bluuebunny/crossref_metadata_2025 huggingface dataset:
Spectral Domain Optical Coherence Tomography Findings in Vision-Threatening Rhino-Orbital Cerebral Mucor Mycosis—A Prospective Analysis
Authors: Singh Ankur , Diwaker Preeti , Agrawal Akanksha , Agarwal Aniruddha , Rohatgi Jolly , Singh Ramandeep , Das Gopal Krushna , Sahoo Pramod Kumar , Arora Vinod Kumar
Published: December 2022

Rhino-orbital cerebral mucor mycosis is a rare disease entity, where retinal involvement is described in the literature mostly as CRAO. However, pathological studies have shown mucor invading the choroid and retina with a neutrophilic reaction. So, it is pertinent that retinal inflammation secondary to invading mucor has some role in microstructural changes seen in the vitreous and retina of these patients. This novel study aims to describe the vitreal and retinal features of patients with vision-threatening rhino-orbital cerebral mucor mycosis and how they evolve on spectral domain optical coherence tomography (SD-OCT). This study shall also provide insight into the pathophysiology of these vitreoretinal manifestations by in vitro analysis of the exenterated orbital content. Fifteen eyes of fifteen patients with vision-threatening ROCM treated with standard care were enrolled in this study and underwent complete ophthalmic examination, serial colour fundus photography, and SD-OCT for both qualitative and quantitative analysis, at baseline and follow-up visits. SD-OCT on serial follow-up revealed thickening and increased inner-retinal reflectivity at presentation followed by thinning of both, other features such as the loss of the inner-retinal organized layer structure, external limiting membrane (ELM) disruption, necrotic spaces in the outer retina, and hyperreflective foci. Vitreous cells with vitreous haze were also seen. There was a significant reduction in CMT, inner and outer retinal thickness, total retinal thickness (all p < 0.05) with time, the quantum of reduction concentrated primarily to the inner retina. In summary, in vivo and in vitro analysis revealed that early microstructural changes were primarily a result of retinal infarctions secondary to thrombotic angioinvasion. With the late microstructural changes, there was possible sequelae of retinal infarction with some contribution from the inflammation, resulting from mucor invading the choroid and retina.
Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms
Authors: de la Luz Escobar María , De la Rosa José I. , Galván-Tejada Carlos E. , Galvan-Tejada Jorge I. , Gamboa-Rosales Hamurabi , de la Rosa Gomez Daniel , Luna-García Huitzilopoztli , Celaya-Padilla José M.
Published: December 2022

Breast cancer is the most common cancer among women worldwide, after lung cancer. However, early detection of breast cancer can help to reduce death rates in breast cancer patients and also prevent cancer from spreading to other parts of the body. This work proposes a new method to design a bio-marker integrating Bayesian predictive models, pyRadiomics System and genetic algorithms to classify the benign and malignant lesions. The method allows one to evaluate two types of images: The radiologist-segmented lesion, and a novel automated breast cancer detection by the analysis of the whole breast. The results demonstrate only a difference of 12% of effectiveness for the cases of calcification between the radiologist generated segmentation and the automatic whole breast analysis, and a 25% of difference between the lesion and the breast for the cases of masses. In addition, our approach was compared against other proposed methods in the literature, providing an AUC = 0.86 for the analysis of images with lesions in breast calcification, and AUC = 0.96 for masses.
Diagnostic Strategy for Suspected Pulmonary Embolism in Emergency Departments Based on the 4-Level Pulmonary Embolism Clinical Probability Score: Study Protocol of SPEED&PEPS Trial
Authors: Roy Pierre-Marie , Moumneh Thomas , Penaloza Andrea , Schmidt Jeannot , Charpentier Sandrine , Joly Luc-Marie , Riou Jérémie , Douillet Delphine
Published: December 2022

Introduction: Several strategies have been devised to safely limit the use of thoracic imaging in patients suspected of pulmonary embolism (PE). However, they are based on different rules for clinical probability (CP) assessment, rendering their combination difficult. The four-level pulmonary embolism probability score (4PEPS) allows the combination of all other strategies using a single CP assessment. Methods and analysis: Pragmatic cluster-randomized trial in 20 EDs. Patients with suspected PE will be included and followed for 90 days (number of patients to be included: 2560, 1280 in each arm). Ten centers will be allocated to the control group where physicians will be free to do as they see fit but they will be given the recommendation to apply a validated strategy. Ten centers will be allocated to the interventional group where the physicians will be given the recommendation to apply the 4PEPS strategy. The primary objective will be to demonstrate that the application of the 4PEPS strategy by the emergency physicians, in comparison to current practices, (i) does not increase the risk of serious events related to diagnostic strategies and (ii) significantly reduces the use of thoracic imaging. Ethics and dissemination: The study will be submitted for approval to an institutional ethics review board for all participating centers. If successful, the SPEED&PEPS trial will have an important impact for patients suspected of PE limiting their irradiation and for public health in substantial savings in terms of the direct cost of diagnostic investigations and the indirect cost of hospitalizations due to waiting times or delayed harmful effects. Funding: This work is funded by a French Public Health grant (PREPS-N 2019). The funding source plays no role in the study design, data collection, analysis, interpretation or the writing of the manuscript. Trial registration: ongoing. Trial status: not started.
Impact of Geographic Location on Diagnosis and Initial Management of Takayasu Arteritis: A Tale of Two Cohorts from Italy and India
Authors: Misra Durga Prasanna , Tomelleri Alessandro , Rathore Upendra , Benanti Giovanni , Singh Kritika , Behera Manas Ranjan , Jain Neeraj , Ora Manish , Bhadauria Dharmendra Singh , Gambhir Sanjay , Kumar Sudeep , Baldissera Elena , Agarwal Vikas , Campochiaro Corrado , Dagna Lorenzo
Published: December 2022

