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, Ei Compendex 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 21.2 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the first half of 2024).
- 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
Hierarchical Dynamic Spatio-Temporal Graph Convolutional Networks with Self-Supervised Learning for Traffic Flow Forecasting
Inventions 2024, 9(5), 102; https://doi.org/10.3390/inventions9050102 - 20 Sep 2024
Abstract
It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on
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It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on local geographic correlations, ignoring cross-region interdependencies in a global context, which is insufficient to extract comprehensive semantic relationships, thereby limiting prediction accuracy. Additionally, most GCN-based models rely on pre-defined graphs and unchanging adjacency matrices to reflect the spatial relationships among node features, neglecting the dynamics of spatio-temporal features and leading to challenges in capturing the complexity and dynamic spatial dependencies in traffic data. To tackle these issues, this paper puts forward a fresh approach: a new self-supervised dynamic spatio-temporal graph convolutional network (SDSC) for traffic flow forecasting. The proposed SDSC model is a hierarchically structured graph–neural architecture that is intended to augment the representation of dynamic traffic patterns through a self-supervised learning paradigm. Specifically, a dynamic graph is created using a combination of temporal, spatial, and traffic data; then, a regional graph is constructed based on geographic correlation using clustering to capture cross-regional interdependencies. In the feature learning module, spatio-temporal correlations in traffic data are subjected to recursive extraction using dynamic graph convolution facilitated by Recurrent Neural Networks (RNNs). Furthermore, self-supervised learning is embedded within the network training process as an auxiliary task, with the objective of enhancing the prediction task by optimising the mutual information of the learned features across the two graph networks. The superior performance of the proposed SDSC model in comparison with SOTA approaches was confirmed by comprehensive experiments conducted on real road datasets, PeMSD4 and PeMSD8. These findings validate the efficacy of dynamic graph modelling and self-supervision tasks in improving the precision of traffic flow prediction.
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(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)
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Open AccessReview
Review of Existing Tools for Software Implementation of Digital Twins in the Power Industry
by
Irina F. Iumanova, Pavel V. Matrenin and Alexandra I. Khalyasmaa
Inventions 2024, 9(5), 101; https://doi.org/10.3390/inventions9050101 - 19 Sep 2024
Abstract
Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization
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Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization of the operation of power systems and their components. Digital twins are used to address different issues, including the management of equipment reliability and efficiency, integration of renewable energy sources, and increased flexibility and adaptability of power grids. Digital twins can be developed with the use of specialized software solutions for designing, prototyping, developing, deploying, and supporting. The existing diversity of software requires systematization for a well-informed choice of digital twin’s development tool. It is necessary to take into account the technical characteristics of power systems and their elements (equipment of power plants, substations and power grids of power systems, mini- and microgrids). The reviews are dedicated to tools for creating digital twins in the power industry. The usage of Digital Twin Definition Language for the description data of electromagnetic, thermal, and hydrodynamic models of a power transformer is presented.
Full article
(This article belongs to the Special Issue From Sensing Technology towards Digital Twin in Applications, 2nd Edition)
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Open AccessArticle
Design of a Fiber Temperature and Strain Sensor Model Using a Fiber Bragg Grating to Monitor Road Surface Conditions
by
Gulzhan Kashaganova, Ainur Kozbakova, Timur Kartbayev, Kulzhan Togzhanova, Zhuldyz Alimseitova and Gani Sergazin
Inventions 2024, 9(5), 100; https://doi.org/10.3390/inventions9050100 - 13 Sep 2024
Abstract
In this paper, the types and principles of operation of fiber sensors based on fiber Bragg gratings (FBGs) are investigated. The influence of strain and temperature on the characteristics of FBGs is considered, and a method for the simultaneous measurement of these parameters
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In this paper, the types and principles of operation of fiber sensors based on fiber Bragg gratings (FBGs) are investigated. The influence of strain and temperature on the characteristics of FBGs is considered, and a method for the simultaneous measurement of these parameters is presented. Laboratory studies were carried out in the temperature range from +18 °C to +135 °C with an incremental step of 5 °C, with the actual temperature not deviating by more than ±0.5 °C. From the data obtained, the Bragg wavelength–temperature relationships were plotted, which showed a linear increase in wavelength with increasing temperature. This study shows that the use of two FBGs with a different sensitivity to temperature and strain allowed for the simultaneous measurement of both parameters. Numerical models created in the MATLAB R2022b environment confirmed the high accuracy and precision of the measurements. The FBG-based sensors demonstrated a robust performance in harsh environments, withstanding temperatures of up to 160 °C and high humidity, making them applicable in various industries and sciences. This work confirms that FBGs are a promising tool for accurate temperature and strain measurements, providing reliable results in harsh environments.
