bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Patients Who Developed Pneumothorax Due to COVID-19
Authors: Çoraplı Gökhan , Tekin Veysi
Published: September 2022

Background: Although SARS-CoV-2 infection often affects the lungs, pneumothorax is extremely rare. The aim of this study was to analyze the characteristics of patients who were hospitalized due to COVID-19 and who developed pneumothorax (PNX) and to analyze their risk factors. Methods: Patients who developed PNX, among the patients who were followed up in the hospital due to COVID-19 between 1 April 2020 and 1 April 2022, were included in the study. The mean and standard deviation values of the descriptive statistics were obtained from patient data. The entire application was carried out using IBM SPSS 26 (IBM Corp., Armonk, NY, USA). p values < 0.05 were considered statistically significant. Results: We observed that advanced age and male gender increase the risk of COVID-19 patientsdeveloping PNX, but smoking, sepsis, and being followed-up with mechanical ventilation do not increase this risk. In addition, we observed that the presence of an additional disease increases the mortality rate. Conclusion: We show that advanced age and male gender increase the risk for PNX, which is a rare complication of COVID-19, and that comorbidity is associated with mortality in these patients.
Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review
Authors: Ghrabli Syrine , Elgendi Mohamed , Menon Carlo
Published: September 2022

In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual’s health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets.
Diagnosis and Treatment of Inflammatory Pseudotumor with Lower Cranial Nerve Neuropathy by Endoscopic Endonasal Approach: A Systematic Review
Authors: Huang Sheng-Han , Chuang Chi-Cheng , Huang Chien-Chia , Jung Shih-Ming , Lee Cheng-Chi
Published: September 2022

Inflammatory pseudotumor (IPT) is a non-neoplastic condition of unknown etiology. IPT with lower cranial nerve (CN IX, X, XI, XII) neuropathies is extremely rare. In this study, we systematically reviewed all previously reported cases regarding the management of IPT with lower cranial nerve neuropathies. We searched the PubMed/MEDLINE database for reports related to IPT with lower cranial nerve neuropathies. A total of six papers with 10 cases met the inclusion criteria (mean age 51.6 years, 70% male). The mean follow-up period was 15.4 months (range: 1–60 months). The most frequent treatment was corticosteroids alone (60%), followed by surgery alone (20%), and multimodal treatment (20%). Corticosteroid therapy was associated with an excellent (100%) response rate at 6 months of follow-up, and half of the patients were in complete remission after 9 months. Both cases who received surgery had persistent neurological deficits. Immediate complete remission of neurological symptoms and resolution on imaging after decompression surgery via an endoscopic endonasal approach (EEA) with corticosteroids was demonstrated in our representative case. This review suggests that EEA is a preferred method for diagnosis and treatment, a promising approach associated with favorable outcomes, and a viable first-line treatment for selected cases, followed by multimodal therapy.
T Cell and Antibody Response Following Double Dose of BNT162b2 mRNA Vaccine in SARS-CoV-2 Naïve Heart Transplant Recipients
Authors: Delrue Leen , Muylaert Annelies , Beernaert Ann , De Pelsmaeker Imke , Boel Elly , Moya Ana , Verstreken Sofie , Dierckx Riet , Heggermont Ward , Bartunek Jozef , Vanderheyden Marc
Published: September 2022

Introduction: Preliminary studies have suggested a low post-vaccination antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in heart transplant(HTx)recipients. Although many studies have focused on the role of antibodies in vaccine-induced protection against SARS-CoV-2, the role of T cell immunity is less well characterized. To date, data regarding seroconversion and T cell response after mRNA SARS-CoV-2 vaccination in patients undergoing HTx are scarce. Therefore, the present study aimed to assess the specific memory humoral and cellular responses after two doses of the BNT162b2 vaccine in HTx recipients. Methods: Blood was drawn from heart transplant (HTx) recipients at two pre-specified time points after the first and second vaccine doses to measure both the anti-SARS-CoV-2 antibody response against the spike protein and the SARS-CoV-2-reactive T cell response. Results: Our study included 34 SARS-CoV-2 naïve HTx recipients (mean age, 61 ± 11 years). The mean time from transplantation to the first vaccine dose is 10 ± 10 years. Subgroup analysis (n = 21) demonstrated that after the first vaccine dose, only 14% had antibodies and 19% had a SARS-CoV-2-reactive T-cell response, which increased to 41% and 53%, respectively, after the second dose. Interestingly, 20% of patients with no antibodies after the second dose still had a positive SARS-CoV-2-reactive T cell response. The percentage of patients with positive S-IgG antibody titers was significantly higher 5 years after transplantation (18% 0–5 years post-TX vs. 65% 5 years post-TX, p = 0.013). Similarly, 5 years after heart transplantation, the percentage of patients with a T cell response was significantly higher (35% 0–5 years post-TX vs. 71% 5 years post-TX, p = 0.030). Conclusions: In SARS-CoV-2 naïve HTx recipients, post-vaccination antibody titers but also SARS-CoV-2 specific T cell response are low. Therefore, the protection from SARS-CoV-2 that is generally attributed to vaccination should be regarded with caution in HTx recipients.
Reducing Visceral-Motion-Related Artifacts on the Liver with Dual-Energy CT: A Comparison of Four Different CT Scanner Techniques
Authors: Grosu Sergio , Vijittrakarnrung Korawan , Wang Zhen J. , Obmann Markus M. , Sun Yuxin , Sugi Mark D. , Yeh Benjamin M.
Published: September 2022

