Search results

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Research and development (R&D) personnel are an organization’s ultimate source of creative knowledge. Thus, their job performance ensures an organization’s innovative capability. Focusing on the process nature of creativity and innovation in organizations, the purpose of this paper is to analyze the influence of R&D personnel’s active knowledge sharing on their job performance and highlight the significance of social skills by examining their moderation effect.

This study conducted a survey of 252 R&D personnel in 60 R&D organizations in large South Korean firms. Data reliability and validity were confirmed, and regression analysis was performed to test the hypotheses.

The authors found that R&D personnel’s knowledge sharing has a significant effect on their job performance. Furthermore, social skills significantly moderate the relationship between knowledge sharing and job performance.

This study addresses the existing literature’s limited understanding of the process nature of creativity and innovation by examining knowledge sharing as a personal engagement in the innovation process, which has a positive effect on job performance. The study also casts new light on the importance of social skills in fortifying personal engagement in the innovation process. The overall results will prove valuable in the selection of effective R&D personnel and the design of competency development programs for R&D organizations.

This study aims to leverage inertial sensors via a walk test to associate kinematic variables with functional assessment results among walkable subjects with chronic stroke.

Adults with first-ever stroke survivors were recruited for this study. First, functional assessments were obtained by using Fugl–Meyer Assessment for lower extremity and Berg balance scales. A self-assembled inertial measurement system obtained walking variables from a walk test after being deployed on subjects’ affected limbs and lower back. The average walking speeds, average range of motion in the affected limbs and a new gait symmetry index were computed and correlated with the two functional assessment scales using Spearman’s rank correlation test.

The average walking speeds were moderately correlated with both Fugl–Meyer assessment scales (γ = 0.62, p < 0.01, n = 23) and Berg balance scales (γ = 0.68, p < 0.01, n = 23). After being modified by the subjects’ height, the new gait symmetry index revealed moderate negative correlations with the Fugl–Meyer assessment scales (γ = −0.51, p < 0.05) and Berg balance scales (γ = −0.52, p < 0.05). The other kinematics failed to correlate well with the functional scales.

Neuromotor and functional assessment results from inertial sensors can facilitate their application in telemonitoring and telerehabilitation.

The average walking speeds and modified gait symmetry index are valuable parameters for inertial sensors in clinical research to deduce neuromotor and functional assessment results. In addition, the lower back is the optimal location for the inertial sensors.

In 2020, the COVID-19 pandemic had a devastating impact on global health care and the economy. The restaurant industry has been especially hit hard by the statewide “stay-at-home” orders. To get back on track, many of these businesses need capital. A new and effective form of fundraising for business startups is crowdfunding (CF). However, there has been little research on the pandemic impact on CF. This study aims to fill this gap by investigating the pandemic-related impact on restaurant CF.

This study extracted all 2,686 restaurant CF projects in the USA from the Kickstarter platform from April 2010 to January 2021. By conducting descriptive analyses and multiple logistic regression models, this study examined the pandemic impact on CF success.

This study finds that, while controlling the effects of other determinants, businesses in the midst of the pandemic are more likely to be successfully funded than businesses unaffected by the pandemic. Findings also reveal that restaurant startups lowered their funding goals and posted more updates/comments/pledge levels during the pandemic, which made projects more likely to be selected as a “Project We Love” and increased the odds of funding success. However, mentioning COVID-19-related information or locating projects in “red zones” are not found to have any significant direct or moderating impact on the funding success.

This study pioneers the research topic restaurant CF and attempts to raise the research attention of small- and medium-sized enterprises and entrepreneurial financing. Using quantitative methods, it provides a new perspective on pandemic-impact research. Social exchange theory is extended to the context of reward-based CF under crisis. Finally, to the best of the authors’ knowledge, this is the first investigation of the possible moderating effect of project location on the relationship between restaurant CF characteristics and success.

The findings of this study suggest restaurateurs to be confident about the fundraising of their startup business through reward-based CF, even when located within so-called pandemic red zones, and perform appropriate communication strategies while using the reward-based CF.

This study is one of the earliest to examine the main and moderating effects of the pandemic-related factors on business CF in the hospitality realm. The findings are reference for researchers and restaurateurs on fundraising in a crisis context.

The promotion of new energy vehicles (EVs) is an effective way to achieve low carbon emission reduction. This paper aims to investigate the optimal pricing of automotive supply chain members in the context of dual policy implementation while considering consumers' low-carbon preferences.

This article takes manufacturers, retailers and consumers in a main three-level supply chain as the research object. Stackelberg game theory is used as the theoretical guidance. A game model in which the manufacturer is the leader and the retailer is the follower is established. The author also considered the impact of carbon tax policies, subsidy policies and consumer preferences on the results. Furthermore, the author investigates the optimal decision-making problem under the profit maximization model.

Through model solving, it is found that the pricing of EVs is positively correlated with the unit price of carbon and the amount of subsidies. The following conclusions can be obtained by numerical analysis of each parameter. Changes in carbon prices have a greater impact on conventional gasoline vehicles. Based on the numerical analysis of parameter β, it is also found that when the government subsidizes consumers, supply chain members will increase their prices to obtain partial subsidies. Compared with retailers, low-carbon preferences have a greater impact on manufacturers.

