bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating the Impact of Maternal Lipid Profiles on Fetal Cardiac Function at Mid-Gestation: An Observational Study
Authors: Belovan Biliana , Popa Zoran Laurentiu , Citu Cosmin , Citu Ioana Mihaela , Sas Ioan , Ratiu Adrian
Published: November 2024

Background: Maternal dyslipidemia during pregnancy may influence fetal cardiac development and function, potentially predisposing offspring to cardiovascular diseases later in life. This study aims to evaluate the relationship between maternal lipid profiles and fetal cardiac function at mid-gestation, utilizing detailed echocardiographic assessments. Methods: In this prospective cohort study conducted at the Obstetrics and Gynecology Clinic of the Timișoara Municipal Emergency Hospital, 19 pregnant women aged 27–40 years were recruited and divided into two groups based on their triglyceride levels: Group A (triglycerides ≤ 150 mg/dL, n = 48) and Group B (triglycerides > 150 mg/dL, n = 28). Maternal demographic data and lipid profiles were recorded. Fetal echocardiographic measurements, including global longitudinal strain and ventricular function parameters, were obtained between 20 and 24 weeks of gestation. Statistical analyses, including subgroup comparisons, correlations, and regression analyses, were performed. Results: Maternal BMI was significantly higher in Group B compared to Group A (31.94 ± 2.80 vs. 27.01 ± 2.40 kg/m2, p < 0.001). Group B showed higher mean triglyceride levels (163.43 ± 11.34 mg/dL) compared to Group A (131.42 ± 10.57 mg/dL, p < 0.001). Fetal echocardiographic measurements indicated reduced global longitudinal strain in fetuses of Group B mothers (LV strain: −19.86% ± 6.83% vs. −26.14% ± 5.92%, p = 0.017). Significant correlations were found between maternal triglyceride levels and fetal LV strain (r = 0.536, p = 0.019). Regression analysis identified maternal triglyceride levels and BMI as significant predictors of reduced fetal LV strain (β = 0.45, p = 0.021 and β = 0.39, p = 0.038, respectively). Conclusions: Elevated maternal triglyceride levels, LDL cholesterol, and BMI are associated with altered fetal cardiac function parameters at mid-gestation, suggesting that maternal lipid profiles may impact fetal cardiac development. These findings underscore the importance of monitoring lipid levels during pregnancy and suggest potential benefits of managing dyslipidemia to improve fetal cardiac outcomes. However, the study included only a small sample; therefore, the study needs to be continued with a larger group.
HfO2/Al2O3 Multilayer on Parabolic Cylinder Substrate to Monochromatize and Collimate Divergent X-Ray Beam
Authors: Zhao Huibin , Yuan Tianyu , Li Yanli , Kong Xiangdong , Sun Xuepeng , Han Li
Published: November 2024

A monochromatic parallel X-ray beam is essential for some X-ray applications and a multilayer on a parabolic cylinder substrate is a good choice to obtain it. In this work, an HfO2/Al2O3 multilayer with a period of 3.80 nm and a bilayer number of 60 is grown on a smooth, flat Si substrate via atomic layer deposition for a monochromatizing Cu kα 0.154 nm X-ray and the first-order peak of the X-ray reflectivity is about 45%. The multilayer-coated Si substrate is then glued on a pre-made stainless steel body with a designed parabolic cylinder profile to convert divergent X-rays from a laboratory X-ray source into a parallel beam. The surface profiles before and after gluing Si on the stainless steel body are almost the same and basically consistent with the designed one. The results show that a monochromatic parallel X-ray (0.154 nm) beam can be acquired by an HfO2/Al2O3 multilayer on a parabolic cylinder substrate and the divergence angle of the reflected beam is 0.67 mrad.
Multiple Behavioral Conditions of the Forward Exchange Rates and Stock Market Return in the South Asian Stock Markets During COVID-19: A Novel MT-QARDL Approach
Authors: Tabash Mosab I. , Ahmed Adel , Issa Suzan Sameer , Mansour Marwan , Varma Manishkumar , Al-Absy Mujeeb Saif Mohsen
Published: November 2024

