Review Papers Collection for Advanced Technologies
A topical collection in Technologies (ISSN 2227-7080).
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Editor
Prof. Dr. Manoj Gupta
Prof. Dr. Manoj Gupta
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Collection Editor
Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore
Interests: metal additive manufacturing; processing; characterization; lightweight materials; nanocomposites
Special Issues, Collections and Topics in MDPI journals
Topical Collection Information
Dear Colleagues,
Technology is the backbone of advancement of society, providing welfare to humans. Most advanced and prosperous countries are technology savvy. The area of technology is very broad, ranging from old and currently relevant materials and manufacturing technologies to the latest technologies, such as those related with artificial intelligence. Thousands of papers are published in each area of technology every year, and it is extremely difficult for researchers to keep pace with recent advancements due to time or access constraints. A logical and amicable solution is to encourage publication of review articles in open access so that they will be accessible to researchers worldwide. In view of this, an attempt is made here to create a devoted section in Technologies that seeks to publish review articles of topical importance. In line with the aims and scope of the journal, review articles in the following areas will be most welcome:
- Internet of Things;
- Neural networks;
- Machine learning;
- Advances in materials science;
- Assistive technologies (adaptive device, healthcare, robotics, wearable, etc.)
- Advances in electronics;
- Environmental technologies (environmental monitoring, model and conserve, etc.);
- Medical technologies (diagnosing, monitoring and treating, etc.).
Prof. Dr. Manoj Gupta
Collection 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 collection 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. Technologies is an international peer-reviewed open access monthly 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 1600 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
- Internet of Things
- neural networks
- machine learning
- advances in materials science
- assistive technologies
- advances in electronics
- environmental technologies
- medical technologies
Published Papers (9 papers)
Open AccessReview
Influence of Insulin Pen Needle Geometry on Pain Perception and Patient’s Acceptability: A Review
by
Francesca De Tommasi and Sergio Silvestri
Viewed by 431
Abstract
Diabetes is one of the most common diseases worldwide, with an increasing number of people affected. Insulin therapy is still the major treatment for both Type 1 and Type 2 diabetes and has evolved from bulky syringes to modern insulin pens introduced in
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Diabetes is one of the most common diseases worldwide, with an increasing number of people affected. Insulin therapy is still the major treatment for both Type 1 and Type 2 diabetes and has evolved from bulky syringes to modern insulin pens introduced in 1985. An insulin pen consists of three major parts: a cartridge, a single-use pen needle (PN), and a precision dosing mechanism. Initially, PNs were long and thick, causing great discomfort and concern. Thanks to advances in design, shorter and thinner needles have appeared on the market, improving patient acceptability and pain perception. Studies highlight the influence of PN geometry and other characteristics on injection-related pain, including length, diameter, bevel design, and hub. Despite a lack of specific international regulations for PN geometry, scientific publications have focused on exploring different PNs’ characteristics to optimize patient comfort and reduce pain. To guide the selection of suitable PNs, this review provides a round-up of literature research findings on the impact of PN geometry on pain perception and patient acceptability. Specifically, it provides an overview of the PN manufacturing process, current international regulations, and the state-of-the-art research on PN geometry affecting pain perception.
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Open AccessReview
Revolutionary Integration of Artificial Intelligence with Meta-Optics-Focus on Metalenses for Imaging
by
Nikolay L. Kazanskiy, Svetlana N. Khonina, Ivan V. Oseledets, Artem V. Nikonorov and Muhammad A. Butt
Cited by 1 | Viewed by 2941
Abstract
Artificial intelligence (AI) significantly enhances the development of Meta-Optics (MOs), which encompasses advanced optical components like metalenses and metasurfaces designed to manipulate light at the nanoscale. The intricate design of these components requires sophisticated modeling and optimization to achieve precise control over light
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Artificial intelligence (AI) significantly enhances the development of Meta-Optics (MOs), which encompasses advanced optical components like metalenses and metasurfaces designed to manipulate light at the nanoscale. The intricate design of these components requires sophisticated modeling and optimization to achieve precise control over light behavior, tasks for which AI is exceptionally well-suited. Machine learning (ML) algorithms can analyze extensive datasets and simulate numerous design variations to identify the most effective configurations, drastically speeding up the development process. AI also enables adaptive MOs that can dynamically adjust to changing imaging conditions, improving performance in real-time. This results in superior image quality, higher resolution, and new functionalities across various applications, including microscopy, medical diagnostics, and consumer electronics. The combination of AI with MOs thus epitomizes a transformative advancement, pushing the boundaries of what is possible in imaging technology. In this review, we explored the latest advancements in AI-powered metalenses for imaging applications.
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Open AccessReview
Technology in Forensic Sciences: Innovation and Precision
by
Xavier Chango, Omar Flor-Unda, Pedro Gil-Jiménez and Hilario Gómez-Moreno
Cited by 1 | Viewed by 9433
Abstract
The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing,
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The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA
® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor & Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator’s prominence and responsibility in the resolution of cases.
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Open AccessReview
Analysis of the Use of Artificial Intelligence in Software-Defined Intelligent Networks: A Survey
by
Bayron Jesit Ospina Cifuentes, Álvaro Suárez, Vanessa García Pineda, Ricardo Alvarado Jaimes, Alber Oswaldo Montoya Benitez and Juan David Grajales Bustamante
Cited by 1 | Viewed by 2584
Abstract
The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve
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The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.
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Open AccessReview
Energy Efficiency in Additive Manufacturing: Condensed Review
by
Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra and Khalid Tantawi
Cited by 7 | Viewed by 4313
Abstract
Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the
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Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community.
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Open AccessFeature PaperReview
A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision
by
Nikoleta Manakitsa, George S. Maraslidis, Lazaros Moysis and George F. Fragulis
Cited by 24 | Viewed by 20409
Abstract
Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics,
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Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.
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Open AccessReview
Recent Technological Progress of Fiber-Optical Sensors for Bio-Mechatronics Applications
by
Mohomad Aqeel Abdhul Rahuman, Nipun Shantha Kahatapitiya, Viraj Niroshan Amarakoon, Udaya Wijenayake, Bhagya Nathali Silva, Mansik Jeon, Jeehyun Kim, Naresh Kumar Ravichandran and Ruchire Eranga Wijesinghe
Cited by 9 | Viewed by 3614
Abstract
Bio-mechatronics is an interdisciplinary scientific field that emphasizes the integration of biology and mechatronics to discover innovative solutions for numerous biomedical applications. The broad application spectrum of bio-mechatronics consists of minimally invasive surgeries, rehabilitation, development of prosthetics, and soft wearables to find engineering
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Bio-mechatronics is an interdisciplinary scientific field that emphasizes the integration of biology and mechatronics to discover innovative solutions for numerous biomedical applications. The broad application spectrum of bio-mechatronics consists of minimally invasive surgeries, rehabilitation, development of prosthetics, and soft wearables to find engineering solutions for the human body. Fiber-optic-based sensors have recently become an indispensable part of bio-mechatronics systems, which are essential for position detection and control, monitoring measurements, compliance control, and various feedback applications. As a result, significant advancements have been introduced for designing and developing fiber-optic-based sensors in the past decade. This review discusses recent technological advancements in fiber-optical sensors, which have been potentially adapted for numerous bio-mechatronic applications. It also encompasses fundamental principles, different types of fiber-optical sensors based on recent development strategies, and characterizations of fiber Bragg gratings, optical fiber force myography, polymer optical fibers, optical tactile sensors, and Fabry–Perot interferometric applications. Hence, robust knowledge can be obtained regarding the technological enhancements in fiber-optical sensors for bio-mechatronics-based interdisciplinary developments. Therefore, this review offers a comprehensive exploration of recent technological advances in fiber-optical sensors for bio-mechatronics. It provides insights into their potential to revolutionize biomedical and bio-mechatronics applications, ultimately contributing to improved patient outcomes and healthcare innovation.
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Open AccessReview
Advancements in Doping Strategies for Enhanced Photocatalysts and Adsorbents in Environmental Remediation
by
Pramita Sen, Praneel Bhattacharya, Gargi Mukherjee, Jumasri Ganguly, Berochan Marik, Devyani Thapliyal, Sarojini Verma, George D. Verros, Manvendra Singh Chauhan and Raj Kumar Arya
Cited by 12 | Viewed by 4761
Abstract
Environmental pollution poses a pressing global challenge, demanding innovative solutions for effective pollutant removal. Photocatalysts, particularly titanium dioxide (TiO
2), are renowned for their catalytic prowess; however, they often require ultraviolet light for activation. Researchers had turned to doping with metals and
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Environmental pollution poses a pressing global challenge, demanding innovative solutions for effective pollutant removal. Photocatalysts, particularly titanium dioxide (TiO
2), are renowned for their catalytic prowess; however, they often require ultraviolet light for activation. Researchers had turned to doping with metals and non-metals to extend their utility into the visible spectrum. While this approach shows promise, it also presents challenges such as material stability and dopant leaching. Co-doping, involving both metals and non-metals, has emerged as a viable strategy to mitigate these limitations. Inthe fieldof adsorbents, carbon-based materials doped with nitrogen are gaining attention for their improved adsorption capabilities and CO
2/N
2 selectivity. Nitrogen doping enhances surface area and fosters interactions between acidic CO
2 molecules and basic nitrogen functionalities. The optimal combination of an ultramicroporous surface area and specific nitrogen functional groups is key to achievehigh CO
2 uptake values and selectivity. The integration of photocatalysis and adsorption processes in doped materials has shown synergistic pollutant removal efficiency. Various synthesis methods, including sol–gel, co-precipitation, and hydrothermal approaches had been employed to create hybrid units of doped photocatalysts and adsorbents. While progress has been made in enhancing the performance of doped materials at the laboratory scale, challenges persist in transitioning these technologies to large-scale industrial applications. Rigorous studies are needed to investigate the impact of doping on material structure and stability, optimize process parameters, and assess performance in real-world industrial reactors. These advancements are promising foraddressing environmental pollution challenges, promoting sustainability, and paving the way for a cleaner and healthier future. This manuscript provides a comprehensive overview of recent developments in doping strategies for photocatalysts and adsorbents, offering insights into the potential of these materials to revolutionize environmental remediation technologies.
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Open AccessEditor’s ChoiceReview
Recent Advances in the 3D Printing of Pure Copper Functional Structures for Thermal Management Devices
by
Yue Hao Choong, Manickavasagam Krishnan and Manoj Gupta
Cited by 4 | Viewed by 3688
Abstract
Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around
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Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices.
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