Developed Model Research Articles

Sedimentary characteristics and development model of the bedded evaporites in the Paleogene Kumugeliemu formation, Kuqa depression, Northwestern China

Sedimentary characteristics and development model of the bedded evaporites in the Paleogene Kumugeliemu formation, Kuqa depression, Northwestern China

The ethical student scale: development of a new measure

Purpose The purpose of this study is to develop the “ethical student scale” to understand the underlying factors that lead to the decisions of cheating and plagiarism and where business students are developmentally from a moral perspective and to help academic institutions assess how best to develop ethical education throughout the curriculum. This three-dimensional nine-item scale based on Kohlberg’s moral development model is proposed to serve as a valuable tool for educators who are struggling with identifying the best approach to help their students make ethical choices both within and outside of the walls of their educational institutions and once they join the workforce. Design/methodology/approach Four survey design studies were conducted to determine what factors had previously been identified to both negatively and positively impact the propensity of a university student to engage in cheating and/or plagiarizing (Study 1, N = 179), to preliminarily validate the three dimension nine item scale that emerged from Study 1 (Study 2, N = 87); to test the construct validity of the three-dimensional nine-item scale (Study 3, N = 235); and to test the nine-item scale for convergent, divergent and predictive validity (Study 4, N = 201). The four surveys were administered to undergraduate students at two universities in the Northeast in the USA. Findings To shed light on the underlying factors that lead to the decisions of cheating and plagiarism, the authors propose three factors that are engaged when students make these types of ethical decisions: rules and enforcement as an external control, personal morality as an internal control and social influences as a social control (Kohlberg, 1976). Through four studies, this paper presents a three-dimensional nine-item scale based on Kohlberg’s moral development model to determine the factors that influence the propensity of a university student to engage in cheating and/or plagiarizing. The proposed scale showed strong reliability across the three dimensions. Research limitations/implications The limitations of the scale are that this research was restricted to an academic setting. The relationship between the academic environment, the resulting behaviors of students and the subsequent behavior of these students as managers also needs investigation to determine if business ethics education does have an impact on increasing ethical decision-making. Practical implications The ethical student scale attempts to measure the development stage of students in a university setting and has the potential utility to help higher education institutions better understand the moral development of their students and what drives their decisions to engage in an ethical manner. Being a short yet reliable tool, ethical student scale may help business schools develop programs beyond a single business ethics course to instill ethical decision-making in students. Social implications One of the goals as business educators is to produce ethical managers. The ethical student scale can help us develop a more integrated approach to business ethics education. As the students become managers and leaders in organizations, the social implications for more ethical decision-makers and organizations are widespread and vital to the community and the economy. Originality/value Ethical student scale is an attempt to quantify what types of controls (external, social, or personal) help develop ethical students and ethical managers. Based on Kohlberg’s moral development model, this three-dimensional nine-item scale which shows strong reliability will serve as a valuable tool for educators who are struggling with identifying the best approach to the issue of unethical decisions and behaviors as they try to create strategies to help their students make ethical choices both within and outside of the walls of their educational institutions and once they join the workforce.

A data-driven decision-making approach for evaluating the projects according to resilience, circular economy and industry 4.0 dimension

Multi-industry organizations constantly define new projects for development and progression. However, the noteworthy point is that there are many options for defining new projects and allocating resources to them, from which the best choices must be selected among the candidate projects. In today's world, due to extensive changes in business conditions, traditional performance metrics are not suitable for project evaluation, and projects that can enhance organizational resilience and serve communities in line with economic and environmental components should be selected. Moreover, considering the widespread changes in digital transformation over the past decade, paying attention to Industry 4.0 standards in project selection is important. In this context, the main goal of this study is to design a data-driven decision-making model based on machine learning for evaluating and selecting projects in line with resilience, circular economy, and Industry 4.0 criteria. In the first step, 21 indicators were identified, and their weights were determined using the fuzzy best-worst method (FBWM). Then, using the weighted fuzzy inference system method (WFIS), rules for labeling data were established, and finally, a model for predicting and evaluating project performance was defined using the Light Gradient Boosting Machine algorithm. The findings indicate that the indicators “Increase market share using Industry 4.0 technologies” and “Net present value” are the most important in project evaluation. The developed model accurately evaluates projects with more than 93% accuracy, performing better than other algorithms and demonstrating the high performance of data-driven algorithms in project evaluation.

