Central Asian Journal of Theoretical and Applied Science

Developing an Intelligent Analytical Method for Monitoring Pesticide Residues and Heavy Metals in Local Vegetables Using Portable Nano-Sensors

2026-05-03T10:36:28+00:00

Monitoring chemical pollutants in vegetables has become a prominent focus in modern analytical chemistry because fresh vegetables may acquire pesticide residues and heavy metals, which might jeopardize food safety and consumer health. Although traditional reference techniques are very precise and sensitive, their use for rapid on-site testing is restricted since they may need expensive equipment, specialized laboratories, and long processing periods. The study problem is the lack of a creative, portable analytical method that combines speed, reliability, and field application for detecting pesticide residues and heavy metals in local vegetables within a single framework. It looked and created a smart analytical method based on portable nanosensors to swab neighborhood vegetables for positive pesticide residues and precise heavy metals. Its analytical effectiveness is assessed through the contrast with respect to technology. The Look at has used virtual reading units, improved record processing, and fabrication of portable nanoelectrochemical sensor systems Local vegetable samples are collected from the market and processed using ideal extraction digestion strategies. Linearity, limit of detection, reproducibility, recovery and consistency regarding the processes were established through study The findings confirmed that the encouraged process has perfect linearity, low limit of detection and satisfactory repeatability. The recovery cost was additionally within reasonable analytical limits, and there was a surprising convergence between the sensor results and the reference strategies This proves its effectiveness as a potential tool for rapid web surface monitoring of vegetable infection.

Paradigm Shifts in Data Science: From Descriptive Analytics to Adaptive Predictive Modeling

2026-05-10T10:12:38+00:00

The data science sector has experienced a radical change in the last 10 years, shifting from less advanced and less dynamic traditional reporting to more robust adaptive predictive systems, which can aid decisions in real time. The present paper focuses on paradigm shifts defining this evolution, tracing the path of descriptive analytics to diagnostic, predictive, and prescriptive models (reaching adaptive predictive modeling models, which are driven by machine learning and artificial intelligence). The study deals with the theoretical foundations of each stage, the facilitating technologies that have led to the changes between stages, and the organizational and technical challenges that are associated with these changes. Based on the extensive literature review of recent literature, the paper brings together evidence about the latest advances in healthcare, industrial systems, business intelligence, and social sciences to demonstrate how adaptive modeling is changing what data-driven decision-making is capable of. The results imply that the development towards adaptive systems cannot be uniform and progression across sectors, and that the only way to achieve success in adoption is to ensure that technical infrastructure is coordinated with strategic organizational intent. The paper provides a formal analytical framework on how the given industries are at this point of this evolutionary continuum and the point where the greatest potential improvements can be realized.

Kinetic and Thermodynamic Insights into Oxygen Evolution Reaction on Defect-Engineered Metal Oxide Nanocatalysts

2026-05-10T12:34:14+00:00

Developing sustainable hydrogen production from water electrolysis is an important topic in which the design of earth-abundant, high-performance electrocatalysts for the oxygen evolution reaction (OER) remains a central challenge. We have synthesized oxygen vacancy-enriched iron–nickel composite oxide nanocatalysts Ov-Fe₂O₃/NiO from Iraqi natural hematite sourced from Derbendikhan district of Sulaymaniyah. In this work, we report. The preparation of catalysts occurred via alkaline co-precipitation and following hydrothermal crystallisation and thermal annealing in dilute H₂/Ar. Using X-ray diffraction (XRD), BET surface area, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and electron paramagnetic resonance (EPR) characterisation the oxygen vacancy defects were confirmed to be introduced in the material and a successful hetero-structure with a surface area of 78.3 m² g⁻¹ (crystallite size 12.4 nm).