The new energy automobile industry involves many policies, including tax cuts, tax exemptions and subsidies. The policy environment faced by the members of a supply chain is complex and diverse. Therefore, the analysis in this article is based only on partial policies.

The authors innovatively combine the three factors of subsidy policy, carbon tax policy and consumer low-carbon preference, with research on the pricing of EVs. The influence of policy factors and consumer preferences on the pricing of EVs is studied.

This study aims to move beyond the current understanding of corporate social responsibility (CSR) to propose the concept of just-in-time (JIT) CSR as a metaphor that reflects hospitality operators’ endeavors to expedite socially responsible measures to both internal and external organizational stakeholders during times when functional and emotional supports are urgently needed.

This research used a qualitative approach in two studies. Study 1 engaged a media analysis to better grasp the knowledge of the research problem at hand. Study 2 involved interviews from stakeholders to assess their emotions and perceptions of meanings of major contents discerned from the first study.

This research highlights a process in which operators’ CSR practices (e.g. for business practices, for organizational strategy and for stakeholder well-being) during the COVID-19 crisis are imbued with connotative meanings (e.g. place-as-safety, place-as-partnership and place-as-warmth) that ultimately give shape to three core outcomes (e.g. individual rejoinder, brand resonance and societal resilience).

While JIT CSR is not an antidote for all devastations caused by COVID-19, it is posited as a needed mechanism that operators could use to ameliorate the situation and to go beyond their own stake to bring a broader array of societal benefits to humanity.

This research underscores how hospitality operators expedite crisis responses to the pandemic, and how their societal objectives transform the image of a place from a commercial venue into a place imbued with meaning associated with safety, partnership and warmth.

This study aims to validate the impact of waiting staff’s attitudes, subjective norms, and perceived behavioural control on customer dissuasion from over-ordering and identify their antecedents using an extended theory of planned behaviour.

We selected three categories of restaurants (30 in total, including fine dining, casual dining, and fast food) in Macao and Zhuhai (China) for conducting the survey using a purposive sampling approach. The respondents were waiting staff who took customers’ orders in the past three months. In total, 393 valid responses were used for a structural-equation-modelling analysis.

The results show that restaurant waiting staff’s attitudes, subjective norms, and perceived behavioural control have positive effects on their intention to dissuade customers from over-ordering. Our study further reveals that perceived behavioural control is far more influential than attitudes and subjective norms on restaurant employees’ intentions to intervene with over-ordering. We also validate seven antecedents, including environmental concern and communication for attitudes, peer influence, supervisor influence, and organisational support for subjective norms, and self-efficacy and training for perceived behavioural control.

The food-waste literature tends to focus on consumers in home and restaurant settings and has paid scarce attention to the role of restaurant waiting staff in intervening in consumers’ waste behaviours. We fill in this research gap by revealing a formation mechanism for waiting staff’s intention to dissuade over-ordering.

Crowdfunding can be an effective fundraising vehicle to independent restaurants to overcome fundraising limitations due to its small-size and high risk. A few studies have identified contents of communication for successful crowdfunding, yet missing is on how to say. The purpose of this paper is to investigate linguistics styles that are effective in pitches in restaurant crowdfunding.

The study analyzed 500 restaurant crowdfunding projects listed on Kickstarter.com that is one of the most popular crowdfunding platforms with sizable restaurant projects. By text analysis, this study identifies four linguistic categories – concrete, precise, interactive and language with low psychological distancing – from the project descriptions. A binominal linear regression analysis identified effective language styles that increases success of restaurant crowdfunding.

Drawing on the language expectancy theory and uncertainty reduction theory, this study found that project descriptions that are concrete and delivering stories with fewer usage of first person pronouns are likely to succeed in restaurant fundraising. Interactive style was counter-effective while preciseness of language did not affect funding success significantly. Findings further reveal the roles of non-linguistic attributes shown in the project pitches.

Despite the increasing practice of restaurant crowdfunding and good matches with small- and medium-sized restaurants’ needs, scant research has been conducted. Notably absent from the research is work on linguistic styles that are effective in restaurant crowdfunding. This study extends the prior literature by investigating how restaurant entrepreneurs should communicate with potential crowdfunders to make crowdfunding successful in the restaurant context. Simply knowing what are important does not provide holistic help to restaurant entrepreneurs; they also should know how to communicate well.

The findings of this study are important for restaurant entrepreneurs who need to present their projects and communicate effectively with potential investors online. Based on the study’s findings, crowdfunding platforms could enhance their project posting systems by developing algorithm that automatically measures language cues embedded in a project description and providing suggestions to improve persuasiveness of languages to maximize funding success for restaurant projects.

Scant research has been conducted in the restaurant crowdfunding; prior crowdfunding literature has focused on the projects other than restaurants such as technology, design and film. This study is the first that uncovers the role of the linguistic styles for the specific context of restaurant crowdfunding. This study sheds the light on the critical communication strategies used by small-sized restaurant entrepreneurs on crowdfunding platform.

Parking choice involves an individual selecting a parking place based upon various inter-related factors. This chapter examines the factors that influence parking choice decisions.

A review of the literature on parking choice has been undertaken. The influence of various factors on parking choice and recommendations for future parking policy will be outlined.

Most often it is a combination of several factors which influence individuals’ choice of parking place.

Increased knowledge of the factors which influence parking-search behaviour will inform urban parking policy applications with associated environmental and economic benefits.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.