Media Pembelajaran Mobile Learning Berbasis Articulate Storyline Materi Kondisi Geografis Wilayah Indonesia Kelas V

This research aims to develop, test the feasibility, and test the effectiveness of articulate storyline-based mobile learning media. The type of research used is research and development (RD) with the Borg and Gall development model, including 10 implementation stages, which are simplified into 8 steps: potential and problems; data collection; product design; design validation; design revision; product trials; product revision; and test use. The subjects of this research were 30 students in fifth grade elementary school, Banjarnegara Regency, 6 of whom were taken as small-scale trial subjects and 24 students as large-scale trial subjects. The data collection techniques used in this research are observation, interviews, documentation, questionnaires, and tests. Data analysis techniques include initial and final data analysis using the t-test and the N-Gain. The results of this research showed that the development of articulate storyline-based mobile learning media has been successful. The results of the feasibility assessment from media experts were 91.67% and material experts 90%, and were confirmed by teacher and student responses of 100% and 94.44% with the criteria "very feasible." The results of student learning completion after using learning media were that 87% of students met the criteria for completion, and 13% had not yet completed. These results showed that there is a significant difference between learning outcomes before and after media use, as indicated by the Sig (2-tailed) 0.000 0.05. The average increase in student learning outcomes in the N-Gain test was 56.0650, or 56%. Based on these results, it is concluded that articulate storyline-based mobile learning media is feasible and effective enough to improve student learning outcomes.

Parental Child Socialization and the Ecclesiastical Community

The aim of the article is to evaluate the effectiveness of child socialization in the Anglican episcopacy of Embu, Kenya. The article delves into socialization in the ecclesiastical circles pertaining to integral development models for teenagers in terms of their physical (body), spiritual (soul), emotional (mind) and social values. In particular, the Anglican Church socializes her children in order to help them grow and become strong Christians; and this is ideally done to mould them from an early age in all dimensions of life, namely: morally, socially and spiritually. However, in spite of being socialized through different rites of passage, the youth members are battling with different forms of moral concerns. The article suggests that there is need for the Anglican Church to re-evaluate her socialization programmes in order to minimize moral decadency among the youth.

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.

Development, implementation and evaluation of a novel quality improvement and patient safety curriculum in an emergency medicine residency training program.

Proficiency in Quality Improvement and Patient Safety (QIPS) methodologies has been identified as a standard of residency training. However, there is no consensus on how to achieve these competencies. We used Kern's model of curricular development to create a QIPS curriculum for the local Emergency Medicine (EM) residency training program. The curriculum was designed following best practice recommendations for QIPS education and took the form of a 10-h educational experience including two in-person live sessions. The curriculum was delivered to a mix of local transition to practice residents and faculty members. Participants reported favorable outcomes and objectively demonstrated QIPS knowledge acquisition. This curriculum serves as a model that could be adapted by other residency training programs seeking to implement their own QIPS curricula.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Policy Model of Thematic Village Development Based Pentahelix in Realizing Sustainable Development Goals

This research aims to explore the policy model of thematic village development based on the pentahelix approach and its implementation in East Java, Indonesia. By examining the concept of pentahelix and its integration into thematic village development, the study discusses the role of each stakeholder in advancing villages towards sustainable goals. The research adopts a policy research approach, utilizing a mix-method (qualitative and quantitative) analysis. Data collection involves macro-level analysis through documents and interviews with government officials, and micro-level analysis focusing on village-level policy implementation through document studies, interviews, and observations. Data analysis follows the Miles, Huberman, and Saldana model, ensuring data validity through triangulation. The study delineates the stages of the policy model, including agenda setting, formulation, implementation, and evaluation. Agenda setting highlights the transition from addressing underdeveloped villages to empowering them. Formulation involves collaborative efforts among various stakeholders, leading to comprehensive policy plans. Implementation includes financial support allocation and technical assistance, ensuring transparency and collaboration across sectors. Evaluation assesses the policy's impact on achieving Sustainable Development Goals, demonstrating positive outcomes such as increased village autonomy and awareness of development indices. This research contributes to understanding the implementation of the pentahelix-based thematic village development model in East Java, providing insights into collaborative policy formulation and its impact on sustainable development goals. The findings offer practical implications for policymakers, emphasizing the importance of multi-stakeholder collaboration in addressing complex societal challenges.

