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The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

With the rapid advancement of generative artificial intelligence (AI), it is important to consider how young people are making sense of these tools in their everyday lives. Drawing on critical postdigital approaches to learning and literacy, this study aims to center the experiences and perspectives of young people who encounter and experiment with generative AI in their daily writing practices.

This critical case study of one digital platform – Character.ai – brings together an adolescent and adult authorship team to inquire about the intertwining of young people’s playful and critical perspectives when writing on/with digital platforms. Drawing on critical walkthrough methodology (Light et al., 2018), the authors engage digital methods to study how the creative and “fun” uses of AI in youths’ writing lives are situated in broader platform ecologies.

The findings suggest experimentation and pleasure are key aspects of young people’s engagement with generative AI. The authors demonstrate how one platform works to capitalize on these dimensions, even as youth users engage critically and artfully with the platform and develop their digital writing practices.

This study highlights how playful experimentation with generative AI can engage young people both in pleasurable digital writing and in exploration and contemplation of platforms dynamics and structures that shape their and others’ literate activities. Educators can consider young people’s creative uses of these evolving technologies as potential opportunities to develop a critical awareness of how commercial platforms seek to benefit from their users.

This study contributes to the development of a critical and humanist research agenda around generative AI by centering the experiences, perspectives and practices of young people who are underrepresented in the burgeoning research devoted to AI and literacies.

The purpose of this study is to critically examine a prevalent online scam mechanism, with the aim of understanding its exploitation of behavioral finance principles and group dynamics and propose effective countermeasures.

This study uses a blend of case study and thematic analysis, drawing from behavioral finance, social psychology and criminology, using primary source testimonies of victims to provide a detailed exploration of the scam’s operations.

This research uncovers the strategic use of four key principles: loss aversion, overconfidence, scarcity bias and social proof, within the scam operation. These tendencies are manipulated to induce victims to progressively invest into the fraudulent scheme, even amid growing suspicions.

While the research elucidates on the workings of one specific online scam, it is necessary to explore if and how these principles are used in various other online fraudulent schemes, to develop comprehensive countermeasures.

The findings underscore the urgent need for enhanced public awareness, stronger corporate responsibility and robust regulatory oversight. There is a call for concerted efforts encompassing public education campaigns, fortified security protocols and strong legal frameworks for preventing such scams.

The research emphasizes the need for collective action in ensuring a safe online space. This involves the individual user’s prudence, businesses’ proactive education initiatives and stringent legal actions against fraudulent activities.

This research offers novel insights into the nuanced manipulation of behavioral finance principles within online scams, based on empirical data from victim testimonials. These findings contribute to the understanding of the psychological mechanisms at play in online scams and are instrumental in formulating effective preventive strategies.

The purpose of this study is to determine what technological strategies were used within each of the phases of the ADDIE framework when developing content for professional training. The study also examined the affordances of those technologies in training.

A PRISMA systematic review methodology (Moher et al., 2015) was utilized to answer the four questions guiding this study. Specifically, the PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P, Moher et al., 2015) was used to direct each stage of the research, from the literature review to the conclusion. In addition, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Liberati et al., 2009) are used to guide the article selection process.

The findings reveal that the majority of the studies were in healthcare (36%) and education (24%) and used an online format (65%). There was a wide distribution of ADDIE used with technology across the globe. The coding for the benefits of technology use in the development of the training solution revealed four trends: 1) usability, 2) learning approaches, 3) learner experience and 4) financial.

This systematic review only examined articles published in English, which may bias the findings to a Western understanding of how technology is used within the ADDIE framework. Furthermore, the study examined only peer-review academic articles from scholarly journals and conferences. While this provided a high level of assurance about the quality of the studies, it does not include other reports directly from training providers and other organizations.

These findings can be used as a springboard for training providers, scholars, funders and practitioners, providing rigorous insight into how technology has been used within the ADDIE framework, the types of technology, and the benefits of using technology. This insight can be used when designing future training solutions with a better understanding of how technology can support learning.

This study provides insight into the uses of technology in training. Many of these findings and uses of technology within ADDIE can also transfer to other aspects of society.

This study is unique in that it provides the scholarly community with the first systematic review to examine what technological strategies were used within each of the phases of the ADDIE structure and how these technologies provided benefits to developing a training solution.

