bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Structural Stability of Oil-Shell Microbubbles via Incorporation of a Gold Nanoparticle Protective Shell for Theranostic Applications
Authors: Ataei Marzieh , Yi Hsiu-Ping , Taravatfard Aida Zahra , Lin Ken Young , Lee Abraham Phillip
Published: April 2023

Phospholipid-stabilized microbubbles are utilized as contrast agents in medical ultrasound imaging, and researchers are currently investigating their potential as theranostic agents. Due to the inadequate water solubility and poor stability of numerous new therapeutics, the development of stable microbubbles with the capacity to encapsulate hydrophobic therapeutics is necessary. Herein, we proposed a flow-focusing microfluidic device to generate highly monodispersed, phospholipid-stabilized dual-layer microbubbles for theranostic applications. The stability and microstructural evolution of these microbubbles were investigated by microscopy and machine-learning-assisted segmentation techniques at different phospholipid and gold nanoparticle concentrations. The double-emulsion microbubbles, formed with the combination of phospholipids and gold nanoparticles, developed a protective gold nanoparticle shell that not only acted as a steric barrier against gas diffusion and microbubble coalescence but also alleviated the progressive dewetting instability and the subsequent cascade of coalescence events.
Proposal for the Clustering of Characteristics to Identify Emotions in the Development of a Foreign Language Exam
Authors: Montenegro Carlos , Medina Víctor , Espitia Helbert
Published: April 2023

Automatic emotion identification allows for obtaining information on emotions experienced by an individual during certain activities, which is essential for improving their performance or preparing for similar experiences. This document aims to establish the clusters of variables associated with the identification of emotions when a group of students takes a foreign language exam in Portuguese. Once the data clusters are determined, it is possible to establish the perception of emotions in the students with relevant variables and their respective decision thresholds. This study can later be used to build a model that relates the measured variables and the student’s performance so that strategies can be generated to help the student achieve better results on the test. The results indicate that the clusters and range values of the variables can be obtained to observe changes in the concentration of the students. This preliminary information can be used to design a fuzzy inference system to identify the student’s state of concentration.
Anodic Potential and Conversion Chemistry of Anhydrous Iron (II) Oxalate in Na-Ion Batteries
Authors: Gromov Vasilii , Noubir Atlas , Keshavarz Fatemeh , Laakso Ekaterina , Barbiellini Bernardo , Bansil Arun
Published: April 2023

Anhydrous ferrous (II) oxalate (AFO) outperforms its hydrated form when used as an anode material in Li-ion batteries (LIBs). With the increasing interest in Na-ion batteries (NIBs) in mind, we examine the potential of AFO as the anode in NIBs through first principles calculations involving both periodic and non-periodic structures. Our analysis based on periodic (non-periodic) modeling scheme shows that the AFO anode generates a low reaction potential of 1.22 V (1.45 V) in the NIBs, and 1.34 V (1.24 V) in the LIBs, which is much lower than the potential of NIBs with mixed oxalates. The conversion mechanism in the underlying electrochemical process involves the reduction of Fe2+ with the addition of Na or Li. Such conversion electrodes can achieve high capacities through the Fe2+ valence states of iron.
Metal-Involving Halogen Bonding Confirmed Using DFT Calculations with Periodic Boundary Conditions
Authors: Eliseeva Anastasiya A. , Khazanova Marina A. , Cheranyova Anna M. , Aliyarova Irina S. , Kravchuk Roman I. , Oganesyan Evfpraksiia S. , Ryabykh Andrey V. , Maslova Olga A. , Ivanov Daniil M. , Beznosyuk Serge A.
Published: April 2023