The present study compares disease characteristics, imaging modalities used, and patterns of treatment in two large cohorts of Takayasu arteritis (TAK) from Italy and India. Clinic files were retrospectively reviewed to retrieve information about initial choices of vascular imaging and immunosuppressive therapies. Unpaired t-tests compared means, and proportions were compared using Fisher’s exact test or Chi square test [Odds ratios (OR) with 95% confidence intervals (95%CI) calculated where appropriate]. The cohorts comprised 318 patients [Italy (n = 127), India (n = 191)] with similar delays to diagnosis. Ultrasound (OR Italy vs. India 9.25, 95%CI 5.02–17.07) was more frequently used in Italy and CT angiography in India (OR 0.32, 95%CI 0.20–0.51). Corticosteroid use was more prevalent and for longer duration in Italy. TAK from Italy had been more often treated with methotrexate, leflunomide or azathioprine, as opposed to tacrolimus in TAK from India (p < 0.05). Biologic or targeted synthetic disease-modifying agents were almost exclusively used in Italy. Survival on first immunosuppressive agent was longer from Italy than from India (log rank test p value 0.041). Considerable differences in the choice of initial vascular imaging modality and therapies for TAK from Italy and India could relate to prevalent socio-economic disparities. These should be considered while developing treatment recommendations for TAK.
Asphyxia-Induced Bacterial Translocation in an Animal Experimental Model in Neonatal Piglets
Authors: Matara Dimitra-Ifigeneia , Sokou Rozeta , Xanthos Theodoros , Pouliakis Abraham , Sarantaki Antigoni , Boutsikou Theodora , Iliodromiti Zoi , Salakos Christos , Gazouli Maria , Iacovidou Nicoletta
Published: December 2022

Background: The term “bacterial translocation” (BT) refers to the migration of bacteria or their products from the gastrointestinal tract to tissues located outside it, and may occur after intestinal ischemia-reperfusion injury. The term “endotoxin” is synonymous, and is used interchangeably with the term lipopolysaccharide (LPS). LPS, a component of Gram-negative gut bacteria, is a potent microbial virulence factor, that can trigger production of pro-inflammatory mediators, causing localized and systemic inflammation. The aim of this study is to investigate if neonatal asphyxia provokes BT and an increased concentration of LPS in an animal model of asphyxia in piglets. Methods: Twenty-one (21) newborn male Landrace/Large White piglets, 1–4 days old, were randomly allocated into three groups, Control (A), Asphyxia (B) and Asphyxia-Cardiopulmonary Resuscitation (CPR) (C). All animals were instrumented, anesthetized and underwent hemodynamic monitoring. In Group A, the animals were euthanized. In Group B, the endotracheal tube was occluded to cause asphyxia leading to cardiopulmonary arrest. In Group C, the animals were resuscitated after asphyxia and further monitored for 30′. Bacterial translocation was assessed by the measurement of endotoxin in blood from the portal vein and the aorta, and also by the measurement of endotoxin in mesenteric lymph nodes (MLNs) at euthanasia. The results are given as median (IQR) with LPS concentration in EU/mL. Results: BT was observed in all groups with minimum LPS concentration in the MLN and maximum concentration in the portal vein. LPS levels in the MLNs were higher in the Group B: 6.38 EU/mL (2.69–9.34) compared to the other groups (Group A: 2.1 EU/mL (1.08–2.52), Group C: 1.66 EU/mL (1.51–2.48), p = 0.012). The aorta to MLNs LPS difference (%) was lower in Group B: 0.13% (0.04–1.17), compared to Group A: 5.08% (2.2–10.7), and Group C: 3.42% (1.5–5.1)) (p = 0.042). The same was detected for portal to MLNs LPS difference (%) which was lower in Group B: 0.94% (0.5–3) compared to Group A: 4.9% (4–15), and Group C: 3.85% (1.5–5.1)) (p = 0.044). Conclusions: Neonatal asphyxia can provoke ΒΤ and increased LPS concentration in blood and tissue located outside the gastrointestinal system.
Development of Mia Phenotyping Using Paper-Based Device
Authors: Chomean Sirinart , Attapong Jirapat , Jitsuvantaya Sumittra , Poomsaard Komin , Dongwilai Chadchadaporn , Bunnun Pished , Kaset Chollanot
Published: December 2022

