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.

In order to reduce the challenges encountered by learners and educators in engaging in educational activities, this paper classifies learners' roles in virtual learning communities, and explores the role of behaviour characteristics and their positions in collaborative knowledge construction networks in promoting the process of knowledge construction. This study begins with an analysis of the relationship structure among learners in the virtual learning community and then applies the FCM algorithm to arrange learners into various dimensional combinations and create distinct learning communities. The test results demonstrate that the FCM method performs consistently during the clustering process, with less performance oscillations, and good node aggregation, the ARI value of the model is up to 0.90. It is found that they play an important role in the social interaction of learners' virtual learning community, which plays a certain role in promoting the development of artificial intelligence.

To explore the computer-aided translation technology based on the intelligent algorithm of edge computing. This paper presents the research on computer-aided translation technology based on edge computing intelligent algorithm. In the K-means computer edge algorithm, it analyses the traditional way of average resource allocation and the way of virtual machine allocation. In the process of online solution, we have a more detailed understanding of the data information at the edge, and also avoid the connection relationship between network users and the platform, which has a certain impact on the internal operation efficiency of the system. The network user group is divided into several different types of existence through K-means computer algorithm, and various information resources are counted according to their own characteristics. Computer-aided translation technology can significantly improve the quality of translation, improve the translation efficiency, and reduce the translation cost.

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.

With the development of urban construction, its spatial evolution is also influenced by behavioural actors such as enterprises, residents, and environmental factors, leading to some decision-making behaviours that are not conducive to urban public space and environmental design. At the same time, some cities are vulnerable to various factors such as distance factors, transportation factors, and human psychological factors during the construction of public areas, resulting in a decline in the quality of urban human settlements. Urban public space is the guarantee of urban life. For this, in order to standardise urban public space and improve the quality of urban living environment, the standardisation of the environment of urban public space is required. The rapid development of intelligent algorithms and fuzzy control provides technical support for the environmental design of urban public spaces. Through the modelling of intelligent algorithms and the construction of fuzzy space, it can meet the diverse.

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.

The application of computer image digitisation has realised the transformation of people's production and lifestyle, and also promoted the development of the construction industry. This article aims to realise the research on architectural decoration design and production under computer network environment and promote the ecological development of indoor and outdoor design in the construction industry. This article proposes to use virtual reality technology in image digitisation to guide architectural decoration design research. In the comparative analysis of the weight of architectural decoration elements, among the calculated weights of secondary elements, the spatial function has the largest weight, which is 0.2155, and the landscape has the smallest weight, which is 0.0113. Among the three-level unit weights, the service area has the largest weight, which is 0.0976, and the fence frame has the smallest weight, which is 0.0119.

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.

E-government serves as a vital channel for citizen interactions with the public sector, where user enjoyment is of paramount importance. To date, few studies have comprehensively examined the determinants of citizen enjoyment in e-government. To address this research gap, we administered a survey and gathered data from 363 Australian residents using myGov for tax filing. Our analysis revealed a pronounced discrepancy between reported enjoyment and the intention to continue using the services. Although users demonstrated a strong intent to use e-government services, this intent did not uniformly align with enjoyment. Additionally, informed by self-determination theory, we developed and tested an e-government service enjoyment model to study the impacts of effort expectancy, technophilia, technology humanness, and engagement in fostering user enjoyment. Unexpectedly, the results showed that information privacy concerns, commonly seen as a deterrent in e-government adoption, did not significantly affect enjoyment. Our findings advance the discourse on e-government service improvement.

Physical and social aspects each make up a separate part of servicescape. Together, these make up the servicescape. Although previous research has frequently investigated these aspects separately, the purpose of this study is to simultaneously find out the impact of both aspects within the casual dining restaurants' context. In total, 462 customers in Delhi were polled for this study, and structural equation modelling was used to analyse the data. According to the results, both the social and physical parts of the servicescape have the ability to affect how satisfied customers are, which in turn can affect how they behave in the future.