Development of an Online-Based Hamilton Anxiety Rating Scale Anxiety Test Model for Field Work Practice Students at Muhammadiyah Lumajang Vocational High School

The research objectives are: (1) to analyze the implementation of anxiety tests on students who will carry out PKL; (2) developing an online-based Hamilton Anxiety Rating Scale anxiety test model; and (3) analyzing the effectiveness of the online-based Hamilton Anxiety Rating Scale anxiety test at SMK Muhammadiyah Lumajang. This research uses a research and development model, where the development consists of five stages, namely analysis, design, development, implementation and evaluation. The validity test of the research instrument was carried out by experts in their field (expert judgment), namely material experts and media experts. The research reliability test used the Cronbach's Alpha formula. The data analysis technique used the SPSS 22 program. The population was carried out at Muhammadiyah Vocational School Lumajang and the sample of class XII Nursing Assistant students was 95 students. The results of the research show (1) the implementation of anxiety tests at SMK Muhammadiyah Lumajang is currently still conventional, namely using paper questionnaires; The development of the online-based HARS anxiety test model began to be developed with a simple image feature when starting to fill in the questions and at the end of filling in the questions there will be an image that can represent the respondent's feelings, namely the emotion of the image; sad, happy, laughing, disappointed, surprised. Online applications are distributed to students in the form of http://harstest.smkmulu.sch.id.; and (3) the effectiveness of the online-based HARS anxiety test developed at SMK Muhammadiyah Lumajang is very feasible to be implemented based on the assessment of media and material experts with a value range of 80-85%. The application of the HARS anxiety test model can be used for other skill competencies, and can be used by schools in provision (consolidation of material and vocationally appropriate practice) before visits to industry and street vendors.

Internal temperature estimation for lithium-ion batteries through distributed equivalent circuit network model

Lithium-ion cells experience significant internal thermal gradients during operation, with a direct impact on their safety, performance, cost and lifetime. The estimation of the internal temperature of cells is therefore particularly important. In this work, a 3D distributed electro-thermal model for internal temperature estimation is developed for a cylindrical cell (LG M50T, NMC811). The model is parameterized and comprehensively validated against experimental data for 21700 cells, including direct core temperature measurements. Multiple types of electrical load are considered, including constant current discharge, pulse discharge, drive cycle and instant discharge/charge switching. The developed model is used to estimate core temperature based on surface temperature measurement. The predictions are shown to have good accuracy at relatively low computational cost. We show that the widely adopted two-node lumped thermal estimation model is increasingly inaccurate for more aggressive discharges, when thermal gradients become higher. Compared to the standard two-node model, the distributed equivalent circuit network model predicts the effects of detailed internal cell structure (electrode, current collector, metal can and tab) and distributed internal heat generation. The results are of immediate interest to cell manufacturers and battery pack designers, while the modelling and parameterization framework is a useful tool for energy storage systems design.

Genesis and Related Reservoir Development Model of Ordovician Dolomite in Shuntogol Area, Tarim Basin

