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This study aims to empirically explore e-banking service quality and its impact on the e-banking loyalty through a mediating impact of e-banking satisfaction. The account holders of three domestic systemically important banks of Pakistan were surveyed during COVID-19 to examine the electronic services provided by these banks.

The data was collected through an adapted questionnaire by using emails and messaging applications. The database of a local marketing company in Pakistan was used, and 976 responses were included in the analysis. The structured equation modeling was used to test the propositions of study.

The findings delineate that reliability and website design proved to increase e-banking loyalty, particularly during COVID-19. The link between e-banking privacy and security and e-banking loyalty was proved as fully mediated by e-banking satisfaction; however, indirect effect of the reliability and website design with e-banking loyalty was partially mediated.

In strategic planning of e-banking mechanisms and the associated consumer behavior, the results of this study can be helpful for policymakers. In case of similar epidemics and natural calamities, consumers may depict similar behavior as shown during the lockdown and social distancing during COVID-19; hence, the study can help regulatory bodies in preparing their safety roadmap.

The mediating effect of e-banking satisfaction between privacy & Security and e-banking loyalty implies that customer give importance to secure e-banking platforms. There can be a variation in their loyalty because of privacy concerns. The application of cognitive–motivational–relational (CMR) theory in a relationship between e-banking service quality and e-banking loyalty with a mediating role of satisfaction is an emotional response to capture the behavioral changes during COVID-19. It enables researchers to understand the CMR theory concerning COVID-19 and e-banking perspective.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

This paper aims to replicate a model already proven in previous research in this field. This will make it possible to explain the possible relationships that may occur among the motivations, perceived value, satisfaction and loyalty of the tourist towards the Alhambra and Generalife inscribed as World Heritage Site (WHS) in 1984.

From a dataset containing 1,612 surveys, a model a model based on structural equations has been carried out through SmartPLS software, focus the analysis on the model dependent variables’ predictive power, as well as the size of the effect and the statistical inference of the structural relationships.

The main conclusions include the influence of perceived value on satisfaction as well as the influence of the latter on loyalty. it is remarkable the effect that the perceived value has on satisfaction, and satisfaction on loyalty. This implies that a positive assessment of world heritage destinations leads a subsequent loyalty to them.

The results obtained in this research can be used as a starting point for the establishment of new strategies for the promotion of the destination in terms of tourism and heritage.

The inclusion in the list of WHS is recognition in terms of material and historical quality, as well as a stimulus for tourism because it increases the number of visits to the destination. Several studies carried out in these types of destinations have shown the existence of a relationship between motivations, perceived value, satisfaction and loyalty. However, there are no previous studies carried out in the Alhambra and the Generalife that sustain this relationship. This work makes a contribution that completes the academic literature on the study of the emotional bonds between the historical and monumental heritage and the tourist who visits it and its behaviour.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

The smoothness of the high-speed railway (HSR) on the bridge may exceed the allowable standard when an earthquake causes vibrations for HSR bridges, which may threaten the safety of running trains. Indeed, few studies have evaluated the exceeding probability of rail displacement exceeding the allowable standard. The purposes of this article are to provide a method for investigating the exceeding probability of the rail displacement of HSRs under seismic excitation and to calculate the exceeding probability.

In order to investigate the exceeding probability of the rail displacement under different seismic excitations, the workflow of analyzing the smoothness of the rail based on incremental dynamic analysis (IDA) is proposed, and the intensity measure and limit state for the exceeding probability analysis of HSRs are defined. Then a finite element model (FEM) of an assumed HSR track-bridge system is constructed, which comprises a five-span simply-supported girder bridge supporting a finite length CRTS II ballastless track. Under different seismic excitations, the seismic displacement response of the rail is calculated; the character of the rail displacement is analyzed; and the exceeding probability of the rail vertical displacement exceeding the allowable standard (2mm) is investigated.

The results show that: (1) The bridge-abutment joint position may form a step-like under seismic excitation, threatening the running safety of high-speed trains under seismic excitations, and the rail displacements at mid-span positions are bigger than that at other positions on the bridge. (2) The exceeding probability of rail displacement is up to about 44% when PGA = 0.01g, which is the level-five risk probability and can be described as 'very likely to happen'. (3) The exceeding probability of the rail at the mid-span positions is bigger than that above other positions of the bridge, and the mid-span positions of the track-bridge system above the bridge may be the most hazardous area for the running safety of trains under seismic excitation when high-speed trains run on bridges.

The work extends the seismic hazardous analysis of HSRs and would lead to a better understanding of the exceeding probability for the rail of HSRs under seismic excitations and better references for the alert of the HSR operation.

Singapore has had a dramatic spike in coronavirus infections in early 2020, with thousands of new cases linked to clusters in migrant workers (MWs) dormitories. To control the spread, the Government attempted to isolate the dormitories, test workers and move symptomatic patients into quarantine facilities. But those measures have left thousands of them trapped in their dormitories, living in cramped conditions that make social distancing near impossible. This paper investigates how COVID-19 has impacted the lives of these workers in varied ways and highlights the migrant workers' belief if Singapore’s effort has been enough for them during the COVID-19 pandemic? The focus is mainly on the low-skilled workers from India and Bangladesh, who are prone to be affected in various ways by COVID-19. My collected data show that migrant workers are grateful to the Singapore state for the support extended during COVID-19. I used the concept of subcultures to explain the condition of the workers in the state of Singapore. Because they expect so little social protection from the state, they are genuinely grateful for its support during the pandemic.

This paper aims to comprehensively clarify the research status of thermal transport of supercritical aviation kerosene, with particular interests in the effect of cracking on heat transfer.

A brief review of current research on supercritical aviation kerosene is presented in views of the surrogate model of hydrocarbon fuels, chemical cracking mechanism of hydrocarbon fuels, thermo-physical properties of hydrocarbon fuels, turbulence models, flow characteristics and thermal performances, which indicates that more efforts need to be directed into these topics. Therefore, supercritical thermal transport of n-decane is then computationally investigated in the condition of thermal pyrolysis, while the ASPEN HYSYS gives the properties of n-decane and pyrolysis products. In addition, the one-step chemical cracking mechanism and SST k-ω turbulence model are applied with relatively high precision.

The existing surrogate models of aviation kerosene are limited to a specific scope of application and their thermo-physical properties deviate from the experimental data. The turbulence models used to implement numerical simulation should be studied to further improve the prediction accuracy. The thermal-induced acceleration is driven by the drastic density change, which is caused by the production of small molecules. The wall temperature of the combustion chamber can be effectively reduced by this behavior, i.e. the phenomenon of heat transfer deterioration can be attenuated or suppressed by thermal pyrolysis.

The issues in numerical studies of supercritical aviation kerosene are clearly revealed, and the conjugation mechanism between thermal pyrolysis and convective heat transfer is initially presented.