According to Sustainable Development Goal 4.2 (SDG 4.2), Equal Access to Quality Pre-primary Education, governments throughout the world are working to ensure that all children have access to high-quality early childhood development, care, and pre-primary education by 2030. In order to organize available evidence into a coherent framework, the current scoping review represents an exploratory synthesis addressing the broad question of what qualitative and inclusive Early Childhood Education and Care strategies are currently being established globally to achieve SDG4 targets. The goal of this scoping review in this respect, was to map the available research and offer an overview of micro-, meso-, and macro-level perspectives on evidence-based interventions and strategies, for the promotion of SDG4 globally. A layered model of early childhood education that is both inclusive and egalitarian education emerged, starting with the micro level: child, family and community, mezo level: nursery, and kindergarten and macro level: national policies and SDG 4.2 Agenda for 2030. The mezzo level connects the micro and macro levels, being the most solicited level of implementing inclusive and qualitative ECEC strategies. Thus, starting with putting a real emphasis on children rights, creating a qualitative and inclusive culture with a holistic understanding of child development, then investing in teacher preparation and instilling a strong belief and positive attitudes toward equity in early childhood services, developing inclusive educational policies with an authentic community support offered by all stakeholders, then adapting curriculum and assessment methods to all early childhood educational contexts and lastly piloting and up-scaling good practices, and investing in infrastructure, facilities and innovative educational services, SDG4.2 targets could transparently and efficiently be attained by 2030, with all the setbacks arisen from the pandemic context. The data provide light on a vast topic range, including human rights and values, policy actions, and ideologies. The micro-level themes emphasized the importance of fostering equitable and inclusive environments for children., as well as instructional approaches that encourage positive attitudes toward diversity and instructors' levels of experience in dealing with diversity. We also discovered the significance of creating chances that promote socialization, connection development, and a sense of belonging. Meso-level principles emphasized the relevance of schooling in a child's holistic development and skill acquisition. Mainstream availability for all children, national curriculum regulations, teacher preparation for inclusive early childhood education, excellent funding and governance, evaluation and monitoring, and research on inclusive early childhood education comprise the macro level. As a concept and an approach, inclusive and qualitative education necessitates the preparedness of all relevant educational components to participate. Providing inclusive education in the early years requires setting the foundation for subsequent levels of schooling. The active engagement of a young kid should be directed by developmentally and individually suitable curricula. Access to and participation in age-appropriate general curricula becomes critical in identifying and providing specialized support services. Inclusive programming does not imply that the educational programs will necessarily be of good quality. Efficiency and wellbeing are synonymous with equity. Equitable education investment benefits everyone in society, not just the most marginalized. Investing in education will help communities achieve all of the Sustainable Development Goals related to education.

Glomerulus diameter and Bowman's space width in renal microscopic images indicate various diseases. Therefore, the detection of the renal corpuscle and related objects is a key step in histopathological evaluation of renal microscopic images. However, the task of automatic glomeruli detection is challenging due to their wide intensity variation, besides the inconsistency in terms of shape and size of the glomeruli in the renal corpuscle. Here, a novel solution is proposed which includes the Particles Analyzer technique based on median filter for morphological image processing to detect the renal corpuscle objects. Afterwards, the glomerulus diameter and Bowman's space width are measured. The solution was tested with a dataset of 21 rats' renal corpuscle images acquired using light microscope. The experimental results proved that the proposed solution can detect the renal corpuscle and its objects efficiently. As well as, the proposed solution has the ability to manage any input images assuring its robustness to the deformations of the glomeruli even with the glomerular hypertrophy cases. Also, the results reported significant difference between the control and affected (due to ingested additional daily dose (14.6mg) of fructose) groups in terms of glomerulus diameter (97.40±19.02μm and 177.03±54.48μm, respectively).

The technology acceptance model (TAM) is a well-known postmodern idea that explains how humans adopt and use new technologies. The model focuses on variables that impact behavioral intention to use new technology from the perspective of the end user. The purpose of this study was to construct a viable questionnaire for assessing preschool teachers' technology acceptability in online instruction in ECEC, based on data collected from 182 Romanian preschool instructors, using the theory of planned behavior framework. Our application of theory of planned behavior in technology adoption in ECEC is extraordinarily good, with 66 percent explained variance of actual usage of technology in class. The research literature supports the findings that the intention to use technology and a good attitude toward technology are the most significant determinants of actual technology usage. Although more research is needed in larger and more complex samples to confirm these findings, there is compelling evidence that the prediction methodology can be used to predict preschool teachers' level of technology acceptance and assist educational decision-makers in designing timely interventions that improve the chances of success. The study's major findings point to crucial variables that might help national educational decision-makers improve technology adoption in ECEC.

Background and Aims: Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree‐based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students.Methods: A social media‐based cross‐sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD‐7), Patient Health Questionnaire (PHQ‐9) and Insomnia Severity Index (ISI‐7) scale for measuring students' anxiety, depression and insomnia problems. The tree‐based supervised decision tree (DT), random forest (RF) and robust eXtreme Gradient Boosting (XGBoost) ML algorithms were used to build the prediction models and their predictive performance was evaluated using confusion matrix and receiver operating characteristic (ROC) curves.Results: Of the 1250 students surveyed, 64.7% were male and 35.3% were female. The students' ages ranged from 18 to 26 years old, with an average age of 22.24 years (SD = 1.30). Majority of the students (72.6%) were from rural areas and social media addicted (56.6%). Almost 83.3% of the students had moderate to severe anxiety, 84.7% had moderate to severe depression and 76.5% had moderate to severe insomnia problems. Students' social media addiction, age, academic performance, smoking status, monthly family income and morningness‐eveningness are the main risk factors of anxiety, depression and insomnia. The highest predictive performance was observed from the XGBoost model for anxiety, depression and insomnia.Conclusion: The study findings offer valuable insights for stakeholders, families and policymakers enabling a more profound comprehension of the pressing mental health disorders. This understanding can guide the formulation of improved policy strategies, initiatives for mental health promotion, and the development of effective counseling services within university campus. Additionally, our proposed model might play a critical role in diagnosing and predicting mental health problems among Bangladeshi university students and similar settings.