bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Paradigm for Non-Invasive Prenatal Genetic Screening: Trophoblast Retrieval and Isolation from the Cervix (TRIC)
Authors: Hong Kirim , Park Hee Jin , Jang Hee Yeon , Shim Sung Han , Jang Yoon , Kim Soo Hyun , Cha Dong Hyun
Published: July 2023

As the prevalence of pregnancies with advanced maternal age increases, the risk of fetal chromosomal abnormalities is on the rise. Therefore, prenatal genetic screening and diagnosis have become essential elements in contemporary obstetrical care. Trophoblast retrieval and isolation from the cervix (TRIC) is a non-invasive procedure that can be utilized for prenatal genetic diagnosis. The method involves the isolation of fetal cells (extravillous trophoblasts) by transcervical sampling; along with its non-invasiveness, TRIC exhibits many other advantages such as its usefulness in early pregnancy at 5 weeks of gestation, and no interference by various fetal and maternal factors. Moreover, the trophoblast yields from TRIC can provide valuable information about obstetrical complications related to abnormal placentation even before clinical symptoms arise. The standardization of this clinical tool is still under investigation, and the upcoming advancements in TRIC are expected to meet the increasing need for a safe and accurate option for prenatal diagnosis.
Quantitative Analysis of Supraspinatus Tendon Pathologies via T2/T2* Mapping Techniques with 1.5 T MRI
Authors: Ece Bunyamin , Yigit Hasan , Ergun Elif , Koseoglu Enver Necip , Karavas Erdal , Aydin Sonay , Kosar Pinar Nercis
Published: July 2023

The aim of this study was to quantitatively assess supraspinatus tendon pathologies with T2/T2* mapping techniques, which are sensitive to biochemical changes. Conventional magnetic resonance imaging (MRI) and T2/T2* mapping techniques were applied to 41 patients with shoulder pathology, and there were also 20 asymptomatic cases included. The patients were divided into two groups: tendinosis and rupture. The supraspinatus tendon was divided into medial, middle, and lateral sub-regions, and the T2/T2* values were measured in both the coronal and sagittal planes for intergroup comparison. Intra-class and inter-class correlation coefficients (ICCs) were calculated to assess test reproducibility. Receiver operating characteristic (ROC) analysis was used to determine the cut-off value in each group. A total of 61 patients (27 males and 34 females)—including 20 asymptomatic individuals, 20 with tendinosis, and 21 with rupture—were evaluated using T2/T2* mapping techniques. In the rupture group, there were significant differences in the values of the lateral region (p < 0.001), as well as in the middle and medial regions (p < 0.05) of the supraspinatus tendon compared to the tendinosis and asymptomatic groups. These were determined using both T2* and T2 mapping in both the coronal and sagittal plane measurements. In the tendinosis group, there were significant differences in the values of the lateral region with T2* mapping (p < 0.001) in both the coronal and sagittal planes, and also with the T2 mapping in the coronal plane (p < 0.05) compared to the asymptomatic groups. The cut-off values for identifying supraspinatus pathology ranged from 85% to 90% for T2 measurements and above 90% for T2* measurements in both planes of the lateral section. The ICC values showed excellent reliability (ICC > 0.75) for all groups. In conclusion, T2 and T2* mapping techniques with 1.5 T MRI can be used to assess tendon rupture and tendinosis pathologies in the supraspinatus tendon. For an accurate evaluation, measurements from the lateral region in both the coronal and sagittal planes are more decisive.
A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images
Authors: Khan Muntakim Mahmud , Chowdhury Muhammad E. H. , Arefin A. S. M. Shamsul , Podder Kanchon Kanti , Hossain Md. Sakib Abrar , Alqahtani Abdulrahman , Murugappan M. , Khandakar Amith , Mushtak Adam , Nahiduzzaman Md.
Published: July 2023

Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.
Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel
Authors: Mahmud Istiak , Kabir Md Mohsin , Mridha M. F. , Alfarhood Sultan , Safran Mejdl , Che Dunren
Published: July 2023

Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.
Lessons from a 3-Year Review of PSMA PET-CT in a Tertiary Setting: Can We Fine Tune Referral Criteria by Identifying Factors Predicting Positivity and Negativity?
Authors: Pant Vineet , Vinjamuri Sobhan , Zanial Ahmad Zaid , Naeem Faisal
Published: July 2023

