17628 research outputs found
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Employing behavioural portfolio theory for sustainable investment: Examining drawdown risks and ESG factors
Get PDFThis study uses behavioural portfolio theory (BPT) within the Markowitz Portfolio Theory framework to enhance portfolio management by focusing on sustainability and risk mitigation during market downturns. It selects portfolios to hedge against market lows using Conditional Drawdown at Risk (CDaR) and Expected Regret of Drawdown (ERoD). These measures help choose securities that perform well during a market decline. This study applies drawdown-based risk metrics to assist institutional investors and fiduciaries in making informed investment and fund management decisions. By merging BPT with Markowitz’s mean–variance framework, selected investments are maintained above a safety threshold, contributing to the portfolio’s overall quality and sustainability. Additionally, by incorporating an Environmental, Social, and Governance (ESG) preference function, the findings suggest that BPT built portfolios meet traditional performance standards and align with socially responsible investment principles, thereby offering higher utility and alignment with investor values focused on sustainable investing
Design phase collaborative risk management factors: a case study of a green rating system in South Africa
Get PDFPurposeWe explore the design risk factors and associated managerial practices driving collaborative risk management for design efficacy in green building projects. By illuminating project design risk as an important project risk category in its own right, the study contributes to our understanding of optimising design efficacies for collaborative project risk management.Design/methodology/approachThe study comprises exploratory interviews conducted with 27 industry project practitioners involved in the design and delivery/implementation of Green Star-certified building projects in South Africa.FindingsThe findings discursively highlight seven sources of design risk. We also identify seven specific collaborative risk management practices for design efficacy emerging from a consideration of how risk environments vary in the Green Star-certified projects, each with its own project design risk implications.Originality/valueThe study advances our understanding of how collaborations emerging from particular relational yet context-specific practices can be optimised to strengthen project risk management
Opportunities and challenges of drones and internet of drones in healthcare supply chains under disruption
Get PDFThis study investigates the use of drones and the Internet of Drones (IoDs) in healthcare supply chains (HSCs), highlighting the opportunities and challenges of integrating them to respond to HSCs disruptions, an area that is currently under-examined in supply chain literature. A mixed methods approach is employed in this research. The article establishes important contributions. Firstly, it evaluates and integrates current research to provide new insights for dealing with pandemics and other similar emergencies. Secondly, the article framework reveals relevant political, economic, social, technological, environmental, and legal influencing factors to respond to disruptions. Thirdly, the analysis through the SCOR provided a hierarchical process model for HSCs enabled by drones and IoDs. Fourthly, the paper provides a framework for a shift from people-dependent HSCs to technology-dependent HSCs. Promising future research directions are indicated to advance the supply chain research on this topic
Experiences and Health Outcomes of Emerging Adults with Type 1 Diabetes: A Mixed Methods Study
Get PDFBackground Emerging adults with type 1 diabetes are at risk of poorer diabetes-related health outcomes than other age groups. Several factors affecting the health and experiences of the emerging adults are culture and healthcare specific.Objectives The aim of this study was to explore the experience of emerging adults living with type 1 diabetes in Lebanon, describe their diabetes self-care and diabetes-related health outcomes (HbA1c and diabetes distress), and identify the predictors of these outcomes.Methods A convergent mixed methods design was used with 90 participants aged 18-29 years. Sociodemographic, clinical data, and measures of diabetes distress, social support, and self-care were collected. Fifteen emerging adults participated in individual semi-structured interviews. Multiple linear regression was used to determine predictors of diabetes outcomes. Thematic analysis was used to analyze qualitative data. Data integration was used to present the mixed methods findings.Results The study sample had a mean HbA1c of 7.7% (SD = 1.36) and 81.1 % reported moderate to severe diabetes distress levels. The participants had good levels of diabetes self-care and high levels of social support. HbA1c was