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Clinical staff working in mental health services experience high levels of work-related stress, burnout and poor well-being. Increased levels of stress, burnout, depression and anxiety and poorer mental well-being among health-care workers are associated with more sick days, absenteeism, lower work satisfaction, increased staff turnover and reduced quality of patient care. Virtual reality (VR) relaxation is a technique whereby experiences of pleasant and calming environments are accessed through a head-mounted display to promote relaxation. The purpose of this paper is to describe the design of a study that assesses the feasibility and acceptability of implementing a multi-session VR relaxation intervention amongst mental health professionals, to improve their relaxation levels and mental well-being.

The study follows a pre–post-test design. Mental health staff will be recruited for five weeks of VR relaxation. The authors will measure the feasibility and acceptability of the VR relaxation intervention as primary outcomes, alongside secondary outcomes evaluating the benefits of VR relaxation for mental well-being.

The study aims to recruit 20–25 health-care professionals working in both inpatient and specialist community mental health settings.

Research indicates the potential of VR relaxation as a low-intensity intervention to promote relaxation and reduce stress in the workplace. If VR relaxation is shown to be feasible and acceptable, when delivered across multiple sessions, there would be scope for large-scale work to investigate its effectiveness as an approach to enable health-care professionals to de-stress, relax and optimise their mental well-being. In turn, this may consequently reduce turnover and improve stress-related sick leave across health-care services.

This study examines the dualities of digital services – that is, how customers’ favorite everyday digital services can positively and negatively contribute to their well-being. Thus, the study describes the meanings of favorite digital services as part of customers’ everyday lives and the types of well-being to which such services can contribute.

We used a qualitative research approach through semi-structured interviews conducted in 2021 to collect data from 14 young adults (22–31 years old) who actively used digital services in their daily lives.

Our findings revealed that customers’ favorite everyday digital services can contribute to their mental well-being, social well-being, and intellectual well-being. Within these three dimensions of well-being, we identified nine dualities of digital services that describe their positive and negative contributions: (1) digital escapism versus digital disruption, (2) digital relaxation versus digital stress, (3) digital empowerment versus digital subjugation, (4) digital augmentation versus digital emptiness, (5) digital socialization versus digital isolation, (6) digital togetherness versus digital exclusion, (7) digital self-expression versus digital pressure, (8) digital learning versus digital dependence, and (9) digital inspiration versus digital stagnation.

These findings suggest that everyday digital services have the potential to contribute to customer well-being in various aspects – both positively and negatively – accentuating the need for service providers to decipher the impacts of their offerings on well-being. Indeed, understanding the relationship between digital services and customer well-being can help companies tailor their services to customers’ needs. Companies that prioritize customer well-being not only benefit their customers but also create sustainable growth opportunities in the long run. Further, companies can use the derived information in service design to develop marketing strategies that emphasize the positive impacts of their digital services on customer well-being.

Although prior transformative service studies have investigated the well-being of multiple stakeholders, such studies have focused on services related to the physical and healthcare domains. Consequently, the role of everyday digital services as contributors to customer well-being is an under-researched topic. In addition, the concept of well-being and its various dimensions has received limited attention in previous service research. By investigating everyday digital services and their multidimensional contribution to customer well-being, this study broadens the perspective on well-being within TSR and aids in refining a more precise conceptualization.

The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

The construction industry is one of the most stressful industries. Thus, quantity surveyors (QSs) who work at sites frequently experience high levels of occupational stress. The gender of a QS also has a significant impact on his/her occupational stress. Hence, this study aims to investigate the management of occupational stress in QSs working at sites for contractors (hereinafter referred to as CQSs).

The study adopted a mixed approach using semi-structured interviews and a questionnaire survey for female and male CQSs to identify, validate and rank the stressors and symptoms of occupational stress in CQSs and the strategies of managing that stress based on their significance levels. Manual content analysis and the mean weighted rating were used to analyse the data collected.

Heavy workload was the most significant occupational stressor of CQSs, whereas sleeping disorders were their primary symptom of occupational stress. Establishing a proper work programme was identified as the most effective stress management strategy for male and female CQSs. This study shows that many site QSs are stressed owing to their heavy workloads and work obligations and that their stress-related attributes significantly depend on their genders.

This study is significant because no previous studies have been conducted on managing occupational stress in CQSs in male and female CQSs. The study findings can be used to identify the stressors and symptoms of occupational stress in CQSs early and use appropriate management strategies to enhance the work satisfaction and productivity of CQSs suffering from occupational stress.

Polymethyl methacrylate (PMMA) is a remarkable biocompatible material for bone cement and regeneration. It is also considered 3D printable but requires in-depth process–structure–properties studies. This study aims to elucidate the mechanistic effects of processing parameters and sterilization on PMMA-based implants.

The approach comprised manufacturing samples with different raster angle orientations to capitalize on the influence of the filament alignment with the loading direction. One sample set was sterilized using an autoclave, while another was kept as a reference. The samples underwent a comprehensive characterization regimen of mechanical tension, compression and flexural testing. Thermal and microscale mechanical properties were also analyzed to explore the extent of the appreciated modifications as a function of processing conditions.

