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This paper is supposed to provide a critical review of current research progress on thermal management in grinding of superalloys, and future directions and challenges. By understanding the current progress and identifying the developing directions, thermal management can be achieved in the grinding of superalloys to significantly improve the grinding quality and efficiency.

The relevant literature is collected from Web of Science, Scopus, CNKI, Google scholar, etc. A total of 185 literature is analyzed, and the findings in the literature are systematically summarized. In this case, the current development and future trends of thermal management in grinding of superalloys can be concluded.

The recent developments in grinding superalloys, demands, challenges and solutions are analyzed. The theoretical basis of thermal management in grinding, the grinding heat partition analysis, is also summarized. The novel methods and technologies for thermal management are developed and reviewed, i.e. new grinding technologies and parameter optimization, super abrasive grinding wheel technologies, improved lubrication, highly efficient coolant delivery and enhanced heat transfer by passive thermal devices. Finally, the future trends and challenges are identified.

Superalloys have excellent physical and mechanical properties, e.g. high thermal stability, and good high-temperature strength. The superalloys have been broadly applied in the aerospace, energy and automobile industries. Grinding is one of the most important precision machining technologies for superalloy parts. Owing to the mechanical and physical properties of superalloys, during grinding processes, forces are large and a massive heat is generated. Consequently, the improvement of grinding quality and efficiency is limited. It is important to conduct thermal management in the grinding of superalloys to decrease grinding forces and heat generation. The grinding heat is also dissipated in time by enhanced heat transfer methods. Therefore, it is necessary and valuable to holistically review the current situation of thermal management in grinding of superalloys and also provide the development trends and challenges.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

The study aims to understand how cultural route heritage is conceptualized and managed in China by systematically reviewing the research literature on Chinese cultural route heritage (CRH). The study intends to inspire further discussion on the theoretical and practical development of cultural routes since the development is still at a liminal stage in China.

A total of 253 research articles related to Chinese cultural rote heritage from major Chinese and English research databases China National Knowledge Infrastructure (CNKI), Web of Science (WOS) and Scopus have been comprehensively identified and reviewed for the purpose of the study.

Four major themes of research on Chinese CRH have been identified: conceptual evaluation, list of the routes and characteristics of the routes, conservation and utilization. The results revealed that China has very rich resources in CRH, many of which were formed a long time ago, which exist across vast geographic regions and have assumed multiple functions and undergone dynamic reciprocal exchanges among diverse cultures and ethnicities.

The paper summarizes some major obstacles faced by CRH in China and proposes a strategic model to address the need for a more sustainable development of CRH in the Chinese context.

The paper offers a comprehensive overview of CRH in China and discusses practical issues in management and development of heritage great in size, number and complexity.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

The purpose of this paper is threefold. First, it aims to clarify the moderating role of self-esteem (SE) and susceptibility to normative influence (SNI) in the relationship between brand love and brand loyalty. Second, the study proposes modeling the mediation role of brand love and outlining how SE and SNI affect the consumer-brand relationship. Finally, the study explores the impact of brand love on brand loyalty: the moderating role of self-esteem and social influences, as the literature regarding this is still lacking.

Data were collected via an online survey, which yielded 218 responses. Structural equation modeling was used to predict the research model.

The findings indicate that both SE and SNI mediate the relationship between brand love and brand loyalty. Additionally, consumers love the focal brands positively relates to SE and SNI. In return, SE and SNI lead to brand loyalty. The tight relationship of SE and SNI affects the connection between brand love and brand loyalty.

The data has been collected in Vietnam, which creates a limitation regarding the study’s cross-cultural nature and the economic context. Thus, the study should be conducted in different cultures and economies (both developing and developed countries) to enhance the generalizability in consumer-brand relationships.

Brand managers should conduct more advertising in brand communities to enhance the influence of SNI and emphasize unique features of the brands, to attract consumers through the overlap of SE.

The findings can contribute to enhancing unique brand identity and self-motivation will increase consumer loyalty, increasing the revenue of a specific brand. Moreover, as acceptable peers contribute to making purchase decisions, boosting the brand community will maintain current consumers and attract additional potential consumers from the current consumer relationships.

This study contributes to consumer psychology by indicating both SNI and SE as the mediators in the relationship between brand love and brand loyalty and how the consumer-brand relationship can be enabled.

El propósito de este trabajo es triple. En primer lugar, pretende aclarar el papel moderador de la autoestima (SE) y la susceptibilidad a la influencia normativa (SNI) en la relación entre el amor y la lealtad a la marca. En segundo lugar, el estudio propone modelar el papel mediador del amor a la marca y esbozar cómo la autoestima y la SNI afectan a la relación consumidor-marca. Por último, el estudio explora los factores que afectan a la relación entre el amor a la marca y la lealtad a la misma, ya que aún no existe literatura al respecto.

Los datos se recogieron mediante una encuesta en línea, que arrojó 218 respuestas. Se utilizó el modelo de ecuaciones estructurales (SEM) para predecir el modelo de investigación.

Los hallazgos indican que tanto la autoestima como la SNI median la relación entre el amor y la lealtad a la marca. Además, el amor de los consumidores por las marcas focales se relaciona positivamente con la autoestima y la SNI. En cambio, la autoestima y la SNI conducen a la lealtad a la marca. La estrecha relación de la autoestima y la SNI afecta la conexión entre el amor a la marca y la lealtad a la misma.

