acids metabolism, pyruvate metabolism, TCA cycle, and pentose phosphate pathway as well as endophytic and epiphytic functional groups in the rhizosphere soil. In particular, NH4+-N, pH, and AK had the greatest effect on rhizosphere microorganisms. Overall, the addition of MOFs after fumigation promoted crop root nutrient uptake, enhanced rhizosphere soil microbial metabolism, allowed more beneficial communities to colonize the roots, and promoted soil microbiological health

The 21st Century Maritime Silk Road: Sino-Sri Lanka Bilateral Maritime Cooperation

张仁平，教授，中国大连海事大学国际海事公约研究中心主任。 姜州阳，助理，中国大连海事大学国际海事公约研究中心。 任袖语，助理，中国大连海事大学国际海事公约研究中心。 杨迎，助理，中国大连海事大学国际海事公约研究中心。【中文摘要】丝绸之路经济带和21世纪海上丝绸之路是促进中国与古丝路沿线各国相互投资与合作的战略构想。2013年10月，中国国家主席习近平在出访东南亚国家期间，提出共建海上丝绸之路的重大倡议，他回顾了中国古代海上丝绸之路的历史，并勾勒出一条新的海上丝绸之路。中国重振海上丝绸之路，就是要弘扬古丝路和平、友好与合作的精神，与沿线各国实现共同发展，打造命运共同体，助推海上安全建设。本文将从3方面论述海上丝绸之路的建设：亚洲基础设施投资银行、基础设施建设和自由贸易区。最后，本文提出从政策、能力建设和培训等方面增强中斯双边海上合作。斯里兰卡是第一个正式支持中国“一带一路”战略的国家。中斯均希望两国的双边合作会成为新时期海上合作的样板，同时也让斯里兰卡成为镶嵌在21世纪海上丝绸之路上的一颗璀璨明珠。 【Abstract】 The Silk Road Economic Belt and the 21st Century Maritime Silk Road are Chinese strategic initiatives to increase investments and foster collaboration along the historic Silk Road. Chinese President Xi Jinping first raised the Maritime Silk Road initiative when he visited Southeast Asia in October 2013.During his visit, he memorialized the ancient Maritime Silk Road and outlined a new Maritime Silk Road, in order to revive the Maritime Silk Road to carry forward the spirit of peace, friendship, and cooperation of the ancient Silk Road.China hopes to realize common development with all countries along the road to improve maritime security, and develop a community of common destiny. This paper examines the building of the new Maritime Silk Road from three aspects: the Asian Infrastructure Investment Bank, infrastructure construction, and free trade areas. Finally, this paper proposes to better bilateral cooperation between China and Sri Lanka in the areas of policy, capacity building and training. Sri Lanka is the first country to officially support China’s initiatives and both countries hope that their bilateral collaboration will become a model for maritime cooperation in the new era, resulting in Sri Lanka becoming a dazzling pearl along the 21st Century Maritime Silk Road

GAN-Assisted YUV Pixel Art Generation

Procedural Content Generation (PCG) in games has grown in popularity in recent years, with Generative Adversarial Networks (GANs) providing a promising option for applying PCG for game artistic asset generation. In this paper, we introduce a model that uses GANs and the YUV colour encoding system for automatic colouring of game assets. In this model, conditional GANs in Pix2Pix architecture are chosen as the main structure and the YUV colour encoding system is used for data preprocessing and result visualisation. We experimented with parameter settings (number of epochs, activation functions, optimisers) to optimise output. Our experimental results show that the proposed model can generate evenly coloured outputs for both small and larger datasets

Intelligent Control Strategies for Vehicle Departure in Urban Complex Parking Lots of the Jinding Area in Shanghai, China

The entrances and exits of underground parking lots of large complexes are the key nodes for the conversion between ground-level dynamic traffic and underground static traffic. Since congestion is caused by a large number of vehicles leaving parking lots at peak hours, the departure control strategy can effectively manage vehicle departure and reduce the congestion of ground-level traffic. In this study, we introduce cooperative control in ramp control into parking lot exit management. The frequency of parking lot exit gate lever lift is used as the control and optimization variable. To ensure the efficiency of regional traffic, we designed timing and inductive control strategies to control the speed of departing vehicles. In an experimental model, we took Shanghai Jinding super-large underground parking lot as an example. The changes in the external road network were simulated when different strategies were implemented on the Simulation of Urban Mobility (SUMO) simulation platform. The experimental results show that the proposed control strategies can significantly ease the congestion of the regional road network, improve the average speed of dynamic traffic, and reduce the queue length at intersections

Intelligent Control Strategies for Vehicle Departure in Urban Complex Parking Lots of the Jinding Area in Shanghai, China

The entrances and exits of underground parking lots of large complexes are the key nodes for the conversion between ground-level dynamic traffic and underground static traffic. Since congestion is caused by a large number of vehicles leaving parking lots at peak hours, the departure control strategy can effectively manage vehicle departure and reduce the congestion of ground-level traffic. In this study, we introduce cooperative control in ramp control into parking lot exit management. The frequency of parking lot exit gate lever lift is used as the control and optimization variable. To ensure the efficiency of regional traffic, we designed timing and inductive control strategies to control the speed of departing vehicles. In an experimental model, we took Shanghai Jinding super-large underground parking lot as an example. The changes in the external road network were simulated when different strategies were implemented on the Simulation of Urban Mobility (SUMO) simulation platform. The experimental results show that the proposed control strategies can significantly ease the congestion of the regional road network, improve the average speed of dynamic traffic, and reduce the queue length at intersections

Mesoporous Silica Loaded with Molybdenum Phosphide Nanoparticles for Hydrogen Evolution

A mesoporous silica loaded with molybdenum phosphide nanoparticles (MoP@MSN) was synthesized using Pluronic F-127 as a hard template. Using the method of XRD and HRTEM, the crystallinity, the phase structure, and the morphologies of the MoP@MSN were investigated. The results showed that the MoP@MSN were composed of nanoflakes with approximately 100 nm. Through the linear sweep voltammetry (LSV), the Tafel slopes of 100 and 341 mV were yielded for MoP@MSN and pure MoP, respectively, meaning that the composite of MSN can significantly improve the conductivity of the products. Meanwhile, the mesoporous MoP@MSN presented excellent electrochemical activity and stability toward hydrogen evolution compared with those of bulk MoP nanoparticles, showing a promising prospect in hydrogen production