Set Of Projects Research Articles

Synthesizing customer satisfaction and loyalty through contractors’ service quality and brand image

PurposeThe present study developed an integrated model to evaluate the relationship between service quality, brand image, customer satisfaction and customer loyalty in the context of Myanmar’s construction industry.Design/methodology/approachData on the study variables were gathered from a cross-sectional survey of 210 client organizations that own private buildings in Myanmar. A factor analysis-validated structural equation model was developed to assess the research hypotheses and conceptual framework.FindingsThe results supported an integrated model in which brand image partly mediated the effects of service quality on customer satisfaction. Besides, the brand image and customer satisfaction fully mediated the impact of service quality on customer loyalty. Brand image exerted indirect effects on customer loyalty through customer satisfaction. The study further identified the direct effects of service quality on brand image and customer satisfaction, brand image on customer satisfaction and customer satisfaction on customer loyalty.Originality/valueWhile prior studies have explored service quality in the construction industry, no integrated model has been developed to identify the relationship between service quality, brand image, customer satisfaction and customer loyalty in construction project settings. This study filled this critical gap in the literature by offering a unique perspective on the study variables and their interrelationships.

A Structured Bipartite Graph Learning method for ensemble clustering

Given a set of base clustering results, conventional bipartite graph-based ensemble clustering methods typically require computing a sample-cluster similarity matrix from each base clustering result. These matrices are then either concatenated or averaged to form a bipartite weight matrix, which is used to create a bipartite graph. Graph-based partition techniques are subsequently applied to this graph to obtain the final clustering result. However, these methods often suffer from unreliable base clustering results, making it challenging to identify a clear cluster structure due to the variations in cluster structures across the base results. In this paper, we propose a novel Structured Bipartite Graph Learning (SBGL) method. Our approach begins by computing a sample-cluster similarity matrix from each base clustering result and constructing a base bipartite graph from each of these matrices. We assume these base bipartite graphs contain a set of latent clusters and project them into a set of sample-latent-cluster bipartite graphs. These new graphs are then ensembled into a bipartite graph with a distinct cluster structure, from which the final set of clusters is derived. Our method allows for different numbers of clusters across base clusterings, leading to improved performance. Experimental results on both synthetic and real-world datasets demonstrate the superior performance of our new method.

Epistemic selection of costly alternatives: the case of participatory budgeting

We initiate the study of voting rules for participatory budgeting using the so-called epistemic approach, where one interprets votes as noisy reflections of some ground truth regarding the objectively best set of projects to fund. Using this approach, we first show that both the most studied rules in the literature and the most widely used rule in practice cannot be justified on epistemic grounds: they cannot be interpreted as maximum likelihood estimators, whatever assumptions we make about the accuracy of voters. Focusing then on welfare-maximising rules, we obtain both positive and negative results regarding epistemic guarantees.

Environmental analysis in UK modern methods of construction (MMC) housing: insights from early-stage architectural design process

PurposeModern Methods of Construction (MMC) have been promoted as a solution to address housing shortages and meet sustainability goals in the UK. However, the practical functioning of MMC, particularly in relation to early-stage environmental analyses, remains underexplored. This study aims to fill this gap by providing empirical insights into how architects engage in environmental analysis in early-stage MMC housing designs.Design/methodology/approachThis qualitative pilot study is based on a detailed case study of a UK architectural firm specializing in modular housing. Semi-structured interviews were conducted with eight architects with varying MMC experience. Thematic analysis of the qualitative data allowed for the exploration of key themes influencing early-stage environmental analysis, offering a narrative account of architects’ experiences in MMC practices.FindingsThe study reveals that environmental analysis in MMC is shaped by social, organisational and project-based factors. Three key themes emerged: uncertainty in environmental assumptions, dependencies on external consultants and the dominance of Passivhaus principles in design decisions. These factors challenge the integration of environmental considerations during the early design stages of MMC projects.Research limitations/implicationsAs this is a single case study, the findings might not be generalizable. Future research should expand on multiple firms and project settings to validate and broaden these findings.Originality/valueThis study provides an in-depth understanding of the challenges architects face when integrating environmental analysis into MMC. This reveals the relational and interdependent nature of environmental analysis in MMC projects, where decisions are shaped by multiple social, organisational and project-specific factors. By emphasizing these interdependencies, this study provides original insights into how environmental considerations can be integrated more effectively into the early design stages of MMC projects.