The cocrystallization of trans-[PtI2(NCN(CH2)5)2] and iodoform (CHI3) yields crystalline adduct trans-[PtI2(NCN(CH2)5)2]∙2CHI3, the structure of which was studied via single-crystal X-ray diffractometry (XRD). In the XRD structure of trans-[PtI2(NCN(CH2)5)2]∙2CHI3, apart from rather predictable C–H∙∙∙I hydrogen bonds (HBs) and C−I∙∙∙I halogen bonds (XBs) with the iodide ligands, we identified C–I∙∙∙Pt metal-involving XBs, where the platinum center functions as an XB acceptor (that includes a metal dz2-orbital) toward the σ-holes of I atoms of CHI3. DFT calculations (PBE-D3/jorge-TZP-DKH with plane waves in the GAPW method) were carried out in the CP2K program for isolated molecules, complex–iodoform clusters, and crystal models with periodic boundary conditions, where the noncovalent nature and the existence of the interactions were confirmed using charge analysis, Wiberg bond indexes, and QTAIM topology analysis of electron density, whereas the philicities of the noncovalent partners were proved using charge analysis, electron localization function, electron density deformation, and one-electron potential projections, as well as electron density/electrostatic potential profiles for cluster models and electrostatic potential surfaces (ρ = 0.001 a.u.) for isolated molecules.
The Effects of Microalloying on the Precipitation Behavior and Strength Mechanisms of X80 High-Strength Pipeline Steel under Different Processes
Authors: Ma Guoqiang , Chen Yimian , Wu Guilin , Wang Shuize , Li Tianyi , Liu Wenyue , Wu Honghui , Gao Junheng , Zhao Haitao , Zhang Chaolei , Mao Xinping
Published: April 2023

Pipeline steel is a special type of steel used for transporting, for example, oil and natural gas. This study focuses on X80-grade pipeline steel modified with the addition of Nb and Nb-V at different cooling rates (air cooling or quenching) after hot rolling and subjecting it to quenching and tempering heat treatment. Based on multiscale characterization techniques, the effects of microalloying and the cooling rate after hot rolling on the microstructure, precipitation behavior, and strengthening mechanisms were studied. The results showed that the strengths of quenched steels were higher than those of air-cooled steels, and the increase in strength was more pronounced with the addition of Nb-V than with the addition of Nb alone in the steels. Under the same cooling condition, the strengths of Nb-V-added steels were larger than those of Nb-added steels. Additionally, the Nb-V addition promotes the formation of lath structures. The yield stress of the steels, calculated by using measured microstructural parameters following the linear addition of strengthening, is in good agreement with the measured values.
Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis
Authors: Rajendran Pillai Vipin , Rajasekharan Nair Valsala Rohit , Raj Veena , Petra Muhammed , Krishnan Nair Satheesh , Mathew Sathyajith
Published: April 2023

Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research.
The Use of Brain Natriuretic Peptide in the Evaluation of Heart Failure in Geriatric Patients
Authors: Marinescu Mihai , Oprea Violeta Diana , Nechita Aurel , Tutunaru Dana , Nechita Luiza-Camelia , Romila Aurelia
Published: April 2023

Heart failure is one of the main morbidity and mortality factors in the general population and especially in elderly patients. Thus, at the European level, the prevalence of heart failure is 1% in people under 55 years of age but increases to over 10% in people over 70 years of age. The particularities of the elderly patient, which make the management of heart failure difficult, are the presence of comorbidities, frailty, cognitive impairment and polypharmacy. However, elderly patients are under-represented in clinical trials on the diagnosis and treatment of heart failure. The need for complementary methods (biomarkers) for differential and early diagnosis of heart failure is becoming more and more evident, even in its subclinical stages. These methods need to have increased specificity and sensitivity and be widely available. Natriuretic peptides, in particular B-type natriuretic peptide (BNP) and its fraction NTproBNP, have gained an increasingly important role in the screening, diagnosis and treatment of heart failure in recent years.
Superinfection of Rectovaginal Endometriosis: Case Report and Review of the Literature
Authors: Barba Marta , Morciano Andrea , Melocchi Tomaso , Cola Alice , Inzoli Alessandra , Passoni Paolo , Frigerio Matteo
Published: April 2023

Background: A peculiar complication of endometriosis is a superinfection. However, the superinfection of extra-ovarian endometriosis is anecdotal, and only a few cases have been described. We wanted to present the first cases of the superinfection of rectovaginal endometriosis and to perform a literature review of the superinfection of extra-ovarian endometriosis. Methods: We present a case of a 24-year-old woman who was referred to our Pelvic Floor Unit for rectal–perineal pain, dyspareunia, and recurrent episodes of dense purulent vaginal discharge for one year, in which the superinfection of rectovaginal endometriosis was diagnosed. Moreover, we performed a systematic search of the literature indexed on PubMed up to 31 January 2023. Results: Laparoscopic drainage was successful in managing this condition. In the literature, clinical presentation and instrumental and microbiological findings are very heterogeneous. However, the gold standard of management is represented by surgical or percutaneous drainage. Conclusions: In the case of a pelvic abscess, the superinfection of endometriosis lesions should be suspected, and this can represent the onset symptom of endometriosis. Ultrasonography may show nodular or flat hypoechoic lesions with hyperechoic debris and peripheral positive color/power Doppler intensities. The goal of management is to drain the abscess, either percutaneously or via traditional surgery, followed by proper hormonal therapy to reduce recurrence.
Advances in the Diagnosis and Management of Psychosis
Authors: Stoyanov Drozdstoy
Published: April 2023

