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

Sura Ekrayym Ahmed — 2026-05-03

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

Alaa Khudhair Ali — 2026-05-05

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

Zeena Tariq Khattab — 2026-05-11

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

Ahmed Nafea Ayesh — 2026-05-13

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.