bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Preparation Atmosphere and Doping Concentration on Scintillation and Photoluminescence Properties of Lu2O3:Eu Scintillation Single Crystals
Authors: Du Wenbin , Lu Baoqi , Li Qianli , Xu Zhan , Yu Haihang , Feng He
Published: April 2023

Lu2O3:1%Eu and Lu2O3:8%Eu series single crystals were grown by optical floating zone method under air, N2, and H2-Ar mixed atmosphere (5 vol%H2 and 95 vol%Ar). The effects of preparation atmosphere and doping concentration on the scintillation and photoluminescence properties have been investigated and discussed. Correlated measurements of afterglow curves, X-ray excited luminescence spectra, photoluminescence excitation and photoluminescence, photoluminescence decay, and thermal stimulated luminescence curves were performed. Although Eu(S6) luminescence can be significantly depressed under oxidizing atmosphere, Lu2O3:8%Eu grown under weak reducing atmosphere presents the lowest afterglow intensity. This phenomenon was explained by different interstitial oxygen atom concentrations in the samples.
DNA Barcoding Data Reveal Important Overlooked Diversity of Cortinarius sensu lato (Agaricales, Basidiomycota) in the Romanian Carpathians
Authors: Szabó Emerencia , Dima Bálint , Dénes Avar L. , Papp Viktor , Keresztes Lujza
Published: April 2023

Cortinarius s.l. is a globally distributed agaricoid genus that has been well studied in Europe with over 1000 described species. However, the information about their taxonomy and diversity in eastern Central Europe is still limited. Only 124 species have been reported so far from Romania, based solely on morphological observations. The aim of this study was to re-examine the diversity of the genus Cortinarius s.l. in the Romanian Carpathian area, employing molecular phylogenetic and morphological methods. During intensive field work in the period 2017–2020, a total of 234 Cortinarius s.l. specimens were collected and studied with integrative taxonomic methods. For all the samples, we amplified and sequenced the nrDNA ITS region, which is the widely used official barcode marker of fungi. These sequences were compared to the data found in public databases (GenBank, UNITE, BOLD). Based on phylogenetic analyses, we identified 109 Cortinarius s.l. species, which represent 40 sections and 3 clades. Out of these species, 43 have previously been documented from Romania based on morphological identification methods, while 66 species are reported as new to the country.
Mitogenome of the Doleschallia bisaltide and Phylogenetic Analysis of Nymphalinae (Lepidoptera, Nymphalidae)
Authors: Liu Ning , Wang Hao , Fang Lijun , Zhang Yalin
Published: April 2023

The complete mitogenome of Doleschallia bisaltide was sequenced with a size of 16,389 bp. Gene orientation and arrangement in the newly sequenced mitogenome are the same as other mitogenomes in Lepidoptera. Except for trnS1(AGN), which lacks the dihydrouridine (DHC) arm, the other 21 tRNA genes all contain a typical cloverleaf structure. Ka/Ks ratio analysis of 13 protein-coding genes (PCGs) from 23 Nymphalinae species indicates that the evolutionary rate of COX1 was slowest, while that of ATP8, ND5, and ND6 was substantially high. Phylogenetic analysis revealed that Nymphalinae and Kallimini were nonmonophyletic. Trees constructed only from the nuclear DNA (nDNA) dataset had lower support than mitochondrial or combined datasets. The addition of RNA genes did not improve the phylogenetic signal, and nodal support decreased. These data provide important information for future studies into the phylogeny of Nymphalinae.
Review: The Potential Role of Vitamin E Analogs as Adjunctive Antioxidant Supplements for Transition Cows
Authors: Kuhn Matthew J.
Published: April 2023

Despite routine supplementation of dairy cattle with vitamins with antioxidant functions, such as α-tocopherol, the high energy demand of the transition period creates a pro-oxidant state that can overcome antioxidant defenses and damage macromolecules. Known as oxidative stress, this condition impairs host immune defenses, predisposing cattle to disease and causing dysfunctional inflammation through a dysregulated production of lipid inflammatory mediators known as oxylipids. Non-α-tocopherol analogs of vitamin E have functions in other species that limit oxidative stress and dysfunctional inflammation but have largely remained unstudied in cattle. As non-α-tocopherol analogs of vitamin E have functions similar to α-tocopherol yet are more rapidly metabolized, they may provide further antioxidative functions with a reduced risk for adverse effects. Indeed, in vitro and in vivo evidence in cattle show a strong safety profile of most non-α-tocopherol analogs, and by several measures, non-α-tocopherol analogs present equally or more potent antioxidative activities than α-tocopherol alone. Further, vitamin E analogs are shown to compete with certain fatty acids for enzymatic metabolism, which may impact proinflammatory mediator production. Given that non-α-tocopherol analogs of vitamin E reduce the production of pro-inflammatory mediators and act as potent antioxidants, their safety and efficacy for these purposes should be further evaluated in cattle.
Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning
Authors: Zhao Aihua , Du Xin , Yuan Suzhen , Shen Wenfeng , Zhu Xin , Wang Wenwen
Published: April 2023

Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.
Effect of the COVID-19 Pandemic on the Psychological Health of Patients Who Underwent Liver Transplantation Due to Hepatocellular Carcinoma
Authors: Akbulut Sami , Kucukakcali Zeynep , Saritas Hasan , Bozkir Cigdem , Tamer Murat , Akyuz Musap , Bagci Nazlican , Unsal Selver , Akbulut Mehmet Serdar , Sahin Tevfik Tolga , Colak Cemil , Yilmaz Sezai
Published: April 2023

Background: The primary aim of this study was to compare liver transplant (LT) recipients with and without hepatocellular carcinoma (HCC) in terms of COVID-19-related depression, anxiety, and stress. Method: A total of 504 LT recipients with (HCC group; n = 252) and without HCC (non-HCC group; n = 252) were included in the present case–control study. Depression Anxiety Stress Scales (DASS-21) and Coronavirus Anxiety Scale (CAS) were used to evaluate the depression, stress, and anxiety levels of LT patients. DASS-21 total and CAS-SF scores were determined as the primary outcomes of the study. Poisson regression and negative binomial regression models were used to predict the DASS and CAS scores. The incidence rate ratio (IRR) was used as a coefficient. Both groups were also compared in terms of awareness of the COVID-19 vaccine. Results: Poisson regression and negative binomial regression analyses for DASS-21 total and CAS-SF scales showed that the negative binomial regression method was the appropriate model for both scales. According to this model, it was determined that the following independent variables increased the DASS-21 total score: non-HCC (IRR: 1.26; p = 0.031), female gender (IRR: 1.29; p = 0.036), presence of chronic disease (IRR: 1.65; p < 0.001), exposure to COVID-19 (IRR: 1.63; p < 0.001), and nonvaccination (IRR: 1.50; p = 0.002). On the other hand, it was determined that the following independent variables increased the CAS score: female gender (IRR:1.75; p = 0.014) and exposure to COVID-19 (IRR: 1.51; p = 0.048). Significant differences were found between the HCC and non-HCC groups in terms of median DASS-21 total (p < 0.001) and CAS-SF (p = 0.002) scores. Cronbach’s alpha internal consistency coefficients of DASS-21 total and CAS-SF scales were calculated to be 0.823 and 0.783, respectively. Conclusion: This study showed that the variables including patients without HCC, female gender, having a chronic disease, being exposed to COVID-19, and not being vaccinated against COVID-19 increased anxiety, depression, and stress. High internal consistency coefficients obtained from both scales indicate that these results are reliable.
Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images
Authors: Anaam Asaad , Al-antari Mugahed A. , Hussain Jamil , Abdel Samee Nagwan , Alabdulhafith Maali , Gofuku Akio
Published: April 2023

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs’ manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells’ existence.
NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets
Authors: Mohanty Ricky , Allabun Sarah , Solanki Sandeep Singh , Pani Subhendu Kumar , Alqahtani Mohammed S. , Abbas Mohamed , Soufiene Ben Othman
Published: April 2023

Spinal cord segmentation is the process of identifying and delineating the boundaries of the spinal cord in medical images such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. This process is important for many medical applications, including the diagnosis, treatment planning, and monitoring of spinal cord injuries and diseases. The segmentation process involves using image processing techniques to identify the spinal cord in the medical image and differentiate it from other structures, such as the vertebrae, cerebrospinal fluid, and tumors. There are several approaches to spinal cord segmentation, including manual segmentation by a trained expert, semi-automated segmentation using software tools that require some user input, and fully automated segmentation using deep learning algorithms. Researchers have proposed a wide range of system models for segmentation and tumor classification in spinal cord scans, but the majority of these models are designed for a specific segment of the spine. As a result, their performance is limited when applied to the entire lead, limiting their deployment scalability. This paper proposes a novel augmented model for spinal cord segmentation and tumor classification using deep nets to overcome this limitation. The model initially segments all five spinal cord regions and stores them as separate datasets. These datasets are manually tagged with cancer status and stage based on observations from multiple radiologist experts. Multiple Mask Regional Convolutional Neural Networks (MRCNNs) were trained on various datasets for region segmentation. The results of these segmentations were combined using a combination of VGGNet 19, YoLo V2, ResNet 101, and GoogLeNet models. These models were selected via performance validation on each segment. It was observed that VGGNet-19 was capable of classifying the thoracic and cervical regions, while YoLo V2 was able to efficiently classify the lumbar region, ResNet 101 exhibited better accuracy for sacral-region classification, and GoogLeNet was able to classify the coccygeal region with high performance accuracy. Due to use of specialized CNN models for different spinal cord segments, the proposed model was able to achieve a 14.5% better segmentation efficiency, 98.9% tumor classification accuracy, and a 15.6% higher speed performance when averaged over the entire dataset and compared with various state-of-the art models. This performance was observed to be better, due to which it can be used for various clinical deployments. Moreover, this performance was observed to be consistent across multiple tumor types and spinal cord regions, which makes the model highly scalable for a wide variety of spinal cord tumor classification scenarios.
Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network
Authors: Zhang Licheng , Xu Fengzhe , Wang Lubing , Chen Yunkui , Nazemi Ehsan , Zhang Guohua , Zhang Xicai
Published: April 2023