Psychosis research in the contemporary sense of scientific inquiry may be traced as far as the formulation of the “unitary psychosis” concept, or Einheitpsychose, which is usually attributed to Wilhelm Griesinger, Ernst von Zeller, and Heinrich Neumann [...]
OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification
Authors: Oh Joonho , Park Chanho , Lee Hongchang , Rim Beanbonyka , Kim Younggyu , Hong Min , Lyu Jiwon , Han Suha , Choi Seongjun
Published: April 2023

The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%.
Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study
Authors: Rajagopalan Venkateswaran , Chaitanya Krishna G. , Pioro Erik P.
Published: April 2023

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70–94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.
Missed and Detected Incidental Breast Cancers on Contrast Enhanced Chest CT: Detection Rates and CT Features
Authors: Park Jiyeon , Kim Chohee , Cha Yoon Ki , Chung Myung Jin
Published: April 2023

This study investigated the rate at which radiologists miss or detect incidental breast cancers on chest CT and to compare the CT features between the two groups. This retrospective study evaluated chest CT examinations and medical records of patients who registered with the diagnosis code of “breast cancer” between January 2016 and December 2020, and who had undergone contrast enhanced chest CT 3–18 months before registration, during which they were unaware of any breast lesions. This study found that out of 84 patients, incidental breast cancer lesions were missed in 54 (64.3%) and detected in 30 (53.7%). The initial treatment was delayed in the missed breast lesions group (p = 0.004). Breast lesions of smaller sizes (<9.0 mm, p = 0.01), or with lower enhancement ratios (<1.4, p = 0.009), were more likely to be missed. When three radiologists re-read the CTs with more attention to breast area, they detected breast cancers with higher accuracies (90.1%, 87.9%, and 81.3%). In summary, this study revealed that radiologists miss 64.3% of incidental breast cancers on chest CT, especially those of sub-centimeter sizes and weak enhancements.
Investigating the Impact of COVID-19 Infection on Dry Eye Parameters
Authors: Liao Xulin , Wong Arthur Chun Chi , Wong June Oi Yau , Jia Ruofan , Chen Wanxue , Wong Hanson Yiu Man , Aljufairi Fatema Mohamed Ali Abdulla , Lai Kenneth Ka Hei , Hu Zhichao , Wei Yingying , Tham Clement Chee Yung , Pang Chi Pui , Chong Kelvin Kam Lung
Published: April 2023

Purpose: This study aims to compare dry eye parameters before and after COVID-19 infection in dry eye patients. Methods: We included 44 dry eye patients (88 eyes) from our existing dry eye cohort, with 22 belonging to the post-COVID-19 group due to a prior COVID-19 infection and the other 22 forming the non-COVID-19 group as they had no history of COVID-19. We examined and compared the dry eye parameters of the post-COVID-19 group, including the ocular surface disease index (OSDI), Schirmer’s test results (ST), non-invasive Keratography tear break-up time (NIKBUT), lipid layer thickness (LLT), Meibomian gland dysfunction (MGD), and the grading of papillae and follicles, both before and after the COVID-19 infection. We also compared the dry eye parameters difference of the post-COVID-19 group with the non-COVID-19 group. Results: The post-COVID-19 group was comprised of individuals with an average age of 38.36 ± 14.99 years, of which 82% were female. The time interval between the two tests was 16.92 ± 5.40 months, which did not differ significantly from the non-COVID-19 group. Compared to the pre-COVID-19 eyes, the post-COVID-19 eyes showed a significant decrease in the average LLT (52.86 ± 18.00 nm vs. 63.00 ± 22.40 nm, p < 0.001), as well as the maximum LLT (67.89 ± 20.81 nm vs. 78.48 ± 20.55 nm, p < 0.001). The MGD in both the upper (1.75 ± 0.84) and lower eyelids (1.43 ± 0.73) worsened after a COVID-19 infection. Additionally, the grading of papillae was worse following a COVID-19 infection (0.61 ± 0.69 vs. 0.16 ± 0.37, p < 0.001). The multivariate linear regression model revealed a negative association between COVID-19 infection and NIKBUT-average (β = −2.98, 95%CI: (−5.82, −0.15), p = 0.039), LLT-average (β = −14.12, 95%CI: (−22.66, −5.59), p = 0.001), and LLT max (β = −15.65, 95%CI: (−23.09, −8.20), p < 0.001). Conclusion: From preliminary results, we concluded that dry eye patients who have been infected with COVID-19 appear to have a more severe dry eye condition, as evidenced by lower LLT, worse papillae and MGD, and shorter NIKBUT. It is important to raise awareness of this potential long-term symptom of COVID-19, especially among existing dry eye patients.
Acute Respiratory Distress Syndrome; A Review of Recent Updates and a Glance into the Future
Authors: Ramji Husayn F. , Hafiz Maida , Altaq Hiba Hammad , Hussain Syed Talal , Chaudry Fawad
Published: April 2023

