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The purpose of this study is to provide a method for designing the shell and tube heat exchangers and examine the total annual cost of heat exchanger networks from the economic view based on the careful design of equipment.

Accurate evaluation of heat exchanger networks performance depends on detailed models of heat exchangers design. The simulations variables include nine design variables such as flow direction determination of each of the two fluids, number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter and tube pitch. The optimal designing of the heat exchangers is based on geometrical and hydraulic modeling and using a hybrid genetic particle swarm optimization algorithm (PSO-GA) technique. In this paper, optimization and minimization of the total annual cost of heat exchanger networks are considered as the objective function.

In this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement. PSO-GA algorithms have been used to minimize the total annual cost, which includes investment costs of heat exchangers and pumps, operating costs (pumping) and energy costs for utilities. Three case studies of four, six and nine streams are selected to demonstrate the accuracy of the method. Reductions of 0.55%, 23.5% and 14.78% are obtained in total annual cost for the selected streams, respectively.

In the present study, a reliable method is used to simulate and optimize design parameters and the economic optimization of the heat exchanger networks. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the PSO-GA methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to case studies demonstrates its ability to accurately design heat exchangers to optimize the objective function of the heat exchanger networks by giving the detail of design.

This study aims to examine and validate the role of electronic word of mouth (eWOM), a mediating factor between the marketing mix elements (product, price, place and promotion) and women's purchasing intention of children's dietary supplements in the Jordanian environment culture context.

The data of the research was collected through online and offline questionnaires using a connivance sample of 370 women who have children and live in Amman, the largest city in Jordan.

The findings showed a partial effect of the eWOM as mediating factor on the relationship between marketing mix elements and women's purchasing intention of children's dietary supplements. The findings also revealed significant positive effects of price and place as marketing mix elements on women's purchasing intention of children's dietary supplements, while the product and promotion elements were found insignificant.

This paper contributed to consumer buying behaviour theories by filling a gap in the literature regarding the role of eWOM as a mediating factor on the relationship between marketing mix elements and women's purchasing intention of children's dietary supplements in a developing country like Jordan for the first time.

The purpose of this paper is to propose a comprehensive framework for integrating big data analytics (BDA) into cyber-physical system (CPS) solutions. This framework provides a wide range of functions, including data collection, smart data preprocessing, smart data mining and smart data visualization.

The architecture of CPS was designed with cyber layer, physical layer and communication layer from the perspective of big data processing. The BDA model was integrated into a CPS that enables managers to make sound decisions.

The effectiveness of the proposed BDA model has been demonstrated by two practical cases − the prediction of energy output of the power grid and the estimate of the remaining useful life of the aero-engine. The method can be used to control the power supply system and help engineers to maintain or replace the aero-engine to maintain the safety of the aircraft.

The communication layer, which connects the cyber layer and physical layer, was designed in CPS. From the communication layer, the redundant raw data can be converted into smart data. All the necessary functions of data collection, data preprocessing, data storage, data mining and data visualization can be effectively integrated into the BDA model for CPS applications. These findings show that the proposed BDA model in CPS can be used in different environments and applications.

User-generated content was explored to understand the public discourse surrounding the Vessel, a star architecture in New York. Through text analytics, the study aims to uncover topics, sentiments and themes in public opinion regarding this controversial building from social media data.

This study utilized a big data and text analytics approach, employing topic modeling with the BERTopic technique, sentiment analysis with roBERTa and thematic analysis on 10,259 Reddit comments pertaining to the Vessel.

The comments were grouped into 20 topics and seven themes, shedding light on discussions regarding the Vessel’s philosophy of existence, critiques of the architect’s approach, evaluations of project success or failure and considerations of the project’s future. Negative sentiments dominate the discourse, reflecting widespread criticism and skepticism towards the project.

The manual data collection method, due to API restrictions, precluded tracking evolving trends over time. Nevertheless, the study provides insights for architects, urban planners, policymakers and stakeholders involved in public space design and management, highlighting the importance of considering user feedback from social media platforms.

This study enriches our comprehension of how users perceive star architecture in the age of social media, focusing on hidden layers of discourse surrounding a controversial iconic building. By combining topic modeling and sentiment analysis, the study offers a novel approach to analyzing architectural public debates on social media platforms like Reddit.

Considering the transition of communicational channels from physical to digital spaces, this study aims to provide a theoretical foundation for understanding engagement in electronic word of mouth (eWoM) among managers and customers in the hospitality and tourism industry.

This study uses the four aggregate dimensions, namely, performance expectancy, efforts expectancy, social influence and facilitations condition. Further, this paper uses the 14 second-order themes of the Unified Theory of Acceptance and Use of Technology with a data set that represents elements that can trigger eWoM, both from managers’ and customers’ perspectives. The process of data structuration follows thematic analysis and axial coding techniques.

The results of this study show that performance expectancy, facilitation conditions, social influence and effort expectancy all trigger positive eWoM generation in the hospitality and tourism industry indicating customers’ and managers’ perspectives.

