bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantifying Visual Differences in Drought-Stressed Maize through Reflectance and Data-Driven Analysis
Authors: Banerjee Sanjana , Reynolds James , Taggart Matthew , Daniele Michael , Bozkurt Alper , Lobaton Edgar
Published: June 2024

Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.
Theoretical Study of the Multiferroic Properties of Ion-Doped CaBaCo4O7
Authors: Apostolova Iliana N. , Apostolov Angel T. , Wesselinowa Julia M.
Published: June 2024

Using a microscopic model and Green’s function theory, we investigated the magnetization, specific heat, and polarization properties of CaBaCo4O7 (CBCO), scrutinizing their variations with temperature, magnetic field strength, and doping effects. Our analysis revealed a conspicuous kink in the specific heat curve near the critical temperature (TC), indicative of a phase transition. Additionally, the observed increase in polarization, P with escalating magnetic field strength serves as compelling evidence for the multiferroic nature of CBCO. Substituting Co ions with Fe ions resulted in an augmentation of the CBCO magnetization, M, while doping with Zn, Mn, or Ni ions led to a decline. Similarly, doping CBCO with Y or Sr ions at the Ca site exhibited divergent effects on magnetization, M, with an increase in the former and a decrease in the latter case. This modulation of the magnetization, M, can be attributed to the varying strains induced by the doping ions, thereby altering the exchange interaction constants within the system. The polarization, P, increases by Ni, Mn, or Zn substitution on the kagome layer Co sites. It can be concluded that Ni, Mn, or Zn doping enhances the magnetoelectric effect of CBCO. Notably, our findings align qualitatively well with experimental observations, reinforcing the validity of our theoretical framework.
APTrans: Transformer-Based Multilayer Semantic and Locational Feature Integration for Efficient Text Classification
Authors: Ji Gaoyang , Chen Zengzhao , Liu Hai , Liu Tingting , Wang Bing
Published: June 2024

Text classification is not only a prerequisite for natural language processing work, such as sentiment analysis and natural language reasoning, but is also of great significance for screening massive amounts of information in daily life. However, the performance of classification algorithms is always affected due to the diversity of language expressions, inaccurate semantic information, colloquial information, and many other problems. We identify three clues in this study, namely, core relevance information, semantic location associations, and the mining characteristics of deep and shallow networks for different information, to cope with these challenges. Two key insights about the text are revealed based on these three clues: key information relationship and word group inline relationship. We propose a novel attention feature fusion network, Attention Pyramid Transformer (APTrans), which is capable of learning the core semantic and location information from sentences using the above-mentioned two key insights. Specially, a hierarchical feature fusion module, Feature Fusion Connection (FFCon), is proposed to merge the semantic features of higher layers with positional features of lower layers. Thereafter, a Transformer-based XLNet network is used as the backbone to initially extract the long dependencies from statements. Comprehensive experiments show that APTrans can achieve leading results on the THUCNews Chinese dataset, AG News, and TREC-QA English dataset, outperforming most excellent pre-trained models. Furthermore, extended experiments are carried out on a self-built Chinese dataset theme analysis of teachers’ classroom corpus. We also provide visualization work, further proving that APTrans has good potential in text classification work.
Emerging Industry for Promoting Human Performance and Health: Opportunities and Challenges
Authors: Yin Peng-Yeng
Published: June 2024

In the 21st century, with a highly developed economy and a diverse cultural society, it is not uncommon to see people suffer spiritual stress and physical pain in their lives [...]
Digital Product Passport Implementation Based on Multi-Blockchain Approach with Decentralized Identifier Provider
Authors: Hulea Mihai , Miron Radu , Muresan Vlad
Published: June 2024

This paper examines the implementation of a digital product passport (DPP) using Hyperledger Fabric technology to enhance product lifecycle management within the European Union’s circular economy action plan. This study addresses the need for detailed product information on materials, origin, usage, and end-of-life instructions to improve recycling practices and promote sustainable consumption. The approach integrates decentralized identifier (DID) technology for unique product identification using the cheqd.io platform with an enterprise tailored Hyperledger Fabric blockchain network for DPP data management, leveraging their strengths to enhance security and efficiency. This paper details the data model for the DPP, including entities like Product, Manufacturer, Supplier, and Material. Performance tests on the Hyperledger Fabric network demonstrate the system’s efficacy, focusing on CRUD operations and scalability. Future work will extend to the development of client applications and more comprehensive performance evaluations considering scalability and network expansion.
Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training
Authors: Nascimento Alexandre M. , Shimanuki Gabriel Kenji G. , Dias Luiz Alberto V.
Published: June 2024