The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as the air kerma (the amount of energy that was deposited in the air when the photon passed through it). Radiation beam intensity is represented by this value. Hospital X-ray equipment has to account for the heel effect, which means that the borders of the picture obtain a lesser radiation dosage than the center, and that air kerma is not symmetrical. The voltage of the X-ray machine can also affect the uniformity of the radiation. This work presents a model-based approach to predict air kerma at various locations inside the radiation field of medical imaging instruments, making use of just a small number of measurements. Group Method of Data Handling (GMDH) neural networks are suggested for this purpose. Firstly, a medical X-ray tube was modeled using Monte Carlo N Particle (MCNP) code simulation algorithm. X-ray tubes and detectors make up medical X-ray CT imaging systems. An X-ray tube’s electron filament, thin wire, and metal target produce a picture of the electrons’ target. A small rectangular electron source modeled electron filaments. An electron source target was a thin, 19,290 kg/m3 tungsten cube in a tubular hoover chamber. The electron source–object axis of the simulation object is 20° from the vertical. For most medical X-ray imaging applications, the kerma of the air was calculated at a variety of discrete locations within the conical X-ray beam, providing an accurate data set for network training. Various locations were taken into account in the aforementioned voltages inside the radiation field as the input of the GMDH network. For diagnostic radiology applications, the trained GMDH model could determine the air kerma at any location in the X-ray field of view and for a wide range of X-ray tube voltages with a Mean Relative Error (MRE) of less than 0.25%. This study yielded the following results: (1) The heel effect is included when calculating air kerma. (2) Computing the air kerma using an artificial neural network trained with minimal data. (3) An artificial neural network quickly and reliably calculated air kerma. (4) Figuring out the air kerma for the operating voltage of medical tubes. The high accuracy of the trained neural network in determining air kerma guarantees the usability of the presented method in operational conditions.
Prevalence and Characteristics of Isolated Nocturnal Hypertension and Masked Nocturnal Hypertension in a Tertiary Hospital in the City of Buenos Aires
Authors: Barochiner Jessica , Díaz Romina R. , Martínez Rocío
Published: April 2023

Isolated nocturnal hypertension (INH) and masked nocturnal hypertension (MNH) increase cardiovascular risk. Their prevalence and characteristics are not clearly established and seem to differ among populations. We aimed to determine the prevalence and associated characteristics of INH and MNH in a tertiary hospital in the city of Buenos Aires. We included 958 hypertensive patients ≥ 18 years who underwent an ambulatory blood pressure monitoring (ABPM) between October and November 2022, as prescribed by their treating physician to diagnose or to assess hypertension control. INH was defined as nighttime BP ≥ 120 mmHg systolic or ≥70 diastolic in the presence of normal daytime BP (<135/85 mmHg regardless of office BP; MNH was defined as the presence of INH with office BP < 140/90 mmHg). Variables associated with INH and MNH were analyzed. The prevalences of INH and MNH were 15.7% (95% CI 13.5–18.2%) and 9.7 (95% CI 7.9–11.8%), respectively. Age, male sex, and ambulatory heart rate were positively associated with INH, whereas office BP, total cholesterol, and smoking habits showed a negative association. In turn, diabetes and nighttime heart rate were positively associated with MNH. In conclusion, INH and MNH are frequent entities, and determination of clinical characteristics such as those detected in this study is critical since this might contribute to a more appropriate use of resources.
Multiple-Drugs-Related Osteonecrosis of the Jaw in a Patient Affected by Multiple Myeloma: A Case Report
Authors: Caggiano Mario , Di Spirito Federica , Acerra Alfonso , Galdi Marzio , Sisalli Laura
Published: April 2023

