Eurasian Journal of Theoretical and Applied Sciences (EJTAS)
Eurasian Journal of Theoretical and Applied Sciences (EJTAS) (E-ISSN : 3062-3413) was started in 2025 to create an environment for all applied sciences research and encourage the budding scientists of the world. It is an international, open-access, peer-reviewed, refereed journal published both online and in print. EJTAS is published in the English and Arabic languages and is a scholarly publication in applied sciences, giving exciting current information on all engineering and applied sciences. YAYIMCI BİLGİLERİ (Publisher information) Yayımcı Adı: İKSAD GLOBAL YAYINCILIK Yayınlayan Kuruluş: İKSAD GLOBAL YAYINCILIK İmtiyaz Sahibinin Adı-Soyadı: MUSTAFA LATİF EMEK Sorumlu Yazı İşleri Müdürü: ETHEM İLHAN ŞAHİN Editör: Dr. Amal BARKOUTI
A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors
A biosensor is an integrated receptor-transducer device that converts a biological response into an electrical signal. The design and development of biosensors have gained significant attention from researchers and scientists in recent decades due to their wide range of applications, including healthcare, disease diagnosis, environmental monitoring, water and food quality assessment, and drug delivery. Sensing technology utilizing nanomaterials, ranging from zero- to three-dimensional structures, offers advantages such as a high surface-to-volume ratio, excellent conductivity, shock-bearing capabilities, and color tenability. Nanomaterials (NMs) employed in the fabrication of Nano biosensors include nanoparticles (NPs), known for high stability and carrier capacity; nanowires (NWs) and Nano rods (NRs), capable of high detection sensitivity; carbon nanotubes (CNTs), offering large surface areas and high electrical and thermal conductivity; and quantum dots (QDs), providing color tenability. Furthermore, these nanomaterials can function as transduction elements. This review summarizes the evolution of biosensors, their classification based on receptors and transducers, and modern approaches utilizing nanomaterials such as noble metal and metal oxide NPs, NWs, NRs, CNTs, QDs, and dendrimers. Recent advancements in bio sensing technology facilitated by the expansion of nanotechnology are also discussed.
Biosensors, Nanomaterials, Nano bio sensing, Gold Nanoparticles, Carbon Nanotubes, Quantum Dots
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Optimizing Face Recognition Accuracy through Eigenfaces and K-Nearest Neighbour: An Empirical Study
Face recognition has emerged as a cornerstone of computer vision research, spurred by its expansive utility in security systems, surveillance, and human-computer interaction. This study develops a robust face recognition framework that integrates the Eigenfaces methodology for sophisticated feature extraction with the K-Nearest Neighbour (KNN) algorithm for classification. By leveraging Principal Component Analysis (PCA), Eigenfaces are generated to significantly condense the dimensionality of facial datasets while preserving the most salient discriminative features. These optimized feature vectors are subsequently processed via a KNN classifier, utilizing a range of K values to perform a thorough evaluation of the system's operational performance. The empirical findings reveal that the proposed methodology yields high recognition accuracy alongside stable computational overhead, suggesting that the selection of an optimal K value is essential for balancing predictive precision and processing efficiency.
Face Recognition (FR), Eigenfaces (EF), Principal Component Analysis (PCA), K-Nearest Neighbour (KNN), Feature Extraction
Design and analysis of a metamaterial shielding chip for mobile phones to reduce EM radiation in Human Head
This study focuses on the development and evaluation of a shielding chip made from metamaterials, designed to decrease the absorption of electromagnetic (EM) radiation by the human head from mobile phones. As public concern rises regarding long-term exposure to radiofrequency (RF) emissions, effective personal protection technologies are increasingly important. The proposed shield uses a custom-engineered metamaterial pattern to absorb and deflect EM waves, which lowers the Specific Absorption Rate (SAR) in cranial tissues. Simulations using the Finite Element Method (FEM) were performed across various 5G frequencies. These simulations utilized a detailed, multi-layered anatomical model of the head, including skin, fat, bone, dura, cerebrospinal fluid (CSF), and brain tissues. The analysis assessed critical metrics under realistic conditions, including SAR distribution, electric field attenuation, penetration depth, reflection coefficient (S11), and induced temperature changes. The findings show that the metamaterial chip substantially reduces SAR levels, especially in tissues with high water content. Both the 1-gram and 10-gram averaged SAR results stayed under the 1.6 W/kg regulatory limit set by the Federal Communications Commission (FCC). The shield exhibited strong wave-absorbing properties, with a notable performance at 2.4 GHz characterized by low reflection and no substantial thermal increase in head tissues. The research concludes that the metamaterial-based shielding chip presents a practical and effective approach for mitigating EM radiation exposure from mobile phone usage.
