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Help! I need somebody: Development and validation of the Romantic Support-Seeking (RoSS) scale
Get PDFResearch has established distinct categories of support-seeking behaviors, including direct, indirect, emotional, and instrumental support-seeking. However, no existing scale incorporates all four types of support-seeking within romantic relationships in one measure. Understanding how individuals seek support from romantic partners is crucial for managing stress, relationship satisfaction, and well-being. We aimed to create and validate the Romantic Support-Seeking (RoSS) scale, grounded in theory and empirical data. In Study 1 (N = 117 students), we used open-ended questions to gain knowledge on support-seeking behaviors and inform item development. In Study 2 (N = 491), we conducted an exploratory factor analysis to assess the factor structure and select the highest-loading items. In Study 3 (N = 355 students), we used confirmatory factor analysis to confirm the factor structure and provide preliminary construct validity evidence by correlating the subscales with measures of attachment, relationship quality, and coping. We identified four reliable subscales: direct emotional support-seeking; direct instrumental support-seeking; indirect support-seeking; and no support wanted. This accounts for each type of support-seeking, and individuals who choose to manage distress alone instead of seeking support. The RoSS is a significant advancement over existing measures because it captures the full spectrum of romantic support-seeking. The samples were predominantly young, White, and female so future work should address whether the scale applies to other demographic groups. This has clinical and research implications for understanding support dynamics in relationships and their links to individual and relational outcomes, which may be used in counselling to help couples navigate distress effectively
Omega-3 polyunsaturated fatty acids in neurodegenerative disorders: mixed designs = mixed results
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A syndemic approach to the study of Covid-19-related death: a cohort study using UK Biobank data
No full textBackground The Covid-19 pandemic showed higher infection, severity and death rates among those living in poorer socioeconomic conditions. We use syndemic theory to guide the analyses to investigate the impact of social adversity and multiple long-term conditions (MLTC) on Covid-19 mortality. Methods The study sample comprised 154 725 UK Biobank participants. Structural equation modeling was used to investigate pathways between traumatic events, economic deprivation, unhealthy behaviors, MLTC, for Covid-19 mortality. Cox regression analysis was used to investigate MLTC and Covid-19 mortality. We also tested effect modification by traumatic events, economic deprivation and unhealthy behaviors. Results Covid-19 mortality (n = 186) was directly explained by overall level of MLTC. Economic deprivation and unhealthy behaviors contributed to Covid-19 death indirectly via their negative impact on MLTC. The risk for Covid-19 mortality grew exponentially for every quintile of predicted scores of MLTC. The presence of traumatic events, economic deprivation or unhealthy behaviors did not modify the impact of MLTC on Covid-19 mortality. Conclusions Results suggest a serially causal pathway between economic deprivation and unhealthy behaviors leading to MLTC, which increased the risk of Covid-19 mortality. Policies to tackle the social determinants of health and to mitigate the negative impact of multimorbidity are needed
A consensus statement on child and family health during the COVID-19 pandemic and recommendations for post-pandemic recovery and re-build
Get PDFIntroduction: As health systems struggled to respond to the catastrophic effects of SARS-CoV-2, infection prevention and control measures significantly impacted on the delivery of non-COVID children's and family health services. The prioritisation of public health measures significantly impacted supportive relationships, revealed their importance for both mental and physical health and well-being. Drawing on findings from an expansive national collaboration, and with the well-being of children and young people in mind, we make recommendations here for post-pandemic recovery and re-build. Methods: This consensus statement is derived from a cross-disciplinary collaboration of experts. Working together discursively, we have synthesised evidence from collaborative research in child and family health during the COVID-19 pandemic. We have identified and agreed priorities areas for both action and learning, which we present as recommendations for research, healthcare practice, and policy. Results: The synthesis led to immediate recommendations grouped around what to retain and what to remove from “pandemic” provision and what to reinstate from pre-pandemic, healthcare provision in these services. Longer-term recommendations for action were also made. Those relevant to children's well-being concern equity and relational healthcare. Discussion: The documented evidence-base of the effects of the pandemic on children's and family services is growing, providing foundations for the post-pandemic recovery and re-setting of child and family health services and care provision. Recommendations contribute to services better aligning with the values of equity and relational healthcare, whilst providing wider consideration of care and support for children and families in usual vs. extra-ordinary health system shock circumstances
