Journal of Engineering Research and Applied Science

Machine Learning (ML) for Artillery War Strategic Readiness Prediction, and Postmortem Analysis for Optimum Performance

2026-01-02T12:17:21+03:00

Computer soft-ware based-tool, a tool to process data that produces accurate results and does not make mistakes like human beings. Based on its efficient storage to secure and protect information stored inside it as well as fast access to information stored. These qualities were needed as aids for artillery war strategic readiness prediction, and postmortem analysis for optimum performance achievement already developed by the authors of this article. Hence, the development of Machine learning (ML) which is a branch of artificial intelligence (AI) that focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data. The Problem was defined, the data used in the manual computation were collected, the right algorithm was developed using Python Programming Language, the data were split, the develop model was trained, the models were evaluated and optimized before being deployed. The results were found to be exactly the same as the manually computed results of Akinnuli et al. (2025). This Machine Learning successfully predicts the case studied artillery level of readiness for an operation at this point in time as 74% and the risk involved (that is unreadiness) as 26%. The score of this case study was 233 out of 315 points. The score is Less than 248 but greater than 186 which fell into “Scenario C” of Decision Conditions (248 > SDS_p ≥ 186). The quantitative assessment is (Good). Time used for manual computation was one hour twenty minutes (1Hr. 20 Minutes) that is eighty minutes (80 minutes) while computer used fifteen (15) minutes, data input time and processing time added for these computations. This is a very good saving time of sixty-five (65) minute. Comparing the Machine Learning results with the manually computed results Statistic correlation model (Product moment coefficient of correlation (r)) was used to prove the reliability of the developed ML algorithm. The correlation r = 1.0071 (Approximately r = 1.00 ) which shows that it is strong positive perfect correlation. This is scientific base that gave approval for deployment of the Machine Learning algorithm developed for its artillery corps usage.

Reaction Kinetics and Mechanistic Shift in Non/Catalytic Esterification of PEGs: Microwave-Assisted vs. Conventional Esterification

2026-01-02T12:17:21+03:00

Esterification reactions play a crucial role in both industrial and biological systems, essential for the production of pharmaceuticals, polymers, and specialty chemicals. The esterification of polyethylene glycol (PEG) with chloroacetic acid (CAA) is important for surfactants, drug delivery systems, etc. This study investigates the esterification kinetics in a microwave-assisted, non-catalytic system, employing an alternative kinetic parameter estimation approach compared to conventional methods. Instead of separately analyzing temperature-dependent reaction rates, an optimization algorithm was used to simultaneously evaluate data from different temperatures. This novel method enhances the accuracy and reliability of kinetic parameters, while the absence of a catalyst significantly influenced the reaction mechanism, emphasizing the role of high-dielectric constant molecules. The esterification kinetics between CAA and PEGs were studied under both conventional and microwave-assisted conditions. Optimized kinetic data revealed that increasing PEG chain length resulted in decreased reaction rates, which was attributed to increased viscosity and steric hindrance effects. In the microwave method, lower rate constants were associated with the shorter thermal exposure of the reaction. Additionally, the collision frequency factor decreased with an increase in microwave power, due to the lower dielectric constants of PEG-based compounds. A significant change in reaction order was also observed under non-catalytic conditions; under microwave irradiation, PEG200 exhibited a reaction order of 1.5, while PEG400 showed second-order dependence. These findings offer new insights into the kinetic behavior of non-catalytic reactions and highlight the influence of molecular properties on reaction pathways. Ester formation was confirmed by FTIR and NMR analyses, providing evidence of successful esterification.

