bluuebunny/crossref_metadata_2025 huggingface dataset:
O-RADS Classification for Ultrasound Assessment of Adnexal Masses: Agreement between IOTA Lexicon and ADNEX Model for Assigning Risk Group
Authors: Vara Julio , Pagliuca Mariachiara , Springer Serena , Gonzalez de Canales Juan , Brotons Isabel , Yakcich Javiera , Ajossa Silvia , Pascual Maria Angela , Guerriero Stefano , Alcazar Juan Luis
Published: February 2023

Background: The O-RADS system is a new proposal for establishing the risk of malignancy of adnexal masses using ultrasound. The objective of this study is to assess the agreement and diagnostic performance of O-RADS when using the IOTA lexicon or ADNEX model for assigning the O-RADS risk group. Methods: Retrospective analysis of prospectively collected data. All women diagnosed as having an adnexal mass underwent transvaginal/transabdominal ultrasound. Adnexal masses were classified according to the O-RADS classification, using the criterion of the IOTA lexicon and according to the risk of malignancy determined by the ADNEX model. The agreement between both methods for assigning the O-RADS group was estimated using weighted Kappa and the percentage of agreement. The sensitivity and specificity of both approaches were calculated. Results: 454 adnexal masses in 412 women were evaluated during the study period. There were 64 malignant masses. The agreement between the two approaches was moderate (Kappa: 0.47), and the percentage of agreement was 46%. Most disagreements occurred for the groups O-RADS 2 and 3 and for groups O-RADS 3 and 4. The sensitivity and specificity for O-RADS using the IOTA lexicon and O-RADS using the ADNEX model were 92.2% and 86.1%, and 85.9% and 87.4%, respectively. Conclusion: The diagnostic performance of O-RADS classification using the IOTA lexicon as opposed to the IOTA ADNEX model is similar. However, O-RADS group assignment varies significantly, depending on the use of the IOTA lexicon or the risk estimation using the ADNEX model. This fact might be clinically relevant and deserves further research.
Circulating Biomarkers for the Early Diagnosis and Management of Hepatocellular Carcinoma with Potential Application in Resource-Limited Settings
Authors: Pan Annabelle , Truong Thai N. , Su Ying-Hsiu , Dao Doan Y
Published: February 2023

Hepatocellular carcinoma (HCC) is among the world’s third most lethal cancers. In resource-limited settings (RLS), up to 70% of HCCs are diagnosed with limited curative treatments at an advanced symptomatic stage. Even when HCC is detected early and resection surgery is offered, the post-operative recurrence rate after resection exceeds 70% in five years, of which about 50% occur within two years of surgery. There are no specific biomarkers addressing the surveillance of HCC recurrence due to the limited sensitivity of the available methods. The primary goal in the early diagnosis and management of HCC is to cure disease and improve survival, respectively. Circulating biomarkers can be used as screening, diagnostic, prognostic, and predictive biomarkers to achieve the primary goal of HCC. In this review, we highlighted key circulating blood- or urine-based HCC biomarkers and considered their potential applications in resource-limited settings, where the unmet medical needs of HCC are disproportionately highly significant.
Spectral CT Imaging of Prosthetic Valve Embolization after Transcatheter Aortic Valve Implantation
Authors: D’Angelo Tommaso , Vizzari Giampiero , Lanzafame Ludovica R. M. , Pergolizzi Federica , Mazziotti Silvio , Gaeta Michele , Costa Francesco , Di Bella Gianluca , Vogl Thomas J. , Booz Christian , Micari Antonio , Blandino Alfredo
Published: February 2023

Transcatheter heart valve (THV) embolization is a rare complication of transcatheter aortic valve implantation (TAVI) generally caused by malpositioning, sizing inaccuracies and pacing failures. The consequences are related to the site of embolization, ranging from a silent clinical picture when the device is stably anchored in the descending aorta to potentially fatal outcomes (e.g., obstruction of flow to vital organs, aortic dissection, thrombosis, etc.). Here, we present the case of a 65-year-old severely obese woman affected by severe aortic valve stenosis who underwent TAVI complicated by embolization of the device. The patient underwent spectral CT angiography that allowed for improved image quality by means of virtual monoenergetic reconstructions, permitting optimal pre-procedural planning. She was successfully re-treated with implantation of a second prosthetic valve a few weeks later.
Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases
Authors: Liao Chiung-Wei , Hsieh Te-Chun , Lai Yung-Chi , Hsu Yu-Ju , Hsu Zong-Kai , Chan Pak-Ki , Kao Chia-Hung
Published: February 2023