Acute respiratory distress syndrome (ARDS) is a rapidly progressive form of respiratory failure that accounts for 10% of admissions to the ICU and is associated with approximately 40% mortality in severe cases. Despite significant mortality and healthcare burden, the mainstay of management remains supportive care. The recent pandemic of SARS-CoV-2 has re-ignited a worldwide interest in exploring the pathophysiology of ARDS, looking for innovative ideas to treat this disease. Recently, many trials have been published utilizing different pharmacotherapy targets; however, the long-term benefits of these agents remain unknown. Metabolomics profiling and stem cell transplantation offer strong enthusiasm and may completely change the outlook of ARDS management in the near future.
Evolution of the Probe-Based Loop-Mediated Isothermal Amplification (LAMP) Assays in Pathogen Detection
Authors: Zhang Xiaoling , Zhao Yongjuan , Zeng Yi , Zhang Chiyu
Published: April 2023

Loop-mediated isothermal amplification (LAMP), as the rank one alternative to a polymerase chain reaction (PCR), has been widely applied in point-of-care testing (POCT) due to its rapid, simple, and cost-effective characteristics. However, it is difficult to achieve real-time monitoring and multiplex detection with the traditional LAMP method. In addition, these approaches that use turbidimetry, sequence-independent intercalating dyes, or pH-sensitive indicators to indirectly reflect amplification can result in false-positive results if non-specific amplification occurs. To fulfill the needs of specific target detection and one-pot multiplex detection, a variety of probe-based LAMP assays have been developed. This review focuses on the principles of these assays, summarizes their applications in pathogen detection, and discusses their features and advantages over the traditional LAMP methods.
Medication-Related Osteonecrosis: Why the Jawbone?
Authors: Kuehn Sydney , Scariot Rafaela , Elsalanty Mohammed
Published: April 2023

Medication-related osteonecrosis of the jaw (MRONJ) has emerged as a complication of anti-resorptive medications. Despite its low incidence rate, this problem has gained attention in recent years due to its devastating consequences and lack of preventive strategy. The fact that MRONJ incidence has been exclusive to the jawbones, despite the systemic effect of anti-resorptive medications, could be a starting point to unravel the multifactorial pathogenesis of this condition. This review aims to negotiate the question of why the jawbone is more susceptible to MRONJ than other skeletal sites. Approaching the problem from this perspective could provide new directions for the prevention of MRONJ and expand our understanding of the unique oral microenvironment.
A Novel Collision Avoidance Strategy with D2D Communications for UAV Systems
Authors: Shan Lin , Li Huan-Bang , Miura Ryu , Matsuda Takashi , Matsumura Takeshi
Published: April 2023

In recent years, safety operation issues related to the autonomous flight of unmanned aerial vehicles (UAVs) have become popular research and development topics worldwide. Among all UAV applications, multiple UAV-related applications are emerging due to the integration of UAVs into 6G networks, which is an important topic for next-generation wireless communication systems. For multiple UAV applications, flight safety among UAVs is the most significant issue. Therefore, collision avoidance for UAVs has become an emerging topic in UAV-related research. In the past, although many UAV collision avoidance methods have been proposed, there is still a probability of other problems, such as no possible avoidance route and unmanaged UAVs that are without centralized control, which both result in an unpredictable risk of collisions. In this study, we investigate the current existing methods and propose novel collision avoidance methods based on the elastic collision principle. To verify the performance of the proposed methods, we also conduct simulations in this paper to demonstrate their effectiveness. From the simulation results, it can be seen that the proposed methods can effectively perform collision avoidance for multiple UAVs. Specifically, using the proposed methods, all UAVs can reach their destination points within reasonable time resources without any collision, validating the effectiveness of the proposed methods.
Extraction and Mapping of Cropland Parcels in Typical Regions of Southern China Using Unmanned Aerial Vehicle Multispectral Images and Deep Learning
Authors: Wu Shikun , Su Yingyue , Lu Xiaojun , Xu Han , Kang Shanggui , Zhang Boyu , Hu Yueming , Liu Luo
Published: April 2023

