L-asparaginase (L-asp) is used in the treatment of acute lymphocytic leukemia (ALL). L-asp catalyzes the L-asp into ammonia and L-aspartic acid through deamination. The formed ammonia finds to be toxic to the organism. Therefore, the search for less cytotoxic anti-proliferative L-asp is of great importance. Because of its costeffectiveness, consistency, and easiness of process control, microbial L-asparaginase is chosen over plant or animal enzymes. L-asp enzyme which was studied in the article was isolated from a newly discovered bacteria Myroides gitamensis. The biochemical characteristics and antiproliferative, cytotoxic effect of purified L-asp were studied in order to develop safer biochemically stable anti-leukemia medication. To provide comprehensive information on the characteristics of the isolated L-asparaginase enzyme analytical tools were utilized, including cancer cell lines, brine shrimps, enzyme reactions etc. MOLT-4 and K-562 cell lines showed an IC50 value of 91.41 IU/mL and 77.42 IU/mL, respectively. Cytotoxicity activity of L-asp on Artemia salina showed that at 24 hours (LC50 value of 217.0 µg/mL) and toxic at 48 hours (LC50 value of 126.4µg/ml). © 2022, Rasayan Journal of Chemistry, c/o Dr. Pratima Sharma. All rights reserved.

The present study demonstrates, for the first time, the fundamental possibility of producing electrode materials for sodium-ion batteries through low-temperature carbothermic smelting of ilmenite concentrate fluxed with calcined soda and diatomite, followed by aqueous refining of titanium slag. The primary phase composition of the slag includes Na2Ti3O7 (48.2%), Na0.23TiO2 (22.0%), Na2TiSiO5 (11%), and Na0.67Al0.1Mn0.9O2 (8.5%), which, upon hydrolysis, transform into a monophase titanium dioxide with intercalated sodium—Na0.23TiO2. Thermodynamic analysis of the heat effects of chemical reactions among raw materials and resulting products substantiates the role of silicon and sodium oxides, carbon, oxygen, and water in the formation of various electrode materials during carbothermic flux conversion and aqueous refining. Insights into the mechanisms of thermochemical formation and hydrothermal phase transformations offer a scientific basis for the development of intercalation systems from abundant and low-cost natural raw materials, bypassing the need for expensive precursor synthesis.

This study assesses the development of start-up ecosystems and the effectiveness of government initiatives, determines issues, and proposes measures to support entrepreneurial ecosystems. The aim of the study was to examine how innovative start-up ecosystems contribute to sustainable development and increased entrepreneurial activity in Asian countries, focusing on their strategic importance for economic transformation and technological growth. Using quantitative and qualitative research methods, the authors conduct a comparative analysis of the development of entrepreneurial ecosystems in five Asian countries (Kazakhstan, China, India, South Korea, and Japan) for 2018-2021. The empirical analysis relies on panel data from global databases, applying regression modeling to assess the influence of ecosystem variables on sustainable innovation activity across 20 observations. The data obtained indicates a different degree of development of entrepreneurial ecosystems in the countries under study. Japan and South Korea demonstrate consistently high levels of ecosystem development, while Kazakhstan, China, and India show lower levels but with growth trends. According to the study results, the most influential factors for the development of entrepreneurial activity are state support and policies and access to financing. The findings show that access to financing and state support are the most influential factors in boosting green start-ups and sustainable innovation, with a 1-point increase in funding leading to a 12.6 percentage point rise in eco-innovation activity. To increase entrepreneurial activity, develop open innovations, and achieve SDGs, the authors pro-pose several support measures. These support measures contribute to the formation of start-up infrastructure, including business incubators and accelerators, creating a favorable environment for the development of the venture capital market, building a knowledge exchange system through international cooperation in the framework of open innovation, and holding international hackathons and other events aimed at stimulating innovation and activating knowledge dissemination. Article results offer practical insights for policy-makers and ecosystem developers to enhance regulatory frameworks, improve financing mechanisms, and foster global partnerships that support innovation-driven entrepreneurship. Its unique contribution lies in presenting a cross-country, data-driven analysis of entrepreneurial ecosystems in Asia with a focus on sustainability, thereby filling an important gap in the literature on green innovation and start-up policy strategies. © 2025, Qubahan. All rights reserved.

The article deals with the issues of determining the factors contributing to the occurrence of road accidents. On the basis of statistical studies, the main factors most fully reflecting the process of occurrence of road traffic accidents were identified. For this purpose, an expert-statistical method was used. Road safety assessment was carried out on the example of the roads of the city of Pavlodar. The results obtained allow, in the future, to build a mathematical model that characterizes the possibility of a traffic accident. © 2024 The Author(s).

The structure of Ge2Sb2Te5 thin films doped with Bi has been studied using TEM, Raman spectroscopy, and Optical Transmission Spectroscopy. Thin films were obtained by direct current (DC) ion–plasma sputtering with thicknesses from 60 to 200 nm. TEM images revealed an amorphous film structure with bismuth nanocrystallites with a size of ∼1 to ∼20 nm. A shift in the Raman peaks of Bi–doped Ge2Sb2Te5 films compared to undoped samples was observed. A shift of the fundamental absorption edge to the long wavelength region of the spectrum along with an increase in the concentration of bismuth were detected in the optical transmission spectra of the films. A decrease in the optical band gap of bismuth-doped films was found. It has been shown that with increasing bismuth content in the films, their conductivity also increases. It has been found from studies of the current–voltage film characteristics reversible switching effect takes place. © 2023 The Author(s)

The field of aftershock prediction has evolved over the years, transitioning from traditional statistical models to more advanced machine learning and deep learning methodologies. The USGS relies on the Reasenberg and Jones (1989, 1994) statistical model which subsequent researchers have refined to adapt to various tectonic regimes globally. Meanwhile, DeVries et al. (2018) explored the potential of Deep Learning in predicting aftershock locations, albeit facing critique for potentially over-complicating the issue. Other researchers have applied machine learning algorithms, such as in predicting aftershock patterns following the Kermanshah Earthquake in Iran, demonstrating the capability of machine learning to outperform traditional Coulomb maps. The dynamic discourse among these varying methodologies highlights the ongoing efforts to improve aftershock prediction, aiming at better preparedness and response strategies in seismic-prone regions. © 2024 Elsevier B.V.. All rights reserved.

