bluuebunny/crossref_metadata_2025 huggingface dataset:
Global Profile of tRNA-Derived Small RNAs in Pathological Cardiac Hypertrophy Plasma and Identification of tRF-21-NB8PLML3E as a New Hypertrophy Marker
Authors: Xu Jingyi , Qian Buyun , Wang Feng , Huang Ying , Yan Xinxin , Li Ping , Zhang Qian , Li Yuan , Sun Kangyun
Published: June 2023

Background: It remains unclear whether transfer RNA-derived small RNAs (tsRNAs) play a role in pathological cardiac hypertrophy (PCH). We aimed to clarify the expression profile of tsRNAs and disclose their relationship with the clinical phenotype of PCH and the putative role. Methods: Small RNA sequencing was performed on the plasma of PCH patients and healthy volunteers. In the larger sample size and angiotensin II (Ang II)-stimulated H9c2 cells, the data were validated by real-time qPCR. Atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) were examined in Ang II-stimulated H9c2 cells. The potential role of tsRNAs in the pathogenesis of PCH was explored by bioinformatics analysis. Results: A total of 4185 differentially expressed tsRNAs were identified, of which four and five tsRNAs were observed to be significantly upregulated and downregulated, respectively. Of the five downregulated tsRNAs, four were verified to be significantly downregulated in the larger sample group, including tRF-30-3JVIJMRPFQ5D, tRF-16-R29P4PE, tRF-21-NB8PLML3E, and tRF-21-SWRYVMMV0, and the AUC values for diagnosis of concentric hypertrophy were 0.7893, 0.7825, 0.8475, and 0.8825, respectively. The four downregulated tsRNAs were negatively correlated with the left ventricular posterior wall dimensions in PCH patients (r = −0.4227; r = −0.4517; r = −0.5567; r = −0.4223). The levels of ANP and BNP, as well as cell size, were decreased in Ang II–stimulated H9c2 cells with 21-NB8PLML3E mimic transfection. Bioinformatics analysis revealed that the target genes of tRF-21-NB8PLML3E were mainly enriched in the metabolic pathway and involved in the regulation of ribosomes. Conclusions: The plasma tRF-21-NB8PLML3E might be considered as a biomarker and offers early screening potential in patients with PCH.
Assessment of Type 2 Diabetes Risk in Young Women with Polycystic Ovary Syndrome
Authors: Livadas Sarantis , Paparodis Rodis , Anagnostis Panagiotis , Gambineri Alessandra , Bjekić-Macut Jelica , Petrović Tijana , Yildiz Bulent O. , Micić Dragan , Mastorakos George , Macut Djuro
Published: June 2023

Women with polycystic ovary syndrome (PCOS) are at increased risk for dysglycemia and type 2 diabetes compared to healthy BMI-matched women of reproductive age: robust evidence exists supporting this notion. The presence of altered glycemic status in young women with the syndrome presents a distinct challenge for the clinician for several reasons. Firstly, the reported incidence of this disorder varies among the limited available studies. Furthermore, there is a lack of consensus on the best screening method, which women to screen, at what frequency, and which strategies need to be implemented to reduce the above risk. We provide data regarding the prevalence of dysglycemia in young women suffering from PCOS and the pathophysiological mechanisms underlying the disorder. In addition, we present evidence suggesting universal screening with the oral glucose tolerance test in young women with the syndrome, irrespective of age or BMI status, to identify and manage glycemic abnormalities in a timely manner. Regarding follow-up, oral glucose testing should be carried out at regular intervals if there are initial abnormal findings or predisposing factors. Finally, the efficacy of a well-balanced diet in conjunction with regular exercise and the use of non-pharmacologic agents in this specific population is discussed.
Non-Contrast and Contrast-Enhanced Cardiac Computed Tomography Imaging in the Diagnostic and Prognostic Evaluation of Coronary Artery Disease
Authors: Pugliese Luca , Ricci Francesca , Sica Giacomo , Scaglione Mariano , Masala Salvatore
Published: June 2023

In recent decades, cardiac computed tomography (CT) has emerged as a powerful non-invasive tool for risk stratification, as well as the detection and characterization of coronary artery disease (CAD), which remains the main cause of morbidity and mortality in the world. Advances in technology have favored the increasing use of cardiac CT by allowing better performance with lower radiation doses. Coronary artery calcium, as assessed by non-contrast CT, is considered to be the best marker of subclinical atherosclerosis, and its use is recommended for the refinement of risk assessment in low-to-intermediate risk individuals. In addition, coronary CT angiography (CCTA) has become a gate-keeper to invasive coronary angiography (ICA) and revascularization in patients with acute chest pain by allowing the assessment not only of the extent of lumen stenosis, but also of its hemodynamic significance if combined with the measurement of fractional flow reserve or perfusion imaging. Moreover, CCTA provides a unique incremental value over functional testing and ICA by imaging the vessel wall, thus allowing the assessment of plaque burden, composition, and instability features, in addition to perivascular adipose tissue attenuation, which is a marker of vascular inflammation. There exists the potential to identify the non-obstructive lesions at high risk of progression to plaque rupture by combining all of these measures.
Quantifying Visual Pollution from Urban Air Mobility
Authors: Thomas Kilian , Granberg Tobias A.
Published: June 2023

