bluuebunny/crossref_metadata_2025 huggingface dataset:
Primary Ciliary Dyskinesia Associated Disease-Causing Variants in CCDC39 and CCDC40 Cause Axonemal Absence of Inner Dynein Arm Heavy Chains DNAH1, DNAH6, and DNAH7
Authors: Wilken Alina , Höben Inga Marlena , Wolter Alexander , Loges Niki Tomas , Olbrich Heike , Aprea Isabella , Dworniczak Bernd , Raidt Johanna , Omran Heymut
Published: July 2024

Disease-causing bi-allelic DNA variants in CCDC39 and CCDC40 are frequent causes of the hereditary disorder of primary ciliary dyskinesia (PCD). The encoded proteins form a molecular ruler complex, crucial for maintaining the 96 nm repeat units along the ciliary axonemes. Defects of those proteins cause a stiff, rapid, and flickery ciliary beating pattern, recurrent respiratory infections, axonemal disorganization, and abnormal assembly of GAS8, CCDC39, and DNALI1. We performed molecular characterization of the defects in the 96 nm axonemal ruler due to disease-causing variants in CCDC39 and CCDC40 and analyzed the effect on additional axonemal components. We identified a cohort of 51 individuals with disease-causing variants in CCDC39 and CCDC40 via next-generation sequencing techniques and demonstrated that the IDA heavy chains DNAH1, DNAH6, and DNAH7 are conspicuously absent within the respiratory ciliary axonemes by immunofluorescence analyses. Hence, we show for the first time that the centrin2 (CETN2) containing IDAs are also affected. These findings underscore the crucial role of CCDC39 and CCDC40 in the assembly and function of IDAs in human respiratory cilia. Thus, our data improve the diagnostics of axonemal ruler defects by further characterizing the associated molecular IDA defects.
Performance of the Wisconsin Card Sorting Test in Oncopediatric Patients in an Oncology Unit in Cali, Colombia: A Cross-Sectional Observational Study
Authors: Jiménez Urrego Ángela María , Santa Valeria , Guerrero Gómez Manuel José , Guerrero Benitez Angie Carolina , Romo-González Tania , Botero Carvajal Alejandro
Published: July 2024

Background: In 2020, the prevalence of cancer rose to 844,778 cases among the population aged 0–19 years. Approximately 90% of individuals under 18 years of age reside in low- and middle-income countries, where cancer survivors report adverse outcomes that negatively impact their general health, emotional state, and external factors such as academic performance due to the effect of these outcomes on executive functions. The Wisconsin Cart Sorting Test (WCST) is the gold standard for evaluating executive functioning. Therefore, this article (1) reports the performance of the Wisconsin Card Sorting Test (WCST) in oncopediatric patients from Cali, Colombia; (2) indicates the reliability of the WCST; (3) describes the association between cancer type and executive functioning in patients; (4) describes the differences between patients with various executive deficits and their executive total scores; and (5) describes the association between cancer type and the presence of brain deficits based on the WCST. Methods: In this cross-sectional observational study, 24 oncopediatric patients were interviewed and evaluated via the WCST. Results: The mean age was 12.08 years (SD 3.98); 20.8% of the patients were women, 70.8% had a primary diagnosis of leukemia, 8% exhibited acquired brain deficits, and more than 75% displayed adequate functional indicators of executive functions. Robust statistics were employed to explore the differences between the types of diagnosis and performance in executive functions, and no statistically significant differences were found (p = 0.156). We found that the WCST has a reliable Cronbach’s α of 0.804. Oncopediatric patients without brain deficits presented strong results in terms of executive functions (p = 0.002), with a moderate effect size (0.727). Conclusions: The WCST is reliable for discriminating executive functioning among pediatric cancer patients. The evidence suggests that there were no differences in the executive functioning of the participants based on the types of cancer being evaluated.
Examination of the Life Habits of Preschool Children Based on Their Screen Use Patterns
Authors: Yazıcı Çakıroğlu Tuğba , Sapsağlam Özkan
Published: July 2024

Children who open their eyes to a digital world begin interacting with screens in the early years of life. The interaction between screens and children starts from the very first moments of life and intensifies over time. The aim of this study is to examine the effects of screen use patterns on the life habits of preschool children. In this context, the relationships between problematic media use, eating habits, and self-care skills among preschool children are analyzed from various perspectives. Structured according to a mixed-methods research approach, this study employs both qualitative and quantitative research designs. The study group consists of 582 children continuing their education in preschool institutions across seven different regions and nine different provinces in Turkey, along with 20 of their parents. Random and non-random sampling methods were used to form the study group. Quantitative data were collected using the Problematic Media Use Measure, Children’s Eating Behavior Inventory, and Preschool Children (36–72 months) Self-Care Skills Scale—Teacher Form, while qualitative data were gathered through a semi-structured parent interview form. The study results indicate that problematic media use and eating behaviors significantly vary according to the screen time of children, with an increase in problematic media use linked to a rise in negative eating behaviors. Parents are found to perceive the use of media devices during mealtime as a necessity, thus employing them, and believe that their children’s social behaviors are shaped according to screen content.
Visible-Light Spectroscopy and Rock Magnetic Analyses of Iron Oxides in Mixed-Mineral Assemblages
Authors: Lepre Christopher J. , Yazzie Owen M. , Klaus Benjamin R.
Published: July 2024

