E-Jurnal Universitas Tunas Husada Tasikmalaya
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A novel methodology applying practice theory in pro-environmental organisational change research: Examples of energy use and waste in healthcare
No full textResponding to the increased interest in addressing organisational sustainability issues using behaviour change strategies, this paper aims to propose a methodology for doing so from a different perspective – namely, sociology and social practice theory. Firstly, the background of behaviour change approaches and practice theory are discussed. Then a methodology for conducting a pro-environmental organisational change project is proposed. The methodology involves five key elements: detailed analysis of context, outlining a theoretical framework, establishing project boundaries, acknowledging connectivity of practices and choosing data collection methods. We illustrate the application of methodology by using examples of everyday consumables, energy and waste in a hospital trust in the South East of England. This approach has been effective for analysing routine and inconspicuous consumption within an organisation, as it considers individual attitudes and motivations as well as the structural and habitual nature of communities of practices. It allows researchers and managers to understand workplace consumption issues from several perspectives and identify the best angle from which to approach potential resolutions
Ethical considerations in design and implementation of home-based smart care for dementia
No full textIt has now become a realistic prospect for smart care to be provided at home for those living with long-term conditions such as dementia. In the contemporary smart care scenario, homes are fitted with an array of sensors for remote monitoring providing data that feed into intelligent systems developed to highlight concerning patterns of behaviour or physiological measurements and to alert healthcare professionals to the need for action. This paper explores some ethical issues that may arise within such smart care systems, focusing on the extent to which ethical issues can be addressed at the system design stage. Artificial intelligence has been widely portrayed as an ethically risky technology, posing challenges for privacy and human autonomy and with the potential to introduce and exacerbate bias and inequality. While broad principles for ethical artificial intelligence have become established, the mechanisms for governing ethical artificial intelligence are still evolving. In healthcare settings the implementation of smart technologies falls within the existing frameworks for ethical review and governance. Feeding into this ethical review there are many practical steps that designers can take to build ethical considerations into the technology. After exploring the pre-emptive steps that can be taken in design and governance to provide for an ethical smart care system, the paper reviews the potential for further ethical challenges to arise within the everyday implementation of smart care systems in the context of dementia, despite the best efforts of all concerned to pre-empt them. The paper concludes with an exploration of the dilemmas that may thus face healthcare professionals involved in implementing this kind of smart care and with a call for further research to explore ethical dimensions of smart care both in terms of general principles and lived experience
Remote Production for Live Holographic Teleportation Applications in 5G Networks
No full text—Holographic Teleportation is an emerging media application allowing people or objects to be teleported in a real-time and immersive fashion into the virtual space of the audience side. Compared to the traditional video content, the network requirements for supporting such applications will be much more challenging. In this paper, we present a 5G edge computing framework for enabling remote production functions for live holographic Teleportation applications. The key idea is to offload complex holographic content production functions from end user premises to the 5G mobile edge in order to substantially reduce the cost of running such applications on the user side. We comprehensively evaluated how specific network-oriented and application-oriented factors may affect the performances of remote production operations based on 5G systems. Specifically, we tested the application performance from the following four dimensions: (1) different data rate requirements with multiple content resolution levels, (2) different transport-layer mechanisms over 5G uplink radio, (3) different indoor/outdoor location environments with imperfect 5G connections and (4) different object capturing scenarios including the number of teleported objects and the number of sensor cameras required. Based on these evaluations we derive useful guidelines and policies for future remote production operation for holographic Teleportation through 5G systems
How, when, and why do inter-organisational collaborations in healthcare work? A realist evaluation
