In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

The paper demonstrates the importance of subnational data on housing to be systematically reported and added to country typologies. We asked which national and local level characteristics of housing regimes can serve as benchmarks for reasonable country groupings. The aim of this paper is to (1) develop a methodological tool enabling the comparison of conditions for housing policy implementation on national and subnational levels and (2) identify the group of countries where conditions for housing policy implementation on national and subnational levels tend to be comparable. This country classification can be used as a practical instrument for comparative analyses and policy learning. As a conceptual framework, we used the international comparative Housing research 2.0 launched by Hoekstra in 2020. For our analysis, we selected 15 basic factors that were tested in 24 European countries. We have identified three key factors having an impact on housing policy implementation: decentralisation level in housing, local budget housing expenditure and the information on which governance level has core competencies within housing. The numeric database has been run through a k-means cluster analysis. Five distinct types of countries with similarities in conditions for housing policy implementation on national and subnational level have been identified and described.

The WRKY gene family plays a very diverse role in plant growth and development. These genes contained an evolutionarily conserved WRKY DNA binding domain, which shows functional diversity and extensive expansion of the gene family. In this study, we conducted a genome-wide comparative analysis to investigate the evolutionary aspects of the WRKY gene family across various plant species and revealed significant expansion and diversification ranging from aquatic green algae to terrestrial plants. Phylogeny reconstruction of WRKY genes was performed using the Maximum Likelihood (ML) method; the genes were grouped into seven different clades and further classified into algae, bryophytes, pteridophytes, dicotyledons, and monocotyledons subgroups. Furthermore, duplication analysis showed that the increase in the number of WRKY genes in higher plant species was primarily due to tandem and segmental duplication under purifying selection. In addition, the selection pressures of different subfamilies of the WRKY gene were investigated using different strategies (classical and Bayesian maximum likelihood methods (Data monkey/PAML)). The average dN/dS for each group are less than one, indicating purifying selection. Our comparative genomic analysis provides the basis for future functional analysis, understanding the role of gene duplication in gene family expansion, and selection pressure analysis.

This paper conducts a comparative analysis of mentoring and metacognition in education, unveiling their intricate connections. Both concepts, though seemingly disparate, prove to be interdependent within the educational landscape. The analysis showcases the dynamic interplay between mentoring and metacognition, emphasizing their reciprocal influence. Metacognition, often perceived as self-awareness and introspection, is found to complement the relational and supportive nature of mentoring. Within this context, metacognitive education within mentoring emerges as a vital component. Practical recommendations are offered for effective metacognitive training, highlighting its role in enhancing cognitive and metacognitive skills. Moreover, the paper introduces the concept of a “mentoring scaffolding system.” This system emphasizes mentor-led gradual independence for mentees, facilitating their professional and personal growth. The necessity of fostering a metacognition culture in education is a central theme. Such a culture promotes improved performance and lifelong learning. The paper suggests integrating metacognition into curricula and empowering learners as essential steps toward achieving this culture. In conclusion, this paper advocates for the integration of metacognition into mentoring and education, fostering self-awareness, independence, and adaptability. These attributes are deemed crucial for individuals navigating the challenges of the information age.

High-risk pregnancies are a global concern, with maternal and fetal well-being at the forefront of clinical care. Pregnancy’s three trimesters bring distinct changes to mothers and fetal development, impacting maternal health through hormonal, physical, and emotional shifts. Fetal well-being is influenced by organ development, nutrition, oxygenation, and environmental exposures. Effective management of high-risk pregnancies necessitates a specialized, multidisciplinary approach. To comprehend this integrated approach, a comparative literature analysis using Atlas.ti software is essential. Findings reveal key aspects vital to high-risk pregnancy care, including intervention effectiveness, case characteristics, regional variations, economic implications, psychosocial impacts, holistic care, longitudinal studies, cultural factors, technological influences, and educational strategies. These findings inform current clinical practices and drive further research. Integration of knowledge across multidisciplinary care teams is pivotal for enhancing care for high-risk pregnancies, promoting maternal and fetal well-being worldwide.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.