Building inclusive Learning Development spaces ‘…but we don’t have time for universal design’

This project sets out to convert a Continuous Professional Development (CPD) workshop on Dyslexia awareness into a series of mini asynchronous learning experiences. The aim is that learning and teaching staff will access the short asynchronous activities by scanning a Quick Response (QR) code; these activities will provide a brief insight into relevant examples of neurodiverse experiences, followed by tips on changes to professional practice that can embed inclusive and universal design. Universal Design for Learning (UDL) is an ethos that we all strive for in our various roles in learning development; It embraces the principles of the social model of disability, which the Office of Students (2019) states ‘is not about “fixing” the individual but about restructuring environments and attitudes around them’. As highlighted by Dettmer and Welton (2024, p.62) the learning community seeks to promote the social model of disability when promoting inclusive learning environments. Stripe and Ntonia’s article on implementing change for inclusive teaching (2023) highlights that the challenges faced include lack of time for staff CPD, negative attitudes towards reasonable adjustments, and mindsets that are not conducive to inclusivity. These factors are sector wide; time is precious for many learning and teaching staff, and attending CPD workshops, particularly when it means adding to an existing heavy workload, are not typically at the top of priority lists. However, as proposed, engaging in a 30-60 second asynchronous activity, might lead to some ‘quick wins’ for the adoption of universal design principles. The proposed short asynchronous activities discussed here are based on experiences of neurodiverse learners when faced with non-inclusive environments, and provide strategies that can be immediately implemented. It is hoped that by scanning a QR code and engaging in learning experiences ‘on the go’, this approach will encourage staff to see that inclusive and universal design can start with small changes and have a fundamental impact on students’ learning experiences.

Carving out space for co-creation in implementation of evidence-based mental health interventions

Abstract Background Mental health is identified as a pressing public health priority for European countries challenged by societal changes. The ADVANCE project aims to strengthen our understanding of mental health promotion and prevention in seven countries in Europe. In Denmark, the research project sets out to understand how WHO’s stress management intervention Self Help Plus can be embedded within social and integration programmes for adults who have experienced migration. Methods We are using a common methodology across the 7 country projects oriented by co-creation with end users and relevant stakeholders to initially develop implementation scenarios and stigma reduction strategies. We will present findings from this process and reflect on the tension and potential of the co-creation methodology in the implementation of evidence-based mental health interventions. Results In the first project phase of problem analysis and co-creation, the project’s society advisory group discussed 10 tailored intervention scenarios. Then, a Delphi survey and two rounds of scenario-based workshops with additional involvement of 16 end users/actors resulted in a final implementation scenario and stigma reduction strategies. We identified and reflected on questions around ownership of the project, boundaries of the proposed intervention, alignment of expectations, and intervention sustainability within the parameters of the ADVANCE project. Conclusions Even when interventions are pre-selected in response to funding calls, co-creation can facilitate the development of acceptable and sustainable implementation strategies. However, meaningful co-creation requires time, flexibility, and negotiation, which is not always guaranteed. Key messages • Co-creation creates productive tension and potential for the implementation of ADVANCE. • Space for co-creation was negotiated with collaborators to ensure meaningful engagement.

