Eng | Special Issue : Feature Papers in Eng 2024

Research

Jump to: Review, Other

23 pages, 3021 KiB

Open AccessArticle

The Role of Productization in End-To-End Traceability

by Janne Harkonen, Javier Mauricio Guerrero Rodriguez and Erno Mustonen

Eng 2024, 5(4), 2943-2965; https://doi.org/10.3390/eng5040153 - 12 Nov 2024

Viewed by 511

Abstract

End-to-end traceability offers significant opportunities for product lifecycle visibility, sustainability enhancement, and regulatory compliance in product management. However, it faces challenges in data integration and management, supplier collaboration, cost and complexity, and the sharing of information across the supply chain. Productization refers to [...] Read more.

End-to-end traceability offers significant opportunities for product lifecycle visibility, sustainability enhancement, and regulatory compliance in product management. However, it faces challenges in data integration and management, supplier collaboration, cost and complexity, and the sharing of information across the supply chain. Productization refers to the representation of a product and connects commercial and technical aspects to the systemic perspective of product management. This includes a focus on the engineering lifecycle with inherent linkages to product data. The product management perspective, specifically in relation to the connection between end-to-end traceability and the productization concept, has not been extensively studied. This study explores the role of both productization and traceability in the context of end-to-end traceability. It combines an extensive literature review and an empirical example of applying productization logic across company borders to support end-to-end traceability. The key findings indicate that productization logic with a product structure focus can support end-to-end traceability in product management by providing consistency and a foundation for tracking both technical and operational data across the engineering lifecycle of a product. By focusing on productization, companies can overcome traceability challenges and unlock the benefits of end-to-end traceability. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

23 pages, 5944 KiB

Open AccessArticle

Examining Sentiment Analysis for Low-Resource Languages with Data Augmentation Techniques

by Gaurish Thakkar, Nives Mikelić Preradović and Marko Tadić

Eng 2024, 5(4), 2920-2942; https://doi.org/10.3390/eng5040152 - 7 Nov 2024

Viewed by 521

Abstract

This investigation investigates the influence of a variety of data augmentation techniques on sentiment analysis in low-resource languages, with a particular emphasis on Bulgarian, Croatian, Slovak, and Slovene. The following primary research topic is addressed: is it possible to improve sentiment analysis efficacy [...] Read more.

This investigation investigates the influence of a variety of data augmentation techniques on sentiment analysis in low-resource languages, with a particular emphasis on Bulgarian, Croatian, Slovak, and Slovene. The following primary research topic is addressed: is it possible to improve sentiment analysis efficacy in low-resource languages through data augmentation? Our sub-questions look at how different augmentation methods affect performance, how effective WordNet-based augmentation is compared to other methods, and whether lemma-based augmentation techniques can be used, especially for Croatian sentiment tasks. The sentiment-labelled evaluations in the selected languages are included in our data sources, which were curated with additional annotations to standardise labels and mitigate ambiguities. Our findings show that techniques like replacing words with synonyms, masked language model (MLM)-based generation, and permuting and combining sentences can only make training datasets slightly bigger. However, they provide limited improvements in model accuracy for low-resource language sentiment classification. WordNet-based techniques, in particular, exhibit a marginally superior performance compared to other methods; however, they fail to substantially improve classification scores. From a practical perspective, this study emphasises that conventional augmentation techniques may require refinement to address the complex linguistic features that are inherent to low-resource languages, particularly in mixed-sentiment and context-rich instances. Theoretically, our results indicate that future research should concentrate on the development of augmentation strategies that introduce novel syntactic structures rather than solely relying on lexical variations, as current models may not effectively leverage synonymic or lemmatised data. These insights emphasise the nuanced requirements for meaningful data augmentation in low-resource linguistic settings and contribute to the advancement of sentiment analysis approaches. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

16 pages, 2883 KiB

Open AccessArticle

Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models

by Su Myat Thwin and Hyun-Seok Park

Eng 2024, 5(4), 2805-2820; https://doi.org/10.3390/eng5040146 - 31 Oct 2024

Viewed by 541

Abstract

This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using [...] Read more.

This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using VGG16, ResNet-50, and Inception-V3). The ISIC dataset is balanced through oversampling in classification, and preprocessing techniques such as data augmentation and post-processing are applied in segmentation to increase robustness. The ensemble model outperformed individual models, achieving a Dice Coefficient of 0.93, an IoU of 0.90, and an accuracy of 0.95 for segmentation, with 90% accuracy on the original dataset and 99% on the balanced dataset for classification. The use of ensemble models and balanced datasets proved highly effective in improving the accuracy and reliability of automated skin lesion analysis, supporting dermatologists in early detection efforts. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

15 pages, 2261 KiB

Open AccessArticle

Optical Fiber Technology for Efficient Daylighting and Thermal Control: A Sustainable Approach for Buildings

by Lokesh Udhwani, Archana Soni, Erdem Cuce and Sudhakar Kumarasamy

Eng 2024, 5(4), 2680-2694; https://doi.org/10.3390/eng5040140 - 18 Oct 2024

Viewed by 641

Abstract

Different direct solar harvesting systems for daylighting are being explored to achieve high uniform illumination deep within buildings at minimal cost. A promising solution to make these systems cost-effective is the use of plastic optical fibers (POFs). However, heat-related issues with low-cost POFs [...] Read more.

