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The purpose of this study was to evaluate the psychosocial impact during the current epidemic situation of COVID-19 in Pakistan.

A total of 1,149 respondents were recruited in the study. Mental health status and psychological impact of COVID-19 outbreak were measured by impact of events scale–revised (IES-R) instrument and depression, anxiety and stress scales (DASS-21), respectively.

Results of IES-R revealed moderate or severe psychological impact in 13.05% respondents (score > 33). DAAS score revealed that severe and extremely severe depression (score: 21–42), anxiety (score: 15–42) and stress (score: 27–42) were reported in 6.35%, 6.87% and 2.78% respondents, respectively. Higher levels of stress, anxiety and depression were recorded in female gender, student, medical professionals, farmer and daily wages employed, exhibiting significant (p < 0.05) association with psychological impact of the COVID-19 outbreak. Majority of respondents received increased support, shared feeling and family care.

Mild to moderate psychological impact on mental health status was recorded in this study, which enables further planning and opportunities for health authorities to design psychological interventions for the improvement of negative psychological impact of COVID-19 epidemic in vulnerable groups.

This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector.

This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice.

The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study.

The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results.

The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors.

First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior.

This paper aims to examine how information and communication technology (ICT) coordination, information reliability, social pressure, perceived usefulness and perceived ease of use affect citizens’ intentions to use e-government services.

The study is a quantitative type of study conducted through a causal study design. Noncontrived and cross-sectional methods were used, targeting Malaysian citizens who were 18 years of age or older. Due to an inaccessible sample frame, convenience sampling was used. After cleaning and removing necessary outliers, the final data set used for hypothesis testing consisted of 323 responses, which is considered sufficient as the study required a minimum sample size of 220.

A study has found that social pressure, perceived ease of use and perceived usefulness positively affect people’s intention to use e-government services. The impact of social pressure is influenced by perceived usefulness and ease of use, suggesting that government agencies can encourage usage by improving perceived usefulness and leveraging social pressure. The study emphasizes the significance of perceived usefulness and social pressure in promoting adoption. To enhance the user experience, agencies can use targeted marketing, improve service quality, collaborate with communities and develop mobile applications.

The study underscores the importance of examining the relationship between perceived usefulness, ease of use and the popularity of e-government services while emphasizing the need to comprehend the impact of ICT coordination, information reliability and social pressure on the adoption of e-government applications in developing countries.

This study investigated the role of brand experience dimensions (behavioral, intellectual, sensory and affective) to predict consumer loyalty (repurchase intention (RPI), word of mouth (WOM) and willingness to pay more (WPM)) through the mediating role of perceived brand authenticity (PBA) in the global branding context.

A total of 422 consumers participated in this study and provided feedback on top authentic global brands after completing a self-administered online survey. Partial least squares structural equation modeling (PLS-SEM) was used to conduct the data analysis.

This study discovered that brand experience dimensions positively influenced PBA (predominantly sensory and intellectual experiences), which significantly predicted consumer loyalty (RPI, WOM and WPM).

This research uncovered some limitations that can be used to investigate new research possibilities. From a theoretical standpoint, this study offers new insights into brand experience dimensions (BEDs), PBA and consumer loyalty in order to develop consumer-brand relationships.

This study offered several managerial recommendations. By considering brand authenticity as a positioning tool, global managers can effectively develop and implement various experiential marketing strategies to develop long-term relationships with consumers to attain their loyalty.

This is a new study that uses Fournier's relationship theory to investigate BEDs on PBA to predict consumer loyalty in the context of authentic global brands.

Offshore industries encounter severe production downtime due to high liquid carryovers in the T-junction. The diameter ratio and flow regime can significantly affect the excess liquid carryovers. Unfortunately, regular and reduce T-junctions have low separation efficiencies. Ansys as a commercial computational fluid dynamics (CFD) software was used to model and numerically inspect a novel diverging T-junction design. The purpose of diverging T-junction is to merge the specific characteristics of regular and reduced T-junctions, ultimately increasing separation efficiency. The purpose of this study is to numerically compute the separation efficiency for five distinct diverging T-junctions for eight different velocity ratios. The results were compared to regular and converging T-junctions.

Air-water slug flow was simulated with the help of the volume of the fluid model, coupled with the K-epsilon turbulence model to track liquid-gas interfaces.

