Assessment Methods Research Articles

Meta-Analysis: Prevalence of Frailty and Associated Adverse Events in Inflammatory Bowel Diseases.

The number of adults aged over 60 years with inflammatory bowel disease (IBD) is increasing. Frailty, rather than chronological age, may be a better predictor of adverse health outcomes. To summarise current knowledge about frailty in adults with IBD including the prevalence and associations of frailty and IBD-related adverse outcomes. We performed an electronic search of MEDLINE, EMBASE and EMBASE Classic databases using search terms for IBD and frailty from inception to 14 February 2024. All studies involving adults aged ≥ 16 with a confirmed diagnosis of IBD that included a frailty assessment were eligible for inclusion. We included 23 observational studies involving 1,893,448 adults. Risk of bias was low for 18 studies and moderate for five. Twelve methods of frailty assessment were used, the most common being the Hospital Frailty Risk Score. Pooled prevalence of frailty in IBD patients was 18% (95% confidence interval (CI) 12.4%-25.6%). Meta-analysis of unadjusted events data demonstrated that frailty increased the risk of infection-related admissions following treatment in two studies (relative risk (RR) 1.9; 95% CI 1.2-3.0), post-operative morbidity in three (RR 2.0; 95% CI 1.4-2.7) and mortality in seven (RR 4.3; 95% CI 2.6-7.4). Frailty is common in patients with IBD and is associated with IBD-related adverse outcomes including infection-related admissions following treatment, post-operative morbidity and death. Future work should focus on developing risk assessment tools to better support decision making for older people with frailty and IBD.

The prognostic value and biological significance of MRI CE-T1-based radiomics models in CNS5-identified GBM: a multi-center study.

Following the publication of the 2021 WHO Classification of Central Nervous System Tumors (CNS5), Following the publication of the 2021 WHO Classification of Central Nervous System Tumors (CNS5), prognostic markers of glioblastoma (GBM) need to be further explored. Radiomics is a non-invasive and reproducible method for the prognostic assessment of multiple solid tumors. This study aimed to explore the prognostic value and biological significance of MRI T1-weighted enhancement (CE-T1) based radiomics in GBM (CNS5). A six-features radiomics prognostic model was created to calculate the radiomics score (RS). High RS (HR = 3.718, 95%CI: 2.222 - 6.220, P < 0.001) was an independent risk factor for overall survival (OS). The correlation between RS and OS was externally verified based on the First Affiliated Hospital of Fujian Medical University cohorts (n = 93; HR = 2.015, 95% CI: 1.079 - 3.762, P = 0.028) and the Second Affiliated Hospital of Zhejiang University School of Medicine cohorts (n = 126; HR = 1.779, 95% CI: 1.023 - 3.091, P = 0.041). Through biological significance exploration, RS was found to be significantly correlated with DNA repair (P = 0.009) and glycolysis (P = 0.001) pathway enrichment scores. RS was associated with γδT cell infiltration and the expression of LAG3. The MRI CE-T1 based radiomics models can predict GBM (CNS5) prognosis noninvasively. RS is relevant to DNA repair, and may guide the screening of radiosensitive populations.

The Effect of Spirulina Supplementation on Blood Pressure in Adults: A GRADE-Assessed Systematic Review and Meta-Analysis of Randomized Clinical Trials.