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(This article belongs to the Section Inventions and Innovation in Advanced Manufacturing)
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Open AccessEditorial
Innovative Research and Applications in Hydrodynamics and Flow Control
by
Peng Du, Haibao Hu and Xiaopeng Chen
Inventions 2024, 9(5), 99; https://doi.org/10.3390/inventions9050099 - 13 Sep 2024
Abstract
This work aims to collect cutting-edge developments in the area of hydrodynamics and flow control, including both fundamental and engineering research [...]
Full article
(This article belongs to the Special Issue Innovative Research and Applications in Hydrodynamics and Flow Control)
Open AccessArticle
Composite Modified Graphite Felt Anode for Iron–Chromium Redox Flow Battery
by
Sheng Wu, Haotian Zhu, Enrui Bai, Chongyang Xu, Xiaoyin Xie and Chuanyu Sun
Inventions 2024, 9(5), 98; https://doi.org/10.3390/inventions9050098 - 9 Sep 2024
Abstract
The iron–chromium redox flow battery (ICRFB) has a wide range of applications in the field of new energy storage due to its low cost and environmental protection. Graphite felt (GF) is often used as the electrode. However, the hydrophilicity and electrochemical activity of
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The iron–chromium redox flow battery (ICRFB) has a wide range of applications in the field of new energy storage due to its low cost and environmental protection. Graphite felt (GF) is often used as the electrode. However, the hydrophilicity and electrochemical activity of GF are poor, and its reaction reversibility to Cr3+/Cr2+ is worse than Fe2+/Fe3+, which leads to the hydrogen evolution side reaction of the negative electrode and affects the efficiency of the battery. In this study, the optimal composite modified GF (Bi-Bio-GF-O) electrode was prepared by using the optimal pomelo peel powder modified GF (Bio-GF-O) as the matrix and further introducing Bi3+. The electrochemical performance and material characterization of the modified electrode were analyzed. In addition, using Bio-GF-O as the positive electrode and Bi-Bio-GF-O as the negative electrode, the high efficiency of ICRFB is realized, and the capacity attenuation is minimal. When the current density is 100 mA·cm−2, after 100 cycles, the coulomb efficiency (CE), voltage efficiency (VE), and energy efficiency (EE) were 97.83%, 85.21%, and 83.36%, respectively. In this paper, the use of pomelo peel powder and Bi3+ composite modified GF not only promotes the electrochemical performance and reaction reversibility of the negative electrode but also improves the performance of ICRFB. Moreover, the cost of the method is controllable, and the process is simple.
Full article
(This article belongs to the Special Issue Advanced Electrode Material for Electrochemical Production Conversion and Storage of Energy)
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Open AccessArticle
Pressure Capacity Assessment of L-PBF-Produced Microchannel Heat Exchangers
by
Jiabao An, Luyao Guo, Junjia Zou, Keliang Zhang, Yiheng Zhong, Taimingwang Liu, Long Huang and Yi Chen
Inventions 2024, 9(5), 97; https://doi.org/10.3390/inventions9050097 - 6 Sep 2024
Abstract
Laser powder bed fusion (L-PBF) manufacturing technology is an emerging field of research that focuses on evaluating constraints in printed products. This study highlights the importance of considering various factors, such as mechanical properties and support structures, during the design phase, particularly in
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Laser powder bed fusion (L-PBF) manufacturing technology is an emerging field of research that focuses on evaluating constraints in printed products. This study highlights the importance of considering various factors, such as mechanical properties and support structures, during the design phase, particularly in the context of microchannel heat exchangers where all limiting factors are critical. This paper presents a methodology for analyzing channel pressure limitations and examines the impact of pipe porosity on the loss of mechanical properties. A combination of simulation experiments and pressure capacity tests is used to elucidate the pressure distribution characteristics of microchannel flat tubes and their true pressure capacity. This study also explores potential methods for improving the performance of L-PBF-printed microchannel flat tubes. The results and the development of the experimental setup are summarized.
