bluuebunny/crossref_metadata_2025 huggingface dataset:
Constructing Lipoparticles Capable of Endothelial Cell-Derived Exosome-Mediated Delivery of Anti-miR-33a-5p to Cultured Macrophages
Authors: Echesabal-Chen Jing , Huang Kun , Vojtech Lucia , Oladosu Olanrewaju , Esobi Ikechukwu , Sachdeva Rakesh , Vyavahare Naren , Jo Hanjoong , Stamatikos Alexis
Published: July 2023

Atherosclerosis is driven by intimal arterial macrophages accumulating cholesterol. Atherosclerosis also predominantly occurs in areas consisting of proinflammatory arterial endothelial cells. At time of writing, there are no available clinical treatments that precisely remove excess cholesterol from lipid-laden intimal arterial macrophages. Delivery of anti-miR-33a-5p to macrophages has been shown to increase apoAI-mediated cholesterol efflux via ABCA1 upregulation but delivering transgenes to intimal arterial macrophages is challenging due to endothelial cell barrier integrity. In this study, we aimed to test whether lipoparticles targeting proinflammatory endothelial cells can participate in endothelial cell-derived exosome exploitation to facilitate exosome-mediated transgene delivery to macrophages. We constructed lipoparticles that precisely target the proinflammatory endothelium and contain a plasmid that expresses XMOTIF-tagged anti-miR-33a-5p (LP-pXMoAntimiR33a5p), as XMOTIF-tagged small RNA demonstrates the capacity to be selectively shuttled into exosomes. The cultured cells used in our study were immortalized mouse aortic endothelial cells (iMAECs) and RAW 264.7 macrophages. From our results, we observed a significant decrease in miR-33a-5p expression in macrophages treated with exosomes released basolaterally by LPS-challenged iMAECs incubated with LP-pXMoAntimiR33a5p when compared to control macrophages. This decrease in miR-33a-5p expression in the treated macrophages caused ABCA1 upregulation as determined by a significant increase in ABCA1 protein expression in the treated macrophages when compared to the macrophage control group. The increase in ABCA1 protein also simulated ABCA1-dependent cholesterol efflux in treated macrophages—as we observed a significant increase in apoAI-mediated cholesterol efflux—when compared to the control group of macrophages. Based on these findings, strategies that involve combining proinflammatory-targeting lipoparticles and exploitation of endothelial cell-derived exosomes appear to be promising approaches for delivering atheroprotective transgenes to lipid-laden arterial intimal macrophages.
Detection of Neutralizing Antibodies in COVID-19 Patients from Steve Biko Academic Hospital Complex: A Pilot Study
Authors: Kgatle Mankgopo , Chalwe Joseph Musonda , van der Westhuizen Donald , Xu Shuting , Damane Botle Precious , Mathebela Precious , Ueckermann Veronica , Mayaphi Simnikiwe , Rodrigues Hosana Gomes , Moura-Alves Pedro , Ndlovu Honest , Mzizi Yonwaba , Zongo Lusanda , Hairwadzi Henry , Vorster Mariza , Zeevaart Jan Rijn , Sathekge Mike
Published: July 2023

A correlation between neutralization activity after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination and protection against coronavirus disease 2019 (COVID-19) has been demonstrated by several studies. Here, we detect SARS-CoV-2 neutralizing antibody (NAB) production in COVID-19 patients from the Steve Biko Academic Hospital complex (SBAH), South Africa (SA). Samples from COVID-19 patients (mild to severe) were collected. SARS-CoV-2 rapid assays, genotyping (Delta and Omicron variants) and enzyme-linked immunosorbent assays (ELISA) were performed. IBM® Statistical Package for the Social Sciences (SPSS®) version 28 was used for inferential statistical analysis, and the data were presented using the Prism9 software (version 9.4.1). A total of 137 laboratory-confirmed COVID-19 patients, 12 vaccine recipients and 8 unvaccinated participants were evaluated. The production of SARS-CoV-2 NABs was observed in some of the COVID-19 cases, mainly in severe cases, although this should be noted with caution due to the small sample size of this pilot study. NABs were also observed in asymptomatic participants, with the most being found in recipients (n = 6) of the BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. We found a strong presence of NABs in COVID-19 patients, specifically in mild and severe cases. Severe infection was associated with higher NAB production (82%).
The Effect of Reduction and Oxidation Processes on the Work Function of Metal Oxide Crystals: TiO2(110) and SrTiO3(001) Case
Authors: Cieślik Karol , Wrana Dominik , Rogala Maciej , Rodenbücher Christian , Szot Krzysztof , Krok Franciszek
Published: July 2023

