bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Native Quercus robur and Non-Native Quercus rubra on Soil Properties during Post-Fire Ecosystem Regeneration
Authors: Woś Bartłomiej , Józefowska Agnieszka , Wanic Tomasz , Pietrzykowski Marcin
Published: April 2023

Following disturbances, ecosystems are more susceptible to invasion by non-native species. Furthermore, it is important to determine the impact of alien tree species on soil regeneration processes during secondary succession. In this study, we analyzed the effect of native and late successional common oak (Quercus robur) and non-native red oak (Q. rubra) on soil physicochemical (pH, carbon, and nutrient content) and microbial properties (microbial biomass [Cmic] and respiration [RESP]) nearly 30 years after severe fire disturbance. Post-fire soils under Q. rubra had organic horizons with a greater mass, lower pH values, and depleted nutrient (N, Ca, K, Mg, and P) contents than soils under Q. robur. The impact of Q. robur as a late successional species on soil properties 30 years after a disturbance was similar to that of pioneer species (Scots pine, European larch, common birch, and black alder), as is indicated in previous studies. Most of the studied physicochemical (bulk density, soil organic carbon, N, Ca, K, and P content) and microbial (RESP and Cmic) soil parameters under Q. robur were within the ranges found for post-fire soils under pioneer tree species. Only the pH and Mg and Na contents in organic horizons were higher under Q. robur than under pioneer species. Our results indicate that Q. robur could be a valuable addition to reforestation sites after fire disturbance, especially in more fertile microhabitats. Due to the depletion of soil nutrients, care should be taken when introducing Q. rubra during the reforestation of post-fire sites, especially in larger groups.
Microclimatic Fluctuation throughout the Day Influences Subtropical Fruit-Feeding Butterfly Assemblages between the Canopy and Understory
Authors: Richter Aline , de Souza Mendonça Milton , Gawlinski Karine , Iserhard Cristiano Agra
Published: April 2023

Vertical stratification is a recognized pattern in tropical forests; however, biotic and abiotic factors driving this pattern are little explored. We investigated the influence of daily climatic variation in the vertical stratification of fruit-feeding butterfly assemblages sampled with bait traps in the understory and canopy of a subtropical Atlantic Forest. Overall, 1347 individuals belonging to 38 species of fruit-feeding butterflies were recorded. The canopy and understory are distinct concerning diurnal but not nocturnal microclimatic conditions, leading to different responses in community structure. Richness did not differ between strata, but we observed an effect of stratum in interaction with variation in microclimatic conditions, with the canopy increasing in abundance compared to the understory. Temperature homogenization at night can hinder vertical stratification in richness, while microclimatic variation influences species abundance. The species composition was affected by strata with high turnover in the understory, without an effect of microclimatic variables in beta diversity. In addition to the difference in composition, our study shows that the understory was represented mainly by species from Satyrinae, while the canopy presented species from different clades. This could be an artefact of habitat structure, and the species adapted to the closed forest have a dispersal limitation compared to in the canopy. These findings help us to better understand the mechanisms generating distinct patterns of vertical stratification of fruit-feeding butterflies in the Neotropics and provide new insights into the role of microclimatic conditions in the structure of insect assemblages.
Length–Weight Relationships and Diversity Status of Fishes in the Midstream of the Jialing River, a Tributary of the Upper Yangtze River, China
Authors: Qin Qiang , Xu Jianghaoyue , Zhang Fubin , He Shan , Zhou Tong , Li Shuyin , Zeng Yu
Published: April 2023

The study described the length–weight relationships (LWRs) and diversity status of fishes in the midstream of the Jialing River, which is the largest tributary of the upper Yangtze River, China. A total of 4592 specimens from 53 fish species belonging to three orders and eight families were collected from December 2021 to November 2022. The results showed that Culter oxycephaloides, Xenocypris davidi, Hemibarbus labeo, Hemiculter tchangi were dominant fish species in the study region. Twenty-five fish species (IRI ≥ 10) were subjected to LWR analysis, and the regression parameters a and b for fish species varied from 0.006 to 0.333 and 2.129 to 3.391. Eleven fish species were determined to have isometric growth, and 14 fish species were determined to have allometric growth. The diversity analyses suggested that the diversity status of fishes were kept relatively stable during the sampling period and that the fishes suffered moderate disturbance in the midstream of the Jialing River. The present study provided basic biology data for fish conservation and management after the fishing ban in the Jialing River.
High-Latitude Dinosaur Nesting Strategies during the Latest Cretaceous in North-Eastern Russia
Authors: Amiot Romain , Golovneva Lina B. , Godefroit Pascal , Goedert Jean , Garcia Géraldine , Lécuyer Christophe , Fourel François , Herman Alexei B. , Spicer Robert A.
Published: April 2023