No full textBackgroundInter-organisational collaborations (IOCs) in healthcare have been viewed as an effective approach to performance improvement. However, there remain gaps in our understanding of what helps IOCs function, as well as how and why contextual elements affect their implementation. A realist review of evidence drawing on 86 sources has sought to elicit and refine context-mechanism-outcome configurations (CMOCs) to understand and refine these phenomena, yet further understanding can be gained from interviewing those involved in developing IOCs.MethodsWe used a realist evaluation methodology, adopting prior realist synthesis findings as a theoretical framework that we sought to refine. We drew on 32 interviews taking place between January 2020 and May 2021 with 29 stakeholders comprising IOC case studies, service users, as well as regulatory perspectives in England. Using a retroductive analysis approach, we aimed to test CMOCs against these data to explore whether previously identified mechanisms, CMOCs, and causal links between them were affirmed, refuted, or revised, and refine our explanations of how and why interorganisational collaborations are successful.ResultsMost of our prior CMOCs and their underlying mechanisms were supported in the interview findings with a diverse range of evidence. Leadership behaviours, including showing vulnerability and persuasiveness, acted to shape the core mechanisms of collaborative functioning. These included our prior mechanisms of trust, faith, and confidence, which were largely ratified with minor refinements. Action statements were formulated, translating theoretical findings into practical guidance.ConclusionAs the fifth stage in a larger project, our refined theory provides a comprehensive understanding of the causal chain leading to effective collaborative inter-organisational relationships. These findings and recommendations can support implementation of IOCs in the UK and elsewhere. Future research should translate these findings into further practical guidance for implementers, researchers, and policymakers.</p
The origins and development of statistical approaches in non-parametric frontier models: A survey of the first two decades of scholarly literature (1998-2020)
No full textThis paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (1998) and Park et al. (2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications.</p
Versatile Ni-Ru catalysts for gas phase CO2 conversion: Bringing closer dry reforming, reverse water gas shift and methanation to enable end-products flexibility
No full textAdvanced catalytic materials able to catalyse more than one reaction efficiently are needed within the CO2 utilisation schemes to benefit from end-products flexibility. In this study, the combination of Ni and Ru (15 and 1 wt%, respectively) was tested in three reactions, i.e. dry reforming of methane (DRM), reverse water-gas shift (RWGS) and CO2 methanation. A stability experiment with one cycle of CO2 methanation-RWGS-DRM was carried out. Outstanding stability was revealed for the CO2 hydrogenation reactions and as regards the DRM, coke formation started after 10 h on stream. Overall, this research showcases that a multicomponent Ni-Ru/CeO2 -Al2O3 catalyst is an unprecedent versatile system for gas phase CO2 recycling. Beyond its excellent performance, our switchable catalyst allows a fine control of end-products selectivity
Evolutionary optimization of many-objective problems with irregular Pareto fronts
No full textDecomposition based evolutionary algorithms have proven to be able to strike a good trade-off between convergence and diversity in handling multi-objective or many-objective optimization problems with regular Pareto fronts. However, the performance of decomposition based algorithms becomes less efficient in dealing with many-objective problems with irregular Pareto fronts (we call it irregular problems hereafter for simplicity). In this thesis, we aim to solve the irregular problems based on the framework of the decomposition based evolutionary algorithms, in which a set of fixed reference vectors covering the whole objective space is usually adopted to guide the search process.To ensure that the adaptation of reference vectors can match well with the solutions during the search process, in our first work, we propose to adjust the distribution of reference vectors using an improved growing neural gas network to solve the irregular problems. By using the solutions found during the search process to train the improved growing neural gas network, the algorithm is able to effectively adapt the reference vectors without determining when and where to adapt the reference vectors and the convergence speed can not be slowed down to some extent. For decomposition based algorithms, apart from the reference vectors, the scalarizing function is also critical and the design of it should take both the convergence and diversity into consideration, since eventually, the scalarizing function will be used to rank the solutions before selection based on the reference vector with which the solutions are associated. If the reference vectors are frequently adjusted, a solution in the population may change its associated reference vector very frequently, resulting in reduced selection pressure along a particular search direction and slowing down the convergence. Therefore, in the second work, we propose a new adaptive scalarizing function tailored for adaptive reference vectors, thereby better balancing the trade-off between convergence and diversity.For the third and fourth work, we aim to handle the expensive irregular problems, which is very challenging. For irregular problems, hundreds of thousands of real function evaluations are usually needed to learn the shape of the irregular Pareto fronts, but only several hundreds of real function evaluations can be afforded for expensive problems. In addition, the model management has to not only consider the balance between the exploitation and exploration but also enhance the sampling of infilled solutions in the region of the irregular Pareto fronts. In the third work, to solve expensive irregular problems, we propose to adapt the reference vectors