As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models’ in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach’s applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort.
Fuzzy Testing Model Built on Confidence Interval of Process Capability Index CPMK
Authors: Lo Wei , Huang Tsun-Hung , Chen Kuen-Suan , Yu Chun-Min , Yang Chun-Ming
Published: June 2024

A variety of process capability indices are applied to the quantitative measurement of the potential and performance of processes in manufacturing. As it is easy to understand the formulae of these indices, this method is easy to apply. Furthermore, a process capability index is frequently utilized by a manufacturer to gauge the quality of a process. This index can be utilized by not only an internal process engineer to assess the quality of the process but also as a communication tool for an external sales department. When the manufacturing process deviates from the target value T, the process capability index CPMK can be quickly detected, which is conducive to the promotion of smart manufacturing. Therefore, this study applied the index CPMK as an evaluation tool for process quality. As noted by some studies, process capability indices have unknown parameters and therefore must be estimated from sample data. Additionally, numerous studies have addressed that it is essential for companies to establish a rapid response mechanism, as they wish to make decisions quickly when using a small sample size. Considering the small sample size, this study proposed a 100 (1 − α)% confidence interval for the process capability index CPMK based on suggestions from previous studies. Subsequently, this study built a fuzzy testing model on the 100 (1 − α)% confidence interval for the process capability index CPMK. This fuzzy testing model can help enterprises make decisions rapidly with a small sample size, meeting their expectation of having a rapid response mechanism.
A Selberg Trace Formula for GL3(Fp)∖GL3(Fq)/K
Authors: Aggarwal Daksh , Ghorbanpour Asghar , Khalkhali Masoud , Lu Jiyuan , Németh Balázs , Yu C Shijia
Published: June 2024

In this paper, we prove a discrete analog of the Selberg Trace Formula for the group GL3(Fq). By considering a cubic extension of the finite field Fq, we define an analog of the upper half-space and an action of GL3(Fq) on it. To compute the orbital sums, we explicitly identify the double coset spaces and fundamental domains in our upper half space. To understand the spectral side of the trace formula, we decompose the induced representation ρ=IndΓG1 for G=GL3(Fq) and Γ=GL3(Fp).
Oxidative Evolution of Different Model Rosé Wines Affected by Distinct Anthocyanin and Tannin Contents
Authors: Baris Federico , Castro Marin Antonio , Chinnici Fabio
Published: June 2024

The quality of rosé wines significantly depends on their phenolic composition, particularly tannins, anthocyanins, and their derivatives, which determine the perceived color of these products and their color evolution throughout the storage and shelf-life periods. This study investigated the impact of phenolic content on the oxidation and color evolution of five different model rosé wines obtained by blending a fixed amount of grape tannins with varying concentrations of oenocyanin to modulate their respective ratio and color intensity. The solutions were monitored for color and pigment changes promoted by oxidation in a Fenton-like environment. The findings revealed a potential correlation between the initial phenolic concentration and the different degrees of oxidation within each solution, resulting in significant variations in CIELAB data. Overall, all solutions exhibited a substantial decrease in redness, with losses ranging from 23% in the darkest solution to 43% in the lightest one compared to their initial levels. Additionally, their color profiles shifted toward yellow hues, up to triple the original value, indicating the degradation of the pigments responsible for the characteristic rosé color. Greater amounts of anthocyanins preserved higher Fe(II) concentrations over time, suggesting the antioxidant role of these compounds. The whole dataset also permitted the evaluation of the different oxidation susceptibilities of individual anthocyanins, among which derived pigments, such as vitisins, proved to be notably more stable than native pigments, particularly delphinidin and petunidin.
Hif3α Plays Key Roles in the Progression of Alzheimer’s Disease Caused by Circadian Rhythm Disruption through Regulating the m6A/KDM3A/TGF-β1 Axis
Authors: Li Xinrui , Han Zhengkun , Li Huiying
Published: June 2024

Disrupted circadian rhythms are associated with the onset of chronic diseases and impairments, including cancer, diabetes, and hypertension. However, whether circadian disruptions accelerate the progression of Alzheimer’s disease and the respective pathway remains unclear. In this study, we constructed animal models using male C57BL/6N and APP/PS1 mice. Irregular illumination during sleeping hours was administered to the mice in our intervention groups to consistently disrupt their circadian rhythms. The impact of the intervention was evaluated through body weight tracking, cerebral index determination, histopathological staining, and biochemical marker analysis. Transcriptomic sequencing identified critical genes, with the data subsequently validated using RNA m6A detection and site analysis. The evaluations revealed that circadian disruptions impaired normal weight gain, liver and kidney functions, neuronal cells, and overall brain function. Transcriptomic sequencing data revealed a trend of elevating expression of Hif3α mRNA in the intervention groups. Further analysis of specific gene sites revealed that m6A methylation of the Hif3α gene at m6A site 3632 primarily drove the observed variations in HIF3A protein expression in our model. Furthermore, the expression of proteins in PC12 cells, N2a cells, and mice brains validated that an increase in HIF3A expression decreased KDM3A and TGF-β1 protein expression. Our study reveals a hitherto unknown pathway through which the disruption of circadian rhythms, by triggering m6A methylation at m6A site 3632 in the Hif3α gene, leads to the initiation and acceleration of AD. These findings provide valuable insights and guidelines for treating AD patients and enhancing caregiving by professionals.
Flower Buds Confirmed in the Early Cretaceous of China
Authors: Huang Weijia , Wang Xin
Published: June 2024