The increasing depletion of high-grade nickel sulfide deposits and the growing demand for nickel have intensified global interest in oxidized nickel ores (ONOs), particularly those located in Kazakhstan. This study presents a comprehensive review of the mineralogical and chemical characteristics of ONOs from the Shevchenkovskoye cobalt–nickel ore deposit and other Kazakhstan deposits, highlighting the challenges they pose for conventional beneficiation and metallurgical processing. Current industrial practices are analyzed, including pyrometallurgical, hydrometallurgical, and pyro-hydrometallurgical methods, with an emphasis on their efficiency, environmental impact, and economic feasibility. Special attention is given to the potential of hydro-catalytic leaching as a flexible, energy-efficient alternative for treating low-grade ONOs under atmospheric conditions. The results underscore the necessity of developing cost-effective and sustainable technologies tailored to the unique composition of Kazakhstani ONOs, particularly those rich in iron and magnesium. This work provides a strategic framework for future research and the industrial application of advanced leaching techniques to unlock the full potential of Kazakhstan’s nickel resources. © 2025 by the authors.

The main aim of this study is to determine the efficiency of enhancement of the strength and heat-resistant characteristics of cementing mortars by addition of metallurgical sludge in the form of iron oxide powder, which is a waste product in steel production, to the composition of a finely dispersed fraction. The use of stabilizing strengthening additives is due to the need to increase the strength properties of cementing solutions, especially those that are used in extreme conditions, at high temperatures and high loads, creating pressure on the formations. At the same time, the use of expensive strengthening stabilizers increases the cost of manufacturing cementing solutions, which in turn leads to an increase in the cost of the entire process of creating oil wells. According to the data presented, the use of finely dispersed and ultra-finely dispersed fractions of metallurgical sludge in the form of hematite powder leads to an increase in the strength properties of cementing solutions, and also increases resistance to long-term thermal heating due to an increase in resistance to high-temperature degradation associated with cracking and embrittlement. It was also determined that the transition from a finely dispersed fraction and an ultrafinely dispersed fraction leads to an increase in strength properties, which makes it possible to reduce the consumption of the stabilizing strengthening additive powder while maintaining strength characteristics.

In-situ uranium recovery features high economic efficiency and low environmental impact as compared with other uranium production techniques. The article presents the experimental results on the application of new geophysical logging methods at a uranium deposit of hydrogenetic type. Complex hydrogenetic uranium deposits have a nonuniform lithology, varied permeability and alternation of permeable and water-resistant rocks. The development of such deposits is often complicated by a number of factors affecting the production process. The common methods of geophysical research in wells provide insufficiently reliable information about the geological structure of interwell space. As a result, it is difficult to control and monitor the production process, which can lead to inefficient consumption of chemical reagents, undesirable leakage beyond the limits of production blocks, incomplete extraction of uranium reserves, etc. To solve the problem of insufficient studies of interwell space, the method of radiowave geointroscopy was tested. This method is based on the measurement of electrical resistance. The tests were carried out in two stages, during the periods of high-rate production. By comparing the measurement results in different dimensions, the electrical resistance maps were plotted. The interpretation of the measurement results made it possible to obtain reliable data on the actual distribution of solutions in the interwell space in a given period of time, to identify areas of excessive concentrations of solutions (stagnant zones), to assess acidification dynamics in production blocks, and to delineate potentially difficult areas of the blocks. © 2022, Ore and Metals Publishing house. All rights reserved.

In this research paper, we delve into the transformative potential of integrating Big Data analytics with machine learning (ML) techniques, orchestrating a paradigm shift in production management methodologies. Traditional production systems, often marred by inefficiencies stemming from data opacity, have encountered bottlenecks that throttle scalability and adaptability, particularly in complex, fluctuating markets. By harnessing the voluminous streams of data-both structured and unstructured-generated in contemporary production environments, and subjecting these data lakes to advanced ML algorithms, we unveil profound insights and predictive patterns that remain elusive under conventional analytical methods. Our discourse juxtaposes the multidimensionality of Big Data-emphasizing velocity, variety, veracity, and volume-with the finesse of ML models, such as neural networks and reinforcement learning, which adapt iteratively to the dynamism inherent in production landscapes. This symbiosis underpins a more holistic, anticipatory decision-making process, empowering stakeholders to pinpoint and mitigate operational hiccups, optimize supply chain vectors, and streamline quality assurance protocols, thereby catalyzing a more resilient, responsive, and cost-effective production framework. Furthermore, we explore the ethical contours of data stewardship in this context, advocating for a judicious balance between technological ascendancy and responsible data governance. The culmination of this exploration is the conceptualization of a predictive, self-regulating production ecosystem that thrives on continuous learning and improvement, dynamically calibrating itself in response to an ever-evolving market tableau and thereby heralding a new era of optimal, sustainable, and intelligent production management. © 2023, Science and Information Organization. All rights reserved.