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This paper aims to design and optimize the threaded fastener of leakage current particulate matter (PM) sensor. The corresponding air-tight test is conducted to ensure the reliability of the installation strategy with screw connection.

Research on the pressure-deformation curve of seal gasket was conducted and the vibration load of engine was considered for the calculation of the minimum installation pre-tightening force. Simultaneously, the danger threaded section area was calculated, and the carrying capacity was verified. The height of the welding line was studied to ensure the reliability of the application. FEA was carried out to acquire the relationship between local structure size and local stress for continuous improvement of thread connection. The installation torque range was acquired from the torque control principle for the pre-tightening force. The sealing reliability of the connector was proved with leakage.

The air tightness of the thread connector is proved to be fine. When the pre-tightening force is over 8,000 N, and its length reaches 2 mm, the connector has good reliability at ambient temperature. The tightening torque of 60-74 Nm can guarantee the reliable fixing ability of thread connector, and its plastic non-deformation ability in the process of repeated tearing down.

This paper provides an installation strategy and an optimization of PM sensor, which has a positive effect on the study and the manufacture of PM sensor. It is helpful to further develop PM sensor and after-treatment technology. This kind of real-time monitoring PM sensor needs to be studied further to achieve its commercial application.

This study aims to use eye-tracking technology to conduct an empirical study about online learning process analysis, thus aiming to understand the attentional preferences and learning paths in online learners.

With eye movement tracking and data analysing technology, the Tobii X120 eye-tracking instrument, Tobii studio and online learning platform are used to record and visualise data of eye moving and learning steps during the real task-based online learning processes of 14 online learners. According to Barbara A. Soloman’s learning style classification framework, these learners’ learning style was presented in four dimensions. Based on data of eye moving, leaning style and operation in online course, the correlation about learners’ preferences of learning content, online learning paths and learning style were analysed based on according data.

The paper provides empirical insights about how change is brought about during online learning. It is found that there is no significant difference in attention preference between the students with the difference on the learning style of visual-verbal, although each person has a different attention preference on the learning content.

The limitation of this study is that only one common type of video learning process is studied. The learning process of various types of instructional videos in online learning will be done in future research.

In this study, eye-movement tracking technology is used to understand students’ learning path and learning preference in the online learning process, which is helpful to optimise the online learning process and improve the efficiency of online learning.

This research findings have been approved by relevant experts and have won the first prize in the school-level competition of South China Normal University in China.

In this study, the technology of psychology (eye-tracking technology) is introduced into the study of real task-based online learning process in the subject of educational technology, realising the integration of multi-disciplinary research techniques and methods.

Recent research advances in artificial intelligence, machine learning, and neural networks are becoming essential tools for building a wide range of intelligent applications. Moreover, machine learning helps to automate analytical model building. Machine learning based frameworks and approaches allow making well-informed and intelligent choices for improving daily eating habits and extension of healthy lifestyle. This book chapter presents a new machine learning approach for meal classification and assessment of nutrients values based on weather conditions along with new and innovative ideas for further study and research on health care-related applications.

There are many investigations in design methodologies, but there are also divergences and convergences as there are so many points of view. This study aims to evaluate to corroborate and deepen other researchers’ findings, dissipate divergences and provide directing to future work on the subject from a methodological and convergent perspective.

This study analyzes the previous reviews (about 15 reviews) and based on the consensus and the classifications provided by these authors, a significant sample of research is analyzed in the design for additive manufacturing (DFAM) theme (approximately 80 articles until June of 2017 and approximately 280–300 articles until February of 2019) through descriptive statistics, to corroborate and deepen the findings of other researchers.

Throughout this work, this paper found statistics indicating that the main areas studied are: multiple objective optimizations, execution of the design, general DFAM and DFAM for functional performance. Among the main conclusions: there is a lack of innovation in the products developed with the methodologies, there is a lack of exhaustivity in the methodologies, there are few efforts to include environmental aspects in the methodologies, many of the methods include economic and cost evaluation, but are not very explicit and broad (sustainability evaluation), it is necessary to consider a greater variety of functions, among other conclusions

The novelty in this study is the methodology. It is very objective, comprehensive and quantitative. The starting point is not the case studies nor the qualitative criteria, but the figures and quantities of methodologies. The main contribution of this review article is to guide future work on the subject from a methodological and convergent perspective and this article provides a broad database with articles containing information on many issues to make decisions: design methodology; optimization; processes, selection of parts and materials; cost and product management; mechanical, electrical and thermal properties; health and environmental impact, etc.

Hoping to uncover the physical principles of the vibration of the functionally graded material (FGM) microplate, by which the authors can make contributions to the design and manufacturing process in factories like micro-electro-mechanical system (MEMS) and other industries.

The authors design a method by establishing a reasonable mathematical model of the physical microplate composed of a porous FGM.