The Yixian Formation (Lower Cretaceous) in China is famous worldwide for its fossils of early angiosperms, but there has been only one record of flower buds (Archaebuda lingyuanensis) hitherto, in which only the surface of the flower bud was documented while no internal details were known. Such a partial knowledge of flower buds hinders our understanding of the evolution of flowers, and this knowledge lacuna needs to be filled. Our new specimen was collected from an outcrop of the Yixian Formation (Barremian–Aptian, Lower Cretaceous) near Dawangzhangzi, Lingyuan, Liaoning, China. Our observations reveal a new fossil flower bud, Archaebuda cretaceae sp. nov., from the Lower Cretaceous of China. This new record of Archaebuda in the Yixian Formation not only confirms the truthful existence of the expected gynoecium (plus possible androecium) in a flower bud but also underscores the occurrence of typical flowers in the Early Cretaceous. This new information adds first-hand data to flower sexuality, pollination, and evolution.
IFN-γ-Preconditioned Human Gingival-Derived Mesenchymal Stromal Cells Inhibit Plasmacytoid Dendritic Cells via Adenosine
Authors: Ríos-Ríos William de Jesús , Sosa-Luis Sorely Adelina , Almaraz-Arreortua Alexia , Vargas-Benitez Patricia , Bernardino-Hernández Héctor Ulises , Vargas-Arzola Jaime , Hernández-Osorio Luis Alberto , Romero-Tlalolini María de los Ángeles , Aguilar-Ruiz Sergio Roberto , Torres-Aguilar Honorio
Published: June 2024

Plasmacytoid dendritic cells (pDCs) are vital players in antiviral immune responses because of their high levels of IFN-α secretion. However, this attribute has also implicated them as critical factors behind the immunopathogenesis of inflammatory diseases, and no currently available therapy can efficiently inhibit pDCs’ aberrant activation. Mesenchymal stromal cells (MSCs) possess stromal immunomodulatory functionality, regulating immune cell activation through several mechanisms, including the adenosinergic (CD39/CD73/adenosine) pathway. The IFN-γ preconditioning of bone marrow MSCs improves their inhibitory properties for therapy applications; however, isolating human gingival tissue-derived MSCs (hGMSCs) is more accessible. These cells have shown better immunomodulatory effects, yet the outcome of IFN-γ preconditioning and its impact on the adenosinergic pathway has not been evaluated. This study first validated the immunoregulatory properties of primary-cultured hGMSCs, and the results showed that IFN-γ preconditioning strengthens CD39/CD73 coexpression, adenosine production, and the regulatory properties of hGMSC, which were confirmed by describing for the first time their ability to reduce pDC activation and their IFN-α secretion and to increase the frequency of CD73+ pDC. In addition, when CD73′s enzymatic activity was neutralized in hGMSCs, adenosine production and the IFN-γ preconditioning effect were restrained. This evidence might be applied to design hGMSCs- and adenosine-based immunotherapeutic strategies for treating inflammatory disorders that are associated with pDC overactivation.
A Study on the Mechanism and Properties of a Self-Powered H2O2 Electrochemical Sensor Based on a Fuel Cell Configuration with FePc and Graphene Cathode Catalyst Materials
Authors: Zhang Yunong , Offenhäusser Andreas , Mourzina Yulia
Published: June 2024

Conventional electrochemical sensors use voltammetric and amperometric methods with external power supply and modulation systems, which hinder the flexibility and application of the sensors. To avoid the use of an external power system and to minimize the number of electrochemical cell components, a self-powered electrochemical sensor (SPES) for hydrogen peroxide was investigated here. Iron phthalocyanine, an enzyme mimetic material, and Ni were used as a cathode catalyst and an anode material, respectively. The properties of the iron phthalocyanine catalyst modified by graphene nanoplatelets (GNPs) were investigated. Open circuit potential tests demonstrated the feasibility of this system. The GNP-modulated interface helped to solve the problems of aggregation and poor conductivity of iron phthalocyanine and allowed for the achievement of the best analytical characteristics of the self-powered H2O2 sensor with a low detection limit of 0.6 µM and significantly higher sensitivity of 0.198 A/(M·cm2) due to the enhanced electrochemical properties. The SPES demonstrated the best performance at pH 3.0 compared to pH 7.4 and 12.0. The sensor characteristics under the control of external variable load resistances are discussed and the cell showed the highest power density of 65.9 μW/cm2 with a 20 kOhm resistor. The practical applicability of this method was verified by the determination of H2O2 in blood serum.
Implementation of an Automated Code Checking Algorithm Based on Site Analysis
Authors: Jeongmin Seong , Sangyun Shin
Published: June 2024

