bluuebunny/crossref_metadata_2025 huggingface dataset:
Contribution of sensory nerves to cutaneous reactive hyperaemia in non‐Hispanic Black and White young adults
Authors: Turner Casey G. , Walker Demetria C. , Wong Brett J.
Published: April 2023

The aim of this study was to assess cutaneous sensory nerve function, independent of nitric oxide, in non‐Hispanic Black and White young adults. We tested the hypothesis that cutaneous reactive hyperaemia and sensory nerve‐mediated vasodilatation would be lower in non‐Hispanic Black young adults relative to non‐Hispanic White young adults. Twenty‐four participants who self‐identified as non‐Hispanic Black (n = 12) or non‐Hispanic White (n = 12) were recruited. All participants underwent three bouts of reactive hyperaemia. An index of skin blood flow was measured continuously using laser‐Doppler flowmetry at a control site and at a site treated with topical 4% lignocaine to inhibit sensory nerve function. Peak reactive hyperaemia was lower in non‐Hispanic Black relative to non‐Hispanic White participants (P < 0.001). Total reactive hyperaemia was lower in non‐Hispanic Black [mean (SD); control, 4085 (955)%CVCmax s; lignocaine, 2127 (639) percent maximal cutaneous vascular conductance * seconds, %CVCmax s] relative to non‐Hispanic White [control: 6820 (1179)%CVCmax s; lignocaine, 3573 (712)%CVCmax s] participants (P < 0.001 for both sites). There was no difference between groups for the calculated contribution of sensory nerves to either the peak [non‐Hispanic Black, 25 (14)%; non‐Hispanic White, 19 (13)%] or total reactive hyperaemic response [non‐Hispanic Black, 48 (10)%; non‐Hispanic White, 47 (10)%]. These data suggest that cutaneous reactive hyperaemia is lower in non‐Hispanic Black young adults, but the sensory nerve contribution is similar in non‐Hispanic Black and White young adults.
SOCE as a regulator of neuronal activity
Authors: Courjaret Raphael , Prakriya Murali , Machaca Khaled
Published: April 2023

Store operated Ca2+ entry (SOCE) is a ubiquitous signalling module with established roles in the immune system, secretion and muscle development. Recent evidence supports a complex role for SOCE in the nervous system. In this review we present an update of the current knowledge on SOCE function in the brain with a focus on its role as a regulator of brain activity and excitability

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Soccer Players’ Sleep Quality and Training Load Were Affected by the COVID-19 Lockdown: An International Survey
Authors: Romdhani Mohamed , Washif Jad Adrian , Taylor Lee , Chamari Karim , _ _
Published: April 2023

Background: The effect of the 2020 COVID-19 lockdown on athlete sleep and training behavior is documented, albeit without a worldwide soccer-specific focus. Method: Soccer (football) players (N = 1639; 30 countries; age 22.5 [5.7] y; 81% ≤25 y; 56% male; 30% elite; 66% Muslim) answered a retrospective, cross-sectional questionnaire related to their behavioral habits before and during COVID-19 lockdown (survey period July to September 2020), including (1) Pittsburgh Sleep Quality Index PSQI, (2) Insomnia Severity Index (ISI), (3) bespoke questions about training behaviors, and (4) Muslim player focused sleep and training behavior questions. Results: During lockdown (compared to prelockdown), PSQI (P < .001; moderate effect size [ES]) and ISI (P < .001; moderate ES) scores were higher in the overall sample and in elite versus nonelite (PSQI: P < .05; small ES and ISI: P < .001; small ES), >25 years versus ≤25 years (PSQI: P < .01; small ES and ISI: P < .001; moderate ES), females versus males (PSQI: P < .001; small ES), <1 month versus >1 month lockdown (PSQI: P < .05; small ES and ISI: P < .05; small ES), and players maintaining versus reducing training intensity (PSQI: P < .001; moderate ES and ISI: P < .001; small ES). Muslim players (41%) reported unfavorable sleep and/or training behaviors during Ramadan in lockdown compared to lockdown outside of Ramadan. Conclusions: Specific subgroups appear more vulnerable to lockdown effects, with training-intensity maintenance moderating negative effects relative to sleep. Policy and support (respectful of subgroup nuances) during lockdown-like challenges that facilitate training (including intensity) appear prudent, given their favorable relationship with sleep, mental health, and physical health, in the present data and elsewhere.
Association of Early Sports Participation With Sedentary Behavior in Community-Dwelling Adults—The Role of Sociodemographic Factors in a Retrospective Epidemiological Study
Authors: Tebar William R. , Borges Luan O. , Delfino Leandro D. , Mota Jorge , Ritti-Dias Raphael M. , Christofaro Diego G.D.
Published: April 2023