Full article
(This article belongs to the Special Issue Revolutionizing Manufacturing: Advances in Additive Manufacturing Technologies)
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Open AccessArticle
Exploring the Inhibitory Efficacy of Resokaempferol and Tectochrysin on PI3Kα Protein by Combining DFT and Molecular Docking against Wild-Type and H1047R Mutant Forms
by
Cristina Paraschiv, Steluța Gosav, Catalina Mercedes Burlacu and Mirela Praisler
Inventions 2024, 9(5), 96; https://doi.org/10.3390/inventions9050096 - 5 Sep 2024
Abstract
This study explores the inhibitory potential of the flavonoids resokaempferol and tectochrysin against both wild-type and H1047R mutant forms of PI3Kα, aiming to expand the repertoire of targeted cancer therapies. Employing an array of computational techniques, including Density Functional Theory (DFT), calculations of
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This study explores the inhibitory potential of the flavonoids resokaempferol and tectochrysin against both wild-type and H1047R mutant forms of PI3Kα, aiming to expand the repertoire of targeted cancer therapies. Employing an array of computational techniques, including Density Functional Theory (DFT), calculations of electronic parameters such as the energies of the frontier molecular orbitals, Molecular Electrostatic Potential (MEP) mapping, and Molecular Docking, we investigate in detail the molecular interactions of these compounds with the PI3Kα kinase. Our findings, corroborated by DFT calculations performed based on the B3LYP (Becke, three-parameter, Lee-Yang-Parr) hybrid functional and the 6-311G++(d,p) basis set, align well with experimental benchmarks and indicate substantial inhibitory efficacy. Further analysis of chemical potential and bioavailability confirmed the drug-like attributes of these flavonoids. Binding affinity and selectivity were rigorously assessed through self-docking and cross-docking against the PIK3CA PDB structures 7K71 and 8TS9. The most promising interactions were validated using Pairwise Structure Alignment and MolProbity analysis of all-atom contacts and geometry. Collectively, these results highlight the flavonoids’ potential as PI3Kα inhibitors and exemplify the utility of natural compounds in the development of precise anticancer treatments.
Full article
(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)
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Open AccessArticle
Anisotropic k-ϵ Model Based on General Principles of Statistical Turbulence
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J. J. H. Brouwers
Inventions 2024, 9(5), 95; https://doi.org/10.3390/inventions9050095 - 29 Aug 2024
Abstract
An upgrade is presented of a recently published model for the calculation of statistical averages of turbulent flow variables. Instead of empirical constructions, important parts of the model are based on general principles of statistical turbulence and physics. The upgrade concerns transparent and
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An upgrade is presented of a recently published model for the calculation of statistical averages of turbulent flow variables. Instead of empirical constructions, important parts of the model are based on general principles of statistical turbulence and physics. The upgrade concerns transparent and simplified descriptions of turbulent diffusion and Reynolds stresses which express their dependency of mean flow gradients in a direct manner. As before, prediction comparisons are satisfactory in relation to the results of DNS of channel flow. Implementation in a CFD code is straightforward and its application provides a significant improvement to the results of the widely used empirical basic k- model.
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 AccessReview
CREC Optical-Fibre Sensors for Hydrodynamic Studies in Gas−Solid Fluidized Beds
by
Nicolas Torres Brauer, Cesar Medina-Pedraza and Hugo de Lasa
Inventions 2024, 9(5), 94; https://doi.org/10.3390/inventions9050094 - 28 Aug 2024
Abstract
Optical probes can be employed in dense and dilute fluidized beds. Their application is useful to determine particle volume fraction, bubble velocity, bubble size, and solid segregation in dense-phase fluidized-bed reactors, as well as particle-cluster velocity, size, and shape, in downer/riser units. The
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Optical probes can be employed in dense and dilute fluidized beds. Their application is useful to determine particle volume fraction, bubble velocity, bubble size, and solid segregation in dense-phase fluidized-bed reactors, as well as particle-cluster velocity, size, and shape, in downer/riser units. The CREC-UWO team has developed a unique and miniaturized CREC Optiprobes System (CREC-GS-OPS) equipped with a GRIN (graded refraction index) lens. The GRIN lens creates a small volume of high light irradiation by focusing a laser a few millimetres away from the front of the probe tip. This design minimizes sensor intrusiveness and, as a result, provides trustworthy measurements of hydrodynamic parameters. Through the application of the CREC-GS-OPS, advances have been achieved, leading to (a) the development of a “Y-back” unit with graphite ferrules that protects the optiprobes from fibre-optic stresses and prevents the loss of sensor calibration and (b) the establishment of statistically-based data analysis. It is envisioned that through the introduction of a few design changes, the CREC Optiprobes will be made suitable for high-temperature applications. This will allow the measurement of catalyst flow recirculation (among other measurements), in industrial-scale fluidized-bed catalytic cracking units involving fluidized riser crackers and catalyst regenerators.