Disease Prediction and Drug Recommendation Using ML

In today's dynamic healthcare landscape, the integration of technology has become indispensable. The project sets out to create a cutting-edge healthcare solution that will transform the very foundations of healthcare decision-making by utilizing the powerful powers of machine learning. The main objective of this project is to develop a dual-dashboard system that will serve both patients and medical staff. The main objective is to reduce the gap between patients and healthcare professionals, which has existed for a long time. It makes it possible for people to receive highly personalized health assessments, enables efficient appointment scheduling, and provides doctors with recommendations for data-driven treatments. Ultimately, this project aims to profoundly impact the accessibility and quality of healthcare services. It seeks to enhance the patient experience, augment the precision of medical decision-making, and foster seamless collaboration between individuals and their healthcare providers.

Seed Grant Programs to Promote Community Transformation in Higher Education Institutions

Used in higher education for many decades, seed grants are now beginning to be applied as a strategy to advance diversity, equity and inclusion goals, including rebuilding community post-pandemic. There is little research on the effectiveness of seed grants for such communal goals. This work is innovative in two key ways. First, these seed grants focus on promoting a strong sense of community at the institution rather than promoting individual investigators and research projects. Second, engaging students and staff as principal investigators (PIs) disrupts power structures in the academy. We present a systematic analysis of seed grant project reports (n = 45) and survey data (n = 56) from two seed grant programs implemented at the same institution. A diverse set of projects was proposed and funded. Projects had a positive impact on awardees and their departments and colleges. Seed grant program activities were successful at building community among awardees and recognizing individual efforts. Most noteworthy are the career development opportunities for graduate students, postdocs and staff, which are afforded by changes to PI eligibility. We conclude that seed grant programs have the potential for organizational learning and change around community building in higher education.

Researcher and partner perspectives on the relationship between engagement in research and three uses of patient-centered comparative clinical effectiveness research study findings

BackgroundThe uptake of research findings into clinical practice is critical to providing health care that improves health outcomes for patients. This study explored how Patient-Centered Outcomes Research Institute (PCORI) awardees perceive the relationship between engagement of patients and other partners in research and three uses, or applications, of patient-centered comparative clinical effectiveness research (CER) study findings, which may lead to uptake in clinical practice: (1) Integration into clinical practice guidelines, recognized point-of-care decision tools, or documents that may inform policy; (2) Implementation beyond the study, including at sites outside of the study setting or patient populations; and (3) Active dissemination of findings to specific audiences by parties external to the study team.MethodsThis exploratory qualitative study examined awardee and partner perceptions of what led to each use of study findings and how engaged partners contributed. We purposively selected PCORI-funded research projects with documentation of each use and conducted virtual interviews with 42 individuals (15 PIs or project leads, 2 research team members, and 25 partners) from 17 projects. We conducted thematic analysis of individual projects or project sets, across projects within each use case, and across the three uses.ResultsParticipants described three primary activities in which engaged partners made contributions before, during and after CER studies that facilitated the use of study findings: (1) generating relevant study findings, (2) distributing study findings strategically, and (3) making connections to people or organizations outside the study team. In addition, engagement continued to facilitate the use of study findings during subsequent PCORI-funded implementation and dissemination-specific projects, with partners adapting interventions and creating and tailoring dissemination messages and products. Finally, participants described attributes of teams’ engagement approaches that may have supported partner contributions, including early and ongoing engagement, leveraging partners’ connections and understanding of community needs, and using multiple engagement approaches.ConclusionThis study identified examples of how engagement can help facilitate the use of CER study findings, especially when engagement contributions occur in meaningful ways. Findings from this study suggest a framework for future research on the relationship between engagement in research and uptake of study findings into clinical practice.

Professional Behavior Assessment During Residency Training: Can We Identify Outliers?

