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The purpose of this paper is to explore how robots are being used in libraries and information centers to transform their services and what are the future possibilities and trends in the application of robots in libraries.

Through a review of the literature, this paper analyzes various library websites and consults literature relating to the use of telepresence robots in libraries; the current application of robots in libraries has been enumerated along with case studies of libraries currently adopting telepresence robots.

With the practical examples of libraries using different types of robots, this study summarizes diverse activities of artificial intelligence-mediated robots. The uses of telepresence technology in libraries help to enhance library services, reach new users and provide a more inclusive and accessible library experience. Telepresence robots enhance the quality and accessibility of library services, expand library outreach and provide new opportunities for virtual engagement and programming. The application of telepresence robots in libraries can offer many benefits, but there are also several challenges that libraries must address to ensure successful implementation.

This study will motivate libraries and library professionals to take advantage of adopting telepresence robots in library and information center services and further accelerate library operations in the right direction.

This paper highlights how the introduction of telepresence robots in libraries improves services and productivity and creates a more engaging environment for the user group. The benefits and challenges of using robots in the library and the future trend in the application of telepresence robots in libraries are also discussed.

Artificial intelligence (AI) is one of the emerging technologies of this time. AI is a widely used technology in library services that can transform the best services in the age of information technology. This paper aims to highlight the use of AI in library operations. Several research has been undertaken on this subject, but that only address a few applications. AI and libraries have a substantial nexus; nevertheless, the use and awareness of AI in library services are still creating question marks addressed in this paper. This study will help the policy stakeholder, librarians and scholars in the field to address these issues before the deployment of AI in library services.

This study is based on a qualitative method using content analysis techniques. An extensive review of literature on “artificial intelligence”, “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Literature was searched against various keywords like artificial intelligence, smart technologies, Internet of Things, electronic resource management, data mining and ambient intelligence. This study highlights the pros and cons of AI in library services and its possible solutions.

The findings of this study show that AI is a vibrant technology that can be used in library services; however, some obstacles like adequate funds, the attitude of librarians and technical skills are a few obstacles that hamper AI in library operations. The findings also reveal that using AI in library operations will accelerate libraries in the right direction. Furthermore, this study highlights various applications that can be deployed without spending costs.

This paper may be of interest to academic, librarians, policymakers, researchers and the government to have a perspective on initiatives in the country on application of technology in library services. This study can introduce the current status and potential of this technology to bring the technology revolution in library and information center services.

This study will motivate library professionals to take advantage of AI in library services and further accelerate library operations in the right direction.

This study covers the understanding of AI in library services that will help the librarian’s and information professionals leverage AI in library scenarios. Furthermore, the practical implication of AI in library services will bring positive change in implementing AI.

The purpose of this study is to investigate the entropy generation in different nanofluids flow over a vertically moving rotating disk. Unlike the classical Karman flow, water-based nanofluids have various suspended nanoparticles, namely, Cu, Ag, Al₂O₃ and TiO₂, and the disk is also moving vertically with time-dependent velocity.

The Keller box technique numerically solves the governing equations after reduction by suitable similarity transformations. The shear stress and heat transport features, along with flow and temperature fields, are numerically computed for different concentrations of the nanoparticles.

This study is done comparatively in between different nanofluids and for the cases of vertical movement of the disk. It is found that heat transfer characteristics rely not only on considered nanofluid but also on disk movement. Moreover, the upward movement of the disk diminishes the heat-transfer characteristics of the fluid for considered nanoparticles. In addition, for the same group of nanoparticles, an entropy generation study is also performed, and an increasing trend is found for all nanoparticles, with alumina nanoparticles dominating the others.

This research is a novel work on a vertically moving rotating surface for the water-conveying nanoparticle fluid flow with entropy generation analysis. The results were found to be in good agreement in the case of pure fluid.

This paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.

The authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.

Fifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.

The authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.

Earlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

Perceived corporate social responsibility (CSR) reflects personal views towards CSR activities that impact emotion, attitude and behaviour. This paper aims to investigate how perceived CSR predicts innovative work behaviour (IWB).

This study used a survey questionnaire to collect data from Gen Y 508 nurses working in the United Arab Emirates health-care sector. This study used a structural equation model to examine the study’s hypotheses.

Perceived CSR was found positively and significantly related to employees’ passion at work (PAW). This study also found that PAW positively and significantly mediates the relationship between perceived CSR and IWB in the organisation.

This study’s main limitation is that it used only quantitative methodology to examine the hypotheses. Thus, this study suggests mixed-method investigations in future research.