Full article
(This article belongs to the Special Issue Inventions and Innovations in Optical Sensing Materials and Devices)
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Open AccessReview
A Study on the Impact of Different PV Model Parameters and Various DC Faults on the Characteristics and Performance of the Photovoltaic Arrays
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Khaled Ibrahim Baradieh, Muhammad Ammirrul Atiqi Mohd Zainuri, Nor Azwan Mohamed Kamari, Huda Abdullah, Yushaizad Yusof, Mohd Asyraf Zulkifley and Mohsin Ali Koondhar
Inventions 2024, 9(5), 93; https://doi.org/10.3390/inventions9050093 - 27 Aug 2024
Abstract
PV systems play a vital role in the global renewable energy sector, and they require accurate modeling and reliable performance to maximize the output power. This research presents a thorough analysis and discussions on the effects of different PV models’ parameters and certain
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PV systems play a vital role in the global renewable energy sector, and they require accurate modeling and reliable performance to maximize the output power. This research presents a thorough analysis and discussions on the effects of different PV models’ parameters and certain specific faults on the performance and behavior of the photovoltaic systems under different temperature and irradiation conditions. It provides a detailed analysis of how several parameters affect the performance of the PV arrays, for instance, the series resistance, shunt resistance, photocurrent, reverse saturation current, and the diode ideality factor. These parameters were extracted mathematically and verified with the help of wide-ranging simulations and practical experiments. Additionally, the investigation of the effect of DC faults, including line-to-line, line-to-ground, partial shading, and complete shading faults on PV arrays, provides important fundamentals for fault detection and classification, thus improving the efficiency and protection of PV systems. It can, therefore, be stated that the outcomes of this research will assist in the enhancement of PV systems in terms of design, operation, and maintainability of photovoltaic plants, as well as contribute positively to the advancement of sustainable solar energy technology.
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(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)
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Open AccessArticle
Parametric Optimization of Entropy Generation in Hybrid Nanofluid in Contracting/Expanding Channel by Means of Analysis of Variance and Response Surface Methodology
by
Ahmad Zeeshan, Rahmat Ellahi, Muhammad Anas Rafique, Sadiq M. Sait and Nasir Shehzad
Inventions 2024, 9(5), 92; https://doi.org/10.3390/inventions9050092 - 27 Aug 2024
Abstract
This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat
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This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat contracting expanding plates of channel alongside radiative heat transmission was considered. The lower fixed plate was externally heated whereas the upper porous plate was cooled by injecting a coolant fluid with a uniform velocity inside the channel. The resulting equations were solved by the Homotopic Analysis Method using MATHEMATICA 10 and Minitab 17.1. The design consists of several input factors, namely a magnetic field parameter ( ), radiation parameter ( ) and group parameter ( ). To obtain the values of flow response parameters, numerical experiments were used. Variables, especially the entropy generation ( ), were considered for each combination of design. The resulting RSM empirical model obtained a high coefficient of determination, reaching 99.97% for the entropy generation number ( ). These values show an excellent fit of the model to the data.