A 60-year-old woman suffering from multiple myeloma (MM) was treated with zoledronic acid (bisphosphonate), dexamethasone (corticosteroid), bortezomib (a chemotherapeutic agent), and lenalidomide (thalidomide analog) for about a year and with lenalidomide alone as maintenance therapy for almost two years and developed stage three medication-related osteonecrosis of the jaws (MRONJ) in the upper left dental arch approximately two weeks after tooth extraction, which was treated with a medical nonoperative conservative approach until reversion to stage one. The present case report describing the development of multi-drug-related osteonecrosis of the jaws during the pharmacologic MM maintenance phase draws attention to the complex multidisciplinary and multistage management of MM subjects and also that during disease remission, crucially involving oral healthcare providers for MRONJ prevention and pharmacovigilance. To prevent similar cases, cancer patient management should ensure proper dental care not only before starting but also throughout therapy duration and ensure continuous interdisciplinary consensus between oncologists and dentists. Moreover, also considering the independent negative and potentially synergistic effect on bone metabolism and mucosal healing processes of employed medicaments, additionally combined with the cumulative one of previous intravenous bisphosphonates, further studies should highlight the polypharmacy effect and hopefully aid in patient-specific MRONJ risk assessment in cancer patients.
Do Share Allocations to the Indigenous Investor Drive the Demand for IPOs?
Authors: Tajuddin Ahmad Hakimi , Gopal Kanesh , Mohd-Rashid Rasidah , Mehmood Waqas , Sadik-Zada Elkhan Richard
Published: April 2023

The purpose of this paper was to investigate the impact of allocating shares to the indigenous (Bumiputera) investors on the oversubscription ratio of IPO. This factor is unique to Malaysian IPOs and would enable us to reflect the signaling theory. Data on 348 IPO firms listed on Bursa Malaysia over a span of 17 years from 2002 to 2018 were examined using a cross-sectional regression analysis. The findings demonstrated no significant impact arising from the fractions of shares allocated to Bumiputera investors on the oversubscription ratios, except that the revised guidelines on the Bumiputera equity requirement had a significant negative influence on oversubscription. Further tests showed that the influence of such share allocation on oversubscription was moderated by firm size, which was proxied by market capitalization. The findings lend support to the signaling theory, indicating that the demand for IPOs will be slightly higher for larger firms listed in bigger markets.
Interactive Games and Plays in Teaching Physics and Astronomy
Authors: Akimkhanova Zhuldyzay , Turekhanova Kunduz , Karwasz Grzegorz P.
Published: April 2023

Physics is a difficult subject in which to trigger interest in pupils, particularly in junior high school classes: this reflects in the results of maturity exams. Therefore, teachers, educators (and authorities deciding on CV contents) should search for new efficient methods, techniques, and contents corresponding to particular topics in physics. What can be done at the level of a single university (even if big) or school is to enrich standard lessons with new elements and observe how the responses of pupils change. In this article, the results of an implementation of interactive plays and games are presented for enhancing pupils’ interest and rate of understanding in physics, astronomy, and engineering. The games were designed by authors and the contents were developed jointly at Nicolaus Copernicus University, Poland and al-Farabi Kazakh National University, Kazakhstan. Implementation was carried out both in schools (a secondary school in Almaty and one primary school in Torun) and in extra-school (secondary school students in Almaty, elementary and secondary school students in Torun, during university-based activities) environments. A preliminary analysis of the didactical efficiency is given. We observed a positive reception of the majority of the didactical means that we proposed. These observations will serve us for further (and possibly permanent) enrichment of forms and contents of teaching physics and astronomy.
Science Teaching at a Distance in Greece: Students’ Views
Authors: Stefanidou Constantina , Mandrikas Achilleas
Published: April 2023

This article presents in a comparative way the findings from two surveys conducted on primary (students in Y6) and secondary (students in Y10) students in Attica, Greece, in order to map their views on distance science education, which was emergently implemented due to the COVID-19 outbreak during the school year 2020–2021. The research was conducted in a sample of 378 primary and 197 secondary students. The findings revealed that students were not satisfied with the distance teaching and learning of science, either in primary or in secondary education, except for the increased use of audiovisual material. Technical issues, such as poor network and infrastructure, lack of face-to-face interaction with classmates and teacher, external and internal distractions, lack of sufficient experimental activities, and limited understanding of concepts were common findings in both levels of education. Concerning the differences between the levels, it seems that in primary education more technical problems were reported, while in secondary education more didactic problems were reported.
Why Can the Flipped Classroom Frustrate Students? Experiences from an Engineering Mathematics Course
Authors: Nielsen Kjetil Liestøl
Published: April 2023

