Technologies

20 pages, 481 KiB

Open AccessSystematic Review

Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review

by Victor Hugo Silva-Blancas, José Manuel Álvarez-Alvarado, Ana Marcela Herrera-Navarro and Juvenal Rodríguez-Reséndiz

Technologies 2023, 11(4), 112; https://doi.org/10.3390/technologies11040112 - 13 Aug 2023

Viewed by 2039

Abstract

With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) [...] Read more.

With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage. Full article

(This article belongs to the Special Issue Advances in Applications of Intelligently Mining Massive Data)

► Show Figures

Figure 1

20 pages, 2975 KiB

Open AccessArticle

Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm

by Rusul Sabah Jebur, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng and Ali Al-Naji

Technologies 2023, 11(4), 111; https://doi.org/10.3390/technologies11040111 - 12 Aug 2023

Cited by 4 | Viewed by 3912

Abstract

Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) [...] Read more.

Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications. Full article

► Show Figures

Figure 1

18 pages, 13150 KiB

Open AccessEditor’s ChoiceArticle

Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems

by Alexander Pastukh, Valery Tikhvinskiy, Svetlana Dymkova and Oleg Varlamov

Technologies 2023, 11(4), 110; https://doi.org/10.3390/technologies11040110 - 10 Aug 2023

Cited by 29 | Viewed by 8024

Abstract

This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks [...] Read more.

This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Inmarsat. This study simulates an NTN satellite network with typical characteristics defined by 3GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites. Full article

(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G and Beyond)

► Show Figures

Figure 1

20 pages, 747 KiB

Open AccessEditor’s ChoiceArticle

Fuzzy Logic System for Classifying Multiple Sclerosis Patients as High, Medium, or Low Responders to Interferon-Beta

by Edgar Rafael Ponce de Leon-Sanchez, Jorge Domingo Mendiola-Santibañez, Omar Arturo Dominguez-Ramirez, Ana Marcela Herrera-Navarro, Alberto Vazquez-Cervantes, Hugo Jimenez-Hernandez and Horacio Senties-Madrid

Technologies 2023, 11(4), 109; https://doi.org/10.3390/technologies11040109 - 9 Aug 2023

Viewed by 2044

Abstract

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on [...] Read more.

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on the opinion of a neurology expert, to classify relapsing–remitting multiple sclerosis patients as high, medium, or low responders to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated with interferon-beta responses is proposed, for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results showed that the fuzzy system presented 100% efficiency, compared to an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model was evaluated, and 0.8 testing accuracy was achieved. Hence, a pipeline model, including data standardization, data compression, and a learning algorithm, could be a useful tool for getting reliable predictions about responses to interferon-beta. Full article

(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)

► Show Figures

Figure 1

18 pages, 7564 KiB

Open AccessArticle

A Novel Approach to Quantitative Characterization and Visualization of Color Fading

by Woo Sik Yoo, Kitaek Kang, Jung Gon Kim and Yeongsik Yoo

Technologies 2023, 11(4), 108; https://doi.org/10.3390/technologies11040108 - 8 Aug 2023

Viewed by 2103

Abstract

Color fading naturally occurs with time under light illumination. It is triggered by the high photon energy of light. The rate of color fading and darkening depends on the substance, lighting condition, and storage conditions. Color fading is only observed after some time [...] Read more.

Color fading naturally occurs with time under light illumination. It is triggered by the high photon energy of light. The rate of color fading and darkening depends on the substance, lighting condition, and storage conditions. Color fading is only observed after some time has passed. The current color of objects of interest can only be compared with old photographs or the observer’s perception at the time of reference. Color fading and color darkening rates between two or more points in time in the past can only be determined using photographic images from the past. For objective characterization of color difference between two or more different times, quantification of color in either digital or printed photographs is required. A newly developed image analysis and comparison software (PicMan) has been used for color quantification and pixel-by-pixel color difference mapping in this study. Images of two copies of Japanese wood-block prints with and without color fading have been selected for the exemplary study of quantitative characterization of color fading and color darkening. The fading occurred during a long period of exposure to light. Pixel-by-pixel, line-by-line, and area-by-area comparisons of color fading and darkening between two images were very effective in quantifying color change and visualization of the phenomena. RGB, HSV, CIE L*a*b* values between images and their differences of a single pixel to areas of interest in any shape can be quantified. Color fading and darkening analysis results were presented in numerical, graphical, and image formats for completeness. All formats have their own advantages and disadvantages over the other formats in terms of data size, complexity, readability, and communication among parties of interest. This paper demonstrates various display options for color analysis, a summary of color fading, or color difference among images of interest for practical artistic, cultural heritage conservation, and museum applications. Color simulation for various moments in time was proposed and demonstrated by interpolation or extrapolation of color change between images, with and without color fading, using PicMan. The degree of color fading and color darkening over the various moments in time (past and future) can be simulated and visualized for decision-making in public display, storage, and restoration planning. Full article

(This article belongs to the Special Issue Image and Signal Processing)

► Show Figures

Figure 1

18 pages, 2892 KiB

Open AccessArticle

Deep Learning Techniques for Web-Based Attack Detection in Industry 5.0: A Novel Approach

by Abdu Salam, Faizan Ullah, Farhan Amin and Mohammad Abrar

Technologies 2023, 11(4), 107; https://doi.org/10.3390/technologies11040107 - 8 Aug 2023

Cited by 15 | Viewed by 5706

Abstract

As the manufacturing industry advances towards Industry 5.0, which heavily integrates advanced technologies such as cyber-physical systems, artificial intelligence, and the Internet of Things (IoT), the potential for web-based attacks increases. Cybersecurity concerns remain a crucial challenge for Industry 5.0 environments, where cyber-attacks [...] Read more.

