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The study investigates the relationship between hospital environmental factors and the well-being of geriatric in-patients. It aims to identify the impact of architectural design on comfort, safety, privacy and stress levels experienced by elderly patients during their hospital stays.

Employing a mixed-methods approach, the research assesses the experiences of 100 geriatric in-patients across various hospital types through surveys, observational checklists and state anxiety measurements. The methodology involves examining architectural features, patient perceptions and correlations among environmental variables and patient experiences. Statistical analyses, including correlations and chi-square tests, were employed to discern associations between environmental variables and patient experiences.

The research identified key architectural features significantly impacting geriatric patients' experiences. Factors such as sturdy beds, furniture quantity, lighting conditions, proximity to facilities and ward occupancy levels were found to influence spatial, sensory and social comfort. Notably, proximity to facilities and control over the immediate environment were crucial for self-control and safety perceptions. Privacy, highly valued by patients, correlated with the presence of curtains and ward occupancy. Moreover, patient stress levels exhibited correlations with autonomy, privacy and ward occupancy.

This research offers significant insights into the criticality of specific architectural elements in enhancing comfort and reducing stress for geriatric in-patients. These findings hold substantial value for healthcare facility design, emphasizing the need to prioritize certain design aspects to promote the well-being of elderly patients during hospitalization.

The “chiralisation” of Euclidean polygonal tessellations is a novel, recent method which has been used to design new auxetic metamaterials with complex topologies and improved geometric versatility over traditional chiral honeycombs. This paper aims to design and manufacture chiral honeycombs representative of four distinct classes of 2D Euclidean tessellations with hexagonal rotational symmetry using fused-deposition additive manufacturing and experimentally analysed the mechanical properties and failure modes of these metamaterials.

Finite Element simulations were also used to study the high-strain compressive performance of these systems under both periodic boundary conditions and realistic, finite conditions. Experimental uniaxial compressive loading tests were applied to additively manufactured prototypes and digital image correlation was used to measure the Poisson’s ratio and analyse the deformation behaviour of these systems.

The results obtained demonstrate that these systems have the ability to exhibit a wide range of Poisson’s ratios (positive, quasi-zero and negative values) and stiffnesses as well as unusual failure modes characterised by a sequential layer-by-layer collapse of specific, non-adjacent ligaments. These findings provide useful insights on the mechanical properties and deformation behaviours of this new class of metamaterials and indicate that these chiral honeycombs could potentially possess anomalous characteristics which are not commonly found in traditional chiral metamaterials based on regular monohedral tilings.

To the best of the authors’ knowledge, the authors have analysed for the first time the high strain behaviour and failure modes of chiral metamaterials based on Euclidean multi-polygonal tessellations.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

Tourism is one of the upcoming service industry in India with high potentials for future growth, particularly in rural areas. Many potential barriers are affecting the growth of tourism in rural India. Therefore, it is essential to explore and prioritize the barriers to tourism growth in rural India.

Qualitative and quantitative responses from “16” experts related to tourism and hospitality management from central India are collected for this study. An integrated Multi-Criteria Decision Making (MCDM) based framework is adopted to identify and relate significant barriers to tourism growth in India.

The result of the study identified many significant barriers and their importance to tourism growth in rural India.

The findings of this study add to the knowledge base of tourism research in line with the previous literature. This study offers an in-depth understanding of barriers focusing on rural tourism growth and devising both the plan of action and the suggestive measures in dealing with rural tourism.

The study provides a robust framework by integrating Interpretive Structural Modelling(ISM) and Decision Making Trial and Evaluation Laboratory (DEMATEL) to explore and prioritizing the critical barriers to rural tourism growth in India. The results of this study can help the decision-maker to fundamentally improve the economy of India through the growth of rural tourism.

Place-based community events fulfil important functions, internally and externally. They provide opportunities for people from diverse communities and cultures to encounter each other, to participate in pleasurable activities in convivial settings and to develop mutual understanding. The purpose of this paper is to investigate the value of such events as a means of resisting or challenging the deleterious effects of territorial stigmatisation.