Unmanned aerial vehicles (UAVs) can bring many benefits, particularly in emergency response and disaster management. However, they also induce negative effects, such as noise and visual pollution, risk, and integrity concerns. In this work, we study visual pollution, developing a quantitative measure that can calculate the visual pollution from one or multiple UAVs. First, the Analytic Hierarchy Process was utilized in an expert workshop to find and rank factors relevant to visual pollution. Then an image-based questionnaire targeted at the general public was used to find relations between the factors. The results show that the two main factors causing visual pollution are the number of UAVs and the distance between a UAV and the observer. They also show that while a UAV used for emergency medical services is as polluting as any other UAV, it is easier to tolerate this pollution. Based on the questionnaire results, two visual pollution functions were developed that can be used when carrying out path planning for one or multiple UAVs. When combining this function with other existing measures for noise pollution, and ground and air risk, it is possible to find paths that will give as little negative impact as possible from urban air mobility.
Distances in Higher-Order Networks and the Metric Structure of Hypergraphs
Authors: Vasilyeva Ekaterina , Romance Miguel , Samoylenko Ivan , Kovalenko Kirill , Musatov Daniil , Raigorodskii Andrey Mihailovich , Boccaletti Stefano
Published: June 2023

We explore the metric structure of networks with higher-order interactions and introduce a novel definition of distance for hypergraphs that extends the classic methods reported in the literature. The new metric incorporates two critical factors: (1) the inter-node distance within each hyperedge, and (2) the distance between hyperedges in the network. As such, it involves the computation of distances in a weighted line graph of the hypergraph. The approach is illustrated with several ad hoc synthetic hypergraphs, where the structural information unveiled by the novel metric is highlighted. Moreover, the method’s performance and effectiveness are shown through computations on large real-world hypergraphs, which indeed reveal new insights into the structural features of networks beyond pairwise interactions. Namely, using the new distance measure, we generalize the definitions of efficiency, closeness and betweenness centrality for the case of hypergraphs. Comparing the values of these generalized measures with their analogs calculated for the hypergraph clique projections, we show that our measures provide significantly different assessments on the characteristics (and roles) of the nodes from the information-transferability point of view. The difference is brighter for hypergraphs in which hyperedges of large sizes are frequent, and nodes relating to these hyperedges are rarely connected by other hyperedges of smaller sizes.
Human Capital, Networks and Segmentation in the Market for Academic Economists
Authors: Faria João , Mixon Franklin , Sawyer William
Published: June 2023

Academic labor markets often exhibit steep hierarchies, with institutions at the top attempting to attract newly minted doctorates from similarly situated institutions in an effort to maintain or improve their reputations. Yet, despite recent research on labor market segmentation in academe, the literature has heretofore been under-theorized. This paper provides a straightforward formal model that generates a three-tiered hierarchy of academic institutions, wherein academic departments affiliated with top-tier universities endeavor to hire only from within the group, while those in the bottom tier are unable to employ faculty with degrees from top departments. The results from statistical tests applied to data from economics departments in the U.S. indicate that top-tier departments employ 3.5 to 3.8 (2.5 to 2.9) more assistant professors from top-tier institutions, ceteris paribus, than bottom (middle) tier departments.
Norwegian Research on ECEC Quality from 2010 to 2021—A Systematic Scoping Review
Authors: Furenes May Irene , Andresen Anne Kristin , Løkken Ingrid Midteide , Moser Thomas , Nilsen Tone Rove , Dahl Anne-Lene Skog
Published: June 2023

This review concerns how quality in ECEC is thematized and examined in Norwegian research published in 2010–2021. The review contributes to developing relevant professional knowledge and insight into what quality in ECEC is, should be or can be. A systematic scoping review includes 97 empirical studies of quality in the Norwegian ECEC context. The results show an increase in studies addressing quality after the year 2017. Most studies use interviews and observations, i.e., qualitative data. Research based on quantitative studies mainly applies to questionnaires, whereas multi-method designs occur with the third greatest frequency. The majority of the studies are descriptive or explore relationships. A few studies concern method development, interventions or reviews of the literature. ECEC staff and ECEC teachers are the most common informants. Two-thirds of the studies included were published in English, and only one-third were published in Norwegian. Thematically, process quality emerges as the most studied dimension of quality, dominated by investigating the aspect of staff–child interactions. About one-third of the studies investigate structural quality, with most attention paid to the physical environment and the organization of the ECEC provisions. Less research is directed at result (outcome) quality and curriculum quality. This overview contributes to increased knowledge about how quality is understood and used in research, as an essential prerequisite for creating a safe and sound everyday life for children in ECEC.
An Efficient Random Access Reception Algorithm for ToA Estimation in NB-IoT
Authors: Li Shengyong , Xiang Tian , Huang Ding , Han Ling , Wu Qi , Kong Dejin
Published: June 2023