Background: The role of sociodemographic factors in the association between early sports participation (ESP) and sedentary behavior is unclear. We analyzed the association of ESP with sedentary behavior and identified the influence of sociodemographic factors in adulthood. Methods: A sample of 264 community-dwelling adults was randomly assessed (42.6 [17.0] y old, 57.6% women). Sociodemographic factors (age, gender, and socioeconomic status) and ESP (retrospectively) were assessed by questionnaire. Sedentary behavior was measured by accelerometer and self-reported for domains identification. The association between ESP and sedentary behavior domains and sociodemographic factors was analyzed by Poisson regression and presented in prevalence ratio (PR). Sociodemographic factors were separately included as covariates to identify their role in the main association analysis. Results: The ESP prevalence was 56.4% (n = 149). ESP participants included a higher proportion of men (59.7% vs 20.0%), had a lower age (36.8 [15.3] vs 50.8 [15.9]), and included a smaller proportion of individuals with low socioeconomic status (24.8% vs 43.5%) compared with those without ESP. The ESP was inversely associated with older age (PR = 0.58, P < .001 for middle aged; PR = 0.34, P < .001 for older participants), female gender (PR = 0.79, P < .001), low socioeconomic status (PR = 0.63, P = .036), and TV watching (PR = 0.67, P = .011). ESP was associated with driving (PR = 1.50, P = .028), office/paper work (PR = 1.63, P = .012), and using a cellphone (PR = 1.60, P = .009). The age was the main confounding factor of association between ESP and sedentary behavior, followed by socioeconomic status. No mediation role was identified. Conclusion: The ESP was associated with mentally active behavior domains and inversely associated with mentally passive sedentary behavior domains, but this association was majorly affected by sociodemographic factors, mainly by age.
The Relationship Between Eating Disorders, Disordered Eating, and Injury in Athletes: A Critically Appraised Topic
Authors: Hamstra-Wright Karrie L. , Bliven Kellie C. Huxel , Coumbe-Lilley John E. , Djelovic Eddin , Patel Jahnvi
Published: April 2023

Clinical Scenario: Eating disorders (EDs) and disordered eating (DE) result in numerous physical and psychological complications for female and male athletes. Besides bone-related injury, little research exists investigating what injuries EDs and/or DE contribute to. Clinical Question: Are EDs and/or DE a risk factor for injury incidence in athletes? Summary of Key Findings: We searched for prospective studies assessing EDs or DE as a risk factor for injury in female or male athletes high school age and older. Our search returned 5 studies. One study found Eds, or DE were not a risk for any type of injury in female cross-country and track-and-field athletes. Two studies found a possible relationship between EDs or DE, as one contributing factor of others, in the incidence of bone stress injuries (BSIs) in female athletes who compete in various sports. One study found female, but not male, cross-country and track-and-field athletes with a history of EDs were more at risk for stress fractures than those without a history. One study found Eds, or DE were not a risk for BSI in female runners and triathletes. Clinical Bottom Line: Large and important gaps in the literature exist investigating injuries related to EDs or DE outside of BSIs. There is low–moderate evidence that EDs and/or DE are either a sole, or contributing, risk factor for BSIs in female athletes. Strength of Recommendation: Grade B evidence exists to support the idea that EDs and/or DE are a risk factor for a specific type of injury (BSI) in female athletes only.
Applications of Data Literacy to Course Design in Sport Performance Analytics
Authors: Pifer Nathan David , Lumpkin Angela , Henry Thomas
Published: April 2023

With sport now fully immersed in the era of big data, there is a need for sport managers who are data literate and, therefore, capable of collecting, managing, evaluating, and applying data to the range of problems and scenarios encountered by industry personnel. However, many in the sport management academy remain unacquainted with the development and delivery of sports analytics courses, unsure of the methods and means by which they can equip students with the necessary skills. This is particularly true in sport performance analytics, the version of sports analytics popularized in the book and movie Moneyball and representative of data analyses applied to the competitive side of sport. Although prior literature has provided pedagogical guidance for instructors in the areas of general data analytics or sport business analytics, sports analytics in this traditional sense has largely been ignored. Using the data literacy framework, this manuscript outlines procedures for designing and delivering an applied course in sport performance analytics. It further provides prospective implementers with effective instructional tools and an overview of the challenges likely to be encountered in this arena.
Gravitational lensing by a charged spherically symmetric black hole immersed in thin dark matter
Authors: Gao Xiao-Jun , Yan Xiao-kun , Yin Yihao , Hu Ya-Peng
Published: April 2023

