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.

Carbon based materials are really an integral component of our lives and widespread research regarding their properties was conducted along this process. The addition of dopants to carbon materials, either during the production process or later on, has been actively investigated by researchers all over the world who are looking into how doping can enhance the performance of materials and how to overcome the current difficulties. This study explores synthesis methods for nitrogen-doped carbon materials, focusing on advancements in adsorption of different pollutants like CO₂ from air and organic, inorganic and ions pollutants from water, energy conversion, and storage, offering novel solutions to environmental and energy challenges. It addresses current issues with nitrogen-doped carbon materials, aiming to contribute to sustainable solutions in environmental and energy sciences. Alongside precursor types and synthesis methods, a significant relationship exists between nitrogen content percentage and adsorption capacity in nitrogen-doped activated carbon. Nitrogen content ranges from 0.64% to 11.23%, correlating with adsorption capacities from 0.05 mmol/g to 7.9 mmol/g. Moreover, an electrochemical correlation is observed between nitrogen atom increase and specific capacity in nitrogen-doped activated carbon electrodes. Higher nitrogen percentage corresponds to increased specific capacity and capacity retention. This comprehensive analysis sheds light on the potential of nitrogen-doped carbon materials and highlights their significance in addressing critical environmental and energy challenges.

This study critically examines the implications of international transport corridor projects for Central Asian countries, focusing on the Western-backed Transport Corridor Europe-Caucasus-Asia (TRACECA), the Chinese initiative “One Belt—One Road”, and the International North-South Transport Corridor (INSTC) supported by the Russian Federation, India, and Iran. The analysis underscores the risks associated with Western projects, highlighting a need for a more explicit commitment to substantial infrastructure investments and persistent contradictions among key investors and beneficiaries. While the Chinese initiative presents significant benefits such as transit participation, infrastructure development, and economic investments, it also carries risks, notably an increased debt burden and potential monopolization by Chinese corporations. The study emphasizes that Central Asian countries, though indirect beneficiaries of INSTC, may not be directly involved due to geographical constraints. Study findings advocate for Central Asian nations to balance foreign investments, promote economic integration, and safeguard political and economic sovereignty. The study underscores the region’s wealth of natural and human resources, emphasizing the potential for increased demand for goods and services with improved living standards, strategically positioning these countries in the evolving global economic landscape.