Background: When cancer has metastasized to bone, doctors must identify the site of the metastases for treatment. In radiation therapy, damage to healthy areas or missing areas requiring treatment should be avoided. Therefore, it is necessary to locate the precise bone metastasis area. The bone scan is a commonly applied diagnostic tool for this purpose. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation. The study evaluated object detection techniques to improve the efficacy of bone metastases detection on bone scans. Methods: We retrospectively examined the data of 920 patients, aged 23 to 95 years, who underwent bone scans between May 2009 and December 2019. The bone scan images were examined using an object detection algorithm. Results: After reviewing the image reports written by physicians, nursing staff members annotated the bone metastasis sites as ground truths for training. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. The optimal dice similarity coefficient (DSC) in our study was 0.6640, which differs by 0.04 relative to the optimal DSC of different physicians (0.7040). Conclusions: Object detection can help physicians to efficiently notice bone metastases, decrease physician workload, and improve patient care.
Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis
Authors: Fekri-Ershad Shervan , Alsaffar Marwa Fadhil
Published: February 2023

Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.
Dysregulated Levels of Circulating Autoantibodies against Neuronal and Nervous System Autoantigens in COVID-19 Patients
Authors: Lavi Yael , Vojdani Aristo , Halpert Gilad , Sharif Kassem , Ostrinski Yuri , Zyskind Israel , Lattin Miriam , Zimmerman Jason , Silverberg Jonathan , Rosenberg Avi , Shoenfeld Yehuda , Amital Howard
Published: February 2023

Background: COVID-19 is a heterogenous disease resulting in long-term sequela in predisposed individuals. It is not uncommon that recovering patients endure non-respiratory ill-defined manifestations, including anosmia, and neurological and cognitive deficit persisting beyond recovery—a constellation of conditions that are grouped under the umbrella of long-term COVID-19 syndrome. Association between COVID-19 and autoimmune responses in predisposed individuals was shown in several studies. Aim and methods: To investigate autoimmune responses against neuronal and CNS autoantigens in SARS-CoV-2-infected patients, we performed a cross-sectional study with 246 participants, including 169 COVID-19 patients and 77 controls. Levels of antibodies against the acetylcholine receptor, glutamate receptor, amyloid β peptide, alpha-synucleins, dopamine 1 receptor, dopamine 2 receptor, tau protein, GAD-65, N-methyl D-aspartate (NMDA) receptor, BDNF, cerebellar, ganglioside, myelin basic protein, myelin oligodendrocyte glycoprotein, S100-B, glial fibrillary acidic protein, and enteric nerve were measured using an Enzyme-Linked Immunosorbent Assay (ELISA). Circulating levels of autoantibodies were compared between healthy controls and COVID-19 patients and then classified by disease severity (mild [n = 74], severe [n = 65], and requiring supplemental oxygen [n = 32]). Results: COVID-19 patients were found to have dysregulated autoantibody levels correlating with the disease severity, e.g., IgG to dopamine 1 receptor, NMDA receptors, brain-derived neurotrophic factor, and myelin oligodendrocyte glycoprotein. Elevated levels of IgA autoantibodies against amyloid β peptide, acetylcholine receptor, dopamine 2 receptor, myelin basic protein, and α-synuclein were detected in COVID-19 patients compared with healthy controls. Lower IgA autoantibody levels against NMDA receptors, and IgG autoantibodies against glutamic acid decarboxylase 65, amyloid β peptide, tau protein, enteric nerve, and S100-B were detected in COVID-19 patients versus healthy controls. Some of these antibodies have known clinical correlations with symptoms commonly reported in the long COVID-19 syndrome. Conclusions: Overall, our study shows a widespread dysregulation in the titer of various autoantibodies against neuronal and CNS-related autoantigens in convalescent COVID-19 patients. Further research is needed to provide insight into the association between these neuronal autoantibodies and the enigmatic neurological and psychological symptoms reported in COVID-19 patients.
Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology!
Authors: Al-Antari Mugahed A.
Published: February 2023

We would like to express our gratitude to all authors who contributed to the Special Issue of “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis” by providing their excellent and recent research findings for AI-based medical diagnosis [...]
Pulmonary Artery Systolic Pressure and Cava Vein Status in Acute Heart Failure with Preserved Ejection Fraction: Clinical and Prognostic Implications
Authors: Ruocco Gaetano , Pirrotta Filippo , Mingiano Christian , Cavati Guido , Tavera Cristina , Palazzuoli Alberto
Published: February 2023

