Performance analysis of concrete-filled aluminum tubes confined with aramid fiber sheets under axial loading:a combined numerical and machine learning approach

This study presents a comprehensive investigation into the axial performance of concrete-filled aluminum tubular (CFAT) columns externally confined with aramid fiber-reinforced polymer (AFRP) sheets, using an integrated finite element analysis (FEA) and machine learning (ML) framework. While CFAT columns offer significant advantages such as reduced weight, high corrosion resistance, and architectural appeal, their structural performance is often limited by the lower stiffness and yield strength of aluminum. To overcome these limitations, this research explores the use of AFRP confinement to enhance load-bearing capacity and ductility. A validated FEA model was developed in ABAQUS based on 23 experimental CFAT stub column tests, showing strong agreement with results (where PEXP/PFEA, the ratio of experimental to FEA-predicted load capacities, ranged from 0.85 to 1.14, confirming model accuracy). A detailed parametric analysis investigated the effects of AFRP layer count, concrete strength, and tube geometry (D/t ratio, the diameter-to-thickness ratio indicating slenderness), revealing that AFRP confinement significantly improves performance—particularly in thin-walled columns. Additionally, four ML models (SVR, RF, ANN, Meta-ANN) were trained on 113 datasets generated from numerical simulations to predict ultimate axial load capacity. The Meta-ANN model achieved the highest accuracy with a MAPE of 2.02% and R of 0.99. To interpret the predictions, SHAP (SHapley Additive exPlanations) analysis was used, identifying column diameter and concrete strength as the most influential parameters. This dual numerical–data-driven approach demonstrates the potential of combining AFRP confinement with AI-based prediction tools for the design and optimization of advanced composite columns

Performance analysis of concrete-filled aluminum tubes confined with aramid fiber sheets under axial loading:a combined numerical and machine learning approach

This study presents a comprehensive investigation into the axial performance of concrete-filled aluminum tubular (CFAT) columns externally confined with aramid fiber-reinforced polymer (AFRP) sheets, using an integrated finite element analysis (FEA) and machine learning (ML) framework. While CFAT columns offer significant advantages such as reduced weight, high corrosion resistance, and architectural appeal, their structural performance is often limited by the lower stiffness and yield strength of aluminum. To overcome these limitations, this research explores the use of AFRP confinement to enhance load-bearing capacity and ductility. A validated FEA model was developed in ABAQUS based on 23 experimental CFAT stub column tests, showing strong agreement with results (where PEXP/PFEA, the ratio of experimental to FEA-predicted load capacities, ranged from 0.85 to 1.14, confirming model accuracy). A detailed parametric analysis investigated the effects of AFRP layer count, concrete strength, and tube geometry (D/t ratio, the diameter-to-thickness ratio indicating slenderness), revealing that AFRP confinement significantly improves performance—particularly in thin-walled columns. Additionally, four ML models (SVR, RF, ANN, Meta-ANN) were trained on 113 datasets generated from numerical simulations to predict ultimate axial load capacity. The Meta-ANN model achieved the highest accuracy with a MAPE of 2.02% and R of 0.99. To interpret the predictions, SHAP (SHapley Additive exPlanations) analysis was used, identifying column diameter and concrete strength as the most influential parameters. This dual numerical–data-driven approach demonstrates the potential of combining AFRP confinement with AI-based prediction tools for the design and optimization of advanced composite columns

Shaping the future:exploring the Chartered Association of Sport and Exercise Sciences (CASES) endorsed undergraduate sport and exercise science curricula in the United Kingdom