As the manufacturing industry advances towards Industry 5.0, which heavily integrates advanced technologies such as cyber-physical systems, artificial intelligence, and the Internet of Things (IoT), the potential for web-based attacks increases. Cybersecurity concerns remain a crucial challenge for Industry 5.0 environments, where cyber-attacks can cause devastating consequences, including production downtime, data breaches, and even physical harm. To address this challenge, this research proposes an innovative deep-learning methodology for detecting web-based attacks in Industry 5.0. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models are examples of deep learning techniques that are investigated in this study for their potential to effectively classify attacks and identify anomalous behavior. The proposed transformer-based system outperforms traditional machine learning methods and existing deep learning approaches in terms of accuracy, precision, and recall, demonstrating the effectiveness of deep learning for intrusion detection in Industry 5.0. The study’s findings showcased the superiority of the proposed transformer-based system, outperforming previous approaches in accuracy, precision, and recall. This highlights the significant contribution of deep learning in addressing cybersecurity challenges in Industry 5.0 environments. This study contributes to advancing cybersecurity in Industry 5.0, ensuring the protection of critical infrastructure and sensitive data. Full article

(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering)

► Show Figures

Figure 1

23 pages, 10373 KiB

Open AccessArticle

Biodegradable Polyhydroxyalkanoates Formed by 3- and 4-Hydroxybutyrate Monomers to Produce Nanomembranes Suitable for Drug Delivery and Cell Culture

by Tatiana G. Volova, Aleksey V. Demidenko, Anastasiya V. Murueva, Alexey E. Dudaev, Ivan Nemtsev and Ekaterina I. Shishatskaya

Technologies 2023, 11(4), 106; https://doi.org/10.3390/technologies11040106 - 7 Aug 2023

Cited by 1 | Viewed by 2196

Abstract

Biodegradable polyhydroxyalkanoates, biopolymers of microbiological origin, formed by 3- and 4-hydroxybutyrate monomers P(3HB-co-4HB), were used to obtain nanomembranes loaded with drugs as cell carriers by electrospinning. Resorbable non-woven membranes from P(3HB-co-4HB) loaded with ceftazidime, doripinem, and actovegin have been obtained. The loading of [...] Read more.

Biodegradable polyhydroxyalkanoates, biopolymers of microbiological origin, formed by 3- and 4-hydroxybutyrate monomers P(3HB-co-4HB), were used to obtain nanomembranes loaded with drugs as cell carriers by electrospinning. Resorbable non-woven membranes from P(3HB-co-4HB) loaded with ceftazidime, doripinem, and actovegin have been obtained. The loading of membranes with drugs differently affected the size of fibers and the structure of membranes, and in all cases increased the hydrophilicity of the surface. The release of drugs in vitro was gradual, which corresponded to the Higuchi and Korsmeyer-Peppas models. Antibiotic-loaded membranes showed antibacterial activity against S. aureus and E. coli, in which growth inhibition zones were 41.7 ± 1.1 and 38.6 ± 1.7 mm for ceftazidime and doripinem, respectively. The study of the biological activity of membranes in the NIH 3T3 mouse fibroblast culture based on the results of DAPI and FITC staining of cells, as well as the MTT test, did not reveal a negative effect despite the presence of antibiotics in them. Samples containing actovegin exhibit a stimulating effect on fibroblasts. Biodegradable polyhydroxyalkanoates formed by 3-hydroxybutyrate and 4-hydroxybutyrate monomers provide electrospinning non-woven membranes suitable for long-term delivery of drugs and cultivation of eukaryotic cells, and are promising for the treatment of wound defects complicated by infection. Full article

(This article belongs to the Special Issue Electrospinning Technologies for Biomedical and Biotechnological Applications—Volume II)

► Show Figures

Graphical abstract

13 pages, 4355 KiB

Open AccessCommunication

Electrochemical Detection of Furaltadone Antibiotic Drug by the Rare Earth Metal Tungstate Decorated Screen Printed Carbon Electrode

by Sivaramakrishnan Vinothini, Te-Wei Chiu and Subramanian Sakthinathan

Technologies 2023, 11(4), 105; https://doi.org/10.3390/technologies11040105 - 6 Aug 2023

Cited by 3 | Viewed by 2434

Abstract

Furaltadone (FLD) is an antibiotic drug that is widely treated for coccidiosis, intestinal infection, and turkey blackhead. Moreover, excessive use of FLD may have some negative consequences for humans and domestic animals. Therefore, practical, sensitive, selective, and facile detection of FLD is still [...] Read more.

Furaltadone (FLD) is an antibiotic drug that is widely treated for coccidiosis, intestinal infection, and turkey blackhead. Moreover, excessive use of FLD may have some negative consequences for humans and domestic animals. Therefore, practical, sensitive, selective, and facile detection of FLD is still needed. In this exploration, a Eu₂(WO₄)₃-nanoparticles-modified screen-printed carbon electrode was developed for the low-level detection of FLD. Hydrothermal techniques were used effectively to prepare the Eu₂(WO₄)₃ complex. Scanning electron microscopy and X-ray diffraction investigations were used to confirm the Eu₂(WO₄)₃. The results revealed that the Eu₂(WO₄)₃ was well formed, crystalline, and uniformly distributed. Furthermore, the electrochemical behavior of the SPCE/Eu₂(WO₄) electrode was examined by differential pulse voltammetry and cyclic voltammetry studies. The SPCE/Eu₂(WO₄) electrode demonstrated improved electrocatalytic activity in the detection of FLD with a detection limit of 97 µM (S/N = 3), linear range of 10 nM to 300 µM, and sensitivity of 2.1335 µA µM⁻¹ cm⁻². The SPCE/Eu₂(WO₄) electrode detected FLD in the presence of 500-fold excess concentrations of other interfering pollutant ions. The practical feasibility of the SPCE/Eu₂(WO₄) electrode was tested on different antibiotic medicines and showed adequate recovery. Moreover, the SPCE/Eu₂(WO₄) electrode shows appreciable repeatability, high stability, and reproducibility. Full article