Background: Peak tricuspid regurgitation (TR) velocity and inferior cava vein (ICV) distention are two recognized features of increased pulmonary artery pressure (PASP) and right atrial pressure, respectively. Both parameters are related to pulmonary and systemic congestion and adverse outcomes. However, few data exist about the assessment of PASP and ICV in acute patients affected by heart failure with preserved ejection fraction (HFpEF). Thus, we investigated the relationship existing among clinical and echocardiographic features of congestion, and we analyzed the prognostic impact of PASP and ICV in acute HFpEF patients. Methods and Results: We analyzed clinical congestion PASP and ICV value in consecutive patients admitted in our ward by echocardiographic examination using peak Doppler velocity tricuspid regurgitation and ICV diameter and collapse for the assessment of PASP and ICV dimension, respectively. A total of 173 HFpEF patients were included in the analysis. The median age was 81 and median left ventricular ejection fraction (LVEF) was 55% [50–57]. Mean values of PASP was 45 mmHg [35–55] and mean ICV was 22 [20–24] mm. Patients with adverse events during follow-up showed significantly higher values of PASP (50 [35–55] vs. 40 [35–48] mmHg, (p = 0.005) and increased values of ICV (24 [22–25] vs. 22 [20–23] mm, p < 0.001). Multivariable analysis showed prognostic power of ICV dilatation (HR 3.22 [1.58–6.55], p = 0.001) and clinical congestion score ≥ 2 (HR 2.35 [1.12–4.93], p = 0.023), but PASP increase did not reach statistical significance (p = 0.874). The combination of PASP > 40 mmHg and ICV > 21 mm was capable of identifying patients with increased events (45% vs. 20%). Conclusions: ICV dilatation provides additional prognostic information with respect to PASP in patients with acute HFpEF. A combined model adding PASP and ICV assessment to clinical evaluation is a useful tool for predicting HF related events.
Early Assessment of Neoadjuvant Chemotherapy Response Using Multiparametric Magnetic Resonance Imaging in Luminal B-like Subtype of Breast Cancer Patients: A Single-Center Prospective Study
Authors: Kovacevic Lucija , Petrovecki Marko , Korsa Lea , Marusic Zlatko , Dumic-Cule Ivo , Prutki Maja
Published: February 2023

This study aimed to evaluate the performance of multiparametric breast magnetic resonance imaging (mpMRI) for predicting response to neoadjuvant chemotherapy (NAC) in patients with luminal B subtype breast cancer. The prospective study included thirty-five patients treated with NAC for both early and locally advanced breast cancer of the luminal B subtype at the University Hospital Centre Zagreb between January 2015 and December 2018. All patients underwent breast mpMRI before and after two cycles of NAC. Evaluation of mpMRI examinations included analysis of both morphological (shape, margins, and pattern of enhancement) and kinetic characteristics (initial signal increase and post-initial behavior of the time-signal intensity curve), which were additionally interpreted with a Göttingen score (GS). Histopathological analysis of surgical specimens included grading the tumor response based on the residual cancer burden (RCB) grading system and revealed 29 NAC responders (RCB-0 (pCR), I, II) and 6 NAC non-responders (RCB-III). Changes in GS were compared with RCB classes. A lack of GS decrease after the second cycle of NAC is associated with RCB class and non-responders to NAC.
Osteomyelitis of the Lower Limb: Diagnostic Accuracy of Dual-Energy CT versus MRI
Authors: Foti Giovanni , Longo Chiara , Sorgato Claudia , Oliboni Eugenio Simone , Mazzi Cristina , Motta Leonardo , Bertoli Giulia , Marocco Stefania
Published: February 2023

Background: MRI is the preferred imaging technique for the identification of osteomyelitis. The key element for diagnosis is the presence of bone marrow edema (BME). Dual-energy CT (DECT) is an alternative tool which is able to identify BME in the lower limb. Purpose: To compare the diagnostic performance of DECT and MRI for osteomyelitis, using clinical, microbiological, and imaging data as reference standards. Materials and Methods: This prospective single-center study enrolled consecutive patients with suspected bone infections undergoing DECT and MRI imaging from December 2020 to June 2022. Four blinded radiologists with various experience levels (range of 3-21 years) evaluated the imaging findings. Osteomyelitis was diagnosed in the presence of BMEs, abscesses, sinus tracts, bone reabsorption, or gaseous elements. The sensitivity, specificity, and AUC values of each method were determined and compared using a multi-reader multi-case analysis. A p value < 0.05 was considered significant. Results: In total, 44 study participants (mean age 62.5 years ± 16.5 [SD], 32 men) were evaluated. Osteomyelitis was diagnosed in 32 participants. For the MRI, the mean sensitivity and specificity were 89.1% and 87.5%, while for the DECT they were 89.0% and 72.9%, respectively. The DECT demonstrated a good diagnostic performance (AUC = 0.88), compared with the MRI (AUC = 0.92) (p = 0.12). When considering each imaging finding alone, the best accuracy was achieved by considering BME (AUC for DECT 0.85 versus AUC of MRI of 0.93, with p = 0.07), followed by the presence of bone erosions (AUC 0.77 for DECT and 0.53 for MRI, with p = 0.02). The inter-reader agreement of the DECT (k = 88) was similar to that of the MRI (k = 90). Conclusion: Dual-energy CT demonstrated a good diagnostic performance in detecting osteomyelitis.
The Value of Deep Learning in Gallbladder Lesion Characterization
Authors: Yin Yunchao , Yakar Derya , Slangen Jules J. G. , Hoogwater Frederik J. H. , Kwee Thomas C. , de Haas Robbert J.
Published: February 2023