Drawn from technology acceptance model, this study establishes a theoretical framework for the analytical interpretation of factors affecting job-seekers intention to use e-recruitment websites. Using the data obtained from 379 respondents in India, ten hypotheses derived from the experimental model are evaluated using a structural equation modelling technique. Vividness, perceived usefulness (PU), and attitude have been shown to have a significant positive impact on the behavioural intentions (BIs) of job-seekers, although perceived ease of use (PEOU) did not. Furthermore, perceived internet stress (PIS) is observed to be a significant antecedent PEOU; and PEOU is of PU. Such findings broaden our knowledge of e-recruiting in various ways and offer qualitative insights into the potential impact of website functionality on the attractiveness of job-seekers.

Software start-ups generally use development practices that are adapted to their agile and innovative environment. However, these practices, although consolidated, may not be the best ones for a specific context. This paper aims to present a set of guidelines for software development in start-ups. It also aims to show the results of three studies to validate and refine the proposed guidelines: a confirmatory survey, a focus group, and an expert panel. The participants were actors from both the industry and the academia. The results revealed that the guidelines obtained a positive perception from the participants of both contexts. Based on their approval, we can infer that those guidelines can increase the quality of products generated by start-ups and the chances of success for these organisations. Besides, the need for some improvements has been identified, and they will be implemented in the next version of the guidelines.

Over recent decades, advanced economies have been characterised by reduced rates of productivity. In this article, we advance the hypothesis that one of the potential causes of this trend might be the new knowledge capitalisation practices. Capitalisation of intangible assets is justified by the limited exhaustibility of knowledge, which implies its slow obsolescence, and hence, having the potential of being capitalised to reflect its prolonged period of contribution to productivity. However, the capitalisation of an increasing proportion of the assets that initially were accounted for as labour or intermediate inputs is having a direct effect on increasing capital and theoretical output and reducing total factor productivity (TFP). Our empirical analysis based on US-listed firms shows that the capitalisation of knowledge strongly reduces both the levels of TFP, and because of its fast increase in the last two decades, its rates of growth.

The notion of creative slack purposefully refers to the notion of organisational slack proposed by Penrose (1959), who suggested that managers in organisations always have some stock of unused resources that inevitably accumulate when developing projects and are the primary factors determining the growth and innovation of the firm. In this contribution, we aim at adding a new dimension to the notion of organisational slack. Our view is that in many innovative organisations the slack of unused ideas is essentially a creative one, which is accumulated in diverse communities through multiple projects. This creative slack is a key intangible asset and a source of knowledge creation and innovation. To explain how organisations may benefit from exploiting the creative slack accumulated by communities, we rely on the analysis of two case studies, that of the Hydro-Québec Research Institute (IREQ), and of Ubisoft Montreal.

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.

Science-based university spin-offs face considerable technology and market uncertainty over extended periods of time, increasing the challenges of commercialisation. Scientist-entrepreneurs can play formative roles in commercialising lab-based scientific inventions through the formation of well-endowed university spin-offs. Through case study analysis of three science-based university spin-offs within a biotechnology innovation ecosystem, we unpack the impact of <i>pre-formation</i> intangible assets of academic scientists (research excellence, patenting, and international networks) and their entrepreneurial capabilities on spin-off performance. We find evidence that the pre-formation entrepreneurial capabilities of academic scientists can endow science-based university spin-offs by leveraging the scientists' pre-formation intangible assets. A theory-driven model depicting the role of pre-formation intangible assets and entrepreneurial capabilities in endowing science-based university spin-offs is developed. Recommendations are provided for scholars, practitioners, and policymakers to more effectively commercialise high potential inventions in the university lab through the development and deployment of pre-formation intangible assets and entrepreneurial capabilities.

For a long time, high school students have been forced to practice selecting correct answers on college scholastic ability tests. Recently, it has been suggested that schools introduce biomimicry activities for STEM education to develop students' 21st century competency. However, there have been arguments about which system is more appropriate in terms of enhancing a student's competency development. Therefore, we evaluated neurological evidence of students' competency using fMRI scans taken during the selecting a correct answer for a biology question and during a biomimicry activity. Results showed that the repetitive practice of selecting correct responses limited a student's neurological activities to the brain network of the visual cortex and the front-parietal working memory cortex. However, the biomimicry activity simultaneously involved diverse prefrontal, parietal and temporal cortexes, and the putamen, limbic and cerebellum lobes. Therefore, this study proposes that the biomimicry activities could stimulate their coordinated brain development.