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Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

Artificial Intelligence (AI) has revolutionized teaching and learning methods in higher education, especially in English language teaching and learning. This chapter contributes to the existing knowledge by exploring how AI has developed within the framework of teaching and learning of English, highlighting the challenges, dangers, and moral issues associated with its application. The typical classroom environment has significantly changed because of the integration of AI-powered tools and platforms in English instruction. Chatbots, automated grading systems, and language learning apps driven by AI have streamlined language education, increasing its effectiveness and accessibility. But these benefits accompany a variety of challenges and worries. Ethical concerns about data privacy, algorithmic biases, and the depersonalization of education arise as AI becomes more deeply ingrained in educational methods. Reliance on AI may inadvertently exacerbate educational disparities as long as learners' access to technology and its advantages remain unequal. In addition, significant thought must be given to the ethical ramifications of AI-generated content as well as the possible loss of human connection in language learning settings. This chapter examines these dangers and challenges and makes the case for a well-rounded strategy that maximizes AI's benefits while minimizing any potential downsides. Together, educators and legislators need to create moral guidelines that balance the potential of AI with human-centered learning experiences. To ensure responsible and fair AI integration and promote an inclusive learning environment that prioritizes students' holistic development while exploiting technology breakthroughs, comprehensive assessment of the associated obstacles, risks, and ethical issues is necessary.

Based on upper echelons theory and social contagion theory, we investigate how environmental leadership affects GIC via green human resource management (GHRM) and examine the moderating role of environmental climate.

We conduct hierarchical regression and use the bootstrap method to analyze the two-waved data from 317 Chinese manufacturers in order to verify the hypotheses.

The results indicate that GHRM mediates the impacts of environmental leadership on green human capital, structural capital and relational capital. In addition, environmental climate strengthens the positive impact of environmental leadership on GHRM.

Our study enriches the literature on GIC by uncovering the “black box” between environmental leadership and GIC, providing a logical framework opposite to mainstream GIC research, and expanding the boundary condition for GIC accumulation. This study provides more logical paths for enterprises and governments to increase the accumulation of GIC and promote green intellectual economy development.

This study aims to evaluate the behavioral predictors that affect tourists’ intentions to visit silver screen destinations.

The survey questionnaire was composed of multi-item measures, and a total of 432 questionnaires were collected by purposive sampling technique. Participants were asked about sensory stimuli, social stimuli, naturalistic stimuli, cultural stimuli, hospitality culture stimuli, cognitive responses, affective responses and behavior approach, which were evaluated using a seven-point Likert scale.

The authors discovered that cognitive and affective responses positively influence the tourists’ intention toward destinations with film-induced tourism, because tourists highly value unique and refreshing symbolic connotations of silver screen destinations, which are commonly predisposed to nostalgia and poetic on the scene.

The originality of this study and the theoretical value of the present research lies with revealing specific relations within the film-induced experienscape constructs that are based on the multistakeholder and multidisciplinary approach. Moreover, this study puts forward constructive suggestions for destination stakeholders in regard to how to market film-induced tourism that uses a multidisciplinary approach that is encompassed by experienscape constructs, which thereby reinforces the film-induced tourists’ experience and their behavior approach.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

According to G20, ‘Inclusiveness basically signifies the accessibility of the various resources (goods, services, and livelihoods) on a commercially viable basis to the economically vulnerable section of the society through making them part of the organizations' value chain as customers, retailers, distributors, and suppliers’. With the increased application of digital technology in every sphere of life, the concept of inclusiveness has moved to e-inclusiveness. So, the present chapter tried to investigate the conceptual journey from inclusiveness to e-inclusiveness from business and financial system aspects. Further, it presents an insight into how the e-inclusiveness aspect impacts the poverty level mainly from the developing country's perspective. The study also suggests that from the perspective of developed as well as developing economies, the public and private sector players strive to develop an effective financial system incorporating an inclusiveness aspect.

Protein-energy wasting is a common complication among patients with kidney failure undergoing dialysis. This study aims to develop a homemade oral nutrition supplement (ONS) to fulfill the energy and protein requirements of these patients.

Three formulations of homemade ONS were developed using soybean milk, whey protein isolates and canola oil. Two of these formulations were flavored with pineapple and honeydew juices, respectively. The energy and macronutrient contents were determined using proximate analyses, and mineral contents were determined using inductively coupled plasma optical emission spectroscopy. The acceptance of homemade ONS for five attributes, namely color, taste, odor, consistency and overall acceptability, was assessed using the nine-point hedonic scale.

The homemade ONS provided 198–212 kcal and 8.4–9.6 g protein per 100 mL, which were comparable to commercial products. Similarly, the sodium (45–65 mg/100 mL) and phosphorus (56–66 mg/100 mL) contents were on par with commercial products. However, the potassium content of homemade ONS was higher, ranging from 141 to 155 mg per 100 mL. The sensory evaluation indicated that the formulation added with honeydew juice had a similar degree of acceptance as the commercial ONS, while formulations containing pineapple juice and without added fruit juice were less favored.

A few studies have investigated the development of food products for individuals with kidney failure on dialysis. However, to the best of the authors’ knowledge, this is the first study to focus on developing a homemade ONS specifically tailored to meet the unique nutritional needs of hemodialysis patients. In addition, this research included a comprehensive assessment of the beverage’s nutritional content and sensory attributes.

The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences.

The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction.

The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction.

This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.

This study investigates the variables that impact initial public offerings (IPO) initial returns witnessed in Pakistan from 1996 to 2019 using pre-listing information variables, namely country-level institutional quality, sponsor ownership and pricing mechanism. IPO oversubscription is included as a moderating variable.

This research is motivated by the premise that the Pakistani IPO market is characterised by a broad range of institutional and regulatory frameworks. Multiple regression studies, i.e. ordinary least square (OLS) and quantile least square (QLS), were performed on 102 IPOs issued on the Pakistan stock market.

The present study findings suggest that the quality of public service, the independence of civil service from political influences and the legitimacy of government increase investors’ confidence in the prospects of companies, hence increasing the demand for IPO and initial returns. In addition, good regulatory quality enhances market transparency and lowers uncertainty, hence signalling high-quality IPOs and leading in substantial initial returns. The negative effect of the lock-up ratio on the initial return of an IPO is consistent with the risk-return trade-off theory, which asserts that the lock-up ratio indicates the quality of the IPO.

The results provide market regulators, policymakers, investors and underwriters with useful data for assuring proper subscriptions of issued shares, as these variables are crucial for company transparency and market efficiency. The findings will also help investors make better IPO subscription decisions.

The present study explains the important influencing factors of IPO initial return in the Pakistani market.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.