Previous studies have yielded controversial results regarding the effect of spirulina on blood pressure (BP), which need updating. So, this updated systematic review and meta-analysis of randomized controlled trials (RCTs) carry out a more accurate estimation of the effect of spirulina on BP in adults. This systematic searches (in PubMed/Medline, Scopus, and ISI Web of Science) until April 1, 2024, to identify related RCTs based on PICOS guidelines (population (individuals > 18 years old), the intervention (spirulina), the comparison (control or placebo group), the outcomes (systolic BP (SBP) and diastolic BP (DBP)), the study design (RCTs)), and PRISMA-checklist (Supporting Information, data S2). We evaluated the impact of spirulina on DBP and SBP. Conventional procedures were employed for analyzing publication bias, heterogeneity, and sensitivity. The GRADE criteria and the Cochrane assessment method were employed to evaluate the risk of bias (ROB) and certainty of evidence across the studies, respectively. The result shows spirulina consumption decreases SBP (WMD: -4.41 mmHg, 95% CI: -6.74 to -2.07, I2 = 66.1%) and DBP (WMD: -2.84 mmHg, 95% CI: -4.65 to -1.03, I2 = 62.3%). Subgroup analysis demonstrated SBP and DBP were still lower in individuals with ≥ 120 and ≥ 80 mmHg, hypertension (HTN) individuals, overweight individuals, age > 50 years, and > 8 weeks of intervention. Indeed, we do not observe publication bias, ROB, or interference studies in the overall results of BPs, and based on GRADE, our outcomes have moderate quality. Because of the low number of studies and participants, the dose-response and meta-regression are not significant. His study demonstrated spirulina intervention decreased SBP and DBP in HTN and overweight individuals, age > 50 years, and > 8 weeks of intervention. So, spirulina intake decreases BP and could be used in clinical practice. Furthermore, more and high-quality RCTs are needed to establish the clinical efficacy of the spirulina and determine cutoff spirulina interventions based on dose and duration. Trial Registration: PROSPERO: CRD42024534608.

Психологические основы системы отбора граждан поступивших на обучение в полицейскую академию Никарагуа

The problem of selecting citizens for admission to the police service is being worked out in almost all countries of the world. The process of selection for the internal affairs bodies of the Russian Federation is a fairly widely developed area of ​​science. In Latin American countries, these studies are at the stage of active development. The subject of this article is the optimization of the system of selection of citizens admitted to training at the Nicaraguan Police Academy. The purpose of the study is to develop a program for the psychological selection of citizens for training at the Nicaraguan Police Academy. To achieve the goal of the study, the expert assessment method was used. Mathematical statistics, quantitative and qualitative analysis were used in the processing of empirical data. 30 psychologists of the National Police of Nicaragua (73% were women and 27% were men) acted as respondents in the expert survey. It is shown that the selection of citizens for the National Police of Nicaragua should be optimized in the direction of developing the psychological part of this process. Optimization should be carried out by updating psychometric tests, including methods for assessing the mental health of candidates; inclusion in the interview protocol of parameters for assessing the psychiatric history in the family environment, as well as organizing additional training and feedback on the results of their work for psychologists upon hiring.

Comprehensive evaluation of water quality grades based on improved CRITIC and multidimensional connection cloud combination

AbstractThis study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science.