In alkaline electrolyte (1 M KOH), the Ov-Fe₂O₃/NiO composite achieved an overpotential of 285 mV at a standard 10 mA cm⁻² current density, a Tafel slope of 48 mV dec⁻¹, and a charge-transfer resistance of 3.2 Ω, which are all significantly superior to those of the undoped composite, single-phase oxides, and the reference IrO₂ catalyst. The scientists conducted electrochemical measurements of the selected nickel catalyst at 298–338 K. This enabled them to carry out rigorous kinetic analysis using the Arrhenius and Eyring formalism. The analysis yielded an activation energy of 28.4 kJ mol⁻¹, an enthalpy of activation of 25.9 kJ mol⁻¹, and an entropy of activation of −48.3 J mol⁻¹ K⁻¹ for the defect-engineered catalyst. The above values reflect lower barriers relative to those of the reference materials. Based on findings related to density of states reasons and XPS analysis, these thermodynamic characteristics suggest that the presence of oxygen vacancies boosts the adsorption of reactive oxygen intermediates while improving the efficiency of charge carriers and reducing O–O bond intrinsic kinetic barriers. This research provides a way to quantify designed structural defects in relation to the thermodynamic landscape of OER catalysis and shows that locally sourced Iraqi mineral precursors can serve as a feedstock for advanced manufactured electrocatalysts.

Enhancing Big Data Processing Performance Using Distributed AI Techniques on High-Performance Computing Systems

2026-05-13T07:04:08+00:00

Big Data processing requires high-performance solutions in today's industries with the increasing growth of data. Traditional computing techniques are not efficient to deal with huge datasets based on process and memory constraints . Distributed AI algorithms on HPC platforms are utilized in this work to enhance Big Data processing performance. Distributed Random Forest and Deep Neural Networks were experimented with multi-core CPUs and GPU clusters. Memory optimization and cache reuse were employed to minimize data access latency. Experiments based on synthetic health-care and financial data sets show remarkable improvement in processing time, prediction accuracy, and power consumption. Experiments prove the efficacy of distributed AI strategies along with HPC for scalable Big Data analysis with high performance.

Methodology of Strategic Designing of The Distance Professional Development Process for Prosecutorial Employees Based on Pedagogical Design Tools

2026-05-20T03:09:22+00:00

This article analyzes the scientific and methodological foundations of strategically designing the distance professional development process for prosecutorial employees based on pedagogical design tools. The study examines the scientific-theoretical essence of the concept of pedagogical design (instructional design), its significance within the modern distance education system, and its potential for organizing professional development processes. Based on the analysis of scientific literature, the main principles, characteristics, and models of pedagogical design, including the content of the ADDIE model, are elucidated.

Integration of Communicative and Interactive Methods in Teaching Russian as a Foreign Language

2026-05-20T03:21:04+00:00

This article presents an integrative review of current research on the combination of communicative and interactive methods in teaching Russian as a foreign language. The relevance of this topic is determined by the changing goals of language education: teaching Russian as a foreign language is increasingly less limited to mastering grammatical structures and basic vocabulary, as the central outcome is the learner's ability to engage in real-life verbal interactions, understand the sociocultural context, use digital resources, and solve communicative problems in educational, professional, and intercultural situations. It has been found that the integration of communicative and interactive methods is most productive when learning is structured around the speech situation, learning interaction, authentic material, digital support, and reflection on communicative experience.

Pedagogical Possibilities of Project-Based Collaboration in Career Guidance For Schoolchildren Based on The STEAM Approach

2026-05-20T03:31:55+00:00

This article presents a theoretical and analytical review of current research devoted to the pedagogical potential of project-based collaboration in career guidance for schoolchildren based on the STEAM approach. The relevance of this topic lies in the fact that traditional career guidance, focused primarily on informing students about professions, is proving insufficient in the context of the technologization of education, the expansion of interdisciplinary professional fields, and the growing importance of project-based, communication, and research skills. The source material includes research conducted between 2020 and 2025 on STEAM/PBL, integrated STEM, career awareness, STEM/STEAM interest, educational technologies, and project-based activities. Analysis shows that project-based collaboration in a STEAM environment serves not only an organizational function but also a career guidance function: it transforms students from observers to participants in professionally significant activities.