The Ordovician thick dolostone in Shuntogol area of the Tarim Basin has the potential to form a large-scale reservoir, but its genesis and reservoir development model are still unclear. Starting from a sedimentary sequence, this study takes a batch of dolostone samples obtained from new drilling cores in recent years as the research object. On the basis of core observation and thin section identification, trace elements, cathodoluminescence, carbon and oxygen isotopes, rare earth elements, and X-ray diffraction order degree tests were carried out to discuss the origin of the dolomite and summarize the development model of the dolostone reservoir. The analysis results show that the Ordovician dolomite in the study area had a good crystalline shape, large thickness, high Fe and Mn values, and mostly showed bright red light or bright orange–red light under cathode rays. The ratio of δ18O values to seawater values at the same time showed a negative bias; the δCe values were negative anomalies, the δEu values were positive anomalies, and the order degree was high. This indicates that the dolomitization process occurred in a relatively closed diagenetic environment. The Ordovician carbonate rocks in the study area were low-lying during the sedimentary period, and with the rise of sea level, the open platform facies continued to develop. When the Middle and Lower Ordovician series entered the burial stage, the main hydrocarbon source rocks of the lower Cambrian Series entered the oil generation peak, and the resulting formation overpressure provided the dynamic source for the upward migration of the lower magnesium-rich fluid, and the dolomitization fluid entered the karst pore system in the target layer to produce all the dolomitization. This set of dolostone reservoirs is large in scale and can be used as a favorable substitute area for deep carbonate exploration for continuous study.

PENGEMBANGAN VIDEO PROMOSI BUKU MENUJU DIGITALISASI PERPUSTAKAAN STKIP TAMAN SISWA BIMA

The research objective of developing this book promotional video was to increase students' interest in reading in an interesting way. The conclusion obtained from this research is a book promotional video towards digitizing the STKIP Taman Siswa Bima library which has been tested for its feasibility. This research uses the Research and Development (R & D) method with the Borg and Gall development model starting from needs analysis, planning, development, testing, evaluation, updating, improvement, correction, modification and ending with product dissemination. According to the final evaluation by media experts, a percentage of 95% shows that the book promotional video that has been made is declared valid. Meanwhile, the final results obtained by small group participants in the book promotional video obtained a percentage of 88.3% which was declared Very Decent. The final results obtained by large group participants in the book promotional video obtained a percentage of 86.5% which was declared Very Decent. According to the results of the study, the mean percentage (%) results involving 30 students obtained a percentage of 86.7% in the Very Eligible classification.

A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash

One of the major challenges in the civil engineering sector is the durability of reinforced concrete structures against carbonation during the physico-chemical process of interaction of hydrated cementitious composites with carbon dioxide. This aggressive process causes carbon penetration into the reinforcement part, which affects the behavior of the structure during its lifetime due to corrosion risk. A countermeasure is using alternative cementitious materials to improve concrete texture and resist increased carbonation depth (CD). Considering that the CD test requires a long time and a skilled technician, this study strives to provide an alternative approach by moving from traditional laboratory-based methods towards artificial intelligence (AI) techniques for modeling the CD of sustainable concrete containing fly ash (CCFA). Despite the development of single AI models so far, it is undeniable that utilizing metaheuristic optimization techniques in the form of hybrid models can improve their performance. To this end, a new hybrid model from the integration of biogeography-based optimization (BBO) technique with artificial neural network (ANN) is developed for the first time to estimate the CD of CCFA. The error distribution results revealed that 59% of the ANN predictions had errors within the range of (− 1 mm, 1 mm], while the corresponding percentage for the ANN-BBO predictions was 70%, indicating an 11% reduction in the prediction errors by the proposed hybrid model. Furthermore, A10-index highlighted a performance improvement of 78% for the hybrid model, which met the closeness of the predicted values to the observed ones, so that the value of this index for models of ANN and ANN-BBO was 0.5019 and 0.8947, respectively. Analyzing the cross-validation confirmed the reliability and generalizability of the developed model. Also, the three most influential variables in estimating the CD were exposure time (27%), carbon dioxide concentration (22%), and water/binder (18%), respectively. Finally, the superiority of the ANN-BBO model was verified by comparing it with previous studies’ models.