Aim of the study: To draw inferences from a retrospective evaluation of PSMA PET CT scans performed for the evaluation of biochemical recurrence. Material and Methods: A retrospective analysis of 295 PSMA PET CT scans spanning 3 years between 2020 and 2022 was undertaken. Results: Of 295 PET CT scans, 179 were positive, 66 were negative and 50 had indeterminate findings. In the positive group, 67 had radical prostatectomy and PSMA avid lesions were seen most commonly in pelvic lymph nodes. The remaining 112 positive scans were in the non-radical prostatectomy group; 25 had recurrence only in the prostate, 17 had recurrence involving the prostate bed; 28 had no recurrence in the prostate gland, while 42 had recurrence in the prostate as well as in extra-prostatic sites. Overall, in the non-prostatectomy group, 75% of the population was harboring a PSMA avid lesion in the prostate gland while in the remaining 25% of the population, recurrence did not involve the prostate gland. The majority of indeterminate findings were seen in small pelvic or retroperitoneal lymph nodes or skeletal regions (ribs/others) and in nine patients indeterminate focus was seen in the prostate bed only. Follow-up PSMA PET CT was helpful in prior indeterminate findings and unexplained PSA rise. Conclusion: A higher recurrence in the prostate bed while evaluating biochemical recurrence prompts the following: question: should prostatectomy be offered more proactively? Follow-up PSMA PET CT is helpful for indeterminate findings; a PSA rise of 0.7 ng/mL in 6 months can result in positive PSMA PET CT while negative scans can be seen up to a 2 ng/mL PSA rise in 6 months.
Sentinel Lymph Node Biopsy in Prostate Cancer: An Overview of Diagnostic Performance, Oncological Outcomes, Safety, and Feasibility
Authors: Rossin Giulio , Zorzi Federico , De Pablos-Rodríguez Pedro , Biasatti Arianna , Marenco Josè , Ongaro Luca , Perotti Alessandro , Tulone Gabriele , Traunero Fabio , Piasentin Andrea , Gomez-Ferrer Alvaro , Zucchi Alessandro , Trombetta Carlo , Simonato Alchiede , Rubio-Briones José , Bartoletti Riccardo , Ramírez-Backhaus Miguel , Claps Francesco
Published: July 2023

Sentinel node biopsy (SNB) for prostate cancer (PCa) represents an innovative technique aimed at improving nodal staging accuracy. The routinary adoption of this procedure in patients undergoing radical prostatectomy (RP) might be crucial to identify candidates who could effectively benefit from extensive pelvic lymph nodal dissection (ePLND). Despite some promising results, SNB for PCa is still considered experimental due to the lack of solid evidence and procedural standardization. In this regard, our narrative review aimed to analyze the most recent literature in this field, providing an overview of both the diagnostic accuracy measures and the oncological outcomes of SNB.
Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features
Authors: Saidani Oumaima , Aljrees Turki , Umer Muhammad , Alturki Nazik , Alshardan Amal , Khan Sardar Waqar , Alsubai Shtwai , Ashraf Imran
Published: July 2023

Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.
Ratio of Extracellular to Intracellular Water Is Associated with Permanent Catheter Patency Survival in Patients Receiving Maintenance Hemodialysis
Authors: Kim Moo-Jun , Jeon Jae-Wan , Kim Hae-Ri , Park Hyerim , Han Suyeon , Hwang Yunkyeong , Park Heewon , Park Kyungho , Lee Eu-Jin , Ham Young-Rok , Na Ki-Ryang , Lee Kang-Wook , Choi Dae-Eun
Published: July 2023

