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This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

The purpose of this paper is an attempt to design a proper incentive coordination mechanism to deal with the channel conflicts between the traditional sales and online direct sales.

With respect to the problems of channel conflicts between the traditional sales and online direct sales, to optimize the sale system and get more profits, considering the influences of consumer network acceptance, the authors establish demand and profit function based on consumer's utility, respectively. What's more, we exploit the game theory to analyze the optional decisions of the supply chain under the incentive coordination condition and no incentive coordination condition, and then we discuss the supply chain's optimal pricing, demand, profit and compensation incentive levels with the different effect of consumer network acceptance.

The level of compensation incentive provided by the manufacturer is influenced by consumer network acceptance and product cost. The lower the consumer network acceptance, the better the compensation incentive coordination effect of manufacturers. Manufacturers, wholesalers, retailers and consumers are all important players in real supply chain relationships. When a manufacturer exists as a dominant role, it should pay full attention to the impact of consumer behavior on supply chain decisions.

The research can clarify the influence and mechanism of consumer behavior on supply chain channel conflict coordination, and deal with channel conflicts.

The proposed incentive coordination can effectively realize supply chain channel conflict resolution, and provide decision-making ideas and methods for manufacturers to develop the supply chain management of online direct sales channels.

This paper aims to investigate how user expectations affect value-added service (VAS) investment and pricing decisions of two-sided platforms. It draws on the information asymmetry theory and offers suggestions on how platform operators can manage user expectations.

According to the game theory, this study considers three user expectations (responsive, passive and wary). By framing the Hotelling duopoly model and comparing the VAS investment, price and platform profits, the optimal platform decision is analyzed and discussed.

The conclusions demonstrate that the monopolistic two-sided platform obtains more profits from the informed users with responsive expectations than uninformed users with passive or wary expectations. The marginal investment cost and cross-network externalities are two key factors that determine the platform's VAS investment and pricing strategies of passive or wary users. Furthermore, considering the expectation preferences, i.e. the uniformed users hold wary expectations with more information and hold passive expectations with less or no information, the results suggest that the proportion of wary users to all uninformed users increases the platform's VAS investment, profits and the price of informed users, and increase (decrease) the price of uninformed users when the cross-network externalities of informed users are relatively small (larger).

These results can provide insightful enlightenment into how platform operators utilize bilateral users' expectations and information level to guide their VAS investment and pricing decisions.

This paper is one of the first to explore the impact of three user expectations and the heterogeneity of preferences in informing users' passive or wary expectations, based on different levels of information on the decision-making of two-sided platforms regarding VAS.

Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling.

First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation.

The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased.

The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

The purpose of this paper is to investigate remanufacturing operational strategies considering uncertain quality of end-of-life (EOL) products and differential consumers’ willingness-to-pay (WTP) for new products and provide suggestions on the remanufacturing mode selection for the original equipment manufacturer (OEM).

This study considers three remanufacturing modes, i.e. in-house, outsourcing and authorization modes. By establishing and comparing decision models of three modes from the perspectives of profit, consumer surplus and environment, the optimal remanufacturing mode is discussed.

The results suggest that if the OEM’s remanufacturing capability is high, the in-house mode brings to the highest environmental performance, OEM’s profit and consumer surplus. Otherwise, the outsourcing mode (authorization) is the best benefit to environment (consumers if the unit production cost of new products is not too high). As for the preference of two decision-makers to outsourcing and authorization modes, if the difference of consumers’ WTP for new products is low, the OEM prefers the outsourcing mode; otherwise, the OEM prefers the authorization mode. The preference of the third-party remanufacturer (TPR) to remanufacturing mode is affected by consumers’ WTP for remanufactured products, WTP difference for new products and remanufacturing quality level standard.

These results can provide operational insights into how to select remanufacturing mode when the quality of EOL products is uncertain and consumers’ WTP for new products is different under three remanufacturing modes.

This paper is among the first to investigate the joint effects of EOL products’ uncertain quality and differential consumers’ WTP for new products on the operational strategies and performance under different remanufacturing modes.

Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches.

This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures.

This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

Under the carbon regulation mechanism, managing operational strategies is a challenging task. Green innovation is introduced into a hybrid system of manufacturing and remanufacturing to handle the carbon emission constraints in a dynamic market environment. This paper aims to investigate the joint dynamic green innovation policy and pricing strategies in a hybrid manufacturing and remanufacturing system.

This paper first considers a monopolistic manufacturer who offers brand-new products and remanufactured items at the same price to consumers. Subsequently, the authors extend their analyses to distinct pricing strategies for both newly manufactured products and refurnished ones in such a hybrid system. Two different cases are considered: a loose carbon emission constraint and a binding carbon emission constraint. By solving the dynamic optimization problem, the differential game and Pontryagin’s maximum principle are used to obtain the joint green innovation and pricing strategies.

The retail price first increases then declines over a single period. The green innovation diminishes in the same pricing decision model, while it first increases then declines in a distinct pricing decision model over a single planning horizon. The green innovation investment as well as the retail price are discouraged by an emission cap and recycling fraction. The distinct retail price fluctuates violently, and they are, in descending order of the highest peak price as follows: the newly manufactured product, the same pricing product and the repaired product. Carbon emission caps that are either too high or too low decrease the revenue of the manufacturer. A small emission constraint margin benefits the manufacturer. The recycling policy, as well as other parameters, affects whether the hybrid system attains the carbon emission constraint or not, which suggests that the recycling policy is complementary to the carbon emission constraint mechanism in the hybrid system.