This novel study provides a theoretical foundation and novel propositions for future research work on the role of novel antecedents that can trigger eWoM in the hospitality and tourism industry. This paper also provides a benchmark for practitioners and policymakers in their strategic decisions-making towards improving business performance through positive eWoM.

Perceived risk and benefits have been proposed as crucial elements in consumers’ technology adoption intention. Therefore, this study aims to use the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, with perceived risks and benefits, to explore how they affect consumers’ Buy-Now-Pay-Later (BNPL) behavioural intention.

An online survey was conducted among 537 users of BNPL. Partial least square structural equation modelling analysis was performed to examine the hypothesized relationships.

The study found that perceived benefits, performance expectancy, effort expectancy, habit and social influence increase consumers’ behavioural intention towards BNPL. In contrast, perceived risk reduces the consumer’s intention to use. Additionally, the study revealed that consumers’ intention towards BNPL services positively affects their usage.

The lack of information makes consumers hesitant to use BNPL. Therefore, marketers should optimize facilitating conditions, such as user-friendly interfaces, accessible resources and efficient customer support, which should be the business strategy of BNPL service providers to ensure a seamless user experience.

The research empirically investigates the effect of perceived benefits on consumer behavioural intention. This contribution is crucial because while there was speculation about benefits as a driving force to consumers’ adoption intention, there was a lack of sufficient empirical evidence to support it.

This paper aims to investigate the motivational drivers for knowledge sharing (KS) through the application of mobile social networking (MSN) and their influence on employee productivity in the Malaysian construction industry.

The data was collected by distributing a self-administered questionnaire among employees working in the construction industry. A total of 250 questionnaires were collected and analysed using partial least squares structural equation modeling. This study used a variety of analysis methodologies including correlation analysis, reliability and validity testing, as well as a structural model in evaluating the relationships between KS, motivational factors and influence on productivity.

The empirical results of this study reveal that utilitarian motivation is positively related to internal and external KS. Similarly, internal and external KS have a positive influence on productivity, while internal and external hedonic motivation does not show any significant relationship with productivity.

Considering the paucity of evidence regarding the determinants of KS motivational drives on employee productivity in the Malaysian construction industry, this study enriches the social cognitive theory-based literature on KS.

This study provides some insights to the players in the construction industry in implementing the most appropriate KS promotion strategies that could potentially influence employee productivity. Organisations in the construction industry that place great emphasis on utilitarian motivation including competency, reward and incentives are more likely to have a better influence on KS and, in turn, positively impact employee productivity.

This study meaningfully contributes to enhancing the understanding of the motivational drivers for sharing knowledge on MSN and its influence on productivity. The findings of this study potentially assist construction practitioners in developing a holistic blueprint for managing their KS towards improving the overall productivity of their employees.

A welding robot is a complicated system with uncertainty, time-varying, strong coupling and non-linear system. It is more complicated if it is used in an underwater environment. It is difficult to establish an accurate dynamic model for an underwater welding robot. Aiming at the tracking control of an underwater welding robot, it is difficult to achieve the control performance requirements by the classical proportional integral derivative control method to realize automatic tracking of the seam. The purpose of this paper is to suggest a novel method to deal with these issues.

To combine the advantages of active disturbance rejection control (ADRC) and sliding mode control (SMC) to improve the shortcomings of a single control method, a hybrid control method for an underwater welding robot trajectory tracking based on SMC_ADRC is proposed in this research work.

The simulation experiment of the proposed approach is carried out by Matlab/Simulink, and the welding experiment is recorded. The seam gets plumper and smoother, with better continuity and no undercut phenomenon.

The proposed approach is effective and reliable, and the system’s tracking performance is stable, which can effectively reduce chattering and improve system robustness.

The aim of the study is to investigate the factors that influence the adoption of e-procurement by businesses in the specific context of Saudi Arabia.

This study uses the firm-level data of World Bank Enterprise Surveys 2022 of Saudi Arabia covering 1,573 enterprises. The association between firm characteristics and the adoption of e-procurement by enterprises has been examined through the chi-square test. Further, a logistic regression model has been fitted to estimate the factors affecting adoption of e-procurement.

About 10 % of the enterprises reported that they adopted e-procurement as the establishment’s main technology to manage procurement and supplier chain by enterprises. A statistically significant association has been found between e-procurement vis-a-vis firm characteristics. Moreover, logistic regression analysis indicated that large and innovative enterprises, with digital presence in terms of website, holding a bank account and access to credit are more likely to adopt e-procurement.

This study offers valuable insights into the factors that influence the adoption of e-procurement by businesses, which helps the supply chain manager to develop and operationalize the digital supply chain through e-procurement in the context of Saudi Arabia.

This paper can provide significant insights for promoters and supply chain managers in Saudi Arabia, who are now focusing on the digital transformation of businesses. By understanding the factors affecting e-procurement by firms, they can smooth the supply chain.