Iron oxide assemblages are central to many pursuits, ranging from Mars exploration to environmental remediation. Oxides and oxyhydroxides of iron both carry the special properties of color and magnetism. In this paper, we use visible-light spectroscopy and rock magnetic data collected at varying temperatures (~77–973 K) to analyze the concentrations and identities of iron oxides found in natural hematite-dominated samples that were obtained from a scientific drill core of Late Triassic red beds in the American Southwest. Our results suggest that hematite colorization of Earth materials varies from red to blue/purple as crystal size increases. Second-derivative analysis of the collected visible-light spectra allows this variation to be measured through the characteristic wavelength band position. Magnetic coercivity data indicate “hardness” differences that also may suggest smaller grain sizes are associated with redder colors. Yellowish maghemite and goethite have overlapping characteristic wavelength band positions that make it challenging to distinguish their contributions to mixed assemblages from visible-light data alone. Remanent magnetizations acquired at ~77 K and room temperature suggest the presence of hematite and a low-coercivity phase that may be maghemite and/or oxidized magnetite. However, we interpret this phase as maghemite in order to explain the changes in iron oxide concentrations indicated by visible-light intensities near ~425 nm and because the thermal demagnetization data suggest that goethite is absent from the samples. Future research that increases the resolution of hematite, maghemite, and goethite detection in experimental and natural samples will provide opportunities to refine the study of past climates and constrain soil iron availability under future changes in global moisture and temperature. Multimethod datasets improve understanding of environmental conditions that cause iron oxides assemblages to shift in phase dominance, grain size, and crystallinity.
Potential Nano/Microcenters of Crystal Nucleation in Reagent-Grade Purity Solvents and Their Differentiation by Fluorescent-Tagged Antiscalant
Authors: Popov Konstantin , Vainer Yuri , Silaev Gleb , Kuryakov Vladimir , Trukhina Maria , Koltinova Elena , Trokhin Vasilii , Oshchepkov Maxim , Butakova Maria , Oshchepkov Alexander
Published: July 2024

A common issue in studies on liquid-phase chemical processes is that the natural solid nanoimpurities present in reagent-grade chemicals are ignored. Little is known about these impurities’ nature, sizes, concentrations, and behavior, yet they significantly affect the efficiency of antiscalants in municipal and laboratory solutions. Recent research has focused on: (i) estimating nanoimpurity concentrations in in-house deionized water and semiconductor-grade isopropanol using “light sheet” optical ultramicroscopy, and (ii) visualizing antiscalant sorption on these impurities. Using a fluorescent-tagged antiscalant aminobis(methylenephosphonic acid) (ADMP-F), we tracked its affinity to particulate matter in deionized water and reagent-grade KCl solutions. Our study showed that the total concentration of nanoparticles with a size larger than 20 nm is about 106 units/mL in deionized water and 105 units/mL in isopropanol. Extrapolation of these values to a size ≥1 nm resulted in concentrations of 1011 and 108 units/mL. The addition of KCl or ADMP-F significantly increased foreign nanoparticle populations. ADMP-F is selectively adsorbed by only some impurities, while most antiscalant molecules remain as true solution. To our knowledge, this is the first instance of fluorescently labeled aminoalkylphosphonates being able to differentiate particulate matter traces in reagent-grade purity solutions. Therefore, the role of nanoparticles as crystallization centers should be seriously reconsidered, especially in their important application in scale inhibition.
Comprehensive Study of the IBMP ELISA IgA/IgM/IgG COVID-19 Kit for SARS-CoV-2 Antibody Detection
Authors: Mattar Sibelle Botogosque , Celedon Paola Alejandra Fiorani , Leony Leonardo Maia , Vasconcelos Larissa de Carvalho Medrado , Sampaio Daniel Dias , Marchini Fabricio Klerynton , Morello Luis Gustavo , Lin Vanessa Hoysan , Crestani Sandra , Camelier Aquiles Assunção , Meireles André Costa , de Oliveira Junior André Luiz Freitas , Bandeira Antônio Carlos , Macedo Yasmin Santos Freitas , Duarte Alan Oliveira , Pavan Tycha Bianca Sabaini , de Siqueira Isadora Cristina , Santos Fred Luciano Neves
Published: July 2024