Purpose: To assess the influence of different dual-energy CT (DECT) scanner techniques on the severity of visceral-motion-related artifacts on the liver. Methods: Two independent readers retrospectively evaluated visceral-motion-related artifacts on the liver on 120-kVp(-like), monoenergetic low- and high-keV, virtual non-contrast (VNC), and iodine images acquired on a dual-source, twin-beam, fast kV-switching, and dual-layer spectral detector scanner. Quantitative assessment: Depth of artifact extension into the liver, measurements of Hounsfield Units (HU) and iodine concentrations. Qualitative assessment: Five-point Likert scale (1 = none to 5 = severe). Artifact severity between image reconstructions were compared by Wilcoxon signed-rank and paired t-tests. Results: 615 contrast-enhanced routine clinical DECT scans of the abdomen were evaluated in 458 consecutive patients (mean age: 61 ± 14 years, 331 men). For dual-source and twin-beam scanners, depth of extension of artifacts into the liver was significantly shorter and artifact severity scores significantly lower for 120-kVp-like images compared with the other image reconstructions (p < 0.001, each). For fast kV-switching and spectral detector scanner images, depth of extension of artifacts was significantly shorter and artifact severity scores significantly lower for iodine images (p < 0.001, each). Dual-source 120-kVp-like and spectral detector iodine images reduced artifacts to an extent that no significant difference in HU or iodine concentrations between artifacts (dual-source: 97 HU, spectral detector: 1.9 mg/mL) and unaffected liver parenchyma (dual-source: 108 HU, spectral detector: 2.1 mg/mL) was measurable (dual-source: p = 0.32, spectral detector: p = 0.15). Conclusion: Visceral-motion-related artifacts on the liver can be markedly reduced by viewing 120-kVp-like images for dual-source and twin-beam DECT scanners and iodine images for fast kV-switching and dual-layer spectral detector DECT scanners.
Loeffler Endocarditis Causing Heart Failure with Preserved Ejection Fraction (HFpEF): Characteristic Images and Diagnostic Pathway
Authors: Lupu Silvia , Pop Marian , Mitre Adriana
Published: September 2022

We report the case of a 69-year-old female patient in which echocardiography and cardiac magnetic resonance imaging were used to diagnose a patient presenting with heart failure with preserved ejection fraction (HFpEF) due to Loeffler endocarditis. Loeffler endocarditis is an uncommon cause of heart failure with preserved ejection fraction, triggered by eosinophil and lymphocyte infiltration of the endomyocardium, followed by the formation of thrombus in the afflicted area, and eventually fibrosis. This condition is due to an increased number of eosinophils associated with allergies, infections, systemic conditions, as well as malignancies and hypereosinophilic syndrome. Loeffler endocarditis can lead to serious complications, such as progressive heart failure, systemic thromboembolic events, or arrhythmias (including sudden cardiac death).
Quantum Dot-Based Lateral Flow Immunoassay as Point-of-Care Testing for Infectious Diseases: A Narrative Review of Its Principle and Performance
Authors: Ahmad Najib Mohamad , Selvam Kasturi , Khalid Muhammad Fazli , Ozsoz Mehmet , Aziah Ismail
Published: September 2022

Infectious diseases are the world’s greatest killers, accounting for millions of deaths worldwide annually, especially in low-income countries. As the risk of emerging infectious diseases is increasing, it is critical to rapidly diagnose infections in the early stages and prevent further transmission. However, current detection strategies are time-consuming and have exhibited low sensitivity. Numerous studies revealed the advantages of point-of-care testing, such as those which are rapid, user-friendly and have high sensitivity and specificity, and can be performed at a patient’s bedside. The Lateral Flow Immunoassay (LFIA) is the most popular diagnostic assay that fulfills the POCT standards. However, conventional AuNPs-LFIAs are moderately sensitive, meaning that rapid detection remains a challenge. Here, we review quantum dot (QDs)-based LFIA for highly sensitive rapid diagnosis of infectious diseases. We briefly describe the principles of LFIA, strategies for applying QDs to enhance sensitivity, and the published performance of the QD-LFIA tested against several infectious diseases.
Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery
Authors: Sangjan Worasit , Carpenter-Boggs Lynne A. , Hudson Tipton D. , Sankaran Sindhuja
Published: September 2022

