Chain Design Research Articles

A novel approach for the atomic scale characterization of Li-ion battery components probed by positron annihilation lifetime spectroscopy

Recent research has focused on solid polymer electrolytes (SPEs) as the best liquid electrolyte replacements. Poly(ethylene oxide) (PEO) is the most frequent one because of its fast segmental movements in the free volume which controls lithium-ion transport between electrodes. Therefore, PEO-based copolymer electrolytes that can be tailored for mechanical strength and ionic conductivity are often studied. The most popular material is PEO chains containing PMMA because PEO transports ions, and PMMA has good mechanical properties. Further investigations on PEO-based solid polymer electrolytes (SPEs) have shown that chain designs with more nonlinear branches prevent crystallinity and maintain fast segmental dynamics. For this purpose, we worked on PEO-grafted-PMMA copolymers in which the free volume probed by positron annihilation lifetime spectroscopy considerably affects the structure-ionic conductivity relationship. Finally, we investigated the free volume theory of ionic condcutivity using Yahsi-Ulutas-Tav (YUT) theory.

Blood Supply Chain Management: A Review of Different Solution Techniques

Managing the blood supply network is crucially important. The lack of blood might result in patient problems and even death. Blood loss, on the other hand, results in hefty expenses. To reduce the levels of shortage and wastage, the blood product supply chain must make the best decisions possible. Numerous writers have researched this field because of the intricacy and significance of the blood supply chain. This essay aims to provide an overview of research on the blood supply chain. Studies that were published from 2015 to 2022 were therefore examined and categorized. This survey's main contribution is to update the body of research on the blood supply chain with a new classification and critically evaluate the state of the art in this field. Environments for making decisions, problems with the blood supply chain's design, working methods, decision-making, modeling, problem-solving methods, and data features are among the suggested categories. In addition, the shortcomings and inadequacies in the existing literature are emphasized, and potential study approaches are presented.

Eigenvalue-Based Stability Analysis for Droop-Free Controlled Islanded Microgrid With Symmetric/Asymmetric Communication Network

Applying droop-based hierarchical control for islanded microgrids would face several unavoidable operation issues, such as large frequency deviation, high dynamic fluctuation, and inefficient coordination between different control layers. To this end, droop-free control has been explored to achieve the power sharing goal through neighboring communication, while maintaining system frequency levels (i.e., keeping the average nodal frequency constant). Although droop-free control shows potential in mitigating the defects of droop-based control, current research on the theoretical understanding of its stability performance remains limited. Hereby, this paper discusses the stability performance of droop-free controlled islanded microgrid via eigenvalue-based analysis. Specifically, a modified eigenvalue analysis method is first proposed to prove that all effective system eigenvalues under the symmetric communication network are located in the left half-plane, indicating that the symmetric droop-free controlled microgrid is asymptotically stable. Then, the asymmetric communication network is discussed, and the analytical eigenvalues with symmetric/asymmetric communication network designs are deduced under the homogenized electrical network. With the deduced analytical eigenvalues, stability performances of the symmetric/asymmetric designs are assessed via stability margin analysis and vulnerability analysis. Numerical case studies illustrate stability performance of the proposed designs, demonstrating that the asymmetric cycle design outperforms symmetric cycle and symmetric/asymmetric chain designs in overall convergence speed and stability against communication errors between droop-free controllers.

Experiment and Analysis of Film-Soil Separation Motion Characteristics of a Chain Drive Residual Film Recovery Mechanism for the Tillage Layer

The working principle and key components of a chain drive residual film recovery machine were studied. A kinematic analysis of the chain drive soil separation mechanism was completed by applying ADAMS software to obtain the trajectory, displacement, velocity and acceleration of the center of mass of the chain plate link_1 in the x- and y-directions within the effective shaking displacement. A field test of the chain drive separation mechanism was carried out. When the shaking roller was in contact with the chain, the change in speed of the chain in the x- and y-directions was not significant, but the force in the y-direction was conducive to breaking the soil pieces and improving the rate of collecting the residual film. The change in the speed of link_1 in the y-direction speed with x-direction speed explained the action of the separation mechanism on conveying and throwing of the film-soil mixture in the horizontal and vertical directions, which revealed the mechanism of the chain drive film-soil separation equipment. The field test showed that the film collection rate of the chain drive separation mechanism was 68%, and the surface residue was 6%. The test results meet the relevant national and industry standards and design requirements. The results of this study provide guidance for the design and optimization of chain drive film-soil separation equipment.