The strict control of the work function of transition metal oxide crystals is of the utmost importance not only to fundamental research but also to applications based on these materials. Transition metal oxides are highly abundant in electronic devices, as their properties can be easily modified using redox processes. However, this ease of tuning is a double-edged sword. With the ease of manipulation comes difficulty in controlling the corresponding process. In this study, we demonstrate how redox processes can be induced in a laboratory setting and how they affect the work function of two model transition metal oxide crystals, namely titanium dioxide TiO2(110) and strontium titanate SrTiO3(001). To accomplish this task, we utilized Kelvin Probe Force Microscopy (KPFM) to monitor changes in work function, Scanning Tunneling Microscopy (STM), and Low-Energy Electron Diffraction (LEED) to check the surface morphology and reconstruction, and we also used X-ray Photoelectron Spectroscopy (XPS) to determine how the surface composition evolves. We also show that using redox processes, the work function of titanium dioxide can be modified in the range of 3.4–5.0 eV, and that of strontium titanate can be modified in the range of 2.9–4.5 eV. Moreover, we show that the presence of an oxygen-gaining material in the vicinity of a transition metal oxide during annealing can deepen the changes to its stoichiometry and therefore the work function.
Specific Features of Formation of Electron and Hole Trapping Centers in Irradiated CaSO4-Mn and BaSO4-Mn
Authors: Nurakhmetov Turlybek. N. , Alibay Temirulan. T. , Zhangylyssov Keleshek. B. , Daurenbekov Dulat. H. , Zhunusbekov Amangeldy. M. , Kainarbay Aset. Zh. , Sadykova Batsaiy. M. , Tolekov Doszhan. A. , Shamiyeva Raushan. K.
Published: July 2023

Spectroscopic and thermoactivation methods were used to study the processes of accumulation of electron and hole trapping centers and energy transfer of electronic excitations to impurities in CaSO4-Mn and BaSO4-Mn. It is shown that electronic trapping centers are created during the excitation of an anionic complex as a result of charge transfer from O2−→SO42− to closely spaced anionic complexes SO42− in CaSO4 and BaSO4. In CaSO4 and BaSO4, energy transfer from the host to impurities occurs at the moment of charge transfer from the excited anionic complex to the combined radiative electronic state at 2.95–3.1 eV. This combined state is formed from electronic trapping centers Mn+-SO4− and SO43−-SO4−. It was found that the emerging combined radiative states at 2.95–3.1 eV of sulfates, which are formed as a result of charge transfer from the excited anionic complexes to the excited state of impurities, Tl+,Cu+,and Mn2+, occupy the same energy levels as the intrinsic electronic trapping center SO43− of the host at 2.95–3.17 eV. Experimental results show that during UV photon irradiation, anionic complexes are excited mainly near impurities in sulfates.
Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women
Authors: Kim Hayoung , Lim Jihe , Kim Hyug-Gi , Lim Yunji , Seo Bo Kyoung , Bae Min Sun
Published: July 2023

The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.
Segmentation of Portal Vein in Multiphase CTA Image Based on Unsupervised Domain Transfer and Pseudo Label
Authors: Song Genshen , Xie Ziyue , Wang Haoran , Li Shiman , Yao Demin , Chen Shiyao , Shi Yonghong
Published: July 2023

Background: Clinically, physicians diagnose portal vein diseases on abdominal CT angiography (CTA) images scanned in the hepatic arterial phase (H-phase), portal vein phase (P-phase) and equilibrium phase (E-phase) simultaneously. However, existing studies typically segment the portal vein on P-phase images without considering other phase images. Method: We propose a method for segmenting portal veins on multiphase images based on unsupervised domain transfer and pseudo labels by using annotated P-phase images. Firstly, unsupervised domain transfer is performed to make the H-phase and E-phase images of the same patient approach the P-phase image in style, reducing the image differences caused by contrast media. Secondly, the H-phase (or E-phase) image and its style transferred image are input into the segmentation module together with the P-phase image. Under the constraints of pseudo labels, accurate prediction results are obtained. Results: This method was evaluated on the multiphase CTA images of 169 patients. The portal vein segmented from the H-phase and E-phase images achieved DSC values of 0.76 and 0.86 and Jaccard values of 0.61 and 0.76, respectively. Conclusion: The method can automatically segment the portal vein on H-phase and E-phase images when only the portal vein on the P-phase CTA image is annotated, which greatly assists in clinical diagnosis.
Budd-Chiari Syndrome Imaging Diagnosis: State of the Art and Future Perspectives
Authors: Porrello Giorgia , Mamone Giuseppe , Miraglia Roberto
Published: July 2023