Pasture management approaches can determine the productivity, sustainability, and ecological balance of livestock production. Sensing techniques potentially provide methods to assess the performance of different grazing practices that are more labor and time efficient than traditional methods (e.g., soil and crop sampling). This study utilized high-resolution satellite and unmanned aerial system (UAS) imagery to evaluate vegetation characteristics of a pasture field location with two grazing densities (low and high, applied in the years 2015–2019) and four fertility treatments (control, manure, mineral, and compost tea, applied annually in the years 2015–2019). The pasture productivity was assessed through satellite imagery annually from the years 2017 to 2019. The relation and variation within and between the years were evaluated using vegetation indices extracted from satellite and UAS imagery. The data from the two sensing systems (satellite and UAS) demonstrated that grazing density showed a significant effect (p < 0.05) on pasture crop status in 2019. Furthermore, the mean vegetation index data extracted from satellite and UAS imagery (2019) had a high correlation (r ≥ 0.78, p < 0.001). These results show the potential of utilizing satellite and UAS imagery for crop productivity assessment applications in small to medium pasture research and management.
Insecticidal Management of Rangeland Grasshoppers Using a Remotely Piloted Aerial Application System
Authors: Martin Daniel E. , Rodriguez Roberto , Woller Derek A. , Reuter K. Chris , Black Lonnie R. , Latheef Mohamed A. , Taylor Mason , López Colón Kiara M.
Published: September 2022

Grasshoppers are integral parts of rangeland ecosystems but also have the potential to reach population densities high enough (outbreaks) to cause serious economic damage from forage loss and affect adjacent crops. The objective of this study was to investigate the efficacy of treating grasshopper population hotspots with a liquid insecticide using a remotely piloted aerial application system (RPAAS), as opposed to fixed-wing aircraft, which is the most common method currently in use. A liquid insecticide, Sevin XLR PLUS (containing carbaryl), was applied on replicated 4.05-hectare (10-acre) plots with an RPAAS on a ranch in New Mexico. Our results demonstrated that Sevin XLR PLUS significantly suppressed grasshopper populations over a 14-day period (normalized population reduction was 79.11 ± 8.35% SEM) and quite rapidly (mostly by day 3) compared to untreated controls. These results are comparable to those achieved with fixed-wing aircraft. The RPAAS covered the whole test area in a single flight in approximately 5 min, making these population hotspot treatment applications relatively rapid, potentially more cost-effective, and more targeted in comparison to fixed-wing aircraft. Before adoption as an application method option, further research is recommended on using an RPAAS to cover larger areas in combination with using diflubenzuron-based insecticides, which are often preferred.
Quantifying Understory Vegetation Cover of Pinus massoniana Forest in Hilly Region of South China by Combined Near-Ground Active and Passive Remote Sensing
Authors: Wang Ruifan , Bao Tiantian , Tian Shangfeng , Song Linghan , Zhong Shuangwen , Liu Jian , Yu Kunyong , Wang Fan
Published: September 2022

Understory vegetation cover is an important indicator of forest health, and it can also be used as a proxy in the exploration of soil erosion dynamics. Therefore, quantifying the understory vegetation cover in hilly areas in southern China is crucial for facilitating the development of strategies to address local soil erosion. Nevertheless, a multi-source data synergy has not been fully revealed in the remote sensing data quantifying understory vegetation in this region; this issue can be attributed to an insufficient match between the point cloud 3D data obtained from active and passive remote sensing systems and the UAV orthophotos, culminating in an abundance of understory vegetation information not being represented in two dimensions. In this study, we proposed a method that combines the UAV orthophoto and airborne LiDAR data to detect the understory vegetation. Firstly, to enhance the characterization of understory vegetation, the point CNN model was used to decompose the three-dimensional structure of the pinus massoniana forest. Secondly, the point cloud was projected onto the UAV image using the point cloud back-projection algorithm. Finally, understory vegetation cover was estimated using a synthetic dataset. Canopy closure was divided into two categories: low and high canopy cover. Slopes were divided into three categories: gentle slopes, inclined slopes, and steep slopes. To clearly elucidate the influence of canopy closure and slope on the remote sensing estimation of understory vegetation coverage, the accuracy for each category was compared. The results show that the overall accuracy of the point CNN model to separate the three-dimensional structure of the pinus massoniana forest was 74%, which met the accuracy requirement of enhancing the understory vegetation. This method was able to obtain the understory vegetation cover more accurately at a low canopy closure level (Rlow2 = 0.778, RMSElow = 0.068) than at a high canopy closure level (RHigh2 = 0.682, RMSEHigh = 0.172). The method could also obtain high accuracy in version results with R2 values of 0.875, 0.807, and 0.704, as well as RMSE of 0.065, 0.106, and 0.149 for gentle slopes, inclined slopes, and steep slopes, respectively. The methods proposed in this study could provide technical support for UAV remote sensing surveys of understory vegetation in the southern hilly areas of China.
The Quantum Geometric Tensor in a Parameter-Dependent Curved Space
Authors: Austrich-Olivares Joan A. , Vergara Jose David
Published: September 2022

