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The purpose of this paper is to propose and optimize plastic optical fiber (POF) probe with macro-bending biconical tapered structure for the relative humidity (RH) sensing.

In this study, the principle is the evanescent wave power modulated by the ambient humidity. The probe is fabricated by using fused biconical taper and heat-setting method and then coated with a fluorescent moisture-sensitive film.

The probe’s sensing performance can be optimized by changing the probe’s curvature radius, biconical tapered transition length and taper waist diameter. The result shows that the sensitivity of the probe is up to 1.60 and 3.40 mV/ per cent, respectively, at low humidity (10-45 per cent) and high humidity (45-90 per cent). Also, this probe has good linearity, repeatability, photostability and long-term stability.

The proposed probe can improve the sensitivity and linearity of RH sensing without complex devices, which is necessary for mass production, remote measurement and convenient operation.

POF probe with macro-bending biconical tapered structure is investigated in this paper, which is proved to be effective in improving the sensitivity and linearity.

Internet-famous food (also known as “online celebrity” food) is very popular in the digital age. This study aims to investigate consumer attitudes and understand consumer behavior towards Internet-famous food.

The authors collected 136,835 online comments regarding “Internet-famous food” from Dianping platform between 2016 and 2019 using a web scraper. A sentiment lexicon for Internet-famous food was constructed, and sentiment analysis is further conducted to understand consumer attitudes. Additionally, the authors use topic analysis and time series analysis to study consumer behavior.

Sentiment analysis showed that the number of consumers' comments decreased over time with the attitudes being overall positive, and the Internet-famous food industry has a positive prospect; time series analysis showed that the consumption of Internet-famous food was not affected by the season; topic analysis showed that consumers' comments on Internet-famous food were rich with a large variety, covering food categories, brand, quality, service, environment and price.

To the authors’ knowledge, limited research has focused on public opinions regarding “Internet-famous food”. This is the first study on consumer behavior towards Internet-famous food. This article provides a unique insight into the purchasing behavior and attitude of Chinese Internet-famous food consumers through text mining.

In the era of climate change, industrial organizations are under increasing pressure from consumers and regulators to reduce greenhouse gas emissions. The purpose of this paper is to examine the effectiveness of product mix as a strategy to deliver the low carbon supply chain under the cap-and-trade policy.

The authors incorporate the cap-and-trade policy into the green product mix decision models by using game-theoretic approach and compare these decisions in a decentralized model and a centralized model, respectively. The research explores potential behavioral changes under the cap-and-trade in the context of a two-echelon supply chain.

The analysis results show that the channel structure has significant impact on both economic and environmental performances. An integrated supply chain generates more profits. In contrast, a decentralized supply chain has lower carbon emissions. The cap-and-trade policy makes a different impact on the economic and environmental performances of the supply chain. Balancing the trade-offs is critical to ensure the long-term sustainability.

The research offers many interesting observations with respect to the effect of product mix strategy on operational decisions and the trade-offs between costs and carbon emissions under the cap-and-trade policy. The insights derived from the analysis not only help firms to make important operational and strategic decisions to reduce carbon emissions while maintaining their economic competitiveness, but also make meaningful contribution to governments’ policy making for carbon emissions control.

Limited research has explored why and how cause-related marketing on social media influences consumers' responses. Drawing upon balance theory and consistency theory, this study aims to identify the mechanism of cause-related marketing on social media.

Data were collected from a sample of 360 users of cause-related marketing campaigns on social media and analyzed using structural equation modeling in Mplus 8.0.

The three types of congruence – self-image congruence, brand-image congruence and value congruence – can serve as sub-dimensions of perceived fit between a consumer, brand and cause of a cause-related marketing campaign on social media. Importantly, these perceived fit sub-dimensions positively influence community identification and, therefore, influence consumer citizenship behaviors.

The findings provide theoretical and practical contributions for a brand to undertake cause-related marketing on social media.

This study clarifies the myth of the perceived fit of cause-related marketing on social media and examines the perceived fit sub-dimensions’ mechanism of consumers' responses through community identification.

This paper aims to explore how external creditors assess firms’ financial aggressiveness in China.

Using bank loan-specific data, the authors investigate whether firms exhibit greater costs of bank loans when they engage in earnings manipulation and whether this association changes when restrictions on lenders’ compensation are promulgated.

The authors find compelling evidence that bank executives charge higher premiums on firms with accrual earnings management to compensate for additional financial risk but do not charge extra loan prices for firms conducting real earnings management (REM). The authors also find that the enactment of Robust Bank Executive Compensation (REBC) enhances the vigilance of bank executives on the overall client firms’ earnings manipulation, with the exception of REM conducted by state-owned firms.

The authors extend the current literature on the cost of external loans by focusing on bank loans and the influence of REBC. This study offers implications for policymakers in China and other emerging economics to control loan default and financial risk.

This paper aims to explore the role of digital inclusive finance (DIF) in influencing household tourism consumption, whether this influence differs between households with different characteristics and determining the intermediate mechanisms that influence the relationship.