This study examines the short- and long-term effects of multiple quantiles of forward exchange rate premiums (FERPs) and COVID-19 cases on the quantiles of stock market returns (SMRs). We extend the Quantile Autoregressive Distributive Lag (QARDL) model, and the Multiple Threshold Non-linear Autoregressive Distributive Lag (NARDL) model propose a new Multiple Threshold Quantile Autoregressive Distributive Lag (MT-QARDL) approach. Unlike MT-NARDL, QARDL, and NARDL, the MT-QARDL model, which integrates the MT-NARDL model and the quantile regression methodology, captures both short- and long-term locational and sign-based asymmetries. For instance, at lower quantiles for Indian and Sri Lankan SMRs, bearish FERP exerts a positive influence, while bullish FERP has a negative effect during COVID-19. Conversely, bullish FERP negatively affects lower quantiles of SMRs of Bangladesh, India, and Sri Lanka, whereas bearish FERP either yields an opposite effect or remain statistically insignificant during COVID-19. The findings underscore long-term sign-based asymmetries due to the differential bearish and bullish FERP impact during COVID-19. However, in the long term, location-based asymmetries also existed as bullish FERP negative influence the SMRs of India, Bangladesh and Sri Lanka at higher quantiles but SMRs at lower quantiles insignificantly respond to the bullish FERP fluctuations during COVID-19.
Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification
Authors: Nguyen Hai-Long , Pham Van Su , Le Hai-Chau
Published: November 2024

The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.
Morphological Studies and Phylogenetic Analyses Unveil Two Notable New Species of Russula Subg. Heterophyllinae from China
Authors: Chen Bin , Liang Junfeng , Yu Fei
Published: November 2024

Russula, a prominent genus of ectomycorrhizal fungi, is notably abundant and diverse in China. We present here the findings from studies on various Chinese Russula collections. Two notable species within subg. Heterophyllinae, namely Russula leucoviridis and R. subswatica, were described and illustrated based on comprehensive morphological characteristics and molecular evidence. Morphologically, Russula leucoviridis is characterized by its pastel-green to green pileus center with light-yellow spots, a white to greenish-white pileus margin, and a cuticle that cracks and breaks into small green patches after maturation, whereas R. subswatica is distinguished by an infundibuliform pileus with a deeply depressed center after maturation, a light-orange to grayish-orange pileus center, and a yellowish-white to light-orange margin with a purplish to purplish-black hue, and a white to yellowish-white stipe that exhibits a purplish hue at the junction with the pileus. The phylogenetic analyses were conducted using a combined dataset of ITS, nrLSU, RPB2, and mtSSU. Relying on both morphological characteristics and multigene phylogeny, the former species is classified within subsect. Virescentinae, while the latter is affiliated with subsect. Griseinae. In this study, we provide new scientific data that enhance the comprehension of species diversity within the subgenus Heterophyllinae in China.
Clinical and Biological Markers of Frailty Syndrome in Patients Undergoing Elective Percutaneous Coronary Intervention
Authors: Krivoshapova Kristina , Tsygankova Daria , Neeshpapa Anastasiya , Kareeva Anastasia , Kokov Alexandr , Bazdyrev Evgeny , Karetnikova Victoria , Barbarash Olga
Published: November 2024

Background: The aim of this study was to analyze the prevalence of prefrailty and frailty syndrome (FS) in patients with coronary artery disease (CAD), and the clinical and biological characteristics of frail patients undergoing elective percutaneous coronary intervention (PCI). Material and Methods: The study included 78 patients with CAD who were admitted to the clinic to undergo PCI. To detect prefrailty and FS in patients, we used a short physical performance test battery (10–12 points—no FS, 8–9 points—prefrailty, 7 or fewer points—FS). We used the RayBio® Human ELISA Kit (Norcross, GA, USA), a highly sensitive and highly specific enzyme-linked immunosorbent assay, to determine the concentration of biological markers of inflammation (IL-6, IL-10, IL-13, IL-15, TNF-α) and bone, muscle, and fat remodeling (leptin, calcitonin, osteoprotegerin, osteocalcin, myostatin) in the serum of patients with coronary artery disease before planned PCI. Results: Taking into account the test battery score, the prevalence of FS in patients with CAD before elective PCI was 24.4%, the prevalence of prefrailty was 33.3%. According to the results of the study, older women with type 2 diabetes in their history were significantly more likely to be frail. Studying a wide range of biological markers of inflammation and musculoskeletal and fat remodeling, we noted lower levels of calcitonin (2.60 [1.50; 5.85] pg/mL, p = 0.018) and osteoprotegerin (0.80 [0.60; 1.20] ng/mL, p = 0.025) in the serum of frail patients with CAD. Later we confirmed the results by correlation analysis. Moreover, we found an association between FS and higher serum leptin levels in patients with CAD before elective PCI. Conclusion: The results of the study confirm the high prevalence of prefrailty (33.3%) and FS (24.4%) in patients with CAD. Older women with type 2 diabetes in their history were significantly more likely to be frail. At the same time, the presence of FS is associated with lower levels of calcitonin and osteoprotegerin, and higher levels of leptin in the serum of frail patients before elective PCI.
PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data
Authors: Xu Jia , Vaeggemose Michael , Schulte Rolf F. , Yang Baolian , Lee Chu-Yu , Laustsen Christoffer , Magnotta Vincent A.
Published: November 2024

