Eng | Special Issue : Feature Papers in Eng 2024

Research

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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

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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

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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

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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

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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

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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

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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 260

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

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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 247

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

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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 473

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

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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 668

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

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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 508

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

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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 445

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

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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

Viewed by 966

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

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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

Viewed by 840

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

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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 977

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

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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 908

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

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Review

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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 350

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

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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

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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

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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

Viewed by 754

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

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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 1 | Viewed by 1508

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)

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