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This paper aims to explore the deep Web as a solution for displacement and replacement challenges in libraries, addressing the challenges, benefits, strategies and case studies.

The paper synthesizes existing literature on deep Web integration in libraries, providing a comprehensive analysis of insights from scholarly articles, case studies and expert opinions.

The deep Web grants libraries access to unique content, improving information access, fostering collaboration and enabling personalized content. However, security, privacy, ethics and data protection must be considered.

This paper contributes to the literature by providing a comprehensive examination of deep Web integration in libraries, offering valuable recommendations for navigating the changing landscape and leveraging the deep Web’s potential.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

This study aims to assess green food packages’ role in sustaining a restaurant’s curbside pickup service on three stages of consumer experiences: choosing a restaurant, evaluating their experiences of a recent purchase and weighing their post-consumption behavioral intentions after the recent purchase.

The service encounters framework and relevant literature guided the development of the questionnaire. A Qualtrics panel data of 314 valid questionnaires were collected and analyzed with choice experience, ordinary least squares regression and PROCESS modeling.

First, word-of-mouth (WOM) and function encounters significantly influence consumers’ first-time curbside pickup purchasing decisions. Then, service results encounter (besides distributor encounter) most significantly affects consumers’ overall curbside pickup experience. Finally, green food packages increase consumers’ shares of future purchases through their positive WOM intentions and extra efforts of revisiting the restaurant. Consumers’ perceived importance of green restaurant practices strengthens green food packages’ positive impact on extra efforts.

This study provides operational and marketing insights for restaurants to use food packages and sustain their curbside pickup service.

Besides assessing consumers’ evaluations and behavioral intentions for an off-premises restaurant service expected to stay beyond the pandemic, this research uniquely focuses on green food packages, a sustainability issue lacking research attention. The findings add new empirical insights to studies about sustainability and restaurant/food–retail operations.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

It is generally known that the perforated section such as the castellated section is good to sustain distributed loads but inadequate to sustain highly concentrated loads. Therefore, it is possible to design the opening in a different arrangement of web opening to achieve section efficiency, thus improving the strength and torsional behaviour of the section with web opening. This study aims to focus on the finite element analysis of I-beam with and without openings in steel section dominated to lateral-torsional buckling behaviour.

In this work, the analysis of different sizes, shapes and arrangements of web opening is performed by using LUSAS application to conduct numerical analysis on lateral-torsional buckling behaviour. This involves three diameter sizes of web opening, five types of opening shapes and two criteria of the model.

The section with c-hexagon web opening was placed about 200-mm centre to centre and 100-mm edge distance, contribute to 7.26% increase of buckling capacity. For the section with 150-mm centre to centre and 50-mm edge distance, the occurrence of local buckling contributes to decrease of lateral buckling section capacity to 19.943 kNm, where pure lateral-torsional buckling mostly occurred because of prevented section. Besides that, the web opening diameter was also analysed. The web crippling was observed because of the increase of opening diameter from 0.67 to 0.80 D.

This contributes to a decrease in buckling capacity as figured in the contour of the deformed shape. For Model 1, an increase of buckling capacity (31.46%) is observed when the opening diameter are changed from 0.67 to 0.80 D.

The professional experiences of teachers of languages and cultures, along with the learning experiences of their students, are embedded in educational contexts, which themselves are informed, and constrained, by national language policies. This study aims to explore 51 English-as-a-foreign-language (EFL) secondary teachers’ perceptions of Web-based technology use to enhance students’ cultural awareness in Chile. Specifically, the study investigated teachers’ use of Web-based resources for cultural awareness, culture content and technology-based tasks, as well as perceived challenges in implementing technology-enhanced language and culture learning.

The study adopted a mixed-method research design combining online questionnaires and interviews as data collection tools. Results were analyzed through the use of descriptive statistics and content analysis.

The teachers in this study emphasized reflection in their classrooms but did not take a critical approach. Their approach to culture was limited to a “country-specific” view, and technology-enhanced activities accentuated differences rather than promoting meaningful intercultural exchange. Challenges to the successful implementation of technology-enhanced language and culture learning included a somewhat out-of-date theoretical approach to intercultural learning in the national curriculum, a nationwide approach to professional development that lacks a focus on critical reflection and inadequate support for effective use of technologies in schools.

The study highlights the importance of periodically revising a country’s EFL language policies, communication methods, support mechanisms and implementation factors to ensure classroom integration of language, culture and technology education.

This paper explores the tension between macro-level national policy and teachers’ perspectives on their classroom practice, including the contextualized limitations of implementing national policy at the micro level.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.

A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.

The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.

First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.