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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.

The vehicular ad hoc network (VANET) is an emerging area for smart cities as observed in last few decades. However, some hurdles for VANET exist that need to be resolved before its full implementation in smart cities. Routing is one of the main factors for having effective communication between smart vehicles that urgently needs to be addressed. One factor that affects communication between the vehicles is the intersection points that obstruct the communication. The paper aims to discuss these issues.

The conventional routing schemes fail to address the intersection problems that occur during the two points of communication. Therefore, this paper analyses the performance of existing position-based routing protocol for inter-vehicle ad hoc networks, considering the impact of a number of intersections. This simulation evaluates different position-based routing protocols such as Intersection-based Distance and Traffic-Aware Routing (IDTAR), Greedy Traffic-Aware Routing, Anchor-based Street and Traffic-Aware Routing and Geographic Source Routing, based on road topology and the number of intersections.

As a result, the protocol IDTAR has a lower end-to-end delay and high packet delivery ratio in terms of the number of intersections as a case study of smart cities. This concludes that IDTAR can be adaptive to smart cities communication, although some questions need to be considered in terms of its security, compatibility, reliability and robustness.

The role of VANET has been highlighted in smart cities due to its implications in day-to-day life. The vehicles in VANET are equipped with wireless communication nodes to provide network connectivity. Such types of network operate without the legacy infrastructure, as well as legacy client/servers.

Additionally, the study contributes to smart cities by measuring the performance of position-based routing protocols for VANETs.

Digital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.

Considering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.

From the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.

The authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

The purpose of this study is to model the theory of the low-velocity impact (LVI) process on sandwich beams consisting of flexible cores and face sheets reinforced with functionally graded carbon nanotubes (CNTs).

A series of parameters derived from molecular dynamics are used to consider the size scale in the mixture rule for the combination of CNTs and resin. A procedure involving the use of the first-order shear deformation theory of the beam is used to provide the displacement field of the sandwich beam. The energy method and subsequently the generalized Lagrange method are used to derive the motion equations. Due to the use of Hertz’s nonlinear theory to calculate the contact force, the equations of motion are nonlinear. Validation of the problem is carried out by comparing natural frequencies with other papers.

The influence of a series of parameters such as CNTs distributions pattern in the face sheets, the influence of the CNTs volume fraction and the influence of the core thickness to the face sheets thickness ratio in the issue of LVI on sandwich beams with clamped-clamped boundary conditions is investigated. The result shows that the type of CNTs pattern in the face sheet and the CNTs volume fraction have a very important effect on the answer to the problem, which is caused by the change in the value of the Young’s modulus of the beam at the contact surface. Changes in the core thickness to the face sheets thickness ratio has little effect on the impact response.

Considering the important application of sandwich structures in vehicles, aviation and ships, in this research, sandwich beams consisting of flexible core and CNTs-reinforced face sheets are investigated under LVI.

The purpose of this paper is to present an applied method to design the low-speed contact between a mass and surface of a beam using an analytical solution based on the first-order shear deformation beam theory. Also, a simulation of impact process is carried out by ABAQUS finite element (FE) code.

In theoretical formulation, first strains and stresses are obtained, then kinetic and potential energies are written, and using a combination of Ritz and Lagrange methods, a set of system of motion equations in the form of mass, stiffness and force matrices is obtained. Finally, the motion equations are solved using Runge–Kutta fourth order method.

The von Mises stress contours at the impact point and contact force from the ABAQUS simulation are illustrated and it is revealed that the theoretical solution is in good agreement with the FE code. The effect of changes in projectile speed, projectile diameter and projectile mass on the results is carefully examined with particular attention to evaluate histories of the impact force and beam recess. One of the important results is that changes in projectile speed have a greater effect on the results than changes in projectile diameter, and also changes in projectile mass have the least effect.

This paper presents a combination of methods of energy, Ritz and Lagrange and also FE code to simulate the problem of sandwich beams under low velocity impact.

The phenomenon of stress is a problem for a large number of interested people and researchers, and it has even become a global phenomenon in recent times. This is due to the effect of this phenomenon on the individual's performance, efficiency and satisfaction with the work he performs. This paper aims to know the level of psychological resilience, life stress and the relationship between them among a sample of Al-Bayda University students in the health-care context in Republic of Yemen.

To achieve the study objectives, the researchers applied the psychological resilience scale, which consisted of (40) items and prepared the psychological stress scale that consisted of (41) items.

The findings of the study indicated that the level of psychological resilience of Al-Bayda University students is high. The findings also indicated that there are statistically significant differences in psychological resilience depending on the gender variable in favor of males and that there are no statistically indicative significant differences according to the variables of specialization, academic level and marital status. The findings showed a high prevalence of life stresses among the members of the study sample and indicated that the sources of stress among the study sample individuals were arranged as follows: academic stress, future stress, social stress, family stress and economic stress.

The findings indicated significant differences among the study sample in the stress of life according to the variable of gender in favor of males and the presence of an inverse significant relationship between the level of psychological resilience and life stress among the study sample members.

