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Pipeline robots are often used in pipeline non-destructive testing. Given the need for long-range in-pipe inspections, this study aims to develop a wireless in-pipe inspection robot for image acquisition.

In this paper, an in-pipe robot with a new mechanical system is proposed. This system combines a three-arm load-bearing structure with spring sleeves and a half-umbrella diametric change structure, which can ensure the stability of the camera when acquiring images while maintaining the robot’s flexibility. In addition, data were transmitted wirelessly via a system that uses a 433 MHz ultra-high frequency and wireless local-area network–based image transmission system. Software and practical tests were conducted to verify the robot’s design. A preliminary examination of the robot’s cruising range was also conducted.

The feasibility of the robot was demonstrated using CATIA V5 and MSC ADAMS software. The simulation results showed that the centre of mass of the robot remained in a stable position and that it could function in a simulated pipeline network. In the practical test, the prototype functioned stably, correctly executed remote instructions and transmitted in near real-time its location, battery voltage and the captured images. Additionally, the tests demonstrated that the robot could successfully pass through the bends in a 200-mm-wide pipe at any angle between 0° and 90°. In actual wireless network conditions, the electrical system functioned for 44.7 consecutive minutes.

A wheeled wireless robot adopts a new mechanical system. For inspections of plastic pipelines, the robot can adapt to pipes with diameters of 150–210 mm and has the potential for practical applications.

The purpose of this paper is to identify corrosion types and corrosion transitions by a novel electrochemical noise analysis method based on Adaboost.

The corrosion behavior of Q235 steel was investigated in typical passivation, uniform corrosion and pitting solution by electrochemical noise. Nine feature parameters were extracted from the electrochemical noise data based on statistical analysis and shot noise theory. The feature parameters were analysis by Adaboost to train model and identify corrosion types. The trained Adaboost model was used to identify corrosion type transitions.

Adaboost algorithm can accurately identify the corrosion type, and the accuracy rate is 99.25%. The identification results of Adaboost for the corrosion type are consistent with corroded morphology analysis. Compared with other machine learning, Adaboost can identify corrosion types more accurately. For corrosion type transition, Adaboost can effectively identify the transition from passivation to uniform corrosion and from passivation to pitting corrosion consistent with corroded morphology analysis.

Adaboost is a suitable method for prediction of corrosion type and transitions. Adaboost can establish the classification model of metal corrosion, which can more conveniently and accurately explore the corrosion types. Adaboost provides important reference for corrosion prediction and protection.

This study examines the impact of social media information sharing and usage on consumer beliefs particularly in the credibility of the information provided by e-commerce vendors, and consumer trust formation.

Drawing on trust through social media usage and surface credibility, the authors have proposed a research model to investigate consumers satisfaction on food and beverage (F&B) products. Empirical support for the research model was provided by using structural equation modelling using survey data drawn from Malaysian consumers with an account with Facebook.

Results indicate that consumer participation in social media communities support higher levels of consumer trust and ratings of the surface credibility of information provided by an F&B vendor, and surface credibility also helped to develop consumer trust. Trust in the vendor also exerted a positive influence on consumer satisfaction with F&B product offerings. Results suggest that F&B that provide credible and transparent information regarding their branded products, enjoy increased levels of consumer trust, leading to higher levels of consumer satisfaction with their F&B consumption experience.

The result of this research contributes to social commerce branch of literature and has practical implications for practitioners in the F&B industry as a means to survival strategies to embrace critical and challenging period during an endemic, particularly. As such, this study analyses the relationship between social media usage, surface credibility, trust and satisfaction for developing consumer trust while managing enterprise social media.

This study aims to explore and conceptualize metaverse adoption using a systematic literature review (SLR). It also aims to propose a conceptual model that identifies significant factors affecting metaverse adoption in the entertainment, education, tourism and health sectors.

A SLR was conducted using the “preferred reporting items for systematic reviews and meta-analyses” report protocol and the “theory, context, characteristics, methods” framework to include all relevant articles published up to March 2023, which were sourced from the Scopus and Web of Science databases.

The reviewed literature revealed that the countries with the highest publications in the field of metaverse were China and the USA. It was also found that the technology acceptance model was the most used theoretical framework. Survey-based research using purposive and convenience sampling techniques emerged as the predominant method for data collection, and partial least square-structural equation modeling was the most used analytical technique. The review also identified the top six journals and the variables that help to develop a proposed model.

This review presents a novel contribution to the literature on metaverse adoption by forming a conceptual model that incorporates the most used variables in the entertainment, education, tourism and health sectors. The possible directions for future research with identified research gaps were also discussed.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

Laser additive manufacturing is widely utilized to fabricate the Ti6Al4V alloy, but it requires post-processing to improve its performance. This paper aims to propose laser peening (LP) as an effective way to improve the surface characteristics of the Ti6Al4V alloy fabricated by direct laser deposition (DLD).

Surface integrity including surface roughness, porosity, residual stress and microhardness are investigated in detail before and after LP treatment. Microstructure evolution is characterized by the electron backscatter diffraction (EBSD) to analyze crystal phase, grain boundary misorientation and texture.

Multiple overlapping layers of LP treatment result in slight influence on the polished surface of DLD-built samples. Porosity measured by the Archimedes test is found to be greatly decreased after LP treatment. Compressive residual stresses are significantly induced, the magnitude of which is greatly increased by increasing layers of LP treatment. And, local weakening or enhancement of residual stress in depth is observed because of pore and inclusion defects in the DLD-built Ti6Al4V alloy. Favorable hardness property can be obtained after multiple overlapping layers of LP treatment. EBSD analysis shows that LP treatment with multiple layers can introduce a large amount of lower-angle boundaries, indicating that dislocations beneath the top surface could induce a strain-hardened layer. The microtexture of the DLD-built Ti6Al4V alloy cannot be eliminated to decrease the anisotropy of the mechanical property.

The variation of porosity observed after LP inside the DLD-built Ti-Al-4V is attractive but requires more detailed work to analyze the evolution of pore geometry.

Surface treatment of an additive manufactured titanium alloy was carried out to improve its fatigue resistance.

This work is original in proposing LP as an effective post process for the surface treatment of an additive manufactured titanium alloy through analyzing the surface integrity and microstructure evolution.

Following the recent surge in research on carsharing, the paper synthesizes this growing literature to provide a comprehensive understanding of the current state of research and to identify directions for future work. Specifically, this study details implications for service theory and practice.

Systematic selection and analysis of 279 papers from the existing literature, published between 1996 and 2020.

The literature review identified four key themes: business models, drivers and barriers, customer behavior, and vehicle balancing.

For managers, the study illuminates the importance of collaboration among stakeholders within the automotive sector for purposes of widening their customer base and maximizing utilization and profits. For policy makers, their important role in supporting carsharing take-off is highlighted with emphasis on balancing support rendered to different mobility services to promote mutual success.

This is the first systematic multi-disciplinary literature review of carsharing. It integrates insights from transportation, environmental, and business studies, identifying gaps in the existing research and specifically suggesting implications for service research.

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time.

This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization.

From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods.

This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.