Different direct solar harvesting systems for daylighting are being explored to achieve high uniform illumination deep within buildings at minimal cost. A promising solution to make these systems cost-effective is the use of plastic optical fibers (POFs). However, heat-related issues with low-cost POFs need to be addressed for the widespread adoption of efficient daylighting technologies. Previous studies have explored solutions for this overheating problem, but their effectiveness remains uncertain. This study proposes a low-cost fiber optic daylighting system integrated with a newly patented mechanical component designed to secure the fiber optic bundle at the focal point, providing three levels of heat filtration while ensuring uniform illumination. Our methodology involves selecting a small area, installing the setup, and measuring both heat and light readings, followed by validation through software simulations. The operational principle of this technology is explained, and experimental tests using lux meters and infrared thermometers were conducted to investigate the system’s characteristics. The three-level heat filtration device reduces temperature by approximately 35 °C at the surface of the optical fiber, and the average illumination of the room is around 400 lux. These results were further verified using RELUX simulation software. The findings demonstrate the promising potential of this new device in solar heat filtration and achieving uniform illumination. Recommendations for mitigating overheating damage and exploring heat filtering possibilities in new parabolic solar daylighting systems for further research are also provided. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

22 pages, 6449 KiB

Open AccessArticle

Development of a Smart Material Resource Planning System in the Context of Warehouse 4.0

by Oleksandr Sokolov, Angelina Iakovets, Vladyslav Andrusyshyn and Justyna Trojanowska

Eng 2024, 5(4), 2588-2609; https://doi.org/10.3390/eng5040136 - 12 Oct 2024

Viewed by 911

Abstract

This study explores enhancing decision-making processes in inventory management and production operations by integrating a developed system. The proposed solution improves the decision-making process, managing the material supply of the product and inventory management in general. Based on the researched issues, the shortcomings [...] Read more.

This study explores enhancing decision-making processes in inventory management and production operations by integrating a developed system. The proposed solution improves the decision-making process, managing the material supply of the product and inventory management in general. Based on the researched issues, the shortcomings of modern enterprise resource planning systems (ERPs) were considered in the context of Warehouse 4.0. Based on the problematic areas of material accounting in manufacturing enterprises, a typical workplace was taken as a basis, which creates a gray area for warehouse systems and does not provide the opportunity of quality-managing the company’s inventory. The main tool for collecting and processing data from the workplace was the neural network. A mobile application was proposed for processing and converting the collected data for the decision-maker on material management. The YOLOv8 convolutional neural network was used to identify materials and production parts. A laboratory experiment was conducted using 3D-printed models of commercially available products at the SmartTechLab laboratory of the Technical University of Košice to evaluate the system’s effectiveness. The data from the network evaluation was obtained with the help of the ONNX format of the network for further use in conjunction with the C++ OpenCV library. The results were normalized and illustrated by diagrams. The designed system works on the principle of client–server communication; it can be easily integrated into the enterprise resource planning system. The proposed system has potential for further development, such as the expansion of the product database, facilitating efficient interaction with production systems in accordance with the circular economy, Warehouse 4.0, and lean manufacturing principles. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

20 pages, 6678 KiB

Open AccessArticle

Vibration Analysis of a Centrifugal Pump with Healthy and Defective Impellers and Fault Detection Using Multi-Layer Perceptron

by Masoud Hatami Garousi, Mahdi Karimi, Paolo Casoli, Massimo Rundo and Rasoul Fallahzadeh

Eng 2024, 5(4), 2511-2530; https://doi.org/10.3390/eng5040131 - 8 Oct 2024

Viewed by 905

Abstract

Centrifugal pumps (CPs) are widely utilized in many different industries, and their operations are maintained by their reliable performance. CPs’ most common faults can be categorized as mechanical or flow-related faults: the first ones are often associated with damage at the impeller, while [...] Read more.

Centrifugal pumps (CPs) are widely utilized in many different industries, and their operations are maintained by their reliable performance. CPs’ most common faults can be categorized as mechanical or flow-related faults: the first ones are often associated with damage at the impeller, while the second ones are associated with cavitation. It is possible to use computational algorithms to monitor both failures in CPs. In this study, two different problems in pumps, the defective impeller and cavitation, have been considered. When a CP is working in a faulty condition, it generates vibrations that can be measured using piezoelectric sensors. Collected data can be analyzed to extract time- and frequency-domain data. Interpreting the time-domain data showed that distinguishing the type of defect is not possible. However, indicators like kurtosis, skewness, mean, and variance can be used as input for the multi-layer perceptron (MLP) algorithm to classify pump faults. This study presents a detailed discussion of the vibration-based method outcomes, emphasizing the benefits and drawbacks of the multi-layer perceptron method. The results show that the suggested algorithm can identify the occurrence of different faults and quantify their severity during pump operation in real time. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

16 pages, 5075 KiB

Open AccessArticle

A Computationally Time-Efficient Method for Implementing Pressure Load to FE Models with Lagrangian Elements

by Adnan Shahriar, Arsalan Majlesi and Arturo Montoya

Eng 2024, 5(3), 2379-2394; https://doi.org/10.3390/eng5030124 - 22 Sep 2024

Viewed by 546

Abstract

A computationally time-efficient method is introduced to implement pressure load to a Finite element model. Hexahedron elements of the Lagrangian family with Gauss–Lobatto nodes and integration quadrature are utilized, where the integration points follow the same sequence as the nodes. This method calculates [...] Read more.