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(This article belongs to the Section Inventions and Innovation in Energy and Thermal/Fluidic Science)
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Open AccessArticle
Technical Requirements of Photovoltaic Inverters for Low Voltage Distribution Networks
by
Krzysztof Chmielowiec, Łukasz Topolski, Mateusz Dutka, Aleks Piszczek, Zbigniew Hanzelka and Tomasz Rodziewicz
Inventions 2024, 9(4), 91; https://doi.org/10.3390/inventions9040091 - 16 Aug 2024
Abstract
The paper presents the results of an experimental study, which was conducted in 2021 and briefly presented at the conference CIGRE Paris Session 2022, as a part of a joint initiative for comparative studies of PV inverters, of AGH University of Science and
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The paper presents the results of an experimental study, which was conducted in 2021 and briefly presented at the conference CIGRE Paris Session 2022, as a part of a joint initiative for comparative studies of PV inverters, of AGH University of Science and Technology and Tauron Dystrybucja (Polish DSO). The study was performed on a representative sample of 29 brand new PV inverters, widely available for sale in the EU, from over 20 various manufacturers from and outside the EU. For the purposes of this research, all tested PV have been made available by their manufacturers or regional distributors, which confirms the origin of the devices from official distribution channels and their designation for the EU market. The conducted research covers the technical aspects of PV inverters’ operation and performance included in the NC RfG network code, technical standard EN-505049-1:2019, and internal regulations of distribution system operators governing PV inverter technical quality and its cooperation with the low-voltage distribution network. The national regulations may differ between individual EU member states. The paper describes the testing procedure, obtained results, conclusions, and recommendations for regulatory bodies and DSOs.
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
Exposing Data Leakage in Wi-Fi CSI-Based Human Action Recognition: A Critical Analysis
by
Domonkos Varga
Inventions 2024, 9(4), 90; https://doi.org/10.3390/inventions9040090 - 15 Aug 2024
Abstract
Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence
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Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence and impact of data leakage in three published Wi-Fi CSI-based human action recognition methods that utilize deep learning techniques. The original studies achieve precision rates of 95% or higher, attributed to the lack of human-based dataset splitting. By re-evaluating these systems with proper subject-based partitioning, our analysis reveals a substantial decline in performance, underscoring the prevalence of data leakage. This study highlights the critical need for rigorous dataset management and evaluation protocols to ensure the development of robust and reliable human action recognition systems. Our findings advocate for standardized practices in dataset partitioning to mitigate data leakage and enhance the generalizability of Wi-Fi CSI-based models.
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(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)
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Open AccessArticle
Flow Rate Sensor inside Infusion Tube
by
Hsiang-Chen Chui, Ying Xu, Zhiyuan Wang, Xianting Zhang, Rui Li and Kai-Rong Qin
Inventions 2024, 9(4), 89; https://doi.org/10.3390/inventions9040089 - 13 Aug 2024
Abstract
Infusion systems are widely used in clinical medicine. Intravenous infusion therapy must be monitored to ensure patient safety. We proposed a compact flow rate sensor device based on the time-of-flight method. This device included one ceramic heater and two infrared sensors. Practical sensor
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Infusion systems are widely used in clinical medicine. Intravenous infusion therapy must be monitored to ensure patient safety. We proposed a compact flow rate sensor device based on the time-of-flight method. This device included one ceramic heater and two infrared sensors. Practical sensor prototypes were fabricated and characterized. The response time was 30 s. The sensor range was estimated to be 33 dB from μL/min to tens of mL/min, covering almost the entire usage range This flow rate sensor can be applied to common infusion tubes. Through the use of a mobile phone app, detailed information can be presented in real time.
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(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)
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Open AccessArticle
A Novel Technique for the Optimization of Energy Cost Management and Operation of Microgrids Inspired from the Behavior of Egyptian Stray Dogs
by
Hatem Y. Diab and Mahmoud Abdelsalam
Inventions 2024, 9(4), 88; https://doi.org/10.3390/inventions9040088 - 30 Jul 2024
Abstract
Managing costs in microgrids presents a formidable challenge due to the intricate blend of renewable and non-renewable energy sources that underpin their power generation. Ensuring seamless integration of microgrids with the national grid is pivotal for continuous load demand satisfaction and adherence to
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Managing costs in microgrids presents a formidable challenge due to the intricate blend of renewable and non-renewable energy sources that underpin their power generation. Ensuring seamless integration of microgrids with the national grid is pivotal for continuous load demand satisfaction and adherence to liberalized energy market mandates. To address this challenge, this paper introduces a new optimization technique for the Cost Management and Operation System (CMOS) of multi-source microgrids through a smart management unit. The cornerstone of this unit is the Egyptian Stray Dog Optimization (ESDO) algorithm, meticulously designed to optimize operational costs in line with load demands, energy cost dynamics, and generation proficiencies. Rigorous testing of the proposed system was conducted on a multi-resource microgrid using MATLAB, encompassing various operational scenarios. The simulation outcomes consistently highlighted the unit’s capability to achieve optimal cost-efficiency. Comparative analysis with other optimization techniques, particularly Particle Swarm Optimization (PSO), demonstrated the superior performance of the Egyptian Stray Dog algorithm, underscoring its potential as a leading solution in this domain.