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.
Impact of Neoadjuvant Therapy on PD-L1 Expression in Triple-Negative Breast Cancer and Correlation with Clinicopathological Factors
Authors: Ilieva Nevena , Pencheva Mina , Hadzhiev Hristo , Tashkova Desislava , Daskalova Elena , Georgiev Petar , Genova Sylvia
Published: November 2024

Background: This study aims to deliver more insights on the impact of neoadjuvant treatment on Pd-L1 expression and to evaluate its correlation with clinicopathological factors. Methods: We reviewed 88 TNBC cases for the period 2021–2023. Data on age, tumor size, stage, and treatment were collected. Histological slides were assessed for subtype, grade, and TILs. A total of 48 received neoadjuvant treatment. HER2 and Ki67 were evaluated via immunohistochemistry. PD-L1 expression was tested on primary and residual tumors. Statistical analysis was performed using IBM SPSS (p < 0.05). Results: In this study, PD-L1 positive expression was found in 44.3% of primary tumors, with 52.9% of initially positive cases losing expression post-treatment. TILs were significantly higher in PD-L1-positive tumors (mean 41.79% vs. 27.55%, p = 0.001). A notable correlation was found between PD-L1 expression and Ki-67 proliferation index, with PD-L1-positive tumors having a median Ki-67 of 64.49 compared to 52.86 in negative cases (p = 0.015). Neoadjuvant immunotherapy led to a lower mean residual cancer burden (0.95 vs. 2.55, p = 0.002) compared to chemotherapy alone. Higher Ki-67 levels (≥50%) were associated with better treatment outcomes, showing a mean RCB score of 1.60 versus 3.16 for lower levels (p = 0.022). HER2-negative cases had a higher prevalence of favorable pathological response (54.5%) compared to HER2-low tumors (25%, p = 0.048), because of the strong correlation to high proliferative index. Conclusions: In conclusion, PD-L1 expression in TNBC shows significant discordance post-treatment, highlighting the need for routine testing and further research on predictive biomarkers.
Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics
Authors: Sufian Md Abu , Alsadder Lujain , Hamzi Wahiba , Zaman Sadia , Sagar A. S. M. Sharifuzzaman , Hamzi Boumediene
Published: November 2024

Background/Objectives: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. Methods: The research utilised quantitative 3D/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project’s (CAP) open challenges to explore AI-driven methodologies for mitigating algorithmic bias in cardiac imaging. The SCIR model, known for its robustness, was adapted with the Capuchin algorithm, adversarial debiasing, Fairlearn, and post-processing with equalised odds. The robustness of the SCIR model was further demonstrated in the fairness evaluation metrics, which included demographic parity, equal opportunity difference (0.037), equalised odds difference (0.026), disparate impact (1.081), and Theil Index (0.249). For interpretability, YOLOv5, Mask R-CNN, and ResNet18 were implemented with LIME and SHAP. Bias mitigation improved disparate impact (0.80 to 0.95), reduced equal opportunity difference (0.20 to 0.05), and decreased false favourable rates for males (0.0059 to 0.0033) and females (0.0096 to 0.0064) through balanced probability adjustment. Results: The SCIR model outperformed the SIR model (recovery rate: 1.38 vs 0.83) with a −10% transmission bias impact. Parameters (β=0.5, δ=0.2, γ=0.15) reduced susceptible counts to 2.53×10−12 and increased recovered counts to 9.98 by t=50. YOLOv5 achieved high Intersection over Union (IoU) scores (94.8%, 93.7%, 80.6% for normal, severe, and abnormal cases). Mask R-CNN showed 82.5% peak confidence, while ResNet demonstrated a 10.4% accuracy drop under noise. Performance metrics (IoU: 0.91–0.96, Dice: 0.941–0.980, Kappa: 0.95) highlighted strong predictive accuracy and reliability. Conclusions: The findings validate the effectiveness of fairness-aware algorithms in addressing cardiovascular predictive model biases. The integration of fairness and explainable AI not only promotes equitable diagnostic precision but also significantly reduces diagnostic disparities across vulnerable populations. This reduction in disparities is a key outcome of the research, enhancing clinical trust in AI-driven systems. The promising results of this study pave the way for future work that will explore scalability in real-world clinical settings and address limitations such as computational complexity in large-scale data processing.
Improving Antimicrobial Stewardship in Acute Sore Throat: Comparison of FeverPAIN and McIsaac Scores with Molecular Point of Care Testing Using Abbott ID NOW
Authors: Daniels Rob , El Omda Tarek , Mokbel Kinan
Published: November 2024

