Journal of Applied Research and Technology

Aggressive Environments’ Effect on HPC Reinforced with Building Waste Steel Fibers.

Rekia Zouini — 2026-02-27

Durability refers to concrete’s ability to withstand deterioration from its surrounding environment. It is important to note that concrete durability encompasses not only its mechanical resistance but also its resistance to aggressive environments. This research paper investigates the chemical and mechanical durability of high-performance concrete reinforced with waste steel fibers. Concrete pecimens were immersed in 5% HCl and MgSO₄ solutions for 90 days, while control samples were stored in water for comparison. The esults show that specimens immersed in water exhibited very low mass loss, ranging between 0.1% and 0.8%, indicating minimal deterioration. In contrast, fiber-reinforced specimens exposed to MgSO₄ and HCl showed slightly higher mass loss, ranging from 0.2% to 0.3%, especially in the fiber-reinforced specimens. Despite minor material loss, a corresponding reduction in compressive strength was observed after immersion. Overall, the findings demonstrate that incorporating waste steel fibers significantly enhances the durability and resistance of high-performance concrete in harsh chemical environments.

Simultaneous Optimization of Investment in Technology Implementation and Regulatory Compliance: A Supply Chain Decision Model

M. Monsreal — 2025-10-31

A mathematical optimization model is proposed for strategic decision-making in supply chain management (SCM). The proposed model simultaneously optimizes investments to comply with government regulations and investments in technology to improve efficiency across three performance dimensions: ordering, just-in-time (JIT), and operating efficiency. Real company data is used to test the model. This data comes from a German company. The behavior of the proposed model is analyzed by solving four scenarios under different investment strategies. Results reveal counterintuitive findings, for example, JIT efficiency does not necessarily increase when technology investment increases; in comparison compliance with government regulations can improve companies’ operational efficiencies. These results demonstrate the sensitivity of companies’ operations to the allocation of technology investment and highlight the importance of simultaneously optimizing investments in government regulations compliance, and in the implementation of new technology. The optimization model informs the decision-making process that companies follow when investing in new technology while ensuring compliance with government regulations. Therefore, the model offers practical insights and utility for both private companies and government policymakers.

A K-NN-Driven Multilateration Approach for Improved Aircraft Positioning

Varsha Reddy Manda — 2026-02-27

For safe and efficient air traffic management, the Air Traffic Control (ATC) should know the precise location of aircraft. Aircraft usually report their positions to ATC using an advanced location-based service known as Automatic Dependent Surveillance–Broadcast (ADS-B). The location of aircraft without position-reporting capabilities is determined using complementary localization methods. A key challenge with traditional positioning techniques, such as multilateration using Time Difference of Arrival (TDOA), is that they involve solving non-linear equations, which require a precise initial position estimate. In this paper, we propose a novel method for aircraft localization that integrates a traditional positioning technique (multilateration) with data-driven learning using the K-Nearest Neighbors (K-NN) algorithm. The K-NN regression model provides a more realistic initial guess of the aircraft’s position. The results were validated against the actual aircraft positions provided by the OpenSky Network, and the proposed technique demonstrated a 2D root-mean-square error of 39.4 m. This work has significant potential for real-world applications in air traffic management, contributing to safer and more precise aircraft positioning.

Deep Reinforcement Learning-Based Optimal Deployment Strategy for UAV-Assisted Wireless Communication

sara A. Owaid — 2026-04-30

Unmanned aerial vehicles (UAVs) are progressively used to improve wireless communication networks, especially in dynamic and complicated environments. This research presents a novel UAV deployment optimization framework utilizing deep reinforcement learning (DRL), epically a deep Q-network (DQN), to enhance user coverage and power efficiency while dynamically adjusting to environmental conditions. In contract to traditional methods such as K-means clustering, the proposed approach uses an adaptive learning mechanism and a multi-metric reward function to optimize UAV placement in real time depending on altitude and noise variance. Simulation outcomes show that the DRL-based method accomplishes up to 11.2 in reward values at 300m altitude with tiny noise variance, in contrast to a maximum of 9.4 in conventional techniques under similar scenarios. Furthermore, power efficiency enhanced by 18% and energy consumption was decreased by 15% in contrast to static optimization methods. The user coverage raised by 12% on average, corroborating the model’s effectiveness in handling unpredictable environmental. These results confirm the superiority of DRL over traditional UAV deployment techniques, making it a viable solution for independent aerial communication networks of the future. This work contributes to enhancing UAV adaptability in real-world applications, providing a more efficient and intelligent approach to wireless network optimization.

Automated Hydroponic System: Demonstrating Robustness in Panama City’s Uncontrolled Climate Conditions

Víctor Zárate — 2026-04-30

Traditional agriculture faces growing challenges such as water scarcity, soil degradation, and the increasing demand for sustainable food production. Hydroponics offers an innovative solution, enabling soil-free cultivation of plants through nutrient-rich water systems, optimizing resource use and ensuring controlled growth conditions. This project focuses on the development and implementation of an automated hydroponic system for lettuce cultivation, designed to perform under the challenging environmental conditions of Panama’s humid and hot climate. The system integrates advanced sensor technologies, programmable controllers, and a digital twin for real-time monitoring and management of critical variables, including pH, electrical conductivity (EC), temperature, and nutrient concentrations. A user-friendly interface facilitates data visualization and manual adjustments when necessary. The implementation process addressed technical challenges such as sensor calibration, hardware-software integration, and the development of efficient control algorithms. Experimental results demonstrated the system’s robustness and adaptability, achieving significant improvements in plant growth consistency and health. The automated features reduced resource consumption, minimized human intervention, and maintained optimal growth conditions despite environmental stressors. These outcomes validate the viability of automation in hydroponic systems and highlight its potential for advancing sustainable and urban agriculture in tropical regions.

