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The occurrence and dissemination of hate videos in social media platform could pose serious harm to both society and individuals. However, the characteristics of the hate videos increase the difficulty of detection task. Hate content is usually presented in a relatively covert manner in videos, and textual content in videos plays an important role in hate video detection. In this work, we propose a textual context enhanced dynamic bimodal fusion (TCE-DBF) method for hate video detection.

The proposed method TCE-DBF introduces dynamic modality gate (DMG) and bimodal fusion transformer network to dynamically integrate multimodalities. Moreover, in order to enhance textual modality in videos, two types of textual context from the video are taken as the input of TCE-DBF. One is extracted from video frames in visual modality. The other is extracted from audio in acoustic modality. Specially, TCE-DBF splits the original audio and learns the sequence representation to capture acoustic temporal information.

Hate video detection has been one of the hotspots in recent works. However, it still faces two serious challenges. The first challenge is the hate content in videos presented in multimodalities. The second challenge is how to evaluate the importance of different modalities for multimodal fusion modeling. TCE-DBF aims to tackle these challenges. Experimental results on hate video dataset HateMM demonstrate that TCE-DBF outperforms the state-of-the-art methods, and the visualization results show that textual modality plays a more important role in hate video detection. Therefore, it is vital to consider the text in videos.

TCE-DBF can be utilized to effectively detect hate videos on social media. Besides transcript, TCE-DBF considers text in video frames, which makes detection more accurate. Meanwhile, to better achieve multimodal fusion, TCE-DBF uses DMG and bimodal fusion transformer network to dynamically assign different weights to three modalities and integrate them. The proposed TCE-DBF is novel in terms of capturing multimodal features, enhancing the textual modality and achieving high detection performance for hate video detection.

This study investigates the impact and role of digital twin technology in building automation (DTBA) from a sustainability viewpoint. It aims to enhance the understanding of how DTBA can boost efficiency, optimize quality and support sustainable practices in contemporary construction. By exploring the integration of DTBA with sustainable practices, the study seeks to demonstrate how DT can revolutionize building management and operations, leading to significant improvements in resource efficiency, environmental impact and overall operational excellence.

This research employs a bibliographic analysis and systematic review of 176 publications from the past five years (January 1, 2019 to December 31, 2023), focusing on the application and development of DTBA. The study methodically analyzes current trends, identifies research gaps and suggests future directions by synthesizing data from various studies, offering a comprehensive overview of the current state of DTBA research. The approach combines quantitative and qualitative analyses to provide robust insights into the advancements and challenges in the field.

The review identifies key development areas in DTBA, such as energy and environmental management, resource utilization within a circular economy and technology integration and interoperability. It highlights the necessity for further research to maximize DTBA’s potential in sustainable building automation. The findings suggest that while significant progress has been made, there is a critical need for innovations in data interoperability, predictive analytics and the integration of renewable energy sources to fully realize the benefits of DTBA in enhancing building sustainability.

This paper provides a thorough review of DTBA from a sustainability perspective, offering valuable insights into its current applications and future development potential. It serves as a crucial resource for researchers and practitioners looking to advance sustainable practices in the construction sector using DT technology. By bridging the gap between theoretical research and practical applications, the paper underscores the transformative potential of DTBA in driving sustainable development and provides a roadmap for future research and innovation in the field.

This study aims to replace petroleum-based lubricating oils with sustainable biomaterials, addressing issues associated with existing alternatives, such as poor performance, high cost and limited availability.

The transformation of agricultural waste cardanol, a nonedible vegetable oil that is abundantly available, into green cardanyl acetate (CA) biolubricating ester oil. The potential of CA as a base stock for lubricants is validated by assessing its lubrication performance.

CA exhibited a higher viscosity index, flash point and thermal stability than commercially available mineral-based (CTL3, coal-to-liquid) and synthetic (PAO2, poly-alpha-olefin) lubricant base stocks. Moreover, CA exhibits excellent anticorrosivity properties as well as PAO2 and CTL3. The tribological properties of CA were evaluated, and the results show that CA exhibits a smaller average wear scar diameter (WSD) of 0.54 mm than that of PAO2 (0.85 mm) and CTL3 (0.90 mm). In extreme pressure tests, acylated CA demonstrated the highest last nonseizure load capacity at 510 N, outperforming commercial CTL3 (491 N) and PAO2 (412 N). All results demonstrate that CA displays an excellent series of base stock properties.

The novelty of this work lies in the utilization of renewable agricultural waste, cashew nut shell liquid, to produce a high-value biolubricant as an alternative to commercial fossil-based lubricants. The renewable nature, low cost, and large-scale availability of raw materials pave a new path for the production and application of biolubricants, showcasing the immense potential of converting agricultural waste into high-value products.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0064/

The aim of this study is to examine the ownership impact on firm sustainable investments (FSIs). In particular, this research examines the link between institutional investor ownership (IIO), managerial ownership (MOWN) and FSIs in the tourism industry in Malaysia.

This study uses a data set of 346 firm-year observations from 2008 to 2020 and applies feasible generalized least squares (FGLS) regression analysis. The study sample is based on tourism firms listed on Bursa Malaysia (the Malaysian Stock Exchange).

There is a significant positive association between IIO and FSIs. When IIO is classified into foreign (FIIO) and local (LIIO), this significant association is mainly driven by FIIO. In addition, there is a significant, positive association between managerial ownership (MOWN) and firm sustainable investments (FSIs). These findings imply that firm ownership has an influence on FSIs in the tourism industry.