Budd-Chiari syndrome (BCS) is a rare hepatic vascular disorder defined by the presence of partial or complete impairment of hepatic venous drainage in the absence of right heart failure or constrictive pericarditis. Several conditions can lead to BCS, from hypercoagulable states to malignancies. Primary BCS is the most common subtype, and usually bartends hypercoagulability states, while secondary BCS involves tumor invasion or extrinsic compression. A combination of clinical and imaging features leads to the diagnosis of BCS, including (1) direct signs: occlusion or compression of the hepatic veins and/or inferior vena cava, and the presence of venous collaterals; (2) indirect signs: morphological hepatic changes with caudate lobe enlargement; inhomogeneous enhancement, and hypervascular nodules. From a clinicopathological point of view, two forms of BCS can be distinguished: acute and subacute/chronic BCS, although asymptomatic and fulminant forms are also possible. Acute presentations are rare, and symptoms include hepatomegaly, ascites, and hepatic insufficiency. Subacute/chronic forms are the most common presentation, with dysmorphic liver and variable degrees of fibrosis deposition. Patients with chronic BCS can develop benign regenerative nodules (large regenerative nodules or FNH [Focal Nodular Hyperplasia]-like lesions), but are also at a higher risk of hepatocellular carcinoma (HCC). The radiologist role is therefore fundamental in both diagnosis and surveillance of BCS. The aim of this review is to present all clinical and imaging signs that can help to reach the diagnosis of BCS, with their clinical significance, providing tips and tricks for the cross-sectional diagnosis of this condition.
Identification of TLE Focus from EEG Signals by Using Deep Learning Approach
Authors: Ficici Cansel , Telatar Ziya , Kocak Onur , Erogul Osman
Published: July 2023

Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30–35% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system.
On Reading Mathematical Texts, Question-Asking and Cognitive Load
Authors: Lavy Ilana , Shriki Atara
Published: July 2023

In this study, we examined aspects relating to the impact of integrating question-asking activities and providing answers to these questions while reading historical mathematical texts on prospective mathematics teachers’ self-reported cognitive load. The research design of the study was quasi-experimental. The study participants included two groups of 20 students each (experimental and control). The experimental group was instructed to ask questions while coping with the texts, whereas the control group received no special instructions. The experimental group participants were asked to think aloud while coping with the texts and audio record themselves. These records were transcribed into written protocols. Both groups had to respond to a self-esteem index questionnaire in which they had to report the level of difficulty they experienced during their attempts to cope with the texts, as an indicator of their sense of cognitive load. This process was repeated at three time points, relating to three different texts. The data were analyzed using quantitative and qualitative methods. Two main observations were obtained: (1) A significant difference was found between the control and the experimental groups regarding the decrease in cognitive load along the time points. Because the only difference between the control and experimental groups was the activity of question-asking, it might be concluded that question-asking affects the reduction in cognitive load. (2) Question-asking supports the assimilation of new information up to a specific limit, depending on the gap between existing knowledge and new information.
A Smart System for Environmental Parameter Monitoring in Underwater Archaeological Sites
Authors: Lupia Marco , Gagliardi Gianfranco , Cario Gianni , Cicchello Gaccio Francesco , D’Angelo Vincenzo , Folino Paolo
Published: July 2023

In this paper, we present a prototype for the remote monitoring of underwater archaeological sites. The system aims to provide autonomous and real-time data collection from environmental probes and also includes biofouling detection based on image processing. The monitoring system is equipped with sensors to measure various parameters of interest, including temperature, pH, conductivity, dissolved oxygen, and oxidation-reduction potential. To detect biofouling, a camera is integrated into the system, and an analysis is performed on the captured images utilizing indices such as the Normalized Difference Vegetation Index (NDVI) and the PYM method. The system consists of a subsea node placed near the monitoring site, connected to a surface buoy through an umbilical cable. The surface buoy contains an LTE router for data transmission to a remote web platform for data storage and visualization. The hardware of the underwater node includes an embedded computer, responsible for providing functionalities for image processing and interfacing with the web platform, and a micro-controller board to read the sensor measurements. The developed remote monitoring system has been tested in a marine environment and offers a comprehensive solution for long-term environmental parameter analysis, biofouling detection, and real-time data access. It holds great potential for applications in marine research, environmental monitoring, and ecosystem studies.
MemoryGAN: GAN Generator as Heterogeneous Memory for Compositional Image Synthesis
Authors: Wang Zongtao , Peng Jiajie , Liu Zhiming
Published: July 2023

