134125 research outputs found
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Generation of Oil-In-Water Emulsion-Core Sodium Alginate Beads
No full textCombined emulsion-hydrogel systems offer versatile platforms for controlled release and enhanced emulsion stability across various applications. This study introduces alginate-based core-shell structures encapsulating oil-in-water emulsions. Mineral oil is emulsified using an aqueous solution containing carboxymethyl cellulose (CMC), calcium lactate, and non-ionic surfactants (Tween 20 and Span 80) at varying concentrations. The influence of oil volume fraction and surfactant concentration on emulsion stability is assessed through bottle tests, droplet size distribution, and zeta potential analysis. Emulsions with 30 v% oil and 2 wt.% Tween 20 demonstrate the highest stability, remaining stable for up to seven days. The optimized emulsion exhibits a small droplet size of 0.55 µm and a polydispersity index of 15%, indicating a uniform and stable dispersion. This formulation is successfully encapsulated into alginate beads, yielding uniform and spherical structures. SEM imaging shows smooth shell surfaces due to the incorporation of oil, further supported by FTIR analysis. Oil content analysis reveals that larger thin-shelled beads retain the most oil (25.5%), demonstrating bead morphology influence. Mechanical and swelling tests also show that large, thin-shelled beads provide superior pressure resistance and swelling capacity. These findings highlight the potential of emulsion-filled core-shell alginate beads for applications requiring structural integrity and controlled release.Full Tex
A Novel Digital Intervention to Facilitate Diabetes Self-Management Among People with Schizophrenia and Related Disorders: Development and Acceptability Testing of SMART
No full textIntroduction: Compared with the general population, people with schizophrenia and schizophrenia-related disorders (SSD) have a higher prevalence of type 2 diabetes (T2D) and T2D risk factors such as poor diet and sedentary lifestyle. Antipsychotic drugs significantly contribute to this risk through metabolic adverse effects, including weight gain and insulin resistance. Prevention and self-management of T2D is challenging in this population due to inherent motivational and cognitive challenges associated with schizophrenia. The objective of this study was to describe the co-design and test the feasibility, acceptability, and usability of a novel digital health intervention, Schizophrenia and diabetes Mobile-Assisted Remote Trainer (SMART), for prevention and self-management of T2D in people with SSD. Methods: SMART was developed through an iterative process including review of relevant literature (eg, disease-specific guidelines), stakeholder involvement, and user testing. A pre-post mixed-methods design was used to assess the acceptability and feasibility of SMART over 4 weeks among five outpatients with schizophrenia/schizoaffective disorder and pre-diabetes/T2D. Results: The co-design process resulted in a digital intervention, which consisted of personalised, interactive text messages, providing psychoeducation and strengthening motivation for self-care behaviours that promote effective diabetes self-management (ie, nutrition, physical activity, weight management, and stress coping). The pilot study demonstrated good acceptability of SMART (response rates 75–95%). Trends towards improved clinical outcomes were observed in well-being, depression, anxiety, and mental health recovery. Barriers to usability included lack of mobile/internet data, precluding the ability to reply to text messages, and a preference for more hyperlinks and additional interactive features. Conclusion: The comprehensive co-design process resulted in the development of a novel digital intervention for prevention and self-management of T2D tailored to unique needs and preferences of people with SSD. The pilot study findings indicate that SMART is acceptable and potentially usable for this population. Results will inform further adaptation and a future feasibility study to examine preliminary effectiveness of SMART.Full Tex
A Scoping Literature Review of Generative Artificial Intelligence for Supporting Neurodivergent School Students
No full textWhile Generative Artificial Intelligence (GenAI) platforms like ChatGPT have gained significant traction in education, their specific applications for neurodivergent learners remain largely unmapped. Through systematic searching of academic databases and grey literature between 2020 and 2024, this scoping literature review examined the emerging landscape of GenAI applications in supporting neurodivergent students within K-12 educational contexts. Twenty-one relevant sources were identified, discussing GenAI usage with neurodivergent students, the analysis revealing discussion of several predominant applications, including personalized learning, administrative assistance for educators, and development of individualized education plans. The review identified both promising approaches and significant concerns. Benefits included GenAI's potential to provide real-time, personalized support for students as well as reducing administrative burdens for educators. However, notable concerns emerged regarding information accuracy, over-reliance on AI, privacy considerations, and the need for human oversight. The limited empirical evidence base was particularly striking, with only nine studies providing original research data. The review identified critical gaps in current understanding, particularly regarding GenAI's effectiveness across different neurodivergent conditions and curriculum areas, and little evidence of approaches detailed in ways that educators could use. This scoping review demonstrates the need for robust empirical research examining GenAI usage in learning for neurodivergent students. These insights are timely and crucial for educators, researchers, and policymakers working to harness GenAI's potential in supporting neurodivergent learners within inclusive educational environments.Full Tex
Machine Learning vs Traditional Approaches to Predict All-Cause Mortality for Acute Coronary Syndrome: A Systematic Review and Meta-analysis