Background: The diagnosis of streptococcal throat infection is an area where current practice results in significant over-diagnosis of bacterial infection, with the resulting implications for antimicrobial use and resistance. The use of molecular point of care testing (POCT) has previously been shown to alter antibiotic prescribing decisions when compared to Centor scoring. This paper explores the impact of the addition of POCT to clinical assessment using the McIsaac and FeverPAIN scoring systems. Methods: Retrospective analysis of the clinical records of 144 patients who had previously received point of care testing as part of a trial of POCT as an adjunct to usual care was undertaken. The McIsaac and FeverPAIN scores were calculated and then compared with the POCT result originally obtained. The records were also reviewed to identify further consultations within 10 days of being tested. Results: Sufficient clinical data were available to calculate the FeverPAIN and McIsaac scores in 81% of patients and outcome data were available for 96.5% of patients. For patients with a FeverPAIN score of 4 or 5, 50–67% of patients had a positive POCT result, while for patients with a McIsaac score of 4 or 5, 50–68% had a positive POCT result. Moreover, 16.7% of patients who tested negative, and 16.3% of patients who tested positive, had a further consultation within 10 days of being assessed. Conclusions: Although relatively few patients in this study had maximum scores on the FeverPAIN and McIsaac scoring, the addition of POCT was shown to alter antibiotic prescribing decisions in a significant number of patients, supporting the use of Abbott ID NOW point of care testing to reduce antibiotic prescribing. Larger studies are required to confirm these results and explore the health economic aspects and potential impacts on health inequalities.
Interrelationship and Volatility Dynamics Among the Seven Main NYSE Mineral ETFs
Authors: Streck Pedro Augusto , Passos Marcelo De Oliveira , Tessmann Mathias Schneid , Trescher Alfrânio Rodrigo , Uhr Daniel De Abreu Pereira , Marques Maria Laura
Published: November 2024

This paper aims to investigate the main mineral exchange-traded funds (ETFs) in terms of trading volumes on the New York Stock Exchange by measuring the volatility transmission among them and the connectivity of this market. Daily closing ETF data from 2019 to 2023 for platinum, silver, copper, lead, nickel, gold, and a diversified set of precious metals are considered to estimate a spillover index and apply complex network metrics that identify and cluster the intensity of these relationships. The results indicate that the ETFs that transmit and receive the most volatility in the modeled complex network, in ascending order, are precious metals: gold, silver, and platinum. They are described by the cluster analysis of the modularity optimization process as the group most used for hedging purposes in critical periods. These findings are helpful for the scientific literature about derivatives by bringing empirical evidence from metals markets, supply chain agents, and investors.
An Efficient Multi-Level 2D DWT Architecture for Parallel Tile Block Processing with Integrated Quantization Modules
Authors: Li Qitao , Zhang Wei , Wu Zhuolun , Dai Yuzhou , Liu Yanyan
Published: November 2024

A multi-level 2D Discrete wavelet transform (DWT) architecture for JPEG2000 is proposed, enhancing speed through parallel processing multiple tile blocks. Based on the lifting scheme, folded architecture and unfolded architecture achieving critical path delay with only one multiplier are designed to increase throughput rate. Connecting the folded and unfolded architecture through a pipeline architecture ensures uniform throughput rates across all DWT levels within a singular clock domain. Computational resource consumption is reduced by adjusting the timing to allow one folded architecture to process three tile blocks of three to five levels of DWT, and a transposing module requiring merely six registers is devised to decrease storage resource consumption. The quantization module, crucial for code-word control in JPEG2000, is integrated into the scaling module with minimal additional resource expenditure. Compared to the existing architecture, the analysis demonstrates that the proposed architecture exhibits enhanced hardware efficiency, with a reduction in transistor-delay-product (TDP) of no less than 14.69%. Synthesis results further reveal an area reduction of at least 26.64%, and a decrease in area-delay-product (ADP) by a minimum of 29.89%. Results from FPGA implementation indicate a significant decrease in resource utilization.
Electric Vehicle Charging Guidance Strategy with Dual-Incentive Mechanisms for Charging and Discharging
Authors: Lyu Shukang , Miu Huiyu , Yuan Xiaodong , Wang Mingshen , Zeng Fei , Pan Yi
Published: November 2024