Synthesis, Characterization, and Molecular Docking of Cefoxitin-Derived Schiff Bases: Biological Activity and Thermal Stability (TGA)

2026-05-22T11:14:46+00:00

In this study, new Schiff base derivatives were prepared via addition and condensation reactions by adding cefoxitin to substituted benzaldehydes or ketones in an acidic medium. To produce Schiff bases (D1-D4), cefoxitin was added to the carbonyl compounds in the presence of ethanol as a solvent, using glacial acetic acid as a catalyst. The structures of the prepared compounds were determined using FT-IR spectroscopy and, for some of them, 1H-NMR and 13C-NMR spectroscopy. The biological activity of prepared Schiff bases (D1-D4) was studied against G-negative bacteria (Klebsiella and Pseudomonas aeruginosa) and G-positive bacteria (Enterococcus faecalis and Staphylococcus aureus). The results showed antibacterial activity of the prepared compounds at high concentrations (0.01 and 0.001 mg/ml) compared to low concentrations (0.001 mg/ml) against Staphylococcus aureus, Enterococcus faecalis, and Klebsiella due to the effect of high concentration. In addition to performing the GTA thermal decomposition of compounds D1 and D3 to measure the change in the mass of these compounds with increasing temperatures and determine their thermal stability. In addition, compounds D1 and D3 underwent GTA thermal decomposition to assess the mass change of these compounds as temperatures increased and establish their thermal stability. Molecular docking of the three compounds (D2 and D3) with the target protein (8C7Y) was carried out using the MOE software. With the target protein, all compounds exhibited high binding affinity values. With important amino acid residues in the active site, hydrogen bonding and hydrophobic interactions were the primary interaction types.

Screening of Ornamental Flower Extracts as Natural Staining Agents

2026-05-28T09:16:15+00:00

This study aimed to provide additional information using natural dyes as a substitute for synthetic dyes in the viewing of plant and animal cells. It utilized the quantitative descriptive experimental research design using standard laboratory procedures. An experimental method was used in the study: it started with collecting flowers, then extract preparation using mechanical extraction, plant cell preparation, animal cell preparation, and staining the specimen for microscopic observation. Basically, there were five (5) treatments, each treatment had 3 replications. The result of the study showed that Begonia x hybrida and Mirabilis jalapa can stain the nucleus, cell wall, and cytoplasm of the plant cell when viewed under 100x and 400x magnification. While in Thunbergia erecta, the parts of the plant cell cannot be distinctly seen. In contrast, none of the extracts successfully stained animal cells, suggesting their limited application in animal histology. Compared to synthetic dyes, which pose health and environmental risks, natural dyes offer an eco-friendly and biodegradable alternative. However, their selective staining ability emphasizes their potential use in plant histology rather than animal tissue staining. Further research is needed to enhance their staining efficiency and expand their applications in microscopy.

Toxicological Evaluation of Selected Heavy Metals in Toothpastes : Quantitative Findings and Public Health Perspectives

2026-05-29T11:00:17+00:00

Toothpastes is important nowadays for frequent daily personal use to clean and whiten. Due to the increasing number of diseases in recent years related to the accumulation of heavy metals in the body , and due to the daily use of toothpaste , it has become important to check its safety from the heavy metals it contains . The importance lies in Knowing the safety of objects that enter or come into contact with human body , especially in the oral area , as they come into contact with the gums and salivary glands , and some children swallow them .This study has shown that there are high levels of some heavy elements examined in this work , namely lead (Pb) and arsenic (As) , among four heavy elements .The highest levels of lead (0.57 mg/kg) and arsenic (0.27mg/kg) were found in the analyzed toothpastes samples .These concentrations have been found to be very high and exceed the limits permitted by global health standards , especially in children`s toothpastes.

Organizational and Economic Analysis of Homeowners' Associations and Property Management Companies: Evidence from Uzbekistan