Narrowband Internet of Things (NB-IoT) aims to provide wide coverage for a massive number of low-cost devices. Therefore, an NB-IoT physical random access channel (NPRACH) preamble based on a single tone signal with frequency hopping was designed, enabling the base station to estimate the time-of-arrival (ToA) values for realizing uplink synchronization among multiple users. However, due to residual carrier frequency offset (RCFO) in the NPRACH preamble, it is crucial to keep the accuracy of the ToA estimation. Recognizing this urgency, in this paper we first judiciously investigate the effect of the hooping distance on ToA estimation. With which, we propose an efficient receiving algorithm to improve the accuracy of ToA estimation. The main aim of the algorithm is to treat two consecutive symbol groups as a whole and then carry out difference calculations on the two newly constituted symbol groups to construct a more logical frequency hopping distance. Extensive numerical results validate the superiorly of our proposed scheme compared against conventional strategies, showing that the probability of the ToA estimation obtained by this method is 99% within the acceptable error range.
A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System
Authors: Cai Yunpiao , Qian Weixing , Dong Jiayi , Zhao Jiaqi , Wang Kerui , Shen Tianxiao
Published: June 2023

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.
X-ray Security Inspection Image Dangerous Goods Detection Algorithm Based on Improved YOLOv4
Authors: Yu Xiaoyu , Yuan Wenjun , Wang Aili
Published: June 2023

Aiming at the problems of multi-scale and serious overlap of dangerous goods in X-ray security-inspection-image samples, an X-ray dangerous-goods-detection algorithm with high detection accuracy is designed based on the improvement of YOLOv4. Using deformable convolution to redesign YOLOv4’s path-aggregation-network (PANet) module, deformable convolution can flexibly change its receptive field based on the shape of the detected object. When the high-level information and low-level information are fused in the PANet module, deformable convolution is used to align features, which can effectively improve the detection accuracy. Then, the Focal-EIOU loss function is introduced, which can solve the problem of the CIOU loss function being prone to causing severe loss-value oscillation when dealing with low-quality samples. During training, the network can converge more quickly and the detection accuracy can be slightly improved. Finally, Soft-NMS was used to improve the non-maximum suppression of YOLOv4, effectively solving the problem of the high overlap rate of hazardous materials in the X-ray security-inspection dataset and improving accuracy. On the SIXRay dataset, this model detected 95.73%, 83.00%, 82.95%, 85.13%, and 80.74% AP for guns, knives, wrenches, pliers, and scissors, respectively, and the detected mAP reached 85.51%. The proposed model can effectively reduce the false-detection rate of dangerous goods in X-ray security images and improve the detection ability of small targets.
Design of a 2.4 & 5.8 GHz Efficient Circularly Polarized Rectenna for Wireless Power Transfer Applications
Authors: Deng Xizhou , Yang Ping , Chen Shengtao , Ren Wang
Published: June 2023

In this paper, we proposed a novel dual-band circularly polarized rectifier antenna for collecting environmental electromagnetic energy at 2.4 and 5.8 GHz. The receiving antenna is a circularly polarized microstrip slot antenna that achieves circular polarization in the low-frequency band (2.4 GHz) and high-frequency band (5.8 GHz) through the use of the “U” slot of the ground and the “L” branch of the upper patch, respectively. The antenna exhibits impedance bandwidths of 320 MHz (2.18–2.50 GHz) and 3.73 GHz (5.33–9.06 GHz) in the 2.4 GHz and 5.8 GHz bands, respectively, while the 3 dB axis specific bandwidths are 260 MHz (2.32–2.58 GHz) and 420 MHz (5.66–6.08 GHz), respectively. The antenna gains are 3.5 dBi and 6.2 dBi at 2.4 GHz and 5.8 GHz, respectively. Furthermore, a dual-frequency voltage doubling rectifier circuit was designed to operate with the antenna. A dual-branch matching circuit was used to achieve impedance matching between the antenna and the rectifier circuit, and the output was optimized to suppress high-order harmonics through sector branches to improve the rectification efficiency. The simulation results show that the maximum rectification efficiency of the circuit is more than 80%, with a measured efficiency of over 60%. This 2.4–5.8 GHz dual-band circularly polarized rectifier antenna has the potential to self-power low-power devices in the Internet of Things, making it a valuable contribution to the field of wireless energy harvesting.
An Improved LSTNet Approach for State-of-Health Estimation of Automotive Lithium-Ion Battery
Authors: Ping Fan , Miao Xiaodong , Yu Hu , Xun Zhiwen
Published: June 2023

Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of conventional SOH estimation approaches, this paper proposes an improved LSTNet network model. Firstly, the discharged battery sequence data are divided into long-term and short-term sequences. A spatially convolutional long short-term memory network (ConvLSTM) is then introduced to extract multidimensional capacity features. Next, an autoregressive (AR) component is employed to enhance the model’s robustness while incorporating a shortcut connection structure to enhance its convergence speed. Finally, the results of the linear and nonlinear components are fused to make predictive judgments. Experimental comparisons on two datasets are conducted in this study to demonstrate that the method fits the electric capacity recession curve well, even without the preprocessing step. For the data of four NASA batteries, the maximum root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the prediction results were maintained at 0.65%, 0.58%, and 0.435% when the proportion of the training set was 40%, which effectively validates the model’s feasibility and accuracy.
Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment
Authors: Abbas Zaheer , Myeong Seunghwan
Published: June 2023

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study’s objectives. This study identifies gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of precision, accuracy, and recall values, as well as F1 scores, R.O.C. curves, and confusion matrices. The results demonstrated that the X.G.B. model outperformed, in terms of all the matrices, with an accuracy of 97.50%, a precision of 97.60%, a recall value of 97.60%, and an F1 score of 97.50%. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.
DRnet: Dynamic Retraining for Malicious Traffic Small-Sample Incremental Learning
Authors: Wang Ruonan , Fei Jinlong , Zhang Rongkai , Guo Maohua , Qi Zan , Li Xue
Published: June 2023

Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory.
Underwater Noise Modeling and Its Application in Noise Classification with Small-Sized Samples
Authors: Song Guoli , Guo Xinyi , Zhang Qianchu , Li Jun , Ma Li
Published: June 2023

Underwater noise classification is of great significance for identifying ships as well as other vehicles. Moreover, it is helpful in ensuring a marine habitat-friendly, noise-free ocean environment. But a challenge we are facing is the small-sized underwater noise samples. Because noise is influenced by multiple sources, it is often difficult to determine and label which source or which two sources are dominant. At present, research to solve the problem is focused on noise image processing or advanced computer technology without starting with the noise generation mechanism and modeling. Here, a typical underwater noise generation model (UNGM) is established to augment noise samples. It is established by generating noise with certain kurtosis according to the spectral and statistical characteristics of the actual noise and filter design. In addition, an underwater noise classification model is developed based on UNGM and convolutional neural networks (CNN). Then the UNGM-CNN-based model is used to classify nine types of typical underwater noise, with either the 1/3 octave noise spectrum level (NSL) or power spectral density (PSD) as the input features. The results show that it is effective in improving classification accuracy. Specifically, it increases the classification accuracy by 1.59%, from 98.27% to 99.86%, and by 2.44%, from 97.45% to 99.89%, when the NSL and PSD are used as the input features, respectively. Additionally, the UNGM-CNN-based method appreciably improves macro-precision and macro-recall by approximately 0.87% and 0.83%, respectively, compared to the CNN-based method. These results demonstrate the effectiveness of the UNGM established in noise classification with small-sized samples.
A Spectral Enhancement Method Based on Remote-Sensing Images for High-Speed Railways
Authors: Zuo Dongsheng , Li Yingjie , Qiu Su , Jin Weiqi , Guo Hong
Published: June 2023

This paper proposes a pansharpening model in order to obtain remote-sensing images with high spatial resolution and high spectral resolution. Based on a generic component substitution (CS) fusion framework, the model utilizes the difference between the high-frequency component of the panchromatic (PAN) image and the high-frequency component of the luminance (L) image to express the missing spatial detail information of the ideal high-resolution multispectral (HRMS) image. A rolling guidance filter (RGF) is used in this framework to achieve the effective extraction of high-frequency information from remote-sensing images while reducing the spectral distortion of subsequent operations. The modulation transfer function (MTF) values of the sensor are also applied to the selection of adaptive weighting coefficients to further improve the spectral fidelity of the fused images. At the same time, the choice of suitable interpolation and gain coefficients improves the generalizability of the model while reducing spectral and spatial distortions. Finally, the use of a guided filter (GF) also greatly improves the quality of the fused image. The experimental results show that the model can effectively improve the spatial resolution for foreign objects at the perimeter of high-speed railways, while also ensuring the color fidelity of foreign objects such as colored steel tiles.
CNTR-YOLO: Improved YOLOv5 Based on ConvNext and Transformer for Aircraft Detection in Remote Sensing Images
Authors: Zhou Fengyun , Deng Honggui , Xu Qiguo , Lan Xin
Published: June 2023

