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Feature Papers in Eng 2024
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Feature Papers in Eng 2024

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2764

Special Issue Editor

Prof. Dr. Antonio Gil Bravo

E-Mail Website
Guest Editor
INAMAT^2-Departamento de Ciencias, Edificio de los Acebos, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain
Interests: preparation, characterization, and catalytic activity of metal-supported catalysts; surface properties of solids; pollutants adsorption; environmental management; industrial waste valorization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the Editor-in-Chief of Eng, I am pleased to announce this Special Issue, entitled "Feature Papers in Eng 2024". This Special Issue will be a collection of high-quality reviews and original papers from editorial board members, guest editors, and leading researchers discussing new knowledge or new cutting-edge developments in the field of engineering. The potential topics include, but are not limited to:

  • Electrical, electronic, and information engineering;
  • Chemical and materials engineering;
  • Energy engineering;
  • Mechanical and automotive engineering;
  • Industrial and manufacturing engineering;
  • Civil and structural engineering;
  • Aerospace engineering;
  • Biomedical engineering;
  • Geotechnical engineering and engineering geology;
  • Ocean and environmental engineering.

We therefore very much look forward to your valued contributions to make this Special Issue a reference resource of essential knowledge for future researchers in the engineering field.

Prof. Dr. Antonio Gil Bravo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Eng is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrical, electronic, and information engineering
  • chemical and materials engineering
  • energy engineering
  • mechanical and automotive engineering
  • industrial and manufacturing engineering
  • civil and structural engineering
  • aerospace engineering
  • biomedical engineering
  • geotechnical engineering and engineering geology
  • ocean and environmental engineering

Published Papers (5 papers)

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Research

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20 pages, 7186 KiB  
Article
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 351
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)
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12 pages, 5756 KiB  
Article
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 443
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)
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19 pages, 6607 KiB  
Article
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 - 05 Feb 2024
Viewed by 646
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)
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20 pages, 6122 KiB  
Article
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 550
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)
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Review

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43 pages, 26325 KiB  
Review
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
Viewed by 457
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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