No full textBackground: Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the GRACE and TIMI risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This review compares ML models with traditional risk scores for predicting all-cause mortality in patients with ACS. Methods: PubMed, Embase, Web of Science, Cochrane, CINAHL, Scopus, and IEEE XPlore databases were searched through October 30, 2024, as well as Google Scholar and manual screening of reference lists from included studies and the grey literature for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) in estimating risk of all-cause mortality. Results: Twelve studies were included (250,510 patients). The summary C-statistic of best-performing ML models across all end points was 0.88 (95% CI 0.86-0.91), compared with 0.82 (95% CI 0.80-0.85) for traditional methods. The difference in C-statistic between ML models and traditional methods was 0.06 (P < 0.0007). Five studies undertook external validation. The PROBAST tool demonstrated high risk of bias for all studies. Common sources of bias included reporting bias and selection bias. Best-performing ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared with traditional risk scores. Conclusions: Despite outperforming well established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the need for greater model validation.No Full Tex
Design uncertainty in long span mass timber floors: proposed band-beam solution
No full textThe low relative density of timber compared to other building materials (concrete and steel) increases its sensitivity to low frequency footfall vibrations. Coupled with the scarcity of experimental data on in-situ floor vibration performance, shorter clear spans (~8 m) and excessive panel thicknesses are often prescribed for mass timber floors. This study combines in-situ performance testing of an existing mass timber floor under a conventional 5.5 m span supported by glulam with a conceptual cross-laminated timber band-beam. The existing floor performance met those of a high frequency floor (>10 Hz natural frequency, 2.93% damping ratio). Sensitivity testing indicated damping ratios and floor classifications (low or high frequency) can greatly impact response factor calculations. A conceptual floor design was implemented by replacing the hardwood glulam with thinner, laboratory-tested band-beams. The numerical results indicated a change in the floor classification and an increase in response factor. Further experimental investigations can help determine the optimal band-beam design.Full Tex
An EMG-Assisted Musculoskeletal Simulation with Concurrent Optimization of Muscle Excitations and Knee Joint Kinematics
No full textIn this study, we developed and validated an electromyography- (EMG) assisted musculoskeletal simulation framework with concurrent optimization of knee kinematics and muscle excitations. The musculoskeletal model had a 12 degree of freedom (DoF) knee joint with personalized articulating surfaces. First, model?s muscle parameters underwent calibration, followed by the EMG-assisted analysis. To assess model?s performance, we compared estimated knee biomechanics against four other simulation approaches, i.e., a 12 DoF knee model with either 1) uncalibrated EMG-assisted and 2) static-optimization (SO) neural solution; and a conventional 1 DoF knee model with either 3) EMG-assisted or 4) SO neural solution. The performance of the models was assessed against in vivo measured values from two grand challenge datasets. For estimated muscle excitations and joint contact force (JCF), the EMG-assisted models outperformed the SO solutions. Compared to the EMG-assisted 1 DoF knee, using EMG-assisted 12 DoF knee improved estimation of muscle excitations, joint moments, and transverse tibiofemoral JCF to a greater extent than compressive tibiofemoral JCF. To estimate compressive tibiofemoral JCF (during walking), the EMG-assisted model with personalized 1 DoF knee may suffice. However, the EMG-assisted 12 DoF knee model is recommended for a more accurate estimation of joint moments, muscle forces, and compressive and transverse tibiofemoral JCF, especially when these quantities can be affected, e.g., due to musculoskeletal disorders. The developed simulation framework provides a viable approach for estimating knee biomechanics accounting for personalized muscle excitation strategy and knee articulating geometries.Full Tex
Comprehensive review on biomass chemical looping combustion: Feedstock type, operating conditions, reactor design, technological advancement, mechanistic insights, and economics
No full textChemical-looping combustion has appeared as an encouraging technology to generate electricity with intrinsic CO2 capture. Utilizing biomass material in the CLC can benefit sustainable energy development and negative carbon emissions. Biomass CLC often includes diverse feedstocks, operating conditions, and multiple reactor designs, which can affect the combustion efficiency and performance of the CLC system. This review aims to critically discuss biomass CLC with the combustion of different feedstocks in the fuel reactor (FR), oxygen carriers (OCs) performance, reactor design, operating conditions, process economics, and environmental impact. The biomass CLC was also compared with conventional combustion technique. The biomass feedstocks mostly produced syngas along with 100% CO2 capture in the CLC process. However, the reactivity and stability of OCs and operational difficulties need to be addressed for diverse biomass feed. The three reactor configurations including air reactor (AR), FR, and steam reactor remained attractive among twin reactor (AR and FR) and fluidized bed systems because of their enhanced combustion efficiency and power output. The FR temperature and pressure between 850-950 °C and 0.5-2.4MPa, respectively, can result in enhanced carbon conversion (84.7%), CO2 recovery (97.2%), combustion efficiency (95.5%), and 50kWth power output. The Ni-based OCs indicated stability (i.e., no agglomeration and carbon deposits) and morphology beyond 900 °C with 99% combustion efficiency, while Fe-based OC showed high-quality hydrogen in syngas at 950 °C. Further, the molecular simulations revealed changes in the crystal structures of OCs due to the reduction and oxidation steps of the CLC. For instance, the lattice oxygen of CuO OCs was utilized to activate biomass C-H bond and transferred to char to generate CO2. Moreover, technological advancement in biomass CLC covered OCs development, multi-fuel burning compatibility, and novel reactors. The economics of biomass CLC relied on integrating it with hydrogen production, using cheap feedstock, employing natural OCs, specialized reactor design, equipment depreciation, and environmentally friendly process.No Full Tex
Recognizing the future utility of a solution: When do children choose to retain and share an object to solve a future problem?
