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Because of the complexity of relationship between surface tension and its decisive factors, such as temperature, concentration, electronic density, molar atomic volume and electro-negativity, a reasonable predicting model of surface tension of Sn-based solder alloys has not been developed yet. The paper aims to address the surface tension issue that has to be considered if the new lead free solder will be applied for electronics.

Using an artificial neural network (ANN) model with back-propagation (BP) algorithm, the surface tension for Sn-based binary solder alloys was simulated, and the comparison between the simulating results and data from experiments and literatures was analyzed as well. In addition, the relationship between surface tension and its decisive factors would be discussed based on the ANN and orthogonal design methods.

It is shown that the predicting model of surface tension of Sn-based solder alloys is constructed according to the BP–ANN theory, and the predicted value from the BP–ANN is in excellent agreement with the experimental results. The surface tension of Sn-based solders is determined by five factors, i.e. temperature, concentration, electronic density, molar atomic volume and electro-negativity. Among of the factors, molar atomic volume is major factor, and the order of degree of influence on surface tension is molar atomic volume > electro-negativity > electronic > density > concentration > temperature. Moreover, a simply reasonable equation is proposed to estimate the surface tension for Sn-based solders.

The five decisive factors of surface tension for Sn-based binary solder alloys have been analyzed theoretically, and a reasonable model of surface tension for Sn-based binary solder alloys is proposed as well. It is shown that ANN theory will be applied well to simulate the surface tension of Sn-based lead free solder.

Viscosity is an important basic physical property of liquid solders. However, because of the very complex nonlinear relationship between the viscosity of the liquid ternary Sn-based lead-free solder and its determinants, a theoretical model for the viscosity of the liquid Sn-based solder alloy has not been proposed. This paper aims to address the viscosity issues that must be considered when developing new lead-free solders.

A BP neural network model was established to predict the viscosity of the liquid alloy and the predicted values were compared with the corresponding experimental data in the literature data. At the same time, the BP neural network model is compared with the existing theoretical model. In addition, a mathematical model for estimating the melt viscosity of ternary tin-based lead-free solders was constructed using a polynomial fitting method.

A reasonable BP neural network model was established to predict the melt viscosity of ternary tin-based lead-free solders. The viscosity prediction of the BP neural network agrees well with the experimental results. Compared to the Seetharaman and the Moelwyn–Hughes models, the BP neural network model can predict the viscosity of liquid alloys without the need to calculate the relevant thermodynamic parameters. In addition, a simple equation for estimating the melt viscosity of a ternary tin-based lead-free solder has been proposed.

The study identified nine factors that affect the melt viscosity of ternary tin-based lead-free solders and used these factors as input parameters for BP neural network models. The BP neural network model is more convenient because it does not require the calculation of relevant thermodynamic parameters. In addition, a mathematical model for estimating the viscosity of a ternary Sn-based lead-free solder alloy has been proposed. The overall research shows that the BP neural network model can be well applied to the theoretical study of the viscosity of liquid solder alloys. Using a constructed BP neural network to predict the viscosity of a lead-free solder melt helps to study the liquid physical properties of lead-free solders that are widely used in electronic information.

The study explored users' tendency of confirmation bias when processing congenial vs. uncongenial electronic-word-of-mouth (e-WOM) about mystery fictions, a hedonic product category with strong experience and hedonic characters.

A two-stage judgment approach was employed where the participants were asked to judge a set of mystery novels twice: one before, and another after they were exposed to positive and negative e-WOM. The first-stage judgment established two favored and two disfavored titles by each participant. They were then asked to read six consumer reviews – three positive and three negative – for each of the four titles. The procedures created four review evaluation situations: two congruent and two incongruent, which allowed the authors to assess the participants' perceptions of congenial and uncongenial reviews and their rating adjustments of the titles. Participants' involvement in mystery novels was also measured to test its moderating effect on confirmation bias.

Confirmation bias in the evaluation of e-WOM was observed and reinforced by the user's involvement in the genre. Congenial reviews were perceived to be significantly more credible, better reflect the intrinsic value of a title and less subjectively motivated than uncongenial reviews. Furthermore, after exposure to equal amount of positive and negative e-WOM, an asymmetrical adjustment of final rating of the titles was observed. A significantly greater downward adjustment was observed for disfavored than favored titles. Stronger positive confirmation bias was also observed in the evaluation of WOM.

Previous studies on e-WOM have shown conflicting findings on the relative efficacy of positive vs. negative reviews. By introducing the factor of prior attitudes, the study demonstrated that whether WOM is consistent with an individual's prior attitude, rather than the valences of WOM in itself, determines its persuasiveness. Thus, it established the confirmation bias in users' processing of e-WOM. The finding highlights the importance for marketers to establish a positive initial impression, which, as the findings demonstrated, helps alleviate the damages caused by negative WOM.