The MNS7 (Mia) blood group antigen is found at a different prevalence among different ethnic groups. Anti-Mia can cause hemolytic disease of the fetus and newborn (HDFN) and both acute- and delayed-type hemolytic transfusion reactions (HTR). Mia typing should be performed in donors to prevent life-threatening hemolytic transfusion reactions. The gel card and standard tube methods still need specialized equipment, centrifugation, and expertise for result interpretation. We used a novel paper-based analytical device (PAD) pre-coated with monoclonal IgM anti-Mia for Mia phenotyping. We measured grey pixel intensity in blood typing results for interpretation processing using OpenCV at the sample (SP) and elution parts (EP); furthermore, we used the SP: EP ratio and F-score as analysis criteria. We typed 214 blood EDTA samples with PAD–Mia and then compared with gel card results for setting an analysis criterion. We observed 100% sensitivity, specificity, and accuracy when we applied the SP: EP ratio and F-score with the optimal criterion (1.07 and 0.17 for SP: EP ratio and F-score, respectively). The validation of PAD–Mia typing for blood donor samples (n = 150) via F-score gave 100% sensitivity and specificity when compared with the gel card method; therefore, we argue that PAD–Mia typing can be used for Mia phenotyping without sero-centrifugation. Moreover, to study the correlation between genotype and phenotype, PCR-SSP was performed to identify GYP(B-A-B) hybrids. The results revealed that all Mia+ blood samples gave a positive with GP. Hut, GP. HF, GP. Mur, GP. Hop, and GP. Bun. Results of the gel card method and PCR-SSP were concordant. Hence, using PAD–Mia typing in blood donors would be helpful for creating a phenotype database of blood donors for reducing alloimmunization risks.
Quantification of Significant Aortic Stenosis by Echocardiography versus Four-Dimensional Cardiac Computed Tomography: A Multi-Modality Imaging Study
Authors: Wang Tom Kai Ming , Abou Hassan Ossama K. , Popović Zoran B. , Griffin Brian P. , Rodriguez Luis Leonardo
Published: December 2022

Transthoracic echocardiography (TTE) grading of aortic stenosis (AS) is challenging when parameters are discrepant, and four-dimensional cardiac computed tomography (4D-CCT) is increasingly utilized for transcatheter intervention workup. We compared TTE and 4D-CCT measures contributing to AS quantification. AS patients (n = 80, age 86 ± 10 years, 71% men) referred for transcatheter replacement in 2014–2017 were retrospectively studied, 20 each with high-gradient AS (HG-AS), classical and paradoxical low-flow low-gradient AS (CLFLG-AS and PLFLG-AS), and normal-flow low-gradient AS (NFLG-AS). Correlation and Bland–Altman analyses were performed between TTE and 4D-CCT parameters. There were moderate-to-high TTE versus 4D-CCT correlations for left ventricular volumes, function, mass, and outflow tract dimensions (r = 0.51–0.88), though values were mostly significantly higher by 4D-CCT (p < 0.001). Compared with 4D-CCT planimetry of aortic valve area (AVA), TTE estimates had modest correlation (r = 0.37–0.43) but were significantly lower (by 0.15–0.32 cm2). The 4D-CCT estimate of LVSVi lead to significant reclassification of AS subtype defined by TTE. In conclusion, 4D-CCT quantified values were higher than TTE for the left ventricle and AVA, and the AS subtype was reclassified based on LVSVi by 4D-CCT, warranting further research to establish its clinical implications and optimal thresholds in severe AS management.
The Genetic and Epigenetic Footprint in Idiopathic Pulmonary Fibrosis and Familial Pulmonary Fibrosis: A State-of-the-Art Review
Authors: Tirelli Claudio , Pesenti Chiara , Miozzo Monica , Mondoni Michele , Fontana Laura , Centanni Stefano
Published: December 2022

Idiopathic pulmonary fibrosis (IPF) is a rare disease of the lung with a largely unknown etiology and a poor prognosis. Intriguingly, forms of familial pulmonary fibrosis (FPF) have long been known and linked to specific genetic mutations. There is little evidence of the possible role of genetics in the etiology of sporadic IPF. We carried out a non-systematic, narrative literature review aimed at describing the main known genetic and epigenetic mechanisms that are involved in the pathogenesis and prognosis of IPF and FPF. In this review, we highlighted the mutations in classical genes associated with FPF, including those encoding for telomerases (TERT, TERC, PARN, RTEL1), which are also found in about 10–20% of cases of sporadic IPF. In addition to the Mendelian forms, mutations in the genes encoding for the surfactant proteins (SFTPC, SFTPA1, SFTPA2, ABCA3) and polymorphisms of genes for the mucin MUC5B and the Toll-interacting protein TOLLIP are other pathways favoring the fibrogenesis that have been thoroughly explored. Moreover, great attention has been paid to the main epigenetic alterations (DNA methylation, histone modification and non-coding RNA gene silencing) that are emerging to play a role in fibrogenesis. Finally, a gaze on the shared mechanisms between cancer and fibrogenesis, and future perspectives on the genetics of pulmonary fibrosis have been analyzed.
Effects of Protective Surface Coating on Fluoride Release and Recharge of Recent Uncoated High-Viscosity Glass Ionomer Cement
Authors: Krajangta Nantawan , Dulsamphan Chayanee , Chotitanmapong Tongjai
Published: December 2022