SAR, electromagnetic radiation, shielding, Metamaterial, human head
Gender and Age as Determinants of COVID-19 Outcomes
Background: SARS-CoV-2, first detected in late 2019, rapidly developed into a global health crisis. Evidence consistently shows that males experience more severe COVID-19 outcomes than females, and age plays a significant role in determining disease severity. Findings from this study suggest that males have a slightly higher risk of acquiring the infection and developing severe illness, while young individuals under twenty years of age show greater resistance. Conversely, those aged sixty years and above are more likely to experience severe manifestations, although susceptibility decreases among males older than seventy-five. Objective: The purpose of this study was to evaluate how gender and age influence COVID-19 infection patterns, disease severity, mortality, and prognosis. Only 198 confirmed cases were analyzed, consisting of 120 males (60.4%) and 78 females (39.6%), from January 2021 to January 2022. Results: Our results indicated that males were more affected by COVID-19 than females. Younger individuals demonstrated the lowest susceptibility, while elderly patients were the most vulnerable. Infection rates among males beyond the age of seventy-five showed a decline.
Wind Energy: an overview of the physical basics and the current types of technology
In recent years, the need for renewable energy has become a primary topic of discussion among all countries worldwide. This is especially true because the use of fossil fuels, such as natural gas and crude oil, has led to more environmental pollution, and the availability of these fuels is also decreasing. Renewable energy is a type of energy that originates from natural sources, and it requires specialized technology to be utilized in a manner that produces clean energy. Wind energy is one of the most commonly used renewable energy sources, and it has been used for many years. This article will explain why wind forms on the Earth's surface, the factors that influence how much energy can be taken from the wind, the effects of wind energy on the environment, and the different types of turbines used in wind power plants. These turbines are designed to collect the energy in the wind and turn it into mechanical energy, and then, through an electric generator attached to the turbine, they create electrical energy
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Ensemble Learning in Clinical Decision Support Systems: A Comprehensive Review of Approaches for Symptom Analysis and Real-Time Decision-Making
Ensemble learning has become one of the most promising methods for clinical decision support as artificial intelligence is increasingly used in the medical industry, especially in symptom analysis and real-time decision-making. With an emphasis on model types, performance accuracy, interpretability, and their integration with medical sensors and smart devices, this paper offers a thorough overview of ensemble techniques used in Clinical Decision Support Systems (CDSS). Current issues with data privacy, technological constraints, and practicality in clinical settings are also highlighted in the research. It also provides a forward-looking viewpoint on how to improve these systems to deliver healthcare that is more precise, effective, and individualized.