We investigate the gravitational lensing effect around a spherically symmetric black hole, whose metric is obtained from the Einstein field equation with electric charge and perfect-fluid dark matter contributing to its energy-momentum tensor. We do the calculation analytically in the weak field limit and we assume that both the charge and the dark matter are much less abundant (only give rise to the next-leading-order contribution) in comparison to the black hole mass. In particular, we derive the light deflection angle and the size of the Einstein ring, where approximations up to the next-leading order are done with extra care, especially for the logarithmic term from perfect-fluid dark matter. We expect our results will be useful in the future to relate the theoretical model of perfect fluid dark matter with observations of celestial bodies immersed in thin dark matter.
An Efficient Framework for Unsupervised Anomaly Detection over Edge-Assisted Internet of Things
Authors: Liu Yumeng , Wang Hongan , Zheng Xu , Tian Ling
Published: April 2023

The detection of anomaly status plays a pivotal role in the maintenance of public transportation and facilities in smart cities. Owing to the pervasively deployed sensing devices, one can collect and apply multi-dimensional sensing data to detect and analyze potential anomalies and react promptly. Current efforts concentrate on offline manners and fail to fit the situation in smart cities, where efficient and online solutions are expected. In this paper, a novel framework is designed for anomaly detection over edge-assisted Internet-of-Things (IoTs). The framework allows periodical data collection from sensors and continuous anomaly detection at the edge node. A novel efficient and unsupervised deep learning model is designed to balance the resource consumption and accuracy for anomaly detection, based on the combination of a convolutional autoencoder and adversarial training. Meanwhile, the proposed framework also adopts an adaptive strategy for continuous anomaly detection to reduce the overall resource consumption. According to theoretical analysis and evaluation on several real-world datasets, the proposed framework can discover the potential correlation features among multi-dimensional sensing data, and efficiently detect the abnormality of public transportation and facilities in smart cities.
Weakly Supervised Hashing with Reconstructive Cross-modal Attention
Authors: Du Yongchao , Wang Min , Lu Zhenbo , Zhou Wengang , Li Houqiang
Published: April 2023

On many popular social websites, images are usually associated with some meta-data such as textual tags, which involve semantic information relevant to the image and can be used to supervise the representation learning for image retrieval. However, these user-provided tags are usually polluted by noise, therefore the main challenge lies in mining the potential useful information from those noisy tags. Many previous works simply treat different tags equally to generate supervision, which will inevitably distract the network learning. To this end, we propose a new framework, termed as Weakly Supervised Hashing with Reconstructive Cross-modal Attention (WSHRCA), to learn compact visual-semantic representation with more reliable supervision for retrieval task. Specifically, for each image-tag pair, the weak supervision from tags is refined by cross-modal attention, which takes image feature as query to aggregate the most content-relevant tags. Therefore, tags with relevant content will be more prominent while noisy tags will be suppressed, which provides more accurate supervisory information. To improve the effectiveness of hash learning, the image embedding in WSHRCA is reconstructed from hash code, which is further optimized by cross-modal constraint and explicitly improves hash learning. The experiments on two widely-used datasets demonstrate the effectiveness of our proposed method for weakly-supervised image retrieval. The code is available at
https://github.com/duyc168/weakly-supervised-hashing
.
EVHA: Explainable Vision System for Hardware Testing and Assurance—An Overview
Authors: Al Hasan Md. Mahfuz , Tahsin Mostafiz Mohammad , An Le Thomas , Julia Jake , Vashistha Nidish , Taheri Shayan , Asadizanjani Navid
Published: April 2023

Due to the ever-growing demands for electronic chips in different sectors, semiconductor companies have been mandated to offshore their manufacturing processes. This unwanted matter has made security and trustworthiness of their fabricated chips concerning and has caused the creation of hardware attacks. In this condition, different entities in the semiconductor supply chain can act maliciously and execute an attack on the design computing layers, from devices to systems. Our attack is a hardware Trojan that is inserted during mask generation/fabrication in an untrusted foundry. The Trojan leaves a footprint in the fabrication through addition, deletion, or change of design cells. To tackle this problem, we propose EVHA (Explainable Vision System for Hardware Testing and Assurance) in this work, which can detect the smallest possible change to a design in a low-cost, accurate, and fast manner. The inputs to this system are scanning electron microscopy images acquired from the integrated circuits under examination. The system output is the determination of integrated circuit status in terms of having any defect and/or hardware Trojan through addition, deletion, or change in the design cells at the cell level. This article provides an overview on the design, development, implementation, and analysis of our defense system.
Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique
Authors: Andrade Pedro , Silva Ivanovitch , Diniz Marianne , Flores Thommas , Costa Daniel G. , Soares Eduardo
Published: April 2023