The ability to release and recharge fluoride is a property of glass ionomer cement materials, which is an advantage for patients with a high caries risk. The purpose of this study was to evaluate the amount of released and recharged fluoride in recent uncoated high-viscosity glass ionomer cement (KetacTM Universal AplicapTM) with different surface coatings and at different time points. In total, 135 cylindrical-shaped specimens were equally divided into the following three groups: KetacTM Universal AplicapTM, KetacTM Molar AplicapTM, and KetacTM Fil Plus AplicapTM. The different coatings performed on each group were as follows: uncoated, coated with KetacTM Glaze, and coated with G-Coat PlusTM. The amounts of released and recharged fluoride were measured at 24 h and at weeks 1, 2, 3, and 4. The recharging agent was a 1.23% APF gel. KetacTM Universal AplicapTM showed the highest released fluoride at all time points and the highest recharged fluoride at weeks 1, 2, and 3. Both the KetacTM Glaze- and G-Coat PlusTM-coated specimens presented significantly lower released and recharged fluoride ions than the uncoated group at all time points (p < 0.001). Coating with G-Coat PlusTM significantly decreased the released and recharged fluoride compared to the coating with KetacTM Glaze at almost all time points (p < 0.05), except for weeks 1 and 2. The application of coating agents reduced the amount of released and recharged fluoride by the KetacTM Universal AplicapTM.
Staff Members’ Professional Agency within the Staff Community and the Education Policies: Supporting Integration in Multicultural and Multilingual School Communities
Authors: Manninen Eveliina , Hökkä Päivi , Tarnanen Mirja , Vähäsantanen Katja
Published: December 2022

In recent decades, increased diversity and migration have challenged school staff members’ ways of working. This study aimed to identify the challenges faced by Finnish school staff in supporting students with migrant backgrounds, and to elaborate on how they enact professional agency toward these challenges. The data consist of 15 thematic interviews with staff members across various work positions in two Finnish lower secondary schools. Based on thematic analysis, the challenges within the staff community and the education policies were found to include the following: (1) the diversification of students makes tensions more visible in the staff community, and (2) inflexible education policies restrict support processes. In these challenges, staff members practiced professional agency via a focus on their own work level, relying on certain colleagues, and trusting their own professionalism, under strong autonomy tradition. However, outside of their own work level, staff members prefer to adapt to the conditions by compromising, and they seem have not strong participation in higher decision-making. As a conclusion, it would be valuable to resource time expressly for establishing new practices, strengthen head teachers’ ability to promote a culture of shared leadership, while clarifying the boundaries of pedagogical autonomy, and facilitate the participation in higher decision-making.
COVID-19 Academic Integrity Violations and Trends: A Rapid Review
Authors: Maryon Thomas , Dubre Vandy , Elliott Kimberly , Escareno Jessica , Fagan Mary Helen , Standridge Emily , Lieneck Cristian
Published: December 2022

The rapid shift from classroom course delivery to online education modalities during the COVID-19 pandemic has had significant impacts on academia. Student loss of face-to-face interaction, the lost social benefits of the educational milieu, and restricted instructor ability to control both the learning environment and assessment process have been significant. The purpose of this paper is to discover if due to the unplanned shift to online course delivery, educators and researchers experienced impacts to academic integrity during the peak of the online shift. A systemic review utilizing the PRISMA methodology of peer reviewed literature published during the period of March 2020 till September 2021 demonstrated that violation types continued to fall within the existing academic integrity constructs of inappropriate information sharing, cheating on exams and assignments, incidents of plagiarism, and falsifying or fabricating information. The results showed that pre-COVID concerns with academic integrity were amplified with previous concerns moving to the forefront. In addition, the rapid shift opened doors for greater opportunity for violations and increased instructor concern especially within the hard sciences and courses with lab-based components. Reinforcing the importance of providing formal academic integrity student and faculty training can be a beneficial intervention to ensure students understand the ethical implications of student behavior and performance during the assessment process. Given the emerging trend pre-COVID that skyrocketed during the pandemic, ensuring academic integrity should remain a key priority for learning institutions.
Imagining and Reimagining the Future of Special and Inclusive Education
Authors: Kauffman James M. , Anastasiou Dimitris , Hornby Garry , Lopes Joao , Burke Mack D. , Felder Marion , Ahrbeck Bernd , Wiley Andrew
Published: December 2022