Dinosaur eggshell fragments attributed to the oofamilies Spheroolithidae and Prismatoolithidae and recovered from the latest Cretaceous Kakanaut Formation of North-eastern Russia (Chukotka) constitute one of the northernmost records of dinosaur reproductive behaviors. The high palaeolatitude of the locality (~75° N), as well as the cool near-polar climate, where summer temperatures only averaged 20 °C during the warmest month, dark near-freezing winters and egg incubation that could have lasted several months, raise questions about dinosaur reproductive strategies, particularly in terms of the timing of egg laying. In order to investigate seasonal aspects of Kakanaut dinosaur reproductions, carbonate from eggshell fragments have been analyzed for their oxygen and carbon isotope compositions, along with the oxygen and carbon isotope compositions of apatite phosphate and structural carbonate of associated theropod, hadrosaur and ankylosaur teeth as well as lepisosteid fish scales. Stable oxygen and carbon isotope compositions of eggshells from the Kakanaut Formation together with those of associated adult dinosaur teeth and fish scales reveal differences in mineralization timing between eggshells and teeth and show that eggs were laid at the very beginning of spring when snowmelt drained from nearby highlands. We propose that Kakanaut dinosaurs laid their eggs at the very beginning of spring in order to accommodate an incubation period that lasted several months. This timing would also benefit from mild temperatures and increasing food availability when the eggs hatch, allowing the hatchlings to grow large enough to survive the next winter or perhaps follow adult animals in their migration southwards.
Collecting and Pre-Processing Data for Industry 4.0 Implementation Using Hydraulic Press
Authors: Hercik Radim , Svoboda Radek
Published: April 2023

More and more activities are being undertaken to implement the Industry 4.0 concept in industrial practice. One of the biggest challenges is the digitization of existing industrial systems and heavy industry operations, where there is huge potential for optimizing and managing these processes more efficiently, but this requires collecting large amounts of data, understanding, and evaluating it so that we can add value back based on it. This paper focuses on the collection, local pre-processing of data, and its subsequent transfer to the cloud from an industrial hydraulic press to create a comprehensive dataset that forms the basis for further digitization of the operation. The novelty lies mainly in the process of data collection and pre-processing in the framework of edge computing of large amounts of data. In the data pre-processing, data normalization methods are applied, which allow the data to be logically sorted, tagged, and linked, which also allows the data to be efficiently compressed, thus, dynamically creating a complex dataset for later use in the process digitization.
Managing Viral Emerging Infectious Diseases via Current and Future Molecular Diagnostics
Authors: Altindiş Mustafa , Kahraman Kilbaş Elmas Pınar
Published: April 2023

Emerging viral infectious diseases have been a constant threat to global public health in recent times. In managing these diseases, molecular diagnostics has played a critical role. Molecular diagnostics involves the use of various technologies to detect the genetic material of various pathogens, including viruses, in clinical samples. One of the most commonly used molecular diagnostics technologies for detecting viruses is polymerase chain reaction (PCR). PCR amplifies specific regions of the viral genetic material in a sample, making it easier to detect and identify viruses. PCR is particularly useful for detecting viruses that are present in low concentrations in clinical samples, such as blood or saliva. Another technology that is becoming increasingly popular for viral diagnostics is next-generation sequencing (NGS). NGS can sequence the entire genome of a virus present in a clinical sample, providing a wealth of information about the virus, including its genetic makeup, virulence factors, and potential to cause an outbreak. NGS can also help identify mutations and discover new pathogens that could affect the efficacy of antiviral drugs and vaccines. In addition to PCR and NGS, there are other molecular diagnostics technologies that are being developed to manage emerging viral infectious diseases. One of these is CRISPR-Cas, a genome editing technology that can be used to detect and cut specific regions of viral genetic material. CRISPR-Cas can be used to develop highly specific and sensitive viral diagnostic tests, as well as to develop new antiviral therapies. In conclusion, molecular diagnostics tools are critical for managing emerging viral infectious diseases. PCR and NGS are currently the most commonly used technologies for viral diagnostics, but new technologies such as CRISPR-Cas are emerging. These technologies can help identify viral outbreaks early, track the spread of viruses, and develop effective antiviral therapies and vaccines.
The Impact of Adding Digital Breast Tomosynthesis to BI-RADS Categorization of Mammographically Equivocal Breast Lesions
Authors: Hassan Rania Mostafa , Almalki Yassir Edrees , Basha Mohammad Abd Alkhalik , Alduraibi Sharifa Khalid , Aboualkheir Mervat , Almushayti Ziyad A. , Aldhilan Asim S. , Aly Sameh Abdelaziz , Alshamy Asmaa A.
Published: April 2023

Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on the BI-RADS categorization of equivocal breast lesions. We prospectively evaluated 148 females with equivocal BI-RADS breast lesions (BI-RADS 0, 3, and 4) with DM. All patients underwent DBT. Two experienced radiologists analyzed the lesions. They then assigned a BI-RADS category for each lesion according to the BI-RADS 2013 lexicon, using DM, DBT, and integrated DM and DBT. We compared the results based on major radiological characteristics, BI-RADS classification, and diagnostic accuracy, using the histopathological examination of the lesions as a reference standard. The total number of lesions was 178 on DBT and 159 on DM. Nineteen lesions were discovered using DBT and were missed by DM. The final diagnoses of 178 lesions were malignant (41.6%) and benign (58.4%). Compared to DM, DBT produced 34.8% downgrading and 32% upgrading of breast lesions. Compared with DM, DBT decreased the number of BI-RADS 4 and 3. All the upgraded BI-RADS 4 lesions were confirmed to be malignant. The combination of DM and DBT improves the diagnostic accuracy of BI-RADS for evaluating and characterizing mammographic equivocal breast lesions and allows for proper BI-RADS categorization.
Pulmonary Ultrasonography in Systemic Sclerosis-Induced Interstitial Lung Disease—A Systematic Review and Meta-Analysis
Authors: Radić Mislav , Đogaš Hana , Gelemanović Andrea , Jurić Petričević Slavica , Škopljanac Ivan , Radić Josipa
Published: April 2023

Background: The aim of the current systematic review was to summarize and evaluate the overall advantages of lung ultrasonography (LUS) examination using high-resolution computed tomography (HRCT) as a reference standard in assessing the presence of interstitial lung disease (ILD) in systemic sclerosis (SSc) patients. Methods: Databases PubMed, Scopus, and Web of Science were searched for studies evaluating LUSs in ILD assessments including SSc patients on 1 February 2023. In assessing risk of bias and applicability, the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used. A meta-analysis was performed and the mean specificity, sensitivity, and diagnostic odds ratio (DOR) with a 95% confidence interval (CI) were obtained. In addition, in a bivariate meta-analysis, the summary receiver operating characteristic (SROC) curve area was additionally calculated. Results: Nine studies with a total of 888 participants entered the meta-analysis. A meta-analysis was also performed without one study that used pleural irregularity to assess the diagnostic accuracy of LUSs using B-lines (with a total of 868 participants). Overall sensitivity and specificity did not differ significantly, with only the analysis of the B-lines having a specificity of 0.61 (95% CI 0.44–0.85) and a sensitivity of 0.93 (95% CI 0.89–0.98). The diagnostic odds ratio of univariate analysis of the eight studies using the B-lines as a criterion for ILD diagnosis was 45.32 (95% CI 17.88–114.89). The AUC value of the SROC curve was 0.912 (and 0.917 in consideration of all nine studies), which indicates high sensitivity and a low false-positive rate for the majority of the included studies. Conclusions: LUS examination proved to be a valuable tool in discerning which SSc patients should receive additional HRCT scans to detect ILD and therefore reduces the doses of ionizing radiation exposure in SSc patients. However, further studies are needed to achieve consensus in scoring and the evaluation methodology of LUS examination.
AI-Based CXR First Reading: Current Limitations to Ensure Practical Value
Authors: Vasilev Yuriy , Vladzymyrskyy Anton , Omelyanskaya Olga , Blokhin Ivan , Kirpichev Yury , Arzamasov Kirill
Published: April 2023

We performed a multicenter external evaluation of the practical and clinical efficacy of a commercial AI algorithm for chest X-ray (CXR) analysis (Lunit INSIGHT CXR). A retrospective evaluation was performed with a multi-reader study. For a prospective evaluation, the AI model was run on CXR studies; the results were compared to the reports of 226 radiologists. In the multi-reader study, the area under the curve (AUC), sensitivity, and specificity of the AI were 0.94 (CI95%: 0.87–1.0), 0.9 (CI95%: 0.79–1.0), and 0.89 (CI95%: 0.79–0.98); the AUC, sensitivity, and specificity of the radiologists were 0.97 (CI95%: 0.94–1.0), 0.9 (CI95%: 0.79–1.0), and 0.95 (CI95%: 0.89–1.0). In most regions of the ROC curve, the AI performed a little worse or at the same level as an average human reader. The McNemar test showed no statistically significant differences between AI and radiologists. In the prospective study with 4752 cases, the AUC, sensitivity, and specificity of the AI were 0.84 (CI95%: 0.82–0.86), 0.77 (CI95%: 0.73–0.80), and 0.81 (CI95%: 0.80–0.82). Lower accuracy values obtained during the prospective validation were mainly associated with false-positive findings considered by experts to be clinically insignificant and the false-negative omission of human-reported “opacity”, “nodule”, and calcification. In a large-scale prospective validation of the commercial AI algorithm in clinical practice, lower sensitivity and specificity values were obtained compared to the prior retrospective evaluation of the data of the same population.
A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach
Authors: Xuan Shurui , Zhang Jiayue , Guo Qinxing , Zhao Liang , Yao Xin
Published: April 2023

Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic value has not yet been fully established in COPD. The purpose of this study was to develop an effective model for the diagnosis of COPD based on circulating miRNAs. We included circulating miRNA expression profiles of two independent cohorts consisting of 63 COPD and 110 normal samples, and then we constructed a miRNA pair-based matrix. Diagnostic models were developed using several machine learning algorithms. The predictive performance of the optimal model was validated in our external cohort. In this study, the diagnostic values of miRNAs based on the expression levels were unsatisfactory. We identified five key miRNA pairs and further developed seven machine learning models. The classifier based on LightGBM was selected as the final model with the area under the curve (AUC) values of 0.883 and 0.794 in test and validation datasets, respectively. We also built a web tool to assist diagnosis for clinicians. Enriched signaling pathways indicated the potential biological functions of the model. Collectively, we developed a robust machine learning model based on circulating miRNAs for COPD screening.
Diagnostic and Prognostic Nomograms for Hepatocellular Carcinoma Based on PIVKA-II and Serum Biomarkers
Authors: An Shu , Zhan Xiaoxia , Liu Min , Li Laisheng , Wu Jian
Published: April 2023

Background: The aim of the present study was to develop an improved diagnostic and prognostic model for HBV-associated HCC by combining AFP with PIVKA-II and other potential serum/plasma protein biomarkers. Methods: A total of 578 patients, including 352 patients with HBV-related HCC, 102 patients with HBV-associated liver cirrhosis (LC), 124 patients with chronic HBV, and 127 healthy subjects (HS), were enrolled in the study. The serum levels of AFP, PIVKA-II, and other laboratory parameters were collected. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors, respectively. The diagnostic efficacy of the nomogram was evaluated using receiver operator curve (ROC) analysis and the prognostic performance was measured by Harrell’s concordance index (C-index). Results: AFP and PIVKA-II levels were significantly increased in HBV-related HCC, compared with those in HBV-associated LC and chronic HBV participants (p < 0.05 and p < 0.001, respectively). The diagnostic nomogram, which included age, gender, AFP, PIVKA-II, prothrombin time (PT), and total protein (TP), discriminated patients with HBV-HCC from those with HBV-LC or chronic HBV with an AUC of 0.970. In addition, based on the univariate and multivariate Cox regression analysis, PIVKA-II, γ-glutamyl transpeptidase, and albumin were found to be significantly associated with the prognosis of HBV-related HCC and were incorporated into a nomogram. The C-index of the nomogram for predicting 3-year survival in the training and validation groups was 0.75 and 0.78, respectively. The calibration curves for the probability of 3-year OS showed good agreement between the nomogram prediction and the actual observation in the training and the validation groups. Furthermore, the nomogram had a higher C-index (0.74) than that of the Child−Pugh grade (0.62), the albumin−bilirubin (ALBI) score (0.64), and Barcelona Clinic Liver Cancer (0.56) in all follow-up cases. Conclusion: Our study suggests that the nomograms based on AFP, PIVKA-II, and potential serum protein biomarkers showed a better performance in the diagnosis and prognosis of HCC, which may help to guide therapeutic strategies and assess the prognosis of HCC.
UAV Trajectory and Energy Efficiency Optimization in RIS-Assisted Multi-User Air-to-Ground Communications Networks
Authors: Yao Yuanyuan , Lv Ke , Huang Sai , Li Xuehua , Xiang Wei
Published: April 2023

An air-to-ground downlink communication network consisting of a reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) is proposed. In conjunction with a resource allocation strategy, the system’s energy efficiency is improved. Specifically, the UAV equipped with a RIS starts from an initial location, and an energy-efficient unmanned aerial vehicle deployment (EEUD) algorithm is deployed to jointly optimize the UAV trajectory, RIS phase shifts, and BS transmit power, so as to obtain a quasi-optimal deployment location and hence improve the energy efficiency. First, the RIS phase shifts are optimized by using the block coordinate descent (BCD) algorithm to deal with the nonconvex inequality constraint, and then integrated with the Dinkelbach algorithm to address the resource allocation problem of the BS transmit power. Finally, for solving the UAV trajectory optimization problem, the complex objective function is transformed into a convex function, and the optimal UAV flight trajectory is obtained. Our simulation results show that the quasi-optimal deployment location obtained by the EEUD algorithm is superior to other deployment strategies in energy efficiency. Moreover, the instantaneous energy efficiency of the UAVs along the trajectory of searching the deployment location is better than other comparison trajectories. Furthermore, the RIS-assisted multi-user air-to-ground communication network can offer up to 145% improvement in energy efficiency over the traditional amplify-and-forward (AF) relay.
Uncovering the Origins of Instability in Dynamical Systems: How Can the Attention Mechanism Help?
Authors: Bahador Nooshin , Lankarany Milad
Published: April 2023