The accurate extraction of cropland distribution is an important issue for precision agriculture and food security worldwide. The complex characteristics in southern China pose great challenges to the extraction. In this study, for the objective of accurate extraction and mapping of cropland parcels in multiple crop growth stages in southern China, we explored a method based on unmanned aerial vehicle (UAV) data and deep learning algorithms. Our method considered cropland size, cultivation patterns, spectral characteristics, and the terrain of the study area. From two aspects—model architecture of deep learning and the data form of UAV—four groups of experiments are performed to explore the optimal method for the extraction of cropland parcels in southern China. The optimal result obtained in October 2021 demonstrated an overall accuracy (OA) of 95.9%, a Kappa coefficient of 89.2%, and an Intersection-over-Union (IoU) of 95.7%. The optimal method also showed remarkable results in the maps of cropland distribution in multiple crop growth stages, with an average OA of 96.9%, an average Kappa coefficient of 89.5%, and an average IoU of 96.7% in August, November, and December of the same year. This study provides a valuable reference for the extraction of cropland parcels in multiple crop growth stages in southern China or regions with similar characteristics.
Assessing the Reactionary Response of High School Engineering Teachers Offering a Novel Pre-College Engineering Curriculum: Lessons Learned from the COVID-19 Pandemic
Authors: Griesinger Tina , Olawale David , Saqib Najmus , Reid Kenneth
Published: April 2023

The coronavirus (COVID-19) pandemic forced a rapid transition of K-16 education to remote and online learning in the final quarter of the 2019–2020 school year. The disruption was extreme for all teachers in K-12 but particularly for teachers involved in pilot programs, such as the NSF-funded Engineering for Us All (e4usa) project. This paper reports the key findings obtained through systematic data collection from a pilot cohort of high school teachers who adapted a brand-new engineering curriculum during the COVID-19 pandemic, students who experienced the adapted curriculum, and a new cohort of teachers who were tasked with teaching the updated curriculum.
Significance of L2 and L3 Motivation in the Context of an English-Taught Program: A Preliminary Study at a Japanese University
Authors: Kojima Naoko
Published: April 2023

This study explores the motivation of international students who simultaneously studied L2 English and L3 Japanese while learning in an English-taught program specializing in policy studies at a Japanese university. Data were collected from five participants using semi-structured interviews, motivation graphs, a biographical questionnaire, and the program’s application form to examine how international students chose the program and the trajectories of their motivations to learn English, Japanese, and policy studies. The results show that all participants had rich experience in learning English and/or intercultural contacts before coming to Japan. Although two participants wanted to live in Japan to learn the language, three had no specific aim of studying abroad. The students’ motivation to learn English was enhanced when their study became more advanced, but their motivation to learn Japanese was more varied and complex. Although sustaining the motivation to learn Japanese over time seemed demanding, one of the participants invested more time in learning Japanese than English. This study highlights that exploring students’ disposition of motivation and international orientation can be beneficial, especially in uncovering why and how students can sustain their motivation to learn Japanese for academic purposes. Furthermore, it could indicate future directions for such programs.
Engaging Students in Scientific Practices in a Remote Setting
Authors: Lager Anna , Lavonen Jari
Published: April 2023

The goal of science education has shifted from teaching scientific concepts to facilitating students’ active role in making sense of phenomena through engaging in scientific practices (SPs). While engaging in scientific practices, students use and develop core ideas. The COVID-19 pandemic forced a shift towards online education, stressing the need to explore how SPs are used in a remote setting. This study aimed to investigate upper secondary students’ use of SPs during collaborative work in a remote setting. The study was conducted in two stages. In Stage 1, the researcher designed collaborative assignments according to the SP approach. In Stage 2, students (N = 16) worked on the designed assignments in small groups. Students’ actions on the computer were recorded with screen-recording software and investigated from three perspectives: use of digital resources, use of SPs, and collaboration. Interviews were conducted to understand students’ perceptions and engagement and were analysed by content analysis means. The results indicated that the collaboration actions were intertwined with SPs use and use of digital resources. The challenges faced by students varied by SPs, with developing models and constructing scientific explanations causing the most challenges. We discuss possible strategies to engage students in SPs in online settings.
After the Pandemic: Teacher Professional Development for the Digital Educational Innovation
Authors: Eradze Maka , De Martino Delio , Tinterri Andrea , Albó Laia , Bardone Emanuele , Sunar Ayşe Saliha , Dipace Anna
Published: April 2023

