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The design method of high-resolution capacitor arrays was proposed to improve the precision of successive approximation register (SAR) analog-to-digital converters (ADCs) without calibration and optimize the circuit area.

According to calculation of equivalent series capacitors and change of voltage at the comparator input node, two three-stage structures of capacitor arrays and a general design flow of the multi-stage capacitor arrays were presented. Non-ideal factors on the capacitor arrays were analyzed, and the applications of the two structures were explained based on the capacitor mismatch.

A multi-stage capacitor array for 16-bit SAR ADCs was implemented. The simulation result shows that its nonlinear error was less than 0.3LSB with no gain error and the sampling capacitance accounted for 92.42% of the total capacitance. Effects of capacitive parasitic and mismatch on capacitor arrays were confirmed.

The proposed method focused on capacitor arrays design of high-resolution SAR ADCs. It effectively reduced nonlinear errors, improved SNR and optimized the area of SAR ADCs. The design method was suitable for SAR ADCs with different resolutions to improve their precision.

Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones).

The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling.

By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective.

This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification.

Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

This study aims at developing a better understanding of the relationship between network embeddedness and incremental innovation capability and further examines the moderating effect of open innovation.

This paper adopts hierarchical regressions to validate the theoretical model and collect the patent data of the top 54 firm patentees in the smartphone industry as empirical sample. Using patent citation network data, this paper estimates the relationship between open innovation, network embeddedness and incremental innovation capability.

This paper empirically shows that structural embeddedness exerts a negative effect on incremental innovation capability, while relational embeddedness is positively related to incremental innovation capability. And open innovation strengthens the relationship between network embeddedness and incremental innovation capability.

This paper shifts the focus of the determinants of incremental innovation capability from internal factors to the external network features by exploring the linkage between network embeddedness and incremental innovation capability. A counterintuitive conclusion is that structural embeddedness shows a negative effect on firm's incremental innovation capability. Furthermore, in contrast to most previous studies, which only focus on the direct effect of open innovation on the firm's incremental innovation capability, our study examines the moderating effect of open innovation on the relationships between network embeddedness and incremental innovation capability. At last, the results provide practical guidance for firms to occupy the beneficial network positions and adopt appropriate open innovation strategies to improve their incremental innovation capability.

This paper aims to review the literature on carbon trading from the perspective of evolution, finds out the evolution path of these literatures and gives out the future research hotspots in this field.

Uses visualization tools (CiteSpace and HistCite) to systematically categorize the literature on carbon-trading schemes in the Web of Science core collection from 1998 to 2018, comprehensively analyzes carbon-trading schemes from four dimensions, namely, discipline evolution, keyword evolution, citation cluster evolution and citation path evolution.

Research on carbon-trading schemes has a specific development and evolution path along four dimensions, namely, in the discipline dimension, the largest change lies in the mathematics pointed to by at least four different disciplines; the keyword evolution dimension shows a gradual deepening emphasis on coordinated development; citation clusters identify three major clusters – carbon prices, China’s carbon trading, carbon market and supply chain; and citation paths identify three major evolutionary paths, the most important of which shows that “What affects carbon price?” has changed to “What is the impact of carbon prices?”

Reveals the evolution path of carbon trading research studies and proposes four possible development directions for carbon-trading scheme research, which is helpful for future carbon trading-related research and serves as a reference for the promotion of and improvements in carbon-trading schemes.

This paper aims to obtain the evolution law of dynamic performance of CR400BF electric multiple unit (EMU).

Using the dynamic simulation based on field test, stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers were tested. Stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to research the evolution law with running mileage of dynamic index of CR400BF EMU.

The results showed that stiffness and damping coefficient subjected to normal distribution, the mean and variance were computed and the evolution law of stiffness and damping coefficient with running mileage was obtained.

Firstly, based on the field test we found that stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers subjected to normal distribution, and the evolution law of stiffness and damping coefficient with running mileage was proposed. Secondly stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to the research to the evolution law with running mileage of dynamic index of CR400BF EMU.

