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Using the remarkable olfaction ability, insects can sense trace amounts of host plant volatiles that are notorious for causing severe damage to fruits and vegetables and in consequence the industry. The purpose of the paper is to investigate the interactions between olfactory proteins, odorant-binding proteins (OBPs) and host plant volatiles through the developed olfactory biosensors. It might be helpful to develop novel pest control strategies.

Using the successfully expressed and purified OBPs of the oriental fruit fly Bactrocera dorsalis, a biosensor was developed by immobilizing the proteins on interdigitated electrodes through nitrocellulose membrane. Based on electrochemical impedance sensing, benzaldehyde emitted by the host plants, such as Beta vulgaris, was detected, which could be used to investigate and analyze the mechanisms of pests’ sense of chemical signals. The relative decreases of charge transfer resistances of the sensor were proportional to the odorant concentrations from 10⁻⁷ M to 10⁻³ M. Meanwhile, the interactions between OBPs and benzaldehyde were studied through the process of molecular docking.

The paper provides a pest OBPs-based biosensor that could sensitively detect the host odorants benzaldehyde. Meanwhile, the most related amino acids of OBPs that bind to host plant volatiles can be distinguished with molecular docking.

An olfactory biosensor was developed to explore interactions and mechanism between the pest OBPs and benzaldehyde, which showed promising potentials for small organic molecule sensing. Simultaneously, it might be helpful for novel pest control strategies.

The purpose of this study is to develop a reliable finite element algorithm based on the transmission line method (TLM) to solve the nonlinear problem in electromagnetic field calculation.

In this paper, the TLM has been researched and applied to solve nonlinear iteration in FEM. LU decomposition method and the Jacobi preconditioned conjugate gradient method have been investigated to solve the equations in transmission line finite element method (FEM-TLM). The algorithms have been developed in C++ language. The algorithm is applied to analyze the magnetic field of a long straight current-carrying wire and a single-phase transformer.

FEM-TLM is more effective than traditional FEM in nonlinear iteration. The results of FEM-TLM have been compared and analyzed under different calculation scales. It is found that the LU decomposition method is more suitable for FEM-TLM because there is no need to repeatedly assemble the global coefficient matrix in the iterative solution process and it is not affected by the disturbance of the right-hand vector.

An effective algorithm is provided for solving nonlinear problems in the electromagnetic field, which can save a lot of computing costs. The efficiency of LU decomposition and CG method in FEM-TLM nonlinear iteration is investigated, which also makes a preliminary exploration for the research of FEM-TLM parallel algorithms.

The demand to reduce carbon emissions has become an increasingly important social factor due to the unprecedented impacts of climate change. However, most existing publications have focused on minimizing emissions during the operational phase of buildings. At the same time, there is a lack of comprehensive research conducted on carbon emissions, specifically during the construction phase. The purpose of this paper is to identify, review and classify current practices related to carbon emissions management in construction operations to gain greater insight into how to reduce and mitigate emissions and achieve more sustainable solutions.

This study reviewed the published literature on carbon emissions from construction. A total of 198 bibliographic records were extracted from the Scopus collection database and analyzed using Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). PRISMA is used as a basis for reporting possible trends, research methods and strategies used in published literatures. A total of 99 papers related to carbon emissions in the construction operations were further reviewed and analyzed. This review paper draws on existing research and identifies current carbon management patterns in construction projects.

Data indicated an upward trend in the number of publications in carbon emissions research during the last few years, particularly in 2015, 2017 and 2019. The most significant contributions to the domain were reported from China, Europe and the USA. This paper found that most studies conduct the Life Cycle Assessment (LCA) method to estimate carbon emissions. This paper found that the primary studies have focused on construction machinery and equipment emissions. The strategies such as establishing uniform standards for carbon emissions policies and regulations, equipment and logistic planning and low carbon design material will potentially impact carbon emissions reductions.

This paper provides information that will be beneficial for the construction industry to design and manage construction operations. It will also be of interest to those looking to reduce or manage construction emissions.

Although there is a diversity of current thinking related to the practical estimation and management of carbon emissions in construction projects, there is no consolidated set of keys of standardized carbon emissions management in practice. By assessing the existing paradigms of carbon assessment methods and tactics in the construction industry, this study contributed to the existing knowledge base by providing insights into current techniques in the construction sector for monitoring and mitigating emissions.

The literature reports inconsistent findings about the effects of social media usage (SMU). Researchers distinguish between active and passive social media usage (ASMU and PSMU), which can generate different effects on users by social support and social comparison mechanisms, respectively. Drawing on social presence theory (SPT), this study integrates an implicit social presence mechanism with the above two mechanisms to explicate the links between SMU and seniors' loneliness.

Data were collected from a field study by interviewing seniors living in eight aging care communities in China. Loneliness, social media activities and experiences with social media in terms of online social support (OSS), upward social comparison (USC) and social presence (SP) were assessed. Factor-based structural equation modeling was used to analyze the data.