Logistics and Supply Chain Modelling for the Biobased Economy: A Systematic Literature Review and Research Agenda

One way to mitigate the negative impacts of climate change, is for society to move towards a biobased economy, where fossil resources are replaced by biobased ones. This replacement requires the development of biobased supply chains that differ significantly from the conventional supply chain. For example, seasonality and variability of the feedstocks create specific challenges for biobased systems and call for customized solutions for the design and operation of biobased chains. As a result, the modelling efforts to support decision-making processes for biobased logistics and supply chains have some different requirements. This paper presents a systematic literature review on logistics and supply chain modelling studies for the biobased economy published in a period of 2011–2020. The literature analysis shows that most modelling studies for the biobased economy are strategic optimization models aiming to minimize economic impact. As biomass source, forest and agricultural residues are mostly used, and fuel and energy are the most common biobased applications. Modelling strategies, biomass sources and applications are however diversifying, which is what we encourage for future research. Also, not only focusing on economic optimization but also optimizing social and environmental performance is an important future research direction, to deal with the sustainability challenges the world is facing.

Quality-based design of supply chains considering supplier and technology effects

Quality-based design of supply chains considering supplier and technology effects

Short term effect evaluation model of rural energy construction revitalization based on ID3 decision tree algorithm

In rural energy construction, systematic research is needed in multiple fields including the following: basic frontier investigation on energy resource evaluation and optimal regulation, development of common key technologies, application demonstration and industrialization promotion for the whole chain design, integrated deployment and sub module promotion. Firstly, this paper studies the short-term effect of decision tree algorithm in rural energy construction and revitalization of energy construction. Secondly, through multiple decision tree ID3 algorithms. It combines the advantages of the two algorithms and sets an appropriate threshold for Relief feature selection algorithm and performs attribute complementary screening. Thirdly, this paper establishes a feature selection model based on Relief feature selection algorithm and decision tree ID3 algorithm. The first result is obtained and screened by the decision tree algorithm. During the process, the threshold of the Relief feature selection algorithm is set, the attributes obtained by the Relief feature selection algorithm and the attributes obtained by the decision tree ID3 algorithm are screened and supplemented to realize the screening of irrelevant attributes. By creating the utilization mode of optimal allocation of rural solar energy space, it has a certain promotion and reference value.

Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

Multi-objective robust optimization for multi-stage-multi-product agile closed-loop supply chain under uncertainty in the context of circular economy

Purpose A circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view. Design/methodology/approach In this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method. Findings The proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems. Practical implications This study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context. Originality/value The main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants.

Incorporating Environmental Perspective in Integrated Strategic-Tactical Economic Optimization Model of Biomass-to-Biofuel Supply Chain—A Real Case Study in Ethiopia

Several optimization models, which consider economic and environmental perspectives, have been developed recently to support the sustainable biomass-to-biofuel supply chain (BBSC) design. All of the economic-environmental optimization models rely on solving long-term planning problems with a conventional hierarchical approach, where tactical decisions are made based on the optimal strategic decisions from the strategic-level model, despite it arousing non-optimal solutions. Moreover, almost all of them have used non-monetary-based environmental indicators, which result in difficulties with clarity when comparing with economic objectives. Therefore, in this work, an effort is made to develop a more reliable planning strategy that offers optimal strategic and tactical decisions simultaneously and maximizes the economic and environmental benefits. Furthermore, the environmental performance of the BBSC has been assessed in terms of monetary value by adopting an ecocost approach after performing an LCA on the system. The integrated model is applied in the real biofuel sector of Ethiopia to optimize the country’s bioethanol and biodiesel supply chain over a 20-year horizon. Despite the abrupt rise in the model size, with it being a real countrywide case with many variables and large quantities of data, an alternative semi-heuristic method that offers a feasible solution to the multi-objective problem is provided.