This is the first attempt to consider IIO and MOWN simultaneously in a single model estimation. The findings contribute to emerging capital markets where the involvement of ownership concentration in the governance of publicly listed firms is a common practice.

Despite the considerable increase in research on entrepreneurship education, few studies examine the role of entrepreneurship educators. Similarly, most frameworks from entrepreneurship education recognize the educator’s importance in facilitating instruction and assessment, but the factors influencing the educator role are not well understood. According to the identity theory, personal factors including self-efficacy, job satisfaction and personal values influence the perspective of self, significance and anticipations that an individual in this role associates with it, determining their planning and actions. The stronger the role identity the more likely entrepreneurship educators will be in effectively developing their entrepreneurial skills as well as the overall learning experience of their students. The objective of this study is to pinpoint the factors that affect entrepreneurial role identity.

Drawing upon the identity theory, this study developed a theoretical framework and carried out an empirical investigation involving a survey of 289 entrepreneurship educators across the globe. Structural equation modeling (SEM) technique was applied to analyze and explore the factors that impact the identity of the educators in their role as entrepreneurship teachers.

The findings show that the role identity of entrepreneurship educators is significantly influenced by their self-efficacy, job satisfaction and personal values. Among these factors, self-efficacy and job satisfaction have the most significant impacts on how educators perceive their role. The implications of these results and directions for future research are also discussed.

The novelty of the current study is derived from its conceptualization of the antecedents of role perception among entrepreneurship educators. This study stands out as one of the earliest attempts to investigate the factors that shape an individual’s scene of self and professional identity as an entrepreneurship educator. The significance of comprehending the antecedents of role perception lies in the insights it can offer into how educators undertake and execute their role, and consequently, their effectiveness in teaching entrepreneurship.

This study identifies and ranks the barriers to manufacturing integrated circuit (IC) chips in the Indian semiconductor manufacturing industry by examining the causal relationships between the different barriers using an advanced causal modeling technique named the grey influence analysis (GINA). Moreover, IC plays a crucial function in reducing the size, boosting the speed and improving the dependability of electronic devices. The widespread use of these technologies has led to advancements in sectors such as communications, healthcare, transportation and entertainment.

This study investigates 12 factors related to the barriers to manufacturing IC chips. In addition, experts from an Indian semiconductor manufacturing organization have validated the presence of these barriers. In addition, the research employs a novel GINA to investigate and comprehend the influence relationships between these barriers.

To successfully manufacture IC chips in India, the “Government Policies and Regulations (B11),” “Lack of Semiconductor Fabrication Facilities (B1)” and “Dependence on Imported Raw Materials and Components (B2),” were the critical barriers that must be addressed and overcome, as per the GINA result.

The author highlighted that a lack of fabrication plants could lead to limited production of the semiconductor chips and that a lack of local fabs can hinder the development of advanced technologies. The complex policy can significantly affect the set-up of new semiconductor fabs, hamper the overall growth of the semiconductor market and divert away foreign investors. This further creates restrictions on employment creation and undermines the economy.

Although semiconductor applications have several benefits, more research is needed to identify the barriers to manufacturing IC chips in India, making this study unique. In addition, the advanced GINA method is also used to examine the critical barriers and their interconnection based on an expert response-based study from an Indian semiconductor organization.

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 present research aims to study the behavioural intention to use the digital currencies issued by the central bank through the lens of technology acceptance and switching behaviour perspective. The study also proposes to analyse the role of financial constructs to explain the adoption intention.

The current study develops a model by integrating the unified theory of acceptance and use of technology (UTAUT) and the push–pull–mooring (PPM) theory of switching behaviour. It amends the same by including financial literacy, financial inclusion and trust. A sample data of 419 respondents has been collected through a structured questionnaire and the PLS-SEM approach has been used for data analysis.

The findings suggest that UTAUT and PPM models can significantly predict individuals' readiness to adopt the central bank digital currency (CBDC). More precisely, performance expectancy, social influence, government support, relative advantage and task-technology fit jointly determine the adoption behaviour. Besides, the financial constructs also affect the intention to use CBDC.

The study is largely based on a quantitative approach with cross-sectional data from an Indian sample. Thus, the findings may benefit from a longitudinal approach with mixed-method data analysis. However, the study elaborates on several implications for policymakers and research scholars.

The present study uniquely integrates the technology adoption perspective with switching behaviour applied to the migration studies. Given the nascent stage of CBDC implementation in many countries, the current study uses a triangulation approach to enhance the understanding of its adoption behaviour.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

The current study investigates the factors influencing graduates’ perceived employability by utilizing the stimulus-organism-response theory, in the post pandemic era.

A quantitative approach was employed to examine the hypotheses of the research framework through partial least squares structural equation modelling (PLS-SEM) on the SmartPLS software.

The result indicates that course structure has a positive effect on students’ grit and community of inquiry (CoI). Also, students’ grit and CoI have a positive relationship with students’ performance, while students’ performance has a positive relationship with perceived employability. Moreover, students’ grit, CoI and students’ performance sequentially mediated course structure and perceived employability, whereas readiness and self-directed learning strengthen the relationship between students’ performance and perceived employability.

The findings will benefit university management, government and potential employers on how confident the student is in the chances of a future career after graduating from a higher institution.