Patients undergoing dialysis through a permanent catheter often experience infection or malfunction. However, few studies have clarified the predictors of permanent catheter patency survival in patients undergoing hemodialysis. We assessed the relationship between the parameters of body composition monitoring (BCM), determined before the initiation of dialysis, and the patency survival of the permanent catheters inserted in 179 patients who commenced hemodialysis between 14 January 2020 and 31 August 2021. The relationships between permanent catheter patency at 6 weeks and BCM parameters, laboratory tests, age, sex, comorbidities, and medications at baseline were studied using Kaplan–Meier survival curves. Permanent catheter patency was observed to be superior at high extracellular-to-intracellular (ECW/ICW) ratio (p < 0.005). After adjustment for covariates, the ECW/ICW ratio remained an independent factor associated with permanent catheter patency survival. When patients with non-patent catheters were subdivided into infection and malfunction groups, and the associations of BCM parameters were evaluated in those groups, the ECW/ICW ratio was not significantly associated with permanent catheter patency survival in the infection group (p = 0.327); instead, a significant association was found for the lean tissue index (p < 0.001). In the malfunction group, the ECW/ICW ratio remained significantly associated with permanent catheter patency survival (p < 0.001).
Multicentre Prospective Study Analysing Relevant Factors Related to Marginal Bone Loss: A Two-Year Evolution
Authors: Fernández-Figares-Conde Iñigo , Castellanos-Cosano Lizett , Fernandez-Ruiz Juan-Alberto , Soriano-Santamaria Ismael , Hueto-Madrid Juan-Antonio , Gómez-Lagunas Javier , Romano-Laureato Roberto , Torres-Lagares Daniel
Published: July 2023

Introduction: The aim of this prospective descriptive study was to analyse the possible variables associated with marginal bone loss in rehabilitated implants (Proclinic S.A.U, Zaragoza, Spain) two years after their prosthetic loading. Materials and Methods: Three clinical centres collaborated for a period of two years after the prosthetic rehabilitation of the implants (Proclinic S.A.U, Zaragoza, Spain), in which marginal bone loss and the possible associated variables were evaluated. The collection form comprised different variables throughout different stages of the implant procedure, from implant insertion to the subsequent prosthetic rehabilitation, over a two-year period. Data of the patients and implant characteristics were studied. Statistical analysis was performed with SPSS for qualitative (univariate logistic regressions, Chi2 test, and Haberman’s corrected standardised residuals) and quantitative variables (Kolmogorov–Smirnov test). Results: The total study sample consisted of 218 implants (Proclinic S.A.U, Zaragoza, Spain). The sample presented a frequency of 99 men (45.4%) and 119 women (54.6%). The mean age of the patients among the reported cases was 58.56 ± 10.12 years. A statistically significant association was found between marginal bone loss 2 years after prosthetic rehabilitation placement and several variables, including age (under 55 years, 0.25 mm ± 0.56; 55–64 years, 0.74 mm ± 0.57; over 65 years, 0.63 mm ± 0.55; p < 0.0001), gender (female, 0.74 mm ± 0.61; male, 0.34 mm ± 0.51; p < 0.0001), bone quality (D1, 0.75 mm ± 0.62; D2, 0.43 mm ± 0.57; D3, 0.65 mm ± 0.60; p < 0.01), implant diameter (up to 4 mm, 0.49 mm ± 0.58; more than 4 mm, 1.21 mm ± 0.30; p < 0.0001), prosthetic connection type (direct to implant, 0.11 mm ± 0.58; transepithelial straight, 0.67 mm ± 0.57; transepithelial angled, 0.33 mm ± 0.25; p < 0001), implant model (internal conical, 0.17 mm ± 0.24; external conical, 0.48 mm ± 0.61; external cylindrical, 1.12 mm ± 0.32; p < 0.0001), prosthetic restoration type (full denture, 0.59 mm ± 0.59; partial denture, 0.50 mm ± 0.85; unitary crown, 0.08 mm ± 0.19; p < 0.05), and insertion torque (>35 N/cm, 0.53 mm ± 0.58; <35 N/cm, 1.04 mm ± 0.63; p < 0.01). Conclusions: At 2 years, marginal bone loss following prosthetic rehabilitation was shown to be influenced by multiple factors. Correct implantological planning is of vital importance for successful rehabilitation.
Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany
Authors: Diebner Hans H.
Published: July 2023

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/ COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government’s Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series shows enormous heterogeneity, both with respect to temporal features as well as spatial distributions, the counties’ incidence curves organise into well-distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods, which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic, which serves as a basis to be further explored with the expertise of different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.
Automating Model Comparison in Factor Graphs
Authors: van Erp Bart , Nuijten Wouter W. L. , van de Laar Thijs , de Vries Bert
Published: July 2023

Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.
Transaction Entropy: An Alternative Metric of Market Performance
Authors: Zhong Hua , Liang Xiaohao , Wang Yougui
Published: July 2023