COVID-19 laboratory diagnosis primarily relies on molecular tests, highly sensitive during early infection stages with high viral loads. As the disease progresses, sensitivity decreases, requiring antibody detection. Since the beginning of the pandemic, serological tests have been developed and made available in Brazil, but their diagnostic performance varies. This study evaluated the IBMP ELISA IgA/IgM/IgG COVID-19 kit performance in detecting SARS-CoV-2 antibodies. A total of 90 samples, including 64 from COVID-19 patients and 26 pre-pandemic donors, were assessed based on time post symptom onset (0–7, 8–14, and 15–21 days). The kit showed 61% sensitivity, 100% specificity, and 72% accuracy overall. Sensitivity varied with time, being 25%, 57%, and 96% for 0–7, 8–14, and 15–21 days, respectively. Similar variations were noted in other commercial tests. The Gold ELISA COVID-19 (IgG/IgM) had sensitivities of 31%, 71%, and 100%, while the Anti-SARS-CoV-2 NCP ELISA (IgG) and Anti-SARS-CoV-2 NCP ELISA (IgM) showed varying sensitivities. The IBMP ELISA kit displayed high diagnostic capability, especially as the disease progressed, complementing COVID-19 diagnosis. Reproducibility assessment revealed minimal systematic and analytical errors. In conclusion, the IBMP ELISA IgA/IgM/IgG COVID-19 kit is a robust tool for detecting anti-SARS-CoV-2 antibodies, increasing in efficacy over the disease course, and minimizing false negatives in RT-PCR COVID-19 diagnosis.
Use of Biomarkers of Inflammation in the Differentiation of Iron Deficiency and Anaemia—Lessons from Inflammatory Bowel Disease
Authors: Farrag Karima , Aksan Aysegül , Ademaj-Kospiri Valëza , Leventi Eleni , Stein Jürgen
Published: July 2024

Iron deficiency and iron deficiency anaemia are common in inflammatory bowel disease (IBD), to the detriment of the patients’ quality of life. Since ferritin, as an acute-phase protein (APP), has limited diagnostic value in IBD, concurrent assessment of C-reactive protein (CRP) is recommended. The World Health Organization suggests using α1-acid glycoprotein (AGP) as an additional biomarker due to its differing half-life. This study aimed to evaluate ferritin levels in patients with IBD using CRP and AGP, individually and in combination. A total of 118 patients with IBD (mean age: 45.48 ± 15.25 years, 47.46% female) were recruited, including 38 with Crohn’s disease, 47 with ulcerative colitis, and 33 controls. The results showed that while CRP alone detected an inflammatory increase in ferritin of 29.76%, this increased to 82.14% when AGP or both AGP and CRP were considered (p < 0.05). Elevated AGP levels were more prevalent in patients with ulcerative colitis. However, concordance between high CRP and AGP levels was confirmed in only 55% of cases. Correcting for inflammation using CRP and/or AGP significantly improved the diagnostic accuracy of ferritin levels in patients with IBD, highlighting the challenge posed by inflammation when assessing iron deficiency.
Recent Advances in Biosensor Technology for Early-Stage Detection of Hepatocellular Carcinoma-Specific Biomarkers: An Overview
Authors: Chinnappan Raja , Makhzoum Tariq , Arai Momo , Hajja Amro , Abul Rub Farah , Alodhaibi Ibrahim , Alfuwais Mohammed , Elahi Muhammad Affan , Alshehri Eman Abdullah , Ramachandran Lohit , Mani Naresh Kumar , Abrahim Shugufta , Mir Mohammad Shabab , Al-Kattan Khaled , Mir Tanveer Ahmad , Yaqinuddin Ahmed
Published: July 2024

Hepatocellular carcinoma is currently the most common malignancy of the liver. It typically occurs due to a series of oncogenic mutations that lead to aberrant cell replication. Most commonly, hepatocellular carcinoma (HCC) occurs as a result of pre-occurring liver diseases, such as hepatitis and cirrhosis. Given its aggressive nature and poor prognosis, the early screening and diagnosis of HCC are crucial. However, due to its plethora of underlying risk factors and pathophysiologies, patient presentation often varies in the early stages, with many patients presenting with few, if any, specific symptoms in the early stages. Conventionally, screening and diagnosis are performed through radiological examination, with diagnosis confirmed by biopsy. Imaging modalities tend to be limited by their requirement of large, expensive equipment; time-consuming operation; and a lack of accurate diagnosis, whereas a biopsy’s invasive nature makes it unappealing for repetitive use. Recently, biosensors have gained attention for their potential to detect numerous conditions rapidly, cheaply, accurately, and without complex equipment and training. Through their sensing platforms, they aim to detect various biomarkers, such as nucleic acids, proteins, and even whole cells extracted by a liquid biopsy. Numerous biosensors have been developed that may detect HCC in its early stages. We discuss the recent updates in biosensing technology, highlighting its competitive potential compared to conventional methodology and its prospects as a tool for screening and diagnosis.
Evaluation and Limitations of the Novel Chemiluminescent Enzyme Immunoassay Technique for Measuring Total Tau Protein in the Cerebrospinal Fluid of Patients with Human Prion Disease: A 10-Year Prospective Study (2011–2020)
Authors: Weijie Kong , Nonaka Toshiaki , Satoh Katsuya
Published: July 2024

