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The purpose of this study is to investigate the behaviour of M40 grade of self-compacting concrete (SCC) with high volume of ground granulated blast furnace slag (GGBS) (50%) and recycled concrete aggregate (RCA) content up to 100% to assess the mechanical properties of SCC. As per guidelines of IS: 383 – 2016, the RCA can be replaced up to 20% of natural coarse aggregate up to M25 grade of concrete. This study assesses the mechanical properties of SCC beyond 20% of RCA content. Based on the experimental investigations, the compressive strength of mixes decreases as the content of RCA increases. It is found that concrete mixes with 20% RCA and shows the maximum compressive strength at 56 days.

The fresh properties as per EFNARC and IS: 10262–2019 guidelines, ultrasonic pulse velocity testing, mechanical properties and microstructure analysis have been conducted to evaluate the performance of SCC with RCA for practical applications.

From the experimental investigations, it is found that up to 50% of recycled coarse aggregate can be used for structural applications.

The environmental pollution and dumping of waste on green land can be reduced by effective utilization of recycled coarse aggregate and GGBS in the production of SCC.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

This study aims to identify the location of cracks in composite plates using a normalized mode shape curve algorithm. Crack in any structure is a destructive occurrence. Detecting these cracks early is pivotal for ensuring safety and preventing potential accidents. To prevent failure of structures, it is crucial to detect these cracks effectively and take the necessary precautions. Hence, crack detection and localization techniques are used to avoid sudden failures of structures while in operation.

An experimental modal analysis is conducted on composite plates with and without cracks to determine the natural frequencies and mode shapes. For this purpose, an impact hammer, uniaxial accelerometer and four-channel vibration analyzer are used to find the natural frequencies and mode shapes. Numerical modal analysis is performed on no crack and cracked composite plates using ANSYS software, and these are validated by the experimental modal analysis results. The normalized mode shapes algorithm is trained using test data of the first three natural frequencies collected from numerical modal analysis on different cracked composite plates for localization of crack.

The natural frequencies derived from both experimental modal analysis and numerical modal analysis exhibit a variance of 9.6%. The estimation of the crack location is achieved with exceptional precision by intersecting the first three normalized mode shapes. The first three normalized mode shape curve intersections provide a solid indication of the crack’s location. As the difference in error between the actual and estimated crack locations is only 0.9%.

This study introduces the first application of experimental modal analysis in conjunction with the normalized mode shape curve algorithm for localizing cracks in composite plates. The normalization process of mode shapes, derived from experimental modal analysis, forms a fundamental component of the mode shape curve algorithm specifically designed for crack localization. Combining experimental modal analysis with a specific algorithm of normalizing mode shapes is used to identify and locate cracks within these composite plates.

Purpose: This chapter investigates the moderating impact of personality and demographic factors on the association between work–life balance (WLB) and the well-being (WB) of Ayurveda doctors in Sri Lanka.

Need for the Study: WB is necessary for everyone’s life. Individuals must meet proper WLB between their private and career life scenarios. On the other hand, employee WB and WLB are considered under the sustainable development goals. Hence, it is required to investigate the effect of WLB on WB.

Methodology: This quantitative, cross-sectional study was conducted with minimal researcher interference. The primary data were collected using structured questionnaires from Ayurvedic Doctors in Sri Lanka. The correlation, regression, and hierarchical regression analyses with multivariate assumptions were conducted using SPSS.

Findings: The findings reveal a robust positive association between the WLB and WB, indicating the same association between the WLB and personality. Moreover, there is a strong positive association between personality and WB. The results of the moderator analysis presented that there is a marginal moderator impact from the personality towards the association between WLB and WB.

Practical Implications: Ayurveda Practitioners and policymakers can use the generated knowledge in decision-making. The results of this study can be used as a reference by all industrial practitioners to improve their business practices. They can do this by raising employee WLB to enhance WB, which will help them keep the best employees within the company.

This study aims to accommodate the mechanical characteristics that comply with the spectra of active sportswear by taking the innovative features of double-layered fabric (double cloth) and polyester yarn. The research focused on blending two types of polyester: Renova^® and polyester microfiber with different weave structures in both face and back layers for fabricated fabrics. Force resistance was investigated through several properties, the mass per unit area and thickness properties were also considered.

The research was divided into two phases. The first phase included three samples with the same double cloth structure and different weft materials to detect the most preferable performance. The second phase included three samples with the same materials and different double cloth structures. Several mechanical properties were tested in accordance with standard test methods, and the results were collected, tabulated and statistically analyzed using a chart line, an ANOVA test with a p-value = 0.05, a calculated least significant difference (value) and a radar chart area.

The results assigned that blending Renova^®/polyester microfiber develops mechanical properties than using them individually, considering the increase in polyester microfiber ratio. The double cloth with different plain structures has a significantly different effect on the examined properties, except for elongation and thickness.

According to the radar chart area, the double cloth with a plain rib structure achieved better performance, and in the same vein, the results showed that the plain with a warp rib structure enhanced fabric behavior more than the weft rib.

In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

The study aims to explore the mediating role of organizational culture in the relationship between on-the-job training (OJT), induction training and employee job performance. This study is conceptually grounded in Albert Bandura’s Social Cognitive Theory. The function that organizational culture plays as a mediator in the links between employee performance and on-the-job training and induction is a unique aspect of this study.

An industry-wide representation was ensured in the study by using a stratified random sampling technique to choose participants. The main characteristics pertaining to organizational culture, training initiatives and worker job performance were measured by using validated scales from earlier studies. For the purpose of validating the measurement model, factor loadings, internal consistency reliability and discriminant validity were evaluated through the use of partial least squares structural equation modelling in SmartPLS.

In support of Bandura’s Social Cognitive Theory, the study’s results show a strong association between work performance, organizational culture, on-the-job training and induction training. The study highlights the positive synergistic effect that supportive organizational culture and well-designed training programmes have on improving employee job performance. The unique contribution of this study is the provision of empirical support for these correlations across a wide range of industries, highlighting the crucial roles that organizational culture plays in promoting employee success.

Authors’ knowledge of how organizations may create environments that maximize worker productivity and potential is expanded by the study’s practical insights.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.