The Internet of Things (IoT) has made it possible to include everyday objects in a connected network, allowing them to intelligently process data and respond to their environment. Thus, it is expected that those objects will gain an intelligent understanding of their environment and be able to process data more efficiently than before. Particularly, such edge computing paradigm has allowed the execution of inference methods on resource-constrained devices such as microcontrollers, significantly changing the way IoT applications have evolved in recent years. However, although this scenario has supported the development of Tiny Machine Learning (TinyML) approaches on such devices, there are still some challenges that require further investigation when optimizing data streaming on the edge. Therefore, this article proposes a new unsupervised TinyML regression technique based on the typicality and eccentricity of the samples to be processed. Moreover, the proposed technique also exploits a Recursive Least Squares (RLS) filter approach. Combining all these features, the proposed method uses similarities between samples to identify patterns when processing data streams, predicting outcomes based on these patterns. The results obtained through the extensive experimentation utilizing vehicular data streams were highly encouraging. The proposed algorithm was meticulously compared with the RLS algorithm and Convolutional Neural Networks (CNN). It exhibited significantly superior performance, with mean squared errors that were 4.68 and 12.02 times lower, respectively, compared to the aforementioned techniques.
Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering
Authors: Ao Jing , Cheng Zehui , Chirkova Rada , Kolaitis Phokion G.
Published: April 2023

We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.
Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-quantized Log-gradient Input Images
Authors: Lu Qianyun , Murmann Boris
Published: April 2023

This article studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable: (i) aggressive 1-bit quantization of first-layer inputs, (ii) potential CNN resource reductions, (iii) inherent insensitivity to illumination changes (1.7% accuracy loss across 2
-5
… 2
3
brightness variation vs. up to 10% for JPEG), and (iv) robustness to adversarial attacks (>10% higher accuracy than JPEG-trained models). We establish these results using the PASCAL RAW image dataset and through a combination of experiments using quantization threshold search, neural architecture search, and a fixed three-layer network. The latter reveals that training on log-gradient images leads to higher filter similarity, making the CNN more prunable. The combined benefits of aggressive first-layer quantization, CNN resource reductions, and operation without tight exposure control and image signal processing (ISP) are helpful for pushing tinyML CV toward its ultimate efficiency limits.
A 20-Gene Signature Predicting Survival in Patients with Clear Cell Renal Cell Carcinoma Based on Basement Membrane
Authors: Yin Zhenjie , Zhao Yu , Zhou Weiwen , You Chengcheng , Bai Yuanyuan , You Bingyong , Lu Dongming , Liao Shangfan , Zheng Luoping , Sun Yingming , Wu Yongyang
Published: April 2023

Objectives. The most common subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC), has a high heterogeneity and aggressive nature. The basement membrane (BM) is known to play a vital role in tumor metastasis. BM-related genes remain untested in ccRCC, however, in terms of their prognostic significance. Methods. BM-related genes were gleaned from the most recent cutting-edge research. The RNA-seq and clinical data of the ccRCC were obtained from TCGA and GEO databases, respectively. The multigene signature was constructed using the univariate Cox regression and the LASSO regression algorithm. Then, clinical features and prognostic signatures were combined to form a nomogram to predict individual survival probabilities. Using functional enrichment analysis and immune-correlation analysis, we investigated potential enrichment pathways and immunological characteristics associated with BM-related-gene signature. Results. In this study, we built a model of 20 BM-related genes and classified them as high-risk or low-risk, with each having its anticipated risk profile. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group in the TCGA cohort, as was confirmed by the testing dataset. Functional analysis showed that the BM-based model was linked to cell-substrate adhesion and tumor-related signaling pathways. Comparative analysis of immune cell infiltration degrees and immune checkpoints reveals a central role for BM-related genes in controlling the interplay between the immune interaction and the tumor microenvironment of ccRCC. Conclusions. We combined clinical characteristics known to predict the prognosis of ccRCC patients to create a gene signature associated with BM. Our findings may also be useful for forecasting how well immunotherapies would work against ccRCC. Targeting BM may be a therapeutic alternative for ccRCC, but the underlying mechanism still needs further exploration.
The Design of Vehicle Profile Based on Multivehicle Collaboration for Autonomous Vehicles in Roundabouts
Authors: Cao Dongfa , Liu Zhandong , Hu Chuangye , Ding Nan
Published: April 2023