Background: Recently, the investigation of cerebrospinal fluid (CSF) biomarkers for diagnosing human prion diseases (HPD) has garnered significant attention. Reproducibility and accuracy are paramount in biomarker research, particularly in the measurement of total tau (T-tau) protein, which is a crucial diagnostic marker. Given the global impact of the coronavirus disease pandemic, the frequency of measuring this protein using one of the world’s fully automated assays, chemiluminescent enzyme immunoassay (CLEA), has increased. At present, the diagnosis and monitoring of neurological diseases mainly rely on traditional methods, but their accuracy and responsiveness are limited. There is limited knowledge of the accuracy of CLEA in tau measurements. We aimed to measure T-tau protein using CLEA and to elucidate its merits and limitations. Methods: We randomly selected 60 patients with rapidly progressive dementia, using ELISA and CLEA analysis of cerebrospinal fluid specimens. Additionally, we used Western blotting to detect the presence of 14-3-3 protein and employed real-time quaking-induced conversion (RT-QuIC) assays to analyze the same set of samples. Furthermore, we examined the correlation coefficient between ELISA and CLEA results in a subset of 60 samples. Moreover, using CLEA, we evaluated the diurnal reproducibility, storage stability, dilutability, and freeze–thaw effects in three selected samples. Results: In 172 patients, 172 samples were extracted, with each patient providing only one sample, and a total of 88 (35 men and 53 women) tested positive for HPD in the RT-QuIC assay. In contrast, all CSF samples from the remaining 84 patients without HPD (50 men and 34 women) tested negative in the RT-QuIC assay. Both ELISA and CLEA showed perfect sensitivity and specificity (100%) in measuring T-tau protein levels. In addition, ELISA and CLEA are similar in terms of measurement sensitivity and marginal effect of detection extrema. CLEA analysis exhibited instability for certain samples with T-tau protein levels exceeding 2000 pg/mL, leading to low reproducibility during dilution analysis. Conclusions: Our findings indicate that CLEA outperforms ELISA in terms of diurnal reproducibility, storage stability, and freeze–thaw effects. However, ELISA demonstrated superior performance in the dilution assay. Therefore, it is imperative to develop innovative approaches for the dilution of biomarker samples for CLEA measurements during clinical trials.
Can Combination Therapy with Endothelin Receptor Antagonist and PDE5 Inhibitors Prevent Echocardiographic Findings Suspicious for Pulmonary Arterial Hypertension? Description of a Real-Life Case Series
Authors: Damiani Arianna , Lepri Gemma , Bonomi Francesco , Fiorentini Elisa , Peretti Silvia , Blagojevic Jelena , Bellando Randone Silvia , Guiducci Serena
Published: July 2024

Objective: To retrospectively evaluate the incidence rate (IR) of elevated echocardiographic estimated systolic pulmonary artery pressure (sPAP), suspected for pulmonary hypertension (PH), in systemic sclerosis (SSc) patients after the introduction of a combination therapy with bosentan and sildenafil for treatment or prevention of digital ulcers. Methods: Patients attending the Scleroderma Unit of the Universital Hospital of Careggi from July 2010 to July 2023 were enrolled. Patients older than 18 years old with a history of digital ulcers, treated with bosentan and sildenafil in combination for at least 12 months, were included. Patients with a diagnosis of PH preceding the introduction of the therapy were excluded. Demographical data, disease duration, laboratoristic, and instrumental data (pulmonary function tests, echocardiographic estimation of sPAP, and ultrasonographic value of renal resistive index) were collected. The IR of echocardiographic signs suspected of pulmonary hypertension and their 95% confidence interval were calculated in events/1000 patients-years. Results: Thirty-five patients were enrolled; the mean disease duration was 12.82 years (SD 5.92). The mean duration of the combination treatment was 81.03 (SD 43.1.3) months, and the total at-risk time was 2674 months. Two patients (5.7%) presented echocardiographic signs of PH (sPAP 50 mmHg and 40 mmHg); the IR was calculated to be 9/1000 patients-years (95% CI 7.95–10.12). In one of the two patients, right heart catheterism (RHC) excluded PAH, while the other patient refused to undergo RHC, and PAH could not be confirmed/excluded. The stability of PFTs and echocardiographic sPAP was observed during the observation time. Conclusions: The results of this retrospective study suggest that combination therapy with endothelin receptor antagonists and phosphodiesterase-5 (PDE5) inhibitors could help in preventing PAH in SSc; prospective case–control studies on a larger population are needed to improve knowledge in this field.
Bonding Effectiveness of Veneering Ceramic to Zirconia after Different Grit-Blasting Treatments
Authors: Zicari Francesca , Monaco Carlo , Vivan Cardoso Marcio , Silvestri Davide , Van Meerbeek Bart
Published: July 2024