The behavior of the network and its stability are governed by both dynamics of the individual nodes, as well as their topological interconnections. The attention mechanism as an integral part of neural network models was initially designed for natural language processing (NLP) and, so far, has shown excellent performance in combining the dynamics of individual nodes and the coupling strengths between them within a network. Despite the undoubted impact of the attention mechanism, it is not yet clear why some nodes of a network obtain higher attention weights. To come up with more explainable solutions, we tried to look at the problem from a stability perspective. Based on stability theory, negative connections in a network can create feedback loops or other complex structures by allowing information to flow in the opposite direction. These structures play a critical role in the dynamics of a complex system and can contribute to abnormal synchronization, amplification, or suppression. We hypothesized that those nodes that are involved in organizing such structures could push the entire network into instability modes and therefore need more attention during analysis. To test this hypothesis, the attention mechanism, along with spectral and topological stability analyses, was performed on a real-world numerical problem, i.e., a linear Multi-Input Multi-Output state-space model of a piezoelectric tube actuator. The findings of our study suggest that the attention should be directed toward the collective behavior of imbalanced structures and polarity-driven structural instabilities within the network. The results demonstrated that the nodes receiving more attention cause more instability in the system. Our study provides a proof of concept to understand why perturbing some nodes of a network may cause dramatic changes in the network dynamics.
Effects of Stochastic Noises on Limit-Cycle Oscillations and Power Losses in Fusion Plasmas and Information Geometry
Authors: Hollerbach Rainer , Kim Eun-jin
Published: April 2023

We investigate the effects of different stochastic noises on the dynamics of the edge-localised modes (ELMs) in magnetically confined fusion plasmas by using a time-dependent PDF method, path-dependent information geometry (information rate, information length), and entropy-related measures (entropy production, mutual information). The oscillation quenching occurs due to either stochastic particle or magnetic perturbations, although particle perturbation is more effective in this amplitude diminishment compared with magnetic perturbations. On the other hand, magnetic perturbations are more effective at altering the oscillation period; the stochastic noise acts to increase the frequency of explosive oscillations (large ELMs) while decreasing the frequency of more regular oscillations (small ELMs). These stochastic noises significantly reduce power and energy losses caused by ELMs and play a key role in reproducing the observed experimental scaling relation of the ELM power loss with the input power. Furthermore, the maximum power loss is closely linked to the maximum entropy production rate, involving irreversible energy dissipation in non-equilibrium. Notably, over one ELM cycle, the information rate appears to keep almost a constant value, indicative of a geodesic. The information rate is also shown to be useful for characterising the statistical properties of ELMs, such as distinguishing between explosive and regular oscillations and the regulation between the pressure gradient and magnetic fluctuations.
A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition
Authors: Li Zipeng , Yang Kunde , Zhou Xingyue , Duan Shunli
Published: April 2023

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
Group Work during Inquiry-Based Learning in Biology Teacher Education: A Praxeological Perspective on the Task of (Collaborative) Protocol Generation
Authors: Olschewski Petra , Herzmann Petra , Schlüter Kirsten
Published: April 2023

Writing protocols is a central activity in the natural sciences, but is also a part of science education. In the context of inquiry-based learning, keeping records is considered beneficial for the comprehension of scientific reasoning and the associated problem-solving process. Previous studies have focused particularly on the evaluation of learner-generated protocols and their potential for learning. The process of protocol writing, especially in the context of inquiry-based group work, as it is usually implemented in practice, has hardly been researched so far. In this video-based study, we use the documentary method, a reconstructive analysis method, in order to investigate how student groups implement joint protocol generation in an experimental inquiry-based setting and which action-guiding orientations emerge in the process. In all groups, action-guiding orientations to “task completion” and using the “protocol as means to provide structure and security” were found. Moreover, we have found differing orientations which can be titled “protocol as a flagship” and “protocol used in a pragmatic manner”. Overall, the protocol seems more to serve as a guide and an assurance in the experimentation process rather than as a tool for improving scientific thinking and problem solving.
Distinguishing between Grit, Persistence, and Perseverance for Learning Mathematics with Understanding
Authors: DiNapoli Joseph
Published: April 2023