Modeling and Dynamic Analysis of Pitch Motion Control of an Aircraft System
Safe and efficient flight operations require accurate and steady aircraft pitch angle control. Conventional control approaches struggle to achieve ideal transient response characteristics like minimum overshoot and fast settling time. This thesis compares aircraft pitch angle control techniques. Due to its instability, the open-loop aircraft pitch system requires powerful feedback control. This paper simulates the longitudinal dynamics of an aircraft pitch control system and builds PID controllers utilizing Trial-and-Error, Ziegler-Nichols, and GA-based optimization. A Model Predictive Control (MPC) technique is also tested. While Trial-and-Error and Ziegler-Nichols conventional PID tuning approaches stabilize the system, simulation studies show that they have higher overshoot and longer settling periods in transient response. The GA-optimized PID controller has a faster reaction (0.204 s, peak time 0.619 s, settling time 1.25 s) and a 5.79% reduction in overshoot. With comparable performance measures (settling time of 1.33 s, overshoot of 6.11%), the MPC technique works well in complex control contexts. A thorough comparison shows that the GA-tuned PID controller beats classical PID approaches and MPC in important transient response characteristics. This shows that artificial intelligence can optimize aircraft pitch control systems better. The results show that GA-based tuning can provide accurate and robust control, providing insights for aerospace control system design.
Forecasting Solar Power Generation Using Real Meteorological Data and Machine Learning Techniques
Accurate short-term forecasting of photovoltaic (PV) power is critical for grid management and energy planning. We analyze a real year-long meteorological dataset (including irradiance, temperature, wind, etc.) and simulate corresponding PV output (derived from irradiance and temperature with efficiency factors). Using this data, we train and evaluate four models Random Forest (RF), XGBoost (XGB), Long Short-Term Memory (LSTM), and a Hybrid Ensemble – for 1-hour, 3-hour, and 6-hour ahead forecasts. Models use features such as global horizontal irradiance (GHI), temperature, wind speed, humidity, and time-derived cyclic variables. Performance is measured by RMSE, MAE, and R². Results show that ensemble tree methods (RF and XGB) outperform LSTM for this task, with RF often giving the lowest error. As horizon increases, forecast accuracy degrades (higher RMSE) due to meteorological variability. Feature importance and correlation analysis indicate that irradiance is the dominant predictor of PV output, with nearly perfect correlation (R²≈0.99) to power. We include detailed experiments, visualizations (e.g. actual vs. predicted curves, error trends), and discuss the implications of hybrid models combining ML and time-series techniques.
The Impact of Modern Medical Technology on the Efficiency of Surgical, Dialysis, and Emergency Departments: An Analytical Survey Study
This study aims to evaluate the current use of modern medical technology in the departments of surgery, dialysis, and emergency care, through surveying the opinions of 51 healthcare professionals. An electronic questionnaire was used to collect data regarding the evaluation of technology levels, challenges, adequacy of training, and future needs. The results showed a variation in the evaluation of technology across departments, with the dialysis department receiving the highest positive rating. The main shared challenges were insufficient training, technical malfunctions, and high costs. The majority of participants emphasized the importance of technology and the need for more, noting a clear deficiency in the adequacy of current training, particularly in the emergency and surgery departments. Future needs focused on improving imaging technologies, artificial intelligence, more efficient dialysis machines, and robotic surgery. The study recommends investing in continuous training, addressing technical challenges, strategic planning for technology, and involving staff in the development process.
Study of the properties of lightweight concrete and its potential use as an alternative to hollow bricks for roof slabs using polystyrene granules
The research summarizes the conduct of tests on lightweight concrete (lightened by the addition of polystyrene beads) in terms of density, compressive strength, weight, and absorption rate. All these tests are conducted on three mixes of lightweight concrete using polystyrene beads, and the best mixes are selected in terms of weight lightness and absorption rate. Consequently, the possible and available uses of this concrete are deducted, such as sloping layer works and the manufacturing of solid bricks as an alternative to hollow bricks. The research shows the possibility of using this type of concrete in the manufacturing of certain types of bricks used for ceiling slabs instead of hollow bricks to avoid water leakage and moisture storage within concrete ceilings, which results in problems. The research discusses part of these issues related to surface moisture and the possibility of avoiding them.