Intrusion detection in smart grids using artificial intelligence-based ensemble modelling
No full textFor efficient distribution of electric power, the demand for Smart Grids (SGs) has dramatically increased in recent times. However, in SGs, a safe environment against cyber threats is also a concern. This paper proposes a novel Fog-based Artificial Intelligence (AI) framework for SG Networks. It uses Machine Learning (ML) and Deep Learning (DL)-based ensemble models to enhance the accuracy of detecting intrusions in SG networks. This work has two main goals, which include addressing class imbalance in network intrusion detection datasets and building interpretable models for targeted security interventions. It is achieved by using ensemble modeling, such as Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) for ML-based ensemble, while the DL ensembles consist of aggregated neural network models trained using TensorFlow. The paper assess their effectiveness in identifying malicious activities in the SG network traffic. The present study utilizes a large dataset that was custom-designed for SG intrusion detection. Most of the previous studies explored different ML techniques using a single dataset; however, the performance improvement by ensemble modeling has not been explored intensively. Therefore, this paper bridges this research gap by suggesting a novel ML-based ensemble model for intrusion detection using two datasets: CIC-IDS-Collection and a specifically designed Power System Intrusion dataset. This study has made benchmark results demonstrating the effectiveness of the proposed ensemble models for intrusion detection in SGs. Results demonstrated better accuracy, precision, recall, and F1 Scores for the proposed ensemble models over the two datasets. The accuracy, precision, recall, and F1 Scores for the proposed Ensemble model 1 for the CIC-IDS Collection dataset are 98.57%, 98.75%, 99.00%, and 98.25% and for the Power System dataset they are 98.75%, 99.05%, 99.20%, and 99.10%, respectively. Similarly, for the proposed Ensemble model 2 for the CIC-IDS Collection dataset, we have 98.84%, 99.00%, 99.00%, and 99.00% accuracy, precision, recall, and F1 Score values. For the Power System dataset, these values are 99.05%, 99.30%, 99.25%, and 99.27% for the mentioned parameters
Mapping Tumor–Stroma–ECM Interactions in Spatially Advanced 3D Models of Pancreatic Cancer
No full textResearch directions for using LLM in software requirement engineering: a systematic review
Get PDFNatural Language Processing (NLP) and Large Language Models (LLMs) are transforming the landscape of software engineering, especially in the domain of requirement engineering. Despite significant advancements, there is a notable lack of comprehensive survey papers that provide a holistic view of the impact of these technologies on requirement engineering. This paper addresses this gap by reviewing the current state of NLP and LLMs in requirement engineering, highlighting their effects on improving requirement extraction, analysis, and specification. We analyze trends in software requirement engineering papers, noting an upward trajectory in the application of LLMs in software engineering tasks. This review underscores the critical role of requirement engineering in the software development lifecycle and emphasizes the transformative potential of LLMs in enhancing precision and reducing ambiguities in requirement specifications. Our findings indicate a growing interest and significant progress in leveraging LLMs for various software engineering tasks, particularly in requirement engineering. This paper aims to provide a foundation for future research and identify key challenges and opportunities in this evolving field
A study on the marine propulsion plant system by simulating the numerical modelling on Simulink/Matlab: a case study of passenger ship
No full textThe improvement of ship performance and propulsive efficiency has been addressed in this article. In this research, the comparative study has been investigated for a certain passenger vessel with the research results of Stapersma and Woud in their research “Matching Propulsion Engine with Propulsor” that has been published on Journal of Marine Engineering & Technology. After that, the marine propulsion plant system will be researched to enlarge the operational ranges between marine propeller-shaft system- marine diesel engines. A case study of passenger ship has been applied from this research namely Sea life Legend 02 in Quang Ninh province, Vietnam. The marine propulsion plant system of passenger ship will be designed in the Simulink/Matlab platform. Each functional block will be presented for the devices of the marine propulsion plant system, including the diesel engine, generator, shaft system, and marine propeller. The improvement of marine propulsion plant will be conducted on this proposed numerical model. The collected results have been shown the priority features of the marine propulsion plant system and they are fundamental to enlarge the propulsion performance of ship. The research results would be analyzed and validated with the actual marine propulsion plant system. This article is significant for ship-operators and ship-owners in the management of marine propulsion plant system for ships nowadays