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This paper aims to trace how Asian American girls engaged with civic learning in a virtual out-of-school literacy community featuring a curriculum of diverse literary texts.

The researcher used practitioner inquiry to construct a virtual literacy education community dedicated to the civic learning of Asian American girls.

The paper explores how participants mobilized critical practices of textual consumption and production rooted in their intersectional identities and embodied experiences to make meaning of the civic constraints and affordances of marginalized identities and to read and (re)design author choices for civic purposes. These findings – examples of youths’ critical civic meaning-making – indicate how they claimed space for Asian American civic girlhoods in literacy education.

This paper foregrounds how Asian American girls mobilize critical processes of text consumption and production to assert civic identities in literacy education – a significantly under-examined topic in literacy studies. This work has implications for how literacy practitioners and scholars can prioritize Asian American civic girlhoods through pedagogy and research.

This study aims to explore motives behind teachers' and students' use of translanguaging and how they use it in Tanzanian public secondary school classrooms.

Data were collected using interviews and non-participant observations.

The findings indicate that translanguaging was used to facilitate content comprehension, promote classroom interaction and increase students' motivation to learn. Translanguaging was implemented using three strategies: paraphrasing an English text into Kiswahili, translating an English text into its Kiswahili equivalent and word-level translanguaging.

By highlighting the motivations for translanguaging and corresponding strategies associated with translanguaging pedagogy in the Tanzanian context, this study has significant practical implications for teachers and students to showcase their linguistic and multimodal knowledge, while fostering a safe learning space that relates to students' daily experiences.

The study offers new insights into previous research on the role of language-supportive pedagogy appropriate for teachers and students working within bi-/multilingual education settings.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Microwave curing (MC) can facilitate rapid concrete repair in cold climates without using conventional accelerated curing technologies which are environmentally unsustainable. Accelerated curing of concrete under MC can contribute to the decarbonisation of the environment and provide economies in construction in several ways such as reducing construction time, energy efficiency, lower cement content, lower carbonation risk and reducing emissions from equipment.

The paper investigates moisture loss and pore properties of six cement-based proprietary concrete repair materials subjected to MC. The impact of MC on these properties is critically important for its successful implementation in practice and current literature lacks this information. Specimens were microwave cured for 40–45 min to surface temperatures between 39.9 and 44.1 °C. The fast-setting repair material was microwave cured for 15 min to 40.7 °C. MC causes a higher water loss which shows the importance of preventing drying during MC and the following 24 h.

Portland cement-based normal density repair mortars, including materials incorporating pfa and polymer latex, benefit from the thermal effect of MC on hydration, resulting in up to 24% reduction in porosity relative to normal curing. Low density and flowing repair materials suffer an increase in porosity up to 16% due to MC. The moisture loss at the end of MC and after 24h is related to the mix water content and porosity, respectively.

The research on the application of MC for rapid repair of concrete is original. The research was funded by the European commission following a very rigorous and competitive review process which ensured its originality. Original data on the parameters of porosity and moisture loss under MC are provided for different generic cementitious repair materials which have not been studied before. Application of MC to concrete construction especially in cold climates will provide environmental, economic and energy benefits.

The study aims to maximize the efficiency of the process under a given current condition by changing the geometry of the coil. This optimization is economically justified by reducing the cost of the process.

The paper presents the author’s optimization process for a case requiring long computational time. The presented optimization is based on a 3D simulation model of an electromagnetic levitation melting (ELM) inductor.

The result of the work is to find a suboptimal inductor geometry for ELM.

To solve the presented problem, a procedure using an evolutionary algorithm was relied on. As for all global search algorithms, it is possible to find a local optimum instead of a global one.

The new inductor geometry for ELM, thanks to its higher process efficiency for its class of inductors, can lead to the reduction of the costs of the process by using this type of equipment.