Optimal design and operation of a second-generation biofuels supply chain

This article investigates how climate influences the value of adding preprocessing depots to a second-generation biorefinery’s supply chain. This is vital because humidity determines the amount of dry matter loss—exponential decay of energy content—suffered by biomass stored without preprocessing. The large volume of biomass required to fuel a biorefinery poses challenges in storage and in transportation. Further complications arise because the biomass is produced seasonally by hundreds of growers. Thus, recent failures of biorefineries may have been avoided, and future success may be achieved, by adding an intermediate layer to a biorefinery’s supply chain. A rigorous climate-based analysis of cost functions for each potential grower/depot pair leads to a stochastic program that optimizes the locations of depots, the assignment of growers to depots, and the volume of biomass stored fieldside vs. the volume stored as pelleted feedstock at depots. A computational study reveals that the humidity of the climate has much greater influence on the value of adding pre-processing depots to a second-generation biofuel supply chain than does transportation consolidation or any other parameter, endogenous or exogenous. Under favorable circumstances, our process reduces a biorefinery’s costs by over 30%, on average.

Dynamic Contract Design of Product-Service Supply Chain considering Consumers’ Strategic Behavior and Service Quality

With increasing market competition and rapid development of service economy, more and more enterprises are shifting from providing products or services to providing product-service systems (PSSs) that integrate products and services, in order to improve competitiveness and profitability. Meanwhile, consumers have strategic delayed purchasing behavior when purchasing the PSS and high requirements for service quality. This paper investigates the two-period pricing and service quality decisions of product-service supply chain (PSSC) considering consumers’ strategic behavior under decentralized and centralized scenarios. The equilibrium results are compared in two scenarios. In order to eliminate performance loss under the decentralized scenario, we design two-period dynamic contracts to coordinate the PSSC. Furthermore, numerical simulation is provided to verify the feasibility of the contracts. The following conclusions can be drawn: (1) the higher the service input-efficiency, the more beneficial for alleviating consumers’ strategic purchase behavior under two scenarios, but this mitigation effect is more obvious under the centralized scenario. (2) Compared with the centralized scenario, the service quality is lower, the two-period PSS sales prices are higher, and the two-period profit is lower under the decentralized scenario. The proportion of service valuation (accounts for the valuation of PSS) will promote the widening of the service quality gap under two scenarios, but in some cases, the service input-efficiency will weaken the promotion effect of the proportion of service valuation. (3) The design of the two-period combined contracts depends on the proportion of service valuation. When the proportion of service valuation is high, the “two-period revenue sharing + service-cost sharing” combined dynamic contract can achieve PSSC perfect coordination. However, when the proportion of service valuation is low, it is necessary to design complexity combined dynamic contract which can achieve PSSC perfect coordination.

Signal Conditioning Stage in S-Band Communication Subsystem for CubeSat Applications

This work presents the design, performance evaluation, manufacture, and characterization of an RF front-end signal conditioning chain on a substrate that achieves the best performance at S-band frequencies and complies with the dimensions of the international standard for CubeSat-type nanosatellites. In this development, the signal conditioning chains were carried out on the high-frequency substrates RO4350B, CuClad 250, and RT/duroid 5880, considering scattering parameters in a small-signal regime. Concerning the power output, after the filtering and amplifying stages, the conditioning chain delivered 2 watts at 2.25 GHz. Moreover, up to 40 dB gain was achieved, and a good impedance matching at −20 dB for both input and output ports was observed. The numerical simulations and experimental results showed that an RO4350B substrate allows the smallest design dimensions, and these comply with the dimensions of the CubeSat standard. The manufactured RF front-end signal conditioning chain on RO4350B requires an area of 95 mm2, and it is ready to be used in a proof-of-concept space mission in a CubeSat.

Study on the Impact of Smart and Innovative Delocalization Practices on International Trade

Faced with cumulative competition linked to the globalization of markets, and increasingly strong demands in terms of performance, many firms are led to ask the question of relocating either part or all of their activities. Relocation is a strategic decision which obliges companies to review and reconfigure their operations management methods, in particular those concerning the supply chain. The working objective is to identify the key factors to take into consideration for the design of the supply chain in the context of delocalization. First, we tackle the problem of defining delocalization and the supply chain. Next, we review the literature to define the specifics of the problem of relocation and their impact on international trade, notably the case of Morocco, and thus identify all the factors and constraints to be taken into account when reconfiguring the global supply chain in order to adapt it to the context of the practice of intelligent delocalization.