This is the first study that has ever attempted to study the effect of confirmation bias during the users' processing of e-WOM in an experimental setting. By having the participants judge the books before and after exposure to congenial and uncongenial e-WOM, the authors were able to establish the link between the users' prior commitment to a book and their subsequent judgment of both the titles and the e-WOM.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2020-0026

Seasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.

First, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.

The data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.

In this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.

This paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

The main cause of aseptic inflammation after an in vivo implantation is that Poly(L-lactide) (PLLA) and Poly(D-lactide) have a slower degradation and absorption rate, while Poly(D, L-lactide) (PDLLA) has a much faster degradation rate than PLLA because of its amorphous structure. Also, the hydrolyzate of Hydroxyapatite (HA) is alkaline, which can neutralize local tissue peracid caused by hydrolysis of Polylactic acid.

In this study, the selective laser sintering (SLS) technique was chosen to prepare bone scaffolds using nano-HA/PDLLA composite microspheres, which were prepared by the solid-in-oil-in-water (S/O/W) method. First, the SLS parameters range of bulk was determined by the result of a single-layer experiment and the optimized parameters were then obtained by the orthogonal experiment. The tensile property, hydrophobicity, biocompatibility, biological toxicity and in vitro degradation of the samples with optimized SLS parameters were characterized.

As a result, the samples showed a lower tensile strength because of the many holes in their interior, which was conducive to better cell adhesion and nutrient transport. In addition, the samples retained their inherent properties after SLS and the hydrophobicity was improved after adding nano-HA because of the OH group. Furthermore, the samples showed good biocompatibility with the large number of cells adhering to the material through pseudopods and there was no significant difference between the pure PDLLA and 10% HA/PDLLA in terms of biological toxicity. Finally, the degradation rate of the composites could be tailored by the amount of nano-HA.

This study combined the S/O/W and SLS technique and provides a theoretical future basis for the preparation of drug-loaded microsphere scaffolds through SLS using HA/PDLLA composites.

Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.

For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.

Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).

The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.

The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.

This study aims to explore the high turnover intention issue in Taiwan’s tourist hotel industry. Due to a lack of empirical research regarding front-line employees’ psychological contract breach perceptions in tourism literature, this study develops an integrated model to examine the causal relationship among transformational leadership behaviors, leader–member exchange (LMX), psychological contract breach and turnover intentions.

Data from the 226 frontline employees in Taiwan’s tourist hotel industry were employed to examine the proposed hypotheses by using a series of structural equation modeling analyses.

Statistic results revealed that transformational leadership behaviors influence LMX and LMX in turn influences psychological contract breach, which consequently leads to lower turnover intention.

The results of this study suggest that hospitality organizations should recruit individuals who have the potential to exhibit transformational leadership skills, along with designing leadership training programs for middle- and high-level managers.

This study provides hospitality organization leaders with the necessary information to formulate a beneficial relationship with their front-line employees, which, in turn, weakens their perception of psychological contract breaches and reduces their willingness to leave the organization.

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption, respectively. For ideal randomized experiments, (i) we propose nonparametric estimators of the sharp bounds on the distribution of treatment effects and construct asymptotically valid confidence sets for the distribution of treatment effects; (ii) we propose bias-corrected estimators of the sharp bounds on the distribution of treatment effects; and (iii) we investigate finite sample performances of the proposed confidence sets and the bias-corrected estimators via simulation.

As its market and society open up, China has transformed itself from a closed agrarian socialist economy to an urban state and an economic force. This has released accumulated tourism demand, led to the development of a diversified industry, and the spread of university and vocational courses in this field. However, the industry faces challenges to recruit and retain staff, with tourism education in higher education blamed for the shortfall in numbers and quality of candidates with suitable purpose, knowledge, and passion to serve. This chapter provides a background to the development of and problems facing tourism education in China, and suggests how to support student engagement and hence the future workforce.

To develop just‐in‐time (JIT) purchasing threshold value (JPTV) models for ready mixed concrete (RMC) suppliers to decide whether or not to switch from an economic order quantity (EOQ) approach to a JIT purchasing approach for the purchase of their raw materials, when a price discount is offered.

The existing economic order quantity (EOQ) with a price discount versus the JIT purchasing cost comparative models neglect some important cost components under the inventory management systems, for example, the out‐of‐stock costs and the impact of inventory policy on product quality and production flexibility. In addition, these models do not empirically study the capability of an inventory facility to hold the EOQ‐JIT cost indifference point's amount of inventory. These models suggest that the JIT purchasing approach is always preferred to the EOQ approach when the JIT purchasing approach can capitalize on physical plant space reduction. The JPTV models developed in this study overcome the two limitations of the existing EOQ and JIT purchasing cost comparative models.

By developing the JPTV models, this study suggests that the theoretical advantages of JIT purchasing may have been overstated.

The field studies conducted in the RMC industries in Chongqing, China and Singapore supported the propositions in this study. The JPTV models, if adopted, would help to enhance performance in the RMC industries in other cities as well.