Learning mathematics with understanding involves productively struggling to make connections between different mathematical ideas. Such productive struggle is associated with three primary constructs: grit, persistence, and perseverance. Each of these constructs has a distinct definition, background, and implied utility in mathematics education research. However, these constructs are often colloquially conceptualized as synonyms, leading to a misconception of what can be learned through the lens of each construct. The purpose of this paper is to carefully examine the literature on grit, persistence, and perseverance and to review and distinguish the ways in which these constructs offer insights into learning mathematics with understanding.
How Sustainable Is a Waldorf School? Exploring the Congruence between Waldorf Education and the Sustainable School Approach in a Greek School Case Study
Authors: Daskolia Maria , Koukouzeli Vassiliki
Published: April 2023

The study reported here aims to explore the relationship between Waldorf education and the sustainable school approach through a small-scale qualitative research study conducted in a Greek Waldorf school. Following a semi-structured interview protocol, four teachers shared their views and beliefs on the congruence between the two approaches as reflected in the philosophy and everyday practice of their school. The study’s findings indicate that the Greek Waldorf school manifests several of the quality criteria that define a sustainable school, with pedagogy identified as the most relevant aspect, particularly in terms of the quality of teaching and learning processes. Participants also identified common features between the two approaches on both the social/organizational and physical/technical levels. Although not all the criteria of a sustainable school are fully met, the teachers believe that the sustainability concept is implicitly interwoven with the philosophy and practice of Waldorf education and that their school is moving in the direction of Education for Sustainable Development (ESD).
Comparing the Use of Two Different Approaches to Assess Teachers’ Knowledge of Models and Modeling in Science Teaching
Authors: Carroll Grace , Park Soonhye
Published: April 2023

Science teacher knowledge for effective teaching consists of multiple knowledge bases, one of which includes science content knowledge and pedagogical knowledge. With the inclusion of science and engineering practices into the national science education standards in the US, teachers’ content knowledge goes beyond subject matter knowledge and into the realm of how scientists use practices for scientific inquiry. This study compares two approaches to constructing and validating two different versions of a survey that aims to measure the construct of teachers’ knowledge of models and modeling in science teaching. In the first version, a 24-item Likert scale survey containing content and pedagogical knowledge items was found to lack the ability to distinguish different knowledge levels for respondents, and validation through factor analysis indicated content and pedagogical knowledge items could not be separated. Findings from the validation results of the first survey influenced revisions to the second version of the survey, a 25-item multiple-choice instrument. The second survey employed a competence model framework for models and modeling for item specifications, and results from exploratory factor analysis revealed this approach to assessing the construct to be more appropriate. Recommendations for teacher assessment of science practices using competence models and points to consider in survey design, including norm-referenced or criterion-referenced tests, are discussed.
Development of the Emirati Child Adaptation Scale (ECAS) for Assessing the Behavioral Adaptation Skills of Children with and without Disabilities in the UAE
Authors: AlMuhairy Ousha , Efthymiou Efthymia , ElHoweris Hala , Alshathly Mahmoud , Sartawi Abdelaziz
Published: April 2023

Behavioral problems cause limitations in the social skills of children without disabilities and affect the functionality of children with disabilities. The Emirati child adaptation scale (ECAS) was developed in the UAE to provide reliable and valid information on the behavioral adaptation skills in children with and without intellectual disability (ID). The scale’s item pool consists of 651 items that identify behavioral adaptation deficits across the 10 domain–skill areas of communication, pre-academic, listening, social, self-care, self-orientation, motor skills, operational society, life, and health and safety. The scale was administered to 1542 children with disabilities and 920 children without disabilities. The scale is deemed essential, as it enables psychologists, special education teachers, health professionals, and researchers to implement a reliable psychometric tool of adaptive and behavioral functioning of 1–18-year-old individuals. The development and structure of the Emirati child adaptation scale is described and discussed.
Students’ Perception of Metaverses for Online Learning in Higher Education: Hype or Hope?
Authors: Rojas Elisa , Hülsmann Xenia , Estriegana Rosa , Rückert Frank , Garcia-Esteban Soraya
Published: April 2023

Online learning environments have been widely adopted, particularly during the COVID-19 pandemic. Today, their usage is envisioned for multiple teaching scenarios, including international courses involving students from different locations. However, as e-learning frameworks lack many advantages of physical interaction, diverse technologies to enhance the learning experience in this regard, including the use of metaverses, are currently in demand. In this study, we implemented an educational metaverse platform for engineering courses at four different European Universities from Poland, Portugal, Germany, and Spain, then had it quantitatively evaluated by students from these higher education institutions. The obtained results illustrate positive student perceptions of its ease of use, satisfaction and fun, and immersion, although their opinions with respect to learning experience and usefulness are less clear. The students seem to agree that interactivity remains insufficient in comparison with face-to-face teaching. For this reason, we provide an examination other related works and finalize this article by suggesting future research directions in the field.
Terahertz Dual-Band Dual-Polarization 3-Bit Coding Metasurface for Multiple Vortex Beams Generation
Authors: Tang Pengcheng , Zheng Xueqi , Ma Tianyu , Cheng Gong , Wu Genhao , Bao Xiue , Sun Houjun , Ding Jun , Si Liming
Published: April 2023