Background: The similarity of gallbladder cancer and benign gallbladder lesions brings challenges to diagnosing gallbladder cancer (GBC). This study investigated whether a convolutional neural network (CNN) could adequately differentiate GBC from benign gallbladder diseases, and whether information from adjacent liver parenchyma could improve its performance. Methods: Consecutive patients referred to our hospital with suspicious gallbladder lesions with histopathological diagnosis confirmation and available contrast-enhanced portal venous phase CT scans were retrospectively selected. A CT-based CNN was trained once on gallbladder only and once on gallbladder including a 2 cm adjacent liver parenchyma. The best-performing classifier was combined with the diagnostic results based on radiological visual analysis. Results: A total of 127 patients were included in the study: 83 patients with benign gallbladder lesions and 44 with gallbladder cancer. The CNN trained on the gallbladder including adjacent liver parenchyma achieved the best performance with an AUC of 0.81 (95% CI 0.71–0.92), being >10% better than the CNN trained on only the gallbladder (p = 0.09). Combining the CNN with radiological visual interpretation did not improve the differentiation between GBC and benign gallbladder diseases. Conclusions: The CT-based CNN shows promising ability to differentiate gallbladder cancer from benign gallbladder lesions. In addition, the liver parenchyma adjacent to the gallbladder seems to provide additional information, thereby improving the CNN’s performance for gallbladder lesion characterization. However, these findings should be confirmed in larger multicenter studies.
Tooth Whitening with Hydroxyapatite: A Systematic Review
Authors: Limeback Hardy , Meyer Frederic , Enax Joachim
Published: February 2023

A steadily increasing public demand for whiter teeth has resulted in the development of new oral care products for home use. Hydroxyapatite (HAP) is a new ingredient to whiten teeth. This systematic review focuses on the evidence of whether HAP can effectively whiten teeth. A systematic search using the PICO approach and PRISMA guidelines was conducted using PubMed, Scopus, Web of Science, SciFinder, and Google Scholar as databases. All study designs (in vitro, in vivo) and publications in foreign language studies were included. Of the 279 study titles that the searches produced, 17 studies met the inclusion criteria. A new “Quality Assessment Tool For In Vitro Studies” (the QUIN Tool) was used to determine the risk of bias of the 13 studies conducted in vitro. Moreover, 12 out of 13 studies had a low risk of bias. The in vivo studies were assigned Cochrane-based GRADE scores. The results in vitro and in vivo were consistent in the direction of showing a statistically significant whitening of enamel. The evidence from in vitro studies is rated overall as having a low risk of bias. The evidence from in vivo clinical trials is supported by modest clinical evidence based on six preliminary clinical trials. It can be concluded that the regular use of hydroxyapatite-containing oral care products effectively whitens teeth, but more clinical trials are required to support the preliminary in vivo evidence.
Comparison of the Accuracy of Two Transfer Caps in Positional Transmission of Palatal Temporary Anchorage Devices: An In Vitro Study
Authors: Quinzi Vincenzo , Salvati Simone Ettore , Brutto Valeria , Tasciotti Giorgia , Marzo Giuseppe , Ferrazzano Gianmaria Fabrizio
Published: February 2023

The aim of this study was to compare the positional information transfer accuracy of palatal temporary anchorage devices (TADs) of two different brands of transfer caps: PSM and Leone. Thirty plaster casts of maxillary dental arches were chosen for master models. A couple of Leone TADs were inserted in each master model. For each master model, two analysis models were created: using two transfer caps, Leone and PSM, the impressions were taken, the analogues were connected on the transfer caps, and the casts were poured. Using digital methods and equipment, such as a 3D scanner, a 3D analysis and a comparison of the accuracy of the two transfer caps in transferring the positional information of the TADs was then made. The data obtained were analyzed using the Mann–Whitney U-test at a significance level of α = 0.05. PSM transfer caps showed higher error frequency in almost all measurements. Only two measurements had a larger error in the analysis models made with Leone transfer caps. The Mann–Whitney U-test found a significant difference between the error levels of TADs found in the analysis models created with PSM transfer caps. Leone transfer caps showed greater reliability in TADs positional information transmission.
Capturing the Effect of the COVID-19 Pandemic Outbreak on the Financial Performance Disparities in the Energy Sector: A Hybrid MCDM-Based Evaluation Approach
Authors: Makki Anas A. , Alqahtani Ammar Y.
Published: February 2023