BIOM-50. SERIAL INTRACRANIAL CEREBROSPINAL CELL-FREE DNA FOR DISEASE MONITORING IN PATIENTS WITH GLIOMAS

Abstract Improved response assessment methods are needed for patients with gliomas. Intracranial cerebrospinal fluid (CSF) is longitudinally accessible and provides an abundance source of candidate biomarkers, such as cell-free DNA (cfDNA), for disease monitoring. To evaluate the clinical feasibility of molecular profiling from longitudinal intracranial CSF, CSF was acquired from patients with CSF access devices, including Ommaya reservoirs and ventriculoperitoneal shunts. cfDNA was extracted and analyzed via PredicineCARE (Next Generation sequencing) or PredicineSCORE (low pass whole genome sequencing). Five patients (2 females, 3 males; median age: 40 years, range 32-64 years) underwent longitudinal intracranial CSF collection via Ommaya reservoirs (n=4) or VP shunt (n=1). In total, forty-eight CSF samples were obtained (median volume: 4.00 mL; 0.5-5 mL), with 39 samples (81.3%) yielding sufficient cfDNA for variant profiling. Our initial findings suggest that tumor fraction increased by 6.28x, 2.87x, and 8.31x with radiographic progression in three patients. Tumor fraction decreased by 0.08x and 0.04x in two patients with paired immediate pre-versus-post chemoradiation samples. Despite ongoing pseudoprogression in one patient, tumor fraction decreased to unmeasurable levels from a post-resection baseline of 0.26. Patient-specific tumor-associated variant allelic frequencies (VAFs), including TP53, PTEN, TERT, and CDKN2A/B, decreased within individual patients after resection and chemoradiation. In two patients with isocitrate dehydrogenase (IDH) mutant gliomas, decreasing IDH1 VAF after resection and chemoradiation correlated with decreased CSF D-2-hydroxyglutarate (D-2-HG) levels (0.64x and 0.62x, respectively, for the first patient, and 0.01x and 0.07x for the other patient). Both CSF IDH1 VAF and CSF D-2-HG increased a patient who had radiographic progression (2.56x and 9.21x, respectively). Moreover, CNB decreased below the limit of quantification during treatment and increased above the limit at progression. In conclusion, longitudinal CSF cfDNA can feasibly be obtained via CSF access devices in patients with gliomas during their disease course toward evaluating candidate monitoring biomarkers.

Geomatics Technology Education – Assessment of Learning Pedagogy

Abstract. The paper investigates multidisciplinary education and specialization at the master's level, analyzes assessment methods, and proposes a technique for evaluating geospatial technology education. It emphasises is on pedagogical approaches, and highlights the importance of capacity building to meet the growing demand for skilled professionals in spatial information management. The research provides guidelines for improving course content, structure, and teaching methodologies, aiming to enhance the quality and relevance of geospatial education for both students and stakeholders. This paper is based on online data gathered from 115 universities across the globe, who are imparting geospatial education, campus-based and e-learning, also a survey was conducted of two thousand students. It supported in analysis and suggestion to develop a specific robust technique for assessment of the geospatial technology education and provides the corrective measures to the users of the technique. It proposed a framework aimed at enhancing the course content, organization, and teaching methods substantially. Further an attempt was made to adopt AHP model for investigating the statistical approach. It was aimed to know the actual values and significance of each parameter used for analysis of the study. In order to prepare a common guideline for improving the course structure, course content and teaching methodology, ANFIS architecture (Artificial Neural Fuzzy-Inference System) was adopted. Spatial information represented in the form of thematic maps in relation with the respective attribute data in Geographical Information System (GIS) environment is now used by all spectrum of the society, be it government organizations, industry, and academia. The result of the study has designed the criteria and common guidelines, which may be implemented, would able to help and curtail the challenges of the geospatial education as global vision for Local Action.

Machine-learning-based prediction of cardiovascular events for hyperlipidemia population with lipid variability and remnant cholesterol as biomarkers.

Dyslipidemia poses a significant risk for the progression to cardiovascular diseases. Despite the identification of numerous risk factors and the proposal of various risk scales, there is still an urgent need for effective predictive models for the onset of cardiovascular diseases in the hyperlipidemic population, which are essential for the prevention of CVD. We carried out a retrospective cohort study with 23,548 hyperlipidemia patients in Shenzhen Health Information Big Data Platform, including 11,723 CVD onset cases in a 3-year follow-up. The population was randomly divided into 70% as an independent training dataset and remaining 30% as test set. Four distinct machine-learning algorithms were implemented on the training dataset with the aim of developing highly accurate predictive models, and their performance was subsequently benchmarked against conventional risk assessment scales. An ablation study was also carried out to analyze the impact of individual risk factors to model performance. The non-linear algorithm, LightGBM, excelled in forecasting the incidence of cardiovascular disease within 3years, achieving an area under the 'receiver operating characteristic curve' (AUROC) of 0.883. This performance surpassed that of the conventional logistic regression model, which had an AUROC of 0.725, on identical datasets. Concurrently, in direct comparative analyses, machine-learning approaches have notably outperformed the three traditional risk assessment methods within their respective applicable populations. These include the Framingham cardiovascular disease risk score, 2019 ESC/EAS guidelines for the management of dyslipidemia and the 2016 Chinese recommendations for the management of dyslipidemia in adults. Further analysis of risk factors showed that the variability of blood lipid levels and remnant cholesterol played an important role in indicating an increased risk of CVD. We have shown that the application of machine-learning techniques significantly enhances the precision of cardiovascular risk forecasting among hyperlipidemic patients, addressing the critical issue of disease prediction's heterogeneity and non-linearity. Furthermore, some recently-suggested biomarkers, including blood lipid variability and remnant cholesterol are also important predictors of cardiovascular events, suggesting the importance of continuous lipid monitoring and healthcare profiling through big data platforms.