Aircraft detection in remote sensing images is an important branch of target detection due to the military value of aircraft. However, the diverse categories of aircraft and the intricate background of remote sensing images often lead to insufficient detection accuracy. Here, we present the CNTR-YOLO algorithm based on YOLOv5 as a solution to this issue. The CNTR-YOLO algorithm improves detection accuracy through three primary strategies. (1) We deploy DenseNet in the backbone to address the vanishing gradient problem during training and enhance the extraction of fundamental information. (2) The CBAM attention mechanism is integrated into the neck to minimize background noise interference. (3) The C3CNTR module is designed based on ConvNext and Transformer to clarify the target’s position in the feature map from both local and global perspectives. This module is applied before the prediction head to optimize the accuracy of prediction results. Our proposed algorithm is validated on the MAR20 and DOTA datasets. The results on the MAR20 dataset show that the mean average precision (mAP) of CNTR-YOLO reached 70.1%, which is a 3.3% improvement compared with YOLOv5l. On the DOTA dataset, the results indicate that the mAP of CNTR-YOLO reached 63.7%, which is 2.5% higher than YOLOv5l.
RDVI: A Retrieval–Detection Framework for Verbal Irony Detection
Authors: Wen Zhiyuan , Wang Rui , Chen Shiwei , Wang Qianlong , Ding Keyang , Liang Bin , Xu Ruifeng
Published: June 2023

Verbal irony is a common form of expression used in daily communication, where the intended meaning is often opposite to the literal meaning. Accurately recognizing verbal irony is essential for any NLP application for which the understanding of the true user intentions is key to performing the underlying tasks. While existing research has made progress in this area, verbal irony often involves connotative knowledge that cannot be directly inferred from the text or its context, which limits the detection model’s ability to recognize and comprehend verbal irony. To address this issue, we propose a Retrieval–Detection method for Verbal Irony (RDVI). This approach improves the detection model’s ability to recognize and comprehend verbal irony by retrieving the connotative knowledge from the open domain and incorporating it into the model using prompt learning. The experimental results demonstrate that our proposed method outperforms state-of-the-art models.
Optimizing Neural Networks for Imbalanced Data
Authors: de Zarzà I. , de Curtò J. , Calafate Carlos T.
Published: June 2023

Imbalanced datasets pose pervasive challenges in numerous machine learning (ML) applications, notably in areas such as fraud detection, where fraudulent cases are vastly outnumbered by legitimate transactions. Conventional ML methods often grapple with such imbalances, resulting in models with suboptimal performance concerning the minority class. This study undertakes a thorough examination of strategies for optimizing supervised learning algorithms when confronted with imbalanced datasets, emphasizing resampling techniques. Initially, we explore multiple methodologies, encompassing Gaussian Naive Bayes, linear and quadratic discriminant analysis, K-nearest neighbors (K-NN), support vector machines (SVMs), decision trees, and multi-layer perceptron (MLP). We apply these on a four-class spiral dataset, a notoriously demanding non-linear classification problem, to gauge their effectiveness. Subsequently, we leverage the garnered insights for a real-world credit card fraud detection task on a public dataset, where we achieve a compelling accuracy of 99.937%. In this context, we compare and contrast the performances of undersampling, oversampling, and the synthetic minority oversampling technique (SMOTE). Our findings highlight the potency of resampling strategies in augmenting model performance on the minority class; in particular, oversampling techniques achieve the best performance, resulting in an accuracy of 99.928% with a significantly low number of false negatives (21/227,451).
Baseline Seroprevalence of SARS-CoV-2 Specific Antibodies in Hot Spot Areas of Great Tunis, up to 3 Months Post Disease Onset in Tunisia
Authors: Dhaouadi Sonia , Letaief Hejer , Hechaichi Aicha , Safer Mouna , Moussa Rym , Bouhali Ridha , Letaief Fethi , Abdelkader Latifa , Ben Salah Hamida , El Mili Nawel , Hammami Mongi , Talmoudi Khouloud , Souteyrand Yves , Nabeth Pierre , Kouni Chahed Mohamed , Bouafif ép Ben Alaya Nissaf
Published: June 2023