On the one hand, the pandemic has posed unprecedented challenges for teachers all across the world; on the other, created opportunities for teacher professional development (TPD) as well. Furthermore, this educational emergency has uncovered the potential of digital technologies authentically and showcased its relation to the TPD and educational innovation. In this article, the concepts such as teacher agency, networked learning and reorganization are reexamined and theorized. The current article is a continuation of the research that has shed light on the TPD patterns and peer learning in the context of the pandemic and represents qualitative research covering 15 representatives of different-level schools from three different countries (Italy, Spain and Turkey), focusing on their experiences and future opportunities for TPD. Based on the said theorization, through iterative and abductive reasoning, and narrative inquiry, the lived experiences of teachers are examined and explored. The results indicate that in times of uncertainty and relaxation of macro-level frames, teachers have reorganized their teaching and learning activities relying on peer networks and collaboration while reinforcing their agentic qualities. The implications of the research contribute to the theory, policy and practice of TPD.
Empowering Elementary Students with Community-Based Engineering: A Teacher’s Experience in a Rural School District
Authors: Boz Tugba , Hammack Rebekah , Lux Nicholas , Gannon Paul
Published: April 2023

This paper presents a case study of an elementary teacher, Holly, who participated in a federally funded summer professional development (PD) program aimed at integrating community-based engineering into elementary education. The study examines how Holly’s teaching practices and beliefs about teaching engineering contributed to the significant improvements in her students’ attitudes toward engineering and their perceptions of engineering as a potential career. Data were collected over three years through multiple methods, including post-PD interviews, lesson recordings, and a post-teaching interview. We analyzed classroom videos using a video analysis protocol. We used open coding to analyze the interviews. Once the analysis of the interviews and videos was completed, we engaged in a sense-making process to identify connections across data points (videos and interviews). Our findings showed that Holly extensively incorporated scientific inquiry into her lessons. This approach enabled students to develop their inquiry skills and facilitated a smooth transition to engineering design activities. By connecting class activities to the local context, students were able to see the relevance of engineering to their everyday lives and take ownership of their learning. This study emphasizes the potential of community-focused engineering to foster meaningful science and engineering practices in elementary education.
Validation of Self-Regulated Writing Strategies for Advanced EFL Learners in China: A Structural Equation Modeling Analysis
Authors: Wang Xuan , Ma Jianting , Li Ximeng , Shen Xinyi
Published: April 2023

This study aims to validate self-regulated writing strategies for advanced EFL learners through a structural equation modeling analysis. Two sets of advanced, university-level EFL learners in China were recruited on the basis of results from a nationwide standardized English test. Sample 1 consisted of 214 advanced learners and served mainly as a data source for exploratory factor analysis. Sample 2 consisted of 303 advanced learners; data from this group were used to conduct confirmatory factor analyses. The results confirmed the goodness of fit of the hierarchical, multidimensional structure of self-regulated writing strategies. This hierarchic model has the higher order of self-regulation and the second order of nine self-regulated writing strategies that belong to four dimensions. In terms of model comparisons, the indices of Model 1 (nine-factor correlated model of EFL writing strategies for SRL) and Model 2 (four-factor second-order model of EFL writing strategies for SRL) mark significant improvements in terms of fit over the indices of Model 3 (one-factor second-order model of EFL writing strategies for SRL). This means the four-factor model (cognition, metacognition, social behavior, and motivational regulation) offered a better explanation for advanced EFL learners than the model treating self-regulated writing strategies as a single convergent factor. These findings, in some ways, differ from the results of earlier research on EFL learners’ self-regulated writing strategies, and the findings of this study have certain implications for L2 writing teaching and learning.
Free-Viewpoint Navigation of Indoor Scene with 360° Field of View
Authors: Xu Hang , Zhao Qiang , Ma Yike , Wang Shuai , Yan Chenggang , Dai Feng
Published: April 2023