High-Resolution Target Detection in TDMA-MIMO FMCW Millimeter-Wave Radar Systems
This paper discusses the integration of Time Division Multiple Access (TDMA) with MIMO (Multiple Input Multiple Output) techniques in FMCW millimeter wave (mm-Wave) radar systems for high-resolution target detection and imaging. Through the proposed TDMA-MIMO, we demonstrate that the system can form a virtual antenna array, which significantly improves the angular resolution in range, and Doppler-azimuth-elevation domains. Challenges, including side lobe artifacts, phase errors, and Doppler ambiguity, are mitigated through advanced signal processing methodologies, specifically employing Iterative Peak Component Elimination (IPCE) and Constant False Alarm Rate (CFAR) algorithms. From the experiments carried out, the radar is shown to be able to resolve targets with the use of IQ signal analyses, range-azimuth spectrum imaging, and range-Doppler distribution mapping. moving target detection was accomplished using adaptive thresholding and non-coherent accumulation techniques, showcasing the effectiveness of the proposed framework. These advancements illustrate the system's potential use in scenarios that require precise spatial and velocity parameter estimation, such as security monitoring or aircraft tracking.
Iterative Peak Component Elimination (IPCE),
Detection of Minerals and Hidden Objects in Airports Using Millimeter-Wave Radar (MMW) A MATLAB and AI-Based Approach
Exposing hidden objects and metals to airport safety has become a serious challenge, especially with the increasing development of smuggling techniques and the limitations of traditional safety methods such as X-rays and metal detectors. The study looked at the use of millimeter wave radar (MMW) in conjunction with artificial intelligence (AI) to detect hidden objects in the airport environment in real time. MMW radar, which is known for its ability to penetrate various materials, is used to take radar signals from various hidden objects including metals, metals and non-metallic elements. Captured radar data is processed and analyzed using MATABs, particularly with AI-based models, particularly convolutional neural networks (CNNs) and support vector machines (SVMs) to classify and identify these objects based on their unique radar signatures. This study provides a detailed methodology for data collection, pre-processing, feature extraction, and AI model training, and evaluates the performance of the proposed system in terms of detection accuracy, accuracy, and memory. These results reflect the ability of MMW radar and artificial intelligence devices to significantly improve airport security and provide a non-invasive, fast and reliable way to detect hidden objects, bridging the limitations of existing security screening technologies. Future work will focus on improving model performance, expanding datasets, and integrating real-time processing capabilities for large-scale deployment in operational airport environments.
AI-Powered Energy Forecasting: ANext-Gen Approach for Smart Grids and Sustainable Power Systems
Integrating artificial intelligence (AI) into energy forecasting has become an important component in developing smart grids and improving renewable energy systems. This research paper presents a next-generation artificial intelligence-driven approach to predicting the production and consumption of renewable energy with a special focus on solar and wind energy systems. The main objective of this study is to explore how artificial intelligence models, including machine learning algorithms such as ARIMA (autoregressive integrated moving average), long short-term memory (LSTM) networks, and regression models, can increase the accuracy and reliability of energy predictions in real time. Through experiments with publicly available energy consumption and weather data, research compares the performance of various artificial intelligence techniques for energy forecasting, focusing on their ability to predict energy production and improve grid management. The results show that artificial intelligence powered models perform better than traditional methods by improve prediction accuracy and enable renewable resources to be better integrated into power grids. Furthermore, the scalability of artificial intelligence models and their ability to transform energy systems making them more sustainable and efficient. The results provide valuable insight into the future role of artificial intelligence in enhancing the resilience and intelligence of energy networks, which contribute to the global transition toward cleaner and more reliable power sources.
The Role of Epigenetics in Disease Mechanisms: Understanding Gene-Environment Interactions
Epigenetics is an evolving field that offers profound insights into how gene expression is regulated by environmental factors, lifestyle choices, and life experiences without altering the underlying DNA sequence. This paper explores the role of epigenetics in disease mechanisms, with a particular focus on cancer development, progression, and metastasis. We delve into how epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNAs, contribute to the silencing of tumor suppressor genes and the activation of oncogenes. The potential of epigenetic therapies, including DNA methyltransferase inhibitors and histone deacetylase inhibitors, is discussed as promising approaches for reversing abnormal gene expression in cancer treatment. We also examine the broader implications of epigenetics in personalized medicine, emphasizing the use of epigenetic biomarkers for early disease detection, monitoring treatment responses, and tailoring therapies. While challenges and ethical considerations remain, the paper highlights the transformative potential of integrating epigenetics into mainstream healthcare, offering a pathway toward more effective, individualized, and preventative approaches to medicine.
