Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

This article studies the application of artificial intelligence technology in natural resource planning and management. This article first introduces the background of NR and AI intelligent technology, then conducts academic research and summary on NR planning management and AI intelligent technology. Then, an algorithm model based on multi-objective intelligent planning algorithm is established. Finally, simulation experiments are conducted, and experiments summary and discussion are provided. The experimental results show that the average efficiency value of the four stages of NR planning and management before use is 5.25, and the average efficiency value of the four stages of NR planning and management after use is 7. The difference in the average efficiency value before and after use is 1.75. It can be seen that the use of AI intelligent technology can effectively improve the efficiency of natural resource planning and management.

As IOT develops drastically these years, the application of cloud computing in many fields has become possible. In this paper, we take low-voltage current transformers in power systems as the research object and propose a TCN-BI-GRU power measurement method that incorporates the signal characteristics based on the transformer input and output. Firstly, the basic signal enhancement extraction of input and output is completed by using EMD and correlation coefficients. Secondly, multi-dimensional feature extraction is completed to improve the data performance according to the established TCN network. Finally, the power prediction is completed by using BI-GRU, and the results show that the RMSE of this framework is 5.69 significantly lower than other methods. In the laboratory test, the device after being subjected to strong disturbance, its correlation coefficient feature has a large impact, leading to a large deviation in the prediction, which provides a new idea for future intelligent prediction.

Physiological indicators are an important basis for reflecting the physiological health status of the human body and play an important role in medical practice. Association rules have also been one of the important research hotspots in recent years. This study aims to create a data mining system of association rules and artificial intelligence in biochemical indicators of sports training. This article uses Markov logic for network creation and system training, and tests whether the Markov logic network can be associated with the training system. The results show that the accuracy and recall rate obtained are about 90%, which shows that it is feasible to establish biochemical indicators of sports training based on Markov logic network, and the system has universal, guiding and constructive significance, ensuring that the construction of training system indicators will not go in the wrong direction.

Since its birth, the stock market has received widespread attention from many scholars and investors. However, there are many factors that affect stock prices, including the company's own internal factors and the impact of external policies. The extent and manner of fundamental impacts also vary, making stock price predictions very difficult. Based on this, this article first introduces the research significance of the stock market prediction framework, and then conducts academic research and analysis on two key sentences of stock market prediction and artificial intelligence in stock market prediction. Then this article proposes a constructive algorithm theory, and finally conducts a simulation comparison experiment and summarises and discusses the experiment. Research results show that the neural network prediction method is more effective in stock market prediction; the minimum training rate is generally 0.9; the agency's expected dilution rate and the published stock market dilution rate are both around 6%.

Based on a survey of 2548 American adults conducted by Pew Research Center in 2021, this study finds that trust in the accuracy of news circulated on X (former Twitter) is positively correlated with following news sites on X, underscoring the crucial role of trust in news accuracy in shaping news-seeking behaviour. Trust in news accuracy also positively relates to political participation via X. Those who trust in news accuracy are more likely to perceive X as an effective tool for raising public awareness about political and social issues, as well as a positive force for democracy. However, exposure to misinformation weakens the connection between trust in news accuracy and users' perception about X as an effective tool for raising public awareness about political or social issues and as a positive driver for democracy.

The aim of the study is to examine the determinants of digitalisation in public sector. This research is particularly relevant as digital transformation has become a crucial factor in modernising public sector and enhancing service delivery to citizens. The method of the systematic literature review (SLR) was implemented by searching documents on the Scopus database. The initial research reached the 7902 documents and after specifying the keywords the authors found 207 relevant documents. Finally; after the careful read of their abstracts and the use of inclusion and exclusion criteria; the most cited and relevant 32 papers constituted the final sample. Findings highlighted the focus of the literature on technological factors such as the sense of trust and safety as well as the ease of use in the adoption of digital governance; emphasising the need for effective; trustworthy and user-friendly digital services. The most discussed internal factors were leadership and organisational culture. The study offers a deeper understanding of the factors that shape the successful implementation of digital governance initiatives.

This empirical study investigates the mediating role of two components of psychological capital (PsyCap), namely self-efficacy and optimism, in the context of the relationship between transformational leadership (TL), work engagement (WE), and innovative work behaviour (IWB). The study was conducted among IT professionals with a minimum of three years of experience employed in Chennai, India. Data collection was executed using a Google Form, and both measurement and structural models were examined using SPSS 25.0 and AMOS 23.0. The findings of this study reveal several significant relationships. Firstly, transformational leadership (TL) demonstrates a robust positive association with work engagement (WE). Furthermore, work engagement (WE) positively correlates substantially with innovative work behaviour (IWB). Notably, the study underscores that two crucial components of psychological capital, specifically self-efficacy and optimism, mediate the relationship between transformational leadership (TL) and work engagement (WE). These findings carry valuable implications for IT company managers. Recognising that transformational leadership positively influences both work engagement and employees' innovative work behaviour highlights the pivotal role of leaders in fostering a productive and innovative work environment within IT organisations.