The Generative Adversarial Network (GAN) has recently experienced great progress in compositional image synthesis. Unfortunately, the models proposed in the literature usually require a set of pre-defined local generators and use a separate generator to model each part object. This makes the model inflexible and also limits its scalability. Inspired by humans’ structured memory system, we propose MemoryGAN to eliminate these disadvantages. MemoryGAN uses a single generator as a shared memory to hold the heterogeneous information of the parts, and it uses a recurrent neural network to model the dependency between the parts and provide the query code for the memory. The shared memory structure and the query and feedback mechanism make MemoryGAN flexible and scalable. Our experiment shows that although MemoryGAN only uses a single generator for all the parts, it achieves comparable performance with the state-of-the-art, which uses multiple generators, in terms of synthesized image quality, compositional ability and disentanglement property. We believe that our result of using the generator of the GAN as a memory model will inspire future work of both bio-friendly models and memory-augmented models.
A Multistable Discrete Memristor and Its Application to Discrete-Time FitzHugh–Nagumo Model
Authors: Shatnawi Mohd Taib , Khennaoui Amina Aicha , Ouannas Adel , Grassi Giuseppe , Radogna Antonio V. , Bataihah Anwar , Batiha Iqbal M.
Published: July 2023

This paper presents a multistable discrete memristor that is based on the discretization of a continuous-time model. It has been observed that the discrete memristor model is capable of preserving the characteristics of the continuous memristor model. Furthermore, a three-dimensional memristor discrete-time FitzHugh–Nagumo model is constructed by integrating the discrete memristor into a two-dimensional FitzHugh–Nagumo (FN) neuron model. Subsequently, the dynamic behavior of the proposed neuron model is analyzed through Lyapunov exponents, phase portraits, and bifurcation diagrams. The results show multiple kinds of coexisting hidden attractor behaviors generated by this neuron model. The proposed approach is expected to have significant implications for the design of advanced neural networks and other computational systems, with potential applications in various fields, including robotics, control, and optimization.
Unsupervised Vehicle Re-Identification Based on Cross-Style Semi-Supervised Pre-Training and Feature Cross-Division
Authors: Zhan Guowei , Wang Qi , Min Weidong , Han Qing , Zhao Haoyu , Wei Zitai
Published: July 2023

Vehicle Re-Identification (Re-ID) based on Unsupervised Domain Adaptation (UDA) has shown promising performance. However, two main issues still exist: (1) existing methods that use Generative Adversarial Networks (GANs) for domain gap alleviation combine supervised learning with hard labels of the source domain, resulting in a mismatch between style transfer data and hard labels; (2) pseudo label assignment in the fine-tuning stage is solely determined by similarity measures of global features using clustering algorithms, leading to inevitable label noise in generated pseudo labels. To tackle these issues, this paper proposes an unsupervised vehicle re-identification framework based on cross-style semi-supervised pre-training and feature cross-division. The framework consists of two parts: cross-style semi-supervised pre-training (CSP) and feature cross-division (FCD) for model fine-tuning. The CSP module generates style transfer data containing source domain content and target domain style using a style transfer network, and then pre-trains the model in a semi-supervised manner using both source domain and style transfer data. A pseudo-label reassignment strategy is designed to generate soft labels assigned to the style transfer data. The FCD module obtains feature partitions through a novel interactive division to reduce the dependence of pseudo-labels on global features, and the final similarity measurement combines the results of partition features and global features. Experimental results on the VehicleID and VeRi-776 datasets show that the proposed method outperforms existing unsupervised vehicle re-identification methods. Compared with the last best method on each dataset, the method proposed in this paper improves the mAP by 0.63% and the Rank-1 by 0.73% on the three sub-datasets of VehicleID on average, and it improves mAP by 0.9% and Rank-1 by 1% on VeRi-776 dataset.
A Computational Case Study on Sustainable Energy Transition in the Kingdom of Saudi Arabia
Authors: Alghassab Mohammed
Published: July 2023