A deep learning-based method for assessing tricuspid regurgitation using continuous wave Doppler spectra

Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human errors leading to measurement variability, with outcomes critically dependent on the operators’ diagnostic expertise. In this study, we present an innovative assessment methodology for evaluating TR severity utilizing an end-to-end deep learning system. This deep learning system comprises a segmentation model of single cardiac cycle TR continuous wave (CW) Doppler spectra and a classification model of the spectra, trained on the TR CW Doppler spectra from a cohort of 11,654 patients. The efficacy of this intelligent assessment methodology was validated on 1500 internal cases and 573 external cases. The receiver operating characteristic (ROC) curves of the internal validation results indicate that the deep learning system achieved the areas under curve (AUCs) of 0.88, 0.84, and 0.89 for mild, moderate, and severe TR, respectively. The ROC curves of the external validation results demonstrate that the system attained the AUCs of 0.86, 0.79, and 0.87 for mild, moderate, and severe TR, respectively. Our study results confirm the feasibility and efficacy of this novel intelligent assessment method for TR severity.

Prevalence of Adverse Childhood Experiences in Child Population Samples: A Systematic Review and Meta-Analysis.

Exposure to adverse childhood experiences (ACEs) before the age of 18 years is a major contributor to the global burden of disease and disability. To meta-analyze data from samples with children 18 years or younger to estimate the average prevalence of ACEs, identify characteristics and contexts associated with higher or lower ACE exposure, and explore methodological factors that might influence these prevalence estimates. Studies that were published between January 1, 1998 and February 19, 2024, were sourced from MEDLINE, PsycINFO, CINHAL, and Embase. Inclusion criteria required studies to report the prevalence of 0, 1, 2, 3, or 4 or more ACEs using an 8- or 10-item ACEs questionnaire (plus or minus 2 items), include population samples of children 18 years or younger, and be published in English. Data from 65 studies, representing 490 423 children from 18 countries, were extracted and synthesized using a multicategory prevalence meta-analysis. These data were analyzed from February 20, 2024, through May 17, 2024. ACEs. The mean age of children across studies was 11.9 (SD, 4.3) years, the age range across samples was 0 to 18 years, and 50.5% were female. The estimated mean prevalences were 42.3% for 0 ACEs (95% CI, 25.3%-52.7%), 22.0% for 1 ACE (95% CI, 9.9%-32.7%), 12.7% for 2 ACEs (95% CI, 3.8%-22.3%), 8.1% for 3 ACEs (95% CI, 1.4%-16.8%), and 14.8% for 4 or more ACEs (95% CI, 5.1%-24.8%). The prevalence of 4 or more ACEs was higher among adolescents vs children (prevalence ratio, 1.16; 95% CI, 1.04-1.30), children in residential care (1.26; 95% CI, 1.10-1.43), with a history of juvenile offending (95% CI, 1.29; 1.24-1.34), and in Indigenous peoples (1.63; 95% CI, 1.28-2.08), as well as in studies where file review was the primary assessment method (1.29; 95% CI, 1.23-1.34). The prevalence of 0 ACEs was lower in questionnaire-based studies where children vs parents were informants (0.85; 95% CI, 0.80-0.90). In this study, ACEs were prevalent among children with notable disparities across participant demographic characteristics and contexts. As principal antecedent threats to child and adolescent well-being that can affect later life prospects, ACEs represent a pressing global social issue. Effective early identification and prevention strategies, including targeted codesigned community interventions, can reduce the prevalence of ACEs and mitigate their severe effects, thereby minimizing the harmful health consequences of childhood adversity in future generations.