As technical barriers gradually become the important tools of trade protection, it is important to understand whether intensified enforcement of border controls is adopted as a hidden tool of trade protectionism and differs across periods and industries.

This article applies a panel structural vector autoregression (PSVAR) model to investigate the potential role of trade protectionism motives in Food and Drug Administration (FDA) import refusals on China's agricultural exports, utilizing newly constructed monthly data at the industry level.

The results show that import refusal is mainly driven by the inspection history, highlighting the importance of the intrinsic product quality and maintaining an excellent inspection history in border inspection. The novel finding is that US employment contractions would also lead to a small increase in FDA import refusals, especially those taking place within ten months and made without sampling tests. Such an association is driven by industry-specific employment shocks and becomes stronger after the financial crisis. It is also more evident in industries where the US lacks competitiveness against China, being manufactured without mandatory safety regulations, and with negative skewness of employment growth.

This research is one of the preliminary attempts to understand whether the de facto border controls are worked as a hidden tool of protectionism to agricultural products, and what the specific trajectory and duration of the impacts at the monthly level. This study provides empirical evidence showing the role of protectionism motives in FDA import refusals and is heterogeneous across industries, which generate new insights and policy implications to predict and cope with additional barriers on agricultural trade.

This study aims to provide a model to predict the service life of a thick organic coating.

A series of thin coating films are rapidly tested under the same exposure condition as the thick coating in its service condition by means of electrochemical impedance spectroscopy, scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction.

The validity of the model is successfully verified. The long-term protectiveness or service life of a thick organic coating can be rapidly predicted.

The prediction model does not require long-term experiments or any test that may alter the degradation mechanism of the thick coating.

The purpose of this paper is to verify the relationship between the features of social media and knowledge sharing, and to examine how ambient awareness mediates this relationship.

An experiment is designed to stimulate the knowledge work in a famous Chinese business college and 156 valid samples were obtained. AMOS was used in this paper to examine the theoretical model.

There is a correlation among features of social media, ambient awareness and knowledge sharing. Surprisingly, network translucence, which indicates individuals’ meta-knowledge of others’ connections, has no influence on knowledge sharing. Although this is inconsistent with conjecture of the existing literature, it can be well explained by the phenomenon in real life, such as privacy setting in social media.

For employees who use social media to promote knowledge sharing within organizations, this study reminds them of the importance of ambient awareness. For managers, this study can give them some suggestions to make employees take full advantage of social media to achieve optimal benefits of knowledge sharing, thus improving organizational performance and innovation. For social media designers, they can make social media more useful in knowledge work by improving its specific features.

This paper proposes that ambient awareness is the mediator of the effect path between communication and knowledge sharing. And the status perception of coworkers’ exchanging information is closely related to knowledge sharing.

Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant.

A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry.

The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants.

This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology.

This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

This paper aims to explore the effect of fairness concerns on supply chain members' optimal decisions and profits, to compare their profits under different policies, and to investigate the impact of each policy on members, consumers, and the environment with fairness concerns.

Considering government policies and fairness concerns in recycling management, this paper develops five recycling and remanufacturing decision models (anarchy policy model, reward-penalty mechanism model, recycling investment subsidies model, government tax model, and fund subsidy system model). In each model, the manufacturer and the online platform form the Stackelberg game. This research further discusses comprehensive environmental benefits and consumer surplus under five scenarios.

First, the fairness concerns of the online platform inhibit the recovery rate and supply chain members' profit while increasing the platform's utility. Second, fairness concerns increase the profit gap between the manufacturer and online platform, and the higher the degree of fairness concerns, the greater the profit gap; however, the four policies reduce the profit gap. Finally, when there are fairness concerns, environmental taxes damage the interests of supply chain members and consumers, but are most beneficial to the environment; recycling investment subsidies are on the contrary; the fund subsidy system depends on the relative size of the treatment fund and the subsidy fund.

This paper provides useful insights on how to regulate government policy to improve supply chain management with fairness concerns.