Thermal and microscale mechanical properties remained almost unaltered, whereas the mesoscale mechanical behavior varied from the as-printed to the after-autoclaving specimens. Although the mechanical behavior reported a pronounced dependence on the printing orientation, sterilization had minimal effects on the properties of 3D printed PMMA structures. Nonetheless, notable changes in appearance were attributed, and heat reversed as a response to thermally driven conformational rearrangements of the molecules.

This research further deepens the viability of 3D printed PMMA for biomedical applications, contributing to the overall comprehension of the polymer and the thermal processes associated with its implementation in biomedical applications, including personalized implants.

The partially prestressed concrete beam with unbonded tendon is still an active field of research because of the difficulty in analyzing and understanding its behavior. The finite-element (FE) simulation of such beams using numerical software is very scarce in the literature and therefore this study is taken to demonstrate the modeling aspects of unbonded partially prestressed concrete (UPPSC) beams. This study aims to present the three-dimensional (3-D) nonlinear FE simulations of UPPSC beams subjected to monotonic static loadings using the numerical analysis package ANSYS.

The sensitivity study is carried out with three different mesh densities to obtain the optimum elements that reflect on the load–deflection behavior of numerical models, and the model with optimum element density is used further to model all the UPPSC beams in this study. Three half-symmetry FE model is constructed in ANSYS parametric design language domain with proper boundary conditions at the symmetry plane and support to achieve the same response as that of the full-scale experimental beam available in the literature. The linear and nonlinear material behavior of prestressing tendon and conventional steel reinforcements, concrete and anchorage and loading plates are modeled using link180, solid65 and solid185 elements, respectively. The Newton–Raphson iteration method is used to solve the nonlinear solution of the FE models.

The evolution of concrete cracking at critical loadings, yielding of nonprestressed steel reinforcements, stress increment in the prestressing tendon, stresses in concrete elements and the complete load–deflection behavior of the UPPSC beams are well predicted by the proposed FE model. The maximum discrepancy of ultimate moments and deflections of the validated FE models exhibit 13% and −5%, respectively, in comparison with the experimental results.

The FE analysis of UPPSC beams is done using ANSYS software, which is a versatile tool in contrast to the experimental testing to study the stress increments in the unbonded tendons and assess the complete nonlinear response of partially prestressed concrete beams. The validated numerical model and the techniques presented in this study can be readily used to explore the parametric analysis of UPPSC beams.

The developed model is capable of predicting the strength and nonlinear behavior of UPPSC beams with reasonable accuracy. The load–deflection plot captured by the FE model is corroborated with the experimental data existing in the literature and the FE results exhibit good agreement against the experimentally tested beams, which expresses the practicability of using FE analysis for the nonlinear response of UPPSC beams using ANSYS software.

The need for precise synthesis of customized designs has resulted in the development of advanced coating processes for modern nanomaterials. Achieving accuracy in these processes requires a deep understanding of thermophysical behavior, rheology and complex chemical reactions. The manufacturing flow processes for these coatings are intricate and involve heat and mass transfer phenomena. Magnetic nanoparticles are being used to create intelligent coatings that can be externally manipulated, making them highly desirable. In this study, a Keller box calculation is used to investigate the flow of a coating nanofluid containing a viscoelastic polymer over a circular cylinder.

The rheology of the coating polymer nanofluid is described using the viscoelastic model, while the effects of nanoscale are accounted for by using Buongiorno’s two-component model. The nonlinear PDEs are transformed into dimensionless PDEs via a nonsimilar transformation. The dimensionless PDEs are then solved using the Keller box method.

The transport phenomena are analyzed through a comprehensive parametric study that investigates the effects of various emerging parameters, including thermal radiation, Biot number, Eckert number, Brownian motion, magnetic field and thermophoresis. The results of the numerical analysis, such as the physical variables and flow field, are presented graphically. The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as fluid parameter increases. An increase in mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid.

Intelligent materials rely heavily on the critical characteristic of viscoelasticity, which displays both viscous and elastic effects. Viscoelastic models provide a comprehensive framework for capturing a range of polymeric characteristics, such as stress relaxation, retardation, stretching and molecular reorientation. Consequently, they are a valuable tool in smart coating technologies, as well as in various applications like supercapacitor electrodes, solar collector receivers and power generation. This study has practical applications in the field of coating engineering components that use smart magnetic nanofluids. The results of this research can be used to analyze the dimensions of velocity profiles, heat and mass transfer, which are important factors in coating engineering. The study is a valuable contribution to the literature because it takes into account Joule heating, nonlinear convection and viscous dissipation effects, which have a significant impact on the thermofluid transport characteristics of the coating.

The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as the fluid parameter increases. An increase in the mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid. Increasing the strength of the magnetic field promotes an increase in the density of the streamlines. An increase in the mixed convection parameter results in a decrease in the isotherms and isoconcentration.

Following the increasing trend of artificial intelligence (AI) research in hospitality literature, this critical reflection paper aims to identify AI-assisted mindfulness as a critical yet under-investigated issue and to contribute feasible directions for future research.

The authors first conceptualize a framework explaining the effects of mindfulness design in AI interventions on improving human mindfulness. The authors then identify opportunities for interventions in AI-assisted mindfulness for the tourism, hospitality and events industries. Finally, the authors propose potential themes for AI-assisted mindfulness research.

This study contributes three major conceptual works. First, we conceptualize a framework of AI-assisted mindfulness, showcasing that the scope of AI-assisted mindfulness spans from AI interventions to state mindfulness and then to trait mindfulness. Second, the authors offer two approaches to strategic thinking, one from mindfulness (i.e. mindfulness-focused niche markets and activities) and one from AI applications (i.e. AI-facilitated devices and platforms), to identify opportunities for AI-assisted mindfulness interventions. Third, for both management- and marketing-oriented AI-assisted mindfulness research, the authors propose 18 themes.

This critical reflection paper offers directions for future knowledge creation in AI-assisted mindfulness in the tourism, hospitality and events industries.

To the best of the authors’ knowledge, this critical reflection paper serves as the first in hospitality and tourism literature to systematically propose the research issue of AI-assisted mindfulness, offering directions and themes for future research.

The purpose of this commentary is discuss how musical intervention and imagined interactions can be used to deal with conflict. Music has been called the universal language because of its tonality and rhythm. It affects conflict and aggression and helps people to deal with stress. Research is reviewed showing physiological arousal with background music. The effects of music on aggression are summarized in terms of emotional regulation, catharsis and empathy, and the use of mental imagery in the form of imagined interactions, including relational maintenance catharsis and conflict linkage, is discussed. The incremental sound organizer (ISO) principle of music therapy is discussed as a mechanism to affect emotions while listening to music. Finally, a tool to measure the emotional effect of music on listeners is discussed in terms of the musical mood wheel.

This is a commentary on the effects of background music on reducing aggression.

Music can reduce aggression, depending on how it is used. Music affects how we manage our emotions, reduces stress, provides catharsis and can be a distracting element. Music can enhance empathic feeling; induce positive moods, social bonding, physiological changes and neurobiological changes; and affect our arousal.

Music therapy and musical intervention can be applied to conflict resolution.

The ISO principle of music therapy is designed to deal with changing a person’s emotions as they listen to a medley of music. The mood of the person is measured using a series of scales reflecting a continuum of sadness to happiness, and the music is designed to match the mood of the patient to the music being played and/or listened to, which in turn fosters the achievement of an altered state of consciousness. For example, if you are angry, start with music that is loud and gradually switch to a more tranquil piece of music. “The vectoring power of music is that we change the mood or emotion of persons from one affective pole (joy) to its opposite (anger) through small incremental changes in the rhythm and intensity of the music” (Honeycutt, 2003, p. 82).

Background music in music psychology literature is often referred to as musical intervention. Background music can help us recall positive and negative scenes as the music triggers endorphins in the brain (Salimpoor et al., 2011). Background music is intended to enhance the surrounding context without drawing significant attention. It is often played in spaces such as restaurants, stores, offices or public places to create a certain mood or ambiance.

It is important to note that the effectiveness of musical intervention in reducing aggression can vary based on individual preferences, the specific type of music used, and the context in which it is applied. Additionally, while music intervention can play a role in aggression reduction, it is often most effective when used as part of a comprehensive therapeutic approach.

This study intend to investigate a theoretical model looking at how particular tourist emotions, such as “joy,” “love,” and “positive surprise,” might predict their behavior by looking at how satisfied they are with their whole experience when visiting spas, and to examine the relationship of emotional experience, destination image, satisfaction and intention to revisit for spa tourism.

A sample of 345 individuals who traveled to Alleppey as domestic tourists participated in the research study. A non-probability (purposive) sampling method in this study. The structural model was analyzed using Structural Equation modeling (SEM), and the path coefficients were examined to test the hypotheses.

The results supported the hypotheses, indicating that specific emotions, image of the destination, and satisfaction significantly impacted tourists' intentions to revisit Alleppey as a spa tourism destination. This study demonstrated that “emotions of joy, love, and positive surprise” have a considerable influence on the image of the destination and satisfaction. The findings reveal a substantial correlation between satisfaction and behavioral intention (“Intention to revisit”). The research suggests that a higher degree of satisfaction would encourage visitors to revisit the location.

The research suggests that a higher degree of satisfaction would encourage visitors to revisit the location. This research offers vital information for developing, planning, and putting into practice tourism policies in the spa tourism sector. This article focuses on domestic travelers who travel to Alleppey, so the conclusions may not be relevant to research utilizing foreign tourists.

According to the literature study, and to the authors` knowledge, only limited number of studies that look at spa tourism from a wellness perspective. Additionally, Alleppey is used in the study as the study’s setting, providing insight into the visitor experiences of this expanding spa tourism business. This study gives understanding about how emotional experience predicts behavioral intentions.