Navigating the Virtual Environment

In the ever-evolving landscape of higher education, the intersection of learning spaces and technology presents unprecedented opportunities to enhance student experiences. This poster highlights the effective use of 360-degree technology to create immersive digital learning experiences across various STEM disciplines in an Australian University. This poster demonstrates the implementation of Universal Design for Learning (UDL) principles in virtual laboratory tours. Our 360-degree virtual tours embody UDL's core principles (Meyer et al., 2014) by offering multiple means of engagement through immersive experiences, representation via diverse media formats (textual overlays, audible descriptions, graphical elements, and interactive hotspots). By adhering to digital accessibility guidelines (Ismailov &amp; Chiu, 2022), it ensures that our virtual laboratory inductions cater to a wide range of learning preferences and needs, potentially enhancing the effectiveness of STEM education for diverse student populations. Traditional induction processes are often delivered face-to-face, facing challenges like time constraints, safety concerns, and limited accessibility. The COVID-19 pandemic has further highlighted the need for flexible, remote learning solutions (Rapanta et al., 2020). 360-degree virtual tours overcome barriers by immersing students in a digital environment, allowing them to become familiar with procedures before entering physical space (Tan &amp; Tan, 2021). This technology also supports dynamic learning environments, enhancing teaching effectiveness and student engagement (Christopoulos et al., 2018). The effectiveness of 360-degree virtual tours has been demonstrated across various areas, from hospitality education to construction safety training (Pham et al., 2018; Patiar et al., 2017). Virtual tours offer numerous benefits, including cost-effectiveness, accessibility, and enhanced engagement. They also support authentic, experiential learning without time or location constraints, potentially improving knowledge retention and attitudes toward learning (Cardona et al., 2023). A key benefit of 360-degree technology is its web-based implementation, making experiences accessible across a wide range of devices and operating systems. While implementation may require some initial investment in hardware and expertise, the availability of consumer-grade tools and simple workflows makes adoption increasingly feasible for educators (Tan &amp; Tan, 2021). This poster outlines the process of developing, implementing, and evaluating 360-degree virtual tours. The development phase began with the implementation of virtual tours in ten laboratories with large enrollments. Using specialised cameras (Insta360 X3), we captured spherical photographs of these laboratories, allowing students to virtually explore as if physically present. To enhance interactivity and educational value, we integrated various elements such as hotspots, videos, and quizzes using SharePoint Space and H5P platforms. The implementation phase involved incorporating these virtual tours into the online laboratory induction process. We seamlessly integrated the tours with existing course management systems to ensure easy access for students. For evaluation, we focused on qualitative assessment methods to gain insights into student experiences and perceptions on aspects such as ease of navigation, perceived realism, and overall utility of the virtual tours. This assessment was conducted through surveys and informal discussions, offering valuable feedback on the virtual tours' effectiveness in preparing students for physical laboratory work.

Implementing Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Risk Assessment of Drug Interactions

Novel drug development is time-consuming, difficult, and often unsuccessful. This has made combining therapies increasingly common and profitable in recent years. Healthcare professionals in the pharmaceutical sector are interested in the combination, but we must instantly solve drug-drug interactions. In few cases, single-perspective DDI evaluations are insufficient. Pharmacological therapy and patient safety depend on accurate drug interaction risk assessments. This work uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess and predict medication interaction hazards. The proposed method uses neural networks' generative skills and fuzzy logic to construct a robust decision-making framework that can handle drug interaction scenarios and their unpredictability. Fuzzy rules provide all the necessary risk assessment components. This includes dose, patient health issues, drug interaction profiles, and pharmacological properties. Accuracy, specificity, and Mean Squared Error evaluate the model after training with known drug interactions. These metrics evaluate model performance. These measures are essential for machine learning evaluation. The ANFIS-based model outperforms previous risk assessment methods in risk classification and prediction. This study found that the ANFIS architecture may improve medication management security and prevent dangerous drug interactions.

