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In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

The purpose of this research study is to empirically investigate the Figma prototyping technology adoption factors among entrepreneurship and innovation libraries for providing support to startups by developing and evolving the prototype solutions in collaboration with health libraries.

This study uses the technology adoption model (TAM) as a framework and the partial least squares structural equation modeling (PLS-SEM) method of structural equation modeling (SEM) using SmartPLS 3.2.9 software version to investigate the prototyping adoption factors among entrepreneurship and innovation libraries for rural health innovations. A total of 40 libraries, spread over 16 entrepreneurship and innovation libraries, participated in this survey, including participants from Europe (35%), Asia (15%) and USA (50%).

The findings show that previous experience, social impact, brand image and system quality have a significant positive impact on entrepreneurship and innovation libraries' perceived usefulness (PU) of prototyping technology. Perceived ease of use of prototype technology is positively influenced by usability, training materials and documentation, experience and self-efficacy. Together, perceived usefulness and perceived ease of use have a significant influence on behavioural intention. Behavioural intention is positively impacted by minimal investment and shallow learning curve. Technology adoption is furthered by behavioural intention. The control variables, for instance location, gender and work experience (as librarian), were found not having any impact on Figma technology adoption.

Through strategic partnerships with other libraries (including health libraries), policymakers, and technology providers, the adoption of prototype technology can be further accelerated. The important ramifications for policymakers, technology providers, public and entrepreneurship and innovation libraries to create a self-reliant innovation ecosystem to foster rural health innovation based on entrepreneurship are also listed in the article.

This research is distinctive since it integrates several areas of study, including entre, advances in rural healthcare and libraries. A novel idea that hasn't been thoroughly investigated is the collaboration between entrepreneurship and innovation libraries and health libraries for supporting businesses. This study offers insights into the factors that drive technology adoption and offers practical advice for policymakers and technology providers. It also advances understanding of the adoption of Figma prototyping technology among libraries for rural health innovation. Overall, this study provides a novel viewpoint on the nexus between different disciplines, showing the opportunity for cooperation and innovation in favour of rural health.

Predicting student performance is crucial in educational settings to identify and support students who may need additional help or resources. Understanding and predicting student performance is essential for educators to provide targeted support and guidance to students. By analyzing various factors like attendance, study habits, grades, and participation, teachers can gain insights into each student’s academic progress. This information helps them tailor their teaching methods to meet the individual needs of students, ensuring a more personalized and effective learning experience. By identifying patterns and trends in student performance, educators can intervene early to address any challenges and help students acrhieve their full potential. However, the complexity of human behavior and learning patterns makes it difficult to accurately forecast how a student will perform. Additionally, the availability and quality of data can vary, impacting the accuracy of predictions. Despite these obstacles, continuous improvement in data collection methods and the development of more robust predictive models can help address these challenges and enhance the accuracy and effectiveness of student performance predictions. However, the scalability of the existing models to different educational settings and student populations can be a hurdle. Ensuring that the models are adaptable and effective across diverse environments is crucial for their widespread use and impact. To implement a student’s performance-based learning recommendation scheme for predicting the student’s capabilities and suggesting better materials like papers, books, videos, and hyperlinks according to their needs. It enhances the performance of higher education.

Thus, a predictive approach for student achievement is presented using deep learning. At the beginning, the data is accumulated from the standard database. Next, the collected data undergoes a stage where features are carefully selected using the Modified Red Deer Algorithm (MRDA). After that, the selected features are given to the Deep Ensemble Networks (DEnsNet), in which techniques such as Gated Recurrent Unit (GRU), Deep Conditional Random Field (DCRF), and Residual Long Short-Term Memory (Res-LSTM) are utilized for predicting the student performance. In this case, the parameters within the DEnsNet network are finely tuned by the MRDA algorithm. Finally, the results from the DEnsNet network are obtained using a superior method that delivers the final prediction outcome. Following that, the Adaptive Generative Adversarial Network (AGAN) is introduced for recommender systems, with these parameters optimally selected using the MRDA algorithm. Lastly, the method for predicting student performance is evaluated numerically and compared to traditional methods to demonstrate the effectiveness of the proposed approach.

The accuracy of the developed model is 7.66%, 9.91%, 5.3%, and 3.53% more than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-1, and 7.18%, 7.54%, 5.43% and 3% enhanced than HHO-DEnsNet, ROA-DEnsNet, GTO-DEnsNet, and AOA-DEnsNet for dataset-2.

The developed model recommends the appropriate learning materials within a short period to improve student’s learning ability.

In January 2021, the state government of NSW, Australia, announced that all year 9 and 10 elective courses developed by schools will be phased out. This paper offers a brief historical account of school-developed board-endorsed courses (SDBECs) in NSW and a close analysis of the policy to phase them out.