This study suggests that organisations should significantly engage in CSR-related activities, as it increases employees’ PAW and cause IWB to be in full swing. Passionate Gen Y nurses will improve the quality of patient care services with productive, IWB.

The study’s findings give an insight into how Gen Y nurses’ perceptions of CSR would affect their attitudinal and emotional variables. This study contributes to the knowledge domain of IWB in the organisation. Passionate Gen Y nurses will add value to the health-care system by implementing IWB.

The purpose of the study is to perform elastic stress and deformation analysis of a functionally graded hollow disk under different conditions (rotation, gravity, internal pressure, temperature with variable heat generation) and their combinations.

The classical method of solution, Navier's equation, is used to solve the governing equation. The analysis considers thermal and mechanical boundary conditions and takes into account the variation of material properties according to a power law function of the radius of the disk and grading parameter.

The findings of the study reveal distinct trends and behaviors based on different grading parameters. The influence of gravity is found to be negligible, resulting in similar patterns to the pure rotation case. Variable heat generation introduces non-linear temperature profiles and higher displacements, with stress values influenced by grading parameters.

The study provides valuable insights into the behavior of displacement and stresses in hollow disks, offering a deeper understanding of their mechanical response under varying conditions. These insights can be useful in the design and analysis of functionally graded hollow disks in various engineering applications.

The originality and value of this study lies in the consideration of various loading combinations of rotation, gravity, internal pressure and temperature with variable heat generation. Furthermore, the study of effect of various angular rotations, temperatures and pressures expands the understanding of the mechanical behavior of such structures, contributing to the existing body of knowledge in the field.

This study aims to examine the relationships between organizational learning capabilities, open innovation and firm performance (FP) in the context of small and medium enterprises (SMEs) in the emerging economies.

Data collected from 384 manufacturing SMEs operating across the seven emirates of the UAE were statistically analyzed using SmartPLS 3 to examine the hypotheses of this study.

The results show that organizational learning capabilities positively influences both inbound and outbound dimensions of open innovation (OI). Inbound open innovation (IP) practice positively impacted both market effectiveness and profitability, while outbound open innovation (OP) practice only affected profitability. Findings further confirmed the mediating role of IP practice on the relationships of organizational learning capabilities with market effectiveness and profitability. In contrast, OP practice did not mediate the relationships of organizational learning capabilities with market effectiveness and profitability.

To the best of the authors knowledge, this is among the first study contributing to the extant innovation literature in terms of investigations into the significant and complex interrelations of organizational learning capabilities, OI and FP in a single study, demonstrating various theoretical implications in the context of manufacturing SMEs in emerging countries. Overall, the findings of this study confirmed that the owners/managers of the UAE’s manufacturing SMEs need to be acquainted with the need of creating a working environment fostering organizational learning processes and capabilities to enhance IP and OP activities, thereby improving their market effectiveness and profitability.

This study aims to explore and synthesize the role of knowledge management (KM) in tourism organizations (including micro, small, medium and large enterprises and destination management organizations).

This study adopts systematic review methods to synthesize the role of KM in tourism from 90 journal articles.

This study identifies the prominent theories adopted to explore the relation and impact of KM in the tourism sector, the geographic distribution of the literature and thorough qualitative synthesis. This study identifies the critical research themes investigated and the outcome of KM applications. Finally, through reviews, this study identifies critical gaps in the literature and offer promising avenues to advance the KM in tourism research.

This is one of the few papers that comprehensively review the role of KM in the tourism industry and offer implications.

This empirical research intends to examine factors influencing the adoption of renewable energy (RE) using a conceptual model of the consumer decision-making process.

This study uses a primary response-based survey to collect data from 668 respondents interested in adopting RE for their daily usage. The sample respondents were chosen through a multi-stage random stratified technique. The responses were analyzed through structural equation-based modeling techniques to discuss the findings and suggest further implications.

The findings suggest that factors like knowledge, policy incentives, sustainable development goals (SDGs-7, 11 and 13), socio-economic benefits and risk perception significantly impact the adoption of RE. Besides, risk perception mediates between environmental concerns and the adoption of RE. Also, age has a significant role in RE adoption.

The study finds the critical role of government in introducing financial incentives to reduce the initial cost of renewable adoption. Doing so will also promote clean and equitable energy access to society leading to further fulfillment of SDGs. Additionally, steps like knowledge enrichment, designing suitable policies for a manufacturer and public-friendly renewable market development will further facilitate renewable adoption in society.

With an objective to study the public perception and attitude towards renewable adoption, this empirical research is the first of its kind to carry out a real-time survey of the Indian population and suggest policy implications which would benefit all the concerned stakeholders.

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.