The extent of the SARS-CoV-2 circulation and the COVID-19 epidemic in Tunisia three months after virus circulation was unknown. The aim of this study was to determine the extent of SARS-CoV-2 infection among household contacts of confirmed COVID-19 cases living in Hot spot areas of Great Tunis, Tunisia by estimating the seroprevalence of antibodies anti SARS-CoV-2 and to identify factors associated to seroprevalence at the first stage of the pandemic in order to guide decision making and to constitute a baseline for further longitudinal analysis of protective immunity to SARS-CoV-2. The National Observatory of New and Emerging Diseases (ONMNE), Ministry of Health Tunisia (MoH), with the support of the Office of the World Health Organization Representative in Tunisia and the WHO Regional Office for the Eastern Mediterranean (EMRO)), conducted a household cross-sectional survey on April 2020 in Great Tunis (Tunis, Ariana, Manouba and Ben Arous). The study was based on the WHO seroepidemiological investigation protocol for SARS-CoV-2 infection. SARS-CoV-2 specific antibodies (IgG and IgM) were qualitatively detected using a lateral immunoassay that detect SARS-CoV-2 nucleocapsid protein and administered by the interviewers. The included subjects were confirmed COVID-19 cases and their households contacts resided in hot spot areas (cumulative incidence rate ≥ 10 cases/100,000 inhabitants) of Great Tunis. Results: In total, 1165 subjects were enrolled: 116 confirmed COVID-19 cases (43 active cases and 73 convalescents cases) and 1049 household contacts resided in 291 households. The median age of participants was 39.0 with 31 years’ interquartile range (Min = 8 months; Max = 96 years). The sex ratio (M/F) was 0.98. Twenty-nine per cent of participants resided in Tunis. The global crude seroprevalence among household contacts was 2.5% (26/1049); 95% CI 1.6–3.6%, 4.8%; 95% CI 2.3–8.7% in Ariana governorate and 0.3%; 95% CI 0.01%–1.8% in Manouba governorate. In multivariate analysis, the associated factors independently related to seroprevalence were age ≥25 years (aOR = 5.1; 95% CI 1.2–22.0), history of travel outside Tunisia since January 2020 (aOR = 4.6; 95% CI 1.7–12.9), symptomatic illness in the previous four months (aOR = 3.5; 95% CI 1.4–9.0) and governorate of residence (p = 0.02). The low seroprevalence estimated among household contacts in Great Tunis reflect the effect of public health measures early taken (national lockdown, borders closed, remote work), the respect of non-pharmaceutical interventions and the efficacy of COVID-19 contact-tracing and case management at the first stage of the pandemic in Tunisia.
Characterization of Bacterial Diversity in Aguamiel and Two Types of Pulque from the Zacatlán Region, México
Authors: Huezo-Sánchez Ana Rosa , Ortega-Rodríguez Eva Mariel , Pérez-Armendáriz Beatriz , El-Kassis Elie Girgis
Published: June 2023

Pulque is a traditional Mexican fermented beverage associated with numerous health benefits. Over time, there has been considerable interest in studying the bacterial diversity of pulque, and microbial characterization has been carried out using traditional microbiological and molecular methods. Therefore, the objective of this research was to characterize the microbiota of artisanal pulque obtained from the Zacatlán region in Puebla, México, by the means of Illumina sequencing, and to compare it with the microbial diversity of aguamiel (sap before fermentation), commercial pulque (supplemented with additives to increase its yield), and its seed (batch of pulque previously fermented). An analysis of the Shannon index showed medium diversity for both aguamiel and pulque samples (score > 2), while the Chao 1 index exhibited a non-significant difference between them. On the other hand, a principal components analysis confirmed the role of the seed as an essential inoculum to define the microbial diversity of pulque, emphasizing the importance of its preservation as a quality standard during the elaboration process. In addition, results showed that the dominant phyla in artisanal and commercial pulque were Firmicutes and Bacteroidetes. As the fermentation process progressed, it was possible to observe an increase in the population of lactic acid bacteria (LAB) in both types of pulque compared to those detected in aguamiel. Of these, the species Lactobacillus, Leuconostoc, and Lactococcus represented almost 95% of the total LAB. Finally, even though the safety of pulque has been in question due to its non-aseptic manufacturing process, the present study confirmed that less than 1% of its microbiota corresponds to the genera with a pathogenic potential such as γ-proteobacteria (Enterobacter and Hafnia), which decreases as the fermentation process advances.
Research on Blockchain Data Availability and Storage Scalability
Authors: Si Honghao , Niu Baoning
Published: June 2023

Blockchain adopts a chain data structure, and the characteristics of blocks that can only be added and cannot be deleted make the total number of blocks accumulate over time, forcing resource-constrained nodes to become degraded nodes in order to alleviate increasingly severe storage pressure. Degraded nodes only store partial blocks, although improving the scalability of blockchain storage and reducing data redundancy will lead to a decrease in data availability. To address the problem of storage scalability, quantitative research is needed on data availability. Based on a summary of the existing definitions of data availability, we propose a definition of data availability for blockchain. By analyzing the data synchronization process and the transaction lifecycle, key factors affecting data availability were extracted, and a data availability measurement model was constructed based on node types. On this basis, a relationship model linking data availability and storage scalability was constructed to find the range of data redundancy that meets the target data availability. The experimental results indicate that the data availability measurement model for blockchain can measure the data availability levels of different scalable storage schemes. The model of the relationship between data availability and storage scalability can guide the setting of data redundancy in scalable storage schemes.
Numerical Analysis of Convective Mass Transfer during Multi-Droplet Impingement on a Structured Surface in the Presence of an Adhered Liquid Film—An Application to Spray Etching of PCBs
Authors: Eßl Werner , Reiss Georg , Raninger Peter , Ecker Werner , Körbler Nadine , Gerold Eva , Antrekowitsch Helmut , Klocek Jolanta , Krivec Thomas
Published: June 2023