MIMC-β: microdosimetric assessment method for internal exposure of β-emitters based on mesh-type cell cluster model

The method combining Monte Carlo (MC) simulation and mesh-type cell models provides a way to accurately assess the cellular dose induced byβ-emitters. Although this approach allows for a specific evaluation of various nuclides and cell type combinations, the associated time cost for obtaining results is relatively high. In this work, we propose a Microdosimetric assessment method for Internal exposure ofβ-emitters based on Mesh-type Cell cluster models (abbreviated as MIMC-β). This approach is applied to evaluate the dose in various types of cells (human bronchial epithelial cells, BEAS-2B; normal human liver cells, L-O2; and normal human small intestine epithelial cells, FHs74Int) exposed toβ-emitters. Furthermore, microdosimetric quantity based on the cell cluster model are employed to estimate the relative biological effectiveness (RBE) ofβ-emitters. The results indicate that this method can accurately and rapidly predict cellular doses caused by different types ofβ-emitters, significantly mitigating the efficiency challenges associated with directly employing MC to estimate the overall dose of the mesh-type cell cluster model. In comparison with results obtained from direct simulations of uniform administration ofβ- sources using PHITS for validation, the cellular cluster overallS-values obtained through MIMC-βshow discrepancies mostly below 5%, with the minimum deviation reaching 1.35%. Small sampling sizes within the cell nucleus led to larger average lineal energies. In comparison to C-14, the differences in cellular cluster average lineal energy for Cs-134, Cs-137, and I-131 are negligible, resulting in close numerical estimations of RBE based on lineal energy. The MIMC-βcan be extended to diverse cell types andβ-emitters. Additionally, the RBE assessment based on the cell cluster model offers valuable insights for predicting radiobiological damage resulting from internal exposure byβ-emitters. This method is expected to find applicability in various realistic scenarios, including radiation protection and radioligand therapy.

The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.

Envirocare

In Higher Education, and across Australia, there is a persistent disparity in the understanding of Indigenous knowledges and histories (Bodkin-Andrews et al., 2022; Page et al., 2020). Therefore, as part of their indigenous strategy Universities Australia calls upon universities to commit to ensuring all students encounter and engage with Aboriginal and Torres Strait Islander content (Unviersities Australia, 2017). The challenge for educators and universities is navigating how to implement this within the curriculum for multifaceted reasons, including the variance between Eurocentric curricula and Indigenous ways of knowing (Cooper et al., 2024). Approaches that bringing the voice of First Nations Australians into university learning, to allow Indigenous peoples to represent themselves in this process, are vital. However, to date there are limited models of how to do this while balancing the cultural load and workload for First Nations peoples (Bodkin-Andrews et al., 2022). As one method, Envirocare has been developed as a digital learning space aimed for bringing into Australian university curricula Indigenous cultural intelligence. The project aimed to create equitable, open-access Technology Enhanced Learning that supports culturally appropriate content delivery and assessment methods nationwide. The concept emerged from collaborative efforts involving Deakin University and the Australian Council of Environmental Deans and Directors. This was guided entirely by collaborative partnerships with First Nations rangers and educators through interviews to bring authentic and relevant voices to leaners (Dahl et al., 2004; Keddie, 2012). Thus, Envirocare represents a milestone in open education practice with digital learning spaces. The project underscores the importance of collaborative, culturally informed approaches in educational innovation, aiming to enhance student learning experiences and promote broader community engagement. Envirocare can be accessed by web address at any time, and from any device with internet connection. As learners enter Envirocare, they are immersed in the content and challenged to examine their own positions, fostering their cultural intelligence, including what they think about First Nations ways of knowing, doing and being and whether they have thought about how they think about science before. Through reflection, and engaging in deep listening, they identify what might be feasible for future actions and choose a way forward. The resultant digital learning space enables educators to incorporate learnings into university curricula, with modular content that is adaptable for various educational levels. This presentation will reflect on the project's evolution, the significance of building trust with 9 different people from different communities and the value of inclusive content creation processes. Through sharing methodologies and outcomes, this Pecha Kucha presentation encourages similar initiatives, advocating for respectful integration of Indigenous knowledge into educational frameworks. The presentation will showcase the project journey to emphasise this emerging digital learning space and how it might influence the sociocultural norms of higher education.