Market uncertainty has a significant impact on market performance. Previous studies have dedicated much effort towards investigations into market uncertainty related to information asymmetry and risk. However, they have neglected the uncertainty inherent in market transactions, which is also an important aspect of market performance, besides the quantity of transactions and market efficiency. In this paper, we put forward a concept of transaction entropy to measure market uncertainty and see how it changes with price. Transaction entropy is defined as the ratio of the total information entropy of all traders to the quantity of transactions, reflecting the level of uncertainty in making successful transactions. Based on the computational and simulated results, our main finding is that transaction entropy is the lowest at equilibrium, it will decrease in a shortage market, and increase in a surplus market. Additionally, we make a comparison of the total entropy of the centralized market with that of the decentralized market, revealing that the price-filtering mechanism could effectively reduce market uncertainty. Overall, the introduction of transaction entropy enriches our understanding of market uncertainty and facilitates a more comprehensive assessment of market performance.
The Kaniadakis Distribution for the Analysis of Income and Wealth Data
Authors: Clementi Fabio
Published: July 2023

The paper reviews the “κ-generalized distribution”, a statistical model for the analysis of income data. Basic analytical properties, interrelationships with other distributions, and standard measures of inequality such as the Gini index and the Lorenz curve are covered. An extension of the basic model that best fits wealth data is also discussed. The new and old empirical evidence presented in the article shows that the κ-generalized model of income/wealth is often in very good agreement with the observed data.
Basin Entropy and Shearless Barrier Breakup in Open Non-Twist Hamiltonian Systems
Authors: Souza Leonardo C. , Mathias Amanda C. , Haerter Pedro , Viana Ricardo L.
Published: July 2023

We consider open non-twist Hamiltonian systems represented by an area-preserving two-dimensional map describing incompressible planar flows in the reference frame of a propagating wave, and possessing exits through which map orbits can escape. The corresponding escape basins have a fractal nature that can be revealed by the so-called basin entropy, a novel concept developed to quantify final-state uncertainty in dynamical systems. Since the map considered violates locally the twist condition, there is a shearless barrier that prevents global chaotic transport. In this paper, we show that it is possible to determine the shearless barrier breakup by considering the variation in the escape basin entropy with a tunable parameter.
Stability of Delay Hopfield Neural Networks with Generalized Riemann–Liouville Type Fractional Derivative
Authors: Agarwal Ravi P. , Hristova Snezhana
Published: July 2023

The general delay Hopfield neural network is studied. We consider the case of time-varying delay, continuously distributed delays, time-varying coefficients, and a special type of a Riemann–Liouville fractional derivative (GRLFD) with an exponential kernel. The kernels of the fractional integral and the fractional derivative in this paper are Sonine kernels and satisfy the first and the second fundamental theorems in calculus. The presence of delays and GRLFD in the model require a special type of initial condition. The applied GRLFD also requires a special definition of the equilibrium of the model. A constant equilibrium of the model is defined. An inequality for Lyapunov type of convex functions with the applied GRLFD is proved. It is combined with the Razumikhin method to study stability properties of the equilibrium of the model. As a partial case we apply quadratic Lyapunov functions. We prove some comparison results for Lyapunov function connected deeply with the applied GRLFD and use them to obtain exponential bounds of the solutions. These bounds are satisfied for intervals excluding the initial time. Also, the convergence of any solution of the model to the equilibrium at infinity is proved. An example illustrating the importance of our theoretical results is also included.
Multi-Task Segmentation and Classification Network for Artery/Vein Classification in Retina Fundus
Authors: Yi Junyan , Chen Chouyu
Published: July 2023

Automatic classification of arteries and veins (A/V) in fundus images has gained considerable attention from researchers due to its potential to detect vascular abnormalities and facilitate the diagnosis of some systemic diseases. However, the variability in vessel structures and the marginal distinction between arteries and veins poses challenges to accurate A/V classification. This paper proposes a novel Multi-task Segmentation and Classification Network (MSC-Net) that utilizes the vessel features extracted by a specific module to improve A/V classification and alleviate the aforementioned limitations. The proposed method introduces three modules to enhance the performance of A/V classification: a Multi-scale Vessel Extraction (MVE) module, which distinguishes between vessel pixels and background using semantics of vessels, a Multi-structure A/V Extraction (MAE) module that classifies arteries and veins by combining the original image with the vessel features produced by the MVE module, and a Multi-source Feature Integration (MFI) module that merges the outputs from the former two modules to obtain the final A/V classification results. Extensive empirical experiments verify the high performance of the proposed MSC-Net for retinal A/V classification over state-of-the-art methods on several public datasets.
Analysis of the Effect of Human Capital, Institutionality and Globalization on Economic Complexity: Comparison between Latin America and Countries with Greater Economic Diversification
Authors: Rivera Bryan , Leon Marcelo , Cornejo Gino , Florez Hector
Published: July 2023

