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The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.

This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context.

This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography.

This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency.

In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

In the 21st century, libraries are experiencing a significant decline in users due to shifting reading habits and the impact of technology, necessitating library transformation and a heightened emphasis on library marketing. Special libraries, in particular, rely heavily on patrons for survival, often due to their private ownership and limited resources. This paper aims to discuss the aforementioned objectives.

This paper examines the Taste Library, a special library in Hong Kong, and analyzes its current practices based on an interview with its founder, website content, and social media presence. The 7Ps Marketing Mix model is employed to assess the strengths and weaknesses of the library's current market position.

The Taste Library's existing practices exhibit limitations in attracting young patrons. To address this issue, we propose marketing strategies focused on enhancing social network presence, offering digitized content, and engaging in school outreach.

By concentrating on youth marketing, this study offers valuable insights for special libraries in developing strategic plans for transitioning and maintaining sustainability.

Few studies concentrate on marketing small special libraries, particularly in the East, within today's digitized economy.

This study addresses the empirical results of the spillover effect with export as the primary economic activity that enhances local businesses' total factor productivity (TFP). A learning mechanism is expected to be generated and used as the basis for the policy implication.

This study adopted the Cobb–Douglas function and multiple estimation approaches, including the generalized method of moments, the Olley–Pakes and the Levinsohn–Petrin estimation techniques. The findings were estimated based on the panel data of a Vietnamese local businesses survey conducted by the General Statistics Office of Vietnam (GSO) from 2010 to 2019.

The results showed that the highest TFP belongs to the businesses in the Southeast region, the Mekong Delta region, the mining industry and the foreign-invested enterprises. The lowest impacted TFP are businesses in the Northwest region and agricultural, forestry and fishery sectors. In addition, the estimated results also show that the positive spillover effect on TFP is shown through forward and backward linkage. The negative spillover effect is expressed through the backward and horizontal channels.

This study offers original empirical evidence on the learning mechanisms via which exports contribute to productivity improvement in a developing Asian economy, so making a valuable contribution to the existing academic literature in this domain. The findings of this research make a valuable contribution to the advancement of understanding on the many ways via which spillover effects manifest such as horizontal, forward, backward and supplied-backward linkage.

The study's findings indicate that it is advisable for governments to give priority to the development and improvement of forward and supply chain linkages between exporters and local suppliers. This approach is recommended in order to optimize the advantages derived from export spillovers. At the organizational level, it is imperative for enterprises to strengthen their technological and managerial skills in order to efficiently incorporate knowledge spillovers that originate from overseas partners and trade counterparts.

This study sheds new evidence on the export spillover effect on productivity in emerging economies, with Vietnam as the case study. The paper contributes to the research's originality by adopting novel methodological aspects to estimate local businesses' impact on total factor productivity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0373

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

This paper studies the relation between corporate tax evasion, job creation and optimal fiscal policy. Job creation depends on firms’ profits since firms open (“create”) new jobs when profits increase. In turn, firms’ profits depend on incentives (rewards) and disincentives (penalties) to comply with tax rules. Hence, any fiscal policy to combat tax evasion also has repercussions on job creation.

This paper is both theoretical and empirical. From a theoretical point of view, a modified and extended version of the search and matching model of the labor market is used. In this framework, moreover, the welfare function of workers and firms is closely related to the job creation condition. Empirically, a panel analysis of a system of two simultaneous equations that covers 54 countries (both developed and developing) and four years (2018–2021) is carried out.

The paper finds that anti-tax evasion policies should be related to job creation policies. Also, anti-tax evasion and job creation policies change according to the extent of tax evasion in the economy. Precisely, when tax evasion is widespread, a lower tax burden (tax cuts or provision of fiscal rewards) requires tighter tax audits, whereas, where most people comply with tax rules, a decrease in tax audits is possible.

The empirical analysis supports the model-generated theoretical relationships. Eventually, therefore, the optimal fiscal policy suggested by this work can counteract corporate tax evasion and, at the same time, reduce the firm’s tax burden, thus promoting job creation.

As far as we are aware, this is the first paper that considers the close and direct link between fiscal policy, corporate tax evasion and job creation.

This chapter examines the significant role of non-fungible tokens (NFTs) and blockchain technology in fostering a sustainable economy in the metaverse. Blockchain allows the saving and transfer of decentralized and secure data. As a primary component of the metaverse economy, NFTs are distinct and secure virtual assets saved on the blockchain. These assets facilitate possessing, trading, and monetizing digital assets. These advancing technologies have also revolutionized the method by which creators and artists test and exchange their digital work, introducing a novel period of ownership and value in the digital realm. However, the negative environmental effects of some blockchain technologies constitute a considerable constraint, pushing a shift to a sustainable economy. Platforms like The Sandbox have implemented initiatives to address environmental concerns. As a case study, The Sandbox play-to-earn model with tokenized assets showcases its ability to create value and encourage user participation. It shows the ability of NFTs and blockchain to support a sustainable economy.