2026-05-30T16:05:17+00:00

This study investigates the organizational and economic performance of homeowners' associations (HOAs) and property management companies (MCs) operating within the residential housing sector of Uzbekistan. The primary objective is to evaluate institutional efficiency, financial governance, and service delivery capacity across both organisational forms in the context of ongoing post-Soviet urban reforms. A mixed-methods research design was adopted, combining quantitative panel data analysis covering 312 HOAs and 94 MCs across five Uzbek regions (2023-2025) with qualitative semi-structured interviews involving 48 housing sector practitioners. Secondary data were sourced from the Ministry of Housing and Communal Services of Uzbekistan, regional statistical offices, and audited annual reports. Comparative performance indicators were constructed using principal component analysis (PCA) and descriptive statistics. The study introduces a composite Organisational Performance Index (OPI) calibrated to transition economies, extending institutional economics frameworks to Central Asian housing governance contexts that have been underexplored in the international literature. The results support targeted policy interventions including mandatory financial literacy programmes for HOA boards, professionalisation standards for MCs, and a graduated subsidy mechanism for low-income housing associations. The study is geographically bounded to five Uzbek regions and may not fully represent rural or peri-urban contexts. Future research should incorporate longitudinal experimental designs and extend the comparative framework to other Central Asian transition economiesand exceed the limits permitted by global health standards , especially in children`s toothpastes.

Procedure for Accounting for Export Transactions and Foreign Exchange Earnings

2026-05-31T12:14:03+00:00

This study examines the current practices of accounting for export operations and foreign currency revenue monitoring in enterprises, with particular emphasis on improving accounting mechanisms under conditions of exchange rate volatility and increasing international trade integration. Effective accounting of export transactions plays a significant role in ensuring financial stability, maintaining liquidity, and strengthening the competitiveness of enterprises in foreign markets. The research analyzes the accounting treatment of export revenues, recognition of foreign currency inflows, exchange rate differences, and debtor obligations in accordance with international financial reporting standards, particularly IFRS 15 and IAS 21. Empirical analysis was conducted using export and foreign currency revenue data of “BETLIS TEKSTIL” enterprise for the period 2021–2025. The findings indicate that while export realization volumes remained relatively stable, the ratio of actual foreign currency revenues to export volumes gradually declined, reflecting increasing debtor risks, payment delays, and foreign exchange exposure. To improve the assessment of export efficiency and foreign currency risks, several innovative economic–mathematical models. These models provide a more accurate evaluation of financial performance and currency-related risks in export activities.

A Study of Effect of Concentration on Efficiency of dye-Sensitized Solar cells (DSSC) Based on ZnS

2026-06-01T12:30:39+00:00

In this work, the effect of concentration on efficiency can be studied based on theoretical approach for N3 dye contact ZnS semiconductor based on dye sensitized solar cells DSSCs . The impact current density based on transition energy, , atomic density, length of charge, concentration as well as polarity of both N3 -ZnS system on the electronic transport process. The chemical Ethanol solvents using a polar media with N3 dye –ZnS semiconductor system. The current density component is evaluated relative to transition energy, driving energy, potential and coupling parameters. The strength coupling and concentration on the current density behavior can be discussion in N3-ZnS devices, that's reflected in the electronic transport results.The current density results reveal the behaviour of current density has been strongly affected by the concentration, it determine the efficiency of N3-ZnS devices. The electron transfer ability has been limited current density due to transition energy, driving force and potential. The efficiency, influenced by the carrier concentration increases of N3-ZnS devices ,it plays a significantly in the increases current density as well as limited the electrical properties of N3-ZnS devices. Specifically, the overlapping coupling between N3 dye and ZnS was influenced on the electronic current density.In general, the increases in concentration from to is favorable and increases efficiency from 4.09 to 4.448 ,it suggests that N3 dye a suitable contact for ZnS -based DSSCs devices.

Study and Analysis of the Quantum Charge Transfer Rate in Ruthenium Dye RuN3 Contacting Semiconductors