The COVID-19 pandemic has impacted the financial performance of companies in different sectors. One of the most important sectors affected is the energy sector. Therefore, this research study aims to conduct a financial performance evaluation of companies in the Saudi energy sector in 2019, 2020, and 2021. This is to capture and analyze the effect on companies before, during, and post the COVID-19 pandemic, respectively. A hybrid multicriteria decision-making (MCDM) approach is followed to achieve the objective. The financial performance evaluation of energy companies is based on four financial dimensions and 11 performance indicators. Results revealed that energy companies’ efficiency and profitability were relatively the most important dimensions, followed by leverage and liquidity. Furthermore, results revealed the relative importance of the indicators from the most to the least important. Results also revealed the effect of COVID-19 on energy companies’ financial performance, demonstrating the change over the three years. Implications include providing insights for energy companies on the financial dimensions and indicators to be maintained and monitored in the case of pandemics. Furthermore, to assist decision-makers in the energy sector in developing strategies to mitigate financial performance disparities during and post-pandemics.
Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection
Authors: Yulita Intan Nurma , Hariz Fauzan Akmal , Suryana Ino , Prabuwono Anton Satria
Published: February 2023

Because the COVID-19 epidemic has limited human activities, it has touched almost every sector. Education is one of the most affected areas. To prevent physical touch between students, schools and campuses must adapt their complete learning system to an online environment. The difficulty with this technique arises when the teachers or lecturers administer exams. It is difficult to oversee pupils one by one online. This research proposes the development of a computer program to aid in this effort. By applying deep learning models, this program can detect a person’s activities during an online exam based on a web camera. The reliability of this system is 84.52% based on the parameter F1-score. This study built an Indonesian-language web-based application. Teachers and lecturers in Indonesia can use this tool to evaluate whether students are cheating on online exams. Unquestionably, this application is a tool that may be utilized to develop distance learning educational technology in Indonesia.
Comparing Contact Education and Digital Distant Pedagogy Strategies: Lockdown Lessons Learnt for University-Level Teacher Education
Authors: Salmi Hannu , Hienonen Ninja , Nyman Laura , Kaasinen Arja , Thuneberg Helena
Published: February 2023

Teaching and learning experienced a rapid change in spring 2020, and the learning environments for university students changed almost overnight. An integrative science centre education informal learning course for Finnish teaching students has been arranged for over 20 years with latest technologies and innovations. This cross-sectional study compared survey data of teaching students between four time points: in 2019 (n = 108), in 2020 (n = 115), in 2021 (n = 110), and in 2022 (n = 90). The course content was the same, only the implementation differed. In the spring of 2019 and 2022 the course was implemented as contact-teaching, but during the critical phase of the pandemic in 2020 and 2021 as distant teaching. Data were analysed by using ANOVA and the prediction of possible effects of the contact/distance learning by the structural equation path model (SEM). The analysis showed that the results favoured the first contact instruction course in 2019: their confidence of integrating the learned contents of the science centre into practical school matters differed from all the other groups. This group also appreciated the usefulness of the course more than the other groups. In turn, the first distant course had a more negative opinion of the usability of the course than all the other groups. Despite that the distant group in 2020 and even more so the other distant group in 2021 felt more confidence in the direct integration of the course content into future teaching based on the path analysis. Gender had two kinds of effects, one in the distance learning group in 2020: being a female directly predicted the future use of science centre type pedagogy, and the other in the contact learning group in 2022: being a female predicted the appreciation of the science centre course. As a limitation of the study, more students’ prior experience and attitudes with online learning is needed from future research. The preliminary results and best practises of this study are utilised internationally in several EU-Erasmus+ projects.
Job Satisfaction and Teacher Education: Correlational Study in Postgraduate Graduates in Education
Authors: Pérez Fuentes Carlos Alfredo , Alvarez Maestre Annie Julieth , Cardona Rivas Angela María , Aguilar Barreto Andrea Johanna , Sanabria Alarcón Ruth Katherine
Published: February 2023