► Show Figures

Graphical abstract

18 pages, 2301 KiB

Open AccessEditor’s ChoiceArticle

The U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Low-Dose CT

by Lu Liu, Runlei Ma, Peter M. A. van Ooijen, Matthijs Oudkerk, Rozemarijn Vliegenthart, Raymond N. J. Veldhuis and Christoph Brune

Technologies 2023, 11(4), 104; https://doi.org/10.3390/technologies11040104 - 5 Aug 2023

Cited by 1 | Viewed by 2196

Abstract

Epicardial adipose tissue (EAT) is located between the visceral pericardium and myocardium, and EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family have been developed to reduce the workload for radiologists. [...] Read more.

Epicardial adipose tissue (EAT) is located between the visceral pericardium and myocardium, and EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family have been developed to reduce the workload for radiologists. The automatic assessment of EAT on non-contrast low-dose CT calcium score images poses a greater challenge compared to the automatic assessment on coronary CT angiography, which requires a higher radiation dose to capture the intricate details of the coronary arteries. This study comprehensively examined and evaluated state-of-the-art segmentation methods while outlining future research directions. Our dataset consisted of 154 non-contrast low-dose CT scans from the ROBINSCA study, with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we performed both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland–Altman analysis. Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance for segmentation and quantification. The U-net++ model trained with label type (a) efficiently provided better EAT segmentation results (hold-out test: DCS =

80.18 \pm 0.20 %

, mIoU =

67.13 \pm 0.39 %

, sensitivity =

81.47 \pm 0.43 %

, specificity =

99.64 \pm 0.00 %

, Pearson correlation = 0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method. Interestingly, our findings indicate that 3D convolutional neural networks do not consistently outperform 2D networks in EAT segmentation and quantification. Moreover, utilizing labels representing the region inside the pericardium proved advantageous in training more accurate EAT segmentation models. These insights highlight the potential of deep learning-based methods for achieving robust EAT segmentation and quantification outcomes. Full article

(This article belongs to the Special Issue Medical Imaging & Image Processing III)

► Show Figures

Figure 1

14 pages, 6158 KiB

Open AccessEditor’s ChoiceCommunication

Adapting the H.264 Standard to the Internet of Vehicles

by Yair Wiseman

Technologies 2023, 11(4), 103; https://doi.org/10.3390/technologies11040103 - 3 Aug 2023

Cited by 8 | Viewed by 1556

Abstract

We suggest two steps of reducing the amount of data transmitted on Internet of Vehicle networks. The first step shifts the image from a full-color resolution to only an 8-color resolution. The reduction of the color numbers is noticeable; however, the 8-color images [...] Read more.

We suggest two steps of reducing the amount of data transmitted on Internet of Vehicle networks. The first step shifts the image from a full-color resolution to only an 8-color resolution. The reduction of the color numbers is noticeable; however, the 8-color images are enough for the requirements of common vehicles’ applications. The second step suggests modifying the quantization tables employed by H.264 to different tables that will be more suitable to an image with only 8 colors. The first step usually reduces the size of the image by more than 30%, and when continuing and performing the second step, the size of the image decreases by more than 40%. That is to say, the combination of the two steps can provide a significant reduction in the amount of data required to be transferred on vehicular networks. Full article

(This article belongs to the Special Issue Image and Signal Processing)

► Show Figures

Figure 1

27 pages, 5171 KiB

Open AccessEditor’s ChoiceReview

Modern DC–DC Power Converter Topologies and Hybrid Control Strategies for Maximum Power Output in Sustainable Nanogrids and Picogrids—A Comprehensive Survey

by Anupama Ganguly, Pabitra Kumar Biswas, Chiranjit Sain and Taha Selim Ustun

Technologies 2023, 11(4), 102; https://doi.org/10.3390/technologies11040102 - 1 Aug 2023

Cited by 14 | Viewed by 3412

Abstract

Sustainable energy exhibited immense growth in the last few years. As compared to other sustainable sources, solar power is proved to be the most feasible source due to some unanticipated characteristics, such as being clean, noiseless, ecofriendly, etc. The output from the solar [...] Read more.

Sustainable energy exhibited immense growth in the last few years. As compared to other sustainable sources, solar power is proved to be the most feasible source due to some unanticipated characteristics, such as being clean, noiseless, ecofriendly, etc. The output from the solar power is entirely unpredictable since solar power generation is dependent on the intensity of solar irradiation and solar panel temperature. Further, these parameters are weather dependent and thus intermittent in nature. To conquer intermittency, power converters play an important role in solar power generation. Generally, photovoltaic systems will eventually suffer from a decrease in energy conversion efficiency along with improper stability and intermittent properties. As a result, the maximum power point tracking (MPPT) algorithm must be incorporated to cultivate maximum power from solar power. To make solar power generation reliable, a proper control technique must be added to the DC–DC power converter topologies. Furthermore, this study reviewed the progress of the maximum power point tracking algorithm and included an in-depth discussion on modern and both unidirectional and bidirectional DC–DC power converter topologies for harvesting electric power. Lastly, for the reliability and continuity of the power demand and to allow for distributed generation, this article also established the possibility of integrating solar PV systems into nanogrids and picogrids in a sustainable environment. The outcome of this comprehensive survey would be of strong interest to the researchers, technologists, and the industry in the relevant field to carry out future research. Full article

(This article belongs to the Collection Electrical Technologies)

► Show Figures

Figure 1

24 pages, 5116 KiB

Open AccessReview

Cleaning Big Data Streams: A Systematic Literature Review

by Obaid Alotaibi, Eric Pardede and Sarath Tomy

Technologies 2023, 11(4), 101; https://doi.org/10.3390/technologies11040101 - 26 Jul 2023

Cited by 4 | Viewed by 3732

Abstract

In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, [...] Read more.