predicted by insulin treatment type, age at diagnosis, and diabetes self-care; while diabetes distress was predicted by diabetes knowledge, blood glucose monitoring approach, and diabetes self-care. “Living with type 1 diabetes during emerging adulthood: the complex balance of a chemical reaction” was the overarching theme of the qualitative data, with three underlying themes: “Breaking of bonds: changes and taking ownership of their diabetes”, “The reactants: factors affecting the diabetes experience”, and “Aiming for equilibrium”. The integrated mixed methods results revealed one divergence between the qualitative and quantitative findings related to the complexity of the effect of received social support.Discussion The suboptimal health of the emerging adults despite good self-care highlights the importance of addressing cultural and healthcare specific factors such as diabetes knowledge and public awareness, social support, and availability of technology to improve diabetes health. Findings of this study can guide future research, practice, and policy development
Breaking away from family control? Collaboration among political organisations and social media endorsement among their constituents
Get PDFCoalitions that engage in political advocacy are constituted by organisations, which are made up of individuals and organisational subunits. Comparing the coalitions formed by organisations to the those formed by their constituent parts provides a means of examining the extent to which their coalition memberships are aligned. This paper applies inferential network clustering methods to survey data collected from organisations engaging in Irish climate change politics and to X (formerly twitter) data extracted from both the primary accounts of these organisations and the accounts of the individuals and subunits affiliated with them. Analysis of the survey-based organisation-level policy network finds evidence of an outsider coalition, formed by non-governmental organisations, labour unions and left-leaning political parties, and an insider coalition formed by the two main political parties in government, energy sector organisations, business and agricultural interests, scientific organisations, and government bodies. An analysis of the X-based account-level endorsement network finds evidence for a nested coalition structure wherein there are multiple distinct communities, which largely align with the organisation-level coalitions. Most interestingly, the largest and most active community is formed by accounts affiliated with the organisations with agricultural interests—the sector most opposed to ambitious climate action in Ireland. The results show how the somewhat disjoint behaviours of formal organisations and their affiliates give rise to nested coalitions, which can only be identified by disaggregating organisations by their constituent parts
Complex PTSD and identification with the aggressor among survivors of childhood abuse
No full textBackground: Childhood abuse (CA) is a risk factor for trauma-related disorders including posttraumatic stress disorder (PTSD) and complex posttraumatic stress disorder (CPTSD). This severe form of interpersonal trauma may result in "identification with the aggressor" (IWA), in which the individual may take on the beliefs, perspectives, and behaviors of the perpetrator. Although previous evidence suggests that IWA may be particularly related to CPTSD as compared to PTSD, there has been no study that investigated this hypothesis. Objective: The current study explored the relations between IWA and PTSD and CPTSD symptoms, and the contribution of IWA to the excess probability of PTSD and CPTSD classifications, as compared to no classification. Participants and Setting: This cross-sectional study was conducted among 320 Israeli adult CA survivors aged 21–63 (M=42.04, SD=10.81). Methods: An online survey was completed by a convenience sample of adult CA survivors. Results: Replacing one's agency with that of the perpetrator as part of IWA had a significant effect on both PTSD and CPTSD symptoms (ES= 0.36 and 0.24, respectively), and served as a risk factor for both PTSD and CPTSD classifications. Moreover, analysis of the models’ predicted values reveals that the predicted probability of CPTSD classification was 3 to 5 times higher than on the probability of PTSD classifications, for low to high values of the replacing one's agency scale, respectively. Conclusions: The current findings suggest that IWA may describe some of the deep and long-lasting detriments of CA on self, and may contribute to the development of CPTSD symptoms
Crossing Lines: Comics about Human Migration
No full textA graphic anthology co-created by migration scholars and comics artists, Crossing Lines explores issues of displacement, identity, and community, offering nuanced perspectives amid rising anti-immigrant populism