Teacher education is recognized as the process of improving teaching practices. During their postgraduate studies, aspiring teachers are encouraged to improve both their teaching practice and their working conditions. Therefore, an expectation for their Masters’ degrees is that upon graduation, they can achieve job satisfaction. This study had a quantitative approach with a cross-sectional and correlational non-experimental research design. Its objective was to analyze the level of job satisfaction of working teachers after graduating with a Master’s degree in Education, and with this, to correlate sociodemographic variables with the level of job satisfaction. As an information-collection technique, the Job Satisfaction Scale for Teachers (ESLA) was used, which has reliability for internal consistency of 0.88 in Cronbach’s alpha. The results show that teachers graduating with a Master’s degree in Education score significant levels of job satisfaction, ranging between high and moderate, confirming that, as in other studies, with greater possibilities of qualification and teacher preparation, job satisfaction is maintained or generated. The statistical hypothesis testing method of inferential statistics confirmed that moderate and high levels of job satisfaction can be maintained in working teachers after graduating from postgraduate training, without implying relationships with gender, the type of hiring, doctoral degrees, type of contracts or the type of educational institution where they work.
An n-Sigmoid Activation Function to Improve the Squeeze-and-Excitation for 2D and 3D Deep Networks
Authors: Mulindwa Desire Burume , Du Shengzhi
Published: February 2023

The Squeeze-and-Excitation (SE) structure has been designed to enhance the neural network performance by allowing it to execute positive channel-wise feature recalibration and suppress less useful features. SE structures are generally adopted in a plethora of tasks directly in existing models and have shown actual performance enhancements. However, the various sigmoid functions used in artificial neural networks are intrinsically restricted by vanishing gradients. The purpose of this paper is to further improve the network by introducing a new SE block with a custom activation function resulting from the integration of a piecewise shifted sigmoid function. The proposed activation function aims to improve the learning and generalization capacity of 2D and 3D neural networks for classification and segmentation, by reducing the vanishing gradient problem. Comparisons were made between the networks with the original design, the addition of the SE block, and the proposed n-sigmoid SE block. To evaluate the performance of this new method, commonly used datasets, CIFAR-10 and Carvana for 2D data and Sandstone Dataset for 3D data, were considered. Experiments conducted using SE showed that the new n-sigmoid function results in performance improvements in the training accuracy score for UNet (up 0.25% to 99.67%), ResNet (up 0.9% to 95.1%), and DenseNet (up 1.1% to 98.87%) for the 2D cases, and the 3D UNet (up 0.2% to 99.67%) for the 3D cases. The n-sigmoid SE block not only reduces the vanishing gradient problem but also develops valuable features by combining channel-wise and spatial information.
Study on Automatic Electric Vehicle Charging Socket Detection Using ZED 2i Depth Sensor
Authors: Tadic Vladimir
Published: February 2023

This article introduces the utilization of the ZED 2i depth sensor in a robot-based automatic electric vehicle charging application. The employment of a stereo depth sensor is a significant aspect in robotic applications, since it is both the initial and the fundamental step in a series of robotic operations, where the intent is to detect and extract the charging socket on the vehicle’s body surface. The ZED 2i depth sensor was utilized for scene recording with artificial illumination. Later, the socket detection and extraction were accomplished using both simple image processing and morphological operations in an object extraction algorithm with tilt angles and centroid coordinates determination of the charging socket itself. The aim was to use well-known, simple, and proven image processing techniques in the proposed method to ensure both reliable and smooth functioning of the robot’s vision system in an industrial environment. The experiments demonstrated that the deployed algorithm both extracts the charging socket and determines the slope angles and socket coordinates successfully under various depth assessment conditions, with a detection rate of 94%.
Handling Class Imbalance and Class Overlap in Machine Learning Applications for Undeclared Work Prediction
Authors: Alogogianni Eleni , Virvou Maria
Published: February 2023

Undeclared work is a composite socioeconomic matter severely affecting the welfare of workers, legitimate companies, and the state by issuing unfair competition in the labour market and causing considerable state revenue losses by tax evasion. Labour inspectorates are tasked to deal effectively with this issue but usually lack adequate resources and proper tools, yet they own large volumes of past inspection data that, if aptly processed through innovative machine learning techniques, may produce understandable insights into the extent and prevailing patterns of undeclared work and efficient tools to address it. Such datasets are typically imbalanced regarding undeclared work, and contain overlapping inspection discoveries, two issues that impede the learning process. This research points to the problems of class imbalance and class overlap in this domain and applies combinations of data engineering techniques to address them using a dataset of 16.7 K actual labour inspections. Three associative classification algorithms are employed, and multiple classifiers are built and assessed for their predictability and interpretability. The study indicates the overall benefits for the inspection authorities when integrating machine learning methods in targeting undeclared work and proves considerable prediction performance improvement when following data engineering approaches to address the class imbalance and class overlap issues.
Automatic Cube Counting System for the Box and Blocks Test Using Proximity Sensors: Development and Validation
Authors: Oña Edwin Daniel , Balaguer Carlos , Jardón Alberto
Published: February 2023