In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified. Full article

(This article belongs to the Special Issue Advances in Applications of Intelligently Mining Massive Data)

► Show Figures

Figure 1

25 pages, 7137 KiB

Open AccessArticle

Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition

by Benjamin Svendsen and Seifedine Kadry

Technologies 2023, 11(4), 99; https://doi.org/10.3390/technologies11040099 - 15 Jul 2023

Cited by 1 | Viewed by 2631

Abstract

Communication is integral to every human’s life, allowing individuals to express themselves and understand each other. This process can be challenging for the hearing-impaired population, who rely on sign language for communication due to the limited number of individuals proficient in sign language. [...] Read more.

Communication is integral to every human’s life, allowing individuals to express themselves and understand each other. This process can be challenging for the hearing-impaired population, who rely on sign language for communication due to the limited number of individuals proficient in sign language. Image classification models can be used to create assistive systems to address this communication barrier. This paper conducts a comprehensive literature review and experiments to find the state of the art in sign language recognition. It identifies a lack of research in Norwegian Sign Language (NSL). To address this gap, we created a dataset from scratch containing 24,300 images of 27 NSL alphabet signs and performed a comparative analysis of various machine learning models, including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Network (CNN) on the dataset. The evaluation of these models was based on accuracy and computational efficiency. Based on these metrics, our findings indicate that SVM and CNN were the most effective models, achieving accuracies of 99.9% with high computational efficiency. Consequently, the research conducted in this report aims to contribute to the field of NSL recognition and serve as a foundation for future studies in this area. Full article

(This article belongs to the Special Issue Image and Signal Processing)

► Show Figures

Figure 1

12 pages, 4764 KiB

Open AccessArticle

Impact of Post-Process Heat Treatments Performed on Ti6Al4V Titanium Alloy Specimens Obtained Using LPBF Technology

by Silvia Gaiani, Elisa Ferrari, Marica Gozzi, Maria Teresa Di Giovanni, Magdalena Lassinantti Gualtieri, Elena Colombini and Paolo Veronesi

Technologies 2023, 11(4), 100; https://doi.org/10.3390/technologies11040100 - 15 Jul 2023

Cited by 4 | Viewed by 2548

Abstract

Additive manufacturing technology has emerged over the past decade as one of the best solutions for building prototypes and components with complex geometries and reduced thicknesses. Its application has rapidly spread to various industries, such as motorsport, automotive, aerospace, and biomedical. In particular, [...] Read more.

Additive manufacturing technology has emerged over the past decade as one of the best solutions for building prototypes and components with complex geometries and reduced thicknesses. Its application has rapidly spread to various industries, such as motorsport, automotive, aerospace, and biomedical. In particular, titanium alloy Ti-6Al-4V, due to its exceptional mechanical properties, low density, and excellent corrosion resistance, turns out to be one of the most popular for the production of parts with additive manufacturing technology across all the market segments listed above. However, when producing components using Laser Powder Bed Fusion (LPBF) technology, it is always necessary to perform appropriate heat treatments whose main purpose is to reduce the residual stresses typically generated during the manufacturing process. Post-process heat treatments on Ti6Al4V components obtained by way of additive technology have been extensively studied in the literature, with the aim of identifying optimal thermal cycles, which may allow for the effective reduction of residual stresses combined with proper microstructural conditions. However, despite the usual target of maximizing relevant mechanical properties, it is mandatory for industrial production to achieve a robust process, i.e., minimizing the sensitivity to noise-induced variation. Therefore, the aim of the present work is to compare several post-process heat treatment strategies by performing different thermal cycles in the temperature range of 750–955 °C and investigating how these affect the average mechanical properties and their variance. The treated samples are then analyzed running a complete mechanical and microstructural characterization, and the latter particularly focused on the determination of the typical microstructure present in the treated samples by using the XRD technique. Full article

(This article belongs to the Section Manufacturing Technology)

► Show Figures

Figure 1

20 pages, 349 KiB

Open AccessEditor’s ChoiceArticle

Quantum Effects in General Relativity: Investigating Repulsive Gravity of Black Holes at Large Distances

by Piero Chiarelli

Technologies 2023, 11(4), 98; https://doi.org/10.3390/technologies11040098 - 14 Jul 2023

Cited by 2 | Viewed by 1583

Abstract

This paper proposes a theoretical study that investigates quantum effects on the gravity of black holes. This study utilizes a gravitational model that incorporates quantum mechanics derived from the classical-like quantum hydrodynamic representation. This research calculates the mass density distribution of quantum black [...] Read more.

This paper proposes a theoretical study that investigates quantum effects on the gravity of black holes. This study utilizes a gravitational model that incorporates quantum mechanics derived from the classical-like quantum hydrodynamic representation. This research calculates the mass density distribution of quantum black holes, specifically in the case of central symmetry. The gravity of a quantum black hole shows contributions coming from quantum potential energy, which is also sensitive to the presence of a background of gravitational noise. The additional energy, stored in quantum potential fluctuations and constituting a form of dark energy, leads to a repulsive gravity in the weak gravity limit. This repulsive gravity overcomes the attractive classical Newtonian force at large distances of order of the intergalactic length. Full article

(This article belongs to the Section Quantum Technologies)

14 pages, 6253 KiB

Open AccessArticle

Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET

by Rafael Ortiz-Feregrino, Saul Tovar-Arriaga, Jesus Carlos Pedraza-Ortega and Juvenal Rodriguez-Resendiz

Technologies 2023, 11(4), 97; https://doi.org/10.3390/technologies11040097 - 13 Jul 2023

Cited by 2 | Viewed by 2316

Abstract

Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has [...] Read more.

Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has been the traditional choice for image analysis tasks. However, the emergence of visual transformers with their unique attention mechanism has proved to be a game-changer. However, visual transformers require a large amount of data and computational power, making them unsuitable for tasks with limited data and resources. To deal with this constraint, we adapted the attention module of visual transformers and integrated it into a CNN-based UNET network, achieving superior performance compared to other models. The model achieved a 0.89 recall, 0.98 AUC, 0.97 accuracy, and 0.97 sensitivity on various datasets, including HRF, Drive, LES-AV, CHASE-DB1, Aria-A, Aria-D, Aria-C, IOSTAR, STARE and DRGAHIS. Moreover, the model can recognize blood vessels accurately, regardless of camera type or the original image resolution, ensuring that it generalizes well. This breakthrough in retinal vein segmentation could improve the early diagnosis of several health conditions. Full article

(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)

► Show Figures

Figure 1

26 pages, 7475 KiB

Open AccessEditor’s ChoiceArticle

A Deep Reinforcement Learning Method for Economic Power Dispatch of Microgrid in OPAL-RT Environment

by Faa-Jeng Lin, Chao-Fu Chang, Yu-Cheng Huang and Tzu-Ming Su

Technologies 2023, 11(4), 96; https://doi.org/10.3390/technologies11040096 - 12 Jul 2023

Cited by 5 | Viewed by 2488

Abstract

This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and [...] Read more.

This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and the charging/discharging control strategy of a battery energy storage system (BESS). Then, a deep reinforcement learning method, the deep deterministic policy gradient (DDPG), is utilized to develop the power dispatch of a microgrid to minimize the total energy expense while considering power constraints, load uncertainties and electricity price. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the deep reinforcement learning method. Furthermore, a comparison of the proposed method with the experience-based energy management system (EMS), Newton particle swarm optimization (Newton-PSO) and the deep Q-learning network (DQN) is provided to evaluate the obtained solutions. In this study, the average deviation of the LSTM forecast accuracy is less than 5%. In addition, the daily operating cost of the proposed method obtains a 3.8% to 7.4% lower electricity cost compared to that of the other methods. Finally, a detailed emulation in the OPAL-RT environment is carried out to validate the effectiveness of the proposed method. Full article

(This article belongs to the Topic Emerging Technologies for Grid-Interactive Efficient Buildings and Communities)

► Show Figures

Figure 1

24 pages, 7842 KiB

Open AccessEditor’s ChoiceArticle

Field Performance Monitoring of Energy-Generating High-Transparency Agrivoltaic Glass Windows

by Mikhail Vasiliev, Victor Rosenberg, Jamie Lyford and David Goodfield

Technologies 2023, 11(4), 95; https://doi.org/10.3390/technologies11040095 - 12 Jul 2023

Cited by 3 | Viewed by 3814

Abstract

Currently, there are strong and sustained growth trends observed in multi-disciplinary industrial technologies such as building-integrated photovoltaics and agrivoltaics, where renewable energy production is featured in building envelopes of varying degrees of transparency. Novel glass products can provide a combination of thermal energy [...] Read more.

Currently, there are strong and sustained growth trends observed in multi-disciplinary industrial technologies such as building-integrated photovoltaics and agrivoltaics, where renewable energy production is featured in building envelopes of varying degrees of transparency. Novel glass products can provide a combination of thermal energy savings and solar energy harvesting, enabled by either patterned-semiconductor thin-film energy converters on glass substrates, or by using luminescent concentrator-type approaches to achieve high transparency. Significant progress has been demonstrated recently in building integrated solar windows featuring visible light transmission of up to 70%, with electric power outputs of up to P_max ~ 30–33 W_p/m². Several slightly different designs were tested during 2021–2023 in a greenhouse installation at Murdoch University in Perth, Western Australia; their long-term energy harvesting performance differences were found to be on the scale of ~10% in wall-mounted locations. Solar greenhouse generated electricity at rates of up to 19 kWh/day, offsetting nearly 40% of energy costs. The objective of this paper is to report on the field performance of these PV windows in the context of agrivoltaics and to provide some detail of the performance differences measured in several solar window designs related to their glazing structure materials. Methods for the identification and quantification of long-term field performance differences and energy generation trends in solar windows of marginally different design types are reported. The paper also aims to outline the practical application potential of these transparent construction materials in built environments, focusing on the measured renewable energy figures and seasonal trends observed during the long-term study. Full article

(This article belongs to the Topic Emerging Technologies for Grid-Interactive Efficient Buildings and Communities)

► Show Figures

Figure 1

15 pages, 3248 KiB

Open AccessEditor’s ChoiceArticle

Regenerating Iron-Based Adsorptive Media Used for Removing Arsenic from Water

by Ilaria Ceccarelli, Luca Filoni, Massimiliano Poli, Ciro Apollonio and Andrea Petroselli

Technologies 2023, 11(4), 94; https://doi.org/10.3390/technologies11040094 - 12 Jul 2023

Viewed by 1576

Abstract

Of all the substances that can be present in water intended for human consumption, arsenic (As) is one of the most toxic. Many treatment technologies can be used for removing As from water, for instance, adsorption onto iron media, where commercially available adsorbents [...] Read more.