Decision Support System of a Solar Powered Baking Oven

2026-01-02T12:17:21+03:00

Abstract

The use of fossil fuel, wood and charcoal in the traditional cooking and baking processes can present serious challenges to the health of the environment, and promote land degradation. Similarly, the present 21^st century energy crisis in Nigeria and the entire globe, has suggested that solar energy is currently the cheapest and cleanest form of energy for diverse applications, through appropriately developed devices. This study therefore, designed a device for bread baking and other confectionary operations, using a decision support system (DSS) which minimizes the drudgery of under-designing or over-designing, thus making the design flexible and for meeting specific bread production targets. This device, which utilizes solar energy for bread baking is termed solar baking oven. It consists of a focusing collector (parabolic concentrator) and a flat plate collector termed the oven chamber or absorber unit. The following collector obtains direct radiation from the sun over a wide range of an area and converges it to its focal point where the oven chamber is positioned to receive both concentrated, and diffuse radiation from the sun. The oven chamber is a double-walled enclosure constructed from thick stainless-steel plates. The external wall measuring is separated from the inner wall by a thick fiberglass insulation serving as lagging material. It is designed to raise low-level radiation to high-level radiation of the flat plate collector, while the flat plate collector collects, absorbs both diffuse and direct radiation (short-wave radiation) from the sun, and since the plain glasses are opaque to long-wave radiation, it traps and stores the heat energy from the sun. it is the heart of the device; hence it is integrated with the oven compartment. The solar baking oven consists of two reflectors made from plane mirrors which reflect the Sun’s rays to the absorber plate placed in the baking chamber. The two reflectors (plain mirrors) intensify the incoming solar radiation into the baking chamber and increase the oven's thermal properties and efficiency, when the oven temperature is raised to and above, the dough is introduced into the oven chamber and heated for about depending on the shelf-life of the intended bread produced. The DSS has shown its capability of assisting solar oven designers by minimizing drudgery of over and under-design, material selection, and time wastage on long-hand calculation, in which design calculation can be gotten in few seconds. Since, the differences in the comparative results is negligible, and the solar oven poses no risk to environmental health, hence, it does not contribute to the greenhouse gases. Its recommendations for further optimization are also extensively discussed.

The Future of Cybersecurity: Predictive Analytics and Machine Learning Applications

2026-01-02T12:17:21+03:00

As cyber threats become increasingly sophisticated, traditional reactive cybersecurity approaches are becoming quite insufficient in the tackling of cyber threats. This paper explores the transformative role of predictive analytics and machine learning in reshaping the future of cybersecurity. By leveraging large-scale data, behavioral modeling, and anomaly detection, predictive systems can identify potential breaches before they occur, thereby offering a proactive defense mechanism. The current landscape of ML-driven cybersecurity tools was examined, while emerging techniques such as deep learning and natural language processing were highlighted, before the evaluation of their effectiveness in threat detection, incident response, and risk assessment. The paper also addressed the challenges including algorithmic bias, data privacy, adversarial attacks, and scalability. Through a multidisciplinary lens, the study argued that the integration of predictive analytics into cybersecurity ecosystems marks a paradigm shift that will define the resilience and adaptability of digital infrastructures in the coming decade.

Design and Hardware Realization of a Boost Converter for PV Applications

2026-01-02T12:17:21+03:00

Off-grid photovoltaic systems are crucial for supplying energy in areas with inadequate energy infrastructure or restricted energy access. Converting the low input voltage to a higher output voltage is required in these systems in order to effectively optimise energy use. However, conventional DC–DC converters' low efficiency, energy losses, and heating issues restrict how effectively solar energy can be used. To get around these restrictions, a high-efficiency DC–DC boost converter is being designed. In this study, the fast switching capability, high voltage durability, and low energy loss characteristics of Silicon Carbide (SiC) MOSFETs were utilized to increase efficiency in the energy conversion process. Compared to conventional silicon MOSFETs and IGBTs, SiC MOSFETs reduce energy losses thanks to their high‑frequency operating capability and provide a more stable output. The goal of this project is to increase the efficiency of standalone energy systems by designing and testing a high-performance DC-DC boost converter. Converting low input voltage into high output voltage is necessary for efficient energy use in such systems; however, traditional DC-DC converters have low efficiency, energy losses, and heating problems, which restrict the efficient use of solar energy. Simulation and experimental verification are the two primary stages of the study. A converter model is created in a simulation environment, and its performance is examined in the first phase. The circuit's physical implementation, including soldering, printed circuit board preparation, and schematic design, is finished in the second phase. The performance of the converter is assessed under actual operating circumstances, and the suggested design is validated by comparing the experimental and simulation results.

