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Previous researchers have studied push and pull contracts in the single product scenario, although in practice, supply chains often produce and sell multiple products. In a multiproduct scenario, the sales of a product will be influenced by its complements or substitutes, which requires consideration when the supply chain members negotiate contracts. This paper aims to fill this gap by studying push and pull contracts in a supply chain which distributes two products to a market and discusses how the degree of complementarity/substitutability between the two products affects the supplier’s decisions and supply chain efficiency.

The paper uses the model of a single-supplier, single-retailer supply chain which sells a product with a long lead time and another product with a short lead time simultaneously in a market. This research compares the production quantity and supply chain efficiency under a push contract with those under a pull contract.

First, when the two products are complements, the equilibrium production quantity of Product 2 is higher under a pull contract than that under a push contract. Second, a pull contract is found to be optimal for both the supplier’s profit and supply chain efficiency when the two products are complements, while if they are substitutes, then a push contract is the better choice in some situations.

The existing literature discusses push and pull contracts in the single product scenario. The current paper pays attention to the two-product scenario and investigates how the complementarity/substitutability degree between the two products affects the supplier’s decisions and supply chain efficiency.

This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.

A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.

The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.

The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

The increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.

Intelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R², T-Value and GOF.

The study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.

Study limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.

The research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog.

It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper.

The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networks with the dehazed results in data quality. Finally, an object-detection test is taken to judge the edge information preservation after haze removal. All results demonstrate that the proposed dehazing network performs better under railway scenes in detail.

This study provides a new method for image enhancing in the railway monitoring system.

This work presents a numerical study of the dynamic and thermal behavior of a turbulent flow in a shell and tube heat exchanger equipped with a new design of baffle type wing. The implementation of this type of baffle makes it possible to lengthen the path of the fluid in the shell, to increase the heat flux exchanged on the one hand and is to capture the weakness of the shell and tube heat exchanger with segmental baffles on the other hand.

This paper aims to analyze numerically the thermo-convective behavior of water using CFD technique by solving the conservation equations of mass, momentum and energy by the finite volume method based on the SIMPLE algorithm for coupling velocity-pressure. To describe the turbulence phenomenon, the Realizable k–ε model is employed. The analysis is done for different mass flow rates. The parameters studied are: the fluid outlet temperature, the average heat transfer coefficient, the pressure drop, the total heat transfer rate, the effect of the geometric shape of the baffle on the thermal behavior. The purpose of this study is to propose a new design of a shell and tube heat exchanger with a high heat transfer coefficient and a lower pressure drop compared to a shell and tube heat exchanger with transverse and segmental baffles.

The results showed that the use of the wing baffles enhanced the heat transfer coefficient significantly and reduced the friction coefficient. Compared with segmental baffles, the wing baffles are 11.67, 18.53 and 11.5 per cent lower in the pressure drop and 1.79, 1.9 and 2.39 per cent higher in the Nusselt number for the three mass flow rates 0.5, 1 and 2 kg/s, respectively.

The originality of this work lies in proposing a three-dimensional analysis for a novel heat exchanger.

The research in competitive facility location (CFL) is quite dynamic, both from a problem formulation and an algorithmic point of view. Research direction has changed immensely over the years to address various competitive challenges. This study aims to explore CFL literature to highlight these research trends, important issues and future research opportunities.

This study utilises the Scopus database to search for related CFL models and adopts a five-step systematic approach for the review process. The five steps involve (1) Article Identification and keyword selection, (2) Selection criteria, (3) Literature review, (4) Literature analysis and (5) Research studies.

The paper presents a comprehensive review of CFL modelling efforts from 1981 to 2021 to provide a depth study of the research evolution in this area. The published articles are classified based on multiple characteristics, including the type of problem, type of competition, game-theoretical approaches, customer behaviour, decision space, type of demand, number of facilities, capacity and budget limitations. The review also highlights the popular problem areas and dedicated research in the respective domain. In addition, a second classification is also provided based on solution methods adopted to solve various CFL models and real-world case studies.

The paper covers 40 years of CFL literature from the perspective of the problem area, CFL characteristics and the solution approach. Additionally, it introduces characteristics such as capacity limit and budget constraint for the first time for classification purposes.