The purpose of this research is to investigate how genuine leaders impact the creativity and innovative behaviour (IWB) of information technology (IT) employees. It also examines the impact of perceived organisational support as a mediator in the correlations between authentic leadership as well as innovative behaviours. This study explores the influence of authentic leadership via the employee's IWB using aspects from social exchange theory as well as social cognitive theory. The data was collected from a sample of 487 employees of the IT sector in India. The partial least square method is applied to test the structural relationship of the research framework. Findings reveal that authentic leadership positively impact innovative work behaviour and perceived organisation support mediates authentic leadership and IWB. Additionally, when organisations and leaders support the employees and value their creative thinking then the employee replicates IWB in the organisation. The practical and theoretical implications are discussed.

The study investigated the influence of ebullient supervision on employee commitment in a Ghanaian public university through the mediating role of employee engagement. The simple random sampling technique was used to draw 302 administrative staff of the university to respond to the self-administered questionnaire on the constructs. Furthermore, the partial least square structural equation technique was deployed to test the research hypotheses in the study. The results showed that ebullient supervision had a significant positive relationship with employee commitment and employee engagement. The findings further revealed that employee engagement positively correlated with employee commitment. Finally, the study's findings established that employee engagement partially mediated the link between ebullient supervision and employee commitment. The study emphasised that various supervisors in a university's administration should create an environment that favours fun where subordinates can form ties with one another.

This paper examines the growing aspect of perceive organisational politics (POPs) in organisations by understanding their employee engagement with mediating effect of emotional intelligence. This study is cross-sectional, wherein a survey is conducted on executives of different sectors holding strategic positions. The purposive sampling technique is applied to find the 117 most suitable executives for this survey. The survey is self-administered, and a questionnaire is used as an instrument with 43 measurement scale items adopted from previous similar studies. Construct's reliability and validity followed by PLS-SEM is performed using JASP statistical application. The result revealed that the dimensionality support and validation of POP based on a new set of measures centred on generalised beliefs of the application and abuse of power, infrastructure, credibility, choice making, and line-of-sight. In line with previous findings, the current findings also showed that POP works as a barrier to individual behavioural demand and can negatively affect work efficiency. Existence of perceive organisational politics due to the normative belief of the situation happing in the organisation, disengagement of employees, and also evaluates new empirical insight into the organisation by mediating emotional intelligence.

Disease pandemics, once thought to be historical relics, are now again challenging healthcare systems globally. Of essential importance is sufficiently investing in preparedness and responsiveness, but approaches to such investments vary significantly by country. These variations provide excellent opportunities to learn and prepare for future pandemics. Therefore, we examine digital health infrastructure and the state of healthcare and public health services in relation to pandemic preparedness and responsiveness. The research focuses on two countries: South Africa and the USA. We apply case analysis at the country level toward understanding digital health policy preparedness and responsiveness to a pandemic. We also provide a teaching note at the end for use in guiding students in this area to formulate digital health policy recommendations for pandemic preparedness and responsiveness.

The supply chains of the Canadian forest products industry are largely dependent on accurate demand forecasts. The USA is the major export market for the Canadian forest products industry, although some Canadian provinces are also exporting forest products to other global markets. However, it is very difficult for each province to develop accurate demand forecasts, given the number of factors determining the demand of the forest products in the global markets. We develop multi-layer feed-forward artificial neural network (ANN) models for demand forecasting of the Canadian forest products industry. We find that the ANN models have lower prediction errors and higher threshold statistics as compared to that of the traditional models for predicting the demand of the Canadian forest products. Accurate future demand forecasts will not only help in improving the short-term profitability of the Canadian forest products industry, but also their long-term competitiveness in the global markets.

This article explores dual affiliated graduate students that conduct research involving both universities and firms, which we conceptualise as a form of academic engagement, e.g., knowledge networks. We explore what they do during their studies, and their perceptions about their contributions to the firm's capacities for technology and innovation. So far, university-industry interactions in engineering are less researched than other fields, and this qualitative study focuses upon one department of Electrical Engineering in Sweden. First, we define and describe how the partner firms and universities organise this research collaboration as a form of academic engagement. Secondly, we propose a conceptual framework specifying how graduate students act as boundary-spanners between universities and firms. This framework is used for the empirical analysis, when exploring their perceptions of impact. Our results reveal that they primarily engage in problem-solving activities in technology, which augment particularly the early stages of absorptive capacities in firms.

Pandemic has compelled the entire world to change their way of life and work. To control the infection rate, academic institutes deliver education online similarly. At least one smartphone is available in every home, and students use their smartphones to attend class. The study investigates the link between smartphone usage (SU) and academic performance (AP) during the pandemic. 490 data were obtained from various institutions and undergraduate students using stratified random sampling. These components were identified using factor analysis and descriptive methods, while the relationship of SU and AP based on gender classification was tested using Smart-PLS-SEM. The findings show that SU has a substantial relationship with academic success, whether done in class or outside of it. Even yet, the study found that SU and AP significantly impact both male and female students. Furthermore, the research focuses on SU outside and within the classroom to improve students' AP.