The purpose of this study is to test the relationship between mall relevance dimensions [(functional relevance (FNR), symbolic relevance (SYR), social relevance (SOR) and environmental relevance (ENR)] and shoppers' well-being (SWB), which in turn influences mall loyalty (ML). In addition, this study aims to investigate the moderating effect of social media celebrities (SMCs) on the association between SWB and ML.

A mall intercept survey was used to collect responses from mall shoppers. The authors received 426 valid responses from mall shoppers in Pakistan's three metropolitan cities (i.e. Karachi, Lahore and Islamabad). To test the hypotheses, partial least squares structural equation modelling (PLS-SEM) was used.

Findings reveal that FNR, SYR, ENR and SOR significantly and positively influence SWB. Similarly, SWB significantly affects ML. Moreover, SMCs moderate the positive relationship between SWB and ML.

This study is one of the pioneer studies examining mall relevance dimensions on SWB. In addition, this study contributes to the retailing literature by testing the moderation effect of SMCs on the relationship between SWB and ML. Likewise, this study provides insights for mall administration to focus on mall relevance in terms of FNR, SYR, ENR and SOR to enhance the current and prospects' SWB. Next, SMCs play a key role in enhancing SWB and ML.

The aim of this study is to conduct an initial investigation of information sharing between the vast number of expatriate employees and the small minority of local employees in Dubai’s private sector workforce. Research on the impact of the workforce localization policy has highlighted the frequent marginalization of locals within the expatriate-dominated private sector. One form of this is the reluctance of expatriates to share information with local recruits, and the authors conducted this study to assess the reality and extent of this phenomenon.

The authors designed a brief interview survey to probe how Emirati employees secure workplace information and whether they experience information withholding on the part of expatriate colleagues. The authors also explored whether any such experience impacts on their attitudes to working in the private sector since this is a key factor in the success of the localization policy. Complete responses were received from 0.9 per cent of the total local private sector workforce.

A notable lack of information sharing emerged with 58 per cent of respondents reporting their expatriate colleagues’ and superiors’ reluctance to share information with them, and 63 per cent describing experiences of discriminatory behavior.

The authors identify key cultural and communication issues relating to localization within Dubai’s multicultural workforce. These include the broader cultural factors that determine how Emiratis conceptualize information sharing. Future research can pursue this issue to help inform the development of supportive information sharing practices. Such practices are an essential part of the creation of a diversity climate, which is necessary to sustain localization.

This study is a pioneering attempt to empirically investigate the information sharing practices that Emirati private sector employees experience. It suggests that the exclusion of citizens from the workplace through practices such as “ghost Emiratization” reverberates in the workplace through a lack of information sharing.

The proliferation of ChatGPT, as in many other digital technologies and social media, has increasingly impacted many aspects of modern life, including second and foreign language education. This study, building on a common theoretical position of all these advances in Artificial intelligence (AI), seeks to establish a model for ChatGPT research on English language learning. It specifically explores the dimensions in which ChatGPT aids students in their English language learning.

The study adopts grounded theory in collecting and analyzing data from 20 ResearchGater (RG) through a two-week RG discussion about ChatGPT uses in their language learning contexts. Data collected via the discussion was analyzed thematically.

Preliminary findings show that ChatGPT can be used in developing learners' language skills; scaffolding the learning process by providing feedback to students on their language use and acting as partners in practicing language with recommended activities for more language practice.

The study contributes a five-dimension model for artificial intelligence assisted language learning (AIALL). The model involves flexible teacher's role to consolidate learner autonomy and provide enjoyable learning, urges future innovation, and celebrates various applications. Examining the AIALL model of ChatGPT for language learning, teachers should provide some directions for properly using this new application.

This study's aim was to investigate the role of the perceived values of gaming on consumers' perceptions of brands as cool as well as the impacts on the consumer–brand equity relationship. The study proposed a framework highlighting the influences of fashion-branded games on brand coolness and building fashion brands' overall equity. As significant factors affecting gamers, gender and gaming have been studied as moderators affecting the overall proposed framework.

A quantitative method was used to assess the significance of the relationships within the proposed model. Partial least squares structural equation modeling technique was implemented to assess the framework's relationships with a sample size of 248 active online gamers.

The findings indicate that brand equity is positively associated with perceived brand coolness. Furthermore, of the three core online game values, perceived enjoyment was most strongly associated with perceived brand coolness, with other values, such as self-expression and perceived emotional challenge, having a weaker association. The multigroup analysis results further suggest that in the fashion industry, building brand equity through online games is strongly related to perceived brand coolness among female respondents, with the role of perceived brand coolness affecting male respondents to a lesser degree.

The contribution of this study to the existing literature consists in providing a deeper understanding of the impact of branded games on fashion brands' overall equity. The results provide insights for fashion brand managers into the significant effect of fashion gaming collaborations on consumers' behavioral outcomes.