Journal of Applied Research and Technology

Topology Optimized Lattice Design and Mechanical Evaluation of 3D Printed PLA Specimens under Tensile and Compressive Conditions

Tue, 30 Jun 2026 00:00:00 -0600

The lightweight design of 3D-printed polylactic acid (PLA) components requires optimization strategies that reduce material usage while preserving mechanical performance under loading conditions. In this study, cubic lattice topology optimization was applied to tensile and compression specimens manufactured by 3D printing using PLA. The mechanical properties of the base material were experimentally determined and incorporated into finite element analyses. The boundary conditions were defined to reproduce the experimental stress state under standardized testing, with tensile and compressive loads selected based on the material’s yield strength. The optimized geometries were subsequently fabricated by 3D printing and mechanically tested. A qualitative agreement was observed between the simulated and experimental responses, confirming that the gradient-driven optimization approach implemented in ANSYS provided physically representative and experimentally validated designs. The printed PLA exhibited ductile-like behavior attributed to the fused deposition modeling process. Thus, this work demonstrates the feasibility of integrating topology optimization, finite element analysis, and experimental validation to develop PLA components under realistic loading conditions.

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

Rekia Zouini, Abdelkadir Makani, Ahmed Tafraoui — Fri, 27 Feb 2026 00:00:00 -0600

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, S. Ozkul, R. B. Carmona-Benítez, O. Cruz-Mejia — Fri, 31 Oct 2025 00:00:00 -0600

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.

Development of Cellulose–Rice Husk Composite: Evaluation of Fire Resistance and Fungal Growth

Sergio González-Serrud, Nacarí Marín-Calvo — Tue, 30 Jun 2026 00:00:00 -0600

This study examines the use of recycled materials in sustainable construction, specifically a rice husk-newspaper-PVAc-borax composite made from recycled newspaper cellulose (9%), rice husk (14%), borax (15%), and polyvinyl acetate-PVAc (62%). Tests for water absorption, density, fire resistance, and mold growth were conducted following ASTM and European standards. The composite showed high water absorption but improved moisture resistance due to rice husk and borax. Its intermediate density balances strength and lightness, making it suitable for various applications. Fire tests revealed reduced fire propagation in samples containing borax, enhancing fireproofing properties. Borax also inhibited fungal growth, aligning with previous studies. While these results are promising, further research is needed to evaluate the composite’s commercial viability and performance.

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

Fri, 27 Feb 2026 00:00:00 -0600

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.

Antioxidant Characterization and Kinetic and Thermodynamic Analysis of Hexane Extracts from Coccoloba uvifera Leaf, Byrsonima crassifolia Bark, and Bursera copallifera Resin

Tue, 30 Jun 2026 00:00:00 -0600

The research focused on the thermal decomposition characteristics and the kinetic and thermodynamic parameters of hexane extracts from Coccoloba uvifera leaf (CU), Byrsonima crassifolia bark (BYR), and Bursera copallifera resin (BUR). Additionally, it explored the relationship between these thermal properties and radical-scavenging activity (RSA). The preexponential factor (A), activation energy (Ea), enthalpy (ΔH), entropy (ΔS), and Gibbs free energy (ΔG) were obtained via the Coats–Redfern method. The CU extract showed higher RSA than the BYR and BUR extracts. In thermal analysis, the BYR and BUR extracts had two mass-loss events, whereas the CU extract had three. BUR had the lowest Ea, A, ΔH, and ΔS values, coinciding with its low RSA. Meanwhile, in CU, these parameters had the highest values. The thermal decomposition of the extracts was an endothermic process. Variations in the thermal profile were associated with composition and RSA.

Face Detection and Recognition in CCTV Security: A Comparative Study of YOLOv5 and YOLOv8

Muhammad Rif’an, Suci Dwijayanti, Bhakti Yudho Suprapto, Muhammad Yulwi Alwan — Tue, 30 Jun 2026 00:00:00 -0600

Security systems traditionally rely on CCTV for monitoring spaces accessible only to authorized personnel, yet they struggle with face detection and recognition at distances beyond a few meters. This limitation hampers their effectiveness in enhancing room security. This study addresses this challenge by developing a remote facial recognition system utilizing CCTV cameras to identify faces from 1-3 meters away. We employed YOLOv5 and YOLOv8 algorithms, testing pre-trained models of varying sizes (M and X) to improve detection accuracy. The training phase involved 200 epochs with a batch size of 32, yielding mean Average Precision (mAP) scores of 82.7%, 83%, 85%, and 85.2% for YOLOv5m, YOLOv5x, YOLOv8m, and YOLOv8x, respectively. Offline evaluations demonstrated average accuracy rates of 94%, 95%, 90%, and 91%. Online testing, conducted under varying conditions with 1-3 faces visible, showed YOLOv5x achieving an accuracy of 87.8%, compared to 80.9% for YOLOv8x. The results indicate that while single-face recognition is quick and accurate, performance declines with multiple faces in view. This research offers a promising solution to enhance room security through effective facial recognition at a distance, highlighting the potential of improved surveillance technology in secure environments.

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

sara A. Owaid, Abbas H. Miry, Tariq M. Salman — Thu, 30 Apr 2026 00:00:00 -0600

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.

Predicting Academic Performance of Students Through Supervised Learning Approaches

Tue, 30 Jun 2026 00:00:00 -0600

In the contemporary educational environment, where the success of millions of students depends on precise forecasting, it is essential to conduct in-depth research into the many factors that influence academic achievement. Beyond simply analyzing students’ grades, our research aims to provide a holistic picture of student achievement by examining a wide range of student demographics, academic backgrounds, and behavioral factors. We use advanced machine learning techniques, such as regression and classification, to decipher the complex patterns embedded in the data. This enables us to gain nuanced insights into the factors that predict student performance. We hope that by using these approaches, we will not only forecast academic outcomes but also identify the underlying factors that influence overall student success. In addition, our research seeks to determine the primary factors that have the greatest
impact on students’ academic performance. Educators receive vital insights that enable them to personalize interventions that target both academic and non-academic aspects that affect student progress. After an in-depth investigation, we concluded that the Artificial Neural Network (ANN) and Decision Tree (DT) models were the most accurate predictors. These models achieved accuracy rates of 81% and 76%, respectively. The results of this study demonstrate that the use of sophisticated machine learning algorithms is an effective method for predicting student performance and guiding interventions specifically designed to support student achievement.

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

Thu, 30 Apr 2026 00:00:00 -0600

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.