“Reimagination” is required to create a new vision of education that better serves individuals with disabilities. Imagination is a way of conceiving possibilities and probabilities. The future of special education is imagined within the limits of possibility and probability of “appropriate” education for children with special educational needs and disabilities. Education is reimagined for full inclusion without special education and an alternative reimagination of inclusive special education. Particular attention is given to the administrative structure of public education and to the training of teachers for the imagined approaches to special education. The importance of imagining special and inclusive education being based on science and rationality and the limitations of proposed approaches to including students with disabilities in education are explained.
Parity Check Based Fault Detection against Timing Fault Injection Attacks
Authors: Zhang Maoshen , Li  He , Wang Peijing , Liu Qiang
Published: December 2022

Fault injection technologies can be utilized to steal secret information inside integrated circuits (ICs), and thus cause serious information security threats. Parity check has been adopted as an efficient method against fault injection attacks. However, the contradiction between security and overhead restricts the further development and applications of parity check in fault injection detection. This paper proposes two methods, mixed-grained parity check and word recombination parity check, based on parity check for the trade-off between security and overhead. The efficiency of the proposed approaches is verified on RC5, AES, and DES encryption implementations by clock glitch attack. Compared with the traditional parity check, the fault coverage rate of the mixed-grained approach can be increased by up to 53.69% by consuming 13.2% registers more. Against the traditional parity check, the fault coverage rate of the word recombination approach can be increased by up to 47.16% by using only 2.35% register more. The proposed approaches provide IC designers with countermeasure options targeting different design skills and design specifications.
Integrated Demand Response Design of Integrated Energy System with Mobile Hydrogen Energy Storage in Time-Domain Two-Port Model
Authors: Cheng Haoyuan , Ai Qian
Published: December 2022

With the development of energy integration technology, demand response (DR) has gradually evolved into integrated demand response (IDR). In this study, for the integrated energy system (IES) on the distribution grid side with electricity, heat, natural gas network, and hydrogen energy equipment, the analogy relationship between the thermal and mobile hydrogen energy storage networks is proposed. Moreover, a unified model that reflects network commonalities across different energy forms is established. Then, considering the time delay of the IES in the nontransient network, a time-domain two-port model of the IES considering the time delay is established. This model shows the joint effect of time and space on system parameters. Finally, this study validates the model in the application of DR. The verification results show that in DR, the time-domain two-port model can accurately “cut peaks and fill valleys” for the IES and effectively reduce the operating cost of the IES system.
Correction: Xiaomei et al. Intelligent Hybrid Deep Learning Model for Breast Cancer Detection. Electronics 2022, 11, 2767
Authors: Wang Xiaomei , Ahmad Ijaz , Javeed Danish , Zaidi Syeda Armana , Alotaibi Fahad M. , Ghoneim Mohamed E. , Daradkeh Yousef Ibrahim , Asghar Junaid , Eldin Elsayed Tag
Published: December 2022

In the published publication [...]
XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques
Authors: Guleria Pratiyush , Naga Srinivasu Parvathaneni , Ahmed Shakeel , Almusallam Naif , Alarfaj Fawaz Khaled
Published: December 2022

Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.
Bitcoin Price Forecasting and Trading: Data Analytics Approaches
Authors: Al-Nefaie Abdullah H. , Aldhyani Theyazn H. H.
Published: December 2022

Currently, the most popular cryptocurrency is bitcoin. Predicting the future value of bitcoin can help investors to make more educated decisions and to provide authorities with a point of reference for evaluating cryptocurrency. The novelty of the proposed prediction models lies in the use of artificial intelligence to identify movement cryptocurrency prices, particularly bitcoin prices. A forecasting model that can accurately and reliably predict the market’s volatility and price variations is necessary for portfolio management and optimization in this continually expanding financial market. In this paper, we investigate a time series analysis that makes use of deep learning to investigate volatility and provide an explanation for this behavior. Our findings have managerial ramifications, such as the potential for developing a product for investors. This can help to expand upon our model by adjusting various hyperparameters to produce a more accurate model for predicting the price of cryptocurrencies. Another possible managerial implication of our findings is the potential for developing a product for investors, as it can predict the price of cryptocurrencies more accurately. The proposed models were evaluated by collecting historical bitcoin prices from 1 January 2021 to 16 June 2022. The results analysis of the GRU and MLP models revealed that the MLP model achieved highly efficient regression, at R = 99.15% during the training phase and R = 98.90% during the testing phase. These findings have the potential to significantly influence the appropriateness of asset pricing, considering the uncertainties caused by digital currencies. In addition, these findings provide instruments that contribute to establishing stability in cryptocurrency markets. By assisting asset assessments of cryptocurrencies, such as bitcoin, our models deliver high and steady success outcomes over a future prediction horizon. In general, the models described in this article offer approximately accurate estimations of the real value of the bitcoin market. Because the models enable users to assess the timing of bitcoin sales and purchases more accurately, they have the potential to influence the economy significantly when put to use by investors and traders.
Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network
Authors: Duan Wensi , Gao Lili , Liu Juan , Li Cheng , Jiang Peng , Wang Lang , Chen Hua , Sun Xiaorong , Cao Dehua , Pang Baochuan , Li Rong , Liu Sai
Published: December 2022