To date, BIM has been primarily utilized in cost and schedule management, an interference check between architectural and structural models and systems based on geometric data in the process of the construction life cycle. However, there is a lack of research that utilizes the information contained in the BIM model to review whether the proposed architectural model is appropriately designed in accordance with each country’s building regulations or building codes or that proposes a model optimized for laws and standards. ‘Building code checking’ is the step of reviewing whether a building designed based on the building codes is suitable for being constructed as a building. However, this process consumes significant time and money and leads to human errors due to the manual review process. This study included implementation of an algorithm based on the Korean building code. In this study, there was the process of selection of codes when architects interpret building codes in common and implementation based on the codes selected. Next, modeling based on DXF files from NGII (National Geographic Information Institute) was applied to the algorithm developed in this study. Last, it includes case studies that compare the outputs of the algorithm with the real buildings, which had received real code checking, to make sure the algorithm in this paper is working properly. The implementation of such an automated system has the potential to significantly improve the efficiency and effectiveness of the building design and construction process. It can help architects to quickly and accurately identify potential legal issues and provide alternative solutions that meet regulatory requirements. This, in turn, can lead to reduced project costs, improved quality of designs, and faster project delivery times.
Prevention of Brain Metastases: A New Frontier
Authors: Pellerino Alessia , Davidson Tara Marie , Bellur Shreyas S. , Ahluwalia Manmeet S. , Tawbi Hussein , Rudà Roberta , Soffietti Riccardo
Published: June 2024

This review discusses the topic of prevention of brain metastases from the most frequent solid tumor types, i.e., lung cancer, breast cancer and melanoma. Within each tumor type, the risk of brain metastasis is related to disease status and molecular subtype (i.e., EGFR-mutant non-small cell lung cancer, HER2-positive and triple-negative breast cancer, BRAF and NRAF-mutant melanoma). Prophylactic cranial irradiation is the standard of care in patients in small cell lung cancer responsive to chemotherapy but at the price of late neurocognitive decline. More recently, several molecular agents with the capability to target molecular alterations driving tumor growth have proven as effective in the prevention of secondary relapse into the brain in clinical trials. This is the case for EGFR-mutant or ALK-rearranged non-small cell lung cancer inhibitors, tucatinib and trastuzumab–deruxtecan for HER2-positive breast cancer and BRAF inhibitors for melanoma. The need for screening with an MRI in asymptomatic patients at risk of brain metastases is emphasized.
Site-Specific Response and Resistance Patterns in Patients with Advanced Non-Small-Cell Lung Cancer Treated with First-Line Systemic Therapy
Authors: Brown Lauren Julia , Ahn Julie , Gao Bo , Gee Harriet , Nagrial Adnan , Hau Eric , Silva Inês Pires da
Published: June 2024

Patients with advanced NSCLC have heterogenous responses to immune checkpoint inhibitors (ICIs) with or without chemotherapy. In NSCLC, the impact of the distribution of metastatic sites and the response to systemic therapy combinations remain poorly understood. In a retrospective cohort study of patients with unresectable stage III/IV NSCLC who received first-line systemic therapy, we sought to assess the association between the site of metastases with patterns of response and progression. Data regarding demographics, tumour characteristics (including site, size, and volume of metastases), treatment, and outcomes were examined at two cancer care centres. The endpoints included organ site-specific response rate, objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Two-hundred and eighty-five patients were included in the analysis. In a multivariate analysis, patients with bone metastases had a reduced ORR, PFS, and OS. Primary resistance was also more likely in patients with bone metastases. Patients with bone or liver metastases had a shorter OS when receiving ICIs with or without chemotherapy, but not with chemotherapy alone, suggesting an immunological basis for therapeutic resistance. A directed assessment of the tumour microenvironment in these locations and a deeper understanding of the drivers of organ-specific resistance to immunotherapy are critical to optimise novel combination therapies and sequencing in these patients.
A Novel CRISPR-Cas9 Strategy to Target DYSTROPHIN Mutations Downstream of Exon 44 in Patient-Specific DMD iPSCs
Authors: Dhoke Neha R. , Kim Hyunkee , Azzag Karim , Crist Sarah B. , Kiley James , Perlingeiro Rita C. R.
Published: June 2024