Of all the substances that can be present in water intended for human consumption, arsenic (As) is one of the most toxic. Many treatment technologies can be used for removing As from water, for instance, adsorption onto iron media, where commercially available adsorbents are removed and replaced with new media when they are exhausted. Since this is an expensive operation, in this work, a novel and portable plant for regenerating iron media has been developed and tested in four real case studies in Central Italy. The obtained results highlight the good efficiency of the system, which was able, from 2019 to 2023, to regenerate the iron media and to restore its capability to adsorb the As from water almost entirely. Indeed, when the legal threshold value of 10 μg/L is exceeded, the regeneration process is performed and, after that, the As concentration in the water effluent is at the minimum level in all the investigated case studies. Full article

(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)

► Show Figures

Figure 1

13 pages, 1160 KiB

Open AccessEditor’s ChoiceArticle

Optical Properties of AgInS₂ Quantum Dots Synthesized in a 3D-Printed Microfluidic Chip

by Konstantin Baranov, Ivan Reznik, Sofia Karamysheva, Jacobus W. Swart, Stanislav Moshkalev and Anna Orlova

Technologies 2023, 11(4), 93; https://doi.org/10.3390/technologies11040093 - 12 Jul 2023

Cited by 2 | Viewed by 2759

Abstract

Colloidal nanoparticles, and quantum dots in particular, are a new class of materials that can significantly improve the functionality of photonics, electronics, sensor devices, etc. The main challenge addressed in the article is modification of the syntheses of colloidal NP to launch them [...] Read more.

Colloidal nanoparticles, and quantum dots in particular, are a new class of materials that can significantly improve the functionality of photonics, electronics, sensor devices, etc. The main challenge addressed in the article is modification of the syntheses of colloidal NP to launch them into mass production. It is proposed to use an additive printing method of chips for microfluidic synthesis, and it is shown that our approach allows to offer a cheap, easily scalable and automated synthesis method which allows to increase the product yield up to 60% with improved optical properties of

{AgInS}_{2}

quantum dots. Full article

(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)

► Show Figures

Figure 1

21 pages, 89425 KiB

Open AccessCommunication

FPGA-Based Implementation of a New 3-D Multistable Chaotic Jerk System with Two Unstable Balance Points

by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas and Brisbane Ovilla-Martínez

Technologies 2023, 11(4), 92; https://doi.org/10.3390/technologies11040092 - 11 Jul 2023

Cited by 4 | Viewed by 1705

Abstract

Mechanical jerk systems have applications in several areas, such as oscillators, microcontrollers, circuits, memristors, encryption, etc. This research manuscript reports a new 3-D chaotic jerk system with two unstable balance points. It is shown that the proposed mechanical jerk system exhibits multistability with [...] Read more.

Mechanical jerk systems have applications in several areas, such as oscillators, microcontrollers, circuits, memristors, encryption, etc. This research manuscript reports a new 3-D chaotic jerk system with two unstable balance points. It is shown that the proposed mechanical jerk system exhibits multistability with coexisting chaotic attractors for the same set of system constants but for different initial states. A bifurcation analysis of the proposed mechanical jerk system is presented to highlight the special properties of the system with respect to the variation of system constants. A field-programmable gate array (FPGA) implementation of the proposed mechanical jerk system is given by synthesizing the discrete equations that are obtained by applying one-step numerical methods. The hardware resources are reduced by performing pipeline operations, and, finally, the paper concludes that the experimental results of the proposed mechanical jerk system using FPGA-based design show good agreement with the MATLAB simulations of the same system. Full article

(This article belongs to the Collection Electrical Technologies)

► Show Figures

Figure 1

18 pages, 7151 KiB

Open AccessArticle

Implementation of Deep Learning Models on an SoC-FPGA Device for Real-Time Music Genre Classification

by Muhammad Faizan, Ioannis Intzes, Ioana Cretu and Hongying Meng

Technologies 2023, 11(4), 91; https://doi.org/10.3390/technologies11040091 - 10 Jul 2023

Cited by 5 | Viewed by 4341

Abstract

Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnecessary processing delays and high energy consumption. Graphical [...] Read more.

Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnecessary processing delays and high energy consumption. Graphical processing units (GPUs) offer reliable hardware acceleration, but their bulky sizes prevent their utilization in portable equipment. System-on-chip field programmable gated arrays (SoC-FPGAs) provide considerable computational power with low energy consumption, making them ideal for edge computing applications, owing to their innovative, flexible, and small design. In this paper, we implement a deep-learning-based music genre classification system on a SoC-FPGA board, evaluate the model’s performance, and provide a comparative analysis across different platforms. Specifically, we compare the performance of long short-term memory (LSTM), convolutional neural networks (CNNs), and a hybrid model (CNN-LSTM) on an Intel Core i7-8550U by Intel Cooperation. The models are fed an acoustic feature called the Mel-frequency cepstral coefficient (MFCC) for training and testing (inference). Then, by using the advanced Vitis AI tool, a deployable version of the model is generated. The experimental results show that the execution speed is increased by 80%, and the throughput rises four times when the CNN-based music genre classification system is implemented on SoC-FPGA. Full article

(This article belongs to the Section Information and Communication Technologies)

► Show Figures

Figure 1

12 pages, 2034 KiB

Open AccessArticle

Characterization of Triplet State of Cyanine Dyes with Two Chromophores Effect of Molecule Structure

by Iouri E. Borissevitch, Pablo J. Gonçalves, Lucimara P. Ferreira, Alexey A. Kostyukov and Vladimir A. Kuzmin

Technologies 2023, 11(4), 90; https://doi.org/10.3390/technologies11040090 - 8 Jul 2023

Viewed by 1878

Abstract

Quantum yields (φ_T) and energies (E_T) of the first triplet state T₁ for four molecules of cyanine dyes with two chromophores (BCDs), promising photoactive compounds for various applications, for example, as photosensitizers in photodynamic therapy (PDT) [...] Read more.

