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This study aims to present the discrepancy in oil film distribution in reciprocating motion experimentally with zero entraining velocity (ZEV) on a conventional ball-disk test rig with oil lubrication.

Driven independently by two individual servomotors, a steel ball and a sapphire disc move at equal speed but in opposite directions in a triangle wave. The oil film images between the ball and the disc were recorded by a camera. After the experiments, the mid-section film thickness was evaluated by using a dichromatic interference intensity modulation approach.

The dimpled oil film in transient condition is shallower than that at steady state with the same load and velocities, and the transient dimple depth decreases with the decrease of time. The increase of the applied load offers a beneficial effect on lubrication. Boundary slippage happens in ZEV reciprocating motion. The slippage at the interface is related to the transient effect and applied load.

This study reveals the significant difference of the oil film variation in ZEV reciprocating motion, especially the complex boundary slippage at the interface of the oil and the sapphire disc.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2020-0021

The influence of the cam angular speed on the pressure, film thickness and temperature profiles at some selected angular positions together with the oil characteristics are investigated.

A high-order polynomial cam is used, and thermal elastohydrodynamic lubrication (EHL) calculations are carried out by the multi-grid method and line-line scanning technique.

It is found that the film thickness decreases with a decrease in angular speed. The depth of the dimple that occurred in the reverse motion is also reduced because of the recession in the “temperature–viscosity wedge” effect.

It is revealed that the reduction in the cam angular speed makes the classical big surface dimple evolve into a small centralized dimple during the opposite sliding motion.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0327

The purpose of this paper is to investigate the role of social capital in the issuances of Rule 144A debt. Using a sample of 1,378 debt offerings from 1997 to 2015 in the US, this paper provides empirical evidence on whether and to what extent social capital affects the cost of Rule 144A debt.

This paper employs a county-level measure of social capital and links social capital to the yield spreads of Rule 144A debt. A Heckman selection model is sued to address the sample selection bias, and an instrumental variable approach and propensity score matching methodology are implemented to deal with the potential endogeneity issue. The authors check for robustness using an alternative measure of social capital.

The results of the analysis provide evidence that issuers headquartered in the counties with higher levels of social capital experience lower yield spreads in their Rule 144A debt offerings. The findings are robust to a Heckman selection model, an instrumental variable approach and propensity score matching. Furthermore, the analysis reveals the marginal effect of social capital that the effect of social capital is more pronounced for the issuing firms with higher agency cost of debt and lower institutional ownership. The effect of social capital is more prominent after financial crisis.

This paper provides novel evidence of the effect of social capital on the cost of privately placed debt. The issuances of Rule 144A debt are subject to significant information asymmetry and are targeted at sophisticated institutional investors. This paper sheds further light on how institutional investors incorporate the regional social capital in their pricing scheme of private placement of Rule 144A debt.

To find the system with minimum investment and best quality performance that is capable of producing all of the product variants, assessing the complexity of designing assembly system at the early concept stage is an essential step, which helps and instructs a designer to create a product- and system-oriented assembly solution with the least complexity. The purpose of this paper is to propose a quantifying measurement of complexity in the design of a modular automated assembly system.

The configurable assembly system is becoming a trend, which enables companies to quickly respond to changes caused by different product variants but without a large investment. One of the enabling factors is the availability of modular solutions of assembly modules that can be configured according to different technical requirements. This paper develops a methodology using fuzzy evaluation to calculate the design complexity in the design phase for a modular automatic assembly system. Fuzzy linguistic variables are used to measure the interaction among the influence factors, to deal with the uncertainty of the judgement. The proposed method investigates three matrices to present how the function-based assembly modules, design complexity factors, part attributes and product components, which are regarded as the main influence factors, complicate the construction of a modular assembly system. The design complexity is derived and quantified based on these assessments.

The proposed approach presents a formal quantification to evaluate the design complexity with regard to a modular assembly system from beginning, which can be identified and used as criteria to indicate the quality of performance and investment cost in advance. A mathematical model based on the fuzzy logic is established to provide both theoretical and practical guidance for the paper. To validate the predictive model, the statistic relationships between the assessed system design complexity, real assembly defect rate and investment cost are estimated based on regression analysis. The application of the presented methodology is demonstrated with regard to a traditional rear drive unit in the automotive industry.

This paper presents a developed method, which addresses the measures of complexity found in the design of a modular assembly system. It would help to run the design process with better resource allocation and cost estimation in a quantitative approach.

As retailers have increasingly embraced an omnichannel retailing strategy, explaining and predicting the helpfulness of online review should consider both online-based and offline-based reviews. The paper aims to discuss this issue.

Based on the signaling theory, this study intends to examine the impacts of review-related and reviewer-related signals on review helpfulness in the context of omnichannel retailing. The proposed research model and corresponding hypotheses were tested by using negative binomial regression.

The results shown that both review-related (review rating and review sentiment strength) and reviewer-related (reviewer real name and reviewer expertise) signals positively affect review helpfulness. Contrary to the authors’ expectations, review length negatively affects review helpfulness. Specifically, when the review submitted from an omnichannel retailer’s offline channel, the positive impacts of reviewer real name on review helpfulness will be stronger, and the positive impacts of reviewer expertise on review helpfulness will be weaker.

Unlike many previous studies tend to explore the antecedents of review helpfulness in a single-channel setting, the study examined the factors that affect review helpfulness in an omnichannel retailing context.

Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms.

A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated.

The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited.

The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications.

The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution.

In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm.

The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods.

The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts.

A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

Existing models of product assembly scheme design often ignore the constraint relations among design thinking. In order to grasp the functions of each part and the constraint relations among them in product assembly system macroscopically, further design and variation of product assembly system should be made according to design thinking. This paper seeks to address these issues.

Through analyzing the similarity between biological organization system and product system and taking biology knowledge for reference, product assembly system was expressed as product function gene, product constraint gene, product function protein, product constraint protein and product cell and so on in this paper. The product gene model composed of product function gene groups and constraint genes was established and a modeling method based on it was proposed.

The author applied this method to model the 5‐DOF manipulator of complex diamond manufacturing special equipment with good results which proved the effectiveness of this modeling method.

By identifying constraint relations and design thinking in the gene model, the system makes the modification process which is conducted by the designers automatically identified and varied to achieve computer‐aided design and assembly.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.