Mutations in the DMD gene cause fatal Duchenne Muscular Dystrophy (DMD). An attractive therapeutic approach is autologous cell transplantation utilizing myogenic progenitors derived from induced pluripotent stem cells (iPSCs). Given that a significant number of DMD mutations occur between exons 45 and 55, we developed a gene knock-in approach to correct any mutations downstream of exon 44. We applied this approach to two DMD patient-specific iPSC lines carrying mutations in exons 45 and 51 and confirmed mini-DYSTROPHIN (mini-DYS) protein expression in corrected myotubes by western blot and immunofluorescence staining. Transplantation of gene-edited DMD iPSC-derived myogenic progenitors into NSG/mdx4Cv mice produced donor-derived myofibers, as shown by the dual expression of human DYSTROPHIN and LAMIN A/C. These findings further provide proof-of-concept for the use of programmable nucleases for the development of autologous iPSC-based therapy for muscular dystrophies.
Primary Ciliary Dyskinesia: A Clinical Review
Authors: Despotes Katherine A. , Zariwala Maimoona A. , Davis Stephanie D. , Ferkol Thomas W.
Published: June 2024

Primary ciliary dyskinesia (PCD) is a rare, genetically heterogeneous, motile ciliopathy, characterized by neonatal respiratory distress, recurrent upper and lower respiratory tract infections, subfertility, and laterality defects. Diagnosis relies on a combination of tests for confirmation, including nasal nitric oxide (nNO) measurements, high-speed videomicroscopy analysis (HSVMA), immunofluorescent staining, axonemal ultrastructure analysis via transmission electron microscopy (TEM), and genetic testing. Notably, there is no single gold standard confirmatory or exclusionary test. Currently, 54 causative genes involved in cilia assembly, structure, and function have been linked to PCD; this rare disease has a spectrum of clinical manifestations and emerging genotype–phenotype relationships. In this review, we provide an overview of the structure and function of motile cilia, the emerging genetics and pathophysiology of this rare disease, as well as clinical features associated with motile ciliopathies, novel diagnostic tools, and updates on genotype–phenotype relationships in PCD.
The Relationship between Cyber Violence and Cyber Sex Crimes: Understanding the Perception of Cyber Sex Crimes as Systemic Issues
Authors: Lee Eugene , Lee Hye Eun
Published: June 2024

This study examines the relationship between cyber violence and cyber sex crimes, specifically focusing on these crimes as systemic issues among adolescents. The research highlights the severe impact of cyber sex crimes, characterized by the non-consensual sharing of sexually explicit content. It examines various factors that may contribute to witnessing cyber sex crimes, including exposure to violent online content, personal experiences of cyber violence (either as a victim or perpetrator), and the role of parental and teacher interventions. Utilizing data from a nationwide survey conducted by the Korea Communications Commission, the study analyzes responses from 9016 adolescents in 2021 and 9693 in 2022. This analysis reveals significant predictors of witnessing cyber sex crimes and examines how perceptions of cyber violence and interventions of authoritative figures may influence adolescents’ perception of cyber sex crimes as either systemic or individual issues. With females disproportionately affected, the findings underscore a gendered aspect of cyber violence. Furthermore, these insights suggest that perceiving cyber violence as a serious issue leads to viewing cyber sex crimes as systemic problems necessitating societal intervention. The study advocates for enhanced digital literacy education and systemic changes to protect adolescents from the widespread threats of cyber violence and sex crimes.
Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning—A Two-Year Follow-Up Cohort Study
Authors: Daskareh Mahyar , Vakilpour Azin , Barzegar-Golmoghani Erfan , Esmaeilian Saeid , Gilanchi Samira , Ezzati Fatemeh , Alikhani Majid , Rahmanipour Elham , Amini Niloofar , Ghorbani Mohammad , Pezeshk Parham
Published: June 2024

Background: The early diagnosis and treatment of rheumatoid arthritis (RA) are essential to prevent joint damage and enhance patient outcomes. Diagnosing RA in its early stages is challenging due to the nonspecific and variable clinical signs and symptoms. Our study aimed to identify the most predictive features of hand ultrasound (US) for RA development and assess the performance of machine learning models in diagnosing preclinical RA. Methods: We conducted a prospective cohort study with 326 adults who had experienced hand joint pain for less than 12 months and no clinical arthritis. We assessed the participants clinically and via hand US at baseline and followed them for 24 months. Clinical progression to RA was defined according to the ACR/EULAR criteria. Regression modeling and machine learning approaches were used to analyze the predictive US features. Results: Of the 326 participants (45.10 ± 11.37 years/83% female), 123 (37.7%) developed clinical RA during follow-up. At baseline, 84.6% of the progressors had US synovitis, whereas 16.3% of the non-progressors did (p < 0.0001). Only 5.7% of the progressors had positive PD. Multivariate analysis revealed that the radiocarpal synovial thickness (OR = 39.8), PIP/MCP synovitis (OR = 68 and 39), and wrist effusion (OR = 12.56) on US significantly increased the odds of developing RA. ML confirmed these US features, along with the RF and anti-CCP levels, as the most important predictors of RA. Conclusions: Hand US can identify preclinical synovitis and determine the RA risk. The radiocarpal synovial thickness, PIP/MCP synovitis, wrist effusion, and RF and anti-CCP levels are associated with RA development.
Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs
Authors: Bettinger Hubert , Lenczner Gregory , Guigui Jean , Rotenberg Luc , Zerbib Elie , Attia Alexandre , Vidal Julien , Beaumel Pauline
Published: June 2024

The purpose of the study was to assess the performance of readers in diagnosing thoracic anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based AI tool (Rayvolve) and to evaluate the standalone performance of Rayvolve in detecting thoracic pathologies on CXRs. This retrospective multicentric study was conducted in two phases. In phase 1, nine readers independently reviewed 900 CXRs from imaging group A and identified thoracic abnormalities with and without AI assistance. A consensus from three radiologists served as the ground truth. In phase 2, the standalone performance of Rayvolve was evaluated on 1500 CXRs from imaging group B. The average values of AUC across the readers significantly increased by 15.94%, with AI-assisted reading compared to unaided reading (0.88 ± 0.01 vs. 0.759 ± 0.07, p < 0.001). The time taken to read the CXRs decreased significantly, by 35.81% with AI assistance. The average values of sensitivity and specificity across the readers increased significantly by 11.44% and 2.95% with AI-assisted reading compared to unaided reading (0.857 ± 0.02 vs. 0.769 ± 0.02 and 0.974 ± 0.01 vs. 0.946 ± 0.01, p < 0.001). From the standalone perspective, the AI model achieved an average sensitivity, specificity, PPV, and NPV of 0.964, 0.844, 0.757, and 0.9798. The speed and performance of the readers improved significantly with AI assistance.
Coverage Path Planning with Adaptive Hyperbolic Grid for Step-Stare Imaging System
Authors: Zhao Jiaxin
Published: June 2024

Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the irregular shape of the sensor’s projection caused by the step-stare rotational motion. To address this problem, this paper proposes an efficient, seamless CPP method with an adaptive hyperbolic grid. First, we convert the coverage problem in Euclidean space to a tiling problem in spherical space. A spherical approximate tiling method based on a zonal isosceles trapezoid is developed to construct a seamless hyperbolic grid. Then, we present a dual-caliper optimization algorithm to further compress the grid and improve the coverage efficiency. Finally, both boustrophedon and branch-and-bound approaches are utilized to generate rotation paths for different scanning scenarios. Experiments were conducted on a custom dataset consisting of 800 diverse geometric regions (including 2 geometry types and 40 samples for 10 groups). The proposed method demonstrates comparable performance of closed-form path length relative to that of a heuristic optimization method while significantly improving real-time capabilities by a minimum factor of 2464. Furthermore, in comparison to traditional rule-based methods, our approach has been shown to reduce the rotational path length by at least 27.29% and 16.71% in circle and convex polygon groups, respectively, indicating a significant improvement in planning efficiency.
Predictive Characteristics of Childhood Emergency Injury Presentations in a Non-Urban Area of Victoria, Australia
Authors: Peck Blake , Terry Daniel , Kloot Kate
Published: June 2024

Injury is the leading cause of death and disability amongst those between 0 and 16 years of age. Routinely associated with a social gradient of health, childhood injury is reportedly higher in rural and regional areas. A retrospective cross-sectional study collected deidentified emergency injury presentation data from nine health services in regional Victoria for children aged 0–14 years between 2017 and 2022. Emergency injury presentation incidence rates and predictor variables were analysed using hierarchical multiple regression. Significance was determined at p < 0.05. Amongst the 15,884 emergency injury presentations, 35.2% (n = 5597) occurred at health services that did not routinely report data to government datasets. Although some findings are consistent with current research, this study highlights specific factors that are predictive of injury amongst 0–4-year-old children who require deeper investigation, including general practitioner access and health literacy-related elements such as preschool health assessment attendance rates, parent education levels, and overseas birth. These findings provide more comprehensive insights for healthcare workers and policymakers as they seek to support people with injuries and accurately address health inequities.
Oil Volatility Uncertainty: Impact on Fundamental Macroeconomics and the Stock Index
Authors: Aladwani Jassim
Published: June 2024