Multi-droplet impingement is a fundamental aspect inherent to all kinds of technical spray processes which typically aim at enhancing the convective exchange of reagents or heat at the impinged surface. In this paper, the impingement of multiple droplets onto a structured surface is investigated by a comprehensive CFD model, which resolves the dynamics of the individual droplets and the film on a micro-scale level based on the Volume of Fluid (VOF) method. The considered surface topology includes cavities and is typical for protective masks used in the spray etching of Printed Circuit Boards (PCBs). The agitation of the liquid film in terms of the convective mass transfer rates across virtual horizontal evaluation planes is studied and the influence of film height and droplet impaction velocity is elaborated. Passive tracer tracking is employed to investigate the release and re-entrainment of fluid at the surface cavities. Two modes of mass exchange between the cavities and the main flow upon droplet impingement are identified, which are central inflow accompanied by lateral outflow (1) and lateral inflow with outflow at the opposing side (2). A statistical analysis of the allocation of tracer particles shows that high impaction velocities and low film heights correlate with an enhanced decay of tracer particles within the cavities. The susceptibility to re-entrainment is also reduced by high impaction velocities, whereas increased film heights are found to promote re-entrainment.
Characterization and Mechanism of Tea Polyphenols Inhibiting Biogenic Amine Accumulation in Marinated Spanish Mackerel
Authors: Xu Zhe , Chang Jiale , Zhou Jiamin , Shi Yixin , Chen Hui , Han Lingyu , Tu Maolin , Li Tingting
Published: June 2023

Putrescine is a low-molecular-weight organic compound that is widely found in pickled foods. Although the intake of biogenic amines is beneficial to humans, an excessive intake can cause discomfort. In this study, the ornithine decarboxylase gene (ODC) was involved in putrescine biosynthesis. After cloning, expression and functional verification, it was induced and expressed in E. coli BL21 (DE3). The relative molecular mass of the recombinant soluble ODC protein was 14.87 kDa. The function of ornithine decarboxylase was analyzed by determining the amino acid and putrescine content. The results show that the ODC protein could catalyze the decarboxylation of ornithine to putrescine. Then, the three-dimensional structure of the enzyme was used as a receptor for the virtual screening of inhibitors. The binding energy of tea polyphenol ligands to the receptor was the highest at −7.2 kcal mol−1. Therefore, tea polyphenols were added to marinated fish to monitor the changes in putrescine content and were found to significantly inhibit putrescine production (p < 0.05). This study lays the foundation for further research on the enzymatic properties of ODC and provides insight into an effective inhibitor for controlling the putrescine content in pickled fish.
Carbon Quantum Dots from Roasted Coffee Beans: Their Degree and Mechanism of Cytotoxicity and Their Rapid Removal Using a Pulsed Electric Field
Authors: Chu Ling , Zhang Yu , He Leli , Shen Qingwu , Tan Mingqian , Wu Yanyang
Published: June 2023

Carbon quantum dots (CQDs) from heat-treated foods show toxicity, but the mechanisms of toxicity and removal of CQDs have not been elucidated. In this study, CQDs were purified from roasted coffee beans through a process of concentration, dialysis and lyophilization. The physical properties of CQDs, the degree and mechanism of toxicity and the removal method were studied. Our results showed that the size of CQDs roasted for 5 min, 10 min and 20 min were about 5.69 ± 1.10 nm, 2.44 ± 1.08 nm and 1.58 ± 0.48 nm, respectively. The rate of apoptosis increased with increasing roasting time and concentration of CQDs. The longer the roasting time of coffee beans, the greater the toxicity of CQDs. However, the caspase inhibitor Z-VAD-FMK was not able to inhibit CQDs-induced apoptosis. Moreover, CQDs affected the pH value of lysosomes, causing the accumulation of RIPK1 and RIPK3 in lysosomes. Treatment of coffee beans with a pulsed electric field (PEF) significantly reduced the yield of CQDs. This indicates that CQDs induced lysosomal-dependent cell death and increased the rate of cell death through necroptosis. PEF is an effective way to remove CQDs from roasted coffee beans.
Bioactive Potential and Chemical Composition of Coffee By-Products: From Pulp to Silverskin
Authors: Machado Marlene , Espírito Santo Liliana , Machado Susana , Lobo Joana C. , Costa Anabela S. G. , Oliveira Maria Beatriz P. P. , Ferreira Helena , Alves Rita C.
Published: June 2023

