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The aim of the study is to investigate the effects of perceived healthcare service quality (human aspects, technical aspects and tangible aspects) on satisfaction and guest loyalty in the hotel industry in the COVID-19 pandemic era.

A total of 130 guests in the hotel were selected purposively in Iraq. Data from self-administered questionnaires were analyzed through the VB-SEM statistical technique using Smart-PLS software towards testing the hypotheses.

The findings indicated that perceived service quality influences satisfaction and guest loyalty of guests in the hotel. This study reveals that human aspects, technical aspects and tangible aspects directly positively affect satisfaction and guest loyalty in the hotel industry.

This study highlights that perceived service quality (human aspects, technical aspects and tangible aspects) are vital and practical strategic tools that could be positioned to accelerate guest loyalty in the hotel industry. Furthermore, satisfaction mediates the relationship between human aspects, technical aspects, tangible aspects and guest loyalty.

Introduction: Based on the given experiences, many government institutions failed in their strategic management and planning for managing COVID-19. Meanwhile, when a crisis disrupts a system, institutions lose their direction and fail to make necessary responses.

Purpose: The current study highlighted the impact of social justice and modern governance in providing equitable healthcare services and dealing with crises during the COVID-19 pandemic in developing countries.

Methodology: Cross-country analyses were used based on captured secondary data. We evaluated several indices, including, for example, Crisis Index Indicators, Worldometers, and the Global Health Security (GHS) Index 2019.

Findings: According to the GHS (2019) data, public health service delivery equity was ineffective, socially unjust, and unfair treatment was experienced in the context of the conflict-affected countries. Most conflict-affected countries (Iraq, Nigeria, Afghanistan, and Venezuela) did not have guidelines or public reports committing to providing prioritized healthcare services to the public and healthcare workers. The experience of conflict-affected countries has shown that healthcare disparities still exist. While many governments in conflict-affected countries failed to give equitable access to healthcare services during the COVID-19 pandemic to the public.

The Metaverse holds vast amounts of user data, making it essential to address threats to its confidentiality, integrity and availability. These threats are not purely technological, as user actions and perceptions, shaped by psychological factors, can influence cybersecurity challenges. Thus, a holistic approach incorporating technological and psychological dimensions is crucial for safeguarding data security and privacy. This research explores users’ cybersecurity behavior in the Metaverse by integrating the technology threat avoidance theory (TTAT) and the theory of planned behavior (TPB).

The model was assessed using data collected from 746 Metaverse users. The empirical data were analyzed using a dual structural equation modeling-artificial neural network (SEM-ANN) approach.

The main PLS-SEM findings indicated that cybersecurity behavior is significantly affected by attitude, perceived behavioral control, subjective norms, perceived threat and avoidance motivation. The ANN results showed that perceived threat with a normalized importance of 100% is the most significant factor influencing cybersecurity behavior. The ANN results also showed that perceived severity with a normalized importance of 98.79% significantly impacts perceived threat.

The novelty of this research stems from developing a unified model grounded in TTAT and TPB to understand cybersecurity behaviors in the Metaverse. Unlike previous Metaverse studies that solely focused on measuring behavioral intentions or user behaviors, this study takes a step further by evaluating users’ cybersecurity behaviors. Alongside its theoretical insights, the study offers practical recommendations for software developers, decision-makers and service providers.

Nanofluids enhance heat transfer due to the inclusion of nanoparticles, but the exact reasons remain debated. Limited nanoscale experiments hinder understanding. To investigate the thermal effects of nanoparticles, understanding nanoparticle aggregation kinetics is crucial. Nanoparticles have applications in various industrial fields. This study compares the effects of nanoparticle aggregation and non-aggregation in a nanofluid flow influenced by an inclined magnetic field around an expanding or shrinking cylinder, incorporating the generalized Fourier law with a prescribed surface temperature.

The model problem is solved numerically with the bvp4c finite difference collocation method, known for its accuracy.

Graphs and tables illustrate how key factors affect velocity and thermal fields. The results revealed that for stretching flows, fluid velocity increases with higher nanoparticle concentrations and velocity slip, while shrinking flows show opposite trends. The drag force decreases with rising Hartmann numbers and nanoparticle volume fraction, irrespective of aggregation. Surface drag is more affected by aggregation than non-aggregation in both shrinking and expanding cases. The study also validates the proposed model.

Before this, numerous attempts discussed aggregation and non-aggregation separately on a deforming cylinder. Nevertheless, no study has yet assessed the impact of a slanted magnetic field on comparing the effects of nanoparticle aggregation versus non-aggregation in nanoliquid flow over a deformable or shrinking cylinder. This seems to be the first attempt to compare nanoparticle aggregation versus non-aggregation in nanoliquid flow.

Additive manufacturing became the most popular method as it enables the production of light-weight and high-density parts in effective way. Selective laser melting (SLM) is preferred by means of producing a component with good surface quality and near-net shape even if it has complex form. Titanium alloys have been extensively used in engineering covering a variety of sectors such as aeronautical, chemical, automotive and defense industry with its unique material properties. Therefore, the purpose of this review is to study the tribological behavior and surface integrity that reflects the thermal and mechanical performances of the fabricated parts.

This paper is focused on the tribological and surface integrity aspects of SLM-produced titanium alloy components. It is aimed to outline the effect of SLM process parameters on tribology and surface integrity first. Then, thermal, thermal heat, thermomechanical and postprocessing surface treatments such as peening, surface modification and coatings are highlighted in the light of literature review.

This work studied the effects of particle characteristics (e.g. size, shape, distributions, flowability and morphology) on tribological performance according to an extensive literature survey.

