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Regenerative braking is an efficient energy saving technology in urban rail system, in which the recovery energy from braking trains is collected by some equipments and released to accelerating trains when needed. However, the high cost and low lifetime of storage devices prevent the widespread use of this technology. The purpose of this paper is to conduct thorough cost-benefit analysis to facilitate China’s urban rail companies to make decisions on the use of such technology.

To evaluate the benefit from regenerative energy storage, the authors formulate an improved integrated scheduling and speed control model to calculate the net energy consumption associated with different energy recovery rates and then define the benefit as the amount of energy saving arising from the usage of storage equipments. With the frequent charge/discharge operations on storage equipments, the energy recovery rate generally decreases which lowers the benefit, but the maintenance cost increases. By trading-off benefit and cost, the authors derive the optimal scrapping time, the maximum profit and the profitability condition for storage devices.

Simulation studies based on the Beijing Metro Yizhuang Line of China are given. The results show that compared with the current timetable and speed profile, the integrated scheduling and speed control approach with energy recovery rate of 0.5 can reduce the net energy consumption by 12.69 per cent; the net energy consumption can be well approximated as a linear function of energy recovery rate; and the maximum profit and the optimal scrapping time on regenerative energy storage devices are both positively related to the electricity price. The allowance proportion and the number of service trains such that busy lines with higher electricity price or allowance proportion have advantages to use the regenerative energy storage devices.

In this work, a linear energy recovery rate and a linear maintenance cost are used in the cost-benefit analysis process. In future research, the more accurate expressions on energy recovery rate and maintenance cost should be considered if more data on recovery rate and maintenance cost can be gathered.

The main values of this paper are to develop the integrated optimization approaches for train scheduling and speed control and, on this basis, make thorough cost-benefit analysis for regenerative energy storage to improve the operations management of urban rail transit.

This study aims to provide a better understanding of the market balance between regular (high-carbon) and green (low-carbon) products. Further, this study analyses the role of government subsidy policy, based on the results of the government’s optimal green subsidy decision and its implication for green market segmentation and social welfare.

This study adopts a Stackelberg game framework to study the interaction between the government’s subsidy regulations and the firms’ marketing regimes. When considering government subsidy decision, we use multi-objective programming theory and turn the problem into weighted single-objective optimisation programming.

This study explores three marketing regimes and identifies the conditions under which each regime should be adopted by a firm. Further, investigating the optimal subsidy decision problem for the government reveals three subsidy regimes corresponding to the three marketing regimes. The government may be stuck in a regime of useless subsidy and the reason for this phenomenon is analysed as well.

Developing the model into a more complex supply chain situation will enhance the applicability of the framework. Incorporating other environmental regulations, such as carbon tax, can be interesting research extensions of this study.

This study provides a quantitative framework, which can help the regulator gain a deeper understanding of green subsidy policies and assist focal companies in acquiring a better appreciation of green marketing segmentation.

The study is one of the first few works to explore the optimal design of green subsidy regulation for the government and its impact on market segmentations of high- and low-carbon products by using quantitative modelling approaches and deriving vital managerial insights.

The performance of oil-filled pressure cores is very much affected by the corrugated diaphragm and the oil filling volume. The purpose of this paper is to show the effects of different corrugated diaphragms, different oil filling volumes and different treatments of the corrugated diaphragms on the performance of pressure sensors.

Pressure-sensitive cores with different diaphragm diameters, different diaphragm ripple numbers and different oil filling volumes are produced, and thermal cycling is introduced to improve the diaphragm performance, and finally the performance of each pressure-sensitive core is tested and the test data are analyzed and compared.

The experimental results show that the larger the diameter of the corrugated diaphragm used for encapsulation, the better the performance. For pressure-sensitive cores using smaller diameter corrugated diaphragms, the performance of one corrugation is better than that of two corrugations. When the number of corrugations and the diameter are the same size, the performance of the outer ring of the diaphragm with concave corrugations is better than that with convex corrugations. At the same time, the diaphragm after thermal cycling treatment and appropriate reduction of encapsulated oil filling can improve the performance of the pressure-sensitive core.

By exploring the effects of corrugated diaphragm and oil filling volume on the performance of oil-filled pressure cores, the design of oil-filled pressure sensors can be guided to improve sensor performance.

This paper aims to address the problem of integrating sensor feedback in robotized interior finishing operations. Its motivation is to finally realize automatic operations necessitating no human intervention. A vision-based approach is proposed for monitoring the execution status and changing the action accordingly.

First, a robotic system is proposed which can realize two typical interior finishing operations, namely, putty applying and wall sanding. Second, a new method based on a deep neural network is proposed to process the visual information capturing the execution status of the interior finishing operations. It helps to determine essential parameters on where should be processed and how to execute the corresponding operation. With the proposed method, vision information is embedded into the execution of interior finishing in a closed loop style.

The experiments demonstrate the feasibility of the proposal and reveal problems for further improvement of the autonomous interior finishing robot.

This provides an original insight into robotized interior finishing by addressing an attempt on integrating visual feedback into the manual process.

The purpose of this paper is to integrate broaden theoretical perspectives in the investigation of the key antecedents of consumers’ continuous review contribution behavior on restaurant guide online opinion platforms (OOPs). Integrating both rational and relational theoretical perspectives with an extension of the investment model, a research model was proposed to explain what factors influence consumers’ intention to continuously contribute electronic WOM (eWOM) on OOPs.