Fine-needle aspiration cytology (FNAC) is regarded as one of the most important preoperative diagnostic tests for thyroid nodules. However, the traditional diagnostic process of FNAC is time-consuming, and its accuracy is highly related to the experience of the cytopathologist. Computer-aided diagnostic (CAD) systems are rapidly evolving to provide objective diagnostic recommendations. So far, most studies have used fixed-size patches and usually hand-select patches for model training. In this study, we develop a CAD system to address these challenges. In order to be consistent with the diagnostic working mode of cytopathologists, the system is mainly composed of two task modules: the detecting module that is responsible for detecting the regions of interest (ROIs) from the whole slide image of the FNAC, and the classification module that identifies ROIs having positive lesions. The system can then output the top-k ROIs with the highest positive probabilities for the cytopathologists to review. In order to obtain the overall good performance of the system, we compared different object detection and classification models, and used a combination of the YOLOV4 and EfficientNet networks in our system.
Performance Analysis of IEEE 802.15.4 Bootstrap Process
Authors: Gallegos Ramonet Alberto , Noguchi Taku
Published: December 2022

The IEEE 802.15.4 is a popular standard used in wireless sensor networks (WSNs) and the Internet of Things (IoT) applications. In these networks, devices are organized into groups formally known as personal area networks (PAN) which require a bootstrap procedure to become operational. Bootstrap plays a key role in the initialization and maintenance of these networks. For this reason, this work presents our implementation and performance analysis for the ns-3 network simulator. Specifically, this bootstrap implementation includes the support of three types of scanning mechanisms (energy scan, passive scan, and active scan) and the complete classic association mechanism described by the standard. Both of these mechanisms can be used independently by higher layers protocols to support network initialization, network joining, and maintenance tasks. Performance evaluation is conducted in total network association time and packet overhead terms. Our source code is documented and publicly available in the latest ns-3 official release.
Visual-GRoup AFFEct Recognition (V-GRAFFER): A Unified Application for Real-Time Group Concentration Estimation in E-Lectures
Authors: Triantafyllou Andreas M. , Tsihrintzis George A.
Published: December 2022

This paper presents the most recent version of V-GRAFFER, a novel system that we have been developing for Visual GRoup AFFEct Recognition research. This version includes new algorithms and features, as well as a new application extension for using and evaluating the new features. Specifically, we present novel methods to collect facial samples from other e-lecture applications. We use screen captures of lectures, which we track and connect with samples during the duration of e-educational events. We also developed and evaluated three new algorithms for drawing conclusions on group concentration states. As V-GRAFFER required such complex functionalities to be combined together, many corresponding microservices have been developed. The current version of V-GRAFFER allows drawing real-time conclusions using the input samples collected from the use of any tutoring system, which in turn leads to real-time feedback and allows adjustment of the course material.
Assessing the Intention to Adopt Cloud Accounting during COVID-19
Authors: Saad Mohamed , Lutfi Abdalwali , Almaiah Mohammed Amin , Alshira’h Ahmad Farhan , Alshirah Malek Hamed , Alqudah Hamza , Alkhassawneh Akif Lutfi , Alsyouf Adi , Alrawad Mahmaod , Abdelmaksoud Osama
Published: December 2022

The information technology (IT) revolutionization aside with the emergence of COVID-19 have catalyzed cloud-computing services and systems with multiple end-user benefits for online business management, specifically in the accounting discipline. For example, cloud accounting enables the significant reduction of organisational IT investment with flexible access to a large group of scalable resources. The cloud accounting enables small and medium size enterprises (SMEs) to independently engage in fundamental bookkeeping responsibilities rather than hiring external auditors for the same services. As cloud-based accounting adoption remains in the preliminary stage within Jordanian businesses, this study applied the technology, organisation, and environment model to explore cloud accounting among Jordanian SMEs. The study data were gathered from 156 Jordanian SME owners or managers with a structured online survey questionnaire. The recommended study framework encompassed seven determinants that influenced the cloud accounting adoption intention. Resultantly, except Perceived knowledge uncertainty factor, the proposed hypotheses were supported as the aforementioned factors (relative advantages, security concerns, top management support, organizational readiness, competitor’s intensity and suppliers computing support) positively and significantly influenced the cloud accounting of Jordanian SMEs. The study outcomes could facilitate IT field decision-makers and practitioners by investigating an actual cloud accounting case based on the essentiality of its application.
Distributed Adaptive NN-Based Attitude Synchronous Tracking Control with Input Saturation
Authors: Feng Zhenyu , Wang Jiawei , Wan Neng , Li Huayi
Published: December 2022