Despite high popularity, economic and social value of the sport and exercise sciences (SES) courses in the United Kingdom (UK), there has been no attempt to provide an overview of its higher education (HE) provision. Therefore, the aim of this study was two-fold. Firstly, to provide a thorough overview of the curricula of the Chartered Association of Sport and Exercise Sciences (CASES) endorsed undergraduate SES degree programmes in the UK. Secondly, to present a foundation of discussion points and considerations for those shaping and (re)designing sport degree programmes.Curricula data from 2024 to 2025 were collected from 53 UK universities (44 English, 4 Scottish, 4 Welsh and 1 Northern Irish) offering CASES endorsed SES courses. Due to different degree structures in Scotland (a 4-year BSc (Hons) degree) and the rest of the UK (a 3-year BSc (Hons) degree), the data were summarised and presented separately as ‘Scotland’ and ‘RUK’. A total of 1328 modules were analysed by type (either ‘core’ or ‘optional’) and categorised into one of fifteen domains.The results show that RUK universities were more prescriptive than those in Scotland, with 57 % of all SES modules being core compared to 45 % in Scottish institutions. However, the number of optional modules increased over the years in both systems reflecting the generally flexible structure of the SES degree. The curricula of Scottish and RUK SES degree programmes were predominantly multidisciplinary allowing institutions to tailor content in response to emerging fields and/or staff expertise. These findings have implications for future (re)design of SES degree curricula, not just in the UK but in similar settings. The current challenges curriculum developers face in keeping SES programmes relevant and preparing graduates for the workplace are discussed. Finally, we offer recommendations for overcoming these challenges

User personas, ideation and Large Language Models:a post-hoc study

Covering the full ideation of design with Large Language Models (LLMs) and user interview data remains an underexplored area in the current scholarship. This paper begins to address this gap and investigates the integration of LLMs in a user-centered design process, creating user personas based on qualitative interview data. This work further explores using these personas for deriving scenarios, and functionality requirements, also with LLMs. First, LLMs are used to identify key themes of users from interviews, subsequently synthesising these into personas. Second, personas are expanded into scenarios and associated functionalities for a digital platform, simulating the ideation phase of a design process. The findings illustrate how LLMs can potentially streamline these early design stages. An evaluation shows that the process discovers a list of functionalities which are, to a reasonable extent, comparable to those that human researchers have produced separately.The study proposes a practical procedure for integrating LLMs into qualitative design ideation workflows. The dataset used comprises 26 Open Access interviews from a previous Horizon project, from which eight personas and related scenarios are derived. To support further experimentation and practical applications, several computational resources used in performing analysis and generating LLM-based personas are shared. This enables reproducibility and encourages broader exploration of LLM-assisted design ideation

Systematic review and synthesis without <i>meta</i>-analysis (SWiM) reveals lack of clinical studies and weak preclinical evidence for interaction between glucose regulating drugs and environmental contaminant exposure

Environmental chemical exposure is associated with T2D incidence and may underly some of the large observed variation in therapeutic responses. Clinical and applicable preclinical studies could help reveal whether environmental chemicals interact with the action of drugs used for glucose management. We systematically searched for studies testing the interaction between environmental contaminants and T2D drug action on glucose regulation outcomes. We found no clinical studies that examined chemical exposure interaction with T2D drug action. Nine of 458 papers were eligible, all of which were preclinical. Four contained in vivo studies, four contained in vitro work and one contained both approaches. Bisphenols were the main focus (n = 4). Inhaled particulates (PM2.5), polychlorinated biphenyls (PCBs), cadmium, arsenic, and per-and-poly-fluorinated-alkalated substances (PFAS) were each examined once. Metformin and rosiglitazone were the most frequently examined drugs (n = 4). Exendin-4 was investigated twice and glibenclamide once. Lack of study design comparability precluded meta-analysis. Instead, we calculated effect sizes and differences in outcome values (mean ± 95 % CI) for synthesis without meta-analysis (SWiM). Five studies reported impairment of drug action. Our analysis shows support for this conclusion was only present in two papers. Small sample sizes, short duration exposures, unrealistic chemical levels, lack of full factorial analysis and absence of testing in suitable T2D models restrict the applicability of the current, limited preclinical evidence for translation to clinical practice. The potential for chemical exposure to impact T2D medication effects on glucose control needs to be addressed with dedicated clinical studies in patients with T2D