The authors explore two place-based community events in areas that have been subject to territorial stigmatisation: Govanhill in Glasgow, Scotland, and South Dunedin, New Zealand. They draw on in-depth case study methods including observation and interviews with key local actors and employ inductive analysis to identify themes across the datasets.

The demonstrate how neighbourhood events in both Glasgow and Dunedin actively seek to address some of the deleterious outcomes of territorial stigmatisation by emphasising strength and asset-based discourses about the areas they reflect and represent. In their planning and organisation, both events play an important mediating role in building and empowering community, fostering intercultural encounters with difference and strengthening mutuality within their defined places. They make use of public and semi-public spaces to attract diverse groups while also increasing the visibility of marginalised populations through larger showcase events.

The empirical element focuses only on two events, one in Glasgow, Scotland (UK), and the other in South Dunedin (New Zealand). Data generated were wholly qualitative and do not provide quantitative evidence of “change” to material circumstances in either case study community.

Helps organisers think about how they need to better understand their communities if they are to attract diverse participation, including how they programme public and semi-public spaces.

Place-based community events have significant value to neighbourhoods, and they need to be resourced effectively if they are to sustain the benefits they produce. These events provide an opportunity for diverse communities to encounter each other and celebrate what they share rather than what divides them.

This paper is the first to examine how place-based community events help resist narratives of territorial stigmatisation, which produce negative representations about people and their environments. The paper draws on ethnographic insights generated over time rather than a one-off snapshot which undermines some events research.

Although supporting and assessing the non-academic “impact” of research are not entirely new developments in higher education, academics and research institutions are under increasing pressure to produce work that has a measurable influence outside the academy. With a view to supporting the solution of complex societal issues with evidence and expertise, and against the background of increased emphasis on impact in the United Kingdom's 2021 Research Excellence Framework (REF2021) and a proliferation of impact guides and tools, this article offers a simple, easy to remember framework for designing impactful research. We call this framework “The 7Cs of Impact” – Context, Communities, Constituencies, Challenge, Channels, Communication and Capture.

Drawing on core elements of the Policy Institute at King's College London's Impact by Design training course and the authors' practical experience in supporting and delivering impact, this paper outlines how this framework can help address key aspects across the lifecycle of a research project and plan, from identifying the intended impact of research and writing it into grants and proposals, to engaging project stakeholders and assessing whether the project has had the desired impact.

While preparations for current and future REF submissions may benefit from using this framework, this paper sets out the “7Cs” with a more holistic view of impact in mind, seeking to aid researchers in identifying, capturing, and communicating how research projects can and do contribute to the improvement in society.

Non-profit organizations (NPOs) are exposed to a highly competitive environment in which they are forced to grow their commercial activity to acquire additional financial resources. This study aims to create an understanding of how NPOs involved in textile reuse as a revenue-generating programme manage their reverse supply chains (RSC).

The research involves an embedded single-case study of NPOs in Finland involved in post-use textile collection. The main data sources are semi-structured interviews and participant observations.

This study is inspired by the microfoundations movement and identifies the underlying microfoundations of the NPOs’ capabilities for managing RSC for textile reuse. The study contributes to the literature by demonstrating NPOs’ lower-level, granular practices and their adaptations for achieving quality outcomes in textile reuse.

The findings have context sensitivity and apply to the NPOs which operate in a context similar to Finland, such as in other Nordic countries.

This study continues the discussion on the adoption of “business-like” practices in the NPOs’ pursuit of additional revenue streams to finance humanitarian work. The findings of this study can also be transferred to the growing area of domestic textile circularity.

Using the case of NPOs in textile reuse, the study illustrates how RSC management can serve a social, non-profit cause and transform unwanted textile products into a source of fundraising for humanitarian work.

This enriches the understanding of NPOs’ practices within the scope of revenue-generating programmes by examining one of them – textile reuse through charity shops from an RSC perspective.