The research model was empirically tested with an online survey study of 438 eWOM contributors from a leading Chinese restaurant guide OOP, Dianping.com.

The results indicated that satisfaction, affective commitment, and continuance commitment were key determinants of consumers’ continuance intention to contribute eWOM on the platform. Satisfaction was driven by concern for other consumers, social benefits, platform assistance, and venting negative feelings. Quality of alternatives and investment size significantly affected continuance commitment.

The selection of respondents is bound to the China area within a particular OOP, although the chosen OOP is a leading one in China. In addition, this study focuses on the perspective of commitment, but some social factors are neglected.

This paper provides an integrated theoretical framework to explore the factors determining consumers’ continuance intention to contribute restaurant reviews on OOPs. From both rational and relational theoretical perspectives, the proposed model reveals the factors that encourage consumers to continue contributing eWOM on OOPs.

This paper aims to develop a climbing robot to help people inspect lamps of high-mast lighting.

The robot consists of driving mechanism, suspension mechanism and compression mechanism. The driving mechanism is realized by link chains and sprockets, which are arranged opposite to each other, to form a dual caterpillar mechanism. The compression mechanism squeezes the caterpillar, and rubber feet “grasps” the steel rope to generate enough adhesion forces. The suspension mechanism is used to compensate the contraction or extension of the chains. The robot is equipped with a DC motor with a rated power of 250 W and a wireless module to communicate with the operator’s console. The dynamic model of the robot and the control strategy is derived, and the stability of the controller is proofed.

The payload experiment shows the robot can afford up to 3.7 times payload versus its own weight. Even when the payload is 30 kg, the robot can maintain a speed of the 1 m/s. The experiments also show that the tracking error of the robot reaches zero.

The proposed moving mechanism has a high load/weight ratio, which is a verified solution for the cable inspection purpose.

A rope climbing robot for high mast lighting inspection is proposed. The developed mechanism can reach a speed of 1 m/s with the payload of 30 kg, while its own weight is only 15.6 kg. The payload/weight ratio of the robot is 2.24; this value is rather good in many climbing robots reported in other renowned journal.

This paper aims to introduce a novel 4D hyperchaotic fractional-order system which can produce one-to-four-wing hyperchaotic attractors. In the study of chaotic systems with variable-wing attractors, although some chaotic systems can generate one-to-four-wing attractors, none of them are hyperchaotic attractors, which is incomplete for the dynamic characteristics of chaotic systems.

A novel 4D fractional-order hyperchaotic system is proposed based on the classical three-dimensional Lü system. The complex and abundant dynamic behaviors of the fractional-order system are analyzed by phase diagrams, bifurcation diagrams and the corresponding Lyapunov exponents. In addition, SE and C₀ algorithms are used to analyze the complexity of the fractional-order system. Then, the influence of order q on the system is also investigated. Finally, the circuit is implemented using physical components.

The most particular interest is that the system can generate one-to-four-wing hyperchaotic attractors with only one parameter variation. Then, the hardware circuit experimental results tally with the numerical simulations, which proves the validity and feasibility of the fractional-order hyperchaotic system. Besides, under different initial conditions, coexisting attractors can be obtained by changing the parameter d or the order q. Then, the complexity analysis of the system shows that the fractional-order chaotic system has higher complexity than the corresponding integer-order chaotic system.

The circuit structure of the fractional-order hyperchaotic system is simple and easy to implement, and one-to-four-wing hyperchaotic attractors can be observed in the circuit. To the best of the knowledge, this unique phenomenon has not been reported in any literature. It is of great reference value to analysis and circuit realization of fractional-order chaotic systems.

The purpose of this paper is to quickly manufacture full Cu₃Sn-microporous copper composite joints for high-temperature power electronics applications and study the microstructure evolution and the shear strength of Cu₃Sn at different bonding times.

In this paper, a novel structure of Cu/composite solder sheet/Cu was designed. The composite solder sheet was made of microporous copper filled with Sn. The composite joint was bonded by thermo-compression bonding under pressure of 0.6 MPa at 300°C. The microstructure evolution and the growth behavior of Cu₃Sn at different bonding times were observed by electron microscope and metallographic microscope. The shear strength of the joint was measured by shear machine.

At initial bonding stage the copper atoms in the substrate and the copper atoms in the microporous copper dissolved into the liquid Sn. Then the scallop-liked Cu₆Sn₅ phases formed at the interface of liquid Sn/microporous copper and liquid Sn/Cu substrates. During the liquid Sn changing to Cu₆Sn₅ phases, Cu₃Sn phases formed and grew at the interface of Cu₆Sn₅/Cu substrates and Cu₆Sn₅/microporous copper. After that the Cu₃Sn phases continued to grow and the Cu₃Sn-microporous copper composite joint with a thickness of 100 µm was successfully obtained. The growth rule of Cu₃Sn was parabolic growth. The shear strength of the composite joints was about 155 MPa.

This paper presents a novel full Cu₃Sn-microporous copper composite joint with high shear strength for high-temperature applications based on transient liquid phase bonding. The microstructure evolution and the growth behavior of Cu₃Sn in the composite joints were studied. The shear strength and the fracture mechanism of the composite joints were studied.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.