A computationally time-efficient method is introduced to implement pressure load to a Finite element model. Hexahedron elements of the Lagrangian family with Gauss–Lobatto nodes and integration quadrature are utilized, where the integration points follow the same sequence as the nodes. This method calculates the equivalent nodal force due to pressure load using a single Hadamard multiplication. The arithmetic operations of this method are determined, which affirms its computational efficiency. Finally, the method is tested with finite element implementation and observed to increase the runtime ratio compared to the conventional method by over 20 times. This method can benefit the implementation of finite element models in fields where computational time is crucial, such as real-time and cyber–physical testbed implementation. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

17 pages, 102572 KiB

Open AccessArticle

Improved Lift for Thick Flatback Airfoils in the Inboard Blades of Large Wind Turbines

by Micol Pucci and Stefania Zanforlin

Eng 2024, 5(3), 2345-2361; https://doi.org/10.3390/eng5030122 - 20 Sep 2024

Viewed by 764

Abstract

Thick airfoils are often used in the inboard sections of blades in commercial wind turbines. The main reason for this is to give the blade greater structural strength, but it is well known that thick airfoils degrade aerodynamic performance by stalling at relatively [...] Read more.

Thick airfoils are often used in the inboard sections of blades in commercial wind turbines. The main reason for this is to give the blade greater structural strength, but it is well known that thick airfoils degrade aerodynamic performance by stalling at relatively small angles of attack. The adoption of flatback airfoils instead of sharp trailing edges allows high lift coefficient to be maintained in thick airfoils. In this paper, we propose a novel airfoil design based on a passive flap to further improve the lift coefficient. This new design was tested by numerical simulation on several airfoils with different maximum thickness and different TE thickness. The improved design for flatback airfoils yields a higher lift coefficient, while the drag behaviour is strictly related to the baseline airfoil shape: some airfoils show a decrease in drag at certain angles of attack, while others exhibit a drag increase. In conclusion, the practical implications of the flap’s utilisation on a state-of-the-art blade designed for a 5 MW wind turbine are analysed. The findings demonstrate that, due to the enhanced lift coefficient, it is feasible to shorten the chord while maintaining the power output, thereby reducing material costs. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

17 pages, 3465 KiB

Open AccessArticle

WebTraceSense—A Framework for the Visualization of User Log Interactions

by Dennis Paulino, André Thiago Netto, Walkir A. T. Brito and Hugo Paredes

Eng 2024, 5(3), 2206-2222; https://doi.org/10.3390/eng5030115 - 5 Sep 2024

Viewed by 794

Abstract

The current surge in the deployment of web applications underscores the need to consider users’ individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by [...] Read more.

The current surge in the deployment of web applications underscores the need to consider users’ individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform’s capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

36 pages, 443 KiB

Open AccessArticle

Balancing the Scale: Data Augmentation Techniques for Improved Supervised Learning in Cyberattack Detection

by Kateryna Medvedieva, Tommaso Tosi, Enrico Barbierato and Alice Gatti

Eng 2024, 5(3), 2170-2205; https://doi.org/10.3390/eng5030114 - 4 Sep 2024

Viewed by 1438

Abstract

The increasing sophistication of cyberattacks necessitates the development of advanced detection systems capable of accurately identifying and mitigating potential threats. This research addresses the critical challenge of cyberattack detection by employing a comprehensive approach that includes generating a realistic yet imbalanced dataset simulating [...] Read more.

The increasing sophistication of cyberattacks necessitates the development of advanced detection systems capable of accurately identifying and mitigating potential threats. This research addresses the critical challenge of cyberattack detection by employing a comprehensive approach that includes generating a realistic yet imbalanced dataset simulating various types of cyberattacks. Recognizing the inherent limitations posed by imbalanced data, we explored multiple data augmentation techniques to enhance the model’s learning effectiveness and ensure robust performance across different attack scenarios. Firstly, we constructed a detailed dataset reflecting real-world conditions of network intrusions by simulating a range of cyberattack types, ensuring it embodies the typical imbalances observed in genuine cybersecurity threats. Subsequently, we applied several data augmentation techniques, including SMOTE and ADASYN, to address the skew in class distribution, thereby providing a more balanced dataset for training supervised machine learning models. Our evaluation of these techniques across various models, such as Random Forests and Neural Networks, demonstrates significant improvements in detection capabilities. Moreover, the analysis also extends to the investigation of feature importance, providing critical insights into which attributes most significantly influence the predictive outcomes of the models. This not only enhances the interpretability of the models but also aids in refining feature engineering and selection processes to optimize performance. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

13 pages, 5089 KiB

Open AccessArticle

Grading Evaluation of Marbling in Wagyu Beef Using Fractal Analysis

by Yuya Suzuki and Bao Yue

Eng 2024, 5(3), 2157-2169; https://doi.org/10.3390/eng5030113 - 2 Sep 2024

Viewed by 494

Abstract

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Currently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and [...] Read more.

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Currently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and external factors causing fatigue, and fluctuations in grading standards due to the grader’s mood at the time. This paper proposes the use of fractal analysis for the grading evaluation of beef marbling to achieve automatic grading without the inconsistencies caused by human factors. In the experiments, cross-sectional images of the parts used for visual judgment were taken, and fractal analysis was performed on these images to evaluate them using fractal dimensions. The results confirmed a correlation between the marbling evaluation and the fractal dimensions, demonstrating that quantitative evaluation can be achieved, moving away from qualitative visual assessments. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

17 pages, 3696 KiB

Open AccessArticle

Evaluating Field-Effect Separation on Rare Earth and Critical Metals

by Benjamin Schroeder, Michael Free, Prashant Sarswat, Easton Sadler, Jacob Burke and Zoe Evans

Eng 2024, 5(3), 2016-2032; https://doi.org/10.3390/eng5030107 - 1 Sep 2024

Viewed by 865

Abstract

The unique electromagnetic properties of rare earth elements (REEs) have led to rapid technological advances, creating a sharp increase in demand for these materials. The inherent challenges of separating REEs and the significant drawbacks of existing processes have driven the development of a [...] Read more.