This study aims to provide a precise understanding of how corporate sustainability information is used in socially responsible investing (SRI). The study is motivated by the lack of a recognised body of knowledge on this issue. This study, therefore, collates and reviews relevant studies (67 studies) to provide guidance to investors interested in SRI and identify a research agenda for academics desiring to contribute to this area.

This study conducts a systemic literature review employing recognised key words and searching the Web of Science. HistCite is utilised to ensure important cited studies are not missed from the collection. The review was conducted from two perspectives: (1) sources of sustainability information and (2) how the information is used in SRI.

The review identifies five major sources of sustainability information, including corporate reports, ESG ratings, industry affiliation, news and private communication with firms. These sources of information play different roles in the cross section of SRI strategies (i.e. negative and positive screening, active ownership and integration). This study provides guidance on how to use this information in SRI and provides recommendations for future research on how analysts interact with the information, how different informational characteristics impact implementation, ways to improve data quality, improvements to analysis methods and where data use needs to be extended into new strategies.

This review contributes to the SRI literature by inventorying studies of an important, yet omitted aspect, namely, sustainability information. This work also enriches the literature on corporate sustainability information by investigating how this information can be used for a specific purpose, namely, SRI. Given the increasing interest in SRI, this review will provide much-needed guidance for a range of practitioners, including investors and regulators.

Clinician engagement in research has positive impacts for healthcare, but is often difficult for healthcare organisations to support in light of limited resources. This scoping review aimed to describe the literature on health service-administered strategies for increasing research engagement by medical practitioners.

Medline, EMBASE and Web of Science databases were searched from 2000 to 2021 and two independent reviewers screened each record for inclusion. Inclusion criteria were that studies sampled medically qualified clinicians; reported empirical data; investigated effectiveness of an intervention in improving research engagement and addressed interventions implemented by an individual health service/hospital.

Of the 11,084 unique records, 257 studies were included. Most (78.2%) studies were conducted in the USA, and were targeted at residents (63.0%). Outcomes were measured in a variety of ways, most commonly publication-related outcomes (77.4%), though many studies used more than one outcome measure (70.4%). Pre-post (38.8%) and post-only (28.7%) study designs were the most common, while those using a contemporaneous control group were uncommon (11.5%). The most commonly reported interventions included Resident Research Programs (RRPs), protected time, mentorship and education programs. Many articles did not report key information needed for data extraction (e.g. sample size).

This scoping review demonstrated that, despite a large volume of research, issues like poor reporting, infrequent use of robust study designs and heterogeneous outcome measures limited application. The most compelling available evidence pointed to RRPs, protected time and mentorship as effective interventions. Further high-quality evidence is needed to guide healthcare organisations on increasing medical research engagement.

This research investigates how consumers who are most active on Facebook during the day vs in the evening differ, differ in their ad consumption, and how advertising effects vary as a function of a key moderator: gender.

Using a survey of 281 people, the research identifies Facebook users who are more intensely using mobile social media during the day versus in the evening, and measures five Facebook mobile advertising outcomes: brand and product recall, clicking on ads, acting on ads and purchases.

The results show that women who are using social media more intensely during the day are more likely to use Facebook to seek information, hence, Facebook mobile ads tend to be more effective for these users compared to those in the evening.

This contributes to the literature by analyzing how the time of day affects social media behavior in relation to mobile advertising effectiveness, and broadening the scope of mobile advertising effectiveness research from other than just clicks on ads to include measures like brand and product recall.

By analyzing the effectiveness of mobile advertising on social media as a function of the time of day, advertisers can be more targeted in their media buys, and so better use their social media budgets, i.e. advertising is more effective for women who use social media (Facebook) more intensely during the day than for those who use social media more intensely in the evening as the former tend to seek more information than the latter.

This research extends media ecology theory by drawing on circadian rhythm research to provide a first demonstration of how the time of day relates to different uses of mobile social media, which in turn relate to social media mobile advertising consumption.

While research on social media advertising has been steadily increasing, little has been explored on how users consume ads when they engage with social media at different periods along the day. This paper extends media ecology theory by investigating time of day, drawing on the circadian rhythm literature, and how it relates to social media usage.

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.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.