Search results

The purpose of this paper is to review the characterisation of the concept of service experience in service marketing research.

Using content analysis, 30 articles and two books published in the period from 2005 to 2007 are analysed.

Three characterisations of the concept of service experience are identified in the literature review: phenomenological service experience (which relates to the value discussion in service‐dominant logic and interpretative consumer research); process‐based service experience (which relates to understanding service as a sequential process); and outcome‐based service experience (which relates to understanding service experience as one element in models of service linking a number of variables or attributes to various outcomes).

To facilitate meaningful research in this area, it is important that researchers critically consider the nature of the concept of service experience in terms of who experiences it, the scope, content, and context of the service experience, and how service experience relates to other concepts, such as value.

No systematic literature review of the characterisation of the concept of service experience has previously been undertaken.

This paper aims to devise a first-of-its-kind methodology to determine the design, operating conditions and actuation strategy of pneumatic artificial muscles (PAMs) for assistive robotic applications. This requires extensive characterization, data set generation and meaningful modelling between PAM characteristics and design variables. Such a characterization should cover a wide range of design and operation parameters. This is a stepping stone towards generating a design guide for this highly popular compliant actuator, just like any conventional element of a mechanism.

Characterization of a large pool of custom fabricated PAMs of varying designs is performed to determine their static and dynamic behaviours. Metaheuristic optimizer-based artificial neural network (ANN) structures are used to determine eight different models representing PAM behaviour. The assistance of knee flexion during level walking is targeted for evaluating the applicability of the developed actuator by attaching a PAM across the joint. Accordingly, the PAM design and the actuation strategy are optimized through a tabletop emulator.

The dependence of passive length, static contraction, dynamic step response for inflation and deflation of the PAMs on their design dimensions and operating parameters is successfully modelled by the ANNs. The efficacy of these models is investigated to successfully optimize the PAM design, operation parameters and actuation strategy for using a PAM in assisting knee flexion in human gait.

Characterization of static and the dynamic behaviour of a large pool of PAMs with varying designs over a wide range of operating conditions is the novel feature in this article. A lucid customizable fabrication technique is discussed to obtain a wide variety of PAM designs. Metaheuristic-based ANNs are used for tackling high non-linearity in data while modelling the PAM behaviour. An innovative tabletop emulator is used for investigating the utility of the models in the possible application of PAMs in assistive robotics.

The ability to characterise highly conducting objects, that may also be highly magnetic, by the complex symmetric rank–2 magnetic polarizability tensor (MPT) is important for metal detection applications including discriminating between threat and non-threat objects in security screening, identifying unexploded anti-personnel landmines and ordnance and identifying metals of high commercial value in scrap sorting. Many everyday non-threat items have both a large electrical conductivity and a magnetic behaviour, which, for sufficiently weak fields and the frequencies of interest, can be modelled by a high relative magnetic permeability. This paper aims to discuss the aforementioned idea.

The numerical simulation of the MPT for everyday non-threat highly conducting magnetic objects over a broad range of frequencies is challenging due to the resulting thin skin depths. The authors address this by employing higher order edge finite element discretisations based on unstructured meshes of tetrahedral elements with the addition of thin layers of prismatic elements. Furthermore, computer aided design (CAD) geometrical models of the non-threat and threat object are often not available and, instead, the authors extract the geometrical features of an object from an imaging procedure.

The authors obtain accurate numerical MPT characterisations that are in close agreement with experimental measurements for realistic physical objects. The assessment of uncertainty shows the impact of geometrical and material parameter uncertainties on the computational results.

The authors present novel computations and measurements of MPT characterisations of realistic objects made of magnetic materials. A novel assessment of uncertainty in the numerical predictions of MPT characterisations for uncertain geometry and material parameters is included.

The accountant has been characterised in popular culture as dull and boring. Accounting scholarship consistently describes this stereotype as negative. The purpose of this research is to enhance our understanding of this stereotype by comparing the characterisation of the accountant found in contemporary jokes with a similar characterisation found in commedia dell'arte, which is a form of improvisational theatre.

This research employs netnography to identify the stereotype of the accountant portrayed in jokes on social networking sites. To enrich our understanding of the stereotype of the characterisation of the accountant in contemporary jokes, it is compared with the characterisation of il dottore, the stock character of the professional man in commedia dell'arte.