Customer Churn Analysis in the Telecom Sector: Prediction and Evaluation with a Machine Learning and Data Science Approach

2026-01-02T12:17:21+03:00

This study presents a comprehensive data analysis conducted for customer churn prediction in the telecom sector. Using IBM’s TELCO dataset, various machine learning libraries were employed. Three different models (Logistic Regression, Random Forest, and XGBoost) were developed on data from 7,043 customers and compared through a hybrid ensemble approach. Class imbalance was addressed with the SMOTE technique, and the best performance was obtained from the ensemble model (Accuracy: 0.8042, F1 Score: 0.6344). In addition, 15+ advanced feature engineering techniques and multiple feature selection algorithms were applied to boost model success. The experimental results include a detailed analysis of the hybrid system’s outcomes under different conditions and constraints.

Molecular Docking Study of Ver-1 Binding Compounds for the Mitigation of Aflatoxin B1 Toxicity

2026-01-02T12:17:21+03:00

Food safety has become a global health issue due to mycotoxins that occur naturally in agricultural products. The most dangerous of these toxins is Aflatoxin B₁ (AFB₁). Aflatoxin B₁ is a potent mycotoxin produced by Aspergillus flavus that exhibits toxic effects in the liver. In this study, the binding potential of 18 natural compounds to the Ver-1 protein, which is involved in the biosynthesis of AFB₁, was investigated using the Molecular Docking method. In analyses conducted using Gaussian and AutoDock/Vina software, the molecule apiol showed the strongest binding. The findings suggest that certain natural compounds may act as potential inhibitors by binding to the VER-1 protein, thereby reducing toxicity. The study presents a preliminary in silico analysis that evaluates the antifungal potential of natural compounds and could contribute to the drug development process.

Use of Butterworth Filters for Real Time RMS Value Measurement

2026-01-02T12:17:21+03:00

RMS (Root Mean Square) value is the most common value that is used quantitative evaluation of currents and voltages in electrical energy systems. The most commonly used method to obtain this value is based on numerical integration of the signal. Methods based on numerical integration have disadvantages such as high computational load, requiring precise determination of frequency and being sensitive to noise. As an alternative method, methods based on filtering for RMS calculation do not have the disadvantages mentioned. In this study, RMS value calculation using digital low-pass Butterworth filter is introduced and investigated. For investigation, Butterworth filters with various orders and cut-off frequencies are simulated and compared in MATLAB. The response of this filtering-based method to sudden changes in the amplitude and frequency of the input signal is also simulated and examined. The results clearly demonstrate that the method is suitable for real-time RMS value computation, with its low computational load and robustness to frequency changes.

Petrography, Mineral Chemistry, and Crystallization Conditions of the Eocene-aged Hasan Dag Plutons (Kurtun, Gumushane, NE Turkey)

2026-01-02T12:17:21+03:00

The Eastern Pontides are home to a wide variety of plutonic rocks that vary greatly in composition and age. The Eocene Hasan Dağ Pluton, exposed around Kürtün in northeastern Turkey, stands out among them due to its distinctive petrographic and mineralogical characteristics. This study aims to determine the physicochemical conditions (temperature, pressure, and oxygen fugacity) under which the Kazıkbeli magma crystallized based on petrographic and mineralogical data. The Hasan Dağ pluton extends in an approximate NE–SW direction and covers an area of about 15 km². The studied rocks are fine- to medium-grained and primarily consist of granodiorite, tonalite, and diorite compositions. The plutonic rocks display granular, poikilitic, monzonitic, graphic, and, rarely, porphyritic textures. The main mineral assemblage includes plagioclase (An₁₄₋₅₆), hornblende (Mg# = 0.78–0.85), biotite, orthoclase, quartz, and Fe-Ti oxides. Thermobarometric calculations indicate crystallization temperatures of 636–1075 °C, pressures of 0.2–5.1 kbar, and oxygen fugacities (ƒO₂) ranging from −13 to −10. Water contents estimated from hornblende compositions range from 3.3 to 5.0 wt.%. These results suggest that the Hasan Dağ pluton crystallized and was emplaced at mid- to shallow crustal depths corresponding to approximately 3–12 km.