I give an historical account of the meaning and place of SDBECs within the NSW school system, before situating the policy decision to phase them out within the broader historical and political context of curriculum reform in NSW. Finally, I offer an analysis of the discourses and framing of the policy both across curriculum review reports and in the government and public rhetoric, by examining policy documents, government media releases, news and blog articles at the time of the policy change.

This policy change and surrounding discourses are contextualised and analysed to show how the curriculum came to be blamed for a host of educational problems, and how the government arrived at their irrational yet politically expedient policy response by distorting the meaning of one metaphor: the crowded curriculum. I conclude with a reading of the policy as indicative of centralisation and de-legitimisation of teachers’ curriculum development work.

The convergence of state and federal discourse about curriculum as a site of cleaning up, reforming or re-organising should concern educators in Australia especially as authority over education is increasingly centralised and made vulnerable to political whim. Close studies of such minor policy decisions provide a window into how larger processes of centralisation are justified and enacted at the local level.

This paper undertakes an extensive and systematic review of the literature on earnings management (EM) over the past three decades (1992–2022). Furthermore, the study identifies emerging research themes and proposes future avenues for further investigation in the realm of EM.

For this study, a comprehensive collection of 2,775 articles on EM published between 1992 and 2022 was extracted from the Scopus database. The author employed various tools, including Microsoft Excel, R studio, Gephi and visualization of similarities viewer, to conduct bibliometric, content, thematic and cluster analyses. Additionally, the study examined the literature across three distinct periods: prior to the enactment of the Sarbanes-Oxley Act (1992–2001), subsequent to the implementation of the Sarbanes-Oxley Act (2002–2012), and after the adoption of International Financial Reporting Standards (2013–2022) to draw more inferences and insights on EM research.

The study identifies three major themes, namely the operationalization of EM constructs, the trade-off between EM tools (accrual EM, real EM and classification shifting) and the role of corporate governance in mitigating EM in emerging markets. Existing literature in these areas presents mixed and inconclusive findings, suggesting the need for further theoretical development. Further, the study findings observe a shift in research focus over time: initially, understanding manipulation techniques, then evaluating regulatory measures, and more recently, investigating the impact of global accounting standards. Several emerging research themes (technology advancements, cross-cultural and cross-national studies, sustainability, behavioral aspects and non-financial indicators of EM) have been identified. This study subsequent analysis reveals an evolving EM landscape, with researchers from disciplines like data science, computer science and engineering applying their analytical expertise to detect EM anomalies. Furthermore, this study offers significant insights into sophisticated EM techniques such as neural networks, machine learning techniques and hidden Markov models, among others, as well as relevant theories including dynamic capabilities theory, learning curve theory, psychological contract theory and normative institutional theory. These techniques and theories demonstrate the need for further advancement in the field of EM. Lastly, the findings shed light on prominent EM journals, authors and countries.

This study conducts quantitative bibliometric and thematic analyses of the existing literature on EM while identifying areas that require further development to advance EM research.

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.

Project management (PM) involves planning, allocating, directing and controlling project resources within a set of predetermined objectives. The modern definition of PM has evolved and grown into a broader concept. This paper supports the notion that PM evolved into four distinct phases: PM 1.0 is primarily concerned with planning, PM 2.0 with collaboration, PM 3.0 with proactive adaptation and PM 4.0 with using innovative technologies. Research efforts tackled critical aspects of PM, but none of them provided a clear foundation for the full context of PM principles and how they complement one another. This study fills this knowledge gap by investigating the evolution of PM over time.

The authors collected a dataset of research papers between 1960 and 2022 and performed a bibliometric analysis on the collected dataset to isolate the main trends that define the evolution of PM phases.

Results show that all PM phases overlap in terms of overarching themes, concepts, principles and contributions. More importantly, PM 5.0 may be around the corner to facilitate effective and efficient handling of time, cost, scope and risks within the ever-growing complexity of project initiatives.

This paper provides a data-driven study for a holistic understanding of the key trends in PM and the associated expectations of future research directions. This will be of interest to stakeholders within the overall PM domain and multidisciplinary work related to the construction industry.

Researchers piloted a problem-based learning (PBL) activity in a master’s degree-granting strategic studies program to explore how students apply knowledge and skills learned from the curriculum to their formulation of a strategy addressing a real-world global security scenario.

This mixed-methods pilot study used ethnographic observation, participant feedback, document analysis and surveys to assess the learning and engagement of multinational postgraduate students in the context of a PBL environment.

Findings revealed gaps in students’ causal logic and literacy, as well as student discomfort with ambiguity and reliance upon heuristic frameworks over willingness to conduct substantive, current and relevant research. Additionally, observed group dynamics represented a lack of inclusive collaboration in mixed gender and multinational teams. These findings suggest foundational issues with the curriculum, teaching methodologies and evaluation practices of the studied institution.

This study highlights the need to include explicit instruction in problem-solving and causal literacy (i.e. logical reasoning) in postgraduate programs for national and global security professionals, as well as authentic opportunities for those students to practice interpersonal communication.