These results offer managerial implications to the hybrid system in terms of green innovation, pricing strategies and recycling policy.

This paper is among the first papers to research the joint dynamic green innovation policy and pricing strategies with/without a carbon emission constraint in a hybrid manufacturing and remanufacturing system with a differential game. Moreover, this paper presents a potential way of investigating other common resource constraints by a differential game in a manufacturing/remanufacturing system or closed loop supply chain.

Most rapid prototyping (RP) relies on energy fields to handle materials, among which electricity has been much more utilized, resulting in distinctive responsiveness of non-linear, overshoot, variable inertia, etc. The purpose of this paper is to eliminate the drawbacks of array nozzle clogging, stringing, melt sagging, particularly in multi-material RP, by focusing on the electrothermal response so as to adaptively distribute thermal more accurate, rapid and balanced.

This paper presents an electrothermal response optimization method of nozzle structure for multi-material RP based on fuzzy adaptive control (FAC). The structural, physical and control model are successively logically built. The fractional order electrothermal model is identified by Riemann Liouville fractional differential equation, using the bisection method to approximate the physical model via least square method to minimize residual sum of squares. The FAC is thereafter implemented by defining fuzzy proportion integration differentiation control rules and fuzzy membership functions for fuzzy inference and defuzzification.

The transient thermodynamic and structural statics, as well as flow field analysis, are conducted. The response time, mean temperature difference and thermal deformation can be found using thermal-solid coupling finite element analysis. In physical experimental research, temperature change, together with material extrusion loading, were measured. Both numerical and physical studies have revealed findings that the electrothermal responsiveness varies with the three-dimensional structure, materials and energy sources, which can be optimized by FAC.

The proposed FAC provides an optimization method for extrusion-based multi-material RP between the balance of thermal response and energy efficiency through fulfilling potential of the hardware configuration. The originality may be widely adopted alongside increasing requirements on high quality and high efficiency RP.

Recycling and reuse of the electric vehicle (EV) batteries are ways to extend their limited lives. If batteries can be traced from production to recycling, it is beneficial for battery recycling and reuse. Using blockchain technology to build a smart EV battery reverse supply chain can solve the difficulties of lack of trust and data. The purpose of this study is to discuss the behavioural evolution of a smart EV battery reverse supply chain under government supervision.

This study adopts evolutionary game theory to examine the decision-making behaviours of the government, EV manufacturers with recycled used batteries and third-party EV battery recyclers lacking professional recycling qualification.

On the smart reverse supply chain integrated by blockchain technology, a cooperative recycling strategy of the third-party EV battery recycler is the optimal choice when the government tends to actively regulate. The probability of the EV manufacturer choosing the blockchain adoption strategy exceeds (below) the threshold, and the government prefers negative (positive) supervision. According to numerical analysis, in the mature stage in the EV battery recycling industry, when the investment cost of applying blockchain is high, EV manufacturers' willingness to apply blockchain slows down, the government accelerates adopting a negative supervision strategy and third-party EV battery recyclers prefer cooperative recycling.

The results of this study provide opinions on the strength of government supervision and the conditions under which EV manufacturers and third-party EV battery recyclers should apply blockchain and cooperate. On the other hand, this study provides theoretical analysis for promoting the application of blockchain technology in smart reverse supply chain.

Compared with previous research, this study reveals the relevance of government supervision, blockchain application and cooperation strategy in smart EV battery reverse supply chain. In the initial stage, even if the subsidy (subsidy reduction rate) and penalty are high and the penalty reduction rate is low, the EV manufacturer should rather give up the application of blockchain technology. In the middle stage in the EV battery recycling industry, the government can set a lower subsidy (subsidy reduction rate) combined with a penalty or a higher penalty (penalty reduction rate) combined with a subsidy to supervise it. The third-party EV battery recycler is advised to cooperate with the EV manufacturer when the subsidy is low or the penalty is high.

In 2016, the Chinese central government decentralized the responsibilities of housing market regulation to the municipal level. This paper aims to assess whether the decentralized market regulation is effective.

This study first investigates the fundamental drivers of urban housing prices in China. Taking into consideration the factors driving housing prices, the authors further investigate the effectiveness of decentralized housing market regulation by a pre- and post-policy comparison test using a panel data set of 35 major cities for the years from 2014 to 2019.

The results reveal heterogenous policy effects on housing price growth among cities with a one-year lag in effectiveness. With the decentralized housing market regulation, cities with fast price growth are incentivized to implement tightening measures, while cities with relatively low housing prices and slow price growth are more likely to do nothing or deregulate the markets. The findings indicate that the shift from a centralized housing market regulation to a decentralized one is more appropriate and effective for the individual cities.

Few policy evaluation studies have been done to examine the effects of decentralized housing market regulation on the performance of urban housing markets in China. The authors devise a methodology to conduct a policy evaluation that is important to inform public policy and decisions. This study helps enhance the understanding of the fundamental factors in China’s urban housing markets and the effectiveness of municipal government interventions.