Quantum yields (φ_T) and energies (E_T) of the first triplet state T₁ for four molecules of cyanine dyes with two chromophores (BCDs), promising photoactive compounds for various applications, for example, as photosensitizers in photodynamic therapy (PDT) and fluorescence diagnostics (FD), were studied in 1-propanol solutions by steady-state and time-resolved optical absorption techniques. BCDs differ by the structure of the central heterocycle, connecting the chromophores. The heterocycle structure is responsible for electron tunneling between chromophores, for which efficiency can be characterized by splitting of the BCD triplet energy levels. It was shown that the increase in the tunneling efficiency reduces E_T values and increases φ_T values. This aspect is very promising for the synthesis of new effective photosensitizers based on cyanine dyes with two interacting chromophores for various applications, including photodynamic therapy. Full article

(This article belongs to the Section Quantum Technologies)

► Show Figures

Figure 1

11 pages, 2121 KiB

Open AccessArticle

Features of Metalorganic Chemical Vapor Deposition Selective Area Epitaxy of Al_zGa_1−zAs (0 ≤ z ≤ 0.3) Layers in Arrays of Ultrawide Windows

by Viktor Shamakhov, Sergey Slipchenko, Dmitriy Nikolaev, Ilya Soshnikov, Alexander Smirnov, Ilya Eliseyev, Artyom Grishin, Matvei Kondratov, Artem Rizaev, Nikita Pikhtin and Peter Kop’ev

Technologies 2023, 11(4), 89; https://doi.org/10.3390/technologies11040089 - 7 Jul 2023

Cited by 2 | Viewed by 1444

Abstract

Al_zGa_1−zAs layers of various compositions were grown using metalorganic chemical vapor deposition on a GaAs substrate with a pattern of alternating SiO₂ mask/window stripes, each 100 µm wide. Microphotoluminescence maps and thickness profiles of Al_zGa_1−z [...] Read more.

Al_zGa_1−zAs layers of various compositions were grown using metalorganic chemical vapor deposition on a GaAs substrate with a pattern of alternating SiO₂ mask/window stripes, each 100 µm wide. Microphotoluminescence maps and thickness profiles of Al_zGa_1−zAs layers that demonstrated the distribution of the growth rate and z in the window were experimentally studied. It was shown that the layer growth rate and the AlAs mole fraction increased continuously from the center to the edge of the window. It was experimentally shown that for a fixed growth time of 10 min, as z increased from 0 to 0.3, the layer thickness difference between the center of the window and the edge increased from 700 Å to 1100 Å, and the maximum change in z between the center of the window and the edge reached Δz 0.016, respectively. Within the framework of the vapor -phase diffusion model, simulations of the spatial distribution of the layer thickness and z across the window were carried out. It was shown that the simulation results were in good agreement with the experimental results for the effective diffusion length D/k: Ga—85 µm, Al—50 µm. Full article

(This article belongs to the Special Issue Advanced Processing Technologies of Innovative Materials)

► Show Figures

Figure 1

18 pages, 753 KiB

Open AccessEditor’s ChoiceReview

Digital Technologies to Provide Humanization in the Education of the Healthcare Workforce: A Systematic Review

by María Gonzalez-Moreno, Carlos Monfort-Vinuesa, Antonio Piñas-Mesa and Esther Rincon

Technologies 2023, 11(4), 88; https://doi.org/10.3390/technologies11040088 - 5 Jul 2023

Cited by 1 | Viewed by 2462

Abstract

Objectives: The need to incentivize the humanization of healthcare providers coincides with the development of a more technological approach to medicine, which gives rise to depersonalization when treating patients. Currently, there is a culture of humanization that reflects the awareness of health professionals, [...] Read more.

Objectives: The need to incentivize the humanization of healthcare providers coincides with the development of a more technological approach to medicine, which gives rise to depersonalization when treating patients. Currently, there is a culture of humanization that reflects the awareness of health professionals, patients, and policy makers, although it is unknown if there are university curricula incorporating specific skills in humanization, or what these may include. Therefore, the objectives of this study are as follows: (1) to identify what type of education in humanization is provided to university students of Health Sciences using digital technologies; and (2) determine the strengths and weaknesses of this education. The authors propose a curriculum focusing on undergraduate students to strengthen the humanization skills of future health professionals, including digital health strategies. Methods: A systematic review, based on the scientific literature published in EBSCO, Ovid, PubMed, Scopus, and Web of Science, over the last decade (2012–2022), was carried out in November 2022. The keywords used were “humanization of care” and “humanization of healthcare” combined both with and without “students”. Results: A total of 475 articles were retrieved, of which 6 met the inclusion criteria and were subsequently analyzed, involving a total of 295 students. Three of them (50%) were qualitative studies, while the other three (50%) involved mixed methods. Only one of the studies (16.7%) included digital health strategies to train humanization. Meanwhile, another study (16.7%) measured the level of humanization after training. Conclusions: There is a clear lack of empirically tested university curricula that combine education in humanization and digital technology for future health professionals. Greater focus on the training of future health professionals is needed, in order to guarantee that they begin their professional careers with the precept of medical humanities as a basis. Full article

(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)

► Show Figures

Figure 1

22 pages, 597 KiB

Open AccessEditor’s ChoiceArticle

Optimizing EMG Classification through Metaheuristic Algorithms

by Marcos Aviles, Juvenal Rodríguez-Reséndiz and Danjela Ibrahimi

Technologies 2023, 11(4), 87; https://doi.org/10.3390/technologies11040087 - 2 Jul 2023

Cited by 15 | Viewed by 2336

Abstract

This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, [...] Read more.