This paper explores students’ experiences with a flipped classroom in a first-year engineering mathematics course with 118 students. While most students were satisfied with the flipped classroom and expressed appreciation for the flexibility, freedom and independence induced by the teaching method, other students expressed frustrations. Based on two surveys with both open-ended and closed-ended questions, this paper explores possible reasons why a flipped classroom can be a source of frustration. Some students expressed having difficulty adjusting their study habits to the flipped classroom approach as well as having difficulty finding the motivation to watch the required learning videos before in-class activities. While some students experienced the group work associated with the flipped classroom format as a positive aspect of their learning, other students expressed irritation because of group members not meeting prepared for the group assignments. The expressed experiences are discussed in light of the self-determination theory and the self-regulated learning theory. Weaknesses with how the flipped classroom was implemented are also discussed.
The Equilibrium Challenge, a New Way to Teach Engineering Mechanics in Architecture Degrees
Authors: Montés Nicolás , Hilario Lucía , Rivera Javier , López Ángel , Ferrer Teresa , Verdejo Pedro , Juan Ignacio , Ábalos Ana
Published: April 2023

Vocation is one of the determining factors taken into account by students when choosing their university studies. However, when the students start their studies, in their first year, they will find a series of basic subjects that barely motivate or stimulate them. In the specific case of mathematics, the problem is aggravated when many of the students already begin the first year showing rejection towards this subject. The lack of motivation for mathematics also affects the subject of physics because “the role of mathematics is to be the language of physics”. The EXPLORIA project proposed by the CEU Cardenal Herrera University is a potential solution to this problem. The objective of this project is the implementation of STEAM learning (Science Technology Engineering Art Mathematics) in the Degree in Fundamentals of Architecture at CEU Cardenal Herrera University through the EXPLORIA project. This article focuses on the activities carried out in the subject of physics in the Degree in Fundamentals of Architecture, corresponding to the part of mechanical engineering in order to show that through the realization of different challenges, we can develop creative products, new buildings with their logos and storytelling, as well as connect with the rest of subjects. For its development, students must use everyday objects within their reach, such as forks, spoons, knives, shoes, etc., to build an object or structure that must remain in a “creative balance” and this will serve as an inspiration for new buildings. These new creations are evaluated by an architecture team who fills in a rubric to evaluate the creativity and originality of the products. The number of students included in this project was 24 and the participants’ age ranged between 18 and 20 (similarly distributed). At the end of the work, an anonymous ad hoc questionnaire was carried out to show the students’ assessment of the new teaching methodology and the challenges developed in the subject of physics.
Redox Flow Batteries: A Glance at Safety and Regulation Issues
Authors: Trovò Andrea , Marini Giacomo , Zamboni Walter , Sessa Sebastian Dambone
Published: April 2023

Redox flow batteries (RFB) are considered one of the most promising electrochemical energy storage technologies for stationary storage applications, especially for long duration energy storage services. RFBs are electrochemical energy converters that use flowing media as or with active materials, where the electrochemical reactions can be reversed. Knowledge of technical standards and other regulations lay the foundations for successful and safe commercialization of products through uniform instructions and generally applicable rules. A small number of papers on safety and regulatory issues of RFBs are reported in the literature, mainly for two reasons. First, because this technology is considered safe; and second, because most of the publications have been limited to short-term characterization studies of materials in chemistry. This paper aims to help fill this gap, providing researchers and students with introductory knowledge on the safety and regulatory aspects of RFBs, mainly from an electrical and hydraulic point of view. The reader is referred to specific regulations for deeper studies and analyses.
A Causal Graph-Based Approach for APT Predictive Analytics
Authors: Liu Haitian , Jiang Rong
Published: April 2023

In recent years, complex multi-stage cyberattacks have become more common, for which audit log data are a good source of information for online monitoring. However, predicting cyber threat events based on audit logs remains an open research problem. This paper explores advanced persistent threat (APT) audit log information and uses a combination of causal graphs and deep learning techniques to perform predictive analysis of APT. The study focuses on two different methods of constructing malicious activity scenarios, including those based on malicious entity evolving graphs and malicious entity neighborhood graphs. Deep learning networks are then utilized to learn from past malicious activity scenarios and predict specific malicious attack events. To validate the effectiveness of this approach, audit log data published by DARPA’s Transparent Computing Program and restored by ATLAS are used to demonstrate the confidence of the prediction results and recommend the most effective malicious event prediction by Top-N.
Overview about E-Mobility Conducted Immunity Tests on ESA Communication Lines
Authors: Righi Stefano , Dossi Luca , Tacchini Alessandro , Camarda Antonio , Vincetti Luca
Published: April 2023

