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This is a literature survey paper. The purpose of this paper is to focus on the latest developments in textual analysis on China’s financial markets, highlighting its differences from existing works in the US markets.

The authors review the literature and carry out an experiment of sentiment analysis based on a small sample of Chinese news articles.

Based on the experiment of sentiment analysis, there is limited evidence on the association between sentiment and other contemporaneous or future returns.

The supply of financial textual information has grown exponentially in the past decades. Technological advancements in recent years make the programming-based analysis an effective tool to digest such information. The authors highlight the use of credible textual information and discuss directions of research in this important field.

The amount, type and addition conditions of additives of lubricants should be continuously adjusted to obtain appealing performance. To obtain the optimal pretreatment parameters and reduce the cost of time-consuming experiments, the purpose of this paper is to establish an optimal back propagation neural network (BPNN) model combined with genetic algorithm (GA) in this work.

Using trimethylolpropane trioleate as the base oil and three types of phosphorus compounds as additives, 25 sets of lubricant formulas were designed regarding lubricant performances of average friction coefficient, average spot diameter, disk wear volume and extreme pressure. The data set was used for training and learning of BPNN and then combined with GA to optimize BPNN with continuously optimization by adjusting various parameters.

Comparing prediction data of BPNN with actual test data, correlation coefficients were above 90%, indicating that the model could accurately predict the performance of lubricants. When combined with GA, all performance errors were less than 5%, indicating that BPNN could be optimized by GA to obtain an accurate combined model for prediction of lubricant performance. The best additive formula with excellent performances was obtained from the BPNN–GA model.

This work developed a new method to study lubricant compounding. The combined model was expected to provide a theoretical basis and guidance for the compounding optimization of lubricant additives with high efficiency and low cost and to expand the scope to practical applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0165/

Iron deficiency anemia (IDA) is the most common form of anemia in the world, affecting children, women and the elderly, while also being a common comorbidity in several medical conditions. Several studies have suggested a possible association between IDA and neurological dysfunction. Epilepsy, one of the common neurological disorders, has an unknown association with IDA. This pa per aims to investigate whether there is a causal relationship between IDA and epilepsy using a two-sample Mendelian randomization (MR) design.

This paper obtained summary data on IDA and epilepsy from the FinnGen consortium. Genetic variants significantly associated with IDA were used as instrumental variables (IVs). Epilepsy, focal epilepsy and generalized epilepsy were the outcomes. This paper used inverse variance weighted (IVW) as the primary estimate, and other MR methods were used as supplementary measures. Sensitivity analysis was also performed to assess heterogeneity and pleiotropy.

IVW estimates genetically predicted a causal relationship between IDA and the risk of epilepsy [odds ratio (OR), 1.15; 95% confidence interval (95% CI), 1.05–1.26; p = 0.002] and focal epilepsy (OR, 1.978, 95% CI, 1.58–2.48, p ≤ 0.0001), while no significant causal relationship was found with generalized epilepsy (OR, 1.1, 95% CI, 0.94–1.3, p = 0.24). There was no evidence of horizontal pleiotropy and heterogeneity in the sensitivity analysis.

This two-sample MR study found that IDA has a negative effect on the development of epilepsy. Clinical control of IDA may be helpful in the prevention of epilepsy. There is a need for further studies to explain the underlying mechanisms of this association.

We study the potential effects of pension underfunding on corporate investment, financial constraints and improved employee bonding using 10 Pacific-Basin countries (including the United States, Australia, and eight Asian countries) at heterogeneous economic development stages and different regulatory environments. We document that corporate pensions are significantly underfunded in most countries of our sample in the period of 2001–2017, when interest rates were ultralow in most countries. In addition, firms from countries with stronger employee protection and more generous retirement benefits tend to show higher levels of underfunding in their defined benefit (DB) pension plans. To the extent of pension underfunding imposing constraints on corporate investment, we find that firms in these countries can face more constraints on investment when their pension is underfunded.

The purpose of this study is to examine the effectiveness of advertisements with different female role portrayals in a second-tier city with “first-class opportunities.” Chinese girls and women represent a huge market for personal as well as household goods.

An experimental study was conducted using a convenience sample of 216 male and female participants aged 17-21 years in Changchun, China. Participants were asked to respond to print advertisements using traditional and modern female images including housewife, cute female, female with classical beauty, sporty, career-minded and neutral (tomboy).