The results of this study indicated that T-junctions with upstream and downstream diameter ratio combinations of 0.8–1 and 0.5–1 achieved separation efficiency of 96% and 94.5%, respectively. These two diverging T-junctions had significantly higher separation efficiencies when compared to regular and converging T-junctions. Results also revealed that over-reduction of upstream and downstream diameter ratios below 0.5 and 1, respectively, lead to declination in separation efficiency.

The present study is constrained for air and water as working fluids. Nevertheless, the results apply to other applications as well.

The proposed T-junction is intended to reduce excessive liquid carryovers and frequent plant shutdowns. Thus, lowering operational costs and enhancing separation efficiency.

Higher separation efficiency achieved by using diverging T-junction enabled reduced production downtimes and resulted in lower maintenance costs.

A novel T-junction design was proposed in this study with a separation efficiency of higher than 90%. High separation efficiency eliminates loss of time during shutdowns and lowers maintenance costs. Furthermore, limitations of this study were also addressed as the lower upstream and downstream diameter ratio does not always enhance separation efficiency.

Single-valued neutrosophic sets (SVNSs) are efficient models to address the complexity issues potentially with three components, namely indeterminacy, truthness and falsity. Taking advantage of SVNSs, this paper introduces some new aggregation operators (AOs) for information fusion of single-valued neutrosophic numbers (SVNNs) to meet multi-criteria group decision-making (MCGDM) challenges.

Einstein operators are well-known AOs for smooth approximation, and prioritized operators are suitable to take advantage of prioritized relationships among multiple criteria. Motivated by the features of these operators, new hybrid aggregation operators are proposed named as “single-valued neutrosophic Einstein prioritized weighted average (SVNEPWA) operator” and “single-valued neutrosophic Einstein prioritized weighted geometric (SVNEPWG) operators.” These hybrid aggregation operators are more efficient and reliable for information aggregation.

A robust approach for MCGDM problems is developed to take advantage of newly developed hybrid operators. The effectiveness of the proposed MCGDM method is demonstrated by numerical examples. Moreover, a comparative analysis and authenticity analysis of the suggested MCGDM approach with existing approaches are offered to examine the practicality, validity and superiority of the proposed operators.

The study reveals that by choosing a suitable AO as per the choice of the expert, it will provide a wide range of compromise solutions for the decision-maker.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

In real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

China–Pakistan Economic Corridor (CPEC) is a framework of regional connectivity in which employees have to work in a cross-cultural environment. This study has extended the leader-member exchange theory by investigating the mediating role of employee commitment (EC) between the relationship of leader-member exchange (LMX) and employee's work-related behaviors.

PLS-SEM technique was used to test the model by utilizing a multi-wave/two-source data collected from employees and their supervisors (n = 500) working in different energy projects of CPEC.

According to the results/findings, LMX has a significant positive impact on employee commitment, employee performance (EP) and open-minded discussions, but insignificant impact on innovative work behaviour (IWB). Mediating role of employee commitment was significant between the relationship of LMX with EP and open-minded discussions, but insignificant with the IWB.

The study contributes empirical evidence to understanding the leader-member exchange relationship among Chinese managers and Pakistani workers. It also contributes to the LMX theory literature by investigating the effect of LMX on followers' outcomes (employee performance, IWB, open-minded discussions) through employee commitment.

This study analyses the organizational consequences of work–family conflict (WFC) among female nurses in health care sector. Moreover, this study focuses on the moderating effect of intrinsic motivation on the association between WFC dimensions with different organizational outcomes.

Data are collected from 347 female nurses working in health care sector at Islamabad, Rawalpindi, Lahore, Multan and Bahawalpur regions of Pakistan, using random sampling technique. Regression analysis is used to test the hypotheses of this study.

The findings demonstrate that WFC conflict lowers job satisfaction, affective commitment and organizational citizenship behaviour. Contrary, WFC reduces job satisfaction, affective commitment and organizational citizenship behaviour and increases turnover intentions among female nurses. Moreover, intrinsic motivation moderates the association between WFC and certain organizational outcomes.

The study offers valuable insights for female nurses at health care sector about WFC and finally leads to theoretical contributions and practical implications for the healthcare sector of Pakistan.

The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle).

This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.

This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification.

The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).