Eng, Volume 6, Issue 5 (May 2025) – 8 articles

The integration of Digital Twin (DT), Internet of Things (IoT), and Long Range Wireless (LoRa) technology in industrial automation increases efficiency, flexibility, and real-time monitoring. This study proposes a decentralized automation architecture for SCARA robots, leveraging wireless sensor networks to improve scalability, reduce the number of infrastructure components, and optimizing data-driven decision-making. Experimental validation demonstrated a 74.9% reduction in cycle time, decreasing from 55.42 s to 13.91 s across all test scenarios. The system achieved a 98.6% packet delivery success rate, ensuring reliable communication, while latency remained between 1 and 2 s, maintaining synchronization between the real robot and its digital twin. The main contributions include the following: (i) a decentralized control framework for SCARA robots, (ii) an evaluation of LoRa-based wireless communication, and (iii) experimental validation of feasibility. The results confirm the effectiveness of the system in stable real-time data transmission and precise robotic movements, offering a cost-effective alternative to conventional structures. Despite the advantages, challenges such as data security, interoperability, and real-time synchronization require further research. This study provides insights into the practical implementation of DT, IoT, and LoRa in industrial robotics, paving the way for advancements in smart manufacturing and Industry 4.0. Full article

Sudden cardiac death (SCD) represents a critical public health challenge, emphasizing the need for predictive techniques that model complex physiological dynamics. Studies indicate that the “V-trough” pattern in sympathetic nerve activity (SNA) could act as an early indicator of potentially fatal cardiac events, which can be effectively modeled using a modified version of Chua’s chaotic system, incorporating the variables of heart rate (HR), SNA, and blood pressure (BP). This paper introduces a Chua circuit with delay, and proposes a novel control design technique based on Lurie-type control systems theory combined with mixed-sensitivity ${\mathcal{H}}_{\infty }$ ($S/KS/T$) methodology. The proposed controller enables precise regulation of HR in Chua’s circuit, both with and without delay, paving the way for the development of advanced devices capable of preventing SCD. Furthermore, the developed theory allows for the project of robust controllers for delayed Lurie systems within the single-input–single-output (SISO) framework. The presented theoretical framework, supported by numerical simulations, demonstrates the effectiveness of the conceptualization, marking a considerable advance in the understanding and early intervention of SCD through robust and nonlinear control systems. Full article

Ensuring occupational safety in underground mines is a fundamental priority due to the major risks associated with this unfriendly work environment. This involves employing a set of technical, organizational, and educational measures to reduce the hazards for workers and minimize the risks of accidents and occupational diseases due to electrical and mechanical causes. Old and precarious coal extraction methods, in conjunction with obsolete infrastructure and electrical and mechanical installations, lead to high accident risk, endangering the lives of underground workers when at work. Precarious working conditions and working materials alongside the carelessness of decision makers make underground mine-based work a major cause of accidents and professional illnesses. In this paper, the authors identify, estimate, prioritize, and evaluate the vulnerabilities within underground mines and discuss the actions and resources necessary to mitigate, stop, and/or eliminate these vulnerabilities, as well as a mitigation strategy for stopping and/or eliminating them to achieve increased occupational safety. Full article

Root crops typically develop and enlarge their storage organs in the soil, where they are naturally shielded from light exposure. This characteristic influences their physiological development and presents challenges for hydroponic cultivation, as taproot enlargement is often inhibited when submerged in water. To overcome this limitation, this study introduced a novel hydroponic system that prevents direct submersion in the nutrient solution. By isolating the taproots from both soil and nutrient solution, this system allows precise control of the root-zone light environment using LED irradiation. Carrot taproots were cultivated under blue, green, and red LED light from 42 days after sowing to assess their specific responses to different wavelengths. The results revealed distinct pigment accumulation patterns influenced by light quality. Blue light induced anthocyanin accumulation in the epidermis and outer cortex within 2 days of exposure and also stimulated chlorophyll synthesis in these outer tissues. In contrast, green and red light treatments promoted chlorophyll accumulation primarily in the stele, with red light having the most pronounced effect. These findings suggest that carrot taproots exhibit specific physiological responses to light exposure, demonstrating their ability to adjust pigment biosynthesis depending on the wavelength. By integrating controlled lighting environments into hydroponic systems, this study provides new insights into root development mechanisms and presents a novel strategy for optimizing root crop cultivation. Full article

In an era marked by sustainability challenges and digital transformation, organizations face heightened uncertainty in strategic decision-making. This paper applies a conceptual tool, a fuzzy-based decision model, in the appraisal of the attractiveness of electric vehicle acquisition and navigates the multifaceted complexities of integrating economic, environmental, and infrastructural factors. A concise overview of fuzzy principles highlights their relevance to strategic management in uncertain contexts. The study uses a practical example to demonstrate how fuzzy set-based decision models assess EV attractiveness by synthesizing costs, environmental impact, vehicle depreciation, and energy independence variables. The findings reveal the fuzzy set-based decision model’s potential to enhance decision clarity and efficiency, offering managers a simple but robust framework for navigating complex trade-offs. Implications for sustainable strategic management and suggestions for future research on advanced decision support systems are discussed. Full article

Heat exchangers play a crucial role in transferring heat between two mediums, directly impacting energy efficiency, product quality, and operational safety in industrial systems. This study presents a novel approach for fouling resistance estimation using two artificial intelligence models, the cascaded forward network (CFN) and the recurrent neural network (RN), with a minimal set of six input parameters. The proposed models utilize temperature and flow sensor data from heat exchangers to predict fouling resistance. The training process is optimized using the Levenberg–Marquardt (LM) algorithm, ensuring rapid convergence and high accuracy. Model performance is assessed based on mean squared error (MSE), regression values (R), and statistical error analysis. The results demonstrate that both models achieve high accuracy in predicting fouling resistance, with the CFN model outperforming the RN model. The CFN model achieves an MSE of 1.54 × 10⁻⁸, significantly lower than the RN model (MSE = 3.05 × 10⁻⁸), resulting in a 49.5% improvement in accuracy. Additionally, statistical analysis, including error histograms and correlation analysis, further confirms the robustness of the proposed models. Compared to traditional methods, the proposed AI-based models reduce computational complexity while maintaining superior accuracy. This study highlights the potential of AI in predictive maintenance and industrial optimization, paving the way for future enhancements in intelligent fouling estimation systems. Full article

This study investigates the use of machine learning techniques to predict the unconfined compressive strength (UCS) of both stabilized and unstabilized soils. This research focuses on analyzing key soil parameters that significantly impact the strength of earth materials, such as grain size distribution and Atterberg limits. Machine learning models, specifically Support Vector Regression (SVR) and Decision Trees (DT), were employed to predict UCS. Model performance was evaluated using key metrics, including the Pearson coefficient of correlation (r²), coefficient of determination (R²), mean absolute error, and root mean square error. The findings reveal that, for unstabilized soils, both SVR and DT models exhibit remarkable performance with r² values of 0.9948 and 0.9947, respectively, with the DT model surpassing the SVR model in estimating UCS. Validation was conducted using data from four types of locally available soils in the Najd region of Saudi Arabia, although some disparities were noted between actual and predicted results due to limitations in the training data. The analysis indicates that, for unstabilized soil, grain size distribution and moisture content during testing are primary influencers of strength, whereas, for stabilized soil, factors such as stabilizer type and content, as well as density and moisture during testing, are pivotal. This research demonstrates the potential of machine learning for developing a robust classification system to enhance earth material utilization. Full article

Brain tumors pose a significant challenge in medical research due to their associated morbidity and mortality. Magnetic Resonance Imaging (MRI) is the premier imaging technique for analyzing these tumors without invasive procedures. Recent years have witnessed remarkable progress in brain tumor detection, classification, and progression analysis using MRI data, largely fueled by advancements in deep learning (DL) models and the growing availability of comprehensive datasets. This article investigates the cutting-edge DL models applied to MRI data for brain tumor diagnosis and prognosis. The study also analyzes experimental results from the past two decades along with technical challenges encountered. The developed datasets for diagnosis and prognosis, efforts behind the regulatory framework, inconsistencies in benchmarking, and clinical translation are also highlighted. Finally, this article identifies long-term research trends and several promising avenues for future research in this critical area. Full article