The American Board of Orthopaedic Surgery (ABOS) Knowledge, Skills, and Behavior (KSB) project sets up a framework for competency-based medical education for orthopaedic surgery residency training. The Behavior aspect of KSB includes use of the American Board of Orthopaedic Surgery Behavior Tool (ABOSBT) to assess 5 domains of professional behavior. The purpose of this study was to analyze the results of 2 years of ABOSBT assessments and to characterize the low score ratings. All ABOSBT assessments from January 1, 2022, to December 31, 2023, were reviewed totaling 51,678 completed assessments with 48,648 (94%) assessments from a 360° process and 3,030 (6%) as end-of-rotation assessments. Distribution of 51,678 ABOSBT assessments with 258,390 scores demonstrated low scores (disagree or strongly disagree) in 0.9% of assessments. All 5 domains identified low scores in a small percentage at all years-in-training with the greatest number in second- and third-year residents. Comparison of scores from 360° vs. end-of-rotation requests demonstrated a higher percentage of low scores given during the 360° process (p < 0.0001). Three thousand seven hundred seven unique evaluators completed ABOSBT assessments, with attending physicians as the most frequent evaluator type (44%); inpatient nurses assessed residents with low ABOSBT scores more frequently (4.5%) than any other evaluator type. Residents with 2 or more low scores by 2 or more different evaluators were analyzed for each of the 5 domains; low scores were most frequently observed in the reliability domain. The ABOSBT was originally validated 5 years ago in 18 programs with 9,892 assessments; this article updates results using the ABOSBT across 95 programs assessing 2,397 residents with 3,707 evaluators. The ABOSBT demonstrates that most residents demonstrate professional behavior across 5 domains of assessment; the ABOSBT identifies residents with low scores. A strength of KSB is the ability to identify professionalism deficiencies while residents are in training and can focus on individualized educational improvement. Tracking residents with low scores on the ABOSBT assessment over time will help determine its effectiveness in identifying unprofessional behavior.

Comparative analysis of biomechanical patterns in sprinting: A machine learning approach to optimize running performance in track athletes

Athletes’ success in field and track competitions has been reported to be determined by their sprinting skills. Therefore, it is crucial to understand what biomechanical and physiological factors contribute to the most effective sprinting attributes. The scientific research on sprint evaluation has predominantly dealt with discrete metrics simultaneously, avoiding the interplay between multiple factors as the sprint progresses. Incorporating all the factors that could potentially influence the impact of excellent sprint ability is the primary objective of the present study. This research investigates the biomechanics of sprinting using a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) hybrid approach, focusing mainly on factors like stride length, ground reaction forces, joint angles, and muscle activation patterns. The hybrid Machine Learning (ML) model accurately identifies between the two groups, and the results indicate that sprinters performing at the national level have more extended movements, higher reaction time to ground forces, and improved joint angles. The research project set up a 20-meter track for the race, and 30 participants, divided 50-50 between two distinct groups that included comparable college-level and national-level performers, participated. With a 92.4% accuracy, 90.2% precision, and 90.9% F1 score, the hybrid approach performed better than standard models in predicting optimum sprinting patterns. The higher efficiency is caused by phase-specific changes that the model unattended, such as enhanced knee angles and joint accelerated motion in the swing phase. In comparison, the SVM model, though respectable, lags behind with an accuracy of 85.7% and a lower precision and recall (82.4% and 80.9%, respectively). The RF model performed better than SVM with an accuracy of 88.1% and a balanced F1-score of 86.8% but still fell short of the CNN-LSTM hybrid. The standalone LSTM model performed relatively well, with an accuracy of 89.3% and an F1 score of 88.1%, showing its capability but still not matching the hybrid model’s performance.