The Latin American export in the manufacturing sector is 18% lower than the world average. Although between the 1980s and 1990s, the sophistication of exportable products increased by 13%, it is evident to see the low progress of the Latin American region to consolidate the change in its productivity and its dependence on oil extraction activities, minerals, and other raw materials. This article evaluates and quantifies the impact of human capital, globalization, and the role of the efficiency of institutions in the sophistication of production in Latin America compared to economies with greater complexity. This is conducted using panel data methodology with cointegration techniques, using data from 17 countries that belong to the continental part of Latin America and 10 countries with the greatest economic complexity according to the Atlas of Economic Complexity Index. These countries are classified by their income level, data compiled by the United Nations Development Program Indicators, the Harvard Growth Lab Atlas of Economic Complexity, the Swiss Economic Institute database, and the Heritage Foundation. The results indicate that there is a significant relationship between the explanatory variables and economic complexity. However, the nature of the relationships differs between the different income levels, finding the same trend in the cointegration analyses. It requires the adoption of public policies in the curricular and evaluative field of knowledge and skills as well as the fight against corruption in public and private sectors, motivating the improvement of bilateral relations with other countries in an economic, political, and social way.
“It Seems Like I’m Doing Something More Important”—An Interpretative Phenomenological Analysis of the Transformative Impact of Research Experiences for STEM Students with ADHD
Authors: Zaghi Arash E. , Grey Alexander , Hain Alexandra , Syharat Connie M.
Published: July 2023

Recent studies have suggested a link between attention deficit hyperactivity disorder (ADHD) and increased creativity and ingenuity. Clinical work suggests that individuals with ADHD generally gravitate toward exploratory rather than exploitative thinking. Yet, these theories have not been tested in the field. This paper is a case study of a transformative undergraduate summer research program that allows engineering students with ADHD to spend 10 weeks in engineering labs at a research-intensive (R1) university. The program intends to show that students with ADHD can excel in engineering and STEM fields when placed in research environments that align with their natural cognitive processes and preferences. Using an interpretative phenomenological analysis (IPA) on post-program participant interviews, this paper suggests that engineering students with ADHD perceive that they learn more effectively when given the opportunity to explore academic material via hands-on research. The traditional structure of STEM education, with its focus on lectures and rigid curricula, causes significant harm to these students, leading to struggles, anxiety, and even thoughts of dropping out. In contrast, the research environment appears to foster creativity and motivation in students with ADHD, as it allows for exploration, provides real-world problem-solving opportunities, and offers tangible, hands-on experiences. This paper highlights the need for a paradigm shift in engineering pedagogy to better engage with and retain this neurodiverse student population and fully harness their creative potential.
Managing “Hot Moments” in Diverse Classrooms for Inclusive and Equitable Campuses
Authors: Muftugil-Yalcin Seda , Brodsky Nicole Willner , Slootman Marieke , Das Amrita , Ramdas Siema
Published: July 2023

Classrooms are fundamental components of university campuses. The creation of inclusive and equitable campus environments is incumbent upon the teachers who need to navigate through “hot moments”, which typically refers to situations or incidents that generate intense emotions or strong reactions among the students or between the students and teachers. The literature suggests that many “hot moments” occur due to microaggressions in the classroom and that issues of difference, power, and privilege underlie “hot moments”. Based on qualitative research done with university teachers in a Dutch institution, our research shows that teacher-described “hot moments” can be analyzed under three interrelated dimensions: individual, interactional, and institutional. We suggest educators and higher education policy makers read through these experiences as a way to better understand the challenges their colleagues face, most often alone as the classroom’s leader; in the hope of advancing change efforts to create more inclusive and equitable class environments which will change how minoritized students experience university education.
Parent Chats in Education System: During and after the Pandemic Outbreak
Authors: Bylieva Daria , Lobatyuk Victoria , Novikov Maxim
Published: July 2023