Similar Vertices and Isomorphism Detection for Planar Kinematic Chains Based on Ameliorated Multi-Order Adjacent Vertex Assignment Sequence

Isomorphism detection is fundamental to the synthesis and innovative design of kinematic chains (KCs). The detection can be performed accurately by using the similarity of KCs. However, there are very few works on isomorphism detection based on the properties of similar vertices. In this paper, an ameliorated multi-order adjacent vertex assignment sequence (AMAVS) method is proposed to seek out similar vertices and identify the isomorphism of the planar KCs. First, the specific definition of AMAVS is described. Through the calculation of the AMAVS, the adjacent vertex value sequence reflecting the uniqueness of the topology features is established. Based on the value sequence, all possible similar vertices, corresponding relations, and isomorphism discrimination can be realized. By checking the topological graph of KCs with a different number of links, the effectiveness and efficiency of the proposed method are verified. Finally, the method is employed to implement the similar vertices and isomorphism detection of all the 9-link 2-DOF(degree of freedom) planar KCs.

Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in‐depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm‐wise sensing network for assisted wireless sensor networking to achieve crowdsourcing‐assisted localization. We consider a framework for crowdsourcing‐assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data‐driven participant selection problem in swarm‐wise sensing networks. The sprocket‐chain engagement frequency has the greatest influence on the horizontal bending‐vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.

Analysis on Infectious Risk Stress in the Supply Chain of Mega Infrastructure Project

In the implementation process of great infrastructure engineering, the structural safety of supply chain in coping with infectious risks (IR) is of important significance to operation of the whole project. Moreover, infectious risk can influence safety of supply chain of mega project (MP). Therefore, this study constructed a function of infectious risk stress, calculated the infectious risk stress value of supply chain of mega infrastructure project (MPSC), and analysed influences of random variables on infectious risk. Results demonstrate that the maximum infectious risk stress is at the end of supply chain of mega infrastructure project. Research conclusions provide references to anti-risk integrated design of supply chain of mega infrastructure project.

Designing a sustainable logistics supply chain

The purpose of study was to identify the main approaches to the formation of an industry solution related to the design of effective supply chains and aimed at increasing the visibility of constraints and bottlenecks, as well as to prevent unwanted blockages in the logistics process. The work uses general scientific methods of logistics research based on economic and methodological developments in the field of transdisciplinary analysis of modern logistics. The necessity of using a qualitatively new approach of fragmented design of the logistics process, based on a system of values, focused on the new code and digital thinking, in the core of which the cognitive platform is in the real economy sector, has been substantiated. The main components of an industry solution for supply chain visibility and traceability are defined. Conceptualized and theoretically divided into stages the process of designing an effective logistics supply chain that meets the conditions of visibility and traceability. The formation of an industry solution related to the design of a fragmented logistics process and an efficient supply chain is of applied importance and can maximally meet the growing needs of stakeholders to create sustainable multi-level distribution channels of all types of resources.

Impact of Supply Chain Human Capability, Responsive Design, and Collaboration on Supply Chain Resilience

This paper adopts supply chain perspective to examine the impact of human capability in supply chain (SC) on SC resilience and business performance. Based on literature review, this paper expands understanding of factors, which contribute to SC resilience and competitive performance. The authors have operationalized elements of human capability in supply chain, SC collaboration, and SC responsive design to achieve this. Questionnaire was developed and was circulated to more than 600 organizations out of which 158 organizations responded and were useful. Hypotheses were formulated on studying the impact of these aspects on SC resilience and were tested using structural equation modelling. The study and the analysis support the hypothesis on the positive impact of supply chain human capability on SC collaboration and SC responsive design. Similarly, the hypothesis on SC collaboration and responsive design have tested positive impact on supply chain resilience which in turn has positive influence on organizations' performance that gives competitive edge.

Activity analysis based modeling of global supply chains for sustainability assessment

Assessing the sustainability of products is an intricate task that requires a thorough understanding of the underlying supply chains. Prominent challenges are the integration of the environmental, the economic, and the social dimension of sustainability as well as the consideration of spatial heterogeneity with regard to technology, environment, markets, and society. This paper proposes a novel modeling approach based on activity analysis to facilitate spatially differentiated sustainability assessments of global supply chains. The production and transportation activities in the supply chain and the exchanges between activities and their natural, economic, and social environments are formalized by means of activity vectors. Linking the activities and exchanges to specific locations allows for the calculation of regionalized sustainability indicators. The approach is used to analyze the environmental, economic, and social impacts of lithium-ion batteries. The comparison of alternative supply chain configurations with the spatially differentiated approach reveals that the assessment results differ significantly from those of the global average supply chain. These insights offer new opportunities to advance the design of sustainable supply chains.