Journal of Applied Research and Technology

A Robust Data-Driven Framework for Evaluating National Industry 4.0 Performance: A Hybrid Learning and Decision-Making Approach

Merve Doğruel, Alev Taşkın — Fri, 30 Oct 2026 00:00:00 -0600

This study aims to suppress This study proposes a robust, data-driven framework to evaluate national Industry 4.0 performance by integrating unsupervised clustering, neural network– based importance estimation, permutation feature importance, and multi-criteria decision-making. Unlike conventional approaches relying on predefined or equal weights, the proposed framework derives criterion weights directly from data while accounting for structural uncertainty across alternative clustering schemes. The resulting robust weights are incorporated into TOPSIS to generate consistent country rankings. An empirical application using the Network Readiness Index, Global Innovation Index, and National Cyber Security Index demonstrates the effectiveness of the framework. The results identify human capital, governance capacity, and cybersecurity preparedness as the most influential dimensions of Industry 4.0 performance. Sensitivity analysis and equal-weight comparisons further confirm the stability of the rankings, providing a transparent and reliable framework for supporting national digital transformation strategies.

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.

An Investigation Into the Cooling Performance of Graphene and Copper Oxide Nanofluids in Electronic Devices

Fri, 30 Oct 2026 00:00:00 -0600

The primary objective is to evaluate the effectiveness of graphene-based nanofluids as heat transfer fluids for electronic devices. For this investigation, graphene was prepared via the recycling of graphite from used batteries, adding a sustainable dimension to the material’s synthesis. The analysis focuses on the effects of the nanofluid’s volume fraction and the Reynolds number on the heat transfer rate. The results demonstrate that both nanofluids significantly improve convective heat transfer compared to the base fluid (water). A comparative analysis reveals that the graphene nanofluid provides superior cooling performance, particularly at low volume fractions, where it shows a 13% increase in heat transfer over the copper oxide-water nanofluid. This enhanced performance is attributed to graphene’s high thermal conductivity, which promotes more effective fluid flow and heat dissipation.

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

Hla Mostafa, Khaldon Yousef, Ziad Saffour — Mon, 31 Aug 2026 00:00:00 -0600

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, 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.

Vapor Smoothing as a Post-Processing Technique for Enhanced Surface Finish in FDM 3D Printing

Dipak Sawant, Adik Yadao, Sunil Raykar — Fri, 30 Oct 2026 00:00:00 -0600

In virtue of its benefits and abilities to produce intricate geometrics fused deposition modeling (FDM) is one of the most preferred additive manufacturing technology. FDM can be used for all polymers amongst which polylactic acid (PLA) is widely used polymer for consumer & industrial product 3D printing. In this investigation analysis of a vapor smoothing as a post processing treatment to improve the surface finish of additive manufactured Polylactic acid components is discussed. During the investigation vapor smoothing is carried out for 1 ml, 2 ml & 3 ml concentration of ethyl acetate. Results suggest that 3 ml concentration improves surface finish by 13.48 % & 3.37 % as compared to 1 ml & 2 ml concentration respectively. The optimize process parameters found during this investigation are 3 ml of concentration, 180 seconds of time and 0.12 mm layer thickness which will leads to highest surface finish of 3D printed parts made with FDM.

Enhanced Multi-Level Electro-Geometric Model for Assessing Lightning Performance of Transmission Lines Under Non-Vertical Strokes

Reza Shariatinasab, Mojtaba Rasouli, Morteza Ghayedi — Fri, 30 Oct 2026 00:00:00 -0600

In this paper, a procedure developed for evaluating lightning flashover rates caused by non-vertical strokes on transmission lines (TLs) is presented. To achieve this, a multi-level electro-geometric model (MEGM) is developed to determine the termination point of impact (TPI) of each lightning stroke on TLs in the presence of shield wires (SWs). The proposed method can detect strokes on each phase conductor and SWs separately. Then, a Monte Carlo simulation is performed using an electromagnetic transient program (ATP/EMTP) and MATLAB to estimate the lightning flashover rates. The outage rates are calculated for a test system through the proposed and conventional EGM models, and the results show that the lightning outage rate is not the same for different models. The difference in the estimated outage rate has a direct effect on the technical and economic protection strategy of power utilities. However, the results show that the outage rate calculated by the proposed EGM model is lower than that calculated by the conventional model and hence the proposed model predicts lower investment costs to implement the protection scheme for the power networks.

Opuntia Ficus-Indica Fibers: Innovative Solution for Crack Mitigation and Optimization of Concrete Mechanical Properties

Fri, 30 Oct 2026 00:00:00 -0600

Plastic shrinkage cracking significantly compromises concrete durability. Opuntia ficus-indica fibers were evaluated as a sustainable reinforcement to mitigate early cracking and enhance mechanical performance. Concrete mixtures (w/c = 0.50) incorporated fiber dosages of 0.50, 0.75, and 1.0 kg/m³. Cracking evolution was monitored via a repeated-measures design at 1, 7, 14, and 28 days; compressive and flexural strengths were assessed at 7, 14, and 28 days. Findings identify 0.75 kg/m³ (EG(2)) as the optimum dosage, completely suppressing new cracks after 14 days and reducing cumulative width (1.53 to 0.21 mm) and length (909 to 72 mm). At 28 days, this dosage achieved increments of 27.12% in compressive strength and 22.64% in flexural strength relative to the control. Dosages exceeding 0.75 kg/m³ exhibited attenuated gains. Opuntia fibers effectively control early cracking and optimize mechanical capacity, providing a renewable alternative for resilient infrastructure.

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

Ali Aziz Jasem Al Noor — Mon, 31 Aug 2026 00:00:00 -0600

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, 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.