Life Cycle Assessment and Process Optimization of Precipitated Nanosilica—A Case Study in China

To mitigate environmental emissions in the industrial nanosilica sector and promote its sustainable development, the life cycle assessment (LCA) method is employed to evaluate the environmental impacts throughout the life cycle of industrial precipitated nanosilica. This LCA spans from the acquisition and transportation of raw materials to the production of nanosilica. By identifying the critical contributing factors, effective optimization strategies have been proposed to enhance the environmental performance of the nanosilica life cycle. The effects of electricity, alkalis, acids, and steam on the life cycle emission factors of nanosilica were examined. The results indicate that substituting traditional coal power and steam with cleaner alternatives like wind energy, hydroelectric power, and solar power (both photovoltaic and thermal), as well as biogas steam, can lead to a significant reduction in the life cycle emission factors of nanosilica, ranging from 50% to 90%. Notably, the types of acids and alkalis used only significantly reduce certain environmental factors. These findings provide valuable theoretical insights and practical guidance for the industrial nanosilica sector, particularly in the areas of energy conservation, emission reduction, and the transition towards a lower-carbon economy.

Voiding sonography: Technical description of a novel ultrasound method for assessment of micturition in women

AbstractVoiding sonography (VS) is a novel non‐invasive method that can be used to assess micturition in real time. When performed in a functional seated position and combined with free uroflowmetry, detailed evaluation of voiding can be conducted. VS presents advantages over current invasive assessment methods such as urodynamics. This methodological paper describes the rationale; advantages and disadvantages of VS; participant preparation; technique; a practical approach to obtain the video/image (including transducer placement to achieve an assessment of voiding with minimal obstruction of urine flow); recommended machine and transducer specifications; and the potential usefulness of ultrasound measurements. These measurements might provide information regarding the function of the pelvic floor muscle and urethral dynamics during voiding. Measures assessed with VS can also provide information regarding urine storage. In summary, VS is a new method to assess female voiding. Future work should evaluate the potential utility of the method to evaluate characteristics and mechanisms for voiding dysfunction in females with a variety of relevant clinical conditions. Use of VS as a screening tool before and after surgical or non‐surgical treatment intervention may also be a future consideration to assess progress and provide feedback to clinicians and patients.

Designing a machine learning-based lending model to enhance access to capital for small and medium enterprises

This paper presents the design of a machine learning-based lending model aimed at enhancing access to capital for Small and Medium Enterprises (SMEs). SMEs play a crucial role in driving economic growth, yet they often face significant barriers in securing funding due to traditional credit assessment methods that rely heavily on financial history and collateral. By leveraging machine learning (ML) algorithms, this proposed model incorporates diverse data sources, including financial statements, market trends, customer behavior, and non-traditional data such as social media activity and payment histories, to better assess creditworthiness. The model utilizes supervised learning techniques, such as decision trees, random forests, and neural networks, to analyze this diverse data and identify patterns that traditional models may overlook. Additionally, it integrates real-time data processing to continuously update credit profiles, allowing for more dynamic and responsive lending decisions. The primary aim is to improve the accuracy of risk assessment, reduce default rates, and ultimately facilitate more inclusive lending practices for SMEs. One of the core advantages of the ML-based model is its ability to offer personalized lending terms and products by identifying the unique characteristics and risk factors of each SME. This approach not only improves access to capital for underbanked businesses but also ensures that lenders can manage risk more effectively. The paper also discusses the potential ethical considerations, such as ensuring data privacy and avoiding biases in algorithmic decision-making, which are critical for the responsible implementation of such a system. In conclusion, this machine learning-based lending model presents an innovative solution to the long-standing challenges SMEs face in accessing capital. By harnessing the power of data analytics and advanced algorithms, it paves the way for more equitable and efficient lending processes, supporting the growth and sustainability of SMEs. Keywords: Machine Learning, Lending Model, Access To Capital, Small And Medium Enterprises, Risk Assessment, Creditworthiness, Financial Inclusion, Data Privacy, Algorithmic Decision-Making, Supervised Learning.