Geochemical Baseline Establishment for Exploration and Environmental Assessment: The Soil–Rock Relationship in the Gümüşhane Granitoid, Eastern Pontides, Türkiye

2026-01-02T16:27:19+03:00

Geochemical baseline data are essential for both mineral exploration and environmental assessment, particularly in regions where naturally elevated element concentrations may mask or mimic mineralization-related anomalies. This study establishes robust soil and rock geochemical baselines for the Early–Late Carboniferous Gümüşhane Granitoid, a key lithological unit situated within one of Türkiye’s major metallogenic provinces in the Eastern Pontides. A total of 16 granitoid and 34 overlying soil samples were analyzed using ICP–MS, and the resulting dataset was evaluated through descriptive statistics, Pollution Index (PI), and Index of Geoaccumulation (Igeo) calculations.

The granitoid bedrock exhibits PI values close to 1.0 and Igeo values near −0.5 for major oxides, indicating compositions consistent with typical upper continental crust and reflecting unmodified lithogeochemical signatures. Trace-element patterns reveal notable depletions in Co, Cs, V, Ni, and Pb, whereas slight enrichments in Ba, Ga, Y, and rare earth elements likely reflect secondary mineral phases or mild hydrothermal modification. Soil samples, however, display markedly elevated PI values (>1.5) for a broad suite of elements including P, Mn, As, Y, Cd, Sn, Sb, REEs, Pb, Th, and U, demonstrating that soils developed on the granitoid host naturally enriched background levels far above global crustal averages.

Median, MAD, and Median + 2×MAD thresholds were used to identify conservative and high-level anomaly limits, offering a statistically robust framework suited for non-normally distributed soil datasets. These locally calibrated thresholds provide critical guidance for mineral exploration, as reliance solely on universal crustal reference values could lead to false anomaly detection. From an environmental perspective, the established background levels allow for a more accurate distinction between natural geochemical signatures and anthropogenic contributions.

Overall, this study provides the first integrated soil–rock geochemical baseline for the Gümüşhane Granitoid and delivers a scientifically rigorous reference framework for both exploration geochemistry and environmental monitoring across the Eastern Pontides.

Evaluation of Transmission Line Fault Based on Various Mother Wavelets

2026-01-02T12:17:21+03:00

Multi-resolution analysis and data feature extraction have received a lot of attention in signal processing. The time-frequency analysis method provides information on joint distribution in both the time and frequency domains and is a potent mathematical tool for analysing time-varying non-stationary signals. The Short-Time Fourier Transform (STFT) is one of the standard time-frequency distribution functions. However, the wavelet transform offers great frequency resolution at low frequencies and high time resolution at high frequencies, which offers constant, equally spaced time-frequency localisation. In this study, to extract the optimal feature vector, the single-phase ground short-circuit fault signal was obtained in the MATLAB environment. Two methods were applied to determine the most suitable wavelet family. The optimal resolution level was determined using Shannon entropy, while the Minimum Description Length (MDL) method was used to select the most suitable mother wavelet family. Accordingly, various wavelet families, including db8, sym5, coif5, bior1.3, and rbio3.1, were tested in discrete wavelet analysis. The results demonstrate that, when the high- and low-frequency components of the fault signal are analyzed in the feature vector extraction using the db8 wavelet, the similarity of the approximation coefficients to the main signal is not significantly affected. Moreover, the feature vector enables the most transparent and accurate identification of transient fault components, particularly in terms of the critical detail coefficients.

