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Introduces a method of food sensory evaluation employing artificial neural networks. The process of food sensory evaluation can be viewed as a multi‐input and multi‐output (MIMO) system in which food composition serves as the input and human food evaluation as the output. It has proved to be very difficult to establish a mathematical model of this system; however, a series of samples have been obtained through experiments, each of which comprises input and output data. On the basis of these sample data, applies the back‐propagation algorithm (BP algorithm) to “train” a three‐layer feed‐forward network. The result is a neural network that can successfully imitate the food sensory evaluation of the evaluation panel. This method can also be applied in other fields such as food composition optimizing, new product development and market evaluation and investigation.

According to the survey and measurement on rural housing in the Northeast severe cold regions of China, this paper analyzed the existing situation and problems of current rural housing in terms of integral development, functional layout, envelop structure, interior thermal environment, heating system and energy utilization etc.. Based on the climatic features of severe cold regions, as well as rural financial and technical conditions, living and production mode, residential construction characteristics and existing resource status etc., the feasible approaches of achieving building energy saving has been proposed, thus acting as a guidance for new rural housing design in severe cold regions.

The purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.

This presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.

The method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.

In this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

The study aims to address a critical gap in existing healthcare payment schemes and care service pricing by recognizing the influential role of patients' decisions on self-management efforts. These decisions not only impact health outcomes but also shape the demand for care, subsequently influencing care costs. Despite the significance of this interplay, current payment schemes often overlook these dynamics. The research focuses on investigating the implications of a novel behavior-based payment scheme, designed to align incentives and establish a direct connection between patients' decisions and care costs. The primary objective is to comprehensively understand whether and how this innovative payment scheme structure influences key stakeholders, including patients, care providers, insurers and overall social welfare.

In this paper, we propose a game-theoretical model to incorporate the performance of self-management with the demand for healthcare service, compare the patient's effort decision for self-management and provider's price decision for healthcare service under a behavior-based scheme with that under two implemented widely payment schemes, that is, co-payment scheme and co-insurance scheme.

Our findings confirm that the behavior-based scheme incentives patient self-management more than current schemes while reducing their possibility of seeking healthcare service, which indirectly induces the provider to lower the price of the service. The stakeholders' utility under various payment schemes is sensitive to the cost of treatment and the perceived health utility of patients. Especially, patient health awareness is not always benefited provider profit, as it motivates patient self-management while diminishing the demand for care.

We provide a novel framework for characterizing behavior-based payment schemes. Our results confirm the need for modification of the current payment scheme to incentivize patient self-management.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

This study aims to identify the counterproductive knowledge behavior (CKB) of volunteers in nonprofit organizations and its influencing factors, based on the theories of planned behavior and well-being.

An online survey was used to collect 496 valid responses. A structural equation model was constructed, and the relationships among the constructs were estimated via the maximum likelihood method. To analyze the direct and indirect effects, 2,000 bootstrapping runs were conducted. A Kruskal-Wallis test was also conducted to analyze the relationship between the variables.

A combination of organizational factors and individual attitudes and perceptions can be used to explain CKB. Insecurity about knowledge sharing had the greatest impact on CKB. A competitive organizational norm induced CKB while a knowledge-sharing organizational norm did not have a significant impact. Further, the more self-determined the volunteer activity was, the more the CKB was suppressed. However, well-being did not have a significant direct effect. Volunteers with high levels of well-being and self-determination had significantly lower levels of insecurity about knowledge sharing compared to those who did not.

Well-being arising from volunteering did not directly suppress CKB. To improve organizational efficiency by reducing CKB, nonprofit organization managers should provide intrinsically motivating tasks and interact with the volunteers.

There is a lack of empirical research on CKB in volunteer organizations; therefore, the authors propose a new approach to knowledge management in volunteer activities.

The body of scholarship on YouTube is an expanding area of scholarly inquiry. Existent research indicates that music videos are one of the most salient features of YouTube. Interactionist research about popular music has provided important insights through interviews with fans and audience members; however, this work has yet to examine audience engagement with music videos on YouTube. Using Qualitative Media Analysis, I illustrate how the researcher of popular music can work with user comments collected from YouTube. Thematic understandings largely consistent with nostalgia that emerged from an analysis of user-generated comments in response to selected music videos on YouTube are explored. I conclude by suggesting some directions for future research.

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

This study aims to examine the effects of audit quality on earnings management and cost of equity capital (COE) considering the impact of two owner types: government ownership and foreign ownership.

The study uses a panel data set of 236 Vietnamese firms covering the period 2007 to 2017. Because the two main dependent variables of the COE capital and the absolute value of discretionary accruals receive fractional values between zero and one, the paper uses the generalised linear model (GLM) with a logit link and the binomial family in regression analyses. The paper uses numerous audit quality measures, including hiring Big 4 auditors or the industry-leading Big 4 auditor, changing from non-Big 4 auditors to Big 4 auditors or the industry-leading Big 4 auditor, and the length of Big 4 auditor tenure. Big 4 companies include KPMG, Deloitte, EY and PwC, whereas the non-big 4 are the other audit companies.

The study finds a negative relationship between audit quality and both the COE capital and income-increasing discretionary accruals. The effects of audit quality on discretionary accruals and the COE capital depend on the ownership levels of two important shareholders: the government and foreign investors. Foreign ownership is negatively associated with discretionary accruals; however, the effect is more pronounced in the sub-sample of state-owned enterprises (SOEs), the firms where the government owns 50% or more equity, than in the sub-sample of Non-SOEs.

To the best of the knowledge, no prior similar study exists that used the GLM with a logit link and the binomial family regression. Global investors may be interested in understanding how unique institutional settings and capital markets of each country impact the financial reporting quality and cost of capital. Further, policymakers of developing markets may have incentives to improve the quality of financial reporting and reduce the cost of capital which should result in attracting more foreign investments.