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This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

This study aims to explore the critical application areas of radio frequency identification (RFID) technology for sustainable buildings.

The quantitative research approach was adopted through a structured questionnaire administered to relevant stakeholders of construction projects. The data collected were analysed with the exploratory factor analysis, relative importance index (RII) and fuzzy synthetic evaluation (FSE).

The study’s results have categorised the crucial areas of application where construction industry stakeholders should focus their attention. These areas are divided into four categories: management technologies, production technologies, sensing technologies and monitoring technologies. The findings from the FSE indicate that monitoring technologies represent the most significant category, whereas management technologies rank as the least significant. Moreover, the RII analysis highlights that tools management stands out as the most important application of RFID, while dispute resolution emerges as the least significant RFID application.

The study establishes the core areas of RFID application and their benefits to sustainable buildings. Consequently, it helps stakeholders (consultants, clients and contractors) to examine the RFID application areas and make informed decision on sustainable construction. Furthermore, it provides systematic proof that can aid the implementation of RFID in developing countries.

The study provides an insight into the possible application areas and benefits of RFID technology in the construction industry of developing countries. It also developed a conceptual frame for the critical application areas of RFID technology in the construction industry of developing countries.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.

The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the effective functioning of the existing supply chain during COVID-19. The focus is to see the disruptions impacting the energy storage supply chains.

The procedure entails a thorough analysis of scholarly literature pertaining to various supply chain interruptions, confirmed and verified by experts working in an energy storage company in India. These experts also confirmed the occurrence of more disruptive factors during their interviews and questionnaire survey. Moreover, this process attempts to filter out the relevant causal disruption factors in an energy storage company by using the integrated approach of qualitative and quantitative methodologies.

The results provide practical insights for the company management in planning and devising new strategies to manage supply chain disruptions. Supply chains for companies in other industry sectors can also benefit from the proposed framework and results in making them more robust to counter future disastrous events.

The study provides an easily adaptable decision framework to different industries by closely examining supply chain disruptions and identifying associated causes for building a robust supply chain focused on the energy storage sector. It examines four disruption dimensions and investigates possible outcomes and impacts of disruptions.

The purpose of this paper is to analyze manufacturers' production decisions and governments' low-carbon policies in the context of influencer spillover effects.

This paper investigates the impact of the social influencer spillover effect on manufacturers' production decisions when they collaborate with intermediary platforms to sell products through marketplace or reseller modes. Game theory and static numerical comparison are used to analyze our models.

Firstly, under low-carbon policies, the spillover effect does not always benefit manufacturer profits and changes non-monotonically with an increasing spillover effect. Secondly, in cases where there are both a carbon emission constraint and a spillover effect present, if either the manufacturer or intermediary platform holds a strong position, then marketplace mode benefits manufacturer profits. Thirdly, regardless of business mode used when environmental damage coefficient is high for products; government should implement cap-and-trade regulation to optimize social welfare while reducing manufacturers’ carbon emissions.

This study offers theoretical and practical research support to assist manufacturers in optimizing production decisions for compliance with carbon emission limits, enhancing profits through the development of effective influencer marketing strategies, and providing strategies to mitigate carbon emissions and enhance social welfare while sustaining manufacturing activities.

This paper addresses the limitations of prior research by examining how the social influencer spillover effect influences manufacturers' business mode choices under government low-carbon policies and analyzing the social welfare of different carbon emission restrictions when such spillovers occur. Our findings provide valuable insights for manufacturers in selecting optimal marketing strategies and business modes and decision-makers in implementing effective regulations.

Microribbon with meander type based on giant magnetoimpedance (GMI) effect has become a research hot spot due to their higher sensitivity and spatial resolution. The purpose of this paper is to further optimize the line spacing to improve the performance of meanders for sensor application.

The model of GMI effect of microribbon with meander type is established. The effect of line spacing (Ls) on GMI behavior in meanders is analyzed systematically.

Comparison of theory and experiment indicates that decreasing the line spacing increases the negative mutual inductance and a consequent increase in the GMI effect. The maximum value of the GMI ratio increases from 69% to 91.8% (simulation results) and 16.9% to 51.4% (experimental results) when the line spacing is reduced from 400 to 50 µm. The contribution of line spacing versus line width to the GMI ratio of microribbon with meander type was contrasted. This behavior of the GMI ratio is dominated by the overall negative contribution of the mutual inductance.