2026-06-01T12:42:14+00:00

In this work, the charge transfer interaction from the donor state in excited ruthenium dye RuN3 to the acceptor state in the semiconductor CdSe was elucidated. The charge transfer rate was calculated using the quantum scenario of charge transfer theory, based on the donor-receiver approach. IN this approach, the injection of the charge from the excited RuN3 dye into the conduction band of CdSe is treated as a quantum transition process subject to the effects of electronic coupling and reorganization energy.The rate of charge transfer in a RuN3-CdSe cell depends greatly on the rearrangement energy, the polarity of the solvent medium, the coupling strength, the distance between the RuN3 dye and CdSe, the atomic density, and the charge concentration. A high reorganization energy increases the probability of charge transfer and reduce reocobination charge , while a low energy increases the rate of charge injection and decreases the rate of transfer. Ethyl alcohol and methyl alcohol were used as polar media to evaluate the effects of the solvent, when in contact with RuN3 with CdSe in the device. Analysis of the reorganization energy and charge transfer rate reveals that RuN3, CdSe, and solvent properties play a crucial role in modifying charge transfer between surfaces under different solvent conditions, coupling strength, and varyied temperatures. The rate of charge transfer increases significantly with increasing temperature, overcoming the activation barrier to inject it into the conduction band of CdSe, as a result of the increased vibrational energy of the molecular system.The charge transfer reaction occurred mainly as a result of the strong bond between the RuN3 dye and CdSe, which led to increased wavefunction overlap at the interface, thus enhancing the electrical transport potential of the RuN3-CdSe device. Based on the results, the overall performance of the RuN3-CdSe device can be improved by optimizing the basic parameters of charge transport, which are governed by fundamental quantum principles, where temperature affects thermal activation, barrier reorganization energy controls, and coupling controls the quantum transport probability.

2D Materials as Thin Films: Graphene and Beyond

2026-05-30T12:09:39+00:00

The paper discusses the exceptional characteristics and vast usage regarding 2D materials, focusing on advancements other than the widely researched graphene. It studies various 2D materials, like metal oxides, transition metal dichalcogenides, and metal-organic frameworks, which exhibit different electronic and structural properties than graphene [1], [2]. The review outlines the different synthesis methodologies used to obtain few-layer, single-layer, and multilayer assemblies of these materials, in solution and on different substrates, also at wafer scales [3]. This involves the observation of developments in the large-area synthesis technologies which are essential in the process of industrial scalability [4]. Moreover, the article explores the importance of the advanced characterization designs in explaining the atomic structure, electronic band properties and quantum phenomena of these emergent 2D systems [5], [6]. This level of understanding is important in exploiting their distinct electrical, optical, chemical and thermal characteristics in next generation technological uses [7]. The isolation of graphene in 2004 was a turning point and sparked the emergence of the research on 2D materials [8]. This resulting in discovering different novel 2D materials, such as black phosphorus, transition metal dichalcogenides, MXenes, and hexagonal boron nitride which all have a different quantum-confined energy band structure because of the in plane lattice periodicity [9]. The expansion of 2D materials has now gone beyond this to include even wider range of compositions, such as metal oxides, phosphides, and other layered perovskites, and further diversification of their potential applications [10], [11]. This fast-moving era has led 2D materials to develop into a sub-discipline of physical sciences with numerous applications [12].

Effect of Optimized Alkaline Treatment on the Chemical, Microscopic, and Mechanical Properties of Banana Fibers for Sustainable Composite Applications

2026-05-31T06:04:35+00:00

In this study, for the first time, attempts to research banana fibers (Musa sapientum), as a new sustainability composite reinforcement material have been taken, which their composition and morphology were affected by chemical treatment with NaOH solution. NaOH treatment on banana fiber: structure and mechanical performance Within the context of this research, the effect of NaOH treatment on the structural integrity of polymeric composites is presented by highlighting differences in both chemical (Figure 2a) and physical properties (quasi-static tensile behaviors) between abaca fibers treated with different concentrations[57]. In this work, two treatment concentrations were implemented (5 wt% and 10 wt%) under controlled circumstances. Scanning electron microscopy (SEM) characterized surface morphology, while Fourier transform infrared spectroscopy (FTIR) analyzed chemical modifications. The mechanical performances of the tensile testing were presented: The Tensile strength and Young's modulus were significantly higher in 5 wt% NaOH, and so less amount of other materials removed from raw jackfruit seeds caused increasing solid surface roughness improving fiber matrix interfacial interaction In comparison, 10 wt% NaOH treatment causes a deterioration in structure and mechanical properties Thus, the results demonstrate that careful optimization of alkaline treatment conditions is essential for improving natural fibers performances and promoting them as fillers of eco-friendly composite materials.