Numerical Simulation of Rainfall-Induced Erosion on Infiltration and Slope Stability

In slopes where a mixture of coarse and fine particles is present, the infiltration of rainfall can cause the migration of fine particles. This migration alters the hydraulic properties of the soil and has implications for slope stability. In this study, the slope under investigation is a tailings dam composed of loosely consolidated soil with a wide particle size distribution. Due to rainfall infiltration, fine particles tend to migrate within the voids of the coarse particle framework, leading to changes in hydraulic properties and inducing slope instability. The classical internal erosion constitutive model, known as the Cividini and Gioda erosion criterion, is commonly used to predict the behavior and effects of fine particle erosion in geotechnical engineering. However, certain parameters in this erosion criterion equation, such as long-term density, are challenging to obtain through experiments. To investigate the coupled evolution of seepage and erosion within landfill slopes under the influence of rainfall infiltration and to understand the mechanisms of slope instability, this research assumes the erosion of fine particle suspension and adopts the Worman and Olafsdottir erosion criterion to establish a coupled model of unsaturated seepage and internal erosion. The developed model simulates the coupled response of seepage and erosion in unsaturated landfill slopes under three different rainfall intensities. It is then combined with the infinite slope model to quantitatively analyze the impact of fine particle migration on soil permeability and slope stability. The numerical simulations provide the following findings: The Worman and Olafsdottir erosion criterion, unlike the Cividini and Gioda erosion criterion, only requires the determination of the soil’s gradation curve to estimate the erosion rate. Internal erosion primarily occurs within the leading edge of moisture penetration, accelerating the advancement of the wetting front and reducing slope stability. When the rainfall intensity is lower than the saturated permeability coefficient, the influence of internal erosion can be disregarded. However, under rainfall intensities equal to or greater than the saturated permeability coefficient, considering internal erosion results in a difference in the depth of the wetting front of up to 34.2 cm after 6 h in the R2 scenario. The safety factor without considering internal erosion is 1.12, whereas considering internal erosion yields safety factors between 1.08 and 1.09. In the R3 scenario, the difference in the depth of the wetting front reaches 53.8 cm after 6 h, with a safety factor of 1.12 without considering internal erosion and safety factors between 1.06 and 1.07 when considering internal erosion.

Teachers' Efforts in Improving Children's Learning Disability

This study aims to determine the services of children with special needs in terms of identification, assessment and curriculum development models carried out by elementary schools. The method used in this study is a qualitative descriptive method. The subjects in this study were one teacher and one student with special needs, namely students with difficulties studying at SDN Babakan Jawa III, Majalengka District, Majalengka Regency. This research procedure includes three stages, namely: the pre-field stage, field implementation, and the member check stage and then data analysis is carried out. This study explained the initial identification activities of students with learning disabilitie using special identification tools for children with special needs then continued with assessment activities. From this, obtaining information related to the ability of students to have learning difficulties from the aspect of reading the beginning and subsequently used in the provision of educational services. The curriculum development model applied is a modification of the curriculum according to the abilities of the child. This activity is still experiencing obstacles because the facilities and infrastructure to support learning for students with learning difficulties are not yet available, besides that the resources are not ready.

Predictive modeling of green water availability: The role of annual plants as an ecological indicator in dryland ecosystems

Green water is primarily associated with the appearance of annual plants and plays a significant role in biomass production in both arid and semi-arid ecosystems. Herein, we aim to estimate the optimal threshold for determining the presence or absence of annual plants and use them as an ecological indicator to assess potential green water areas in Kuwait as a case study. We integrate remote sensing techniques and MaxEnt modeling. The AUC for the annual plant distribution with all examined factors is 0.847, and the standard deviation is 0.050. The results demonstrated that potential locations with high levels of green water cover <20% of the country. The annual plant distribution was significantly correlated with several types of perennial plants, maximum temperature, precipitation, and sandy soils. It was also found that annual plants are controlled by the spring and winter temperature decline and the timing of precipitation occurrence, especially the pattern and amount of rainfall received in November. Sandy loam and loam soils were found to be ideal for annual plants, although land depressions and soil types are crucial factors in determining annual plant distribution. Additionally, annual plants enhanced the growth of several perennial communities. To reiterate, our study's model helped to comprehend the significance of annual plants as an ecological indicator in sustaining soil moisture over a prolonged period, as well as factors controlling the distribution of annual plants. The developed model and indicators could support decision-makers in determining appropriate locations with adequate levels of green water for revegetation planning in arid landscapes.