This paper explores the effect of line spacing on the GMI ratio of meander type by comparing the simulation results with the experimental results. The superior line spacing is found in the identical sensing area. The findings will contribute to the design of high-performance micropatterned ribbon with meander-type GMI sensors and the establishment of a ribbon-based magnetic-sensitive biosensing system.

This study aims to regarding the application of traditional pulse frequency modulation control full-bridge LLC resonant converters in wide output voltage fields such as on-board chargers, there are issues with wide frequency adjustment ranges and low conversion efficiency.

To address these issues, this paper proposes a fixed-frequency pulse width modulation (PWM) control strategy for a full-bridge LLC resonant converter, which adjusts the gain by adjusting the duty cycle of the switches. In the full-bridge LLC converter, the two switches of the lower bridge arm are controlled by a fixed-frequency and fixed duty cycle, with their switching frequency equal to the resonant frequency, whereas the two switches of the upper bridge arm are controlled by a fixed-frequency PWM to adjust the output voltage. The operation modes of the converter are analyzed in detail, and a mathematical model of the converter is established. The gain characteristics of the converter under the fixed-frequency PWM control strategy are deeply analyzed, and the conditions for implementing zero-voltage switching (ZVS) soft switching in the converter are also analyzed in detail. The use of fixed-frequency PWM control simplifies the design of resonant parameters, and the fixed-frequency control is conducive to the design of magnetic components.

According to the fixed-frequency PWM control strategy proposed in this paper, the correctness of the control strategy is verified through simulation and the development and testing of a 500-W experimental prototype. Test results show that the primary side switches of the converter achieve ZVS and the secondary side rectifier diodes achieve zero-current switching, effectively reducing the switching losses of the converter. In addition, the control strategy reduces the reactive circulating current of the converter, and the peak efficiency of the experimental prototype can reach 95.2%.

The feasibility of the fixed-frequency PWM control strategy was verified through experiments, which has significant implications for improving the efficiency of the converter and simplifying the design of resonant parameters and magnetic components in wide output voltage fields such as on-board chargers.

This paper analyzes how air pollution and the public attention to it influence the returns of stocks in the Indian context.

The study uses firm-level data for the stocks listed on National Stock Exchange in India. Air quality is measured using the Air Quality Index (AQI) values provided by US Embassy and Consulates’ Air Quality Monitor in India. Google Search Volume Index (GSVI) of the relevant terms acts as the measure of public attention. Appropriate regression models are used to address how AQI and attention influence stock returns.

It is observed that degrading air quality alone is unable to explain the stock returns. It is the combined effect of increasing AQI and subsequent rise in associated public attention that negatively impacts these returns. Returns of firms with poor environment score component in their environmental, social, governance (ESG) scores are more negatively affected compared to firms with higher environment scores.

Investors can make use of this knowledge to formulate effective trading strategies and ensure higher chances of profitability in the share market.

To the knowledge of the authors, no earlier study has investigated the effects of AQI and attention together to explain stock price movements. The study is conducted in the Indian context providing a unique opportunity to study the behavioral impact of these effects in one of the fastest growing global economies, which is also plagued by an alarming increase in ambient air pollution.

The rise of the mobile Internet has accumulated much text information in various online financial forums. Such information often contains the emotional attitudes of investors toward financial technology (fintech) platforms, so extracting the sentimental tendency information has great practical value for the development of fintech platforms. Based on the investor sentiment theory, the paper aims to analyze the relevant social media data and test the influence path of online news evaluation on the stock price fluctuation of fintech platforms.

Taking Oriental Fortune as the research object, this paper selects multiple variables such as stock bar popularity, snowball popularity, news popularity and news sentiment scores collected by UQER and combines the sentiment scores of single daily news into a daily sentiment score. Based on the period from November 1, 2019 to March 31, 2020, during the emergence of the coronavirus disease 2019 (COVID-19) pandemic as the background, the authors conduct the Granger causality test based on the vector autoregressive (VAR) model and analyze the relevant evaluation of Oriental Fortune through the empirical model.

The authors' results show that different online evaluations impact the rise and fall of stock prices differently, while news popularity has the most significant impact. Besides, news sentiment scores on share price fluctuation have a relatively substantial influence. These findings indicate that the authoritative news evaluation can strongly guide investors to make relevant investment behavior operations in the information dissemination process, significantly affecting stock prices.

The research findings of this paper have good inspiration and reference values for investors and financial regulators.