The attitude synchronization tracking problem for spacecraft formation flying is investigated in this paper based on sliding-mode control and a Chebyshev neural network (ChNN). A distributed attitude cooperative controller is designed for a group of spacecrafts to guarantee that each individual spacecraft will track the reference attitude of the virtual leader in the presence of external disturbances, system uncertainties and input saturation. An adaptive ChNN is introduced to approximate the system nonlinear uncertainties and bounded external disturbances online, and a switch function, which acts as a switching signal between the adaptive ChNN controller and the robust control law, is applied to limit the output of the ChNN approximator. Then, utilizing Nussbaum-type functions, an auxiliary control system is designed to counteract the nonlinearities caused by input saturation. Finally, a numerical simulation example is provided to illustrate the robustness and effectiveness of the proposed attitude control scheme.
Physical Features of High-Density Barium–Tungstate–Phosphate (BTP) Glasses: Elastic Moduli, and Gamma Transmission Factors
Authors: Zakaly Hesham M. H. , Tekin Huseyin O. , Rammah Yasser S. , Issa Shams A. M. , Alomari Ali Hamed , Ali Fatema T. , Baykal Duygu Sen , Elshami Wiam , Abulyazied D. E. , ALMisned Ghada , Mostafa A. M. A. , Ene Antoaneta
Published: December 2022

We present elastic moduli, gamma radiation attenuation characteristics, and transmission factor of barium–tungstate–phosphate (BTP) glasses with the chemical formula (60-y)BaO-yWO3-40P2O5, where y = 10 (S1)–40 (S4) in steps of 10 mole%. Different types of mathematical and simulation approaches, such as the Makishima-Mackenzie model, the Monte Carlo method, and the online Phy-X/PSD software, are utilized in terms of determining these parameters. The total packing density (Vt) is enriched from 0.607 to 0.627, while the total energy dissociation (Gt) is enriched by increasing the WO3 content (from 52.2 (kJ/cm3). In the investigated glasses, increasing tungstate trioxide (WO3) contribution enhanced Young’s, shear, bulk, and longitudinal moduli. Moreover, Poisson’s ratio is improved by increasing the WO3 content in the BTP glasses. The 20BaO-40WO3-40P2O5 sample possessed the highest values of both linear (µ) and mass attenuation (µm) coefficients, i.e., (µ, µm)S4 > (µ, µm)S3 > (µ, µm)S2 > (µ, µm)S1. Moreover, the 20BaO-40WO3-40P2O5 sample had the lowest values of half (HVL) and tenth (TVL) layers, i.e., (half, tenth)S4 < (half, tenth)S3 < (half, tenth)S2 < (half, tenth)S1. The effective atomic number (Zeff) of the studied glasses has the same behavior as µ and µm. Finally, the 20BaO-40WO3-40P2O5 is reported with the minimum values of transmission factor (TF) for all the BTP investigated at a thickness of 3 cm. In conclusion, the sample with composition 20BaO-40WO3-40P2O5 which has the maximum WO3 reinforcement may be a beneficial glass sample, along with its advanced mechanical and gamma ray shielding properties.
An Improved Multimodal Trajectory Prediction Method Based on Deep Inverse Reinforcement Learning
Authors: Chen Ting , Guo Changxin , Li Hao , Gao Tao , Chen Lei , Tu Huizhao , Yang Jiangtian
Published: December 2022

With the rapid development of artificial intelligence technology, the deep learning method has been introduced for vehicle trajectory prediction in the internet of vehicles, since it provides relative accurate prediction results, which is one of the critical links to guarantee security in the distributed mixed-driving scenario. In order to further enhance prediction accuracy by making full utilization of complex traffic scenes, an improved multimodal trajectory prediction method based on deep inverse reinforcement learning is proposed. Firstly, a fused dilated convolution module for better extracting raster features is introduced into the existing multimodal trajectory prediction network backbone. Then, a reward update policy with inferred goals is improved by learning the state rewards of goals and paths separately instead of original complex rewards, which can reduce the requirement for predefined goal states. Furthermore, a correction factor is introduced in the existing trajectory generator module, which can better generate diverse trajectories by penalizing trajectories with little difference. Abundant experiments on the current popular public dataset indicate that the prediction results of our proposed method are a better fit with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.
Fatigue Driving Recognition Method Based on Multi-Scale Facial Landmark Detector
Authors: Xiao Weichu , Liu Hongli , Ma Ziji , Chen Weihong , Sun Changliang , Shi Bo
Published: December 2022

Fatigue driving behavior recognition in all-weather real driving environments is a challenging task. Accurate recognition of fatigue driving behavior is helpful to improve traffic safety. The facial landmark detector is crucial to fatigue driving recognition. However, existing facial landmark detectors are mainly aimed at stable front face color images instead of side face gray images, which is difficult to adapt to the fatigue driving behavior recognition in real dynamic scenes. To maximize the driver’s facial feature information and temporal characteristics, a fatigue driving behavior recognition method based on a multi-scale facial landmark detector (MSFLD) is proposed. First, a spatial pyramid pooling and multi-scale feature output (SPP-MSFO) detection model is built to obtain a face region image. The MSFLD is a lightweight facial landmark detector, which is composed of convolution layers, inverted bottleneck blocks, and multi-scale full connection layers to achieve accurate detection of 23 key points on the face. Second, the aspect ratios of the left eye, right eye and mouth are calculated in accordance with the coordinates of the key points to form a fatigue parameter matrix. Finally, the combination of adaptive threshold and statistical threshold is used to avoid misjudgment of fatigue driving recognition. The adaptive threshold is dynamic, which solves the problem of the difference in the aspect ratio of the eyes and mouths of different drivers. The statistical threshold is a supplement to solve the problem of driver’s low eye threshold and high mouth threshold. The proposed methods are evaluated on the Hunan University Fatigue Detection (HNUFDD) dataset. The proposed MSFLD achieves a normalized mean error value of 5.4518%, and the accuracy of the fatigue driving recognition method based on MSFLD achieves 99.1329%, which outperforms that of state-of-the-art methods.
Numerical Modeling of Hydrothermal System Circulation Beneath Asal Rift, Republic of Djibouti
Authors: Hassan Aden Abdek , Raymond Jasmin , Giroux Bernard
Published: December 2022