The authors discover that the porosity, the distributions of porosity, the power law of the FGM and the length-to-thickness ratio all affect the natural frequency of the vibration of the microplate, but in different ways.

Originally proposed a model of the micro FGM plate considering the different distributions of the porosity and scale effect and analyzed the vibration frequency of it.

Industrial vending solutions are unique in that they represent a very specific form of vendor-managed inventory (VMI). The purpose of this paper is to investigate performance outcomes associated with industrial vending implementation, a topic that has been largely ignored by the academic community.

A survey instrument was developed from earlier work on VMI success. Structural equation modeling is used to identify relationships between three enablers (information exchange, information quality, and relationship quality), perceived vending system implementation success, and three outcomes (cost benefits, customer service benefits, and inventory benefits).

Statistical outcomes demonstrate support for the benefits arising from successful vending system implementation. This study demonstrates that industrial vending implementation success is strongly tied to the amount and quality of the information shared between the relationship partners.

Successful industrial vending implementation results in improved inventory control, increased levels of customer service, and tighter cost control. This study provides supply chain managers with current findings, which should aid them in evaluating their current and proposed vending solutions.

Although VMI has been studied in the past, little work has been conducted on industrial vending as a specific form of VMI. This is the first study to explore industrial vending from the viewpoint of VMI implementation and performance. Empirically tested study results that are grounded in transaction cost theory confirm a series of performance outcomes of industrial vending from a buyer’s perspective as well as a number of enablers for successful industrial vending implementation.

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

This study explores the impact of carbon neutrality policies on Chinese stock market from a supply chain perspective. Specifically, the carbon policy refers to the Action Plan for Carbon Dioxide Peaking Before 2030 (the Plan) in China.

This paper is based on the resource dependence theory (RDT) and applies the event study methodology to explore the impact. It uses the cross-sectional regression model to reveal the moderating effect of supply chain characteristics on the stock market reaction with a data set of 354 listed companies in A-shares (excluding ChiNext and SME board).

The promulgation of the Plan shows a significant negative stock market reaction. Customer concentration, out-degree centrality and smart supply chains (SSCs) have a significant negative moderating effect. In-degree centrality and supplier concentration have a significant positive moderating effect. Furthermore, the conclusions concerning out-degree centrality, supplier concentration and SSCs are counterintuitive.

For policymakers, the study results provide a reference for evaluating the carbon neutrality policy. For managers, this study provides theoretical support for strategically adjusting and designing supply chain structures in the context of advocating peak carbon dioxide emissions and carbon neutrality.

This paper is the first attempt that includes the supply chain structure factors into the impact of carbon neutrality policies on the stock market.

The purpose of this article is to determine the parameters of the preparation process for devulcanized and pyrolytic crumb rubber modified asphalt (DCRMA) and then study the rheological and microscopic properties of DCRMA through experiments.

In this study, a new preparation process for DCRMA was developed, then the penetration, softening point and viscosity tests were employed to determine the parameters of the preparation process. The crumb rubber (CR) solubility, Fluorescence microscopy (FM), Fourier Transform Infrared (FTIR) spectroscopy and thermogravimetric analysis tests were conducted to verify the devulcanized and pyrolytic effectiveness of the preparation process. Furthermore, dynamic shear rheometer and bending beam rheometer were used to characterize the high and low-temperature rheological properties of DCRMA.

The results showed that the penetration balanced the CR degradation and the virgin asphalt aging well and thus could be used as a main parameters control indicator. The CR solubility, FM and FTIR tests proved that the CR has been fully devulcanized and pyrolytic via the preparation process. The DCRMA exhibited better low-temperature and fatigue performance and lower rutting performance than the conventional crumb rubber modified asphalt (CRMA) with the same CR content. Finally, the time–temperature superposition principle could be employed for all binders in this study.

A new preparation process for DCRMA was developed.

A huge number of events can affect the operations of a long and complicated supply chain. This paper deals with the development of a supply chain risk mitigation index (SCRMI) based on a risk mitigation maturity framework. A comprehensive list of supply chain risks has been ascertained and segregated into risks faced at various supply chain echelons through a detailed literature review.

This paper is based on an extensive literature review and questionnaire to identify risks. order of magnitude analytic hierarchy process (OM-AHP) was used as the methodology to assess the prioritization of supply chain risks under two clusters, viz., Probability and severity leading to risk were tested in a distillery.

SCRMI was determined and used to categorize their maturity level in facing supply chain risks. Thus, organization can focus on improvements for their specific needs.

The model was tested in the distillery industry. It should be tested in other contexts with other methods to provide generalizability.

This research provides direction to managers for choosing risk mitigation strategies based on the global supply chain environment. SCRMI can be a performance metric for the supply chain managers.

The manufacturer's readiness to take action in the face of disruptions in the supply chain is a critical challenge in today's complex business environment and SCRMI framework is instrumental in such business environment.