Due to the complexity of the Automotive Electromagnetic Compatibility legislation in force, this article aims to describe a simplified overview of several technical standards relating to conducted immunity tests on electronic sub-assemblies, where communication lines are involved. The discussed automotive standards reported in this article are: ISO 11452-1 and ISO 11452-4 for continuous narrowband electromagnetic fields immunity test, bulk current injection and tubular wave coupler, IEC 61000-4-5 for immunity against surge events, IEC 61000-4-4 for electrical fast transient/burst events immunity, ISO 10605 for electrostatic discharge events immunity, ISO 7637-2 and ISO 7637-3 for transient disturbances events immunity. For each cited standard, disturbance bandwidth evaluation was performed. Practical examples are reported, with analysis and discussion of some of the adoptable disturbance countermeasures applicable on controlled area network communication lines, and possible design advantages and disadvantages with different types of filtering and suppression circuit solutions.
A Novel Hybrid Approach for Computing Electromagnetic Scattering from Objects with Honeycomb Structures
Authors: Yuan Xiaowei , Yang Zeng , He Weijia , Yang Minglin , Sheng Xinqiing
Published: April 2023

We propose in this paper a novel hybrid numerical modeling method for computing electromagnetic scattering from inhomogeneous targets containing honeycomb structures. In the proposed approach, the whole honeycomb structure is divided into the inner and outer two subregions. Each thin wall of a unit cell in the outer subregion is replaced by a zero-thickness surface, with the aid of a resistive sheet boundary condition (RSBC) to describe the electric and magnetic field discontinuities across the surface. Each unit cell in the inner subregion is homogenized by using the Hashin–Shtrikman and the Mori–Tanaka formulae. The two subregions are further divided into smaller subdomains by introducing the Robin-type transmission condition to couple subregion interfaces, as well as subdomain interfaces. The whole solution region is then discretized and solved using the nonconformal domain decomposition-based hybrid finite element–boundary integral–multilevel fast multipole algorithm (FE-BI-MLFMA). The numerical results demonstrate that the proposed approach exhibits a high accuracy, efficiency, and flexibility. Solutions of scattering by a wing-like object and a practical unmanned aerial vehicle (UAV) model with honeycomb radar-absorbing structures are presented, showing the superior performance of the proposed algorithm.
Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases
Authors: Almurayziq Tariq S. , Senan Ebrahim Mohammed , Mohammed Badiea Abdulkarem , Al-Mekhlafi Zeyad Ghaleb , Alshammari Gharbi , Alshammari Abdullah , Alturki Mansoor , Albaker Abdullah
Published: April 2023

The immune system is one of the most critical systems in humans that resists all diseases and protects the body from viruses, bacteria, etc. White blood cells (WBCs) play an essential role in the immune system. To diagnose blood diseases, doctors analyze blood samples to characterize the features of WBCs. The characteristics of WBCs are determined based on the chromatic, geometric, and textural characteristics of the WBC nucleus. Manual diagnosis is subject to many errors and differing opinions of experts and takes a long time; however, artificial intelligence techniques can help to solve all these challenges. Determining the type of WBC using automatic diagnosis helps hematologists to identify different types of blood diseases. This work aims to overcome manual diagnosis by developing automated systems for classifying microscopic blood sample datasets for the early detection of diseases in WBCs. Several proposed systems were used: first, neural network algorithms, such as artificial neural networks (ANNs) and feed-forward neural networks (FFNNs), were applied to diagnose the dataset based on the features extracted using the hybrid method between two algorithms, the local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM). All algorithms attained superior accuracy for WBC diagnosis. Second, the pre-trained convolutional neural network (CNN) models AlexNet, ResNet-50, GoogLeNet, and ResNet-18 were applied for the early detection of WBC diseases. All models attained exceptional results in the early detection of WBC diseases. Third, the hybrid technique was applied, consisting of a pair of blocks: the CNN models block for extracting deep features and the SVM algorithm block for the classification of deep features with superior accuracy and efficiency. These hybrid techniques are named AlexNet with SVM, ResNet-50 with SVM, GoogLeNet with SVM, and ResNet-18 with SVM. All techniques achieved promising results when diagnosing the dataset for the early detection of WBC diseases. The ResNet-50 model achieved an accuracy of 99.3%, a precision of 99.5%, a sensitivity of 99.25%, a specificity of 99.75%, and an AUC of 99.99%.
Research on Non-Invasive Floating Ground Voltage Measurement and Calibration Method
Authors: Suo Chunguang , He Mingxing , Zhou Guoqiong , Shi Xinghua , Tan Xiangyu , Zhang Wenbin
Published: April 2023

Voltage measurement is an important part of power system operation, and non-intrusive voltage sensors have the advantages of low insulation difficulty, simple structure, easy loading and unloading, and high construction safety, which have become a new direction for voltage measurement. Based on the principle of electric field coupling, this paper constructs a non-intrusive floating ground three-capacitance voltage measurement model, which can complete the accurate measurement of voltage without connecting with the line to be measured and the earth in the measurement process. In non-intrusive voltage measurement, the change of the object to be measured or the measurement environment will cause the change of the coupling capacitance, which leads to the uncertainty of the transmission relationship of the sensor and the large error of measurement results. In order to solve this problem, a new method of sensor calibration is proposed in this paper. By sampling capacitance in parallel between two electrodes of the sensor, changing the capacitance value, and establishing an input output equation, the coupling capacitance value and the voltage value to be measured under different operating conditions are solved. In addition, the sampling capacitance is often several orders of magnitude larger than the sensor’s own capacitance, making the sensor’s voltage division ratio significantly higher and more conducive to the measurement of high voltages. The experimental results show that the measurement error is less than 2%, which verifies the feasibility of the method and the accuracy of the voltage measurement.
Optimized Polarization Encoder with High Extinction Ratio for Quantum Key Distribution System
Authors: Wang Pengcheng , Zhang Qianqian , Xie Huanwen , Guo Banghong
Published: April 2023

Polarization encoding is a promising approach for practical quantum key distribution (QKD) systems due to its simple encoding and decoding methodology. In this study, we propose a self-compensating polarization encoder (SCPE) based on a phase modulator, which can be composed of commercial off-the-shelf (COT) devices. We conducted a proof-of-concept experiment to test the SCPE, which demonstrated an in-system quantum bit error rate (QBER) of 0.53% and long-term running stability without any active adjustments. Additionally, we conducted experiments with transmission over commercial fiber spools of lengths up to 100 km and obtained a secure finite key rate of 3 kbps. Our polarization encoder is a promising solution for various polarization encoding protocols, including BB84, MDI, and RFI.
DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs
Authors: Ullah Naeem , Marzougui Mehrez , Ahmad Ijaz , Chelloug Samia Allaoua
Published: April 2023

Infectious disease-related illness has always posed a concern on a global scale. Each year, pneumonia (viral and bacterial pneumonia), tuberculosis (TB), COVID-19, and lung opacity (LO) cause millions of deaths because they all affect the lungs. Early detection and diagnosis can help create chances for better care in all circumstances. Numerous tests, including molecular tests (RT-PCR), complete blood count (CBC) tests, Monteux tuberculin skin tests (TST), and ultrasounds, are used to detect and classify these diseases. However, these tests take a lot of time, have a 20% mistake rate, and are 80% sensitive. So, with the aid of a doctor, radiographic tests such as computed tomography (CT) and chest radiograph images (CRIs) are used to detect lung disorders. With CRIs or CT-scan images, there is a danger that the features of various lung diseases’ diagnoses will overlap. The automation of such a method is necessary to correctly classify diseases using CRIs. The key motivation behind the study was that there is no method for identifying and classifying these (LO, pneumonia, VP, BP, TB, COVID-19) lung diseases. In this paper, the DeepLungNet deep learning (DL) model is proposed, which comprises 20 learnable layers, i.e., 18 convolution (ConV) layers and 2 fully connected (FC) layers. The architecture uses the Leaky ReLU (LReLU) activation function, a fire module, a maximum pooling layer, shortcut connections, a batch normalization (BN) operation, and group convolution layers, making it a novel lung diseases classification framework. This is a useful DL-based method for classifying lung disorders, and we tested the effectiveness of the suggested framework on two datasets with a variety of images from different datasets. We have performed two experiments: a five-class classification (TB, pneumonia, COVID-19, LO, and normal) and a six-class classification (VP, BP, COVID-19, normal, TB, and LO). The suggested framework’s average accuracy for classifying lung diseases into TB, pneumonia, COVID-19, LO, and normal using CRIs was an impressive 97.47%. We have verified the performance of our framework on a different publicly accessible database of images from the agriculture sector in order to further assess its performance and validate its generalizability. This study offers an efficient and automated method for classifying lung diseases that aids in the early detection of lung disease. This strategy significantly improves patient survival, possible treatments, and limits the transmission of infectious illnesses throughout society.
Separating Malicious from Benign Software Using Deep Learning Algorithm
Authors: Aslan Ömer
Published: April 2023