With the increasing urgency for sustainable development and energy transition, decision-makers face complex challenges in evaluating and prioritizing viable alternatives. Traditional decision-making techniques often struggle to capture the inherent uncertainty and imprecision associated with the latest sustainable energy transition issues. This paper presents a research framework based on fuzzy set theory and the technique for order of preference by similarity to ideal solution (TOPSIS) method to address these complexities and uncertainties. Our proposed approach offers a comprehensive evaluation and ranking of alternatives for sustainable energy transition. To demonstrate the effectiveness and applicability of this system, we employ a case study in the Kingdom of Saudi Arabia (KSA). As a global leader in fossil fuel production and export, particularly oil, the KSA has recognized the need to address climate change and diversify its energy sector. By leveraging the fuzzy TOPSIS-based framework, we provide decision-makers with a powerful tool to navigate the challenges and uncertainties involved in the energy transition process. This research yields promising results, demonstrating the superior capabilities of the proposed fuzzy TOPSIS-based framework compared to traditional decision-making techniques. The case study in the KSA highlights how our approach effectively captures and addresses the uncertainties and complexities involved in sustainable energy transition decision making. Through comprehensive evaluations and rankings, decision-makers gain valuable insights into alternative solutions, facilitating informed and strategic decision-making processes. Our research contributes to sustainable energy transitions by introducing a robust decision-making framework that integrates fuzzy set theory and the TOPSIS method. Based on the fuzzy TOPSIS-based evaluation, the research findings indicate that solar energy (EA1) ranked as the most favourable alternative among the evaluated options for the sustainable energy transition in the KSA. Using our framework, stakeholders in the KSA and similar contexts can make informed decisions to accelerate their energy transition efforts and achieve sustainable development goals.
A Review of Sensor Applications in Electric Vehicle Thermal Management Systems
Authors: Cheng Anyu , Xin Yi , Wu Hang , Yang Lixin , Deng Banghuai
Published: July 2023

With the rapid development of the automotive industry, the application of sensors is of great importance in maintaining the reliability of electric vehicles and ensuring the safe operation of electric vehicles. Faced with the increasing data of thermal management system condition monitoring, sensor detection is widely used in the monitoring of electric vehicle thermal management system. In recent years, a large number of related studies and contributions to the literature have been published. Although a number of reviews have summarized this, these reviews lack an overview of the issues and methods raised in these studies. This paper reviews recent sensor applications for electric vehicle thermal management systems. Currently, battery internal sensors, battery external sensors and related multi-sensor fusion, traditional motor sensors, positionless motor sensors, and component-level sensors of air conditioning systems are the main application sensors in the field of thermal management systems. This article introduces the basic principles of each type of sensor, reviews the relevant applications of various thermal management modules, and summarizes the usage characteristics of each type of sensor. The main problems faced by the existing research on the application of thermal management system-based sensors, such as the detection accuracy of traditional sensors and the detection stability of advanced sensors, are summarized, and the solutions proposed by the existing research are also summarized. Finally, some future research directions, trends, and hotspots are outlined. It is hoped that this review can help readers to understand the problems and existing solutions for thermal-management-system-based sensor applications, and to conduct related research more effectively.
Surface Waterproofing Techniques: A Case Study in Nova Lima, Brazil
Authors: Gomes Lorena Carias de Freitas , Gomes Henrique Comba , Reis Elvys Dias
Published: July 2023

Considering the various problems caused by infiltration in civil construction, this study aimed to identify the most appropriate waterproofing methods for different types of surfaces. A study was conducted on the mechanisms of water infiltration on surfaces and the waterproofing methods available on the market, focusing on asphalt blankets, in addition to a literature review highlighting state-of-the-art methods on this topic. A case study was also conducted in a residence in Nova Lima, Brazil, analyzing different waterproofing techniques, including their characteristics and stages. Among the conclusions, it is highlighted that the implementation of adequate project, installation, inspection, and maintenance techniques can significantly reduce the waterproofing failure rate and repair costs, and that the excellent choice of materials, along with the skill of the labor force in the application, is fundamental to guarantee the adequate performance of these materials in buildings.
The Nutritional Qualities of Walnuts and Their Planted Soils from China—Level and Relationship
Authors: Cui Maokai , Li Qingyang , Ni Zhanglin , Han Yongxiang , Zheng Yuewen , Mo Runhong , Shen Danyu , Liu Yihua
Published: July 2023

