Journal Description
Inventions
Inventions
is an international, scientific, peer-reviewed, open access journal published bimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.4 days after submission; acceptance to publication is undertaken in 4.5 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.1 (2023);
5-Year Impact Factor:
2.7 (2023)
Latest Articles
Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Automatic Labeling Segmentation
Inventions 2024, 9(4), 69; https://doi.org/10.3390/inventions9040069 (registering DOI) - 28 Jun 2024
Abstract
Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation
[...] Read more.
Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots.
Full article
(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)
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Open AccessArticle
Design Principles for Laser-Printed Macrofluidics
by
Gilad Gome, Ofra Benny, Oded Shoseyov and Jonathan Giron
Inventions 2024, 9(4), 68; https://doi.org/10.3390/inventions9040068 - 26 Jun 2024
Abstract
This paper presents a novel method for fabricating fluidic circuits using laser printing technology. The method allows for rapid prototyping of macrofluidic devices with control over fluid manipulation and environmental conditions. We employed a high-resolution laser cutter to etch fluidic channels into various
[...] Read more.
This paper presents a novel method for fabricating fluidic circuits using laser printing technology. The method allows for rapid prototyping of macrofluidic devices with control over fluid manipulation and environmental conditions. We employed a high-resolution laser cutter to etch fluidic channels into various substrates, optimizing parameters such as laser power, speed, and substrate material. Our results demonstrate excellent performance in controlling fluid flow and maintaining environmental conditions, handling a wide range of fluids and flow rates. The devices were tested in multiple settings such as with high school students and in research laboratories in universities. We tested the laser-printed macrofluidcs mechanically for durability. We present previous works in microbiology with plants, microbial, and mammalian cell lines showing reliable operation with minimal leakage and consistent fluid dynamics. The versatility and scalability of this approach make it a promising tool for advancing research and innovation in fluidics, providing a robust platform for growing, manipulating, and experimenting with diverse biological systems from cells to whole organisms. We conclude that laser-printed macrofluidics can significantly contribute to fields such as biomedical research, synthetic biology, tissue engineering, and STEM education.
Full article
(This article belongs to the Section Inventions and Innovation in Biotechnology and Materials)
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Open AccessReview
Microrobots Based on Smart Materials with Their Manufacturing Methods and Applications
by
Jiawei Sun, Shuxiang Cai, Wenguang Yang, Huiwen Leng, Zhixing Ge and Tangying Liu
Inventions 2024, 9(3), 67; https://doi.org/10.3390/inventions9030067 - 14 Jun 2024
Abstract
In recent years, the field of microrobots has exploded, yielding many exciting new functions and applications, from object grasping and release to in vivo drug transport. Smart responsive materials have had a profound impact on the field of microrobots and have given them
[...] Read more.
In recent years, the field of microrobots has exploded, yielding many exciting new functions and applications, from object grasping and release to in vivo drug transport. Smart responsive materials have had a profound impact on the field of microrobots and have given them unique functions and structures. We analyze three aspects of microrobots, in which the future development of microrobots requires more efforts to be invested, and in which smart materials play a significant role in the development of microrobots. These three aspects are smart materials for building microrobots, manufacturing methods, and the functions and applications they achieve. In this review, we discuss the deformation mechanism of materials in response to external stimuli, starting from smart materials, and discuss fabrication methods to realize microrobots, laying the theoretical foundation for future smart material-based microrobots to realize their intelligence and programmability.
Full article
(This article belongs to the Section Inventions and Innovation in Biotechnology and Materials)
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Open AccessArticle
Dynamic Purkinje Meter as a Tool for Intraocular Lens Position Measurement
by
Eliska Palkovicova, Jiri Cendelin and Petr Kudrna
Inventions 2024, 9(3), 66; https://doi.org/10.3390/inventions9030066 - 10 Jun 2024
Abstract
Due to the increasing demands of today’s society on visual quality and patient comfort, and due to the growing interest in the implantation of new and more complex intraocular lens (IOL) designs, determining the IOL position occupies an important position in current ophthalmological
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Due to the increasing demands of today’s society on visual quality and patient comfort, and due to the growing interest in the implantation of new and more complex intraocular lens (IOL) designs, determining the IOL position occupies an important position in current ophthalmological practice. The dynamic Purkinje meter combines the construction of static Purkinje meters, presented in recent years, with dynamic examination of the IOL position according to the optical axis of the IOL.
Full article
(This article belongs to the Special Issue Inventions and Innovations in Optical Sensing Materials and Devices)
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Open AccessArticle
Control on Flow Separation over a Cylinder by a Ferrofluid Film Adsorbed by a Magnet
by
Chunyun Wei, Hongjia Xie, Zixuan Liu, Xinfeng Zhai, Hongna Zhang and Xiaobin Li
Inventions 2024, 9(3), 65; https://doi.org/10.3390/inventions9030065 - 4 Jun 2024
Abstract
Flow separation can lead to increased resistance and vibration generation, which is a difficult problem that cannot be ignored in engineering. In this paper, we propose a method of controlling flow separation by adsorbing ferrofluid onto the surface of a magnetized cylinder, taking
[...] Read more.
Flow separation can lead to increased resistance and vibration generation, which is a difficult problem that cannot be ignored in engineering. In this paper, we propose a method of controlling flow separation by adsorbing ferrofluid onto the surface of a magnetized cylinder, taking the common flow around a cylinder as an example. Parametric effects of the ferrofluid film, including its viscosity and thickness, on the flow behavior were investigated in terms of the vortex shedding process, velocity distribution, dominant frequency, pressure distribution, and the flow motion inside the ferrofluid film. The results indicate that the ferrofluid film can suppress the generation of flow separation and achieve effective control, which is mainly caused by wall slip and the internal movement of the ferrofluid film. Furthermore, the flow separation control effect of ferrofluid thin films with different parameters varies, with low-viscosity ferrofluid exhibiting a superior control effect.
Full article
(This article belongs to the Special Issue Innovative Research and Applications in Hydrodynamics and Flow Control, 2nd Edition)
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Open AccessArticle
Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading
by
Fernando Marcos de Oliveira, Marcelo Henrique Manzke Brandt, Fabiano Salvadori, José Enrique Eirez Izquierdo, Marco Roberto Cavallari and Oswaldo Hideo Ando Junior
Inventions 2024, 9(3), 64; https://doi.org/10.3390/inventions9030064 - 30 May 2024
Abstract
Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes
[...] Read more.
Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation.
Full article
(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 2nd Edition)
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Open AccessArticle
Enhancing Structural Capacity Assessment with a Novel Failure Decision Function for Rectangular Reinforced Concrete Columns
by
Petros Christou, Marios Charalambides, Demetris Nicolaides and Georgios Xekalakis
Inventions 2024, 9(3), 63; https://doi.org/10.3390/inventions9030063 - 29 May 2024
Abstract
This study introduces the Failure Decision Function, a novel approach for evaluating the structural capacity of rectangular reinforced concrete columns under axial forces and moments, both uniaxial and biaxial. The method simplifies existing practices, enhancing accuracy and integration into design software. The methodology
[...] Read more.
This study introduces the Failure Decision Function, a novel approach for evaluating the structural capacity of rectangular reinforced concrete columns under axial forces and moments, both uniaxial and biaxial. The method simplifies existing practices, enhancing accuracy and integration into design software. The methodology hinges on deriving exact biaxial bending failure surfaces, utilizing integral expressions based on material properties and cross-sectional geometry. This direct integration process uncovers failure surface characteristics previously undocumented. Results confirm the utility of the Failure Decision Function through comparative analysis with established literature, showcasing its potential for simplifying and improving structural capacity assessments. The analytic procedure developed enables efficient computation of failure surfaces, streamlining the inclusion of these functions in structural engineering software in two key ways: (1) compiling a library of pre-calculated functions for quick capacity checks and (2) creating a dynamic application that generates these functions based on specific design parameters, allowing users to explore various load and moment scenarios. In conclusion, the Failure Decision Function represents a significant advancement in structural engineering design, offering an accurate and user-friendly method for assessing column performance under critical loading conditions.
Full article
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)
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