Objective: To determine the effect of grit-blasting before and after sintering on the surface roughness of zirconia and the micro-tensile bond strength of a pressable veneering ceramic to zirconia. Methods: Pre-sintered zirconia blocks (IPS e.max ZirCAD, Ivoclar) were divided into four test groups of three specimens each and a control group (‘CTR’; no surface treatment). Pre-S-30, Pre-S-50, and Pre-S-110 were grit-blasted with 30-µm SiO2-coated Al2O3, 50-µm Al2O3 and 110-µm Al2O3 particles, respectively, before sintering. Post-S-30 was grit-blasted with 30-µm SiO2-coated Al2O3 after sintering. For each treatment, the surface roughness was measured (Ra, Perthometer M4P, Mahr Perthen). After sintering the zirconia blocks, a liner was applied and a pressable ceramic (IPS e.max ZirPress, Ivoclar) was heat-pressed. Sixteen microbars were obtained from each block and submitted to micro-tensile bond-strength (µTBS) testing. Data were analyzed with one-way ANOVA. Any correlation between Ra and µTBS was evaluated (Sperman test). Results: Grit-blasting before sintering with 110-µm Al2O3 (RaPre-S-110 = 3.4 ± 0.4 µm), 50-µm Al2O3 (RaPre-S-50 = 2.3 ± 0.5 µm), and 30-µm SiO2-coated Al2O3 (RaPre-S-30 = 1.2 ± 0.2 µm) resulted in significantly higher roughness than grit-blasting after sintering with 30-µm SiO2-coated Al2O3 (RaPost-S-30 = 0.5 ± 0.1 µm). The highest µTBS was measured when the sintered zirconia was grit-blasted with 30-μm SiO2-coated Al2O3 (µTBSPost-S-30 = 28.5 ± 12.6 MPa), which was significantly different from that of specimens that were grit-blasted before sintering (µTBSPre-S-30 = 21.8 ± 10.4; µTBSPre-S-50 = 24.1 ± 12.6; µTBSPre-S-110 = 26.4 ± 14.1) or were not grit-blasted (µTBSCTR = 20.2 ± 11.2). Conclusions: Grit-blasting zirconia before sintering enhanced the surface roughness proportionally to the particle size of the sand used. Grit-blasting with 30-µm SiO2-coated Al2O3 after sintering improved bonding of the veneering ceramic to zirconia. Clinical Significance: As grit-blasting with 30-µm SiO2-coated Al2O3 after sintering improved bonding of the veneering ceramic to zirconia, it may reduce veneering ceramic fractures/chipping.
Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
Authors: Song Yi , Zhu Yiwen , Chen-Hu Kun , Lu Xinhua , Sun Peng , Wang Zhongyong
Published: July 2024

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.
Research on a Three-Way Decision-Making Approach, Based on Non-Additive Measurement and Prospect Theory, and Its Application in Aviation Equipment Risk Analysis
Authors: Xia Ruicong , Tong Sirong , Wang Qiang , Sun Bingzhen , Xu Ziling , Liu Qiuhan , Yu Jiayang , Wu Fan
Published: July 2024

Due to the information non-independence of attributes, combined with a complex and changeable environment, the analysis of risks faces great difficulties. In view of this problem, this paper proposes a new three-way decision-making (3WD) method, combined with prospect theory and a non-additive measure, to cope with multi-source and incomplete risk information systems. Prospect theory improves the loss function of the original 3WD model, and the combination of non-additive measurement and probability measurement provides a new perspective to understand the meaning of decision-making, which could measure the relative degree by considering expert knowledge and objective data. The theoretical basis and framework of this model are illustrated, and this model is applied to a real in-service aviation equipment structures risk evaluation problem involving multiple incomplete risk information sources. When the simulation analysis is carried out, the results show that the availability of this method is verified. This method can also evaluate and rank key risk factors in equipment structures, which provides a reliable basis for decisions in aviation safety management.
Navigating Schools through the Pandemic Crisis: A Study on the Determinants Influencing the Well-Being of Secondary Students in Northeast Thailand
Authors: Meekaew Nattapon , Weeranakin Pattaraporn , Chamaratana Thanapauge , Promphakping Buapun , Bhiasiri Siribhong
Published: July 2024

The COVID-19 pandemic has disrupted education systems worldwide, with students facing challenges related to online learning, social isolation, and mental well-being. This study explores the determinants of well-being in education among secondary school students in northeast Thailand during the post-pandemic recovery phase. Employing a multistage sampling approach and Cochran’s formula for sample size determination, the research engaged 400 students from 30 schools. Data were collected using an interview schedule based on the OECD’s framework for well-being in education, assessing psychological, social, cognitive, and physical dimensions. Multiple regression analysis revealed that students’ perceptions of the pandemic’s social impact, family relationships, school personnel, and the school’s physical environment significantly influenced their well-being in education, collectively explaining 45.30% of the variance. The findings underscore the importance of fostering supportive family environments, ensuring adequate school staffing, and improving educational infrastructure to enhance students’ well-being in the post-pandemic context. This study offers valuable insights for educators and policymakers in developing targeted interventions and policies that prioritize the holistic well-being of students, ensuring a resilient and inclusive educational environment in the face of global crises.
From Early Interest to Career Persistence: Understanding and Supporting STEM Pathways
Authors: Paechter Manuela , Luttenberger Silke , Starr Christine R. , Mackenzie Erin , Ertl Bernhard
Published: July 2024

Knowledge, competencies, and reflective attitudes regarding STEM (science, technology, engineering, and mathematics) are essential for navigating global and societal changes in the economy and workforce [...]
An Adaptive Virtual-Impedance-Based Current-Limiting Method with the Functionality of Transient Stability Enhancement for Grid-Forming Converter
Authors: Hu Xuekai , Li Zifan , Pan Cunyue , Li Hao , Liang Yingyu
Published: July 2024

Grid-forming (GFM) converters are regarded as the most promising solution for grid-connected converters of renewable energy due to their robustness against weak grids. However, attributable to their voltage source characteristics, GFM converters may experience overcurrent issues during large disturbances. The virtual impedance (VI) method is an effective method to solve this problem. Nevertheless, there exists a contradiction between the demands for VI posed by current limitations and transient stability. Firstly, the analytical equations of virtual impedance for limiting the fault steady-state current of a GFM converter with different depths of grid voltage sag are solved. On that basis, an adaptive method based on virtual impedance is proposed to limit the fault current. Moreover, the analytical equation of the output active power of the converter when using adaptive virtual impedance to limit the fault current is solved. On this basis, the effect of the virtual impedance ratio on the transient stability is investigated. Finally, an adaptive virtual-impedance-based current-limiting method with the functionality of transient stability enhancement for a grid-forming converter is innovatively proposed. The enhancement effect of the method is verified by the equal area criterion method and phase portrait method. Finally, the efficacy of the proposed method in limiting fault currents and enhancing transient stability is validated through hardware-in-the-loop (HIL) experiments.
Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction
Authors: Liu Shudong , Zhong Wenlong , Guo Furong , Cong Jia , Gu Boyu
Published: July 2024

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mixed Residual Attention Block (MRA Block), combining channel attention and window-based self-attention to capture local details in images. Additionally, the Dual Reconstruction Feature Fusion (DRFF) module is designed to enhance the model’s adaptability to both inter-class and intra-class variations by integrating features of different scales across layers. Cosine similarity networks are employed for similarity measurement, enabling precise predictions. The experiments demonstrate that the proposed method achieves classification accuracies of 96.99%, 98.53%, and 89.78% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively, confirming the method’s efficacy in fine-grained classification tasks.
A 3216 μm2 MOS-Based Temperature Sensor with a Wide Temperature Measurement Range and Linear Readout
Authors: Li Hao , Yang Zhao , Kong Dezhu , Yin Aiguo , Chen Zefu , Zhang Peiyong
Published: July 2024

This paper introduces an MOS-based intelligent temperature sensor with a linear readout. Compared with similar designs, the proposed sensor utilizes the DIBL effect to reduce the precision requirement for the voltage reference source and compensate for the temperature measurement range. A compact voltage reference circuit is introduced, which generates two reference voltage bases using only three transistors. In addition, the proposed digital readout circuit does not require a subtractor or a divider, further saving area. Fabricated in a 55 nm CMOS process, the proposed sensor occupies a compact area of 3216 μm2. Post-simulation results show it has a maximum error of −0.52/+0.28 °C within the temperature range of −20 °C to 120 °C after two-point calibration. The power supply voltage range of the sensor is 0.8 to 1.8 V. It has a maximum voltage sensitivity of 5.7 °C/V and its power consumption is only 166 nW, with a power supply voltage of 0.8 V.
Hybrid Quantum-Dot Cellular Automata Nanocomputing Circuits
Authors: Alharbi Mohammed , Edwards Gerard , Stocker Richard
Published: July 2024

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy-efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing irreversible, reversible, and partially reversible QCA gates to establish an optimal balance between power consumption, delay time, and occupied area. This hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We evaluated the hybrid design method by examining the half-adder circuit as a case study. We developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level within a microscopic quantum mechanical model.
Multi-Source Information Graph Embedding with Ensemble Learning for Link Prediction
Authors: Hou Chunning , Wang Xinzhi , Luo Xiangfeng , Xie Shaorong
Published: July 2024

Link prediction is a key technique for connecting entities and relationships in a graph reasoning field. It leverages known information about the graph structure data to predict missing factual information. Previous studies have either focused on the semantic representation of a single triplet or on the graph structure data built on triples. The former ignores the association between different triples, and the latter ignores the true meaning of the node itself. Furthermore, common graph-structured datasets inherently face challenges, such as missing information and incompleteness. In light of this challenge, we present a novel model called Multi-source Information Graph Embedding with Ensemble Learning for Link Prediction (EMGE), which can effectively improve the reasoning of link prediction. Ensemble learning is systematically applied throughout the model training process. At the data level, this approach enhances entity embeddings by integrating structured graph information and unstructured textual data as multi-source information inputs. The fusion of these inputs is effectively addressed by introducing an attention mechanism. During the training phase, the principle of ensemble learning is employed to extract semantic features from multiple neural network models, facilitating the interaction of enriched information. To ensure effective model learning, a novel loss function based on contrastive learning is devised, effectively minimizing the discrepancy between predicted values and the ground truth. Moreover, to enhance the semantic representation of graph nodes in link prediction, two rules are introduced during the aggregation of graph structure information. These rules incorporate the concept of spreading activation, enabling a more comprehensive understanding of the relationships between nodes and edges in the graph. During the testing phase, the EMGE model is validated on three datasets, including WN18RR, FB15k-237, and a private Chinese financial dataset. The experimental results demonstrate a reduction in the mean rank (MR) by 0.2 times, an improvement in the mean reciprocal rank (MRR) by 5.9%, and an increase in the Hit@1 by 12.9% compared to the baseline model.
Compact, Ultra-Wideband Butler Matrix Beamformers for the Advanced 5G Band FR3—Part I
Authors: Empliouk Tzichat , Kapetanidis Panagiotis , Arnaoutoglou Dimitrios , Kolitsidas Christos , Lialios Dimitrios , Koutinos Anastasios , Kaifas Theodoros N. F. , Georgakopoulos Stavros V. , Zekios Constantinos L. , Kyriacou George A.
Published: July 2024

Butler Matrix networks are well established as beamforming networks for phased antenna arrays. The challenge we address in this work is to cover the entire (advanced 5G or 6G) FR3 band (7–24 GHz) with a single network, while retaining low losses and minimal size. The employed multilayer topology is also well established; however, the matching between the utilized hybrid couplers and the phase shifters constitutes a major challenge for such a wideband operation. This is achieved herein by employing meander lines with appropriate curvature and introducing two distinct design methods for the Butler Matrix. The first method focuses on designing individual components separately, followed by their integration into the overall Butler Matrix structure. This approach is demonstrated through the design, prototyping, measurements, and validation of an 8 × 8 Butler Matrix beamformer, which operates across the 6–16 GHz band (FR3 Low). The second method introduces a wideband-matching technique which simplifies the implementation process by designing the Butler Matrix as a single, unified structure. This technique is applied to both 4 × 4 and 8 × 8 Butler Matrices, which are implemented and simulated for the low FR3 band. Both design methods result in wideband operation and compact size and meet the desired performance criteria.
Double-Timescale Multi-Agent Deep Reinforcement Learning for Flexible Payload in VHTS Systems
Authors: Feng Linqing , Zhang Cheng , Zhang Qiuyang , Zeng Lingchao , Qin Pengfei , Wang Ying
Published: July 2024

With the expansion of the very-high-throughput satellite (VHTS) system, the uneven distribution of traffic demands in time and space has become increasingly significant and cannot be ignored. It is a significant challenge to efficiently and dynamically allocate scarce on-board resources to ensure capacity and demand matching. The advancement of flexible payload technology provides the possibility to overcome this challenge. However, computational complexity is increasing due to the unsynchronized resource adjustment and the time-varying demands of the VHTS system. Therefore, we propose a double-timescale bandwidth and power allocation (DT-BPA) scheme to effectively manage the available resources in the flexible payload architecture. We use a multi-agent deep reinforcement learning (MADRL) algorithm aiming to meet the time-varying traffic demands of each beam and improve resource utilization. The simulation results demonstrate that the proposed DT-BPA algorithm enhanced the matching degree of capacity and demand as well as reduced the system’s power consumption. Additionally, it can be trained offline and implemented online, providing a more cost-effective solution for the VHTS system.
Methods for Determining Losses and Parameters of Cylindrical-Rotor Medium-Power Synchronous Generators
Authors: Komeza Krzysztof , Dems Maria
Published: July 2024