With the rapid development of electric vehicles (EVs) and charging facilities, EV charging guidance is currently mainly based on charging incentives. Without an in-depth exploration of the superimposed benefits to charging guidance caused by discharging incentives, it is difficult to maximize the benefits of charging station operators and to stimulate the enthusiasm of users to participate in the guidance. In this study, firstly, a traffic network model based on the Logit model is established, and the spatiotemporal distribution characteristics of EV users’ traveling demand based on the O-D matrix and the Monte Carlo Markov method are proposed. Secondly, we analyze the impact of charging and discharging incentive levels on users’ psychological responses to charging guidance. We assess battery degradation during irregular discharging processes of electric vehicles (EVs) while considering users’ personalized travel needs and anxiety levels. We propose a dual-incentive mechanism for charging and discharging to enhance users’ active participation in charging guidance. Then, we construct a model that incorporates users’ travel and waiting time costs, as well as the economic costs of charging and discharging. Subsequently, we consider the economic benefits for users under the discharging incentive mechanism and establish a user charging decision model based on prospect theory. Finally, considering the goal of maximizing the revenue of the charging station, a charging guidance strategy considering users’ participation in the charging and discharging incentive mechanism during the traveling process is proposed. The effectiveness of the EV charging guidance strategy under three different incentive scenarios is verified with comparative results. The proposed guidance strategy enhances operator revenue while taking user interests into account, achieving a 7% increase in operator revenue compared to a strategy that only considers charging incentives. This dual-incentive mechanism promotes operators’ enthusiasm for participating in vehicle-to-grid interactions.
Survey of Transformer-Based Malicious Software Detection Systems
Authors: Alshomrani Mohammed , Albeshri Aiiad , Alturki Badraddin , Alallah Fouad Shoie , Alsulami Abdulaziz A.
Published: November 2024

In the recent past, the level of cyber threats has changed drastically, leading to the current transformation of the cybersecurity landscape. For example, emerging threats like Zero-day and polymorphic malware cannot be detected by conventional detection methods like heuristic and signature-based methods, which have proven useful in the identification of malware. In view of this shift in the cybersecurity paradigm, this study proposes to discuss the utilization of transformer models to improve malware detection effectiveness and the accuracy and efficiency in detecting malicious software. In this regard, this study adopts the application of transformers in identifying different forms of malicious software: ransomware, spyware, and trojans. Transformers are endowed with the ability to handle sequential data and capture intricate patterns. By employing deep learning techniques and conducting thorough contextual analysis, these models enhance the detection process by identifying subtle indications of compromise, which traditional methods may overlook. This research also explains the challenges and limitations related to the application of transformer-based models in real-world cybersecurity settings, which include computing requirements and large-scale labeled datasets’ requirements. By the end, the article suggests potential future research avenues in order to improve and integrate these models into cybersecurity systems.
Variable-State-Trigger: A Formal Model of Smart Contracts Based on Conditional Response and Finite State Automata and Its Application
Authors: Liu Shangqi , Xu Yuntao , Yang Xingyu , Hui Bozhi , Hu Feng
Published: November 2024

As one of the technical elements supporting the blockchain to realize decentralized autonomous organizations, smart contracts are crucial for understanding the inherent properties of blockchain systems through formal research. Most existing formal models of smart contracts focus primarily on static properties, lacking the depiction of dynamic processes such as conditional responses and internal state migration during contract execution, which complicates effective supervision. In this paper, a novel formal model of smart contracts, based on finite state automata, named Variable-State-Trigger (VST), is proposed, which presents state migration and conditional response mechanisms during smart contract execution. The VST model is verified to portray both the static and dynamic properties of smart contracts, providing new avenues for effective supervision during contract execution. Moreover, the VST model is used to illustrate the life cycle of a UAV mission smart contract, demonstrating its feasibility. With the growing integration of blockchain and emerging technologies, smart contracts have found applications in various fields, including drone swarm coordination. In this context, smart contracts enable secure, decentralized management and automation of interactions between autonomous drones, ensuring compliance with predefined conditions and enhancing the reliability of complex drone operations. The VST model can be extended to such applications, offering dynamic supervision and enhancing the coordination efficiency in decentralized autonomous drone systems.
Application and Optimization of a Fast Non-Local Means Noise Reduction Algorithm in Pediatric Abdominal Virtual Monoenergetic Images
Authors: Kim Hajin , Park Juho , Shim Jina , Lee Youngjin
Published: November 2024