This study aims to determine the antecedents that influence attitudes towards the use of environmentally friendly household appliance products and consumers' green purchase intention among consumers in Harare, Zimbabwe.

Data were collected from 329 consumers in Harare, Zimbabwe's commercial capital who were served from five using a structured questionnaire via an online web-based cross-sectional survey. Hypothesised relationships were tested through structural equation modelling with the aid of Smart PLS software.

Green product awareness, social influence, perceived benefit and attitude towards green appliances were found to have a significant positive effect on green purchase intention.

The study's findings may not be generalised to other contexts as sample data was only collected in Zimbabwe. Complementary cross-sectional research studies can be done in other parts of the world to enable cross-cultural comparisons and methodological validations.

The green appliance and energy saving practices are vastly growing, with many multinational appliance companies introducing green products within their product lines and adopting the concept of sustainability through modifications in production, design and consumption of household appliance products that encompass fewer harmful consequences on the environment in response to their concerns about the scarcity of natural resources, environmental well-being and the potential detriment of future generations.

Notwithstanding the limitations of the current study, the results have the potential to contribute to an improved understanding of influence attitudes towards the use of environmentally friendly household appliance products.

This research explores support vector machine (SVM) with Gaussian radial basis function kernel (RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation re-examination decisions of China invention patents, it is beneficial to support patent monetization for corporate intellectual capital.

There were 8,666 China invention patents with their existing invalidation re-examination decisions during 2000∼2021 chosen to conduct classification model training and prediction for the accuracy of invalidation re-examination decisions through SVM with RBF. Statistical significance was performed by ANOVA to identify indicators for these invention patents selected in this research. These selected 8,666 China invention patents were divided into two groups based on their invalidation re-examination decisions during 2000∼2021 in Table 1, which Group 1 included 5,974 invention patents with all valid or partially valid claims, and Group 0 included 2,692 invention patents with all invalid claims. Thereafter, each group was further divided into sub-groups based on 13 major regions where the applicants filed invalidation re-examination. The training sets for Group 1, Group 0 and the sub-groups were selected based on the patent issued in January, February, April, May, July, August, October and November; while the prediction sets were selected from the invention patents issued in March, June, September and December.

The training and prediction accuracies were compared to the existing invalidation re-examination decisions. Accuracies of training sets were ranged from 100% in region 7 (Beijing) and region 9 (Shanghai) to 95.95% in region 1 (US), and the average accuracy of invalidation re-examination decisions was 98.95%. While the accuracies of prediction sets for Group 1 were ranged from 100.00% in region 7 (Beijing) to 90.78% in region 13 (Overseas-others), and the average accuracy of classification was 95.96%, this research’s outcomes confirmed the purpose of applying SVM with RBF to predict the patentability sustainability.

This research developed an empirical method through SVM with RBF to predict patentability sustainability which is crucial for corporate intellectual capital on patents. In particular, the investments on patents are huge, including the patent cultivation and maintenance, developments into products or services, patent litigations and dispute managements. Therefore, this research is beneficial not only for corporation, but also for research organisations to perform cost-effective and profitable patent strategies on intellectual capital.

The primary objective of this paper is to determine the factors that affect the entrepreneurship propensity of students undergoing compulsory entrepreneurship education courses at various universities.

A research instrument was developed and implemented on a sample of 380 students who were offered compulsory entrepreneurship education courses at six major universities in the Jammu and Kashmir region of India. The study employed multiple cross-sectional designs with a simple random sampling technique to gather data. The collected data was subjected to descriptive statistics and structural equation modeling using SMART-PLS (Version 4).

The findings reveal that conceptualization, opportunity identification and implementation are the three antecedents of entrepreneurship propensity. The results indicate that the conceptualization factor is one of the most important predictors of entrepreneurship propensity, followed by opportunity identification, whereas implementation through education has the weakest influence on students' entrepreneurship propensity.

This research provides important insights to universities for designing and developing entrepreneurship courses that can foster the start-up culture. The results will be helpful for policymakers to devise various programs to boost entrepreneurship.

The study integrated the theories of planned behavior and human capital to evaluate the effectiveness of entrepreneurship courses at the university level. The three factors, namely, conceptual factors, actualization factors and implementation factors of entrepreneurship propensity are under-researched.