AI in Renewable Energy Forecasting: Bridging the Gap Between Data Science and Energy
This research paper explores the transformative role of artificial intelligence (AI) in renewable energy forecasting, focusing on its application in solar, wind, and hydropower systems. AI-driven models, such as machine learning techniques (Random Forests, LSTM networks) and hybrid models, have significantly improved the accuracy and efficiency of energy production forecasting by processing large, complex datasets including weather patterns, real-time sensor data, and environmental variables. These models outperform traditional methods by providing precise, real-time predictions, optimizing energy generation, improving grid stability, and enhancing energy storage management. Despite the benefits, challenges related to data quality, model complexity, scalability, and geographic generalization remain barriers to broader adoption. The paper also discusses emerging trends such as AI integration with IoT and smart grids, the use of advanced deep learning architectures like transformers, and the rise of decentralized AI models through edge computing. As AI continues to evolve, its role in enabling more sustainable and efficient energy systems will expand, supporting global transitions to renewable energy.
Drone Technology in Logistics Revolutionizing Last-Mile Delivery Solutions
The exponential growth of e-commerce has reshaped consumer expectations for delivery services, demanding faster and more efficient solutions. In the United States, e-commerce represented 15.5% of total retail sales in 2021, indicating a significant shift towards online shopping. Drone technology emerges as a transformative force in logistics, particularly in addressing the complexities of last-mile delivery. This paper explores the integration of drones into existing delivery systems, emphasizing their potential to enhance delivery speed, reduce operational costs, and mitigate environmental impacts. The analysis includes a review of current applications, cost-benefit comparisons with traditional delivery methods, and the environmental sustainability of drone operations. Findings suggest that drones not only promise substantial improvements in efficiency and accessibility but also pose unique challenges, including regulatory hurdles and technological limitations. This study contributes to the ongoing discourse on sustainable logistics practices, offering insights into the operational and strategic implications of drone technology in last-mile delivery.
The Evolution of Electrical Grid Systems from Centralized to Decentralized Networks
The evolution of electrical grid systems from centralized to decentralized networks marks a significant transformation in the energy sector. Traditionally, centralized grids, with power generated at large facilities and transmitted over long distances, have been the norm. However, the increasing demand for sustainable, resilient, and flexible energy solutions has spurred a shift toward decentralized networks. These decentralized grids distribute electricity generation across multiple, often renewable, sources closer to the point of use, reducing transmission losses and enhancing grid reliability. This paper explores the historical development of centralized grids, the driving forces behind the move toward decentralization, and the benefits and challenges associated with decentralized systems. By examining technological advancements, policy changes, and the economic and social implications, the paper provides a comprehensive analysis of the future of electrical grid systems and the potential for decentralized networks to become the new standard in energy distribution.
AI Ethics in Autonomous Vehicles: Balancing Innovation and Safety
The development of autonomous vehicles (AVs) has introduced significant advancements in transportation, largely driven by artificial intelligence (AI). However, the deployment of AVs raises critical ethical questions, particularly in balancing the pursuit of innovation with the need to ensure safety. This paper explores the ethical frameworks that guide AV decision-making, including utilitarianism, deontological ethics, and virtue ethics, and examines their implications for real-world scenarios. Through a review of regulatory challenges, public perception, and case studies, the paper highlights the complexities of integrating ethical considerations into AV systems. The findings suggest that a balanced approach, combining ethical guidelines, transparent communication, and continuous evaluation, is essential to fostering public trust and ensuring the safe and responsible development of autonomous vehicles.