Digital technology has significantly changed the face of education by, among other things, creating many communication channels between the participants in the process. This study reveals the role of parent chat rooms in supporting the learning process. The pandemic experience has shown that parent chat rooms can serve a controlling and regulatory function, noting problems and inconsistencies in the learning system. The chats contain background routine messages related to informing and “events”, that is, messages of bewilderment, resistance, elation, and other emotional reactions. A total of 143 chats were analyzed, and 326 communication events were identified. During the regular period, the basic topics, including chat rules and regulations (22%), homework (20%), school activities and holidays (24%), and behavioral problems (24%), were evenly distributed, while during the distance learning period, the assignment problems (36%) and technical problems (28%) came to the fore. In the traditional offline period, parents are not direct participants in the educational process, so the information in the chats sometimes comes in a one-sided or distorted form; however, parental activity can serve to improve the educational system and monitor the processes taking place.
Experienced vs. Novice Participants Perception of Overall Quality and Intention to Join in Future Sport Trials: Case European Duathlon Championship
Authors: Magaz-González Ana-María , Sahelices-Pinto César , Mendaña-Cuervo Cristina , García-Tascón Marta
Published: July 2023

Even though athletes’ experience has been ascertained to shape the perception of quality in sports events, scarce studies have addressed this issue jointly with the intent to join in upcoming comparable sports challenges. (1) Background: The present research investigates how the experience determines the evaluation of the championship and future intentions. (2) Methods: The PLS (SEM) method was evaluated specifically for both novice and experienced duathlon participants, and secondly, the fsQCA methodology was applied with the intention to estimate combinatorial net effects to confirm the hypothesis proposed. (3) Results reveal that the overall quality is equally important for novice and experienced individuals as a determinant of future intentions. However, novices tend to value more positively all the dimensions analyzed, while experienced ones noted higher levels of demand. (4) These findings highlight the importance of designing adequate management strategies for the participants with different levels of performance.
A Master Multi-Slave System Based on Structural Modal Frequencies
Authors: Leoni Alfiero , Barile Gianluca , Esposito Paolo , Paolucci Romina , Stornelli Vincenzo , Ferri Giuseppe
Published: July 2023

Structural Health Monitoring (SHM) is a process where, through sensors-based electronic systems and output data analysis, the structural integrity of a building or an infrastructure is observed and determined through periodically sampled measurements for material and geometric variations, ensuring safety and minimizing risk factors. Over the years, the approach to this problem evolved both in terms of system technology, with the use of more accurate sensors, and the parameters used to determine the building status, i.e., the extraction of the damage index. In these terms, structural modal harmonics-based analysis is acquiring more and more relevance. In this paper, we propose a fully custom master multi-slave system used for SHM purposes, capable of acquiring data from six different channels coming from two independent accelerometers suitably configured as to synchronously produce spectrograms and static trim of the monitored structure. A test of the system has been performed on a modular structure subject to random perturbation. Time and frequency domain results were analyzed before and after structural alterations. Their effects were studied in a 500 Hz bandwidth, showing a drift in the resonant frequency of the structure from 145 Hz to 152 Hz, and, additionally, a harmonic displacement: medium-frequency components experienced a 50 Hz drift, while low-frequency components collapsed into two harmonics at 60 Hz and 90 Hz. These tests proved the system’s functionality and the feasibility of this method for structural integrity analysis and, eventually, damage evaluation.
Rotated Object Detection with Circular Gaussian Distribution
Authors: Xu Hang , Liu Xinyuan , Ma Yike , Zhu Zunjie , Wang Shuai , Yan Chenggang , Dai Feng
Published: July 2023