OSS can mediate the relationship between ASMU and seniors' loneliness. Moreover, SP mediates between ASMU, PSMU, and seniors' loneliness, and between OSS, USC and seniors' loneliness. OSS mediates the relationship between ASMU and SP, and USC mediates the relationship between PSMU and SP.

This study shows that social media can alleviate seniors' loneliness, which could help relieve the pressures faced by health and social care systems. Social presence features are suggested to help older users interact with social health technologies in socially meaningful ways.

This study not only demonstrates that SP can play a crucial role in the relationship between both ASMU and PSMU and loneliness, but also unravels the links between SP and OSS, as well as USC.

The purpose of this paper is to explain how individuals form microblogging habits and why they continue to use microblogs from the perspective of direction social networks.

Drawing on the social network theory and the social presence theory, the authors develop a theoretical framework to explain how individuals form microblogging habits and why they continue to use microblogs. To test the proposed model and examine its external validity, the authors collected data from two microblogs: Twitter and Sina Weibo.

Satisfaction and habit have a significant influence on microblogging continuance intention. Whereas, users’ microblogging habits are developed by two key factors – satisfaction and frequency of past behavior – that are further determined by social presence and social network centrality.

Larger sample size with diverse populations is highly recommended for future studies. In addition, exploring the distinct technical functionalities of microblogs when conceptualizing habit formation would be of benefit in future studies.

In this study, it was found that social presence increases both the satisfaction of users and the frequency of past use behavior. Hence, microblog designers should provide users with greater freedom to modify the form and content of their interface, and enable these modifications to be visible in real time to increase the interactivity of microblogs.

In contrast to past studies that have largely neglected the impacts of the directed social network structure, this study aims to focus on microblogging continuance intention from the directed social network perspective. The results from two independent data sets converge on the conclusion that users’ continuance intention to use is affected by both their conscious evaluations (i.e. satisfaction) and unconscious reactions (i.e. habit).

Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.

First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.

The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.

This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.

This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.

The purpose of this paper is to discuss the influence of different aspects of top management team (TMT) functional background on short-term performance, long-term performance, innovation performance and oversea performance separately. This research aims to verify whether the social categorization theory and information and decision-making theory are applicable in listed companies of China’s information technology (IT) industry so as to provide key theoretical references for TMT enhancement ad corporate performance improvement.

This paper takes A-share listed companies in Shanghai Stock Exchange and Shenzhen Stock Exchange as its study subjects, and it chooses the data from 2004 to 2010 in all of the 105 companies in IT industry in terms of the classification of Wind Database. The stepwise multiple regressions were run utilizing the regression program in Statistical Product and Service Solutions (SPSS).

The research results show that the social categorization theory can better explain TMT’s influence on corporate performance. TMT functional heterogeneity does not contribute to improving corporate performance and shows significant negative influence on short-term performance and innovation performance in particular. Among the three basic functional backgrounds, TMTs dominated by “throughput” backgrounds show significant positive influence on short-term performance, long-term performance, innovation performance and overseas performance, and the influence turns out to be the largest among these three backgrounds. In terms of the three special professional experiences, top executives with overseas backgrounds have significant positive influence on all of short-term, long-term, innovation and overseas performances. Externally hired executives, however, would impede corporate innovation development, while those with government background would increase corporate overseas performance.

This paper analyzes the relationship between TMT functional background and corporate performance in a comprehensive way for the first time and then takes the lead in considering the dynamics and complexity of corporate performance as well as discussing the influence of TMT functional background on four corporate performances. This study not only supports the effect that the social categorization theory has on TMTs but also offers some inspirations on the development of China’s IT companies.

Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.

This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.

Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.

The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

This study aims to complement existing studies by investigating the impact of different corporate social responsibility (CSR) information disclosed by peer listed stars (i.e. governance information [GI] and output information [OI]) on focal firms’ responsive CSR (RCSR) and strategic CSR (SCSR) practices. The authors also investigate the influence of different boundary conditions (i.e. founders’ social status [SS] and industry pollution intensity).

Based on the listed stars of 16 industries and their 4,096 private peers in China, the authors use the least squares method and logistic regression models to analyze the data set.

The results indicate that the GI of peer listed stars can only positively affect firms’ RCSR behavior. The OI of peer listed stars has a positive effect on firms’ SCSR behavior while negatively affecting firms’ RCSR behavior. The SS of focal firms’ founders and their interaction with the industry’s pollution level strengthen the abovementioned positive relationships while weakening the negative ones.

This study provides insights into the role of listed stars in influencing peer firms’ CSR activities, offering important practical implications for both policymakers and managers.

This study extends the recent discussion on peer effects of CSR by elucidating the peer star effect on CSR and confirms that firms may adopt heterogeneous CSR practices to achieve sustainable growth by investigating peer firms’ different responses to their listed stars’ different CSR information. Moreover, by introducing the SS of founders and the pollution intensity of the industry as boundary conditions, this study enriches the research context on CSR activities.

The purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.

To analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.

The analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.

This paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.