Systematic review and synthesis without <i>meta</i>-analysis (SWiM) reveals lack of clinical studies and weak preclinical evidence for interaction between glucose regulating drugs and environmental contaminant exposure

Environmental chemical exposure is associated with T2D incidence and may underly some of the large observed variation in therapeutic responses. Clinical and applicable preclinical studies could help reveal whether environmental chemicals interact with the action of drugs used for glucose management. We systematically searched for studies testing the interaction between environmental contaminants and T2D drug action on glucose regulation outcomes. We found no clinical studies that examined chemical exposure interaction with T2D drug action. Nine of 458 papers were eligible, all of which were preclinical. Four contained in vivo studies, four contained in vitro work and one contained both approaches. Bisphenols were the main focus (n = 4). Inhaled particulates (PM2.5), polychlorinated biphenyls (PCBs), cadmium, arsenic, and per-and-poly-fluorinated-alkalated substances (PFAS) were each examined once. Metformin and rosiglitazone were the most frequently examined drugs (n = 4). Exendin-4 was investigated twice and glibenclamide once. Lack of study design comparability precluded meta-analysis. Instead, we calculated effect sizes and differences in outcome values (mean ± 95 % CI) for synthesis without meta-analysis (SWiM). Five studies reported impairment of drug action. Our analysis shows support for this conclusion was only present in two papers. Small sample sizes, short duration exposures, unrealistic chemical levels, lack of full factorial analysis and absence of testing in suitable T2D models restrict the applicability of the current, limited preclinical evidence for translation to clinical practice. The potential for chemical exposure to impact T2D medication effects on glucose control needs to be addressed with dedicated clinical studies in patients with T2D

Systematic review and synthesis without <i>meta</i>-analysis (SWiM) reveals lack of clinical studies and weak preclinical evidence for interaction between glucose regulating drugs and environmental contaminant exposure

Environmental chemical exposure is associated with T2D incidence and may underly some of the large observed variation in therapeutic responses. Clinical and applicable preclinical studies could help reveal whether environmental chemicals interact with the action of drugs used for glucose management. We systematically searched for studies testing the interaction between environmental contaminants and T2D drug action on glucose regulation outcomes. We found no clinical studies that examined chemical exposure interaction with T2D drug action. Nine of 458 papers were eligible, all of which were preclinical. Four contained in vivo studies, four contained in vitro work and one contained both approaches. Bisphenols were the main focus (n = 4). Inhaled particulates (PM2.5), polychlorinated biphenyls (PCBs), cadmium, arsenic, and per-and-poly-fluorinated-alkalated substances (PFAS) were each examined once. Metformin and rosiglitazone were the most frequently examined drugs (n = 4). Exendin-4 was investigated twice and glibenclamide once. Lack of study design comparability precluded meta-analysis. Instead, we calculated effect sizes and differences in outcome values (mean ± 95 % CI) for synthesis without meta-analysis (SWiM). Five studies reported impairment of drug action. Our analysis shows support for this conclusion was only present in two papers. Small sample sizes, short duration exposures, unrealistic chemical levels, lack of full factorial analysis and absence of testing in suitable T2D models restrict the applicability of the current, limited preclinical evidence for translation to clinical practice. The potential for chemical exposure to impact T2D medication effects on glucose control needs to be addressed with dedicated clinical studies in patients with T2D

Shaping the future:exploring the Chartered Association of Sport and Exercise Sciences (CASES) endorsed undergraduate sport and exercise science curricula in the United Kingdom