The characterisation of the accountant in contemporary jokes is consistent with characterisations of the accountant in other areas of popular culture, confirming that the stereotype is widely entrenched in popular culture. Contemporary jokes provide a more detailed characterisation than previously identified in accounting scholarship. Since the stereotype of il dottore in commedia dell'arte resembles the stereotype of the accountant as portrayed in contemporary jokes, studying commedia dell'arte provides an approach that enhances our understanding of the characterisation of the accountant in popular culture.

The contribution of this research to accounting scholarship is the deployment of commedia dell'arte to capture the essence of the stereotype of the accountant as portrayed in popular culture. Extending the definition of popular culture to include contemporary jokes brings a unique use of netnography to accounting scholarship.

Considers a system (machine) that is subject to failure (breakdown). Two characterizations are presented. In the first characterization, the state of the system is described by the real age of the machine and the number of failures incurred to date. In the second characterization, the state of the system is described by the real age of the machine and the virtual age of the machine. In either characterization, upon failure, the unit may undergo a repair which can partially reset the failure intensity of the unit. The degree of reset assumed by the repair is a function of the characterization utilized. The other alternative, at a failure, is to conduct a major overhaul that serves to refresh the failure intensity of the unit. General cost structures, depending upon (real age, number of failures) in characterization one or (real age, virtual age) in characterization two are permitted. The decision, on failure to repair or renew is formulated as a discrete semi‐Markov Decision process. Optimal decisions are of the threshold type. The threshold rules depend upon the characterization.

Micro-texture is processed on the surface to reduce the friction of the contact surface, and its application is more and more extensive. The purpose of this paper is to create a texture function model to study the influence of surface parameters on the accuracy of the simulated surface so that it can more accurately reflect the characteristics of the real micro-textured surface.

The microstructure function model of rough surfaces is established based on fractal geometry and polar coordinate theory. The offset angle θ is introduced into the fractal geometry function to make the surface asperity normal perpendicular to the tangent of the surface. The 2D and 3D contour surfaces of the surface groove texture are analyzed by MATLAB simulation. The effects of fractal parameters (D and G) and texture parameter h on the curvature of the surface micro-texture model were studied.

This paper more accurately characterizes the textured 3D curved surface, especially the surface curvature. The scale coefficient G significantly affects curvature, and the influence of fractal dimension D and texture parameters on curvature can be ignored.

The micro-texture model of the rough surface was successfully established, and the range of fractal parameters was determined. It provides a new method for the study of surface micro-texture tribology.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0298/

This chapter sets forth a plan designed to encourage and enable teachers to engage in first-person characterization in their classrooms. The author draws on his extensive background in social studies teaching, administration, and consulting to argue for the value of historical interpretation within the context of today's curricular emphasis on science, technology, engineering, and mathematics (STEM). This chapter then explores and explains historical interpretation from a classroom perspective, focusing on pedagogical best practices. In a first-person presentation, the presenter assumes the identity of a historical figure. The first question to be asked then is, Who is the individual I wish to represent, and why? This person should be selected from subject matter being studied in your class. Be aware that it is necessary to anticipate some element of controversy when you undertake this activity. With rare exceptions, any historical figure selected for portrayal will have something questionable in their background, and you will have to contend with this. So, the next question to ask is, Why engage in first-person interpretation in the first place? In this chapter, experienced teachers provide reasons for doing so, and consider necessary preparations for effectively implementing such a characterization. That discussion leads to examining ways to ensure successful presentations that achieve established lesson goals, followed by suggestions for debriefing and effectively bringing closure to the exercise. As the accompanying lesson extension demonstrates, a characterization can ground further study of an issue associated with the individual being depicted.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.

This paper aims to concentrate on research that has been conducted in the previous decade on metamaterial (MTM)-based sensors for material characterization, which includes solid dielectrics, micro fluids and biomolecules.

There has been a vast advancement in sensors based on MTM since the past few decades. MTM elements provide a sensitive response to materials while having a tiny footprint, making them an appealing alternative for realizing diverse sensing devices.

Related research papers on MTM sensors published in reputable journals were reviewed in this report, with a specific emphasis on the structure, size and nature of the materials characterized. Because electromagnetic wave interaction excites MTM structures, sensing applications around the electromagnetic spectrum are possible.

The paper contains valuable information on MTM sensor technology for material characterization, and this study also highlights the challenges and approaches that will guide future development.