LANDASAN PENGEMBANGAN KURIKULUM PENDIDIKAN AGAMA ISLAM DALAM UPAYA MEMAJUKAN LEMBAGA PENDIDIKAN ISLAM: Kajian Kurikulum Pendidikan Madrasah dan Pesantren

The PAI curriculum development model needs serious attention, especially in its application during teaching and learning activities in educational institutions (madrasas and Islamic boarding schools), to develop students' knowledge through IMTAK and science and technology. This research aims to explain the basis for developing a PAI curriculum that is by learning models and development models based on madrasa growth. In this research, the literature study method was carried out to obtain valid and convincing data. The primary data sources used were journals, books, articles, papers, and other relevant sources based on the principles of PAI curriculum development. The analysis used in this research is descriptive data analysis objectively and systematically. Data analysis is methodical and results are taken impartially. The research results show that the curriculum is a system consisting of various components that are interconnected and linked to achieving educational goals. Implementing the Islamic Education curriculum functions as a road map that teachers must follow to direct their students toward the ultimate goal of Islamic education. So the PAI curriculum needs to pay attention to religious components, morals, then cultural aspects, and their utilization. The implications of this curriculum development include: 1) Curriculum renewal, 2) Integration of modern sciences, 3) Development of learning methods, 4) Empowerment of teaching staff, 5) collaboration with stakeholders, 6) Increasing curriculum relevance.

Predicting atrial fibrillation risk following embolic stroke of unknown source using cardiac magnetic resonance imaging

Abstract Background Approximately 25% of embolic strokes of unknown source (ESUS) are attributed to undiagnosed atrial fibrillation (AF).1 The European Stroke Organisation (ESO) recommends implantable loop recorder (ILR) monitoring to identify ESUS patients with AF, permit the initiation of anticoagulation and reduce risk of recurrent stroke.2 However, this approach delays treatment and carries significant cost and resource implications. An alternative approach, where early anticoagulation is initiated in the absence of AF has failed to reduce recurrent stroke in four randomised controlled trials (NAVIGATE ESUS, RE-SPECT ESUS, ATTICUS and ARCADIA).3-6 An urgent need exists for new strategies to identify ESUS patients with undiagnosed AF that are at high risk of future thromboembolism. Purpose The primary objective of this study was to produce a substrate-based predictive model for AF in ESUS patients using atrial cardiac magnetic resonance imaging (CMRi). Method The Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented AF (CARM-AF) Study is a prospective, multi-centre, observational study. All patients underwent CMRi and ILR insertion for AF detection within 3 months of ESUS. The main inclusion criteria were expected survival &amp;gt;12 months, CHA2DS2VASc≥3 and eGFR &amp;gt;30ml/min. Patients were allocated to the study group if AF &amp;gt;30 seconds was detected by ILR during the first year of follow-up and control group if no AF detected (Figure 1). Univariable analysis was used to identify clinical, echocardiographic and CMR parameters associated with AF diagnosis. Parameters with p&amp;lt;0.2 and no co-linearity were used to develop multivariable logistic regression models for AF prediction. Results From September 2020 to September 2022, 102 patients were enrolled and 91 were included in the final analysis. AF was detected in 17 patients during the first year of follow-up (18.6%). In univariable analysis, patients with AF were more likely to be female (p=0.02), but there were no significant differences in age, race, body mass index (BMI) or CHA2DS2VASc score (p=0.06, 0.272, 0.195, 0.211) between groups. Increased left atrial (LA) volume, surface area and reduced LA ejection fraction were associated with increased risk of AF detection at 1 year (p=0.006, 0.044, 0.008). LA fibrosis and sphericity were not associated with AF (p=0.84, 0.98). Logistic regression models for AF risk prediction were developed using patient, echocardiographic and CMR parameters. A combined model of patient and CMR parameters outperformed assessment of patient characteristics alone, achieving an AUC of 0.85 for predicting AF occurrence (p&amp;lt;0.005). Pseudo R2 of 0.30 (McFadden) indicated excellent model fit (Figure 2). Conclusion CARM-AF is the first study to demonstrate feasibility and utility of CMR imaging to predict AF risk following ESUS. A randomised controlled trial initiating anticoagulation using the developed model is recommended.Study DesignModel Comparison (ROC Curves)