China is the world’s largest walnut producer. There is still a scarcity of comprehensive information on the nutritional quality of Chinese walnuts and the influence of soil on walnut quality. In this study, 273 samples and the corresponding soils from the main English walnut (Juglans regia L.)-producing areas in China were collected. The results revealed that walnut oil contained a high percentage of linoleic acid and γ-tocopherol, with an average content of 62.36% and 77.70%, respectively. Walnut flour is rich in nutritional elements, with the highest content of macro-element K at 3961.29 mg kg−1. In addition, 12 micro-elements with concentrations ranging from 0.01 to 42.34 mg/kg were discovered in walnut flour. The total amino acid profile of walnut flour contains 27.23% of the seven essential amino acids, with the highest amount allocated to leucine at 2.07 g 100 g−1. The Pearson correlation analysis revealed that almost all amino acids showed a significant positive correlation (p < 0.01, r > 0.70). The soil’s content in terms of three trace elements (Fe, Cu, and Zn) has the greatest influence on the nutritional qualities of walnuts. Furthermore, the pH of the soil played a role in determining the qualities of the walnut. These findings provide fundamental information for the consumer selection and trade of walnuts, as well as guidance for the development of high-quality walnuts in China.
On the Analysis of a Fractional Tuberculosis Model with the Effect of an Imperfect Vaccine and Exogenous Factors under the Mittag–Leffler Kernel
Authors: Ahmad Saeed , Pak Sedat , Rahman Mati ur , Al-Bossly Afrah
Published: July 2023

This research study aims to investigate the effects of vaccination on reducing disease burden by analyzing a complex nonlinear ordinary differential equation system. The study focuses on five distinct sub-classes within the system to comprehensively explore the impact of vaccination. Specifically, the mathematical model employed in this investigation is a fractional representation of tuberculosis, utilizing the Atangana–Baleanu fractional derivative in the Caputo sense. The validity of the proposed model is established through a rigorous qualitative analysis. The existence and uniqueness of the solution are rigorously determined by applying the fundamental theorems of the fixed point approach. The stability analysis of the model is conducted using the Ulam–Hyers approach. Additionally, the study employs the widely recognized iterative Adams–Bashforth technique to obtain an approximate solution for the suggested model. The numerical simulation of the tuberculosis model is comprehensively discussed, with a particular focus on the assumptions made regarding vaccination. The model assumes that only a limited portion of the population is vaccinated at a steady rate, and the efficacy of the vaccine is a critical factor in reducing disease burden. The findings of the study indicate that the proposed model can effectively assess the impact of vaccination on mitigating the spread of tuberculosis. Furthermore, the numerical simulation underscores the significance of vaccination as an effective control measure against tuberculosis.
Modeling Supply Chain Firms’ Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data
Authors: Sakas Damianos P. , Giannakopoulos Nikolaos T. , Margaritis Markos , Kanellos Nikos
Published: July 2023

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly.
Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics
Authors: Zhang Die , Ge Yong , Wu Xilin , Liu Haiyan , Zhang Wenbin , Lai Shengjie
Published: July 2023

Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.
Phase Formation of Co and Cr Co-Doped Bismuth Niobate with Pyrochlore Structure
Authors: Zhuk Nadezhda , Badanina Ksenia , Korolev Roman , Makeev Boris , Krzhizhanovskaya Maria , Kharton Vladislav
Published: July 2023

The formation mechanisms of pyrochlore-type Bi2Co1/2Cr1/2Nb2O9+Δ (space group Fd-3m, a = 10.4838(8) Å), in the temperature range from 400 to 1050 °C were studied by employing X-ray diffraction, scanning electron microscopy and energy-dispersive spectroscopy. An extensive reaction between the binary metal oxides was found to begin at temperatures above 550 °C, following the transition of monoclinic α-Bi2O3 into a tetragonal β-Bi2O3 polymorph. The synthesis process occurs in several stages when Bi-rich intermediate products (Bi6CrO12, Bi6Cr2O15, and Bi5Nb3O15) transform into bismuth-depleted BiNbO4 and a chromium–cobalt spinel is formed. The formation of a single pyrochlore phase occurs at the final reaction stage at 1050 °C via the doping of bismuth ortho-niobate, BiNbO4, by the transition metal cations. The observed mechanism is essentially similar to the mechanism of tantalate-based phases except for the formation of Bi5Nb3O15 at the intermediate reaction stages.
Detection of La Crosse Virus In Situ and in Individual Progeny to Assess the Vertical Transmission Potential in Aedes albopictus and Aedes aegypti
Authors: Darby Christie S. , Featherston Kyah M. , Lin Jingyi , Franz Alexander W. E.
Published: July 2023