In this study, we applied and optimized a fast non-local means (FNLM) algorithm to reduce noise in pediatric abdominal virtual monoenergetic images (VMIs). To analyze various contrast agent concentrations, we produced contrast agent concentration samples (20, 40, 60, 80, and 100%) and inserted them into a phantom model of a one-year-old pediatric patient. Single-energy computed tomography (SECT) and dual-energy computed tomography (DECT) images were acquired from the phantom, and 40 kilo-electron-volt (keV) VMI was acquired based on the DECT images. For the 40 keV VMI, the smoothing factor of the FNLM algorithm was applied from 0.01 to 1.00 in increments of 0.01. We derived the optimized value of the FNLM algorithm based on quantitative evaluation and performed a comparative assessment with SECT, DECT, and a total variation (TV) algorithm. As a result of the analysis, we found that the average contrast to noise ratio (CNR) and coefficient of variation (COV) of each concentration were most improved at a smoothing factor of 0.02. Based on these results, we derived the optimized smoothing factor value of 0.02. Comparative evaluation shows that the optimized FNLM algorithm improves the CNR and COV results by approximately 3.14 and 2.45 times, respectively, compared with the DECT image, and the normalized noise power spectrum result shows a 10−1 mm2 improvement. The main contribution of this study is to demonstrate the effectiveness of an optimized FNLM algorithm in reducing noise in pediatric abdominal VMI, allowing high-quality images to be acquired while reducing contrast dose. This advancement has significant implications for minimizing the risk of contrast-induced toxicity, especially in pediatric patients. Our approach addresses the problem of limited datasets in pediatric imaging by providing a computationally efficient noise reduction technique and highlights the clinical applicability of the FNLM algorithm. In addition, effective noise reduction enables high-contrast imaging with minimal radiation and contrast exposure, which is expected to be suitable for repeat CT examinations of pediatric liver cancer patients and other abdominal diseases.
Comparison of Medical Opinions About the Decrease in Autopsies in Mexican Hospitals Using Data Mining
Authors: Olmos-Vallejo Araceli , Rodríguez-Mazahua Lisbeth , Palet-Guzmán José Antonio , Machorro-Cano Isaac , Alor-Hernández Giner , Cervantes Jair
Published: November 2024

Subgroup discovery (SD) is a data mining technique that allows us to obtain the properties of each element given a particular population; these properties are of interest for a specific study, finding the most important or significant subgroups of the population. Also, the larger the population, the more successful the analysis and the creation of the subgroups, since, on this basis, the possibility of finding more unusual characteristics among the elements of the population is greater. The principal purpose of SD is not to obtain a predictive function, but to achieve a result that users can comprehend and interpret easily, and at the same time provide a more complete and suggestive description of the data. In this paper, we present an application of this technique to the medical field to analyze the opinions of physicians on the decreasing rates of autopsies in Mexican hospitals, utilizing five SD algorithms. The results obtained are the rules that allow for the comparison of medical opinions in three hospitals.
Deep Learning Approaches for Chest Radiograph Interpretation: A Systematic Review
Authors: Iqbal Hammad , Khan Arshad , Nepal Narayan , Khan Faheem , Moon Yeon-Kug
Published: November 2024

Lung diseases are a major global health concern, with nearly 4 million deaths annually, according to the World Health Organization (WHO). Chest X-rays (CXR) are widely used as a cost-effective and efficient diagnostic tool by radiologists to detect conditions such as pneumonia, tuberculosis, COVID-19, and lung cancer. This review paper provides an overview of the current research on diagnosing lung diseases using CXR images and Artificial Intelligence (AI), without focusing on any specific disease. It examines different approaches employed by researchers to leverage CXR, an accessible diagnostic medium, for early lung disease detection. This review shortlisted 11 research papers addressing this problem through AI, exploring the datasets used and their sources. Results varied across studies: for lung cancer, Deep Convolutional Neural Network (DCNN) achieved 97.20% accuracy, while multiclass frameworks like ResNet152V2+Bi-GRU (gated reccurent unit) reached 79.78% and 93.38%, respectively. For COVID-19 detection, accuracy rates of 98% and 99.37% were achieved using EfficientNet and Parallel Convolutional Neural Network-Extreme Learning Machine (CNN-ELM). Additionally, studies on the CXR-14 dataset (14 classes) showed high accuracy, with MobileNet V2 reaching 94%. Other notable results include 73% accuracy with VDSNet, 98.05% with VGG19+CNN for three classes, and high accuracy in detecting pediatric pneumonia, lung opacity, pneumothorax, and tuberculosis.
Optimal Selection Technology of Business Data Resources for Multi-Value Chain Data Space—Optimizing Future Data Management Methods
Authors: Fan Bo , Sun Linfu , Tan Dong , Pan Meng
Published: November 2024