using the improved growing neural gas network by utilizing both the estimated solutions predicted by Gaussian process models and the solutions that have been evaluated using real objective functions as the training data. This way, the algorithm is able to learn the irregularity of the Pareto fronts to some extent. We also propose a new model management strategy by balancing the diversity and convergence according to the adaptive reference vectors tuned by the growing neural gas network. It is well known that effective model management heavily relies on the design and optimization of the acquisition functions. For expensive many-objective problems, in order to obtain a set of solutions trading off between different objectives, the design of the acquisition functions shall be able to well balance the convergence and diversity. Moreover, the optimization of the acquisition function can be difficult because of the multi-modal property. Therefore, in the last piece of work, we propose a reference vector based adaptive model management strategy to balance the convergence and diversity. Two optimization processes on top of two sets of reference vectors are adopted to optimize an amplified upper confidence bound acquisition function
Air pollution and plant health response-current status and future directions
No full textAir pollutants influence the morphological, physiological, and biochemical status of plants, and their impacts vary substantially among different species and cultivars. Current review synthesises published literature on the assessment of air pollution impacts on vegetation, with a specific focus on chronicling and summarizing scientific methods that quantify those impacts. Investigations carried out globally on pollutant-plant exposure-response, and articles that describe impact of air pollutants on plants and pollutant abatement using green infrastructure (GI) were systematically reviewed. 273 articles reviewed indicated that a substantial number of past explorations were on a small spectrum of certain species, mainly wheat, rice, soybean and maize; and fewer on non-crop plant species, which cover most of the urban areas and are part of GI. Furthermore, in lower middle-income countries which face significant pollution loads, even studies on crop species are limited. Most studies either use Air Pollution Tolerance Index, which is not pollutant dependent or concentrate on either Ozone or Particulate Matter (PM) and rarely investigate the impact of multiple pollutants in the atmosphere. Also, very few studies differentiate the effect of PM on plants based on its composition. Subsequently, the best possible experimental set ups and wide array of plant health parameters for determining and understanding the effects of different air pollutants on a variety of plant species has been emphasized. While this review compiled literature-based commendations for academic federations wanting to study and quantify air pollutant impacts on vegetation, numerous pertinent vital topics for future research were identified
Patterns of Internet Use, and Associations with Loneliness, amongst Middle-Aged and Older Adults during the COVID-19 Pandemic
No full textLoneliness among older adults is a major societal problem with consequences for health and wellbeing; this has been exacerbated by the coronavirus pandemic. The present study investigated associations between internet use, including frequency and type of use, and loneliness in a large UK sample of middle-aged and older adults, aged 55–75 (n = 3500) from the English Longitudinal Study of Ageing (ELSA) cohort study. Our findings indicated a clear relationship between the frequency of internet use and subjective loneliness. Those who used the internet more than once a day reported feeling less lonely than those who used the internet once a week or less. We also found that those who used the internet for e-mail communication were less lonely. However, individuals indicated higher levels of loneliness when the internet was used for information searches about health. Regarding sociodemographic factors underlying internet usage, less frequent use was seen amongst individuals who lived alone, people who were not employed, who had lower education levels, and lower sociodemographic status. Additionally, gender differences were found in the type of internet use: males report using the internet for e-mail communication more than females, while females’ internet use for health-related information searches was higher than in males. In sum, findings suggest that intervention strategies that promote internet access amongst middle-aged and older people could be useful for tackling loneliness and point to the groups within society that should be the focus of such interventions
Value creation in an algorithmic world: Towards an ethics of dynamic pricing
No full textChoice of pricing strategy plays a central role in value creation and the effective functioning of markets. Shifts in technology and the growing availability of data are facilitating ever more innovative forms of pricing strategy. Within the emerging literature on pricing ethics, there is a gap in our understanding of the specific challenges of algorithmically generated dynamic pricing. Increasing pricing automation shifts the managerial focus from the selection of prices to the choice of algorithms. This paper expands the literature on pricing ethics by conceptualizing the ethical challenges raised by the contemporary use of dynamic pricing. We propose a governance model for algorithmically generated dynamic pricing, taking into account the role of the customer as a stakeholder in value generation