Los datos se han recogido en Vietnam, lo que crea una limitación en cuanto a la naturaleza transcultural del estudio y el contexto económico. Así pues, el estudio debería realizarse en diferentes culturas y economías (tanto de países en desarrollo como desarrollados) para aumentar la posibilidad de generalización en las relaciones entre consumidores y marcas.

Los gerentes de marca deberían hacer más publicidad en las comunidades de marcas para aumentar la influencia de la SNI y hacer hincapié en las características singulares de las marcas, a fin de atraer a los consumidores mediante la superposición de la autoestima.

Las conclusiones pueden contribuir a mejorar la identidad de una marca única, y la automotivación aumentará la lealtad de los consumidores, incrementando los ingresos de una marca específica. Además, como los pares aceptables contribuyen a la toma de decisiones de compra, el impulso de la comunidad de marcas mantendrá a los consumidores actuales y atraerá a otros consumidores potenciales de las relaciones de consumo actuales.

Este estudio contribuye a la psicología del consumidor al indicar que tanto la SNI como la autoestima son los mediadores en la relación entre el amor y la lealtad a la marca y la forma en que se puede habilitar la relación consumidor-marca.

摘要

本文有三方面的研究目的。第一，明确自尊（SE）和易受规范影响（SNI）在品牌喜爱与品牌忠诚关系中的调节作用。第二，建立品牌喜爱的中介作用模型，并概述SE和SNI如何影响消费者与品牌的关系。最后，探讨品牌喜爱对品牌忠诚度的影响：自尊和社会影响因素的调节作用，目前有关这方面的文献还比较缺乏。

通过在线调查收集数据，共收到218份答复。采用结构方程模型进行预测研究模型。

研究结果表明，自尊和易受规范影响都对品牌喜爱和品牌忠诚度之间的关系起到了调节作用。此外，消费者对焦点品牌的喜爱与自尊和易受规范影响具有正相关关系。反过来，自尊和易受规范影响又会导致品牌忠诚。自尊和易受规范影响的紧密关系影响了品牌喜爱和品牌忠诚之间的联系。

由于本次研究数据是在越南收集的，造成了研究的跨文化性质和经济背景的局限性。因此，未来的研究可以在不同的文化和经济体（包括发展中国家和发达国家）进行，以提高关于消费者与品牌关系结论的普遍性。

品牌管理者可以在品牌社区中增强广告宣传，提高易受规范影响的影响力，同时强调品牌的独特性，与自尊因素相结合来吸引消费者。

本文研究结果有助于增强独特的品牌识别和自我激励，提高消费者的忠诚度，增加特定品牌的收入。此外，由于合适的同伴有助于消费者做出购买决策，提升品牌社区可以维持现有消费者，并通过现有消费者关系吸引更多的潜在消费者。

本研究提出了自尊和易受规范影响是品牌喜爱和品牌忠诚关系的调节变量，并且进一步阐释了它们如何促成消费者与品牌的关系，从而为消费者心理学做出了贡献。

Studies addressing rural residents’ understanding of global warming and their willingness to pay higher prices to mitigate it are very limited. The purpose of this study is to examine the general understanding and attitudes of rural residents in China regarding global warming and their willingness to pay higher prices to mitigate it.

This study surveyed 1,185 rural residents in three counties of coastal, middle and western China. Multivariate regression analysis was conducted to reveal the relationships between the willingness to pay higher prices to mitigate global warming and influencing factors.

The majority of respondents had heard of global warming; however, their knowledge of the phenomenon and its causes was very limited. Most respondents admitted the likelihood of risks from global warming. Although most respondents thought they had an obligation to mitigate global warming, only a small percentage of them were willing to pay higher prices to address the problem; the unwillingness of respondents to pay higher prices to mitigate global warming may have been associated with their low income and perceived inability to handle the cost, externalisation of responsibility and causes and lack of knowledge of how to affect it.

This study examines the general understanding and attitudes of rural residents in China regarding global warming and their willingness to pay higher prices to mitigate it. The research is conducive to climate change communications and the implementation of climate policies in China’s rural areas.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

The purpose of this paper investigates dynamic ease distributions of clothes at bust and waist lines with different body surface angle by using a Qualisys three-dimensional motion capture system (3DMCS).

The current method first obtain the specific markers of participants and their clothes along the bust and waist lines through 3DMCS, then using the least square method and four piecewise polynomial fitting participants and their clothes' bust and waist curves. The coordinates of the markers were tracked by the 3DMCS, while the participants under different body surface angle walked on a treadmill calculated the distances of markers coordinates to the participants' bust and waist curves. Finally, the data of samples were analyzed. It was found that the dynamic ease distributions showed different patterns at different body surface angle.

The results revealed the bust convex angle is 26.53 degrees (Specification:X3) and back slope angle is 13.96 degrees (Specification: Y1), the fluctuation of participant ease distributions on bust section was most obvious, and the maximum fluctuation value was ±20 mm and ±25 mm. The ease distributions of participant waist section fluctuated most obviously when the bust convex angle is 28.10 degrees (Specification: X5) and the back slope angle is 13.96 degrees (Specification: Y1), and the maximum fluctuation was ±30 mm and ±20 mm. The bust convex angle has the greatest influence on 1# garment, and the back slope angle has the greatest influence on 2# garment.

Currently, there is little information in the literature about dynamic ease distributions of garment on a different body types. This paper takes different body surface angles as the research objects to analyze the ease distributions of different clothes, the conclusion can provide reference data for 3D garment modeling and improve the authenticity of virtual garment fitting.

This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.