In the field of industrial big data, the key issue in discovering data value lies not in overcoming the bottlenecks formed by analysis methods and data mining algorithms but in the difficulty of providing data element resources that meet business analysis needs. Due to the surge in data volume and the increasing reliance of enterprises on data-driven decision-making, future data management strategies are constantly evolving to meet higher quality and efficiency requirements. Data metadata resources that meet business analysis needs require high-quality data integration, standardization, and metadata management. The key is to ensure the consistency and availability of data to support accurate analysis and decision-making. By leveraging automation and machine learning, organizations can more effectively integrate and manage data metadata resources, thereby improving data quality and analytical capabilities. The multi-value chain data space is a digital ecological platform for organizing and managing industrial big data. Research on optimizing the supply of its business data resources is a significant topic. This paper studies the evaluation index system of data quality and data utility, constructs an evaluation matrix of business data resources, and addresses the issues of data sparsity and cold start in evaluation calculations through a data quality-utility-based evaluation model of business data resources. It investigates a business data resource algorithm based on collaborative filtering, forming a recommendation set of similar data quality-utility data resources to provide to data analysis users. Finally, using actual production datasets, the paper validates the business data resource evaluation model, compares the performance and effectiveness of three business data resource recommendation algorithms based on collaborative filtering, empirically demonstrates the recommendation accuracy and stability performance of the combined improved data quality-utility collaborative filtering algorithm (CFA-DQU), and provides technical research recommendations for optimization of business data resources.
Measurement Method of Deeply Saturated Excitation Characteristics of Converter Transformer Under AC-DC Hybrid Excitation
Authors: Lv Jinzhuang , Hou Mingchun , Pan Zhicheng , Deng Jun , Pei Xichen , Yang Yu , Zhang Zhanlong
Published: November 2024

During operation, converter transformers enter a saturation state, leading to phenomena such as magnetising inrush currents. Accurately measuring the excitation characteristic curve of an iron core under deep-saturation conditions is essential for analysing low-frequency transient phenomena in transformers. This paper presents a method for calculating the excitation characteristics of a converter transformer under deep iron core saturation. The method involves establishing an improved T model for the converter transformer and conducting open-circuit experiments in the linear working region to obtain the excitation characteristic curve and knee point parameters. AC-DC hybrid excitation is used to achieve deep saturation, and measurements of saturated inductance at different levels of saturation at the transformer terminals are taken. The mathematical relationship between saturated inductance and magnetic impedance is derived, allowing deduction of the magnetising characteristic curve of the converter transformer under deep-saturation conditions based on measured saturated inductance values. A finite element simulation analysis was performed on a single-phase four-column converter transformer with a capacity of 250 MVA. Additionally, a test platform for toroidal transformers and dry-type transformers has been set up to carry out excitation characteristic measurement and verification. Experimental results demonstrate that errors are maintained within 10% or less, validating this approach’s effectiveness.
A Coloring-Based Packet Loss Rate Measurement Scheme on Network Nodes
Authors: Wang Shuhe , Han Rui , Wang Xu
Published: November 2024

Network measurement is an efficient way to understand network behavior. Traditional measurement techniques focus on internet protocol (IP) networks, where the processing capacity of network nodes is limited and primarily dedicated to packet forwarding. As a result, these techniques typically rely on end hosts or external systems to analyze traffic and evaluate network performance. This reliance introduces several challenges, such as increased measurement latency and scalability limitations, particularly in large-scale networks. With the emergence of next-generation internet architectures, especially information-centric networking (ICN), network nodes have gained enhanced capabilities, enabling measurement tasks to be performed directly at these nodes. This paper proposes a distributed measurement scheme where network nodes collaborate to monitor the packet loss rate on the intermediate link. By setting an unused bit in the packet header, the upstream node “colors” the packets into different color blocks. The minimum duration of each block is determined by the degree of reordering on the link, and the number of packets in each block must be a power of two. The downstream node recognizes blocks, assigns packets to the right block, and deduces the original number of packets for each block to calculate packet loss. Moreover, the upstream node adjusts the number of packets in each block based on the packet transmission rate on the link, aiming to balance measurement accuracy and frequency. A P4-based implementation on a BMv2 software switch is presented to demonstrate the feasibility of the proposed scheme. Simulations show that this scheme improves measurement accuracy and is more robust against packet reordering. Additionally, the proposed scheme maintains relatively low network overhead and, at higher measurement frequencies, exhibits the lowest overhead compared to existing methods.
Tracking the Rareness of Diseases: Improving Long-Tail Medical Detection with a Calibrated Diffusion Model
Authors: Zhang Tianjiao , Ma Chaofan , Wang Yanfeng
Published: November 2024