Terahertz technology and vortex beams have demonstrated powerful capabilities in enhancing the channel capacity of communication systems. This work proposes a design strategy of dual-band and dual-function 3-bit coding metasurface based on beam polarization characteristics. The unit cell of the metasurface is composed of two pattern structures, which has the ability to flexibly and independently control the reflection phases of incident plane wave at two frequency bands. The metasurface designed in this work is a combination of two patterns according to the addition operation and the convolution operation. The 3-bit coding metasurface generates two orbital angular momentum (OAM) beams with a deflection of 12.1° with modes l1=+1 and l2=−1 under the y-polarized incidence at 0.6 THz. Similarly, the designed metasurface produces two OAM beams with a deflection of 16.5° under the incidence of x-polarized wave at 0.9 THz, and the modes are l3=+1 and l4=−2. The full-wave simulation results agree well with the theoretical predictions, which could prove the correctness and effectiveness of the proposed method. The metasurface designed according to this method has potential applications in multiple-input multiple-output (MIMO) communication systems.
A New FPGA-Based Task Scheduler for Real-Time Systems
Authors: Kohútka Lukáš , Mach Ján
Published: April 2023

This research demonstrates a novel design of an FPGA-implemented task scheduler for real-time systems that supports both aperiodic and periodic tasks. The periodic tasks are automatically restarted once their period has expired without any need for software intervention. The proposed scheduler utilizes the Earliest-Deadline First (EDF) algorithm and is optimized for multi-core CPUs, capable of executing up to four threads simultaneously. The scheduler also provides support for task suspension, resumption, and enabling inter-task synchronization. The design is based on priority queues, which play a crucial role in decision making and time management. Thanks to the hardware acceleration of the scheduler and the hardware implementation of priority queues, it operates in only two clock cycles, regardless of the number of tasks in the system. The results of the FPGA synthesis, performed on an Intel FPGA device (Cyclone V family), are presented in the paper. The proposed solution was validated through a simplified version of the Universal Verification Methodology (UVM) with millions of test instructions and random deadline and period values.
The Development of an Autonomous Vehicle Training and Verification System for the Purpose of Teaching Experiments
Authors: Wu Chien-Chung , Wu Yu-Cheng , Liang Yu-Kai
Published: April 2023

To cultivate students’ skills in building autonomous vehicle neural network models and to reduce development costs, a system was developed for on-campus training and verification. The system includes (a) autonomous vehicles, (b) test tracks, (c) a data collection and training system, and (d) a test and scoring system. In this system, students can assemble the hardware of the vehicle, configure the software, and choose or modify the neural network model in class. They can then collect the necessary data for the model and train the model. Finally, the system’s test and scoring system can be used to test and verify the performance of the autonomous vehicle. The study found that vehicle turning is better controlled by a motor and steering mechanism, and the camera should be mounted in a high position and at the front of the vehicle to avoid interference with the steering mechanism. Additionally, the study revealed that the training and testing speeds of the autonomous vehicle are dependent on each other, and high-quality results cannot be obtained solely by training a model based on camera images.
Trusted Access Authentication Technology for Large-Scale Heterogeneous Terminals in a Vehicle Charging Network System
Authors: Zou Jing , Xin Peizhe , Xiao Zhihong , Fang Jiang , Li Ting , He Haonan , Zhuang Shangyuan , Liu Yinlong
Published: April 2023

A vehicle charging network system has to access large-scale heterogeneous terminals to collect charging pile status information, which may also give malicious terminals an opportunity to access. Though some general access authentication solutions aimed at only allowing trusted terminals have been proposed, they are difficult to work with in a vehicle charging network system. First, among various heterogeneous terminals with significant differences in computing capabilities, there are inevitably terminals that cannot support computations required for cryptography-based access authentication schemes. Second, though access authentication schemes based on device fingerprints are independent of terminal computing capabilities, their authentication performance is weak in robustness and high in overhead. Third, the access authentication delay is huge since the system cannot withstand heavy concurrent access requests from large-scale terminals. To address the above problems, we propose a reliable and lightweight trusted access authentication solution for terminals in the vehicle charging network system. By cloud, edge, and local servers cooperating to execute authentication tasks, our Cloud-Edge-End Collaborative architecture effectively alleviates the authentication delay caused by high concurrent requests. Each server in the architecture deploys our well-designed unified trusted access authentication (UATT) model based on device fingerprints. With ingenious data construction and the powerful swin-transformer network, the UATT model can provide robust and low-overhead authentication services for heterogeneous terminals. To minimize authentication latency, we further design an A2C-based authentication task scheduling scheme to decide which server executes the current task. Comprehensive experiments demonstrate our solution can authenticate terminals with an accuracy higher than 98% while reducing the required data packets by two orders of magnitude, and it can effectively reduce authentication latency.
SVD-SLAM: Stereo Visual SLAM Algorithm Based on Dynamic Feature Filtering for Autonomous Driving
Authors: Tian Liangyu , Yan Yunbing , Li Haoran
Published: April 2023