Development of a New Brake Failure Early Warning System to Reduce Brake Failure Accidents in Indonesia

Achmad Syaifudin — 2026-04-30

The increasing number of vehicles in Indonesia has heightened the risk of traffic accidents, with brake failure being a significant contributor, particularly in heavy vehicles, such as buses and trucks, on long downhill roads. According to the Korlantas Polri (Traffic Corps of the Indonesian National Police), brake failure is a leading cause of traffic accidents in Indonesia. KNKT (Indonesian National Transportation Safety Committee) investigations into 14 cases of brake failure revealed that brake overheating was the primary factor, exacerbated by high speeds, heavy loads, and steep terrain. In response, the Anti Blong system was developed to prevent brake failure through early detection using smart sensors to monitor both the brake-lining temperature and road slope. Integrated with IoT, the system provides real-time warnings via a virtual assistant, both on the driver’s smartphone and through an audible siren for nearby vehicles. Experimental tests of Anti Blong showed high effectiveness, with 98% accuracy in detecting brake temperature, 76% in slope detection, and 90% accuracy in its virtual assistant’s performance, making it a promising solution for improving heavy vehicle safety in Indonesia.

Enhancing Data Plane Security Against Cyber Attacks for Wired and Wireless Communication Software Defined Networks Based on Encrypted Algorithms

Fadia Noori Hummadi — 2026-04-30

Information technology is constantly evolving in terms of structure, hardware, and software, and one of the most important areas of information technology that is continually updated is networks, which take up a large area in terms of development and innovation in
applications, services, tools, etc. Now is the time for Software Defined Networks (SDN) in wired and wireless topologies to emerge. SDN is a modern networking technology in which the data plane is separated from the control plane, and control functions are gathered in one device called a controller, which leads to many security problems and exposure to attacks by intruders. The aim of this article is to enhance data plane security against cyber-attacks for wired and wireless communication software-defined networks based on encrypted algorithms. In this article, a Developed Encryption Algorithm (DEA) is proposed to protect wired and wireless communication
SDN against malicious and cyber-attacks. The proposed algorithm was tested and verified by sending and receiving ten text files of different sizes through three scenarios of SDN topology, and then calculating the encryption and decryption time. Finally, the National Institute of Standards and Technology (NIST) test was applied to verify the complexity and strength of the proposed algorithm, and the DEA algorithm gives an accuracy of 95.39% as compared with the SG algorithm, which gives an accuracy of 94.93%.

Development of an Epileptic Seizure Monitoring Information System Based on Intelligent Algorithms

Mahmood Zaki Abdullah — 2026-04-30

Epilepsy is a common neurological disorder that needs a direct interference to control the seizures properly. However, the detection of seizure often requires the presence of experienced neurologists, which is not always accessible. This challenge highlights the need to develop an automated system for seizure detection. In response, the main aims and objectives of this article are to propose and develop an algorithm for efficiently detecting instant notifications based on machine learning algorithms using EEG signals. Filtering technique is applied to the captured data to eliminate noise from the data before doing feature extraction, power spectral density (PSD) and spectral entropy. These features are then computed to train several classifiers like support vector machines (SVM), K-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB). KNN turns out to be the most successful classifiers of all. In this model, precision was shown to be at 0. 94, recall of 0. 93, and F1-score of 0. 93 percentage accuracy for seizure identification which, in turn, gives a total of 95% for total accuracy. The contribution and novelty of this article is that it can classify the situations of epileptic seizure monitoring into three classes (pre-seizure), (seizure), and (non-seizure), the alert messages are sent through the GSM using an Arduino microcontroller that connects to a SIM900 module to send alert message during the seizure event.

Estimation of the elastic modulus of Au nanofilms by subtracting the polymeric substrate influence

A. I. Oliva — 2025-10-31

The elastic modulus of Au nanofilms deposited onto a polymeric substrate was estimated by subtracting the substrate mechanical influence from the Au/polymer system under axial tensile test. To select the most appropriate substrate between polyethylene- terephthalate (PET) and Kapton (polyimide), the crystallinity and structural isotropic conditions were previously examined by X-ray diffraction. Kapton shows a lower % of crystallinity and enhanced isotropic conditions than PET. The elastic modulus of 60 nm-thick Au films deposited onto Kapton was measured, resulting in an average value of 37 GPa, which is lower than the Au bulk value. Crystalline structure and elastic properties of polymeric substrates are found to be of great relevance to be considered for the determination of the mechanical properties of metallic films.

Optimized Machine Learning Models for Early Prediction of Kidney Failure

Araek Tashkandi — 2026-04-30

Kidney failure’s poor prognosis, prevalence, and progression to end-stage renal disease pose serious health risks and healthcare burdens, yet early detection and treatment save lives and decrease the demand on the healthcare system. On average, a person may survive only 18 days without functioning kidneys. Kidney failure can be predicted using machine learning approaches, and the optimal algorithms for accurate diagnosis can be identified by using clinical data to identify relevant variables. Our research uses clinical data to help medical professionals anticipate kidney failure using machine learning techniques. Using variables such as creatinine, diabetes, and hypertension, we employ machine learning to predict kidney disease and select the most informative parameters for precise prediction. To predict kidney failure from multiple parameters, various algorithms were used to develop predictive models. The most effective model was identified among Random Forest, decision trees, support vector machines, K Nearest Neighbor, and logistic regression. The modified Random Forest model outperformed the default model with an F1-measure of 99% and an exceptional prediction accuracy of 99.75%, according to the results. In the end, this will help doctors save lives by enabling early diagnosis of kidney disease, allowing early treatment, and proper monitoring of patients’ health before dialysis is necessary.