Search results

In recent years, the updating speed of products has been significantly accelerated, which not only provides diversified styles for consumers to select from but also makes consumers face selection problems sometimes. In addition, a large number of online reviews for products emerge on many e-commerce websites and influence consumers’ purchasing decisions. The purpose of this study is to propose a method for product selection considering consumer’s expectations and online reviews to support consumers’ purchasing decisions.

The product attributes are divided into two categories, i.e. demand attributes and word-of-mouth (WOM) attributes. For the demand attributes, for which the consumers can give specific quantified expectations, the value function of prospect theory is used to determine the consumer’s perceived values to the alternative products according to consumers’ expectations for these attributes and products’ specifications. For the WOM attributes, for which the consumers cannot give specific quantified expectations, the sentiment analysis method is used to identify the sentiment strengths for these attributes in the online reviews, and then the consumer’s perceived values to the alternative products are determined. On this basis, the product selection methods for single consumers and group consumers are given respectively.

Finally, taking the data of JD.com (https://www.jd.com/) as an example, the practicability and rationality of the method proposed in this paper is validated.

First, a new product selection problem considering consumer’s expectations and online reviews is extracted. Second, the product attributes are considered more comprehensively and are classified into two main categories. Third, the bounded rationality of the consumers in the decision-making process is described more reasonably. Fourth, the sentiment dictionaries for each WOM attribute are constructed and the algorithm step of identifying the sentiment strengths is designed, which can help to identify the sentiment strengths in the online reviews more accurately. Fifth, the situation that a group plans to purchase the same products and the members have inconsistent expectations for the product attributes is considered.

The purpose of the study is to investigate and analyze the dynamics of the government-enterprise grain joint storage mechanism, particularly, focusing on profit-driven speculative behaviors exhibited by enterprises within this context. The study aims to understand the various factors influencing the behavior of stakeholders involved in grain storage, including government storage departments, agent storage enterprises and quality inspection agencies.

The study employs a tripartite evolutionary game model to investigate profit-driven behaviors in government-enterprise grain joint storage. It analyzes strategies of government departments, storage enterprises and quality inspection agencies, considering factors like supervision costs and speculative risks. Simulation analysis examines tripartite payoffs, initial probabilities and the impact of digital governance levels to enhance emergency grain storage effectiveness.

The study finds that leveraging digital governance tools in government-enterprise grain joint storage mechanisms can mitigate risks, enhance efficiency and ensure the security of grain storage. It highlights the significant impact of supervision costs, speculative risks and digital supervision levels on stakeholder strategies, offering guidance to improve the effectiveness of emergency grain storage systems.

The originality of this study lies in its integration of digital governance tools into the analysis of the government-enterprise grain joint storage mechanism, addressing profit-driven speculative behaviors. Through a tripartite evolutionary game model, it explores stakeholder strategies, emphasizing the impact of digital supervision levels on outcomes and offering insights crucial for enhancing emergency grain storage effectiveness.

The purpose of this paper is to present the methodology and the results on the design and development of an autonomous, golf ball picking robot, for driving ranges.

The strategy followed to develop a commercial product is presented, based on prior identification requirements, which consist of picking up golf balls on a driving range in a safe and efficient way.

A fully working prototype robot has been developed. It uses two driving wheels and a third cast wheel, and pushes a standard gang which collects the balls from the ground. A hybrid information system was implemented in order to provide a statistically relevant prediction of golf balls location, to optimize the path the robot has to follow in order to reduce time and cost. Autonomous navigation was developed and tested on a simulation environment.

Preliminary results showed that the new path planning algorithm Twin‐RRT* is able to form closed loop trajectories and improve the result over time. Kinematic constraints were already taken into account on the algorithm. This sampling based algorithm has potential usage in solving other TPP (Travelling Purchaser Problem) related problems.

The prototype feasibility is being tested in real driving ranges. It has autonomy of up to 8 h per day. It is capable of collecting up to 1,200 balls in one single journey. It weighs 130 kg and is capable of climbing slopes of up to 22°. The maximum speed is 8 km/h and the robot takes 140 min to completely sweep a 25,000 m² field at 7.2 km/h (2 m/s) average speed.

There are about 30,000 golf practice fields, of which 18,000 are located in the USA and Canada. In some countries the golf industry represents more than 15 per cent of tourism GNP. In a typical practice field, about 10,000 balls have to be picked up every day.

An important contribution of this paper is the algorithm for path planning in order to optimize the ball pick up task, reducing time and cost. There are two patents are pending concerning the technological novelties of this work.

The purpose of this paper is to find out the characteristics and evolutionary trends of China’s inter-regional innovation correlation network, the status and roles of each province in China’s inter-regional innovation correlation network and the influencing factors of China’s inter-regional innovation correlation effect.

Based on the patent data of 30 provinces (autonomous regions and municipalities) in China from 1991 to 2017, social network analysis was used to find out the characteristics and evolutionary trends of China’s inter-regional innovation correlation network and the status and roles of each province in China’s inter-regional innovation correlation network. Furthermore, the QAP method was used to find out the influencing factors of China’s inter-regional innovation correlation effect.