Full article
(This article belongs to the Special Issue Innovative Strategy of Protection and Control for the Grid)
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Open AccessArticle
Tailoring Laser Powder Bed Fusion Process Parameters for Standard and Off-Size Ti6Al4V Metal Powders: A Machine Learning Approach Enhanced by Photodiode-Based Melt Pool Monitoring
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Farima Liravi, Sebastian Soo, Sahar Toorandaz, Katayoon Taherkhani, Mahdi Habibnejad-Korayem and Ehsan Toyserkani
Inventions 2024, 9(4), 87; https://doi.org/10.3390/inventions9040087 - 30 Jul 2024
Abstract
An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using
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An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using machine learning and melt pool monitoring data collected with photodiode sensors, the goal of this research was to efficiently customize LPBF process parameters. A novel aspect of this study is the application of standard and off-size powder feedstocks. Ti6Al4V (Ti64) powder was used in three size ranges of 15–53 µm, 15–106 µm, and 45–106 µm to print the samples. This facilitated the development of a process parameters tailoring system capable of handling variations in powder size ranges. Ultimately, per each part, the associated set of light intensity statistical signatures along with the powder size range and the parts’ density, surface roughness, and hardness were used as inputs for three regressors of Feed-Forward Neural Network (FFN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The laser power, laser velocity, hatch distance, and energy density of the parts were predicted by the regressors. According to the results obtained on unseen samples, RF demonstrated the best performance in the prediction of process parameters.
Full article
(This article belongs to the Special Issue Revolutionizing Manufacturing: Advances in Additive Manufacturing Technologies)
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Open AccessReview
Review on Research Progress of Pulsating Heat Pipes
by
Shiqiang Fang, Chong Zhou, Ye Zhu, Zhong Qian and Cheng Wang
Inventions 2024, 9(4), 86; https://doi.org/10.3390/inventions9040086 - 30 Jul 2024
Abstract
Since their invention by Akachi in 1990s, pulsating heat pipes (PHPs) have attracted widespread interest and application in practice, e.g., grinding, chip cooling, the thermal management of batteries, etc., owing to their notable efficiency in heat transfer and their simplicity and flexibility in
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Since their invention by Akachi in 1990s, pulsating heat pipes (PHPs) have attracted widespread interest and application in practice, e.g., grinding, chip cooling, the thermal management of batteries, etc., owing to their notable efficiency in heat transfer and their simplicity and flexibility in structure. Key factors influencing the heat transfer efficacy of pulsating heat pipes are mainly attributed to the thermophysical properties of the working fluid, the structural parameters, and the operating conditions. Research on pulsating heat pipes is conducted through theoretical investigations, numerical simulations, and visual experiments. In this paper, the research on PHPs in recent decades is reviewed with the consideration of the heat transfer performance mechanism and application of pulsating heat pipes, especially research under operation conditions such as with “status with motion” and with “inconsistent heat flux”.
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(This article belongs to the Special Issue Innovations in Heat Exchangers)
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Open AccessArticle
PI3SO: A Spectroscopic γ-Ray Scanner Table for Sort and Segregate Radwaste Analysis
by
Gaetano Elio Poma, Chiara Rita Failla, Simone Amaducci, Luigi Cosentino, Fabio Longhitano, Gianfranco Vecchio and Paolo Finocchiaro
Inventions 2024, 9(4), 85; https://doi.org/10.3390/inventions9040085 - 26 Jul 2024
Abstract
The current scenario of radioactive waste management requires innovative and automated solutions to ensure its effectiveness and safety. In response to this need, the Proximity Imaging System for Sort and Segregate Operations (PI3SO) project was proposed. It is a gamma radiation proximity scanner
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The current scenario of radioactive waste management requires innovative and automated solutions to ensure its effectiveness and safety. In response to this need, the Proximity Imaging System for Sort and Segregate Operations (PI3SO) project was proposed. It is a gamma radiation proximity scanner system for radioactive waste with the primary goal of speeding up some aspects of the waste management cycle while reducing direct human operations. The system will provide proximity imaging for hot-spot finding and spectral analysis for radiological characterization, enabling semiautomatic recognition, sorting and separation of radioactive waste. The core of the proposed scanning system consists of an array of 128 CsI(Tl) scintillators, 1 cm3 size, coupled with silicon photomultipliers (SiPMs), installed on a motorized bridge sliding along a suitable table in order to scan the materials under investigation.