We introduce a quantum geometric tensor in a curved space with a parameter-dependent metric, which contains the quantum metric tensor as the symmetric part and the Berry curvature corresponding to the antisymmetric part. This parameter-dependent metric modifies the usual inner product, which induces modifications in the quantum metric tensor and Berry curvature by adding terms proportional to the derivatives with respect to the parameters of the determinant of the metric. The quantum metric tensor is obtained in two ways: By using the definition of the infinitesimal distance between two states in the parameter-dependent curved space and via the fidelity susceptibility approach. The usual Berry connection acquires an additional term with which the curved inner product converts the Berry connection into an object that transforms as a connection and density of weight one. Finally, we provide three examples in one dimension with a nontrivial metric: an anharmonic oscillator, a Morse-like potential, and a generalized anharmonic oscillator; and one in two dimensions: the coupled anharmonic oscillator in a curved space.
The Impact of Information Flow by Co-Shareholder Relationships on the Stock Returns: A Network Feature Perspective
Authors: An Pengli , Guo Sui
Published: September 2022

One shareholder may invest in different listed energy companies, so the information held by common shareholders can be transmitted among companies. Based on the two-mode complex network method, we construct an information flow shareholder-based network and employ different network indicators representing features of information flow as variables to construct panel regression models to analyze the impact of information flow among listed energy companies on the stock returns. The results indicate that the information flow of listed energy companies are increasingly important and play a significant role over a period. The efficiency of information flow among listed energy companies is increasingly high and the network information is concentrated among a few of these companies. The efficiency of information flow and the independence of listed energy companies are significantly positively related to stock returns, while the listed energy companies’ ability to control information is not significantly related to stock returns. We employ a new perspective to analyze the information flow on how to influence stock returns, and offer some related suggestions for investors and policy makers in the future.
Quantum Correlation Swapping between Two Werner States Undergoing Local and Nonlocal Unitary Operations
Authors: Xie Chuanmei , Zhang Zhanjun , Chen Jianlan , Yin Xiaofeng
Published: September 2022

In this paper, quantum correlation (QC) swapping between two Werner-like states, which are transformed from Werner states undergoing local and nonlocal unitary operations, are studied. Bell states measures are performed in the middle node to realize the QC swapping and correspondingly final correlated sates are obtained. Two different QC quantifiers, i.e., measurement-induced disturbance (MID) and ameliorated MID, are employed to characterize and quantify all the concerned QCs in the swapping process. All QCs in the concerned states are evaluated analytically and numerically. Correspondingly, their characteristics and properties are exposed in detail. It is exposed that, through the QC swapping process, one can obtain the long-distance QC indeed. Moreover, the similarities of monotony features of MID and AMID between the initial states and final states are exposed and analyzed.
Research on Throughput-Guaranteed MAC Scheduling Policies in Wireless Networks
Authors: Zhang Fan , Li Baozhu , Yang Gangqiang
Published: September 2022

In wireless networks, MAC scheduling methods can be divided into two types according to the implementation model: centralized and distributed scheduling. By reasonably designing MAC scheduling policies, both centralized and distributed schedulers can ensure a reliable throughput capacity region, i.e., realizing throughput-guaranteed. However, it can be found that some existing throughput-guaranteed scheduling schemes cannot further ensure bounded end-to-end average delay, and the reason for this phenomenon has not been deeply analyzed. In practical communication networks, throughput and delay are equally important. Based on this idea, the existing MAC scheduling strategies are investigated systematically in this paper from two aspects of throughput and delay, and their performances are evaluated and compared through both theoretical analysis and simulation experiments. The work of this paper provides a theoretical basis for the improvement of MAC scheduling technology in the next-generation wireless networks.
Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora
Authors: Zong Yuan , Lian Hailun , Chang Hongli , Lu Cheng , Tang Chuangao
Published: September 2022