Work-life balance (WLB) is a widely explored topic in the academic discourse. The researchers are trying to find strategies to effectively balance their work and home responsibilities for women in management. This study aims to analyse how gender roles and inequalities shape the strategies of female academics in higher education institutions. Eighteen faculty members participated in the semi-structured interviews. The trustworthiness of qualitative inquiry was ascertained by using triangulation, thick descriptions, and peer reviews. Three major themes emerged from the analysis: emotional, religious and social strategies. Despite available support, faculty noted challenges in managing work and family roles and fighting with gender stereotypes. This research adds to the emerging concept of WLB literature from the developed countries' viewpoint. It also shows how WLB discourse varies from Western sensibilities and collaborates with the previously established strategies that female academics formulate in WLB.

The use of blended learning is well suited for classes that involve a high level of experiential inquiry such as internship courses. These courses allow students to combine applied, face-to-face fieldwork activities with a reflective academic component delivered online. Therefore, the purpose of this article is to describe the pedagogical design and implementation of a pilot blended learning format internship course. After implementation, the pilot class was assessed. Results of the survey and focus group revealed high levels of student satisfaction in the areas of course structure, faculty-student interaction, and application of theory to the “real-world” experience undertaken by students during the internship. Lower levels of satisfaction with the course’s academic rigor and a sense of community were also reported. Notably, students with experience in blended learning expressed lower levels of overall satisfaction, but reported higher levels of satisfaction with the course’s rigor and sense of community. The paper concludes by offering implications for instructors seeking to implement blended learning approaches.

Many parents, educators, and policy makers see great potential for leveraging tools like laptop computers, tablets, and smartphones in the classrooms of the world. Although increasing students’ technology access may be associated with increased student achievement, there is little research directly investigating objective measures of student achievement. This study addresses the short-term and long-term quantitative impacts of one of the world’s first school efforts to provide Kindergarten through 3rd grade classrooms with 1:1 iPad access and a range of English Language Arts (ELA) and math Apps. This report summarizes two investigations conducted during this iPad implementation. First, a 9-week pre/post randomized control trial was conducted in which 8 Kindergarten classes used literacy and numeracy apps while another 8 Kindergarten classes used their traditional (non-iPad) resources. At the end of this short implementation period, slightly stronger literacy performance gains were observed in the iPad settings. In a second longitudinal study, three years of assessment data were explored before and after the 1:1 iPad implementation in grades K to 2. Results from the longitudinal study provide emerging evidence of potential increases in ELA achievement, but no consistent results in math achievement. This paper adds to the sparse literature in this area and provides a springboard for further research.

Mobile devices are the constant companions of technology users of all ages. Studies show, however, that making calls is a minimal part of our engagement with today’s smart phones and that even texting has fallen off, leaving web browsing, gaming, and social media as top uses. A cross-disciplinary group of faculty at our university came together in the mLearning Scholars group to study the potential for using mobile devices for student learning. The group met bi-weekly throughout a semester and shared thoughts, ideas, resources, and examples, while experimenting with mobile learning activities in individual classes. This paper summarizes student perceptions and adoption intent for using mobile devices for learning, and discusses implementation issues for faculty in adding mobile learning to a college course. Outcomes reflect that mobile learning adoption is not a given, and students need help in using and understanding the value in using personal devices for learning activities.

The pilot research presented here explores the classroom use of Emerging Literacy in Mathematics (ELM) software, a research-based bilingual interactive multimedia instructional tool, and its potential to develop emerging numeracy skills. At the time of the study, a central theme of early mathematics curricula, Number Concept, was fully developed. It was broken down into five mathematical concepts including counting, comparing, adding, subtracting and decomposing. Each of these was further subdivided yielding 22 online activities, each building in a level of complexity and abstraction. In total, 234 grade one students from 12 classes participated in the two-group post-test study that lasted about seven weeks and for which students in the experimental group used ELM for about 30 minutes weekly. The results for the final sample of 186 students showed that ELM students scored higher on the standardized math test (Canadian Achievement Test, 2008) and reported less boredom and lower anxiety as measured on the Academic Emotions Questionnaire than their peers in the control group. This short duration pilot study of one ELM theme holds great promise for ELM’s continued development.

The paper describes the development of four learning modules that focus on technical details of how a person’s privacy might be compromised in real-world scenarios. The paper shows how students benefited from the addition of hands-on learning experiences of privacy and data protection to the existing information technology courses. These learning modules raised students’ awareness of potential breaches of privacy as a user as well as a developer. The demonstration of a privacy breach in action helped students to design, configure, and implement technical solutions to prevent privacy violations. The assessment results demonstrate the strength of the technical approach.