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors’ previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines.
Resilience against Catastrophic Cyber Incidents: A Multistakeholder Analysis of Cyber Insurance
Authors: Bace Brianna , Dubois Elisabeth , Tatar Unal
Published: July 2024

Catastrophic cyber incidents—events of low probability but high impact, with the potential to incur billions of dollars in damages—are prompting insurers to elevate premiums, create higher barriers for potential buyers, and tighten policies with exclusions. While these responses of the insurance industry are important to prevent its insolvency during catastrophic incidents due to excessive claims, they lead to a notable gap in market protection. Using a content analysis of multistakeholder comments submitted in response to a Treasury Department Request for Information (RFI), this study seeks to define what constitutes a catastrophic cyber event, identify mitigation strategies, evaluate the current capacity of the cyber insurance sector to handle such incidents, and investigate the potential roles and support mechanisms that the government can provide to enhance the insurance sector’s capacity to manage these extreme risks. This paper is one of the pioneering studies using data and a multistakeholder perspective to provide essential guidance for policymakers, regulators, the insurance industry, and the cybersecurity sector in formulating robust policies and strategies to address catastrophic cyber risks, ultimately enhancing national economic and technological resilience.
Clustering Network Traffic Using Semi-Supervised Learning
Authors: Krajewska Antonina , Niewiadomska-Szynkiewicz Ewa
Published: July 2024

Clustering algorithms play a crucial role in early warning cybersecurity systems. They allow for the detection of new attack patterns and anomalies and enhance system performance. This paper discusses the problem of clustering data collected by a distributed system of network honeypots. In the proposed approach, when a network flow matches an attack signature, an appropriate label is assigned to it. This enables the use of semi-supervised learning algorithms and improves the quality of clustering results. The article compares the results of learning algorithms conducted with and without partial supervision, particularly non-negative matrix factorization and semi-supervised non-negative matrix factorization. Our results confirm the positive impact of labeling a portion of flows on the quality of clustering.
The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis
Authors: Zhao Qing , Han Sicun , Chen Wenhao , He Jing , Guo Chengjun
Published: July 2024

Deep learning has presented its spectacular potential in the jamming recognition field. Yet, sufficient samples required by normal deep learning analysis methods are not always available, especially in the 6G communication field. This situation appears to be more challenging in the communication field. In this article, Model-Agnostic Meta-Learning (MAML) is imported into the jamming recognition field in order to accomplish compound jamming recognition in the circumstances of few-shot learning. Further, the existing research on jamming recognition techniques is mostly based on single-node recognition. This technique cannot make full and efficient use of the jamming information collected. Therefore, this article adds a multi-node collaborative technique into the compound jamming recognition algorithm that is based on MAML and time-frequency analysis. Based on the fact that each cognitive node can recognize independently, the recognition results are be sent to the fusion center. The fusion center completes the fusion of the recognition results according to the majority rule. The experiments demonstrate that, with the fusion of the multi-node collaborative technique, the precision of compound jamming recognition in the condition of few-shot learning has been effectively improved.
Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9
Authors: Yang Shiman , Cao Zheng , Liu Ningbo , Sun Yanli , Wang Zhongxun
Published: July 2024

The offshore environment is complex during automatic target annotation at sea, and the difference between the focal lengths of visible and infrared sensors is large, thereby causing difficulties in matching multitarget electro-optical images at sea. This study proposes a target-matching method for visible and infrared images at sea based on decision-level topological relations. First, YOLOv9 is used to detect targets. To obtain markedly accurate target positions to establish accurate topological relations, the YOLOv9 model is improved for its poor accuracy for small targets, high computational complexity, and difficulty in deployment. To improve the detection accuracy of small targets, an additional small target detection head is added to detect shallow feature maps. From the perspective of reducing network size and achieving lightweight deployment, the Conv module in the model is replaced with DWConv, and the RepNCSPELAN4 module in the backbone network is replaced with the C3Ghost module. The replacements significantly reduce the number of parameters and computation volume of the model while retaining the feature extraction capability of the backbone network. Experimental results of the photovoltaic dataset show that the proposed method improves detection accuracy by 8%, while the computation and number of parameters of the model are reduced by 5.7% and 44.1%, respectively. Lastly, topological relationships are established for the target results, and targets in visible and infrared images are matched based on topological similarity.