This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications. Full article

(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence)

► Show Figures

Figure 1

18 pages, 4056 KiB

Open AccessEditor’s ChoiceArticle

Enhancement of Handshake Attraction through Tactile, Visual, and Auditory Multimodal Stimulation

by Taishu Kumagai and Yoshimune Nonomura

Technologies 2023, 11(4), 86; https://doi.org/10.3390/technologies11040086 - 1 Jul 2023

Cited by 3 | Viewed by 2222

Abstract

“Handshaking parties”, where pop idols shake hands with fans, can be exciting. The multimodal stimulation of tactile, visual, and auditory sensations can be captivating. In this study, we presented subjects with stimuli eliciting three sensory responses: tactile, visual, and auditory sensations. We found [...] Read more.

“Handshaking parties”, where pop idols shake hands with fans, can be exciting. The multimodal stimulation of tactile, visual, and auditory sensations can be captivating. In this study, we presented subjects with stimuli eliciting three sensory responses: tactile, visual, and auditory sensations. We found that the attraction scores of subjects increased because they felt the smoothness and obtained a human-like sensory experience grasping a grip handle covered with artificial skin, faux fur, and abrasive cloth with their dominant hand as they looked at a picture of a pop idol or listened to a song. When no pictures or songs were presented, a simple feeling of slight warmth was correlated with the attraction score. Results suggest that multimodal stimuli alter tactile sensations and the feelings evoked. This finding may be useful for designing materials that activate the human mind through tactile sensation and for developing humanoid robots and virtual reality systems. Full article

(This article belongs to the Section Information and Communication Technologies)

► Show Figures

Figure 1

10 pages, 217 KiB

Open AccessArticle

Self-Directed and Self-Designed Learning: Integrating Imperative Topics in the Case of COVID-19

by Alireza Ebrahimi

Technologies 2023, 11(4), 85; https://doi.org/10.3390/technologies11040085 - 29 Jun 2023

Cited by 2 | Viewed by 3204

Abstract

Self-directed learning and self-design became unexpectedly popular and common during the COVID-19 era. Learners are encouraged to take charge of their learning and, often the opportunity to independently design their learning experience. This research illustrates the use of technology in teaching and learning [...] Read more.

Self-directed learning and self-design became unexpectedly popular and common during the COVID-19 era. Learners are encouraged to take charge of their learning and, often the opportunity to independently design their learning experience. This research illustrates the use of technology in teaching and learning technology with a central theme of promoting self-directed learning with engaging self-design for both educators and learners. The technology used includes existing tools such as web page design, Learning Management Systems (LMS), project management tools, and basic programming foundations and concepts of big data and databases. In addition, end-users and developers can create their own tools with simple coding. Planning techniques, such as Visual Plan Construct Language with its embedded AI, are used to integrate course material and rubrics with time management. Educators may use project management tools instead. The research proposes a self-directed paradigm with self-designed resources using the existing technology with LMS modules, discussions, and self-tests. The research establishes its criteria for ensuring the quality of content and design, known as 7x2C. Additionally, other criteria for analysis, such as Design Thinking, are included. The approach is examined for a technology-based business course in creating an experiential learning system for COVID-19 awareness. Likewise, among other projects, an environment for educating learners about diabetes and obesity has been designed. The project is known as Sunchoke, which has a theme of Grow, Eat, and Heal. Educators can use their own content and rubrics to adapt this approach to their own customized teaching methods. Full article

(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering)

17 pages, 3624 KiB

Open AccessArticle

Modernizing the Legacy Healthcare System to Decentralize Platform Using Blockchain Technology

by Abdulaziz Aljaloud and Abdul Razzaq

Technologies 2023, 11(4), 84; https://doi.org/10.3390/technologies11040084 - 29 Jun 2023

Cited by 7 | Viewed by 2980

Abstract

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By [...] Read more.

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient. Full article

(This article belongs to the Special Issue The Applications and Challenges of Cybersecurity in Science, Healthcare and Medicine)

► Show Figures

Figure 1

20 pages, 305 KiB

Open AccessSystematic Review

A Survey of Advancements in Real-Time Sign Language Translators: Integration with IoT Technology

by Maria Papatsimouli, Panos Sarigiannidis and George F. Fragulis

Technologies 2023, 11(4), 83; https://doi.org/10.3390/technologies11040083 - 22 Jun 2023

Cited by 11 | Viewed by 17030

Abstract

Real-time sign language translation systems are of paramount importance in enabling communication for deaf and hard-of-hearing individuals. This population relies on various communication methods, including sign languages and visual techniques, to interact with others. While assistive technologies, such as hearing aids and captioning, [...] Read more.

Real-time sign language translation systems are of paramount importance in enabling communication for deaf and hard-of-hearing individuals. This population relies on various communication methods, including sign languages and visual techniques, to interact with others. While assistive technologies, such as hearing aids and captioning, have improved their communication capabilities, a significant communication gap still exists between sign language users and non-users. In order to bridge this gap, numerous sign language translation systems have been developed, encompassing sign language recognition and gesture-based controls. Our research aimed to analyze the advancements in real-time sign language translators developed over the past five years and their integration with IoT technology. By closely examining these technologies, we aimed to attain a deeper comprehension of their practical applications and evolution in the domain of sign language translation. We analyzed the current literature, technical reports, and conference papers on real-time sign language translation systems. Our results offer insights into the current state of the art in real-time sign language translation systems and their integration with IoT technology. We also provide a deep understanding of the recent developments in sign language translation technology and the potential for their fusion with Internet of Things technology to improve communication and promote inclusivity for the deaf and hard-of-hearing population. Full article

(This article belongs to the Collection Technology Advances on IoT Learning and Teaching)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Technologies, Volume 11, Issue 4 (August 2023) – 31 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI