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This paper aims to reduce environment pollution caused by benzotriazole. The authors chose one of the best inhibitors from 2-aminobenzimidazole, 2-methylbenzimidazol, 2-mercaptobenzimidazole and benzimidazole in combination with benzotriazole.

The electrochemical measurement indicated that 2-methylbenzimidazol had the best inhibition behavior. Then, it was mixed with benzotriazole. Techniques such as field emission scanning electron microscopy, atomic force microscopy, Raman spectroscopy and optical contact angle measurements were used.

The results showed that the inhibition efficiency was up to 99.98%, when the mixture concentration was 20 mmol/L and the molar ratio 1:1.

1-benzotriazole was mixed with 2-methylbenzimidazol for the first time. During the exist of methyl, 2-methylbenzimidazol has the better inhibition; this point was ignored by researchers.

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.

Public housing delivered in the Canadian Arctic has been ill-adapted to the social and cultural realities of Inuit communities and to northern climate. Inadequate consultation has resulted in dwellings that fails to adapt to the needs of growing families, impedes the ability of residents to engage in land-based activities, and is inappropriate for local climate. This paper examines how a user-led, flexible approach can help tailor the design of new public homes to the needs of the local housing authority and future occupants. Flexibility is incorporated into the pre-occupancy, post-occupancy and refurbishment stages of the units life-cycle, ensuring that they can be easily adapted over time. A menu of interior and exterior design components has been developed for selection by all stakeholders. The redevelopment of Widow's Row, in Iqaluit, Nunavut demonstrates how appropriate design can play a pivotal role in addressing the housing crisis.

This paper aims to solve the disadvantages of content-based domain ontology (CBDO) and metadata-based domain ontology (MDO) and improve organization and discovery efficiency of library resources by resource ontology (RO).

The paper constructed an RO model. Methods of informetrics are utilized to reveal semantic relationships among library resources. Methods of ontology, ontology-relational database mapping (O-R mapping) and relational database modelling are utilized to construct RO. Take author co-occurrence for example, the paper demonstrated the capability of RO model.

RO not only revealed the deep-level semantic relationships of metadata of library resources but also realized totally computer-automated processing. RO improved the efficiency of knowledge organization and discovery.

Semantic relationships revealed by RO are limited to simple metadata, which makes it difficult to reveal fine-grained semantic relationships. Ongoing research focuses on the revelation of semantic relationships based on the title and abstract.

The paper includes implications for utilizing methods of Informetrics to construct ontology.

This paper proposed a standardized process of ontology construction in library resources. It may be of potential interest for anyone who needs to effectively organize library resources.

In order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.

Firstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.

The results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.

The estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

This paper aims to implement quality of service(QoS) dynamic optimization for the integrated satellite-terrestrial network(STN) of the fifth-generation Inmarsat system(Inmarsat-5).

The structure and operational logic of Inmarsat-5 STN are introduced to build the graphic evaluation and review technique(GERT) model. Thus, the equivalent network QoS metrics can be derived from the analytical algorithm of GERT. The center–point mixed possibility functions of average delay and delay variation are constructed considering users' experiences. Then, the grey clustering evaluation of link QoS is obtained combined with the two-stage decision model to give suitable rewards for the agent of GERT-Q-learning, which realizes the intelligent optimization mechanism under real-time monitoring data.

A case study based on five time periods of monitoring data verifies the adaptability of the proposed method. On the one hand, grey clustering based on possibility function enables a more effective measurement of link QoS from the users' perspective. On the other hand, the method comparison intuitively shows that the proposed method performs better.

With the development trend of integrated communication, STN has become an important research object in satellite communications. This paper establishes a modular and extensible optimization framework whose loose coupling structure and flexibility facilitate management and development. The grey-clustering-based GERT-Q-Learning model has the potential to maximize design and application benefits of STN throughout its life cycle.

This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

The purpose of this study is to examine the impact of chief executive officer (CEO) hometown identity on company audit fees in the Chinese setting.

This study uses data from Chinese public companies in the Shanghai Stock Exchange and the Shenzhen Stock Exchange for the period 2008–2019. This study investigates the impact path of CEO hometown identity on company audit fees and further examines the moderating role of internal and external governance level.

This study finds that CEO hometown identity is significantly and negatively related to company audit fees. In addition, CEO hometown identity can reduce audit fees by alleviating agency risk and litigation risk. Moreover, the negative effect of CEO hometown identity on audit fees is more pronounced in companies with a higher percentage of institutional investors shareholding and more analysts tracking quantity.

This study may provide new references for executives’ selection, auditors’ optimization decisions and regulators’ information disclosure system.

This study contributes to the literature by exploring the effect of CEO hometown identity on audit fees in the context of China.

The growing number of firms leveraging social media ads highlight the urgent need for firms to understand social media ads and their effects on consumer perceptions and attitudes. This research examines whether and how different types of social media ads exert influences on ad engagement and how consumers’ psychological characteristics including regulatory orientation and lay rationalism moderate the influence of social media ads on ad engagement.

This research conducts three quasi experiments featuring two types of social media ads – interactive and transactional – to collect empirical evidence. Results are analyzed using ANOVA and Process in SPSS.

This research reveals that interactive ads lead to higher perceived enjoyment and perceived responsiveness, which further lead to higher ad engagement. Promotion focus moderates the effect of social media ads (interactive vs transactional ads) on perceived enjoyment and perceived responsiveness, and lay rationalism moderates the effect on perceived responsiveness, while prevention focus has no such effect.

First, this research establishes the connection between ad types and ad engagement in social media, which offers a new perspective to understand ad engagement. Second, it explores the underlying mechanisms of processing different types of social media ads. Third, it justifies the moderating effects of consumers’ regulatory orientation and lay rationalism on the effects of ad types on ad engagement, providing the first evidence on the moderating role of consumers’ lay rationalism in ad processing. This research helps firm marketers to fine-tune their social media ads according to consumer characteristics.