Journal of Applied Research and Technology

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

Alfonso Monzamodeth Román-Sedano — 2026-06-30

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.

From Structure to Feel: Statistical Modeling Using Spss to Perform Some Textile Compositions of Flame Retardant Fabrics

Hla Mostafa — 2026-08-31

Studying the performance of fabric compositions of flame-retardant-treated fabrics is gaining increasing scientific and practical importance, especially in the fields of safety and personal protection. This research focuses on analyzing the mechanical and sensory properties of flame-retardant-treated (2/1, 3/1) and warp-stretch (2/2) fabrics, and correlating these properties using SPSS statistical analysis to understand the relationship between the structural variables of the fabric composition and the functional performance of the treated fabric. The research aims to present a quantitative predictive model that helps improve the design of protective fabrics
while evaluating the changes in properties after treatment based on objective sensory analysis. The results showed that the warp-stretch fabrics (2/2) generally showed greater flame resistance and were the best among the composites in tensile, stiffness, and tear properties (mechanically), while the filbert fabrics showed lower flame resistance, especially the filbert (1/3) with improved crease resistance after treatment (physical), while air permeability was not significantly affected by treatment. The sensory analysis results also showed variations in some sensory properties of the treated samples, especially those measured using fingertips (warmth, thickness, and drape), while the averages of the remaining properties were relatively common to all treated samples. Statistical correlation using SPSS also showed that mechanical properties can be predicted based on sensory analysis, as a strong significant correlation was observed for the test properties: stiffness, wrinkling, tensile strength, and friction resistance, with 14 sensory properties measured based on human hand sensation, with a (Pearson >0.92) and a statistical significance level of (P≤0.1* ʻP≤0.05**). The study successfully predicted some physical and mechanical properties of fabrics, while also shortening the testing step on measuring devices by determining the corresponding sensory properties according to the fabric texture based on tactile sensation.

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.

Development of an Ultra-Low Energy Communication Protocol for 6G Networks: Integration with IoT and Wearable Technologies

Ali Aziz Jasem Al Noor — 2026-08-31

This paper develops and evaluates an ultra-low-energy communication protocol for 6G integration with IoT and wearable devices. The protocol combines energy harvesting support, lightweight data compression, and adaptive duty cycling to minimize radio on-time while preserving throughput and reliability. Experiments in a smart-city IoT testbed (1,000 nodes) and a wearable health-monitoring scenario (500 devices) show a 33.46% reduction in energy consumption versus a baseline 5G protocol and an increase in wearable battery life from 6.97 to 10.00 days. Average latency rises from 7.99 to 13.25 ms but remains below the 20 ms target for most IoT services, and reliability stays high (95.11%). These results indicate that ultra-low-energy protocols are feasible enablers for sustainable, scalable 6G IoT and wearable deployments.

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.

An approach for Association Rule Mining: Hash-Based Reverse Apriori using the Masking technique

Sharayu Bonde — 2026-08-31

Consumer buying patterns are a type of purchase made by consumers, whether by an individual or many individuals, to get the desired thing by making a purchase transaction. Apriori algorithm helps to find patterns among purchased items. But for larger transaction datasets, Apriori has high time complexity, as the database is scanned multiple times with the help of expensive resources. This, in turn, impacts the algorithm when the computer memory is inadequate, and there are a voluminous number of frequent transactions. This study aims to create an optimization of Apriori that is used in determining consumer purchasing patterns. The principal objective of this exploration is to make an advanced proposal framework utilizing hash-based Apriori that can assist with investigating the purchasing behavior of clients and, in view of that, suggest the most appropriate items to them as per their needs.

Adaptive Fire System: Leveraging Image Analysis and Multi-Deep Learning Approaches for Dynamic Classification and Quality Assessment

Ammar Wisam Altaher — 2026-08-31

The major roles in the control and prevention of fire breakouts remain fire classification and quality evaluation. In this paper, a new methodology that integrates image analysis with multi-deep models such as ResNet50, MobileNetV2, and lightweight deep learning models for accurate fire classification and quality assessment is proposed. The proposed methodology intends to use deep learning models to extract meaningful features from fire images automatically. ResNet50 and MobileNetV2 are very prominent deep-learning architectures that have proven to perform well in image classification tasks. A lightweight convolutional neural network (CNN), targeted for fire analysis, is supposed to be computationally light but highly accurate. In such a context, concerning obtaining information about the fire’s intensity, spread, and danger, the proposed image analysis techniques come into play. Our approach provides a comprehensive evaluation of fire incidents by a combination of the outputs from the deep learning models and the image analysis results. The experimental results show that our proposed methodology was effective in fire classification and quality valuation against ResNet50 and MobileNetV2. The proposed approach has huge potential for deployment in real-world applications involving fire prevention and control. It employs deep learning and image analysis techniques, hence providing a reliable and efficient way for fire classification and quality evaluation to ensure timely response and proper allocation of resources during fire emergencies.

Deep Learning-Driven Throughput Prediction in 5G for UAV-Assisted Emergency Response

Ahmed Al-Saadi — 2026-08-31

Mobile and 5G networks offer high data rates, yet they remain vulnerable to congestion during emergency events, potentially degrading service for users and first responders. Accurate throughput prediction is essential for optimizing resource allocation in such scenarios. This paper proposes a deep learning-driven framework for forecasting LTE/5G throughput and dynamically guiding the deployment of Unmanned Aerial Vehicles (UAVs) as temporary base stations to mitigate network congestion. We evaluate two prediction models using real-world 5G metrics from Chicago and Minneapolis (2022–2024): (1) a hybrid CNN-BiLSTM model that captures spatiotemporal dependencies, and (2) a CNN-Image model that transforms sequential metrics into image representations. Results show that both models achieve high prediction accuracy, with CNN-Image outperforming CNN-BiLSTM in mean absolute error and training efficiency. The predicted throughput is then integrated into a Proximal Policy Optimization (PPO) reinforcement learning strategy to guide UAV placement. In simulated emergency scenarios, the PPO-based approach significantly improves average user hroughput and service coverage compared to random placement and no UAV support. This work demonstrates the effectiveness of
combining deep learning and DRL for enhancing network resilience in disaster-stricken areas.

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

Sergio González-Serrud — 2026-06-30

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

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.