CanESM5-derived ocean wave projections — Considerations for coarse resolution climate models

This study presents the first set of CanESM5-driven wave projections for two emission scenarios (SSP5-8.5 and SSP2-4.5) and two time periods for mid- and end-century. While coarse resolution climate models, like CanESM5, might be less attractive for development of ocean wave projections, their results are needed to explore the full range of inter-model uncertainty in CMIP6 projections. Considering the coarse resolution limitation, wave simulations were obtained with a proposed computationally efficient 2-step bias-correction approach that consists of (i) calibrating the wind-to-wave energy transfer in the ocean wave model to reduce the underestimation of extremes resulting from coarse resolution, and (ii) bias-correcting the surface winds with a multivariate bias-correction to reduce remaining systematic biases. Results showed overall good performance in comparison with state of the art reanalysis and satellite data. Resulting projections provide increased understanding of future changes in wave conditions, confirming previously reported global-scale changes, such as higher waves in the eastern tropical Pacific and lower waves in the North Atlantic. They also provide more detailed information for areas affected by sea ice conditions in comparison to the latest CMIP5-based wave ensembles, which is critical for the Arctic region, a hotspot for ocean wave changes. Moreover, while the largest changes are typically seen by the end-century under SSP5-8.5, this study reveals that for some variables and areas, such as the mean wave period, larger changes occur for lower warming levels as a result of competing driving factors. Finally, the presented projections can contribute to ongoing efforts to generate a large multi-model ensemble of wave projections based on CMIP6.

Improving the Monitoring and Management of Clozapine-Induced Gastrointestinal Hypomotility (CIGH) in Community Mental Health Services: A Quality Improvement Approach.

Clozapine is the only approved antipsychotic for refractory schizophrenia to date. It can cause a range of serious and fatal adverse effects, including Clozapine-Induced Gastrointestinal Hypomotility (CIGH). While guidance is readily available to help manage CIGH effectively in hospital inpatients, practical recommendations applicable to the community (outpatient) setting are lacking. This project set out to improve the prevention, detection and management of CIGH in psychiatric outpatients. An initial baseline audit followed by quality improvement work was undertaken in a busy support worker-run community clozapine clinic focusing on, education and training, risk assessments and clinical documentation. The project was registered and managed using the Life QI web-based platform, where a set of primary and secondary drivers were defined and change ideas were executed. Qualitative and quantitative data were collected over a three-month period, demonstrating a significant improvement in clinical documentation (up from 36% to 99%). 23% of enhanced risk assessments resulted in treatment recommendations, modifiable risk factors were proactively discussed in 53% of clinic appointments and 65% of patients were provided with additional written information on CIGH. It was evident from staff and our patient feedback that further efforts would be required to continue to raise awareness about harms of unmanaged constipation among this client group. Future approaches may include enhanced collaborative efforts with primary care, and improving the skill mix in existing clozapine clinics, which could include the utilisation of mental health pharmacists.