The Box and Blocks Test (BBT) is a widely used outcome measure for manual dexterity assessments in neurological rehabilitation. The BBT score is based on the maximum number of cubes that a person is able to displace during a 60s time window. In this paper, a low-cost instrumented system to automatically obtain the number of cubes using proximity sensors is presented. For that purpose, the central partition of the BBT was sensorized, aiming to minimise the employed sensors and minimally alter the physical BBT box. The counting system, connected to the mobile app, allows for the self-administration of the test as users only need to follow the presented instructions. Firstly, the methodology used to automate the test scoring is presented, including the sensors’ description and the prototype design. Then, the obtained success rate in cube counting is shown, with an average of 98% in trials with five healthy users. Finally, the conclusions and future work are shown. The results support the use of automated methods for upper limb assessment, providing more objective results and additional information about user performance.
A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
Authors: Sun Wenyu , Zhang Weijia , Ma Ning , Jia Min
Published: February 2023

In light of the increasing scarcity of frequency spectrum resources for satellite communication systems based on the transparent transponder, fast and efficient satellite resource allocation algorithms have become key to improving the overall resource occupancy. In this paper, we propose a reinforcement learning-based Multi-Branch Deep Q-Network (MBDQN), which introduces TL-Branch and RP-Branch to extract features of satellite resource pool state and task state simultaneously, and Value-Branch to calculate the action-value function. On the one hand, MBDQN improves the average resource occupancy performance (AOP) through the selection of multiple actions, including task selection and resource priority actions. On the other hand, the trained MBDQN is more suitable for online deployment and significantly reduces the runtime overhead due to the fact that MBDQN does not need iteration in the test phase. Experiments on both non-zero waste and zero waste datasets demonstrate that our proposed method achieves superior performance compared to the greedy or heuristic methods on the generated task datasets.
Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability
Authors: Kim Jongwook , Lim Byungjin
Published: February 2023

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.
A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification
Authors: Ma Pengfei , Liu Yuesen , Li Lin , Zhu Zhigang , Li Bin
Published: February 2023

Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment.
Fast Wide-Band RCS Analysis of the Coated Target Based on PBR Using EFIE-PMCHWT and the Chebyshev Approximation Technique
Authors: Wang Xing , Yang Fufu , Liu Chunheng , Liu Ying , Gong Haoxuan , Zhang Hairong
Published: February 2023

The Chebyshev approximation technique (CAT) combined with the MoM based on the electric-field integral equation (EFIE) and the Poggio–Miller–Chang–Harrington–Wu–Tsai (PMCHWT) integral equation is proposed to efficiently calculate the wide-band radar cross-section (RCS) based on passive bistatic radars (PBR). The EFIE-PMCHWT equations can be used to analyze the electromagnetic scattering of coated targets. The combination with CAT only requires computing the electric and magnetic currents at a few Chebyshev frequency points, which can be employed to obtain the electric and magnetic currents over the entire frequency band. In this study, the RCS values of a coated target calculated by the hybrid EFIE-PMCHWT-CAT based on passive bistatic radar (PBR) were found to be consistent with that calculated by the MoM based on the EFIE-PMCHWT. The validity of the hybrid method is verified by several numerical examples. Compared with the conventional MoM method, the hybrid method can greatly improve the efficiency for electromagnetic scattering problems over a wide frequency band.
Recent Advances in Educational Robotics
Authors: Chatzichristofis Savvas A.
Published: February 2023

The widespread use of artificial intelligence and robotics contributes, among other things, to create a new scientific field that aims to modernize and disrupt education. The term ’educational robotics’ is being introduced as a learning tool and definitively transforming young people’s education. At the same time, however, it is helping to create a fast-growing new industry that produces educational robots and tools. Companies with a long tradition, either in the creation of robotic equipment or in the production and distribution of toys, are setting up appropriate divisions and supplying the market with electronic devices for educational robotics. This new market is overgrowing and is rapidly becoming an investment attraction. According to MarketsandMarkets research, the educational robotics market is projected to grow from USD 1.3 billion in 2021 to USD 2.6 billion by 2026. Notably, the educational robotics market is expected to grow at a Compound Annual Growth Rate (CAGR) of 16.1% from 2021 to 2026. At the same time, however, the field is attracting many startups securing independent funding for equipment design and implementation and independent efforts competing for funding from crowdfunding platforms. More than 2000 ideas have recently secured funding to build and distribute educational robotics tools through Kickstarter-type platforms. However, what is educational robotics, and how is it expected to transform how the next generation is educated?
A Novel Ensemble Based Reduced Overfitting Model with Convolutional Neural Network for Traffic Sign Recognition System
Authors: Shanmugavel Anantha Babu , Ellappan Vijayan , Mahendran Anand , Subramanian Murali , Lakshmanan Ramanathan , Mazzara Manuel
Published: February 2023