Despite high popularity, economic and social value of the sport and exercise sciences (SES) courses in the United Kingdom (UK), there has been no attempt to provide an overview of its higher education (HE) provision. Therefore, the aim of this study was two-fold. Firstly, to provide a thorough overview of the curricula of the Chartered Association of Sport and Exercise Sciences (CASES) endorsed undergraduate SES degree programmes in the UK. Secondly, to present a foundation of discussion points and considerations for those shaping and (re)designing sport degree programmes.Curricula data from 2024 to 2025 were collected from 53 UK universities (44 English, 4 Scottish, 4 Welsh and 1 Northern Irish) offering CASES endorsed SES courses. Due to different degree structures in Scotland (a 4-year BSc (Hons) degree) and the rest of the UK (a 3-year BSc (Hons) degree), the data were summarised and presented separately as ‘Scotland’ and ‘RUK’. A total of 1328 modules were analysed by type (either ‘core’ or ‘optional’) and categorised into one of fifteen domains.The results show that RUK universities were more prescriptive than those in Scotland, with 57 % of all SES modules being core compared to 45 % in Scottish institutions. However, the number of optional modules increased over the years in both systems reflecting the generally flexible structure of the SES degree. The curricula of Scottish and RUK SES degree programmes were predominantly multidisciplinary allowing institutions to tailor content in response to emerging fields and/or staff expertise. These findings have implications for future (re)design of SES degree curricula, not just in the UK but in similar settings. The current challenges curriculum developers face in keeping SES programmes relevant and preparing graduates for the workplace are discussed. Finally, we offer recommendations for overcoming these challenges

Performance analysis of concrete-filled aluminum tubes confined with aramid fiber sheets under axial loading:a combined numerical and machine learning approach

This study presents a comprehensive investigation into the axial performance of concrete-filled aluminum tubular (CFAT) columns externally confined with aramid fiber-reinforced polymer (AFRP) sheets, using an integrated finite element analysis (FEA) and machine learning (ML) framework. While CFAT columns offer significant advantages such as reduced weight, high corrosion resistance, and architectural appeal, their structural performance is often limited by the lower stiffness and yield strength of aluminum. To overcome these limitations, this research explores the use of AFRP confinement to enhance load-bearing capacity and ductility. A validated FEA model was developed in ABAQUS based on 23 experimental CFAT stub column tests, showing strong agreement with results (where PEXP/PFEA, the ratio of experimental to FEA-predicted load capacities, ranged from 0.85 to 1.14, confirming model accuracy). A detailed parametric analysis investigated the effects of AFRP layer count, concrete strength, and tube geometry (D/t ratio, the diameter-to-thickness ratio indicating slenderness), revealing that AFRP confinement significantly improves performance—particularly in thin-walled columns. Additionally, four ML models (SVR, RF, ANN, Meta-ANN) were trained on 113 datasets generated from numerical simulations to predict ultimate axial load capacity. The Meta-ANN model achieved the highest accuracy with a MAPE of 2.02% and R of 0.99. To interpret the predictions, SHAP (SHapley Additive exPlanations) analysis was used, identifying column diameter and concrete strength as the most influential parameters. This dual numerical–data-driven approach demonstrates the potential of combining AFRP confinement with AI-based prediction tools for the design and optimization of advanced composite columns

User personas, ideation and Large Language Models:a post-hoc study

Covering the full ideation of design with Large Language Models (LLMs) and user interview data remains an underexplored area in the current scholarship. This paper begins to address this gap and investigates the integration of LLMs in a user-centered design process, creating user personas based on qualitative interview data. This work further explores using these personas for deriving scenarios, and functionality requirements, also with LLMs. First, LLMs are used to identify key themes of users from interviews, subsequently synthesising these into personas. Second, personas are expanded into scenarios and associated functionalities for a digital platform, simulating the ideation phase of a design process. The findings illustrate how LLMs can potentially streamline these early design stages. An evaluation shows that the process discovers a list of functionalities which are, to a reasonable extent, comparable to those that human researchers have produced separately.The study proposes a practical procedure for integrating LLMs into qualitative design ideation workflows. The dataset used comprises 26 Open Access interviews from a previous Horizon project, from which eight personas and related scenarios are derived. To support further experimentation and practical applications, several computational resources used in performing analysis and generating LLM-based personas are shared. This enables reproducibility and encourages broader exploration of LLM-assisted design ideation