Processing the coffee cherry into roasted beans generates a large amount of by-products, which can negatively impact the environment. The aim of this study was to analyze the bioactive potential and chemical composition of different coffee by-products (pulp, husk, parchment, silverskin, defective beans, and green coffee sieving residue) having in mind their bioactive potential for health and well-being. The coffee by-products showed a distinct nutritional composition. The content of ash, protein, fat, and total dietary fiber was significantly higher (p < 0.05) in coffee pulp (10.72% dw), silverskin (16.31% dw), defective beans (8.47% dw), and parchment (94.19% dw), respectively. Defective beans and the sieve residue exhibited a higher content of total phenolics (6.54 and 5.11 g chlorogenic acid eq./100 g dw, respectively) as well as higher DPPH• scavenging activity (3.11 and 2.85 g Trolox eq./100 g, respectively) and ferric-reducing antioxidant power (17.68 and 17.56 g ferrous sulfate eq./100 g dw, respectively). All the coffee by-products considered in this study are sources of caffeine and chlorogenic acids, in particular 5-caffeoylquinic acid (5.36–3787.58 mg/100 g dw, for parchment and defective beans, respectively). Thus, they can be recycled as functional ingredients for food, cosmetic and/or pharmaceutical industries, contributing to the social, economic, and environmental sustainability of the coffee industry.
Legitimate or Not, Does It Really Matter? A Reading of the PDO Label’s Legitimacy through Consumers’ Perception
Authors: Bouhaddane Maria , Halawany-Darson Rafia , Rochette Corinne , Amblard Corinne
Published: June 2023

The proliferation of quality labels for the same food product questions the relevance of labeling schemes. Based on the theory of legitimacy and research on food-related consumer behavior, this study aims to examine the influence of the perceived legitimacy of a label (PDO) on consumers’ perceptions of the quality and purchase intentions of the labeled product. A conceptual model was, therefore, developed to estimate the influence of four dimensions of legitimacy on the perceived quality and purchase intention of PDO-labeled cheese, French cheeses being products whose quality is traditionally linked to their regional origin. Our model was tested on a sample of 600 consumers representative of the French population. Using Partial Least Square Structural Equation Modeling, results show that for surveyed consumers, the pragmatic, regulative, and moral legitimacy of the PDO label positively influences the perceived quality of PDO-labeled cheese. Furthermore, pragmatic legitimacy has a substantial and direct influence on purchase intention, whereas both regulative and moral legitimacy influence purchase intention only indirectly through perceived quality. Unexpectedly, our findings do not show a significant influence of cognitive legitimacy either on perceived quality or purchase intention. The output of this research contributes to a better understanding of the link between a label’s legitimacy, perceived quality, and purchase intention.
Investigation of Egyptian Banks’ Competition through a Riesz–Caputo Fractional Model
Authors: Omar Othman A. M. , Ahmed Hamdy M. , Hamdy Walid
Published: June 2023

In this paper, a four-dimensional competition model, driven by the Riesz-Caputo operator, is established. Then, the presented model’s uniqueness, existence, and stability are discussed. After that, the model is applied to describe the profit competition between Egyptian banks. Using the Hamiltonian definition of optimal control, we set a control strategy for banks’ profits during crises as a pre-step measure to counteract their negative impact. Finally, the model is solved numerically using an extended Adams–Bashford scheme. The valid data between the end of 2016 and 2020 are used for numerical simulations, while the data between the end of 2021 and 2022 are used for prediction measurements. From the results, it can be seen that the COVID-19 crisis resulted in a sudden decrease in banks’ profits during 2020, and if there were a control system enabled, it could have compensated for this decrease.
Genome-Wide Identification and Analysis of the Hsp40/J-Protein Family Reveals Its Role in Soybean (Glycine max) Growth and Development
Authors: Razzaq Muhammad Khuram , Rani Reena , Xing Guangnan , Xu Yufei , Raza Ghulam , Aleem Muqadas , Iqbal Shahid , Arif Muhammad , Mukhtar Zahid , Nguyen Henry T. , Varshney Rajeev K. , Siddique Kadambot H. M. , Gai Junyi
Published: June 2023

The J-protein family comprises molecular chaperones involved in plant growth, development, and stress responses. Little is known about this gene family in soybean. Hence, we characterized J-protein genes in soybean, with the most highly expressed and responsive during flower and seed development. We also revealed their phylogeny, structure, motif analysis, chromosome location, and expression. Based on their evolutionary links, we divided the 111 potential soybean J-proteins into 12 main clades (I–XII). Gene-structure estimation revealed that each clade had an exon-intron structure resembling or comparable to others. Most soybean J-protein genes lacked introns in Clades I, III, and XII. Moreover, transcriptome data obtained from a publicly accessible soybean database and RT-qPCR were used to examine the differential expression of DnaJ genes in various soybean tissues and organs. The expression level of DnaJ genes indicated that, among 14 tissues, at least one tissue expressed the 91 soybean genes. The findings suggest that J-protein genes could be involved in the soybean growth period and offer a baseline for further functional research into J-proteins' role in soybean. One important application is the identification of J-proteins that are highly expressed and responsive during flower and seed development in soybean. These genes likely play crucial roles in these processes, and their identification can contribute to breeding programs to improve soybean yield and quality.