By providing a 360° field of view, spherical panoramas can convey vivid visual impressions. Thus, they are widely used in virtual reality systems and street view services. However, due to bandwidth or storage limitations, existing systems only provide sparsely captured panoramas and have limited interaction modes. Although there are methods that can synthesize novel views based on captured panoramas, the generated novel views all lie on the lines connecting existing views. Therefore these methods do not support free-viewpoint navigation. In this paper, we propose a new panoramic image-based rendering method for novel view generation. Our method represents each input panorama with a set of spherical superpixels and warps each superpixel individually so the method can deal with the occlusion and disocclusion problem. The warping is dominated by a two-term constraint, which can preserve the shape of the superpixel and ensure it is warped to the correct position determined by the 3D reconstruction of the scene. Our method can generate novel views that are far from input camera positions. Thus, it supports freely exploring the scene with a 360° field of view. We compare our method with three previous methods on datasets captured by ourselves and by others. Experiments show that our method can obtain better results.
Property Preservation of Object-Oriented Petri Reduction Net Based Representation for Embedded Systems
Authors: Xia Chuanliang , Sun Yan , Shen Bin , Guo Maibo
Published: April 2023

Embedded systems are widely used in automotive electronics, smart home, smart medical, aerospace and other fields. Aiming at the problem of formal modeling and verification analysis of embedded systems, a solution is proposed using extended Petri net reduction operations. Petri net based representation for embedded system (PRES+) and the object-oriented technology are combined to obtain the object-oriented PRES+ (OOPRES+). Two kinds of subnet reduction rules of OOPRES+ are presented. The preservation of boundedness and liveness of the reduction net system has been investigated to alleviate the problem of state space explosion of OOPRES+. The modeling and analysis of the embedded control system of a smart restaurant is used as an example to verify the effectiveness of the subnet reduction rules. Results obtained can provide an effective way to examine the reduction property of Petri net systems, and present a powerful means to model and verify the large-scale complex embedded systems.
1-Bit Hexagonal Meander-Shaped Wideband Electronically Reconfigurable Transmitarray for Satellite Communications
Authors: Ali Qasim , Xiao Yu , Shafiq Shozab , Tan Wenhao , Shahzad Waseem , Abbas Syed Muzahir , Sun Houjun
Published: April 2023

This paper proposes a hexagonal meander-shaped wideband electronically reconfigurable transmitarray (HMRTA) at Ku band for satellite communications and radar applications. The proposed transmitarray offers high gain, low profile, and wideband characteristics with beam-scanning and beam-forming features. The cascaded structure is a low-profile and compact transmitarray. The transmitter (Tx) layer has an angular hexagonal patch with a meandered shape and resonating parasitic patches to enhance the bandwidth. The receiver (Rx) layer comprises a two-part hexagonal receiver patch and a dual ring impedance-matching receiver layer. The current reversal phenomena have executed the 180° phase shift by integrating two diodes in opposite directions. The measured results of a unit cell achieve a minimum insertion loss of 0.86 dB and 0.92 dB for state I and state II. The maximum insertion loss is 2.58 dB from 14.12 GHz to 18.02 GHz and is about 24.83% at 16.5 GHz. The full-wave simulations of a 20 × 20 space-fed reconfigurable transmitarray were performed. Good radiation patterns at all scanning angles of two principal planes are achieved, and the cross-polarization level remains less than −20 dB. The simulated 3 dB gain fluctuation bandwidth of the array is 15.85~18.35 GHz, and the wideband characteristics are verified. The simulation results show that the array can perform beam scanning ±60° in the elevation (y-z) plane and obtain the beam-scanning characteristics for ±60° in the Azimuth (x-z) plane.
Multi-Strategy Fusion of Sine Cosine and Arithmetic Hybrid Optimization Algorithm
Authors: Liu Lisang , Xu Hui , Wang Bin , Ke Chengyang
Published: April 2023