Inequity in exam experience

Online digital exams, both with and without online invigilation, have become a common form of assessment in Higher Education, particularly since the COVID-19 pandemic. However, the impact of online exams and online invigilation (proctoring) on student experience, stress levels, and academic performance remains underexplored, especially for disadvantaged or underrepresented student groups. Despite growing concerns over inequitable outcomes in Higher Education, there is limited large-scale research investigating how these online assessment methods may exacerbate existing disparities. This paper addresses this gap by analysing data from a large-scale survey (N = 16,010) conducted across five semesters at an Australian University (2021-2023). Specifically, it examines the exam preparedness and experiences of on-campus students who sat online digital exams with and without proctoring. By exploring the intersectionality of gender, international status, and language, the study aims to assess whether and how online proctoring exacerbates inequities among disadvantaged student populations. Our findings suggest that social and cultural factors significantly influence exam preparedness, stress levels, and perceived academic performance, with proctoring amplifying these disparities.

Validation of assessment methods for creep crack growth rates in irradiated components from Nimonic PE16 Tie-Bar tests

Creep crack growth is a life-limiting failure mechanism in high-temperature metallic components. The creep crack growth response is linked to the underlying creep deformation and failure properties, such as creep ductility. Irradiation effects such as helium (He) embrittlement in high-temperature reactor components trigger decreases in creep ductility. Creep crack growth (CCG) data obtained from unirradiated and irradiated Nimonic PE16 tie-bars confirm the accelerated crack growth rates under neutron irradiated conditions. The exact same data were also used to validate the analytical defect assessment procedure for transient creep crack growth in Code Case N-934 of ASME BPVC Section XI Division 2, developed from unirradiated material data. To do so, the mechanics-based creep crack growth law of Nikbin-Smith-Webster (NSW) is adopted to estimate the crack growth rates in unirradiated PE16, and then adjusted through changes in bulk mechanical properties in the irradiated case to estimate the changes in crack growth rates. The estimated ȧ-C∗ trend was employed as part of the analytical defect assessment procedure for transient creep crack growth in Code Case N-934 of ASME BPVC Section XI Division 2. Evaluation of results via the procedure and the estimated trends confirm the validity of the approach to predict increases in creep crack growth in neutron irradiated components, as observed in experimental data. The approach yields moderately conservative predictions for crack growth rates, and accurately captures the relative increase in creep crack growth rates for the irradiated case. The predictions were independently validated by predicting the C*-values expected in the loading configuration for a given recorded crack growth rate ȧ. The validation of the defect assessment method introduced in Code Case N-934 against both unirradiated and irradiated PE16 CCG data provides a practical path to its implementation in metallic components across high-temperature reactor designs. This is particularly useful where crack growth data in irradiated material may not be readily available. The technique also allows for propagating uncertainties in crack growth predictions which stems from compounding effects such as variability in unirradiated properties, degree of irradiation and corresponding embrittling effects, crack size and load perturbations. The relevance of mechanical properties accurately depicting aspects of the He embrittlement process for practical applications in plant and the importance of creep crack growth testing are discussed.