Rotated object detection is a challenging task due to the difficulties of locating the rotated objects and separating them effectively from the background. For rotated object prediction, researchers have explored numerous regression-based and classification-based approaches to predict a rotation angle. However, both paradigms are constrained by some flaws that make it difficult to accurately predict angles, such as multi-solution and boundary issues, which limits the performance upper bound of detectors. To address these issues, we propose a circular Gaussian distribution (CGD)-based method for angular prediction. We convert the labeled angle into a discrete circular Gaussian distribution spanning a single minimal positive period, and let the model predict the distribution parameters instead of directly regressing or classifying the angle. To improve the overall efficiency of the detection model, we also design a rotated object detector based on CenterNet. Experimental results on various public datasets demonstrated the effectiveness and superior performances of our method. In particular, our approach achieves better results than state-of-the-art competitors, with improvements of 1.92% and 1.04% in terms of AP points on the HRSC2016 and DOTA datasets, respectively.
Industrial Internet Intrusion Detection Based on Res-CNN-SRU
Authors: Cai Zengyu , Si Yajie , Zhang Jianwei , Zhu Liang , Li Pengrong , Feng Yuan
Published: July 2023

Nowadays, the industrial Internet is developing rapidly, but at the same time it faces serious information security risks. At present, industrial Internet data generally have the problems of complex attack sample types, large numbers, and high feature dimensions. When training a model, the complexity and quantity of attack samples will result in a long detection time for the intrusion detection algorithm, which will fall short of the system’s real-time performance. Due to the high feature dimension of the data, shallow feature extraction will be unable to extract the data’s more significant features, which will render the model’s overall detection capacity insufficient. Aiming at the above problems, an industrial Internet intrusion detection method based on Res-CNN-SRU is proposed. This method not only considers the temporality of network traffic data but can also effectively capture the local features in the data. The dataset used in the experiment is the gas pipeline industry dataset proposed by Mississippi State University in 2014. Experiments show that the algorithm can effectively improve the recognition rate of the system and reduce the false-alarm rate. At the same time, the training time required for this method is also greatly shortened, and it can perform efficient intrusion detection on the industrial Internet.
Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement
Authors: Dounis Anastasios , Avramopoulos Andreas-Nestor , Kallergi Maria
Published: July 2023

A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image.
A Robust Video Watermarking Algorithm Based on Two-Dimensional Discrete Fourier Transform
Authors: Yang Xiao , Zhang Zhenzhen , Jiao Yueshuang , Li Zichen
Published: July 2023

Due to the continuous development and popularity of digital video technology, the copyright protection of digital video content has become an increasingly prominent issue. Digital video watermarking, as an effective means of digital copyright protection, has attracted widespread attention from academia and industry. The two-dimensional discrete Fourier transform (2D-DFT) template-based method has the advantages of good real-time performance and robustness, but the embedding capacity is small and cannot resist frame-dropping attack. To address this problem, a new template construction method is proposed in this paper, which can extend the embedding capacity from 1 bit each group of pictures (GOP) to theoretically n bits each GOP. In addition, by changing the GOP pattern, the method gains the ability to resist frame-dropping attacks. Experimental results show that the proposed method can achieve larger watermark capacity while maintaining strong robustness against image-processing attacks, geometric attacks, video-processing attacks and compression attacks.
Blind Separation of the Measured Mixed Cyclostationary Waveforms in Transmission Lines of the PCB
Authors: Kuznetsov Yury V. , Baev Andrey B. , Konovalyuk Maxim A. , Gorbunova Anastasia A.
Published: July 2023

Crosstalk is an undesirable factor that degrades the quality of data transmission in printed circuit boards (PCBs). The signal integrity (SI) in multiconductor transmission lines is controlled by using a large number of multiport tests and measurements, which require a lot of time and expensive laboratory equipment. Proposed signal processing methods based on blind identification allow a reduction in the measurement burden. Contrary to the traditional approach requiring knowledge of sampling time offset, input pseudorandom bit sequence (PRBS), and time delay between received data and transmitted PRBS, the proposed alternative method performs blind separation of measured data for the linear fit pulse response (LFPR) procedure. The waveform identification of the partial pulse responses is evaluated for additively mixed cyclostationary sources of the data, intersymbol interference, and crosstalk. A mixed matrix model of composed random vectors is considered. The proposed estimation procedure is based on preprocessing of measured data using principal component analysis (PCA) and following independent component analysis (ICA). It is shown that the proposed component analysis allows diagnostics of signal integrity using eye-diagram patterns and the channel operating margin (COM).
An Analytical Method for Determining Flux Density in Surface-Mounted Permanent Magnet Machines with Unequal Magnetic Poles
Authors: Zhai Fengchen , Yu Shenbo , Shan Weiguang , Wu Liping , Zhai Fengjun , Zhang Kun , Dou Rutong , Xia Pengpeng
Published: July 2023