The goal was to address the problems of slow convergence speed, low solution accuracy and insufficient performance in solving complex functions in the search process of an arithmetic optimization algorithm (AOA). A multi-strategy improved arithmetic optimization algorithm (SSCAAOA) is suggested in this study. By enhancing the population’s initial distribution, optimizing the control parameters, integrating the positive cosine algorithm with improved parameters, and adding inertia weight coefficients and a population history information sharing mechanism to the PSO algorithm, the optimization accuracy and convergence speed of the AOA algorithm are improved. This increases the algorithm’s ability to perform a global search and prevents it from hitting a local optimum. Simulations of SSCAAOA using other optimization algorithms are used to examine their efficacy on benchmark test functions and engineering challenges. The analysis of the experimental data reveals that, when compared to other comparative algorithms, the improved algorithm presented in this paper has a convergence speed and accuracy that are tens of orders of magnitude faster for the unimodal function and significantly better for the multimodal function. Practical engineering tests also demonstrate that the revised approach performs better.
Lightweight Deep Learning Model for Real-Time Colorectal Polyp Segmentation
Authors: Jeong Seung-Min , Lee Seung-Gun , Seok Chae-Lin , Lee Eui-Chul , Lee Jun-Young
Published: April 2023

In colonoscopy, computer vision and artificial intelligence technology have enabled the automatic detection of the location of polyps and their visualization. These advancements have facilitated considerable research in the field. However, deep learning models used in the segmentation problem for capturing various patterns of polyps are becoming increasingly complex, which has rendered their operation in real time difficult. To identify and overcome this problem, a study was conducted on a model capable of precise polyp segmentation while increasing its processing speed. First, an efficient, high-performance, and lightweight model suitable for the segmentation of polyps was sought; the performance of existing segmentation models was compared and combined to obtain a learning model that exhibited good accuracy and speed. Next, hyperparameters were found for the MobileNetV3-encoder-based DeepLabV3+ model and, after tuning the hyperparameters, quantitative and qualitative results were compared, and the final model was selected. The experimental results showed that this model achieved high accuracy, with a Dice coefficient of 93.79%, while using a limited number of parameters and computational resources. Specifically, the model used 6.18 million parameters and 1.623 giga floating point operations for the CVC-ClinicDB dataset. This study revealed that increasing the amount of computation and parameters of the model did not guarantee unconditional performance. Furthermore, for the search and removal of polyps in cases in which morphological information is critical, an efficient model with low model complexity and high accuracy was proposed for real-time segmentation.
A Novel Fully Digital Feedforward Background Calibration Technique for Timing Mismatch in M-Channel Time-Interleaved ADCs
Authors: Xiong Wei , Zhang Zhenwei , Lang Lili , Dong Yemin
Published: April 2023

This paper presents a novel digital background calibration technique for timing mismatches in time-interleaved analog-to-digital converters (TIADCs). We reconstruct and eliminate the timing mismatch based on the theory of timing-mismatch-induced spurious signals in the frequency domain. The proposed compensation algorithm utilizes the conjugate property between spurious signals to achieve low complexity. A coarse-fine correction architecture is adopted to eliminate higher-order time-skew errors. A novel feedforward estimation algorithm based on the correlation of adjacent channels is proposed to extract the time-skew errors. Our proposed calibration technique is suitable for an arbitrary number of channels. The simulation results demonstrate that the utilization of the proposed calibration technique yields significant improvements in both the signal-to-noise-and-distortion ratio (SNDR) and spurious-free dynamic range (SFDR). The SNDR and SFDR are improved from 55.82 dB and 56.64 dB to 79.92 dB and 105.98 dB, respectively.
Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information
Authors: Zhang Chaoqin , Yang Wentao , Zhang Qiuwen
Published: April 2023

In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality.
Autoencoder and Incremental Clustering-Enabled Anomaly Detection
Authors: Connelly Andrew Charles , Zaidi Syed Ali Raza , McLernon Des
Published: April 2023

Many machine-learning-enabled approaches towards anomaly detection depend on the availability of vast training data. Our data are formed from power readings of cycles from domestic appliances, such as dishwashers or washing machines, and contain no known examples of anomalous behaviour. Moreover, we are limited to the machine’s voltage, amperage, and current readings, drawn from a retrofitted power outlet in 60-s samples. No rich sensor data or previous insights are available as a training basis, limiting our ability to leverage the existing work. We design a system to monitor the behaviour of electrical appliances. This system requires special consideration as different power cycles from the same machine can exhibit different behaviours, and it accounts for this by clustering unseen cycle patterns into siloed training datasets and corresponding learned parameters. They are then passed in real-time to an autoencoder ensemble for reconstruction-based anomaly detection, using the error in reconstruction as a means to flag anomalous points in time. The system correctly identifies and trains appropriate cycle clusters of data streams on a real-world machine dataset injected with stochastic, proportionate anomalies.