No full textHumans' ability to recognize the future utility of a solution is fundamental to our capacity for innovation. It motivates us to—for instance—retain and share useful tools, transforming one-time solutions into innovations that change the future. However, developmental research on innovation has thus far primarily focused on children's capacity to create solutions, rather than recognition of their future utility. Here we examined children's tendency to retain and share a solution that would be useful again at a later point. Across two rooms, 4- to 9-year-olds (N = 83, M = 83.59 months, SD = 21.21 months, 43 girls) were given a series of time-limited tasks which could be solved by building and using a tool. When given the opportunity to transport a tool between the first and second rooms, children from age 6 onwards took the tool that would be useful again above chance levels. When subsequently asked to secure a solution for another child, only 8- to 9-year-olds chose this tool above chance. Positive age-partialled correlations between children's retaining and sharing suggest that these behaviours may reflect a common underlying capacity for recognizing future utility.Full Tex
Examining Foot Shape Variations in Individuals With and Without Diabetes
No full textFor people with diabetes, a good-fitting shoe is essential for reducing the risk of ulcers and eventual amputation. However, there is a lack of 3D data explaining differences in foot shape between people with and without diabetes and how individual factors might influence these differences; these data are vital for adequate shoe design and prevention of diabetic foot ulcers. This study quantifies the differences in external foot shapes of people with and without diabetes and peripheral neuropathy and examines which factors might affect these variations. One-hundred thirty-six foot scans of older adults with and without diabetes and peripheral neuropathy were used to create and assess foot shape models against demographic and health factors. Principal component analysis (PCA) showed that the feet of people with diabetes and with neuropathy are not necessarily clustered into a particular foot shape but have more pronounced features in specific foot variations (e.g., ankle width, arch height, hallux abduction, and edema vs. atrophic feet) compared to people without diabetes and neuropathy. The mean pairwise distance (intrascore spread) in PC1 and PC2 for individuals with diabetes and neuropathy was 43% larger than for those without diabetes and neuropathy and 24% larger than for those with neuropathy but no diabetes. Partial least squares regression (PLSR) showed potential predicting the presence of diabetes and neuropathy; however, additional data are required to support the trend. Analyses, such as PCA and PLSR, could be useful for determining how to quantify these changes to design more appropriate footwear for these populations.Full Tex
Conceptions to classrooms: The influence of teacher knowledge on inclusive classroom practice
No full textThe broad and subjective nature of inclusive education has led to varied interpretations, posing a significant challenge for its advocates, as there is no universally agreed-upon method for its implementation. Additionally, ongoing concerns persist regarding the "inclusion for all" approach, with arguments suggesting it cannot adequately meet the educational needs of every student due to limitations in time, resources, and support. Teachers play a pivotal role in inclusive education. Their actions influence classroom culture, and the decisions they make about how lessons are taught and assessed, directly impact on student engagement and success in learning. This study examined 140 primary teachers’ perceptions of inclusion and inclusive education practices across New South Wales, Australia. Semi-structured interviews were carried out and thematic analysis was used to investigate and probe the qualitative data. Findings reveal that while teachers who perceive inclusive education as a categorical and ambivalent paradigm enact teaching practices supported by research, they may not necessarily demonstrate inclusion in all practices. Inclusion can often be conceptually confusing, making the need for consistency across all stakeholders (e.g., policymakers, school leaders) imperative if teachers are going to meet the educational and social needs of diverse cohorts of students.Full Tex