Formation of Methodological Competence of Informatics and Information Technology Teachers in a Digital Learning Environment Through Universal Design and Virtual Collaboration Approaches

2026-06-02T03:08:57+00:00

This article analyzes the issues of developing the methodological competence of Informatics and Information Technology teachers in a digital learning environment. In particular, it highlights the significance of the Universal Design for Learning (UDL) concept developed by CAST, as well as virtual collaboration-based teaching models. The results show that UDL principles contribute to the individualization of learning and improvement of educational effectiveness, while virtual collaboration supports teachers’ professional development. The integration of these approaches enables the effective development of teachers’ methodological competence.

Hybrid Identities in Multicultural Societies: Between Tradition and Modernity

2026-06-04T11:52:52+00:00

In the twenty-first century, identity has become one of the most contested and dynamically evolving concepts in social and political thought. The acceleration of globalization, intensification of migration flows, and rapid expansion of digital technologies have fundamentally transformed the conditions under which identities are formed, negotiated, and experienced. Traditional paradigms that conceptualized identity as stable, essential, and rooted in singular cultural, national, or religious frameworks are increasingly inadequate in explaining the complexities of contemporary social life. Instead, identity must be understood as fluid, relational, and hybrid.

This article provides a comprehensive socio-philosophical analysis of hybrid identities in multicultural societies, focusing on the tension between tradition and modernity as a central axis of transformation. It argues that hybrid identities are not merely transitional or marginal phenomena but represent a structural condition of late modernity and a dominant paradigm of identity formation in the global digital age. Drawing on interdisciplinary perspectives from cultural theory, sociology, political philosophy, and media studies, the study develops an integrated theoretical framework that connects cultural hybridity with globalization, transnationalism, and digital identity formation.

Methodologically, the study adopts a qualitative and analytical approach, combining conceptual analysis, critical synthesis of literature, and interpretive reflections on contemporary social realities. The findings suggest that hybrid identities are shaped by multiple intersecting forces, including migration, global cultural flows, digital platforms, and linguistic interaction. While hybridity offers significant opportunities for creativity, adaptability, and intercultural dialogue, it also generates challenges related to identity fragmentation, ontological insecurity, and the crisis of belonging.

The article concludes that hybrid identities simultaneously reflect the possibilities and contradictions of multicultural societies. They challenge traditional models of multiculturalism and necessitate the development of more flexible, dynamic, and inclusive frameworks of identity, citizenship, and governance. Understanding hybrid identities is therefore essential for addressing broader issues of social cohesion, political participation, and cultural integration in an increasingly interconnected world.

Predicting Alzheimer's Disease Using Artificial Intelligence Techniques

2026-06-05T08:20:51+00:00

Alzheimer disease (AD) is a progressive neurodegenerative disorder beginning with the accumulation of pathological proteins in brains and ultimately leading to neuronal death. Alzheimer's disease is among the most severe of cases that have a significant deterioration in cognitive ability with particular emphasis on detrimental effects to memory, intellect and more general behavioral functions. How: There is no cure at the moment, but researchers are working tirelessly in hopes of finding one. The immediate need for early stage diagnosis and manifestation of biomarkers has streamlined the therapeutic algorithms in terms of potential drug trials & preventive medication regimens, instituted at a very early developmental phase. Electroencephalography (EEG) is simple, faster and cost-effective non-invasive technique which can be used as adjunct for automation of Alzheimer's disease diagnosis. It merits inption that epoch length of segment EEG signals data might impact the performance for classification. To tackle this issue, we presented a device-free diagnostic EEG framework, where the ideal segment length estimation for classification is obtained using machine learning and deep learning-based approaches. It consists of the data collection of EEG, preprocessing by removing noise, and segmentation in time axis. In this work, we run a comparison of using deep learning models (multilayer neural networks and convolutional neural networks) to the more traditional machine learning models. Model: Training (logospheric regression, decision tree, random forest, gradient enhancement, AdaBoost, XGBoost; CNN and MLP); Classification; Evaluation The accuracy we obtained using open data Kaggle set is 0.83%, 96,7%. and 99,3% respectively. We tested the proposed models on an entirely novel application of identifying frontotemporal dementia and achieved substantial advances relative to previous publications. Moreover, we performed several analyses and graphically presented extracted categories contents to justify the developed model. The study will set a standard in the realm of neurological disorder research and one that will support future researchers and technical experts focused on this field.