The ELVD (Ensemble-based Lenet VGGNet and DropoutNet) model is used in this paper to examine hypothetical principles and theoretical identification of a real-time image classification and object, tracking, and recognition device running on board a vehicle. Initially, we obtained the dataset from Kaggle. After loading the images, they were converted into 4D tensors and then into a grid. The model has to set the training to 70% training and 30% testing. The ELVD model uses 39,209 32 × 32-pixel color images for preparation, and 12,630 images specifically for research, in the GTSD (German Traffic Sign Detection) dataset. Each picture is a photograph of a traffic sign that corresponds to one of the 43 classes. The picture is a 32 × 32 × 3 sequence of pixel quality values in the RGB color region, defined as pixel values. The image’s class is hidden as a numerical value from 0 to 42. The image collection is somewhat unbalanced, and a few classes are represented significantly better than in the alternative model. The contrast and brightness of the images also differ significantly. The proposed model was created with CNN with Keras and applied with ensemble-based combined LeNet, VGGNet, and DropoutNet pooling layer for tuning the information. The proposed model compares the predicted class with the correct class for all input images and time calculation for predicting different road sign detection images. Underfitting is shown by a standard of low accuracy on the training and testing sets. For a small dataset, the trained model achieved a 98% accuracy level which implied that overfitting the model with the best results on classification accuracy, tested with 15 epochs, resulted in a loss of information of 0.059% and test accuracy of 98%, respectively. Next, the ELVD proposed models trained and validated with different class presents, dataset 2 achieved 93% training accuracy and testing accuracy predicted with 91%. Finally, the ELVD proposed model predicted the test results of unseen class information measured with the 60/km ph, which predicted 99% accuracy. The proposed model predicted noisy as well as unseen multiclass information with fast-moving vehicles. The usage of convolutional layer filter with ensemble-based VGGNet, DropouNet, and LeNet trained and predicted with a high classification accuracy of more than 99% combined ELVD model with fastest time calculation also the high accuracy prediction of selected image dataset labels that enables these models to be used in real-time applications. The ELVD model was also compared with other traditional models of VGGNet, LeNet, and DropoutNet; its detection time outperformed the other models, and it achieved a 98% detection label set of information. In the ELVD model, closure to various human abilities on a related responsibility differs from 97.3% to 99.5%; consequently, the ELVD model performs better than an average human.
Knowledge Engineering and Data Mining
Authors: Konys Agnieszka , Nowak-Brzezińska Agnieszka
Published: February 2023

Knowledge engineering and data mining are the two biggest pillars of modern intelligent systems [...]
Intrusion Detection System Based on One-Class Support Vector Machine and Gaussian Mixture Model
Authors: Wang Chao , Sun Yunxiao , Lv Sicai , Wang Chonghua , Liu Hongri , Wang Bailing
Published: February 2023

Intrusion detection systems (IDSs) play a significant role in the field of network security, dealing with the ever-increasing number of network threats. Machine learning-based IDSs have attracted a lot of interest owing to their powerful data-driven learning capabilities. However, it is challenging to train the supervised learning algorithms when there are no attack data at hand. Semi-supervised anomaly detection algorithms, which train the model with only normal data, are more suitable. In this study, we propose a novel semi-supervised anomaly detection-based IDS that leverages the capabilities of representation learning and two anomaly detectors. In detail, the autoencoder (AE) is applied to extract representative features of normal data in the first step, and then two semi-supervised detectors, the one-class support vector machine (OCSVM) and Gaussian mixture model (GMM), are trained on the derived features. The two detectors collaborate to detect anomalous samples. The OCSVM predicts the abnormal samples initially, and after that, the GMM is applied to recheck the misclassified samples further. The experiments demonstrate that the AE improves the detection rate, and two detectors are more promising than a single one.
Solid State Transformers: A Critical Review of Projects with Relevant Prototypes and Demonstrators
Authors: Cervero David , Fotopoulou Maria , Muñoz-Cruzado Jesús , Rakopoulos Dimitrios , Stergiopoulos Fotis , Nikolopoulos Nikos , Voutetakis Spyros , Sanz José Francisco
Published: February 2023

The ongoing energy transition has changed the architecture of electricity networks in ways that conventional power transformers are not able to cope with the new required functionalities. For this purpose, the solid state transformer (SST), which comprises state of the art power electronics with galvanic isolation to interconnect two separate alternating current (AC) or direct current (DC) power grids, is considered to be the dominant solution. The purpose of this paper is to provide a practical, application-oriented review of the SST. In this context, the main functionalities and possible applications of the SST are presented, including smart grids (SGs), data centres, railways, offshore wind farms, etc. Furthermore, the main developed SST prototypes are analysed with special focus on the related projects, demonstrators, stakeholders and rated values, e.g., voltage, switching frequency and power. The analysis is concluded with the future trends and challenges regarding the wider implementation of SST technology in the electrical grid.