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(This article belongs to the Section Inventions and Innovation in Applied Chemistry and Physics)
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Open AccessReview
Aircraft Innovation Trends Enabling Advanced Air Mobility
by
Raj Bridgelall
Inventions 2024, 9(4), 84; https://doi.org/10.3390/inventions9040084 - 26 Jul 2024
Abstract
This study presents a comprehensive exploration of vertical take-off and landing (VTOL) aircraft within advanced air mobility (AAM), examining the crucial challenges of integrating these innovative technologies into transportation systems. AAM promises transformational social change by enhancing transportation energy efficiency, safety, and operational
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This study presents a comprehensive exploration of vertical take-off and landing (VTOL) aircraft within advanced air mobility (AAM), examining the crucial challenges of integrating these innovative technologies into transportation systems. AAM promises transformational social change by enhancing transportation energy efficiency, safety, and operational effectiveness. This research utilizes a methodical approach that juxtaposes a systematic review of patents with an extensive analysis of the academic literature to map the innovation landscape of VTOL technology. This dual analysis reveals a dynamic progression in VTOL advancements, highlighting significant strides in aerodynamic optimization, propulsion technology, and control systems. The novelty of this study lies in its dual-method approach, combining patent analysis with the academic literature to provide a holistic view of VTOL technological evolution. The patent analysis reveals that companies have been most productive on innovations relating to VTOL aircraft transition efficiency, control enhancement, and energy management. The literature review identifies key trends such as the rise in electric propulsion technologies and the integration of AI-driven control mechanisms. These results provide new engineering knowledge that can guide future VTOL development and policy formulation. The original contributions include a detailed mapping of VTOL innovation trends, identification of key technological advancements, and a predictive lens into future directions. These findings offer a valuable resource for aerospace engineers, policymakers, and urban planners. This study contributes a detailed assessment of both theoretical foundations and practical applications, fostering a holistic view of the challenges and innovations shaping the future of AAM. By connecting research and practical development, this study serves as a critical tool for strategic decision making and policy formulation towards advancing the integration of VTOL aircraft into sustainable urban transportation networks.
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(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)
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Open AccessArticle
The Effect of Individual Hydrocarbons in the Composition of Diesel Fuel on the Effectiveness of Depressant Additives
by
Yana Morozova, Ilya Bogdanov, Andrey Altynov, Alina Titaeva and Maria Kirgina
Inventions 2024, 9(4), 83; https://doi.org/10.3390/inventions9040083 - 24 Jul 2024
Abstract
The use of depressant additives is the most effective and cost-effective way to improve the low-temperature properties of diesel fuels, like the cloud point, cold filter plugging point and pour point. However, the effectiveness of depressant additives depends on the composition of the
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The use of depressant additives is the most effective and cost-effective way to improve the low-temperature properties of diesel fuels, like the cloud point, cold filter plugging point and pour point. However, the effectiveness of depressant additives depends on the composition of the diesel fuel and the content of certain groups of hydrocarbons in it. In this work, the effect of adding individual hydrocarbons of various groups and structures on the effectiveness of depressant additives is studied. This study is carried out on model aromatic (toluene, tetralin) and n-paraffin hydrocarbons (cetane, heptadecane, heneicosane, docosane) in various concentrations. It is shown that the most negative effect on the depressant additives’ effectiveness is due to the content of the most polar aromatic hydrocarbons and light n-paraffins in the composition of diesel fuel, and the most positive effect is exerted by the content of heavy n-paraffins in small quantities. It is proposed to involve small concentrations (1–3% vol.) of heavy n-paraffin hydrocarbons (heneicosane, docosane) to increase the effectiveness of the depressant additive. It has been established that for the more effective action of the depressant, it is necessary to take into account the content and structure of individual hydrocarbons in the diesel fuel’s composition.
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(This article belongs to the Section Inventions and Innovation in Applied Chemistry and Physics)
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