This study utilized both single-regime GARCH and double-regime GARCH models to investigate oil price volatility, Spanish macroeconomic factors, and stock prices during major crises such as geopolitical conflicts, the global financial crisis (GFC), and COVID-19, covering the period from Q2-1995 to Q4-2023. Additionally, the impact of crude oil price volatility on these factors was examined. The empirical results confirmed the presence of the leverage effect and identified multiple volatility switches associated with remarkable events like the GFC, the European debt crisis, the COVID-19 pandemic, and the Russian war. ARDL model analysis revealed a statistically significant positive relationship between oil prices and both unemployment and inflation rates in the long term, while other factors showed a negative correlation.
Design and Psychometric Properties of the Student Perception of Teacher Care Scale in University Students
Authors: Dávila-Villavicencio Roussel Dulio , Salinas Arias Saulo Andrés , Casildo Bedón Ana Fabri , Perez-Brenis Jose Luis , Echabaudes-Ilizarbe Robert Ivan , Cunza-Aranzábal Denis Frank
Published: June 2024

Teacher care in the university setting is crucial for the academic, emotional, and social development of students, encompassing moral virtues such as compassion and professional competence. The students’ perception of teacher care directly relates to their well-being and active participation in learning. Despite the recognized importance of teacher care, there is a lack of adequate instruments to measure it, especially in specific cultural and educational contexts like Peru. The aim of this research is to develop and assess the psychometric properties of a scale measuring student perception of teacher care in the Peruvian university context, to provide a valid and reliable tool for future research and educational practices. An instrumental study was conducted with 910 university students aged between 18 and 50 years (M = 20.2, SD = 3.45). The Student Perception of Teacher Care Scale (SPTCS) was used, divided into five dimensions (Compassion, Competence, Confidentiality, Trust, and Awareness). Exploratory (EFA) and confirmatory (CFA) factor analyses were conducted to evaluate the scale’s internal structure, along with reliability analyses and measurement invariance across genders. The EFA identified a four-factor structure reflecting the dimensions of Confidentiality and Trust, Competence, Awareness, and Compassion. The CFA confirmed this structure with good fit indices for four-factor models, which was then adjusted to a five-factor model, one of which is a second-order factor (χ2 = 720.727, df = 369, CFI = 0.951, TLI = 0.946, RMSEA = 0.046, and SRMR = 0.032). Internal reliability was high (α and ω > 0.9 for all factors). Measurement invariance was established across genders, allowing for valid comparisons between men and women. It is concluded that the Student Perception of Teacher Care Scale proves to be a valid and reliable instrument for measuring teacher care in the Peruvian university context. The results emphasize the multidimensionality of teacher care and its significance for the educational environment and student experience. This study contributes to educational research and teaching practice, offering a means to assess and improve caring relationships in higher education.
A Review of the Antenna Field Regions
Authors: Capozzoli Amedeo , Curcio Claudio , D’Agostino Francesco , Liseno Angelo
Published: June 2024

We review the field regions and their boundaries around an electromagnetic source. We consider the cases of sources whose dimensions are comparable or larger than the wavelength, of planar sources/apertures, and of sources whose dimensions are small with respect to the wavelength and the criteria involving the strength of the reactive components of the electromagnetic field with respect to the radiative ones. The Fraunhofer and the Fresnel Regions are detailed, along with references to the paraxial approximation for planar apertures. The near-field and intermediate regions are also discussed. We review the standard boundaries between the regions. However, the standard boundaries are not clearly marked, nor are the regions uniquely defined. Accordingly, we also discuss different criteria that have been proposed during the years, which depend on the application and typically rely on numerical arguments, but are not necessarily universally accepted.
Ergodic Rate Analysis for Full-Duplex and Half-Duplex Networks with Energy Harvesting
Authors: Zhong Bin , Chen Liang , Zhang Zhongshan
Published: June 2024

Considering energy harvesting, the ergodic data rates for both in band full-duplex (FD) and half-duplex (HD) wireless communications were studied. The analytic expressions of downlink and uplink ergodic rates for the proposed system were first derived with independent and identically distributed (i.i.d.) Rayleigh fading link. It was revealed that the uplink data rate can be improved by decreasing the downlink data rate. Furthermore, the uplink/downlink data rates are also shown to be influenced by some significance parameters, for example, the power split parameter and signal-to-noise ratio (SNR) (i.e., PS/σ2) of each link. Additionally, unlike the HD, the proposed FD node is capable of harvesting energy during the communication process; however, this is at the cost of performance loss induced by the residual self-interference (RSI), which is caused by the essence of simultaneous uplink and downlink transmissions in a single frequency band.
A Compact C-Band Multiple-Input Multiple-Output Circular Microstrip Patch Antenna Array with Octagonal Slotted Ground Plane and Neutralization Line for Improved Port Isolation in 5G Handheld Devices
Authors: Khan Asad Ali , Wang Zhenyong , Li Dezhi , Ahmed Ali
Published: June 2024