La Crosse virus (LACV) is circulating in the midwestern and southeastern states of the United States and can cause human encephalitis. The main vector of the virus is the eastern tree-hole mosquito, Aedes triseriatus. Ae. albopictus has been also described as a natural LACV vector, while Ae. aegypti has been infected with the virus under laboratory conditions. Here, we compare the vertical transmission potential of LACV in Ae. albopictus and Ae. aegypti, with emphasis given to the ovarian infection patterns that the virus generates in both species. Both mosquito species received artificial bloodmeals containing LACV. At defined time points post-infection/bloodmeal, midguts, head tissue, and ovaries were analyzed for the presence of virus. Viral infection patterns in the ovaries were visualized via immunofluorescence confocal microscopy and immunohistopathology assays using an LACV-specific monoclonal antibody. In Ae. aegypti, LACV was confronted with midgut infection and escape barriers, which were much less pronounced in Ae. albopictus, resulting in a significantly higher prevalence of infection in the latter. Following the ingestion of a single virus-containing bloodmeal, no progeny larvae were found to be virus-infected. Regardless, females of both species showed the presence of LACV antigen in their ovariole sheaths. Furthermore, in a single Ae. albopictus female, viral antigen was associated with the nurse cells inside the primary follicles. Following the ingestion of a second non-infectious bloodmeal at 7- or 10-days post-ingestion of an LACV-containing bloodmeal, more progeny larvae of Ae. albopictus than of Ae. aegypti were virus-infected. LACV antigen was detected in the egg chambers and ovariole sheaths of both mosquito species. Traces of viral antigen were also detected in a few oocytes from Ae. albopictus. The low level of vertical transmission and the majority of the ovarian infection patterns suggested the transovum rather than transovarial transmission (TOT) of the virus in both vector species. However, based on the detection of LACV antigen in follicular tissue and oocytes, there was the potential for TOT among several Ae. albopictus females. Thus, TOT is not a general feature of LACV infection in mosquitoes. Instead, the TOT of LACV seems to be dependent on its particular interaction with the reproductive tissues of a female.
Closed-Form Hyper-Rayleigh Mode Analysis of the Fluctuating Double-Rayleigh with Line-of-Sight Fading Channel
Authors: Gvozdarev Aleksey S. , Artemova Tatiana K. , Alishchuk Aleksandra M. , Kazakova Marina A.
Published: July 2023

The research studies hyper-Rayleigh behavior of a wireless communication system functioning in the presence of the generalized multipath fading. Although the initial metric (hyper-Rayleigh mode (HRM)) is quite informative, it is defined only asymptotically (i.e., for the infinitely increasing SNR). In spite of mathematical simplifications brought by such a definition (i.e., in this case most of the performance characteristics defining the HRM can be easily evaluated), it sufficiently limits its applications since, evidently, the real-life systems function in the presence of a finite (and usually not very high) SNR. The study presents a novel approach to the fading channel analysis (i.e., finite signal-to-noise ratio hyper-Rayleigh mode (fHRM)). The proposed metric (fHRM) is studied on the newly presented channel model-fluctuating double-Rayleigh with Line-of-Sight (fdRLoS) fading model. To accomplish this, the novel expressions for two channel-dependent system characteristics (i.e., the Amount of Fading (AoF) and the Outage Probability (OP)) were derived in exact form valid for arbitrary fading parameters. Based on the derived expressions, the finite SNR hyper-Rayleigh map is obtained, which helps to identify the parameters’ values corresponding to the specific propagation scenarios, which were further deployed to analyze the problem of the communication link physical layer security quantified in terms of the probability of strictly positive secrecy capacity (SPSC). Numerical verification of the derived closed-form expressions was performed. Several peculiarities of the system performance are observed and discussed.
Wear Behavior of AZ61 Matrix Hybrid Composite Fabricated via Friction Stir Consolidation: A Combined RSM Box–Behnken and Genetic Algorithm Optimization
Authors: Abebe Samuel Kefyalew , Beri Habtamu , Sinha Devendra Kumar , Rajhi Ali A. , Hossain Nazia , Duhduh Alaauldeen A. , Zainuddin Shaik , Ahmed Gulam Mohammed Sayeed
Published: July 2023