In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER. Unlike conventional SER, a feature distribution mismatch may exist between the labeled source (training) and target (testing) speech samples in cross-corpus SER because they come from different speech emotion corpora, which degrades the performance of most well-performing SER methods. To address this issue, we propose a novel transfer subspace learning method called multiple distribution-adapted regression (MDAR) to bridge the gap between speech samples from different corpora. Specifically, MDAR aims to learn a projection matrix to build the relationship between the source speech features and emotion labels. A novel regularization term called multiple distribution adaption (MDA), consisting of a marginal and two conditional distribution-adapted operations, is designed to collaboratively enable such a discriminative projection matrix to be applicable to the target speech samples, regardless of speech corpus variance. Consequently, by resorting to the learned projection matrix, we are able to predict the emotion labels of target speech samples when only the source label information is given. To evaluate the proposed MDAR method, extensive cross-corpus SER tasks based on three different speech emotion corpora, i.e., EmoDB, eNTERFACE, and CASIA, were designed. Experimental results showed that the proposed MDAR outperformed most recent state-of-the-art transfer subspace learning methods and even performed better than several well-performing deep transfer learning methods in dealing with cross-corpus SER tasks.
Teachers’ Self-Efficacy during the COVID-19 Pandemic in Greece: The Role of Risk Perception and Teachers’ Relationship with Technology
Authors: Andreou Eleni , Roussi Christina , Tsermentseli Stella , Menabò Laura , Guarini Annalisa
Published: September 2022

The COVID-19 pandemic has led to a sudden and radical transition from face-to-face to online teaching across the globe. This shift has required teachers to quickly adapt their educational practices, which has had an impact on their perceived self-efficacy. The limited literature on the impact of COVID-19 on teachers’ self-efficacy (TSE) has focused mostly on online teaching. The purpose of this study was to investigate TSE in both face-to-face and distant learning environments amid the COVID-19 pandemic. It also aimed to explore the impact of teachers’ relationship with technology and COVID-19 risk perception on their TSE. The sample was composed of 290 pre-service and in-service teachers from Greece who completed an online self-report survey. Results showed that pre-service teachers reported higher levels of TSE than in-service teachers, especially in distance learning environments. COVID-19 risk perception was not a significant predictor of TSE. By contrast, higher scores in self-assessed skills of the pedagogical applications of technology predicted TSE in both instructional settings. The present findings confirm the importance of promoting technological skills to enhance the self-efficacy of schoolteachers in both traditional and virtual instructional settings, regardless of the crisis due to the COVID-19 pandemic.
Moral Disengagement, Dark Triad and Face Mask Wearing during the COVID-19 Pandemic
Authors: Chávez-Ventura Gina , Santa-Cruz-Espinoza Henry , Domínguez-Vergara Julio , Negreiros-Mora Nancy
Published: September 2022

Not wearing a face mask, despite the sanitary recommendation, represented a public health risk during the COVID-19 pandemic. For this reason, the aim of the study was to determine the mediating role of moral disengagement in the relationship between the dark triad and face mask wearing during the second wave of the pandemic. We worked with a sample made up of 534 adults, who were administered the Dirty Dozen Dark test, the Moral Disengagement Mechanisms Scale and a questionnaire on the frequency of use of face masks. The results showed that moral disengagement mediates the effect of each trait of the dark triad (Machiavellianism, psychopathy and narcissism) on the use of face masks. It is concluded that those who possess any of the dark personality traits morally disengage in order not to use a face mask, exercising a reckless behavior of the possible contagion of COVID-19 to others.
Effect of Recombinant Antibodies and MIP Nanoparticles on the Electrical Behavior of Impedimetric Biorecognition Surfaces for SARS-CoV-2 Spike Glycoprotein: A Short Report
Authors: Thomaz Douglas Vieira , Goldoni Riccardo , Tartaglia Gianluca Martino , Malitesta Cosimino , Mazzotta Elisabetta
Published: September 2022

Electrochemical immunosensors are often described as innovative strategies to tackle urgent epidemiological needs, such as the detection of SARS-CoV-2 main biomarker, the spike glycoprotein. Nevertheless, there is a great variety of receptors, especially recombinant antibodies, that can be used to develop these biosensing platforms, and very few reports compare their suitability in analytical device design and their sensing performances. Therefore, this short report targeted a brief and straightforward investigation of the performance of different impedimetric biorecognition surfaces (BioS) for SARS-CoV-2, which were crafted from three commonly reported recombinant antibodies and molecularly-imprinted polymer (MIP) nanoparticles (nanoMIP). The selected NanoMIP were chosen due to their reported selectivity to the receptor binding domain (RBD) of SARS-CoV-2 spike glycoprotein. Results showed that the surface modification protocol based on MUDA and crosslinking with EDC/NHS was successful for the anchoring of each tested receptor, as the semicircle diameter of the Nyquist plots of EIS increased upon each modification, which suggests the increase of Rct due to the binding of dielectric materials on the conductive surface. Furthermore, the type of monoclonal antibody used to craft the BioS and the artificial receptors led to very distinct responses, being the RBD5305 and the NanoMIP-based BioS the ones that showcased the highest increment of signal in the conditions herein reported, which suggests their adequacy in the development of impedimetric immunosensors for SARS-CoV-2 spike glycoprotein.
Intelligent Hybrid Deep Learning Model for Breast Cancer Detection
Authors: Wang Xiaomei , Ahmad Ijaz , Javeed Danish , Zaidi Syeda , Alotaibi Fahad , Ghoneim Mohamed , Daradkeh Yousef , Asghar Junaid , Eldin Elsayed
Published: September 2022

Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (accuracy 86.21%, precision 85.50%, sensitivity 85.60%, specificity 84.71%, F1-score 88%, while AUC 0.89 which overcomes the pathologist’s error and miss classification problem. Additionally, the efficiency of the proposed hybrid model was tested and compared with CNN-BiLSTM, CNN-LSTM, and current machine learning and deep learning (ML/DL) models, which indicated that the proposed hybrid model is more robust than recent ML/DL approaches.
A Piece-Wise Linear Model-Based Algorithm for the Identification of Nonlinear Models in Real-World Applications
Authors: Carnevale Claudio , Sangiorgi Lucia , Mansini Renata , Zanotti Roberto
Published: September 2022

In this work, a data-driven approach for the identification of a piece-wise linear model for nitrogen oxide daily concentration simulation is presented and applied. The model has been identified by using daily measured concentrations, meteorological variables, and emission levels estimated starting from the results contained in suitable emission databases. We propose an innovative methodology that jointly optimizes clustering and parameter identification. The procedure has been applied considering data from the Milan (Italy) metropolitan area. The methodology has been compared with two state-of-the-art approaches based on a two-step, cluster-based algorithm and on Hammerstein–Wiener models. The results show how, in the presented application, the devised approach ensures better performance with respect to the two literature methods, both in terms of statistical indexes (correlation, normalized mean absolute error) and in terms of problem-specific metrics (hit ratio, false alarm). For this reason, the approach can be considered suitable to be used in the definition of optimal emission control strategies.
Performance of a Pd-Zn Cathode Electrode in a H2 Fueled Single PEM Fuel Cell
Authors: Bampos Georgios , Bebelis Symeon
Published: September 2022

A 21.7 wt.% Pd—7.3 wt.% Zn/C electrocatalyst prepared via the wet impregnation (w.i.) method was deposited onto commercial carbon cloth (E-TEK) and tested towards its electrocatalytic performance as a cathode electrode material for oxygen reduction reaction (ORR) in a H2 fueled single proton-exchange membrane fuel cell (PEMFC). A commercial PtRu electrode (E-TEK) was used as PEM anode for hydrogen oxidation reaction (HOR). The performance of the aforementioned PEMFC was compared with that of the same PEMFC with two different Pt-based cathodes, which were prepared by deposition onto commercial carbon cloth (E-TEK) of 29 wt.% Pt/C synthesized via w.i. and of commercial 29 wt.% Pt/C (TKK). The metal loading of the tested cathode electrodes was 0.5 mgmet cm−2. Comparison was based both on polarization curves and on electrochemical impedance spectroscopy (EIS) measurements at varying cell potential. In terms of power density, the lowest and highest performance was exhibited by the PEMFC with the 21.7 wt.% Pd—7.3 wt.% Zn/C cathode and the PEMFC with the commercial 29 wt.% Pt/C (TKK) cathode electrode, respectively. This behavior was in accordance with the results of EIS measurements, which showed that the PEMFC with the 21.7 wt.% Pd—7.3 wt.% Zn/C cathode exhibited the highest polarization resistance.
Edge-Based Heuristics for Optimizing Shortcut-Augmented Topologies for HPC Interconnects
Authors: Fuad Kazi Ahmed Asif , Zeng Kai , Chen Lizhong
Published: September 2022

Interconnection network topology is critical for the overall performance of HPC systems. While many regular and irregular topologies have been proposed in the past, recent work has shown the promise of shortcut-augmented topologies that offer multi-fold reduction in network diameter and hop count over conventional topologies. However, the large number of possible shortcuts creates an enormous design space for this new type of topology, and existing approaches are extremely slow and do not find shortcuts that are globally optimal. In this paper, we propose an efficient heuristic approach, called EdgeCut, which generates high-quality shortcut-augmented topologies. EdgeCut can identify more globally useful shortcuts by making its considerations from the perspective of edges instead of vertices. An additional implementation is proposed that approximates the costly all-pair shortest paths calculation, thereby further speeding up the scheme. Quantitative comparisons over prior work show that the proposed approach achieves a 1982× reduction in search time while generating better or equivalent topologies in 94.9% of the evaluated cases.
Conception and Experimental Validation of a Standalone Photovoltaic System Using the SUPC5 Multilevel Inverter
Authors: El Ouardi Hind , El Gadari Ayoub , Ounejjar Youssef , Al-Haddad Kamal
Published: September 2022