The unique electromagnetic properties of rare earth elements (REEs) have led to rapid technological advances, creating a sharp increase in demand for these materials. The inherent challenges of separating REEs and the significant drawbacks of existing processes have driven the development of a new method known as field-effect separation (FES). This technology leverages electrical and magnetic fields to achieve separation by exploiting the differences in magnetic moments or effective charges of REEs in solution. Experiments on REEs were conducted using a microchannel based separation device, which confines fluid flow to facilitate separation within a field, with metal cations in solution being transported based on their respective electrostatic or magnetic properties. The results demonstrate that separation based on effective charge or paramagnetic properties is achievable. The confinement of fluid flow to microchannels allowed advective and osmotic forces to be suppressed sufficiently such that a reasonable separation of ions was achieved, though the impact of these forces were not completely removed. This innovative approach promises to improve the efficiency and effectiveness of REE separation, addressing both the growing demand and the limitations of current methods. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

19 pages, 7970 KiB

Open AccessArticle

Assessing CNN Architectures for Estimating Correct Posture in Cruise Machinists

by Fabian Arun Panaite, Monica Leba and Andreea Cristina Ionica

Eng 2024, 5(3), 1785-1803; https://doi.org/10.3390/eng5030094 - 5 Aug 2024

Viewed by 696

Abstract

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. [...] Read more.

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. This paper presents a novel application of artificial intelligence tools for real-time posture estimation specifically designed for cruise machinists. The primary aim is to enhance occupational health and safety by providing precise, real-time feedback on ergonomic practices. We developed a dataset by capturing video recordings of cruise machinists at work, which were processed to extract skeletal outlines using advanced computer vision techniques. This dataset was used to train deep neural networks, optimizing them for accuracy in diverse and constrained shipboard environments. The networks were tested across various computational platforms to ensure robustness and adaptability. The AI model demonstrated high efficacy in recognizing both correct and incorrect postures under real-world conditions aboard ships. The system significantly outperformed traditional ergonomic assessment tools in terms of speed, accuracy, and the ability to provide instant feedback. The findings suggest that AI-enhanced ergonomic assessments could be a transformative approach for occupational health across various industries. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

12 pages, 4455 KiB

Open AccessArticle

Analysis and Prediction of Spring-Back in Cylindrical Helical Springs Using Analytical and Numerical Models

by Nicola Zani and Luigi Solazzi

Eng 2024, 5(3), 1696-1707; https://doi.org/10.3390/eng5030089 - 2 Aug 2024

Viewed by 738

Abstract

This research focuses on cylindrical helical springs with circular cross-sections made from carbon steel (SH 0.82% C) and stainless steel (AISI 302). The transformation from a linear bar to a circular spiral involves numerous factors such as material mechanical behavior, stress–strain relationships and [...] Read more.

This research focuses on cylindrical helical springs with circular cross-sections made from carbon steel (SH 0.82% C) and stainless steel (AISI 302). The transformation from a linear bar to a circular spiral involves numerous factors such as material mechanical behavior, stress–strain relationships and residual stresses. This research investigates the spring-back phenomenon, which affects the final diameter of helical springs post-manufacture, using analytical, experimental and numerical methods. An analytical model, derived from the mechanical bending process, was proposed to predict spring-back, and its accuracy was validated against experimental data. This study also employed finite element simulations to analyze elastic recovery, confirming the analytical predictions. Results indicated that the spring-back ratio k could be expressed as an exponential function of the spring index C (the ratio between the final diameter of the spring D₂ and the diameter of the wire D_W), with a maximum error of 4.80% for stainless steel and 3.62% for carbon steel. This study’s findings provide valuable insights into optimizing the spring manufacturing process, enhancing the precision of spring diameter predictions, and potentially reducing production errors and material waste. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

26 pages, 18274 KiB

Open AccessArticle

Development, Designing and Testing of a New Test Rig for Studying Innovative Polycrystalline Diamond Bearings

by Alessio Cascino, Andrea Amedei, Enrico Meli and Andrea Rindi

Eng 2024, 5(3), 1615-1640; https://doi.org/10.3390/eng5030085 - 25 Jul 2024

Viewed by 603

Abstract

This paper reports the preliminary experimental studies carried out on an innovative sliding bearing made of polycrystalline diamond, a material with excellent mechanical and chemical characteristics, used mainly in the drilling industry. Bearings crafted from this material do not necessitate lubrication due to [...] Read more.

This paper reports the preliminary experimental studies carried out on an innovative sliding bearing made of polycrystalline diamond, a material with excellent mechanical and chemical characteristics, used mainly in the drilling industry. Bearings crafted from this material do not necessitate lubrication due to their extremely low coefficient of friction and high resistance to wear. For this reason, they are prime candidates for replacing traditional oil bearings, eliminating the need for auxiliary systems and thereby reducing environmental risks. In this regard, an innovative test rig was designed, capable of reaching speeds up to 6000 rpm both in vertical and horizontal configurations thanks to a novel tilting frame. Moreover, with a high modularity it was possible to test three different kinds of radial PCD bearings. Dynamic data were acquired and elaborated to evaluate orbits, acceleration and absorbed torque, to finally compare these different configurations to better understand how dynamic behavior is influenced by bearings’ geometrical characteristics. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

21 pages, 4496 KiB

Open AccessArticle

Engineering Biomedical Problems to Detect Carcinomas: A Tomographic Impedance Approach

by Filippo Laganà, Danilo Prattico, Domenico De Carlo, Giuseppe Oliva, Salvatore A. Pullano and Salvatore Calcagno

Eng 2024, 5(3), 1594-1614; https://doi.org/10.3390/eng5030084 - 25 Jul 2024

Viewed by 714

Abstract

Computed tomography (CT), magnetic resonance imaging (MRI), and radiography expose patients to electromagnetic fields (EMFs) and ionizing radiation. As an alternative, Electrical Impedance Tomography (EIT) offers a less EMF-influenced method for imaging by measuring superficial skin currents to provide a map of the [...] Read more.