The novelty of the article is a proprietary optimization algorithm and the use of an advanced 3D simulation model which was necessary due to the lack of symmetry of the ELM inductor.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

The present study aims to identify significant barriers to adopting prefabricated construction (PFC) in developing economies using a study in Sri Lanka and develop an integrated strategy framework to mitigate and overcome the obstacles.

The research process included a comprehensive literature review, a pilot study, a questionnaire survey for data collection, statistical analysis and a qualitative content analysis.

Ranking method revealed that all 23 barriers were significant. Top significant barriers include challenges in prefabricated component transportation, high capital investment costs and lack of awareness of the benefits of PFC among owners/developers. Factor analysis clustered six barrier categories (BCs) that fit the barrier factors, explaining 71.22% of the cumulative variance. Fuzzy synthetic evaluation revealed that all BCs significantly influence PFC adoption in Sri Lanka. Finally, the proposed mitigation strategies were mapped with barriers to complete the integrated framework.

The study outcomes are relevant to construction industry stakeholders of Sri Lanka, who are keen to enhance construction efficiencies. The implications can also benefit construction industry stakeholders and policymakers to formulate policies and regulations and identify mitigation solutions.

The study provides deeper insights into the challenges to adopting prefabrication in South Asian countries such as Sri Lanka. Furthermore, the integrated framework is a novel contribution that can be used to derive actions to mitigate barriers in developing economies.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Pandemics are a serious challenge for humanity, as their social and economic impacts can be tremendous. This study aims to understand how innovation based in the sharing economy (SE) business models can contribute to overcoming the challenges arising from the Covid-19 pandemic.

Following a netnographic approach, the authors studied the computer-mediated social interactions of internet-based virtual innovation communities.

This study found that the SE business models contribute to overcome the challenges of the Covid-19 pandemic by redistributing idle resources to lessen the impacts of confinement. This was achieved through process innovations and an innovative use of the network, which enabled fast-open and decentralized innovation processes, and quick implementation of innovations. This innovation process is based on a decentralized decision-making approach, clear rules, informal relationship among community members and open communication channels, as well as in evasive strategies to avoid facing challenges, institutional restrictions and barriers in the adoption of innovations.

This study was limited to a virtual innovation community of highly specialized and educated experts and nine community projects focused on institutional contexts of a developed country. Future research should focus on the institutional contexts of less specialized communities and developing countries and study other community innovation projects in pandemics to understand the processes of fast-open, decentralized and evasive innovation and the importance of relational capabilities for innovation in digital contexts.

The findings can guide innovation managers and public policymakers in implementing effective strategies and policies to overcome pandemic challenges using SE business models. This research also provides important insights into the types and processes of innovation in organizations that create solutions to overcome social and business challenges during pandemics. In addition, this study highlights the contributions of netnographic approaches to conducting research on innovation and in pandemic periods when measures of confinement are in place.

This study uses an innovative framework to map the types of innovation and highlights two different types of innovation processes.

This paper aims to examine the congruency effects of physically embodied robots in service encounters, which addressed a significant research gap concerning the synthesis of robot design elements (e.g., appearance and voice) and their service purposes.

Grounded in congruity theory and human-robot interaction literature, this study conducted a pretest and two experimental studies revealing the need to view robot design holistically and recognizing the pivotal role of congruity in shaping consumers’ service robot adoption. The moderating role of service purposes (utilitarian vs hedonic) was also investigated in terms of robot design and consumer reactions.

Consumers generally tend to favor robots with congruent designs, particularly for utilitarian service purposes. The serial mediation through perceived congruence and perceived intelligence explains such a favorite tendency.

This study advances service robot design research by highlighting the critical role of congruity in enhancing consumer engagement. It supports the use of comprehensive, congruent designs for services with utilitarian purposes and recommends adaptable designs for hedonic settings.

This study addressed the research gap by examining service robot design from a holistic perspective. The research findings highlight the importance of congruency effects in service robot design and deployment and provide valuable insights and guidelines to industry practitioners for optimal investment in service robots.