The conventional Simultaneous Localization and Mapping (SLAM) algorithm assumes a static world, which is easily influenced by dynamic elements of the surrounding environment. For high-precision localization in dynamic scenes, a dynamic SLAM algorithm combining instance segmentation and dynamic feature point filtering is proposed to address this issue. Initially, YOLACT-dyna, a one-stage instance segmentation network, was developed in order to perform instance segmentation on the input image, eliminate potential moving objects in the scene, and estimate the camera pose roughly. Second, based on the camera pose and polar constraint, the motion probability of each possible moving object was computed. Finally, the moving feature points were filtered out, and the static feature points were used to calculate the pose. The experimental results reveal that this algorithm’s recall rate in the dynamic regional KITTI dataset was 94.5% in public datasets. Accuracy is enhanced in environments with dynamic object location. At the same time, it can guarantee the positioning accuracy of a static scene, effectively enhancing the visual SLAM system’s position precision and robustness in a dynamic environment. It can meet the requirements of the automatic driving system’s real-time operation.
Unsupervised Image Enhancement Method Based on Attention Map Network Guidance and Attention Mechanism
Authors: Wu Mengfei , Lan Taiji , Xue Xucheng , Xu Xinwei
Published: April 2023

Low-light image enhancement is a crucial preprocessing task in complex vision tasks. It directly impacts object detection, image segmentation, and image recognition outcomes. In recent years, with the continuous development of deep learning techniques, an increasing number of image enhancement methods based on deep learning have emerged. However, due to the high cost of data collection and the limited content of supervised learning datasets, more and more scholars have shifted their focus to the field of unsupervised image enhancement. Unsupervised image enhancement methods do not require paired images of the same scene during the training process, which greatly reduces the threshold for network training. Nevertheless, current unsupervised methods still suffer from issues such as unstable enhancement effects and limited generalization ability. To address these problems, we propose an improved low-light image enhancement method. The proposed method employs the LSGAN as the training architecture and utilizes an attention map network to dynamically generate attention maps that best fit the network enhancement task, which can effectively improve the generalization ability and enhancement performance of the network. Additionally, we adopt an attention mechanism to enhance the subtle details of the image features. Regarding the network training, considering that the traditional convolutional neural network discriminator may not provide effective guidance to the generator in the early stages of training, we propose an improved discriminator structure. The experimental results demonstrate that our method can achieve good enhancement performance on different datasets and has practical value. Although our method has advantages in enhancing low-light images, it also has certain limitations, such as the network size not meeting the requirements for lightweight models and the potential for further improvement under extremely low-light conditions. We will strive to address these issues as comprehensively as possible in our future research.
Advances in Tangible and Embodied Interaction for Virtual and Augmented Reality
Authors: Cardoso Jorge C. S. , Perrotta André , Silva Paula Alexandra , Martins Pedro
Published: April 2023

Virtual Reality (VR) and Augmented Reality (AR) technologies have the potential to revolutionise the way we interact with digital content [...]
Thermoelectric Field Analysis of Trapezoidal Thermoelectric Generator Based on the Explicit Analytical Solution of Annular Thermoelectric Generator
Authors: Niu Wei , Cao Xiaoshan
Published: April 2023

The geometrical configuration is one of the main factors that affect the thermoelectric performance of a device. Research on the trapezoidal thermoelectric generator (TTEG) with varied cross section is mainly based on finite element simulation and experiment. In this paper, an explicit analytical solution of the maximum output power of annular thermoelectric generators (ATEG) is proposed, which has been proved to have high accuracy. Then, the maximum output power between ATEG and TTEG is compared. Results show that, for the appropriate geometric parameter δ, the relative error of maximum output power between explicit analytical ATEG and the simulated solution of TTEG can reach the order of 10−3. When the hot end is at the a side, the high temperature and θ is 510 K and 10°, respectively. For Bi2Te3 material and PbTe material, the relative error of maximum output power between the explicit analytical and simulated solution is 0.0261% and 0.074%, respectively. Under suitable working conditions, the explicit analytical results of ATEG can provide some reference for the performance optimization of TTEG.
Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions
Authors: Mansour Mohammad , Gamal Amal , Ahmed Ahmed I. , Said Lobna A. , Elbaz Abdelmoniem , Herencsar Norbert , Soltan Ahmed
Published: April 2023

The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.