An analytical method was proposed for obtaining the magnetic field distribution for surface-mounted permanent magnet machines with the structure of unequal magnetic poles. Firstly, the Laplace equation and Poisson equation were solved using the method of the separation of variables, and then the mathematical model of the motor structure with unequal magnetic poles was obtained. Secondly, according to the design parameters of the two types of prototypes motors, the flux density in the air gap region of the two types of prototypes motors was calculated, respectively. Thirdly, by comparing the results calculated using the analytical method with the results obtained using the finite element method, the analytical method proposed in this paper was proven to be effective. Moreover, the armature reaction field and the effect of the stator slotting were considered in the mathematical model as well. Overall, the analytical method presented in this paper provides a theoretical basis for the analytical calculation of motors with the structure of unequal magnetic poles.
An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention
Authors: Liu Kejian , Feng Yuanyuan , Zhang Liying , Wang Rongju , Wang Wei , Yuan Xianzhi , Cui Xuran , Li Xianyong , Li Hailing
Published: July 2023

While user-generated textual content on social platforms such as Weibo provides valuable insights into public opinion and social trends, the influence of personality on sentiment expression has been largely overlooked in previous studies, especially in Chinese short texts. To bridge this gap, we propose the P-BiLSTM-SA model, which integrates personalities into sentiment classification by combining BiLSTM and self-attention mechanisms. We grouped Weibo texts based on personalities and constructed a personality lexicon using the Big Five theory and clustering algorithms. Separate sentiment classifiers were trained for each personality group using BiLSTM and self-attention, and their predictions were combined by ensemble learning. The performance of the P-BiLSTM-SA model was evaluated on the NLPCC2013 dataset and showed significant accuracy improvements. In particular, it achieved 82.88% accuracy on the NLPCC2013 dataset, a 7.51% improvement over the baseline BiLSTM-SA model. The results highlight the effectiveness of incorporating personality factors into sentiment classification of short texts.
A 32-GHz Eight-Way Power Amplifier MMIC in 150 nm GaN HEMT Technology
Authors: Park Hyeong-Geun , Trinh Van-Son , Lee Mun-Kyo , Lee Bok-Hyung , Na Kyoung-Il , Park Jung-Dong
Published: July 2023

This paper presents a 32 GHz high-power amplifier (HPA) with a design strategy to achieve high-power output with reliable operation for Ka-band deep space satellite communication in 150 nm GaN HEMT technology. The presented Ka-band HPA employs a cascaded two-stage common source amplifier topology, and the output stage comprises an eight-way power combining network in the current mode. The interstage matching network is designed with the bandpass configuration utilizing capacitors and transmission lines to provide better stability at the low-frequency regime. The implemented Ka-band HPA achieved a power gain of 7.3 dB at the input level with the maximum PAE at 32 GHz, and the 3 dB gain bandwidth was 3.5 GHz (31.3~34.8 GHz). The saturated output power at the peak power-added efficiency (PAE) of 19.3% was 38.2 dBm, and the output 1 dB gain compression point (OP1 dB) was 27.4 dBm in the measurement. The designed HPA consumes an area of 19.35 mm2 including RF pads and DC pads.
A Quantitative Analysis of Non-Profiled Side-Channel Attacks Based on Attention Mechanism
Authors: Pu Kangran , Dang Hua , Kong Fancong , Zhang Jingqi , Wang Weijiang
Published: July 2023

In recent years, the deep learning method has emerged as a mainstream approach to non-profiled side-channel attacks. However, most existing methods of deep learning-based non-profiled side-channel attack rely on traditional metrics such as loss and accuracy, which often suffer from unclear results in practical scenarios. Furthermore, most previous studies have not fully considered the properties of power traces as long time-series data. In this paper, a novel non-profiled side-channel attack architecture is proposed, which incorporates the attention mechanism and derives a corresponding attention metric. By attaching the attention mechanism after the network layers, the attention mechanism provides a quantitative prediction of correct key. Moreover, this architecture can effectively extract and analyze the features from long power traces. The success rate on different datasets is at least 86%, which demonstrates the superior reliability of this architecture compared to other works when facing various countermeasures and noise. Notably, even in scenarios where traditional loss and accuracy metrics fail to provide reliable results, the proposed attention metric remains capable of accurately distinguishing the correct key.