Friction stir consolidation (FSC) is a promising manufacturing process for metal matrix hybrid composites (MMHC) with excellent mechanical properties. The originality of this study involves the exploration of the fabrication technique (FSC), the selection of materials and the optimization of wear behavior via a systematic investigation of the process parameters. The aim of this study was to optimize and investigate the wear behavior of MMHCs fabricated using FSC. The optimum sample was nominated for thermogravimetric analysis (TGA) and wear morphology analysis using SEM imaging. Material compositions of 7.5%wt of SiC, 7.5%wt of ZrO2 and 85%wt of AZ61 were considered for the experimental investigation. The RSM Box–Behnken design followed by a genetic algorithm (GA) was implemented to optimize the process parameters of sliding distance, speed and load at 350 m, 500 m and 650 m; 220 rpm, 240 rpm and 260 rpm; and 20 N, 30 N and 40 N, respectively. The RSM Box–Behnken result showed that the minimum wear rate of 0.008 mg/m was obtained at 350 m, 20 N and 240 rpm, whereas GA predicted the optimum parametric setup at 350 m, 20 N and 220 rpm. Additionally, TGA showed the material’s thermal stability from 375 °C to 480 °C. Generally, MMHCs exhibited a promising wear performance, proving the effectiveness of the FSC.
A Modified Equivalent Factor Method Evaluation Model Based on Land Use Changes in Tianfu New Area
Authors: Li Jing , Qiu Jian , Amani-Beni Majid , Wang Yuyang , Yang Mian , Chen Juewen
Published: July 2023

Scientific understanding of urban ecosystem service value (ESV) is fundamental to building an urban ecological landscape pattern and improving urban environmental quality. The equivalent factor method (EFM) is widely used in evaluating ESV for natural ecosystems. In this study, using the EFM and sensitivity analysis, our research explored the space–time changes in land use and ESV during the planning and construction of Tianfu New Area from 2010 to 2020. This study selected correction factors from natural geography and social economy aspects, and established space–time correction models for standard equivalent coefficients as well as a comprehensive dynamic evaluation model for the ecosystem service value of specific urban areas. In terms of land use, the area of farmland decreased the most. The areas of construction land, grassland, and water bodies increased significantly. The reduced farmland was mainly converted into construction land, followed by grassland and water bodies. Other land use types had smaller changes. Due to the increased area of water bodies and their high-value coefficient per unit area, the urban ecosystem service value showed an increasing trend. During the study period, the conversion of about 1% of land led to about a 0.25% change in the urban ecosystem service value. Farmland in 2010 and water body in 2020 are the most sensitive land factors for ESV in Tianfu New Area. The results might have important insights for urban ecological environment protection and improving ecosystem services during the construction of newly built urban areas.
Prediction of Tool Wear Rate and Tool Wear during Dry Orthogonal Cutting of Inconel 718
Authors: Zhang Ziqi , Liu Zhanqiang , Ren Xiaoping , Zhao Jinfu
Published: July 2023

A new prediction method was proposed based on the positive feedback relationship between tool geometry and tool wear rate. Dry orthogonal cutting of Inconel 718 was used as a case study. Firstly, tool wear rate models and a tool wear prediction flowchart were proposed. Secondly, the evolution of the tool geometry during tool wear was analyzed considering the combined effect of tool crater wear and tool flank wear. Thirdly, the evolution of the cutting temperature, normal stress and tool–chip relative sliding velocity on the tool wear surface was studied, the evolution of the tool wear rate during tool wear was revealed. Finally, the evolution of the tool geometry and tool wear rate during tool wear were applied to the tool wear prediction method to accurately predict the tool wear. The prediction error of KT is less than 15% in comparison with the experimental results. The tool wear prediction method in this paper is helpful to improve the prediction accuracy of tool crater wear.
Enhanced Properties of Ti/Al Laminated Composite Reinforced by High-Entropy Alloy Particles
Authors: Wang Enhao , Lv Lisong , Kang Fuwei , Li Shangzhuo , Li Jiaqi , Tian Yao , Jiang Wei , Song Xiaogang
Published: July 2023

Novel HEAp-Ti/Al laminated composites embedded with particles of the high-entropy alloy Al0.5CoCrFeNi (HEA) were fabricated by vacuum hot-press sintering at 730 °C. The phase composition and microstructure of the composites were studied with X-ray diffractometer (XRD), scanning electron microscope SEM, energy dispersive spectrometer (EDS), transmission electron microscope (TEM), and electron backscattering diffraction (EBSD) techniques. At this temperature, it has been observed that Al3Ti intermetallic compound is the favored phase and the reaction results in the dispersion of Al3Ti in the original Al layer. A large number of interfaces are formed between Al3Ti and Al. The deformed Al3Ti grains are concentrated in the interface near the Ti side. The mechanical properties, including tensile and compressive properties at room temperature, were analyzed. The tensile test results indicate that the composite exhibited an average tensile strength of 258 MPa and an average yield strain of 9.86%. Compression test results show that when a load perpendicular to the layer is applied, the yield strain and yield stress of the material are 9.67% and 474.09 MPa, respectively. Moreover, under a load parallel to the layer, the material fails due to interfacial debonding.