In this work, an advanced pulse width modulation (PWM) technique was developed to provide the auto-balancing of the capacitors voltages of the five-level split-packed U-Cells (SPUC5) single-phase inverter, and then, the latter was applied to a photovoltaic (PV) system in standalone mode to evaluate its performance in this kind of application. The SPUC5 inverter makes use of only five switches (four active bidirectional switches and one four quadrant switch), one DC source and two capacitors to generate five levels of output voltage and a current with a quasi-sinusoidal waveform which reduces the total harmonic distortion (THD) without the need to add filters or sensors, and also reduces its cost compared to the other multilevel inverters. In the proposed system; the incremental conductance (INC) algorithm is combined with a DC/DC boost converter to reach the maximum power (MP) of the PV array by tracking the MP point (MPP). The offered concept has been constructed and then simulated in the MATLAB/Simulink environment to evaluate its efficiency. According to the results, the self-balancing of the capacitors voltages has been achieved. A comparative study was performed with the traditional PWM technique. The proposed PV system has been validated by experimental results.
Application of Improved Quasi-Affine Transformation Evolutionary Algorithm in Power System Stabilizer Optimization
Authors: Huang Jing , Liu Jiajing , Zhang Cheng , Kuang Yu , Weng Shaowei
Published: September 2022

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to the power system. Since the damping ratio is one of the criteria with which to measure the dynamic stability of the power system, this paper sets the objective function according to the principle of maximization of the damping coefficient of the electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the safety factor of the system as much as possible. Finally, the method’s rationality and validity were validated by applying it to the simulation examples of the IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, can restrain the low-frequency oscillation of the power system more effectively and can enhance the system’s stability.
RERB: A Dataset for Residential Area Extraction with Regularized Boundary in Remote Sensing Imagery for Mapping Application
Authors: Liu Songlin , Zhang Li , Liu Wei , Hu Jun , Gong Hui , Zhou Xin , Gong Danchao
Published: September 2022

Due to the high automaticity and efficiency of image-based residential area extraction, it has become one of the research hotspots in surveying, mapping, and computer vision, etc. For the application of mapping residential area, the extracted contour is required to be regular. However, the contour results of existing deep-learning-based residential area extraction methods are assigned accurately according to the actual range of residential areas in imagery, which are difficult to directly apply to mapping due to the extractions being messy and irregular. Most of the existing ground object extraction datasets based on optical satellite images mainly promote the research of semantic segmentation, thereby ignoring the requirements of mapping applications. In this paper, we introduce an optical satellite images dataset named RERB (Residential area Extraction with Regularized Boundary) to support and advance end-to-end learning of residential area mapping. The characteristic of RERB is that it embeds the prior knowledge of regularized contour in the dataset. In detail, the RERB dataset contains 13,892 high-quality satellite images with a spatial resolution of 2 m acquired from different cities in China, and the size of each image is approximately 256 × 256 pixels, which covers an area of more than 3640 square kilometers. The novel published RERB dataset encompasses four superiorities: (1) Large-scale and high-resolution; (2) well annotated and regular label contour; (3) rich background; and (4) class imbalance. Therefore, the RERB dataset is suitable for both semantic segmentation and mapping application tasks. Furthermore, to validate the effectiveness of the RERB, a novel end-to-end regularization extraction algorithm of residential areas based on contour cross-entropy constraints is designed and implemented, which can significantly improve the regularization degree of extraction for the mapping of residential areas. The comparative experimental results demonstrate the preponderance and practicability of our public dataset and can further facilitate future research.
Prediction of Highway Blocking Loss Based on Ensemble Learning Fusion Model
Authors: Guo Honglie , Zhang Jiahong , Zhang Jing , Li Yingna
Published: September 2022

Road blocking events refer to road traffic blocking caused by landslides, debris flow, snow disasters, rolling stones and other factors. To predict road blocking events, the limit gradient lifting model (XGBoost), random forest regression model (RF regression) and support-vector regression model (SVR) are used as the prediction meta-models, and then the meta-models are fused by a logical regression algorithm to construct a road blocking loss prediction fusion model based on ensemble learning. The actual road blocking event data are used to train the model. Using the same blocking location and similar blocking loss characteristics between adjacent points to fill in the missing value and conducting one-hot encoding for other short character sets with obvious category characteristics such as letters, numbers, and Chinese characters overcomes the problems of inherent data loss, error and time logic disorder in the blocking event data set. The test results show that the R2 score based on the stacking fusion model reaches 0.91, which is 18% higher than RF and 11% and 5.8% higher than SVR and XGBoost, respectively, and the RMSE and MAE values are 0.1707 and 0.0341, respectively. Therefore, the proposed road blocking data preprocessing method and road blocking loss prediction fusion model can be used to predict the amount of blocking event loss.
A Typed Iteration Approach for Spoken Language Understanding
Authors: Pang Yali , Yu Peilin , Zhang Zhichang
Published: September 2022