The Effect of Workpiece Geometry on Heat Transfer in Induction Heating

2026-01-02T12:17:21+03:00

Computational study of static and dynamic nonlinear optical properties of the Zn (II) complex

2026-01-02T12:17:21+03:00

The static and dynamic (frequency-dependent) nonlinear optical (NLO) behaviors of a new complex of zinc (II) ion, [ZnCl₂(peta)₂] [peta: 4-Pyridinethioamide] were investigated using the density functional theory (DFT)/B3LYP method with 6-311++G (d, p) basis set. Frontier molecular orbital (FMO) energies, band gap, and global reactivity descriptors were calculated at the same levels using the 6-311++G (d, p) basis set. The relationship between molecular hardness and both the static and dynamic first/second-order hyperpolarizabilities was analyzed using the DFT method. The dynamic NLO parameters of the Zn (II) complex were computed at ω = 532 nm (0.0856 au) laser frequency.

Wind Turbine Standards and Certification: A Global Framework for Reliability and Performance

2026-01-02T12:17:21+03:00

In recent years, the combined effects of climate change and recurring energy supply risks have pushed many countries to reconsider the way they produce and manage electricity. As a result, an increasing number of energy systems are moving away from conventional fossil fuels and turning toward a more diverse mix that includes renewables, particularly wind energy. The expansion of this sector, however, depends not only on technological progress butt also on the presence of reliable standards and certification procedures. These frameworks play an essential role in verifying that wind turbines operate safely and continue to perform throughout their expected lifetime. Within this context, the IEC 61400 standard series occupy a central place. The present study focuses on how this series is structured, what technical issues it covers, and how it is applied in different parts of the world. A special emphasis is given to IEC 61400-1, which outlines design load assumptions, fundamental safety concepts, and the turbine classification system. Additionally, the study discusses Tukey’s domestic content incentives under the Renewable Energy Support Mechanism (YEKDEM) and evaluates how these incentives interact with international certification requirements.

DFT and Experimental Studies on Synthesis of Bisphenol A: Determination of Optimal Feed profile in Semi-batch Reactor with Homogenous and Heterogonous Catalysts

2026-01-02T12:17:21+03:00

Bisphenol A (BPA) is theoretically synthesized with 2 moles of phenol and 1 mol of acetone. During the reaction, a stoichiometric ratio or high acetone concentration causes the formation of by-products. This situation has been confirmed by density functional theory (DFT) calculations in addition to the literature information. In these calculations, the B3LYP method and the 6-311++G(d, p) basis set were used. DFT calculations show that by-products can be formed in the synthesis of bisphenol a. The common method used to solve this problem is to work with high molar phenol/acetone ratios. But this brings additional operating and investment costs. In this study, semi-batch reaction experiments were performed which stoichiometric acetone was fed in reactor with various pulsed modes in the presence of homogenous and heterogonous catalysts such as HCL or Amberlyst w/wo enhancer. As a result, it has been shown that high conversion and selectivity can be achieved by providing energy efficiency.

Application of AHP and TOPSIS Approaches in Evaluating Pacemaker Manufacturing Methods Using Multi-Criteria Decision Making

2026-01-02T12:17:21+03:00

Pacemakers are vital biomedical devices used in the treatment of cardiovascular diseases. The selection of the most appropriate manufacturing technique for these devices is crucial in terms of biocompatibility, precision, and production quality. In this study, eight different manufacturing techniques were evaluated using Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The criteria weights determined by AHP were machining precision (0.4369), surface roughness (0.2942), defect rate (0.1947), and processing time (0.0742). According to the TOPSIS analysis, the most suitable manufacturing method was determined as EDM (Cᵢ = 0.9621). This analysis provides a systematic and objective approach for decision-makers in the pacemaker production field.