Hitting Sets for Orbits of Circuit Classes and Polynomial Families

The orbit of an n -variate polynomial \(f({\mathbf {x}})\) over a field \(\mathbb {F}\) is the set \(\text {orb}(f) := \lbrace f(A{\mathbf {x}}+{\mathbf {b}}) : A \in \mathrm{GL}(n,\mathbb {F}) \text{ and } {\mathbf {b}}\in \mathbb {F}^n\rbrace\) . The orbit of a polynomial f is a geometrically interesting subset of the set of affine projections of f . Affine projections of polynomials computable by seemingly weak circuit classes can be quite powerful. For example, the polynomial \(\mathsf {IMM}_{3,d}\) —the \((1,1)\) th entry of a product of d generic \(3 \times 3\) matrices—is computable by a constant-width read-once oblivious algebraic branching program (ROABP), yet every polynomial computable by a size- s general arithmetic formula is an affine projection of \(\mathsf {IMM}_{3,\text {poly}(s)}\) as shown by Ben-or and Cleve [ 12 ]. To our knowledge, no efficient hitting set construction was known for \(\text {orb}(\mathsf {IMM}_{3, d})\) before this work. In this article, we initiate the study of explicit hitting sets for the orbits of polynomials computable by several natural and well-studied circuit classes and polynomial families. In particular, we give quasi-polynomial time hitting sets for the orbits of: Low-individual-degree polynomials computable by commutative ROABPs . This implies quasi-polynomial time hitting sets for the orbits of the elementary symmetric polynomials and the orbits of multilinear sparse polynomials . Multilinear polynomials computable by constant-width ROABPs . This implies a quasi-polynomial time hitting set for the orbits of the family \(\lbrace \mathsf {IMM}_{3,d}\rbrace _{d \in \mathbb {N}}\) . Polynomials computable by constant-depth, constant-occur formulas . This implies quasi-polynomial time hitting sets for the orbits of multilinear depth-4 circuits with constant top fan-in , and also polynomial-time hitting sets for the orbits of the power symmetric polynomials and the sum-product polynomials . Polynomials computable by occur-once formulas . We say a polynomial has low individual degree if the degree of every variable in the polynomial is at most \(\text {poly}(\log n)\) , where n is the number of variables. The first two results are obtained by building upon and strengthening the rank concentration by translation technique of Agrawal, Saha, and Saxena [ 6 ]; the second result additionally uses the merge-and-reduce idea from Forbes and Shpilka [ 30 ] and Forbes, Shpilka, and Saptharishi [ 27 ]. The proof of the third result adapts the algebraic independence-based technique of Agrawal, Saha, Saptharishi, and Saxena [ 5 ] and Beecken, Mittmann, and Saxena [ 11 ] to reduce to the case of constructing hitting sets for the orbits of sparse polynomials. A similar reduction using the Shpilka-Volkovich (SV) generator-based argument in Shpilka and Volkovich [ 90 ] yields the fourth result. The SV generator plays an important role in all four results.

Social Innovation and Sustainable Development: An Approach in the Portuguese Municipal Context

With the aim of identifying the determinants and impacts of social innovation on local sustainable development, this article analyses a set of projects funded by the Portugal Social Innovation public initiative in 10 municipalities in the country where there was local authority investment. Using documentary analysis and semi-structured interviews with entrepreneurs and local authority investors, it concludes that the experience led to the promotion of new practices, new collaborative relationships and the empowerment of local social action networks. However, it is noted that sustainable development is not yet seen as an operative concept by the stakeholders involved.

Automata-based Model of Scientific Activity

The automata model of scientific activity is defined as an extension of the automata model of arbitrary activity. The automata model is built hierarchically and consists of a static part in the form of a set of data sections and a dynamic part defining a set of model processes. The structure of arbitrary activity is considered from the perspective of requirements engineering. To describe the structure of objects, a new data type is introduced: tree set. Tree sets of objects and activities are defined in the activity model. Science as a whole has a hierarchical tree structure and is defined as a tree set of scientific disciplines. The objects of the model of scientific activity are a theory and a set of scientific projects for each scientific discipline. The organisation of scientific activity in Russia is still inefficient. As an alternative, an organisation based on a portfolio of scientific projects formed by a team of leaders for each scientific discipline is considered. The team of leaders is created and updated in accordance with the principle of maximum competence. The apparatus of formal methods and requirements engineering is used to build a model of scientific activity. The static part of the model proposed by the author is formalised.

Dynamic three-dimensional reconstruction with phase shift coding division multiplexing

Fringe projection profilometry with temporal phase unwrapping (FPP-TPU) has been gotten more and more attention in reconstructing the object with complex surfaces. However, a general contradiction in the existing FPP-TPUs is that more auxiliary coded patterns may limit sampling rate in dynamic three-dimensional (3D) measurement. In this paper, inheriting the single-frame auxiliary pattern of the phase-shifting temporal phase unwrapping (PS-TPU), an improved phase shift coding temporal phase unwrapping with higher robustness is proposed. And new projection stratagem with phase shift coding division multiplexing (PSC-DM) is also proposed to enhance the reconstructing rate in dynamic 3D scene. Firstly, six codewords instead of the traditional eight codewords are embedded into the single-frame auxiliary coding pattern, which guarantees the decoding robustness and provides a faster decoding process with single-step direction unifying. Then, the new projection sequence stratagem is designed with every two sets of 3-step phase-shifting patterns projection followed by the single-frame phase-shifting coding pattern projection. During phase unwrapping, every adjacent three frames of phase-shifting pattern sequentially resolving a wrapped phase and a highly robust 3D reconstruction can be achieved by using the closest phase-shifting coding pattern so as to achieve phase-shifting coding division multiplexing. It can obtain higher reconstructing rate. In addition, a phase shift segmentation is used to correct mismatched errors of fringe order. The experimental results demonstrate that the proposed method can increase the reconstructing rate from the rate of 30 fps in traditional PS-TPU method to 68.57 fps at the digital light projector (DLP) projection rate of 120 fps, which shows its potential prospective in dynamic 3D shape reconstruction.