The conceptual framework of this study was designed on the basis of the research on DIF in residential consumption practices. The China Household Finance Survey (CHFS) and the Peking University DIF Index were used in the study, which included four years of unbalanced panel data from 25 provinces in China. A fixed effects model was used to validate the conceptual framework and hypothesis testing.

Both hypothesis paths proposed in this study were supported. Results of this study show that DIF has a significant contribution to household tourism consumption and shows a positive impact in terms of both breadth of coverage and depth of use, and that Internet usage is an important mediating mechanism for DIF to promote household tourism consumption. Thus, the use of DIF as a tool can have a positive impact on tourism consumption.

Results of this study will help researchers and tourism businesses understand the relationship and mechanisms at play between DIF and household tourism consumption and leverage financial tools to drive tourism revival. However, the lack of third-country data for comparative analysis may render the conclusions inapplicable to every economy.

This study is the first to examine the relationship between DIF and household tourism consumption, using an “individual + time + region” fixed effects model to conduct specific empirical tests.

This paper aims to report a flexible position-sensitive sensor that can be applied as large-area electronic skin over the stiff media.

The sensor uses a whole piezoresistive film as a touch sensing area. By alternately constructing two uniform electric fields with orthogonal directions in the piezoresistive film, the local changes in conductivity caused by touch can be projected to the boundary along the equipotential line under the constraint of electric field. Based on the change of boundary potential in the two uniform electric fields, it can be easy to determine the position of the contact area in the piezoresistive film.

Experiment results show the proposed tactile sensor is capable of detecting the contact position and classifying the contact force in real-time based on the changes of the potential differences on the boundary of the sensor.

The application example of using the sensor sample as a controller in shooting game is presented in this paper. It shows that the sensor has excellent touch sensing performance.

In this paper, a position-sensitive electronic skin is proposed. The experiment results show that the sensor has great application prospects in the field of interactive tactile sensing.

This paper aims to focus on the tracking of a moving magnetic target by using total field magnetometers and to present a tracking method based on the gradient of a magnetic anomaly. In the tracking, it is assumed that the motion of the target is equivalent to a first-order Markov process. And the unit direction vector of the magnetic moment from the gradient of the magnetic anomaly can be obtained. According to the unit direction vector, the inverse problem is turned into an optimization problem to estimate the parameters of the target. The particle swarm optimization algorithm is used to solve this optimization problem. The proposed method is validated by the numerical simulation and real data. The parameters of the target can be calculated rapidly using the proposed method. And the results show that the estimated parameters of the mobile target using the proposed method are very close to the true values.

The authors focus on the tracking of a moving magnetic target by using total field magnetometers and present a tracking method based on the gradient of a magnetic anomaly.

The paper provides an effective method for tracking the magnetic target based on an array with total field sensors.

Comparing with a vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. In this paper, a moving magnetic target was tracked by using total field magnetometers and a tracking method presented based on the gradient of a magnetic anomaly.

The purpose of this paper is to help the financial institutions improve the predictability of business failure of supply chain finance (SCF) clients with the use of external big data set.

A prediction model for the business failure of SCF clients was built upon different theoretical perspectives. Logistic regression method was deployed to test the model.

The authors develop a model that illustrates several key determinants to predict the probability of business failure of SCF clients based on several theoretical perspectives. The results show that taxable sales revenue, frequency of making value added tax (VAT) payment, number of counterparty for VAT invoice issuance, frequency of VAT invoice issuance and firm age are negatively correlated with business failure of SCF clients while the VAT paid and industry clockspeed are positively correlated with their business failure.

This paper shows how financial institutions can effectively leverage the external information sources through “unconventional” predictor variables in order to reduce the credit risks associated with business failure of SCF clients.

This paper is one of the first to focus on the potential use of financial big data set from external sources to improve of predictability of financial institutions on the business failure of SCF clients. In addition, this paper is a pivotal study on the financial client risk assessment based on taxpaying behaviors, tax amount, firm and industry characteristics.

Shortest path problem has always been a hot topic in the study of graph theory, because of its wide application field, extending from operational research to the disciplines of geography, automatic control, computer science and traffic. According to its concrete application, scholars in the relevant field have presented many algorithms, but most of them are solely improvements based on Dijkstra algorithm. The purpose of this paper is to enrich the kinds of (and improve the efficiency of) the shortest path algorithms.

This paper puts forward an improved calculation method of shortest path using cellular automata model, which is designed to search the shortest path from one node to another node. Cellular state set is adjusted with combination of breeding and mature states. Evolution rule is improved to enhance its parallelism. At the same time, recording manner of cellular state turnover is modified to record all information sources.

The result indicates that the improved algorithm is correct and more efficient, in that it could reduce the times of cellular state turnover; meanwhile, it can solve multi‐paths problem.

In this paper, cellular state set in exiting shortest path algorithm based on cellular automata theory is adjusted; evolution rule is improved; and recording manner of cellular state turnover is modified to record all information sources. All of which make the parallelism of this algorithm enhanced and the multi‐paths problem solved.