Motivation: Chest X-ray (CXR) is a routine diagnostic X-ray examination for checking and screening various diseases. Automatically localizing and classifying diseases from CXR as a detection task is of much significance for subsequent diagnosis and treatment. Due to the fact that samples of some diseases are difficult to acquire, CXR detection datasets often present a long-tail distribution over different diseases. Objective: The detection performance of tail classes is very poor due to the limited number and diversity of samples in the training dataset and should be improved. Method: In this paper, motivated by a correspondence-based tracking system, we build a pipeline named RaTrack, leveraging a diffusion model to alleviate the tail class degradation problem by aligning the generation process of the tail to the head class. Then, the samples of rare classes are generated to extend the number and diversity of rare samples. In addition, we propose a filtering strategy to control the quality of the generated samples. Results: Extensive experiments on public datasets, Vindr-CXR and RSNA, demonstrate the effectiveness of the proposed method, especially for rare diseases.
Assessing the Impact of Digital Technologies on the Sustainable Development Goals Within the European Union
Authors: Vărzaru Anca Antoaneta , Bocean Claudiu George , Gheorghe Maria , Simion Dalia , Mangra Mădălina Giorgiana , Cioabă Andreea Adriana
Published: November 2024

In the contemporary digital era, emerging digital technologies are rapidly transforming society and the economy, offering considerable potential for addressing global challenges tied to sustainable development. This study investigates the influence of key digital technologies, including artificial intelligence, big data, cloud computing, the Internet of Things, and autonomous robots, on achieving the Sustainable Development Goals within European Union countries. This study uses artificial neural network analysis and cluster analysis to examine patterns of technology adoption and their measurable impacts on the Sustainable Development Goals based on a dataset covering technology adoption and sustainable development metrics across EU member states. The findings reveal that artificial intelligence, Big Data, and cloud computing have a substantial effect on the progress toward the Sustainable Development Goals. At the same time, the influence of the internet of things and autonomous robots remains moderate at this stage. Cluster analysis underscores the importance of a coordinated digital strategy and targeted policies for integrating these technologies to maximize their benefits while managing associated risks. This study contributes to the field by providing an empirical groundwork for understanding the role of digital technologies in sustainable development.
ENSG: Enhancing Negative Sampling in Graph Convolutional Networks for Recommendation Systems
Authors: Hai Yan , Zheng Jitao , Liu Zhizhong , Wang Dongyang , Ding Chengrui
Published: November 2024

In the field of recommendation, negative samples that are close to positive samples are referred to as “hard negative samples”. These hard negative samples are more likely to be incorrectly recommended to users. Therefore, researching how to enhance the ability to identify hard negative samples and thereby improve recommendation accuracy is an important direction in the field of recommendation systems. To address this issue, we propose a new model named Enhancing Negative Sampling in Graph Convolutional Networks for Recommendation Systems (ENSG). Firstly, part of the information in the positive sample was injected into the negative sample, and the high-quality, difficult negative sample was selected by the inner product method. Secondly, a lightweight graph convolutional network is used for feature extraction to obtain node representations for both users and items. Then, contrastive learning is used to maximize the similarity between the hard negative samples and the positive samples, so that the hard negative samples are as close to the positive samples as possible in the feature space. Ultimately, the model is trained by taking hard negative samples as input and minimizing the loss function to optimize model parameters; the recommendation accuracy of the model is enhanced. To validate the effectiveness of our approach, we conducted comparative evaluations utilizing three publicly accessible datasets. The experimental outcomes indicate that our proposed model surpasses the baseline model in terms of performance. On three public datasets, the algorithm proposed in this paper achieved the highest improvements of 16.27% and 12.72% in the evaluation metrics Recall@20 and NDCG@20, respectively.
Reconstructing 450 Years of Pollarding Events in Spanish Deciduous Oak Woodlands Using Machine Learning
Authors: Sanmiguel-Vallelado Alba , Sangüesa-Barreda Gabriel , García-Hidalgo Miguel , Coca María Encarnación , Olano José Miguel
Published: November 2024

Pollarding, the practice of pruning tree branches at a specific height, has been crucial for managing open forests in Europe. This practice has supported the persistence of highly biodiverse open woodlands featuring ancient trees. Understanding historical management patterns is essential for interpreting past socioeconomic conditions and developing strategies to mimic traditional practices for biodiversity conservation. Current methods for reconstructing past management in pollarded forests often rely on techniques for large-scale forest disturbances, which may be suboptimal for detecting short-term perturbations like pollarding. To address this gap, we applied a random forest algorithm to detect pollarding events using tree-ring traits, reconstructing the multi-centennial management history of four deciduous oak dehesas in northern Spain. Our analysis revealed that short-term changes in latewood were the most reliable indicator of pollarding events. Pollarding typically reduced latewood production for about three years, with the most pronounced declines occurring toward the end of the pollarding effect period. Pollarding patterns underwent a major shift starting in the last third of the 20th century. Key historical decades of both high and low pollarding pressure were consistently observed across the studied dehesas. These findings enhance our understanding of these unique ecosystems and offer critical insights for their conservation.