In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom layer is a ground plane with four etched octagonal slots. Each resonating element is fed by 50-ohm sub-miniature connectors. To mitigate the detrimental effects of mutual coupling of ports and enhance overall isolation between the adjacent microstrip-fed circular patch elements, a neutralization line is strategically implemented between the feed lines of the antenna array. The design configuration involves two elements at each vertex of the printed circuit board (PCB). The overall dimensions of the PCB are 150 × 75 mm2. Each slot and its corresponding radiating elements exhibit linear dual polarization and diverse radiation patterns. The proposed antenna design achieves the required operating bandwidth of more than 1000 MHz spanning from 3 to 4.2 GHz, effectively covering all the upper C-band frequency range of 3.3 GHz to 4.2 GHz allocated for 5G n77 and n78 frequency range 1 (FR1). Required port isolation and lower envelop correlation coefficient (ECC) are achieved for the band of interest. The proposed design gives a peak gain of up to 4 dB for the said band. In addition to these results, degradation in the performance of the antenna array is also investigated during different operating modes of the handheld device. Measured results from the fabricated unit cell and whole array also have a good match with simulated results. On the whole, the proposed antenna possesses the potential to be used in 5G and the open radio access network (ORAN) compliant handheld devices.
Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications
Authors: Issa Razan , Clerc Guy , Hologne-Carpentier Malorie , Michaud Ryan , Lorca Eric , Magnette Christophe , Messadi Anes
Published: June 2024

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.
Virtual Inertia Control for Power Electronics-Integrated Power Systems: Challenges and Prospects
Authors: Shobug Md Asaduzzaman , Chowdhury Nafis Ahmed , Hossain Md Alamgir , Sanjari Mohammad J. , Lu Junwei , Yang Fuwen
Published: June 2024

In modern power systems, conventional energy production units are being replaced by clean and environmentally friendly renewable energy resources (RESs). Integrating RESs into power systems presents numerous challenges, notably the need for enhanced grid stability and reliability. RES-dominated power systems fail to meet sufficient demand due to insufficient inertia responses. To address this issue, various virtual inertia emulation techniques are proposed to bolster power system stability amidst the increased integration of renewable energy sources into the grid. This review article explores state-of-the-art virtual inertia support strategies tailored to accommodate the increased penetration of RESs. Beginning with an overview of this study, it explores the existing virtual inertia techniques and investigates the various methodologies, including control algorithms, parameters, configurations, key contributions, sources, controllers, and simulation platforms. The promising virtual inertia control strategies are categorised based on the techniques used in their control algorithms and their applications. Furthermore, this review explains evolving research trends and identifies promising avenues for future investigations. Emphasis is placed on addressing key challenges such as dynamic response characteristics, scalability, and interoperability with conventional grid assets. The initial database search reveals 1529 publications. Finally, 106 articles were selected for this study, adding 6 articles manually for the review analysis. By synthesising current knowledge and outlining prospective research directions, this review aims to facilitate the current state of research paths concerning virtual inertia control techniques, along with the categorisation and analysis of these approaches, and showcases a comprehensive understanding of the research domain, which is essential for the sustainable integration of renewable energy into modern power systems via power electronic interface.
Optimization of Dropwise Condensation of Steam over Hybrid Hydrophobic–Hydrophilic Surfaces via Enhanced Statistically Based Heat Transfer Modelization
Authors: Croce Giulio , Suzzi Nicola
Published: June 2024

Steam condensation over a hybrid hydrophobic–hydrophilic surface is modeled via simplified heat transfer modelization. Filmwise condensation is assumed over the hydrophilic region. The standard film model is improved, accounting for the liquid flow rate crossing the hydrophobic–hydrophilic boundaries. A threshold for flooding occurrence is also presented. Dropwise condensation is assumed over the hydrophobic region. Compared to the heat transfer models in the literature, based on the statistical drop size distribution, a novel correlation is used for the size distribution of small droplets. The correlations of both the liquid flow rate crossing the hydrophobic–hydrophilic boundary and the size distribution of small drops are derived via Lagrangian simulations, using an in-house code previously developed and validated by the authors. The heat transfer model is validated with experimental data in the literature involving a hybrid surface, composed by alternate vertical hydrophobic–hydrophilic stripes. Then, the optimization of the hybrid surface geometry is performed in terms of hydrophobic width and hydrophilic width, with the aim of enhancing the heat flux.