Asal rift is an aerial rift segment resulting from the westward propagation of the Aden ridge into the Afar Depression. Geothermal manifestations such as hot springs and fumaroles, fault creep, conductivity anomaly, and high geothermal gradient were observed both at the surface and in the subsurface. Despite many scientific works conducted in Asal to understand the rifting mechanisms, the hydrothermal fluid circulation still needs to be evaluated since it is based on simplified conceptual models. To further contribute and progress toward a quantitative evaluation of fluid circulation, a 2D numerical model perpendicular to the rift axis was developed with the objective of better understanding the role of subsurface anisotropy in fluid flow and heat transfer in the Asal rift. Numerical modeling of multiphase flow and heat transfer was carried out with an equivalent porous medium intersected by fault zones having greater permeability. Horizontal anisotropic permeability and magmatic fluid release were taken into account with different simulation scenarios. The results indicate that fault zones act as recharge/discharge areas depending on their location, permeability, and number. Simulations considering horizontal anisotropic permeability allowed the reproduction of the thermal state observed in geothermal wells with the expected general pattern of fluid circulation in the Asal rift. Comparing our result with a recent study made with a 2D numerical modeling parallel to the rift axis, we suggest the presence of a saddle point where fluid flow is both to the northeast and to the southwest direction of the rift. Moreover, magmatic fluid release assumed in two simulation scenarios showed to have an impact on the hydrological behavior of fault zones and facilitate the development of super-critical flow at the center of the rift.
Deep Learning for Knock Occurrence Prediction in SI Engines
Authors: Tajima Haruki , Tomidokoro Takuya , Yokomori Takeshi
Published: December 2022

This research aims to predict knock occurrences by deep learning using in-cylinder pressure history from experiments and to elucidate the period in pressure history that is most important for knock prediction. Supervised deep learning was conducted using in-cylinder pressure history as an input and the presence or absence of knock in each cycle as a label. The learning process was conducted with and without cost-sensitive approaches to examine the influence of an imbalance in the numbers of knock and non-knock cycles. Without the cost-sensitive approach, the prediction accuracy exceeded 90% and both the precision and the recall were about 70%. In addition, the trade-off between precision and recall could be controlled by adjusting the weights of knock and non-knock cycles in the cost-sensitive approach. Meanwhile, it was found that including the pressure history of the previous cycle did not influence the classification accuracy, suggesting little relationship between the combustion behavior of the previous cycle and knock occurrence in the following cycle. Moreover, learning the pressure history up to 10° CA before a knock improved the classification accuracy, whereas learning it within 10° CA before a knock did not noticeably affect the accuracy. Finally, deep learning was conducted using data, including multiple operating conditions. The present study revealed that deep learning can effectively predict knock occurrences using in-cylinder pressure history.
Dynamic Strategy for Effective Current Reduction in Brushless DC Synchronous Motors Fault Tolerant Operation
Authors: Campos Rafael de Farias , Liberato Cesar da Silva , Oliveira José de , Dezuo Tiago Jackson May , Nied Ademir
Published: December 2022

This work presents a flexible strategy for RMS current reduction of healthy phases for brushless DC synchronous motors (BLDC) operating in phase opening failure, avoiding motor degradation without reducing its performance and allowing safe shutdown when a phase failure is detected. After the diagnosis of an open-phase failure, a corrective action divided into three steps is proposed. First, the traditional Six-Step operating mode with 120° electric degrees is changed to a new operating mode that uses the two healthy phases at 180° electric degrees to reduce torque loss due to phase failure. Second, a trapezoidal shape (with adjustable angles according to the RMS current level) is imposed as a current reference for the controller to reduce the current level and, consequently, improve the efficiency of the motor. Third, the passband of the speed control loop is reduced to minimize the influence of speed oscillations in controller failure. The experimental results presented show that the mode of operation with the proposed dynamic current reduction strategy allows an approximate reduction of up to 27% in the effective current and up to 41% in the motor temperature variation, compared to the usual failure mode of operation of the BLDC motor without the proposed strategy. The dynamics of change in the trapezium angle allowed a weighting between the current level and the oscillation of the speed, preventing the motor in phase failure from having a high-speed variation.