China’s inter-regional innovation correlation is becoming increasingly close and inter-regional innovation correlation network is becoming increasingly stable. Jiangsu, Zhejiang, Beijing, Shanghai, Guangdong and other eastern coastal provinces are at the core in the inter-regional innovation correlation network, while the western regions are marginal actors. China’s regional innovation development territory can be divided into four blocks, namely, “bidirectional spillover block,” “net spillover block,” “main beneficial block” and “net beneficial block,” and gradient transfer mechanism is obvious between the blocks. The geographical adjacency and similarity in regional industrial structure, urbanization level and government attention degree have significant positive effect on China’s inter-regional innovation correlation effects.

This paper only uses patent application as a measure of regional innovation level to analyze inter-regional innovation correlation effect. Meanwhile, this paper carries out an empirical study only from the provincial level and not from the city level.

This paper provides the practical basis for further promoting the coordinated development of regional innovation and promoting the construction of regional innovation systems with different characteristics.

This paper contributes to understand the status and role of each province in inter-regional innovation correlation network. Meanwhile, this paper also helps to understand the influence of the proximity and external environmental factors on inter-regional innovation correlation effect.

The purpose of this paper is to propose an optimization method by combining artificial immune algorithm and finite element analysis to find the optimal exciting electrode of a piezoceramic plate type ultrasonic motor vibrator.

The artificial immune algorithm is selected as optimizer for its merit of fast convergence to global optimal solution. The finite element analysis is used to calculate the motion trajectory of contact point. The objective function is the work that the vibrator does to rotor. The design variables are the boundaries of exciting electrode on piezoceramic plate vibrator surface.

The calculated results and the experimental results show that using this method, both the position and the size of optimal exciting electrode of this ultrasonic motor can be quickly and accurately determined.

In order to successfully design an ultrasonic motor, both the position and the size of the exciting electrode must be investigated, so as to change more electric energy into mechanical energy. In this paper, an optimization method by combining artificial immune algorithm and finite element analysis is proposed for the exciting location optimization of a piezoceramic plate type ultrasonic motor to obtain large power output.

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

This paper aims to study the relationship between leakage flux coefficient and the coreless axial magnetic field permanent magnet synchronous generator (AFPMSG) size and obtain the expressions of leakage flux coefficient.

In this paper, a magnetic circuit model of coreless AFPMSG is proposed. Four kinds of leakage permeances of permanent magnet (PM) are considered, and the expression of no-load leakage flux coefficient is obtained. Solving the integral region of leakage permeances by generator size, which improves the accuracy of the solution.

Finite element method and magnetic circuit method are used to obtain the no-load leakage flux coefficient and its variation trend charts with the change of pole arc coefficient, air gap length and PM thickness. The average errors of the two methods are 2.835%, 0.84% and 1.347%, respectively. At the same time, the results of single-phase electromotive force obtained by magnetic circuit method, three dimensional finite element method and prototype experiments are 19.36 V, 18.82 V and 19.09 V, respectively. The results show that the magnetic circuit method is correct in calculating the no-load leakage flux coefficient.

The special structure of the coreless AFPMSG is considered in the presented equivalent magnetic circuit and equations, and the equations in this paper can be applied for leakage flux evaluating purposes and initial parameter selection of the coreless AFPMSG.

This study aims to analyse the scientific relationship between technological innovation and carbon emissions. Taking the Chinese electric power industry as the empirical research object, this study examined the effect of power technological innovation on carbon emissions and proposed policy recommendations for the development of technological innovation in China.

This study first calculated the energy consumption and carbon emission level of the Chinese electric power industry from 2005 to 2018. Secondly, this study built an evaluation index system for technological innovation of electric power with six indicators: average utilisation hours of power generation equipment; power consumption rate of power plant; line loss rate; standard coal consumption for power generation; standard coal consumption for power supply; and number of patent applications granted for generation, conversion or distribution of electric power in China. Finally, from a practical point of view, the relationship between technological innovation and carbon emissions of the Chinese electric power industry from 2005 to 2018 is evaluated and analysed.

Power technology innovation has been found to have a long-term and relatively large effect on carbon emissions, and carbon emissions have a short-term and insignificant impact on power technology innovation.

This study puts forward relevant suggestions for developing technological innovation and technology transfer, which is essential to establishing a low-carbon or zero-carbon power system in China.

This study provides empirical evidence for clarifying the relationship between technological innovation and carbon emissions in the power industry and further develops research theories on technological innovation and carbon emissions.

Relevant authorities will adopt measures to promote technological innovation and development in the power sector to reduce carbon emissions.

This study built an evaluation index system with six indicators for technological innovation of electric power. The evaluation method was used to measure the technological innovation level of the Chinese electric power industry. The causal link between technological innovation and carbon emissions in China was analysed.

The purpose of this paper is to investigate the tribological properties of carbonized polydopamine/reduced graphene oxide (CPDA/rGO) composite coatings.

CPDA/rGO composite coatings were prepared using the spray technique and subsequent pyrolysis under argon. The transmission electron microscopy, field-emission scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and Raman spectroscopy revealed the conversion of PDA and GO into CPDA and rGO, respectively.

The results of tribological investigations show that the CPDA/rGO composite coatings with heat treatment at 300°C possess much better friction-reduction and anti-wear properties.

The worn surfaces of the PDA/GO composite films after heat treatment at 300°C were much smoother than that of the copper substrate. The tribofilms containing C, N, O and Cu played an important role on reducing friction and increasing wear resistance.