Results revealed that female participants responded more favorably toward advertisements using female images than male participants. There was no difference in the responses to the six different female images among both male and female participants.

Young consumers in China are not sensitive to the different female images used in the print advertisements. Advertisers can, therefore, enjoy flexibility in the selection of female gender roles for advertisements.

Little is known about how marketers and advertisements can best communicate with young consumers in China using advertisements with different female images. This study fills this literature gap.

Because of increasing wealth inequality, China has been confronted with resentment against the rich (referred to hereafter as RAR or Choufu in Chinese), which is a growing concern owing to its potential to foment social conflict. Drawing on social comparison and deonance theories, this paper aims to provide theoretical insights into RAR within the Chinese context and to develop an RAR scale. Following spillover theory, the attitudinal and behavioral outcomes of RAR in organizational settings will be explored.

This research consists of two studies. Study 1 conceptualizes RAR and develops an RAR scale by using three separate samples. Exploratory and confirmatory factor analyses are conducted to establish scale reliability and validity. Study 2 uses hierarchical linear regression analysis to test whether employees’ RAR attitude spills over from the societal to the organizational setting.

Results suggest that RAR can be conceptualized as two distinct but related dimensions – emotional RAR and moral RAR. These two forms spill over to the workplace, influencing employees’ work attitudes and behaviors. Emotional RAR relates negatively to life satisfaction and prosocial organizational behaviors and positively to unethical organizational behaviors. Moral RAR relates negatively to pay satisfaction and positively to prosocial behaviors.

This research suggests that RAR has spillover effects from societal to organizational settings and demonstrates that a more robust understanding of employees’ workplace experience requires acknowledging social experiences, such as conflicts beyond the workplace.

This research contributes to the conflict management literature by exploring RAR as a negative attitude that serves to potentially ignite social conflict. It not only develops a theory-grounded, conceptual RAR model and a reliable RAR scale but also for the first time explores RAR attitudinal and behavioral outcomes beyond the social domain. This study serves as a meaningful touchstone for future research to incorporate social attitudes into organizational behavior research.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

This study examines the relationship between domain‐based life satisfaction (LS) and subjective well‐being (SWB) as well as the role of spiritual well‐being as a moderator. Domains of LS include family cohesion, social connectedness, career success, and self‐esteem.

A survey was completed by 145 full‐time Hong Kong Chinese employees working in a variety of jobs and organizations.

Multiple regression analyses show that career success, social connectedness, and self‐esteem are associated with both psychological and physical well‐being. Spiritual well‐being moderated the relationship between career success and psychological well‐being. The relationship is stronger for low than for high spirituality.

All data were self‐reported and collected at one point in time. Thus, common method variance may be an issue and causal inferences are not warranted.

Domain‐specific LS and spiritual well‐being appear to be related to employees' well‐being. Managers and human resources professionals may need to adopt a more holistic approach to staff development.

The current study indicates that domain‐specific LS improves the explanation of variations in well‐being. Implications of these findings, the limitations of the study, and directions for future research are discussed.

The purpose of this paper is to analyze the historical pattern of environmental cost due to grain production in China and to provide further implications of technologies and policies for the transformation of China’s agricultural development toward sustainable intensification.

The data sets about grain production, arable land and chemical fertilizer use in China were collected from FAO, NBSC, and IFA. Greenhouse gas emissions were estimated using life cycle assessments. The policies concerning grain production and the environment were collected from the Ministry of Agriculture, and the State Council of China.

China has produced enough food to feed its growing population, but has neglected the resource-environmental costs of grain production since 1978. Consequently, China’s grain production is always accompanied with a high cost of resource and environment sustainability. However, from 2006 to 2015, the growth rate of grain production has surpassed that of chemical fertilizer consumption, resulting in improvement in nutrient use efficiency and decreasing trends of environmental cost for grain production. This could be partially attributed to technology innovations, such as Soil-Testing and Fertilizer-Recommendations (STFR), soil quality and crop management improvement, and so on, and policy supports (policies of STFR, soil quality improvement, and high-yield construction). This indicated that China’s grain production is starting to transform from high-input and high-output model to “less for more.”

This study is the first to determine the detailed, historical role of technological innovation and agri-environmental policy on the sustainability of grain production in China. The findings should have significant implications for technology and policy for the transformation of China’s agriculture development to sustainable intensification.