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The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design.

The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning.

The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems.

Future work will consider the application of the proposed ACACO to the recurrent fuzzy network.

The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.

Due to the globalization of trade, hundreds of millions containers pass every year through world ports. Such a situation is extremely challenging in terms of securing freight transport operations. However, costs and lead-times are still very important components of supply chains' performance models. Therefore, the drive for enhanced safety and security cannot be made at the expense of these other two factors of competitiveness, and the processes implemented by the global supply chain links, including the maritime port one, should tend to a joint optimization of trade facilitation and operational safety / security.

The research on which this paper feeds back falls within the frame of this mixed performance requirement. More specifically, the paper presents a decision-support system dedicated to managing the risks associated with land and maritime container transportation; this system is based on the modeling of the knowledge of a group of experts, and covers the three phases of risk identification, assessment and avoidance / mitigation.

Existing control circuits for piezoelectric energy harvesting (PEH) suffers from long startup time or high power consumption. This paper aims to design an ultra-low power control circuit that can harvest weak ambient vibrational energy on the order of several microwatts to power heavy loads such as wireless sensors.

A self-powered control circuit is proposed, functioning for very brief periods at the maximum power point, resulting in a low duty cycle. The circuit can start to function at low input power thresholds and can promptly achieve optimal operating conditions when cold-starting. The circuit is designed to be able to operate without stable DC power supply and powered by the piezoelectric transducers.

When using the series-synchronized switch harvesting on inductor circuit with a large 1 mF energy storage capacitor, the proposed circuit can perform 322% better than the standard energy harvesting circuit in terms of energy harvested. This control circuit can also achieve an ultra-low consumption of 0.3 µW, as well as capable of cold-starting with input power as low as 5.78 µW.

The intermittent control strategy proposed in this paper can drastically reduce power consumption of the control circuit. Without dedicated cold-start modules and DC auxiliary supply, the circuit can achieve optimal efficiency within one input cycle, if the input signal is larger than voltage threshold. The proposed control strategy is especially favorable for harvesting energy from natural vibrations and can be a promising solution for other PEH circuits as well.

The purpose of this study is to explore how nationalistic appeals may affect consumers’ perception and purchasing of targeted brands. Qualitative historical data from old China (1900–1949) reveal that social movement groups can adopt nationalistic appeals assisted by meaning framing – defined as a creative interpretation of symbols, designs, behaviors, social events and cultural identities to serve social and political goals – to shape consumers’ attitudes toward foreign brands. After examining the mechanisms and processes underlying consumer boycotts from 1900 to 1949, the responsive strategies of affected foreign companies are illustrated.

Critical historical research method is applied to historical data and historical “traces” from China’s corporate documents, memoirs, posters, advertisements, newspapers and secondhand sources documenting Chinese boycotts from 1900 to 1949.

Consumers may pursue interests beyond economic interests. Nationalistic appeals can mobilize consumer boycotts against foreign brands that were perceived to support or relate to targeted countries. Political framing of certain events shapes consumers’ perceptions and concomitant brand choices.

Although differences between historical and current contexts may require tailoring past marketing strategies to current conditions, past strategies can inform current and future strategies.

Strategies adopted by foreign companies in old China (1900–1949) can help contemporary companies design effective marketing strategies for a hostile marketplace infused with nationalistic appeals and competing interests.

Although local companies can adopt economic or political nationalism to realize their economic goals, it represents a double-edged sword that can harm national brands.

A historical analysis of nationalistic business appeals in pre-1949 China can inform the counterstrategies modern companies adopt to overcome consumer boycotts.

This paper seeks to modify the determinations of flow rate and fluid resistance, which can be realized and confident from the measurements of flow rates in experiments.

According to coupled physics of solid membrane and lubrication fluid, finite element method is used simultaneously to determine membrane deflection and film thickness. Several cases are simulated by traditional method, finite element method and compared with experimental method for the flow rates and fluid resistances to present the modification of determination results.

The FEM results for the fixed eight‐section are approximated to actual flow rate and are consistent with the modified determination of the flow rates, and so the modified determinations of the flow rates are verified. When a computer of P4 with 1.8 GHz CPU and 512 MB RAM is utilized, time needed for traditional method or modified formula is fewer than one second. However, more than 4 h is required for FEM by using the same computer.

This study provides the modified method for the determinations of flow rate and fluid resistance in membrane‐type restrictors by using FEM. The FEM results can increase the determination accuracy of the flow rate and restriction coefficient in the design of membrane‐type restrictors.

“Idol pilgrimage tour” is a popular trend among young Chinese fans who travel for idol-related purposes, engage in interactive events and have co-created experiences at destinations. With the growing market size of fan economy, fan tourists generate significant revenue for the local. However, many destinations have not fully utilised this opportunity, and there is a lack of research on this niche form of tourism. This research was undertaken to address this research gap.

This study adopted an idol worship–motivation–co-created experience–tour satisfaction–destination loyalty framework in the context of idol pilgrimage tours. In addition, this study investigated the direct influence of idol worship on the other four constructs. Data were collected from 354 Chinese fans who had such experience through online questionnaires. The partial least squares–structural equation modelling technique was used to examine the research model.

It was demonstrated that idol worship has a direct influence on motivation, co-created experience, satisfaction and loyalty and that there is a positive relationship between motivation, co-created experience, satisfaction and loyalty. The results advance the brand sacralisation literature by studying worship in a tourism context and contribute to interactive marketing literature by clarifying the interactive mechanism between relationships among the idol, fans and destinations. The study suggests some practical implications for destination management organisations attempting to target tourist fans.

This is the first study introducing the concept of the idol pilgrimage tour. Empirical results of this study reveal the underlying mechanism of how idols influence fans' travel-related psychology and behaviour.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

Due to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.

This study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.

The degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.

This study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.