The increased usage of the Internet raises cyber security attacks in digital environments. One of the largest threats that initiate cyber attacks is malicious software known as malware. Automatic creation of malware as well as obfuscation and packing techniques make the malicious detection processes a very challenging task. The obfuscation techniques allow malware variants to bypass most of the leading literature malware detection methods. In this paper, a more effective malware detection system is proposed. The goal of the study is to detect traditional as well as new and complex malware variants. The proposed approach consists of three modules. Initially, the malware samples are collected and analyzed by using dynamic malware analysis tools, and execution traces are collected. Then, the collected system calls are used to create malware behaviors as well as features. Finally, a proposed deep learning methodology is used to effectively separate malware from benign samples. The deep learning methodology consists of one input layer, three hidden layers, and an output layer. In hidden layers, 500, 64, and 32 fully connected neurons are used in the first, second, and third hidden layers, respectively. To keep the model simple as well as obtain optimal solutions, we have selected three hidden layers in which neurons are decreasing in the following subsequent layers. To increase the model performance and use more important features, various activation functions are used. The test results show that the proposed system can effectively detect the malware with more than 99% DR, f-measure, and 99.80 accuracy, which is substantially high when compared with other methods. The proposed system can recognize new malware variants that could not be detected with signature, heuristic, and some behavior-based detection techniques. Further, the proposed system has performed better than the well-known methods that are mentioned in the literature based on the DR, precision, recall, f-measure, and accuracy metrics.
Horizontal IoT Platform EMULSION
Authors: Ganchev Ivan , Ji Zhanlin , O’Droma Máirtín
Published: April 2023

This article presents an overview of an Internet of Things (IoT) platform design based on a horizontal architectural principle. The goal in applying this principle is to overcome many of the disadvantages associated with the default design approach which, within this context, could be classed as “vertical” in that the IoT system and service are usually designed as stand-alone “silo-like” entities on their own autonomous platform. In a pure sense, each new IoT system and service is a new design ab initio. With the “horizontal principle”, the goal is that in the creation of a new IoT system and service, the provider needs only provide or adapt relevant architectural elements within a horizontal slice of an existing IoT architecture to enable the delivery of the desired IoT service. This article shows how embedding the horizontal principle into an IoT platform design brings the benefits of system design efficiency, effectiveness, and flexibility, together with at least the same scalability attributes inherent in the existing platform, an easily accessible adjustment, fine-tuning, and an openness to new use cases and application scenarios. The vision is the enabling of the realization of a potential multipurpose use of the IoT systems and services built on top of such platforms. The article presents a selective survey on the state of the art in IoT domains of application and in IoT platform architectural design solutions and lines of development from a vertical–horizontal categorization perspective. It presents examples of both IoT platform design solution types in use today. Within the context of strongly recommending the application of the horizontal design principle, the multitiered structure of the authors’ own EMULSION IoT platform based on this horizontal principle is presented in detail in the final part of the article.
Accounting Information Systems as Mediator for Digital Technology and Strategic Performance Interplay
Authors: Nassani Abdelmohsen A. , Yousaf Zahid , Grigorescu Adriana , Oprisan Oana , Haffar Mohamed
Published: April 2023

Digital technology (DT) has been broadly studied in industry and in scholarly research. This study aimed to examine the use of digital technology in the attainment of strategic performance and considered the mediating role of the accounting information systems (AISs) between them. Furthermore, this study explored the moderation of digital innovation in the linkage between DT and strategic performance. For data collection, 326 questionnaires were collected, and quantitative methods and random sampling techniques were used. The results showed that digital technology is directly associated with strategic performance. The findings also confirm that AISs play a mediating role, and digital innovation plays a moderating role in the association between digital technology and strategic performance. The results of this research encourage businesses to utilize opportunities for growing advanced technologies and developments in the industry to take up novel digital technologies, to advance their digital abilities to grow to be innovation leaders, and to boost the strategic performance of their firms. This study is one of the first pieces of research to provide information on how the latest technologies could have an influence in making innovative products/services and, afterward, boost firms’ strategic performance. This study also fills a gap in the literature regarding the driving factors of strategic performance by defining the mediating role of AISs and the moderating role of digital innovation in the association between dynamic factors and performance. The outcomes of this research demonstrate that AISs are extremely helpful and have an influence on the strategic performance of electronics firms.
A Triple Boost Seven-Level Common Ground Transformerless Inverter Topology for Grid-Connected Photovoltaic Applications
Authors: Gopinath Narayanan Pandurangan , Vijayakumar Krishnasamy , Mohd Ali Jagabar Sathik , Raghupathi Kumutha , Selvam Sivakumar
Published: April 2023

This article proposes a single-stage, seven-level (7L), switched-capacitor-based grid-connected inverter architecture with a common ground feature. This topology has the ability to boost the output voltage up to three times the input voltage. The proposed topology can diminish the leakage current in grid-connected photovoltaic (GC-PV) applications, and its capacitor voltages are self-balanced without any additional control strategies. The different operating modes are described in detail with their related mathematical expressions. The design of passive components and a detailed power loss analysis are presented. The merits of the proposed structure are demonstrated using a detailed comparative assessment. The grid-connected operation of the proposed inverter structure is simulated in the MATLAB/Simulink environment, and the results are presented. The laboratory prototype of 935 W is built and analyzed to validate the performance of the proposed structure.