Technologies最新文献

{"title":"An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification","authors":"S. Miclaus, D. Deaconescu, D. Vatamanu, A. Buda","doi":"10.3390/technologies11050113","DOIUrl":"https://doi.org/10.3390/technologies11050113","url":null,"abstract":"To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80900768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review","authors":"Victor Hugo Silva-Blancas, J. M. Álvarez-Alvarado, A. Herrera-Navarro, J. Rodríguez-Reséndíz","doi":"10.3390/technologies11040112","DOIUrl":"https://doi.org/10.3390/technologies11040112","url":null,"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) 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77982314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm","authors":"Rusul Sabah Jebur, Mohd Hazli Mohamed Zabil, D. Hammood, Lim Kok Cheng, Ali Al-Naji","doi":"10.3390/technologies11040111","DOIUrl":"https://doi.org/10.3390/technologies11040111","url":null,"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) 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82826034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems","authors":"Alexander Pastukh, V. Tikhvinskiy, S. Dymkova, O. Varlamov","doi":"10.3390/technologies11040110","DOIUrl":"https://doi.org/10.3390/technologies11040110","url":null,"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 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74017345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Logic System for Classifying Multiple Sclerosis Patients as High, Medium, or Low Responders to Interferon-Beta","authors":"Edgar Rafael Ponce de Leon-Sanchez, J. Mendiola-Santibañez, O. Dominguez-Ramirez, A. Herrera-Navarro, A. Vázquez-Cervantes, Hugo Jiménez-Hernández, H. Sentíes-Madrid","doi":"10.3390/technologies11040109","DOIUrl":"https://doi.org/10.3390/technologies11040109","url":null,"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 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86367779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Techniques for Web-Based Attack Detection in Industry 5.0: A Novel Approach","authors":"Abdu Salam, Faizan Ullah, Farhan Amin, Mohammad Abrar","doi":"10.3390/technologies11040107","DOIUrl":"https://doi.org/10.3390/technologies11040107","url":null,"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 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85490324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach to Quantitative Characterization and Visualization of Color Fading","authors":"W. Yoo, K. Kang, Jung Gon Kim, Yeon-Kwon Yoo","doi":"10.3390/technologies11040108","DOIUrl":"https://doi.org/10.3390/technologies11040108","url":null,"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 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76396138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biodegradable Polyhydroxyalkanoates Formed by 3- and 4-Hydroxybutyrate Monomers to Produce Nanomembranes Suitable for Drug Delivery and Cell Culture","authors":"T. Volova, A. Demidenko, A. Murueva, Alexey E. Dudaev, I. Nemtsev, E. Shishatskaya","doi":"10.3390/technologies11040106","DOIUrl":"https://doi.org/10.3390/technologies11040106","url":null,"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 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83689355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical Detection of Furaltadone Antibiotic Drug by the Rare Earth Metal Tungstate Decorated Screen Printed Carbon Electrode","authors":"S. Vinothini, T. Chiu, S. Sakthinathan","doi":"10.3390/technologies11040105","DOIUrl":"https://doi.org/10.3390/technologies11040105","url":null,"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 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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90891711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Low-Dose CT","authors":"Lu Liu, R. Ma, P. V. van Ooijen, M. Oudkerk, R. Vliegenthart, Raymond N. J. Veldhuis, C. Brune","doi":"10.3390/technologies11040104","DOIUrl":"https://doi.org/10.3390/technologies11040104","url":null,"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. 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±0.20%, mIoU = 67.13±0.39%, sensitivity = 81.47±0.43%, specificity = 99.64±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.","PeriodicalId":22341,"journal":{"name":"Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89113242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}