Computed tomography (CT), magnetic resonance imaging (MRI), and radiography expose patients to electromagnetic fields (EMFs) and ionizing radiation. As an alternative, Electrical Impedance Tomography (EIT) offers a less EMF-influenced method for imaging by measuring superficial skin currents to provide a map of the body’s conductivity. EIT allows for functional monitoring of anatomical regions using low electromagnetic fields and minimal exposure times. This paper investigates the application of EIT for the morphological and functional assessment of tissues. Using the Finite Element Method (FEM) (Comsol 5.2), both two-dimensional and three-dimensional models and simulations of physiological and pathological tissues were developed to replicate EIT operations. The primary objective is to detect carcinoma by analysing the electrical impedance response to externally applied excitations. An eight-electrode tomograph was utilised for this purpose, specifically targeting epithelial tissue. The study allowed the characterisation of tomographs of any size and, therefore, the possibility to verify both their geometric profile and the ideal value of the excitation current to be delivered per second of the type of tissue to be analysed. Simulations were conducted to observe electrical impedance variations within a homogeneously modelled tissue and a carcinoma characterized by regular geometry. The outcomes demonstrated the potential of EIT as a viable technique for carcinoma detection, emphasizing its utility in medical diagnostics with reduced EMF exposure. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

21 pages, 7849 KiB

Open AccessArticle

Control of Floating Body Waves Due to an Airplane Takeoff from a Very Large Floating Airport

by Taro Kakinuma and Yusei Fukuura

Eng 2024, 5(3), 1513-1533; https://doi.org/10.3390/eng5030081 - 22 Jul 2024

Viewed by 585

Abstract

Numerical simulations were generated to investigate the response of a very large floating airport to an airplane takeoff, using the set of nonlinear shallow water equations of velocity potential for water waves interacting with a floating thin plate. We have proposed two methods [...] Read more.

Numerical simulations were generated to investigate the response of a very large floating airport to an airplane takeoff, using the set of nonlinear shallow water equations of velocity potential for water waves interacting with a floating thin plate. We have proposed two methods to reduce persistent airport vibration: reflectance reduction by decreasing the flexural rigidity in airport edge parts and amplification reduction by decreasing the still water depth partially under airport runways. First, when the flexural rigidity is uniformly decreased in an airport edge part, the reflectance of the floating body waves due to a B737 was reduced because of the multiple reflections. However, the wave reflectance for a B747 increased, depending on the conditions. A too-long edge part was not effective in reducing the wave reflectance. Conversely, when the flexural rigidity is linearly decreased in an airport edge part, the wave reflectance was reduced for both airplanes. Second, when the still water depth under an airport runway is partially reduced at the location where floating body waves are amplified, the wave heights of floating body waves tended to decrease as the still water depth in the shallower area decreased. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

14 pages, 3512 KiB

Open AccessArticle

Development of a Methodology for Railway Bolster Beam Design Enhancement Using Topological Optimization and Manufacturing Constraints

by Alessio Cascino, Enrico Meli and Andrea Rindi

Eng 2024, 5(3), 1485-1498; https://doi.org/10.3390/eng5030079 - 19 Jul 2024

Viewed by 711

Abstract

Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicle components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries that are easier to design and produce combined with innovative materials. [...] Read more.

Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicle components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries that are easier to design and produce combined with innovative materials. In this framework, the present paper proposes the development of a design methodology to innovate a railway bolster beam using topological optimization techniques, assessing the effect of different manufacturing constraints oriented to the casting process. A comprehensive numerical testing campaign was conducted to establish an effective testing procedure. Two different designs were obtained and compared, statically and dynamically, evaluating the difference in terms of mass, mechanical performance and manufacturability. Reductions in stress values up to 70% were observed, along with an 8% increase in the first natural frequency of the component, leading to beneficial effects in terms of stiffness. The methodology shows encouraging results to streamline the design of complex casting components, moving to a new generation of structural railway components. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

16 pages, 2584 KiB

Open AccessArticle

Correlation Analysis between Young Driver Characteristics and Visual/Physiological Attributes at Expressway Exit Ramp

by Zeng’an Wang, Xinyue Qi, Chenzhu Wang, Said M. Easa, Fei Chen and Jianchuan Cheng

Eng 2024, 5(3), 1435-1450; https://doi.org/10.3390/eng5030076 - 12 Jul 2024

Viewed by 529

Abstract

More collisions occur at the exit ramps of expressways due to frequent lane-changing behavior and interweaving between vehicles. Young drivers with shorter driving mileage and driving experience, radical driving styles, and worse behavior prediction are likelier to be involved in collisions at the [...] Read more.

More collisions occur at the exit ramps of expressways due to frequent lane-changing behavior and interweaving between vehicles. Young drivers with shorter driving mileage and driving experience, radical driving styles, and worse behavior prediction are likelier to be involved in collisions at the exit ramps. This paper focuses on the correlation analysis between young drivers’ characteristics and their visual and physiological attributes at expressway exit ramps. First, the driver’s gender, driving experience, and mileage are classified. Then, seven expressway exit models are established using the UC/Win road modeling software. The driver’s driving plane vision is divided into four areas using the K-means clustering algorithm. In addition, the driver’s visual and heart rate attributes were analyzed at 500 m, 300 m, 200 m, and 100 m away from an expressway exit. The results show that the visual attributes, gender, and driving mileage of young drivers strongly correlate with the fixation times and average saccade amplitude. In contrast, the driving experience has almost no correlation with the fixation behavior of young drivers. Young drivers’ driving experience and mileage strongly correlate with cardiac physiological attributes, but there is virtually no correlation with gender. The practical implications of these results should be helpful to highway planners and designers. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

13 pages, 2736 KiB

Open AccessArticle

Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning

by Petros Paplomatas, Dimitris Rigas, Athanasia Sergounioti and Aristidis Vrahatis

Eng 2024, 5(3), 1422-1434; https://doi.org/10.3390/eng5030075 - 10 Jul 2024

Viewed by 1018

Abstract

The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models [...] Read more.

The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

18 pages, 7896 KiB

Open AccessArticle

An Investigation of Increased Power Transmission Capabilities of Elastic–Plastic-Designed Press–Fit Connections Using a Detachable Joining Device

by Jan Falter, Daniel Herburger, Hansgeorg Binz and Matthias Kreimeyer

Eng 2024, 5(3), 1155-1172; https://doi.org/10.3390/eng5030063 - 21 Jun 2024

Viewed by 1029

Abstract

Drive systems are an important part of general mechanical engineering, automotive engineering, and various other fields, with shaft–hub connections being an important part of such systems. Decisive aspects in the development of such systems today are, for example, high transmittable forces and torques, [...] Read more.

Drive systems are an important part of general mechanical engineering, automotive engineering, and various other fields, with shaft–hub connections being an important part of such systems. Decisive aspects in the development of such systems today are, for example, high transmittable forces and torques, low masses, and the cheapest possible production of components. A possibly threefold increase in the force and torque transmission capacity can be achieved by using press–fit connections with an elastic–plastic design as opposed to regular elastically designed alternatives. An elastic–plastic design of the press–fit connection is achieved by using a large interference. A large transition geometry on the shaft (which replaces the conventional chamfer) is required to join such an interference. The material and space requirements have a negative impact on lightweight applications and limited building spaces. Therefore, the objective of the research presented in this paper is to design and analyze a detachable joining device that substitutes this geometry. A simulation study was conducted to determine the geometry of the joining device that improves the stress state and consequently the force and torque transmission capacity of the connection. Moreover, the influence of manufacturing tolerances of the joining device and the shaft, corresponding risks, and measures to mitigate them are analyzed using finite element analysis. The results show that large transition radii, enabled by using a joining device, lead to a homogenous distribution of plastic strain and pressure in the press–fit connection, even for large interferences ξ and soft hub materials like wrought aluminum alloys. The influence of manufacturing tolerances on the stress state was quantified, leading to design guidelines that minimize the risk of, e.g., the front face collision of a shaft and hub, while maximizing the power transmission of the connection. The results show the capability of a detachable joining device to enable elastic–plastic press–fit connections and the corresponding threefold increase in the force and torque transmission capacity in lightweight applications, resulting from the substitution of the installation space consuming and mass increasing the transition geometry of the shaft. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

16 pages, 3505 KiB

Open AccessArticle

Assessing the Suitability of Automation Using the Methods–Time–Measurement Basic System

by Malte Jakschik, Felix Endemann, Patrick Adler, Lennart Lamers and Bernd Kuhlenkötter

Eng 2024, 5(2), 967-982; https://doi.org/10.3390/eng5020053 - 24 May 2024

Viewed by 1017

Abstract

Due to its high complexity and the varied assembly processes, hybrid assembly systems characterized by human–robot collaboration (HRC) are meaningful. Suitable use cases must be identified efficiently to ensure cost-effectiveness and successful deployment in the respective assembly systems. This paper presents a method [...] Read more.

Due to its high complexity and the varied assembly processes, hybrid assembly systems characterized by human–robot collaboration (HRC) are meaningful. Suitable use cases must be identified efficiently to ensure cost-effectiveness and successful deployment in the respective assembly systems. This paper presents a method for evaluating the potential of HRC to derive automation suitability based on existing or to-be-collected time data. This should enable a quick and favorable statement to be made about processes, for efficient application in potential analyses. The method is based on the Methods–Time–Measurement Basic System (MTM-1) procedure, widely used in the industry. This ensures good adaptability in an industrial context. It extends existing models and examines how much assembly activities and processes can be optimized by efficiently allocating between humans and robots. In the process model, the assembly processes are subdivided and analyzed with the help of the specified MTM motion time system. The suitability of the individual activities and sub-processes for automation are evaluated based on criteria derived from existing methods. Two four-field matrices were used to interpret and classify the analysis results. The process is assessed using an example product from electrolyzer production, which is currently mainly assembled by hand. To achieve high statement reliability, further work is required to classify the results comprehensively. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

17 pages, 3468 KiB

Open AccessArticle

Effects of Initial Small-Scale Material Nonlinearity on the Pre-Yield and Pre-Buckling Response of an Externally Pressurized Ring

by Reaz A. Chaudhuri and Deokjoo Kim

Eng 2024, 5(2), 733-749; https://doi.org/10.3390/eng5020040 - 30 Apr 2024

Viewed by 628

Abstract

The effects of initial small-scale material nonlinearity on the pre-yield and pre-buckling response of externally pressurized metallic (plane strain) perfect rings (very long cylindrical shells) is investigated. The cylindrically curved 16-node element, based on an assumed quadratic displacement field (in surface-parallel coordinates) and [...] Read more.

The effects of initial small-scale material nonlinearity on the pre-yield and pre-buckling response of externally pressurized metallic (plane strain) perfect rings (very long cylindrical shells) is investigated. The cylindrically curved 16-node element, based on an assumed quadratic displacement field (in surface-parallel coordinates) and the assumption of linear distribution of displacements through thickness (LDT), is employed to obtain the discretized system equations. The effect of initial small-scale material nonlinearity (assumed hypo-elastic) on the deformation and stress in the pre-yield and pre-buckling regime of a very long relatively thin metallic cylindrical shell (plane strain ring) is numerically investigated. These numerical results demonstrate that the enhanced responses for metallic rings due to initial small-scale nonlinearity are significant enough to not miss attentions from designers and operators of submersibles alike. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

20 pages, 7186 KiB

Open AccessArticle

Numerical Analysis of Bearing Capacity in Deep Excavation Support Structures: A Comparative Study of Nailing Systems and Helical Anchors

by Seyyed Alireza Taghavi, Farhad Mahmoudi Jalali, Reza Moezzi, Reza Yeganeh Khaksar, Stanisław Wacławek, Mohammad Gheibi and Andres Annuk

Eng 2024, 5(2), 657-676; https://doi.org/10.3390/eng5020037 - 18 Apr 2024

Cited by 1 | Viewed by 1402

Abstract

The increasing demand for deep excavations in construction projects emphasizes the necessity of robust support structures to ensure safety and stability. Support structures are critical in stabilizing excavation pits, with a primary focus on enhancing their bearing capacity. This paper employs finite element [...] Read more.

The increasing demand for deep excavations in construction projects emphasizes the necessity of robust support structures to ensure safety and stability. Support structures are critical in stabilizing excavation pits, with a primary focus on enhancing their bearing capacity. This paper employs finite element modeling techniques to conduct a numerical analysis of nails and helical anchors’ bearing capacity. To reinforce the stability of pit walls, selecting an appropriate method for guard structure construction is imperative. The chosen method should efficiently redistribute forces induced by soil mass weight, displacements, and potential loads in the pit vicinity to the ground. Various techniques, including trusses, piles, cross-bracing systems, nailing, and anchorage systems, are utilized for this purpose. The study evaluates numerical models for two guard structure configurations: nailing systems and helical anchorage. It examines the impact of parameters such as displacement, helical helix count, helix diameter variations, and the integration of nailing systems with helices. Comparative analyses are conducted, including displacement comparisons between different nailing systems and helical anchor systems, along with laboratory-sampled data. The research yields significant insights, with a notable finding highlighting the superior performance of helical bracings compared to nailing systems. The conclusions drawn from this study provide specific outcomes that contribute valuable knowledge to the field of deep excavation support structures, guiding future design and implementation practices. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

12 pages, 5756 KiB

Open AccessArticle

Investigating Collaborative Robotic Assembly: A Case Study of the FANUC CRX-10 iA/L in Industrial Automation at i-Labs

by Albin Bajrami, Daniele Costa, Matteo Claudio Palpacelli and Federico Emiliani

Eng 2024, 5(2), 532-543; https://doi.org/10.3390/eng5020029 - 22 Mar 2024

Cited by 4 | Viewed by 1299

Abstract

This study examines the practicality and limitations of using a FANUC CRX-10 iA/l collaborative robot to assemble a product component, highlighting the trade-offs between increased robotization and reduced manual intervention. Through a detailed case study in the i-Labs laboratory, critical factors affecting precision [...] Read more.

This study examines the practicality and limitations of using a FANUC CRX-10 iA/l collaborative robot to assemble a product component, highlighting the trade-offs between increased robotization and reduced manual intervention. Through a detailed case study in the i-Labs laboratory, critical factors affecting precision assembly such as station layout, tooling design and robot programming are discussed. The findings highlight the benefits of robots for nonstop operation, freeing up human operators for higher value tasks despite longer cycle times. In addition, the paper advocates further research into reliable gripping of small components, a current challenge for robotics. The work contributes to open science by sharing partial results and methods that could inform future problem solving in robotic assembly. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

19 pages, 6607 KiB

Open AccessArticle

Process Mining Organization (PMO) Based on Machine Learning Decision Making for Prevention of Chronic Diseases

by Angelo Rosa and Alessandro Massaro

Eng 2024, 5(1), 282-300; https://doi.org/10.3390/eng5010015 - 5 Feb 2024

Viewed by 1334

Abstract

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic [...] Read more.

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

20 pages, 6122 KiB

Open AccessArticle

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

by Brijesh Patel, Varsha Dubey, Snehlata Barde and Nidhi Sharma

Eng 2024, 5(1), 246-265; https://doi.org/10.3390/eng5010013 - 28 Jan 2024

Viewed by 1268

Abstract

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In [...] Read more.

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

Review

Jump to: Research, Other

12 pages, 430 KiB

Open AccessReview

Exploring the Frontier of 3D Bioprinting for Tendon Regeneration: A Review

by Josée Rosset, Emmanuel Olaniyanu, Kevin Stein, Nátaly Domingues Almeida and Rodrigo França

Eng 2024, 5(3), 1838-1849; https://doi.org/10.3390/eng5030098 - 7 Aug 2024

Cited by 1 | Viewed by 844

Abstract

The technology of 3D bioprinting has sparked interest in improving tendon repair and regeneration, promoting quality of life. To perform this procedure, surgical intervention is often necessary to restore functional capacity. In this way, 3D bioprinting offers a scaffold design, producing tendons with [...] Read more.

The technology of 3D bioprinting has sparked interest in improving tendon repair and regeneration, promoting quality of life. To perform this procedure, surgical intervention is often necessary to restore functional capacity. In this way, 3D bioprinting offers a scaffold design, producing tendons with precise microarchitectures, promoting the growth of new tissues. Furthermore, it may incorporate bioactive compounds that can further stimulate repair. This review elucidates how 3D bioprinting holds promise for tendon repair and regeneration, detailing the steps involved and the various approaches employed. They demonstrate future challenges and perspectives and provide valuable information on the concept, bioprinting design, and 3D bioprinting techniques for the repair of tendon injuries. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

28 pages, 8165 KiB

Open AccessReview

Bone Drilling: Review with Lab Case Study of Bone Layer Classification Using Vibration Signal and Deep Learning Methods

by Wahyu Caesarendra

Eng 2024, 5(3), 1566-1593; https://doi.org/10.3390/eng5030083 - 23 Jul 2024

Viewed by 1522

Abstract

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling [...] Read more.

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling task depends on the orthopedist and surgeon’s high level of skill and experience. This paper aimed to provide a summary of previously published review studies in the field of bone drilling. This review paper also presents a comprehensive review of the application of machine learning for bone drilling and as a future direction for automation systems. This review can also help medical surgeons and bone drillers understand the latest improvements through parameter selection and optimization strategies to reduce bone damage in bone drilling procedures. Apart from the review, bone drilling vibration data collected in a university laboratory experiment is also presented in this study. The vibration data consist of three different layers of femur cow bone, which are processed and classified using several deep learning (DL) methods such as long short-term memory (LSTM), convolutional neural network (CNN), and recurrent neural network (RNN). These DL methods are used in the bone drilling lab case study to prove that the layers of bone drilling are associated with the vibration signal and that they can be classified and predicted using DL methods. The result shows that LSTM is outperformed by CNN and RNN. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

21 pages, 1443 KiB

Open AccessReview

Machine and Deep Learning Trends in EEG-Based Detection and Diagnosis of Alzheimer’s Disease: A Systematic Review

by Marcos Aviles, Luz María Sánchez-Reyes, José Manuel Álvarez-Alvarado and Juvenal Rodríguez-Reséndiz

Eng 2024, 5(3), 1464-1484; https://doi.org/10.3390/eng5030078 - 16 Jul 2024

Cited by 1 | Viewed by 1576

Abstract

This article presents a systematic review using PRISMA methodology to explore trends in the use of machine and deep learning in diagnosing and detecting Alzheimer’s disease using electroencephalography. This review covers studies published between 2013 and 2023, drawing on three leading academic databases: [...] Read more.

This article presents a systematic review using PRISMA methodology to explore trends in the use of machine and deep learning in diagnosing and detecting Alzheimer’s disease using electroencephalography. This review covers studies published between 2013 and 2023, drawing on three leading academic databases: Scopus, Web of Science, and PubMed. The validity of the databases is evaluated considering essential factors such as the arrangement of EEG electrodes, data acquisition methodologies, and the number of participants. Additionally, the specific properties of the databases used in the research are highlighted, including EEG signal classification, filtering, segmentation approaches, and selected features. Finally, the performance metrics of the classification algorithms are evaluated, especially the accuracy achieved, offering a comprehensive view of the current state and future trends in the use of these technologies for the diagnosis of Alzheimer’s disease. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

32 pages, 4267 KiB

Open AccessReview

A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions

by Leonidas Theodorakopoulos, Alexandra Theodoropoulou and Yannis Stamatiou

Eng 2024, 5(3), 1266-1297; https://doi.org/10.3390/eng5030068 - 3 Jul 2024

Cited by 2 | Viewed by 3546

Abstract

The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough [...] Read more.

The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

43 pages, 26325 KiB

Open AccessReview

Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review

by Shree O. Bade, Ajan Meenakshisundaram and Olusegun S. Tomomewo

Eng 2024, 5(2), 677-719; https://doi.org/10.3390/eng5020038 - 18 Apr 2024

Cited by 4 | Viewed by 2591

Abstract

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, [...] Read more.

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

Other

Jump to: Research, Review

15 pages, 1238 KiB

Open AccessPerspective

Decarbonizing Nitrogen Fertilizer for Agriculture with Nonthermal Plasma Technology

by Xiaofei Philip Ye

Eng 2024, 5(3), 1823-1837; https://doi.org/10.3390/eng5030097 - 7 Aug 2024

Cited by 2 | Viewed by 1169

Abstract

Synthetic nitrogen fertilizer is the backbone of modern agriculture, helping to feed ~50% of the world’s population. However, the current industrial production, distribution, and use of nitrogen fertilizers are built on an unsustainable foundation of fossil resources, and are energy-intensive, environmentally polluting, and [...] Read more.

Synthetic nitrogen fertilizer is the backbone of modern agriculture, helping to feed ~50% of the world’s population. However, the current industrial production, distribution, and use of nitrogen fertilizers are built on an unsustainable foundation of fossil resources, and are energy-intensive, environmentally polluting, and inefficient in their usage. With the rapidly declining cost of renewable electricity, such as solar and wind, it is time to develop and implement the decentralized production and application of nitrogen fertilizer with nonthermal plasma technologies. Such locally sourced production at the farm site, using only air and water as feedstock, circumvents the need for the extensive capital investment and infrastructure required for synthetic nitrogen fertilizer production and storage, as well as the complex and costly distribution networks. It will be adaptive to the intermittency of the solar/wind electricity supply, leave no carbon footprint, and also have the advantage of being easily switched on/off, immediately responding to weather changes and local conditions, such as soil, climate, crops, and farming business models, for precision agriculture. Full article

(This article belongs to the Special Issue Feature Papers in Eng 2024)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Feature Papers in Eng 2024

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (33 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI