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The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets.

A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart–Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training.

In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.

This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.

A novel modelling approach is proposed to study ozone distribution and destruction in indoor spaces. The level of ozone gas concentration in the air, confined within an indoor space during an ozone-based disinfection process, is analysed. The purpose of this work is to investigate how ozone is distributed in time within an enclosed space.

A computational methodology for predicting the space- and time-dependent ozone concentration within the room across the consecutive steps of the disinfection process (generation, dwelling and destruction modes) is proposed. The emission and removal of ozone from the air volume are possible by means of a generator located in the middle of the room. This model also accounts for ozone reactions and decay kinetics, and gravity effect on the air.

This work is validated against experimental measurements at different locations in the room during the disinfection cycle. The numerical results are in good agreement with the experimental data. This comparison proves that the presented methodology is able to provide accurate predictions of the time evolution of ozone concentration at different locations of the enclosed space.

This study introduces a novel computational methodology describing solute transport by turbulent flow for predicting the level of ozone concentration within a closed room during a COVID-19 disinfection process. A parametric study is carried out to evaluate the impact of system settings on the time variation of ozone concentration within the space considered.

The PCL‐R has been heralded as the ‘unparalleled’ (Salekin et al, 1996) risk assessment tool for assessing risk of violent and non‐violent recidivism. In the UK, the PCL‐R looks likely to become an industry standard assessment in psychological evaluation of individuals thought to have a dangerous and severe personality disorder. However, current knowledge about the PCL‐R is unsatisfactory, and a number of issues need to be addressed before clinicians can be confident in the use of this measure. This paper highlights these issues from the perspective of the practising clinician. Questions are raised about the theoretical, methodological and treatment implications of the use of the PCL‐R. Future research needs are established in this context of caution over the use of the measure in routine clinical and academic assessment.

This paper aims to investigate the effects on material properties of layer-by-layer application of pressure during fabrication of polymeric parts by additive manufacturing (AM). Although AM, also known popularly as 3D printing, has set a new standard for ease of use and minimal restraint on geometric complexity, the mechanical part properties do not generally compare with conventional manufacturing processes. Contrary to other types of polymer processing, AM systems do not normally use (in-process) pressure during part consolidation.

Tensile specimens were produced in Somos 201 using conventional laser sintering (LS) and selective laser printing (SLP) – a process under development in the UK, which incorporates the use of pressure to assist layer consolidation.

Mechanical testing demonstrated the potential to additively manufacture parts with significantly improved microstructure and mechanical properties which match or exceed conventional processing. For example, the average elongation at break and ultimate tensile strength of a conventionally laser-sintered thermoplastic elastomer (Somos 201) increased from 136 ± 28 per cent and 4.9 ± 0.4 MPa, to 513 ± 35 per cent and 10.4 ± 0.4 MPa, respectively, when each layer was fused with in-process application of pressure (126 ± 9 kPa) by SLP.

These results are based on relatively small sample size, but despite this, the trends observed are of significant importance to the elimination of voids and porosity in polymeric parts.

Layerwise application of pressure should be investigated further for defect elimination in AM.

This is the first study on the effects of layerwise application of pressure in combination with area-wide fusing.

The paper argues that virtual situated learning environments (VSLE), designed as authentic learning experiences, can provide managers with broader learning opportunities while also cater for the learning needs of the increasing number of peripatetic managers.

An action learning methodology, using first person observation of practice, was used. This first person observation is inclusive of the designers and facilitators of the VSLE (the authors), and of the managers participating in virtual professional practice activities as students. This methodology was chosen in recognition of the need to qualitatively demonstrate the effectiveness of the VSLE for management education.

The findings suggest that on‐line learning environment has, when designed to supplement rather than replace face‐to‐face (F2F) learning, significant advantages for the peripatetic manager.

It is recognised that there are limitations in generalising from particular case studies, particularly when a first‐person action methodology is undertaking. However, this needs to be weighed against the opportunity provided to present the qualitative depth, particularly important when dealing with the intangibility of knowledge.

The implications are that the on‐line learning environment has significant potential for augmenting the F2F environment for managers, particularly in providing the flexibility required by the increasing number of managers working in a global workplace.

The research has significant value for both peripatetic managers seeking to engage in learning environment and universities and academics seeking to provide learning opportunities that are both accessible to, and relevant for, managers, particularly the newly emerging, geographically flexible managers.

An amateur boxer; A professional soldier turned indestructible zombie; A fast-driving heister; A combat pilot on another world; A taco truck driver with a heart of gold; A Smurf; Michelle Rodriguez, American actress, has played them all. As Leticia ‘Letty’ Ortiz, Rodriguez' most famous role offers both a sensitive portrayal of a tenacious woman living out a tough existence who exhibits as much courage, strength, moral standing and fibre as her male counterparts, whilst also revealing a softer, emotional side and one that focuses on family and ideals of accepting Motherhood. This is what makes Rodriguez such a fascinating contradiction. Whilst much praise is heaped on other actresses for their roles in action films, this chapter will offer, through both an overview of her action-hero career and in-depth look at Rodriguez's work in the Fast and Furious films, an insight into the importance of this actress to the growing canon of action hero(ine) characters and film stars.

This paper aims to investigate a stereo vision-based hybrid (additive and subtractive) manufacturing process using direct laser metal deposition, computer numerical control (CNC) machining and in-process scanning to repair metallic components automatically. The focus of this work was to realize automated alignment and adaptive tool path generation that can repair metallic components after a single setup.

Stereo vision was used to detect the defect area for automated alignment. After the defect is located, a laser displacement sensor is used to scan the defect area before and after laser metal deposition. The scan is then processed by an adaptive algorithm to generate a tool path for repairing the defect.

The hybrid manufacturing processes for repairing metallic component combine the advantages of free-form fabrication from additive manufacturing with the high-accuracy offered by CNC machining. A Ti-6Al-4V component with a manufacturing defect was repaired by the proposed process. Compared to previous research on repairing worn components, introducing stereo vision and laser scanning dramatically simplifies the manual labor required to extract and reconstruct the defect area’s geometry.

This paper demonstrates an automated metallic component repair process by integrating stereo vision and a laser displacement sensor into a hybrid manufacturing system. Experimental results and microstructure analysis shows that the defect area could be repaired feasibly and efficiently with acceptable heat affected zone using the proposed approach.

Retro branding is gaining unprecedented momentum. This study aims to empirically examine the moderating impact of nostalgia proneness on the relationship between retro branding and consumer behavioral intentions in the music industry. Nostalgia and retro branding are two paramount elements conceptually discussed in literature but rarely investigated together empirically despite their interconnections.

An experiment including four different scenarios blending retro and contemporary stimuli was conducted on 181 subjects. Two rock band variables were manipulated: song set list (i.e. list of songs) and band lineup.

The findings suggest that mixing the past and present for a retro brand impacts consumer behavior. A more nuanced explanation is suggested by showing that a retro brand has a strong effect on consumers’ intentions to attend and willingness to pay, but not on their WOM intentions, when these consumers are more prone to feeling nostalgia.

Nostalgia and retro branding appear to be interconnected concepts, but few studies have assessed how nostalgia proneness can impact consumers’ intentions toward a retro brand. Fewer have investigated consumers’ intentions toward an experiential, intangible retro brand.