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded information of the two subtasks to realize mutual interaction, nor can they achieve the bidirectional connection between them. In this paper, we propose a typed abstraction mechanism to enhance the performance of intent detection by utilizing the encoded information of SF tasks. In addition, we design a typed iteration approach, which can achieve the bidirectional connection of the encoded information and mitigate the negative effects of error propagation. The experimental results on two public datasets ATIS and SNIPS present the superiority of our proposed approach over other baseline methods, indicating the effectiveness of the typed iteration approach.
An Improved RandLa-Net Algorithm Incorporated with NDT for Automatic Classification and Extraction of Raw Point Cloud Data
Authors: Ma Zhongli , Li Jiadi , Liu Jiajia , Zeng Yuehan , Wan Yi , Zhang Jinyu
Published: September 2022

A high-definition map of the autonomous driving system was built with the target points of interest, which were extracted from a large amount of unordered raw point cloud data obtained by Lidar. In order to better obtain the target points of interest, this paper proposes an improved RandLa-Net algorithm incorporated with NDT registration, which can be used to automatically classify and extract large-scale raw point clouds. First, based on the NDT registration algorithm, the frame-by-frame raw point cloud data were converted into a point cloud global map; then, the RandLa-Net network combined random sampling with a local feature sampler is used to classify discrete points in the point cloud map point by point. Finally, the corresponding point cloud data were extracted for the labels of interest through numpy indexing. Experiments on public datasets senmatic3D and senmatickitti show that the method has excellent accuracy and processing speed for the classification and extraction of large-scale point cloud data acquired by Lidar.
Assessment of Exposure to Time-Varying Magnetic Fields in Magnetic Resonance Environments Using Pocket Dosimeters
Authors: Acri Giuseppe , Anfuso Carmelo , Vermiglio Giuseppe , Hartwig Valentina
Published: September 2022

Staff working in Magnetic Resonance environments are mainly exposed to the static and spatially heterogeneous magnetic field. Moreover, workers movements in such environments give slowly time-varying magnetic field that reflects in an induced electric field in conductive bodies, such as human bodies. It is very important to have a practice method to personal exposure assessment, also to create a list of procedures and job descriptions at highest risk of exposure, to provide complete information for the workers. This is important especially for the “workers at particular risk”, such as pregnant workers or medical devices wearers. The purpose of this work is to measure the exposure of the staff to time-varying magnetic field in Magnetic Resonance clinical environments, using pocket dosimeter. We present here the assessment of exposure in two different working conditions relative to routine procedures for different kinds of workers. The obtained results show compliance with the safety limits imposed by regulation for controlled exposure conditions. However, during the activity of replacement of the oxygen sensor performed by a maintenance technician, some exposure parameters exceeded the limits, suggesting to pay attention with specific conditions to prevent vertigo or other sensory effects.
Evaluation of the Economic Potential of Photovoltaic Power Generation in Road Spaces
Authors: Hu Mengjin , Song Xiaoyang , Bao Zhongxu , Liu Zhao , Wei Mengju , Huang Yaohuan
Published: September 2022

Photovoltaic (PV) power generation has become an important clean energy generation source. In the context of transportation development and its very large energy demand, scholars have begun to use PV power generation technology on roads and their surrounding road spaces. Current research on PV power generation in road spaces has mostly focused on its feasibility and technical potential, but there have been few studies on its economic potential. For this reason, this paper used the Zhengding County of Hebei Province, China, to study the evaluation method of the technical and economic potential of PV power generation in road spaces and to analyze the development potential of PV power generation in road spaces. The results show that Zhengding County has a very high amount of road space available for PV power generation, with an effective PV installation area of 20.98 km2 and an annual theoretical power generation capacity of 1.5 billion kWh. If the PV road space project is fully operational in 2021, it could be profitable by 2026, and the net profit (NP) could reach $705 million in 2030. The application of photovoltaic power generation in road spaces is a very promising method of sustainable energy supply.
VSG Control Applied to Seven-Level PV Inverter for Partial Shading Impact Abatement
Authors: Mateus Tiago H. de A. , Pomilio José A. , Godoy Ruben B. , Pinto João O. P.
Published: September 2022

This paper describes the use of a Cascaded H-Bridge Multilevel Inverter to deal with the problem of partial shading in a photovoltaic system connected to the grid. Combined with the topology, it proposes the use of a virtual synchronous generator for power sharing between photovoltaic arrays and to ensure the connection to the power grid even in extreme shading situations with no power buffer. The experimental results demonstrate the feasibility of the strategy adopted and the limitations to overcome.