Inflammatory Monocytes Are Rapidly Recruited to the Post-Ischaemic Liver in Patients Undergoing Liver Transplantation and Cytokines Associated with Their Activation Correlate with Graft Outcomes
Get PDFLiver ischaemia–reperfusion (IR) injury remains a major cause of morbidity and mortality following liver transplantation and resection. CD4+ T cells have been shown to play a key role in murine models; however, there is currently a lack of data that support their role in human patients. Methods: Data on clinical outcomes and complications were documented prospectively in 28 patients undergoing first elective liver transplant surgery. Peripheral blood samples were collected at baseline (pre-op), 2 h post graft reperfusion, immediately post-op, and 24 h post-op. A post-reperfusion biopsy was analysed in all patients, and in five patients, a donor liver biopsy was available pre-implantation. Circulating cytokines were measured, and T cells were analysed for activation markers and cytokine production. Results: Circulating levels of cytokines associated with innate immune cell recruitment and activation were significantly elevated in the peri-transplant period. High circulating IL-10 levels corresponded with the development of graft-specific complications. The proportion of CD4+ T cells in the peripheral circulation fell throughout the peri-operative period, suggesting CD4+ T cell recruitment to the graft. Although TNFα was the predominant cytokine produced by CD4+ T cells in the intrahepatic environment, the production of IFNγ was significantly upregulated by circulating CD4+ T cells. Furthermore, we demonstrated clear recruitment of inflammatory monocytes in the peri-operative period. In donor-and-recipient pairs with a mismatch at the HLA-A2 or A3 allele, we demonstrated that inflammatory monocytes in the liver are recipient-derived. Discussion: This is the first study to our knowledge that tracks early immune cell responses in humans undergoing liver transplantation. The recruitment of inflammatory monocytes from the recipient and their cytokine release is associated with liver-specific complications. Inflammatory monocytes would be an attractive target to ameliorate ischaemia–reperfusion injury
Driving total factor productivity: The spillover effect of digitalization in the new energy supply chain
No full textDigitalization assumes a crucial role in augmenting productivity within the new energy sector. However, whether this impelling effect can permeate the entire supply chain remains to be discussed. This study delves into the spillover effect of customers’ digitalization on suppliers’ total factor productivity (TFP), endeavoring to explicate the sources of competitive advantage in China’s new energy industry from a supply chain perspective. Based on Chinese new energy enterprises from 2006 to 2022, we find that: (1) Customers’ digitalization significantly impels suppliers’ TFP, with variations depending on market influence. (2) Mechanism tests reveal that supply chain efficiency represents a pivotal channel, especially manifested in three successive aspects: consolidating, expanding, and deepening vertical connections in the supply chain. (3) Customers’ digitalization impels the growth of new energy suppliers’ TFP, while new energy enterprises do not display the spillover effect on their suppliers. This study provides targeted policy implications for policymakers to guide the growth of supply chain efficiency and TFP
Arabic Short-text Dataset for Sentiment Analysis of Tourism and Leisure Events
Get PDFThe focus of this study is to present the detailed process of collecting a dataset of Arabic short-text in the tourism context and annotating this dataset for the task of sentiment analysis using an automatic zero-shot labelling technique utilising transformer-based models. This is benchmarked against a baseline manual annotation approach utilising native Arab human annotators. This study also introduces an approach exploiting both manual/handcrafted and automatically generated annotations of the dataset tweets for the task of sentiment analysis as part of a cross-domain approach using a model trained on sarcasm labels and vice versa. The total collected corpus size is 2293 tweets; after annotation, these tweets were labelled in a three-way classification approach as either positive, negative or neutral. We run different experiments to provide benchmark results of Arabic sentiment classification. Comparative results on our dataset show that the highest performing baseline model when utilising manual labels was MARBERT, with an accuracy of up to 87%, which was pre-trained for Arabic on a massive amount of data. It should be noted that this model enhanced its performance additionally after pre-training on a dialectical Arabic and modern standard Arabic corpus. On the other hand, zero-shot automatically generated labels achieved an 84% accuracy rate in predicting sarcasm classes from sentiment labels
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