Navigating Maritime Heritage: An Immersive Virtual Tour of the USS Drum Submarine Museum

Virtual Reality (VR) technology has revolutionized the preservation and interpretation of heritage sites. This study focuses on developing an immersive 360-degree virtual tour (VT) for the USS Drum Submarine Museum in Mobile, Alabama, USA, incorporating oral histories to enhance the accessibility and visitor experiences. The project addresses the need for innovative methods to present maritime history effectively. Using Matterport technology, detailed 3D imagery of the USS Drum was captured and processed, integrating multimedia elements and oral histories from a veteran USS Drum crew member to provide a richer historical narrative. A user experience study gathered feedback from virtual visitors, who offered quantitative and qualitative responses. The research findings indicate that the VT significantly enhances visitor engagement and historical understanding, with high satisfaction rates for visual quality and oral histories, though some users experienced technical challenges and difficulties. This study demonstrates the potential of combining immersive VTs with oral histories to create engaging educational experiences, preserving the USS Drum’s legacy and making it accessible to a broader audience, including those unable to visit in person. Furthermore, this project sets a precedent for museums to leverage digital tools in preserving and promoting maritime heritage and oral histories.

Storylines of summer Arctic climate change constrained by Barents–Kara seas and Arctic tropospheric warming for climate risk assessment

Abstract. While climate models broadly agree on the changes expected to occur over the Arctic with global warming on a pan-Arctic scale (i.e. polar amplification, sea ice loss, and increased precipitation), the magnitude and patterns of these changes at regional and local scales remain uncertain. This limits the usability of climate model projections for risk assessments and their impact on human activities or ecosystems (e.g. fires and permafrost thawing). Whereas any single or ensemble mean projection may be of limited use to stakeholders, recent studies have shown the value of the storyline approach in providing a comprehensive and tractable set of climate projections that can be used to evaluate changes in environmental or societal risks associated with global warming. Here, we apply the storyline approach to a large ensemble of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with the aim of distilling the wide spread in model predictions into four physically plausible outcomes of Arctic summertime climate change. This is made possible by leveraging strong covariability in the climate system associated with well-known but poorly constrained teleconnections and local processes; specifically, we find that differences in Barents–Kara sea warming and lower-tropospheric warming over polar regions among CMIP6 models explain most of the inter-model variability in pan-Arctic surface summer climate response to global warming. Based on this novel finding, we compare regional disparities in climate change across the four storylines. Our storyline analysis highlights the fact that for a given amount of global warming, certain climate risks can be intensified, while others may be lessened, relative to a “middle-of-the-road” ensemble mean projection. We find this to be particularly relevant when comparing climate change over terrestrial and marine areas of the Arctic which can show substantial differences in their sensitivity to global warming. We conclude by discussing the potential implications of our findings for modelling climate change impacts on ecosystems and human activities.

Design and architecture of the CARA infrastructure for visualising and benchmarking patient data from general practice

ObjectiveCollaborate, Analyse, Research and Audit (CARA) project set out to provide an infrastructure to enable Irish general practitioners (GPs) to use their routinely collected patient management software (PMS) data to...