This study addresses this blind spot in existing industrial-academic knowledge and goals to determine the impact of SLM process parameters, posttreatments (especially peening operations) and particle characteristics on the SLMed Ti-based alloys, which are increasingly used in biomedical applications as well as other many applications ranging from automobile, aero, aviation, maritime, etc. This review paper is created with the intention of providing deep investigation on the important material characteristics of titanium alloy-based components, which can be useful for the several engineering sectors.

The chapter examining the impact of artificial intelligence (AI) on audit quality to achieve business sustainable practices, trying to found that using of AI as a substitute for human intelligence affects auditor’s capability and experience, from other hand affects audit process, starting from audit planning until issuance of reporting. Based on literature review about AI and audit quality, the chapter findings showed that AI adoption is a necessary requirement for auditors due to time constraints, accuracy requirements, and job speed requirements. The impact of AI on audit quality is profound and multifaceted. By leveraging the capabilities of AI, auditors can enhance the accuracy, efficiency and objectivity of their work.

To guarantee that the application of AI benefits the auditing profession and the public it serves, it is imperative to solve the obstacles that come with it. AI has the ability to greatly improve audit quality, but its deployment needs to be closely monitored to prevent any early problems. With appropriate training, data, and governance, AI can enhance the efficiency and effectiveness of audits, leading to more accurate and reliable financial reporting. However, it is important for auditors to be aware of the challenges and risks associated with AI and to take steps to mitigate these risks. Where cybersecurity is a protection line for external threats and it is considered as a new risk management tool.

The chapter examines the impact of artificial intelligence (AI) on audit quality to achieve business sustainable practices, trying to find that using AI as a substitute for human intelligence affects auditors’ capability and experience, and it also affects audit process, starting from audit planning to the issuance of reporting to improve business sustainability. Based on the literature review about AI and audit quality, the chapter findings showed that AI adoption is a necessary requirement for auditors due to time constraints, accuracy requirements and job speed requirements. The impact of AI on audit quality is profound and multifaceted. By leveraging the capabilities of AI, auditors can enhance the accuracy, efficiency and objectivity of their work.

To guarantee that the application of AI benefits the auditing profession and the public it serves, it is imperative to solve the obstacles that come with it. AI has the ability to greatly improve audit quality, but its deployment needs to be closely monitored to prevent any early problems. With appropriate training, data and governance, AI can enhance the efficiency and effectiveness of audits, leading to more accurate and reliable financial reporting, which directly achieve business sustainability through enhancing financial reporting quality, investors’ confidence, corporate governance and mitigation of agency conflicts.

The purpose of this study is to examine the factors that influence people’s behaviour in paying zakat, explore their perceptions of the institutions, examine the factors that influence their trust and analyse the impact of trust on their behaviour in paying zakat to the institutions.

A questionnaire was distributed to 740 potential Zakat payers in the Federal Territory, Malaysia. In designing the questionnaire, a systematic literature review, focus group discussions and pilot study were conducted. Descriptive analysis and partial least squares structural equation model were used with SmartPLS software.

The result shows that trust, intention to pay zakat and age of the respondents have statistically significant impacts on people’s behaviour to pay zakat through institutions. Intention to pay zakat is influenced by attitudes, subjective norms and perceived behavioural control. In addition, the main common concerns expressed were lack of awareness of the importance of paying zakat, lack of transparency in zakat administration, especially in collection and distribution, and inefficiency in administration and distribution. Moreover, trust in the institutions could be established if the institution is able to fulfil its mission of collecting and distributing zakat to the entitled Asnaf and improve their welfare, as trust not only has a direct impact on people’s behaviour, but also strengthens people’s intention and influences their behaviour to pay zakat to the institutions.

This study focuses on a specific geographical area and zakat institution; hence, the study’s generalisability is limited. The use of self-reported and cross-sectional data may introduce bias and fail to capture the dynamic change of trust, intention and behaviour across time. The proposed solution of leveraging digital platforms may provide numerous hurdles and obstacles for adoption by the zakat institution.

This study shows the significant role of trust in influencing people’s intentions and behaviour in supporting organisations. Therefore, it can serve as an indicator of the performance or success of a particular institution. Thus, there is a need to find strategies to gain people’s trust by improving their ability, integrity and benevolence in performing their tasks.

This study aims to investigate the interconnectedness of sustainability-linked and AI-based cryptocurrencies returns and volatility over five years (2018–2024). It aims to uncover the dynamic relationships between these two sectors under various market conditions, providing insights into their behavior and influence within the broader cryptocurrency market.

The research employs a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyze key cryptocurrencies associated with AI and sustainability. This approach is complemented by a quantile-based perspective, allowing for an in-depth examination of return and volatility spillovers across different market conditions. Thus, facilitating an understanding of the intricate dynamics between sustainability-linked and AI-based cryptocurrencies.

The findings reveal distinct market dynamics with the Sustainable sector consistently acting as a net transmitter, while the AI sector predominantly as a net receiver, indicating its reactive nature. In bearish markets, both sectors display high interconnectedness, with the Sustainable sector shaping dynamics. In bullish markets, the Sustainable sector maintains influence, while the AI sector adopts a more proactive role, influencing the market more than in bearish conditions. Post-Chat GPT 3 the Sustainable sector decreases influence, becoming a net receiver in bullish markets. In contrast, the AI sector strengthens as a net transmitter, signaling growing investor confidence and prominence.

This study explores the interconnectedness of sustainability-linked and AI-based cryptocurrencies through a TVP-VAR model and a quantile-based analysis. It provides insights into how these sectors interact and influence each other across different market conditions, especially highlighting the significant shifts in dynamics following the advent of advanced technologies like Chat GPT 3. This contributes to a deeper understanding of the evolving landscape of the cryptocurrency market in the context of sustainability and AI.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.