The collaborative strategy of vehicle-road-environment based on intelligent and connected vehicles (ICVs) to assist in driving vehicles safely and relieve traffic congestion has become an effective solution. This paper proposed a strategy for vehicle lane change and roundabouts traffic based on vehicle profile (VP) in combination with the driving characteristics of roundabouts. Initially, in order to solve the confusion problem of multisource heterogeneous data of ICVs in roundabouts, this paper defines VP to describe and characterize the multidimensional data of ICVs, so the data in ICVs can be further applied. Furthermore, the weights of relevant parameters in the VP are updated based on the random forest algorithm. In addition, the payoff function is designed for the lane change decision at the exit of roundabouts based on the VP and dynamic weights. Finally, the performance of the proposed algorithm is compared with other algorithms through the SUMO platform and three scenarios are used in the simulation verification, including traffic congestion, normal, and sparse. The experimental results verify the optimization effect of vehicle profile on roundabout traffic strategy and also show that this algorithm can effectively improve the efficiency of vehicle traffic in roundabouts. In particular, the efficiency and comfort of vehicles in roundabouts are effectively improved in normal traffic scenarios.
Network Intrusion Anomaly Detection Model Based on Multiclassifier Fusion Technology
Authors: Hang Feilu , Guo Wei , Chen Hexiong , Xie Linjiang , Bai Xiaoyu , Liu Yao
Published: April 2023

With the increasing development of the industrial Internet, network security has attracted more and more attention. Among the numerous network security technologies, anomaly detection technology based on network traffic has become an important research field. At present, a large number of methods for network anomaly detection have been proposed. Most of the better performance detection methods are based on supervised machine learning algorithms, which require a large number of labelled data for model training. However, in a real network, it is impossible to manually filter and label large-scale traffic data. Network administrators can only use unsupervised machine learning algorithms for actual detection, and the detection effects are much worse than supervised learning algorithms. To improve the accuracy of the unsupervised detection methods, this study proposes a network anomaly detection model based on multiple classifier fusion technology, which applies different fusion techniques (such as Majority Vote, Weighted Majority Vote, and Naive Bayes) to fuse the detection results of the five best performing unsupervised anomaly detection algorithms. Comparative experiments are carried out on three public datasets. Experimental results show that, in terms of RECALL and AUC score, the fusion model proposed in this study achieves better performance than the five separate anomaly detection baseline algorithms, and it has better robustness and stability, which can be effectively applied to a wide range of network anomaly detection scenarios.
Riparin‐B as a Potential Inhibitor of AdeABC Efflux System from Acinetobacter baumannii
Authors: Leão Patrícia Virna Sales , Ferreira Ana Laura da Silva , Oliveira Felipe Araújo de Alcântara , Mesquita Avilnete Belém de Souza , Lima-Net José de Sousa , Gutierrez Stanley Juan Chavéz , Nogueira Carlos Emídio Sampaio , Cruz-Martins Natália , Arcanjo Daniel Dias Rufino , Barreto Humberto Medeiros , Lima Ferreira Josie Haydée
Published: April 2023

Acinetobacter baumannii is an important opportunistic pathogen that causes serious health‐related infections, especially in intensive care units. The present study aimed to investigate the antimicrobial activity of Riparin‐B (Rip‐B) alone and in association with norfloxacin against multidrug‐resistant clinical isolates of A. baumannii. For this, the minimum inhibitory concentrations were determined by the microdilution method. For the evaluation of resistance‐modulating activity, MIC values for antibiotics were determined in the presence or absence of subinhibitory concentrations of Rip‐B or chlorpromazine (CPZ). The AdeABC‐AdeRS efflux system genes from these isolates were detected by PCR. Docking studies were also carried out to evaluate the interaction of Riparin‐B and the AdeABC‐AdeRS efflux system. The study was conducted from 2017 to 2019. The results showed that Rip‐B showed weak intrinsic activity against the strains tested. On the other hand, Rip‐B was able to modulate norfloxacin’s response against A. baumannii strains that express efflux pump‐mediated resistance. Docking studies provided projections of the interaction between Rip‐B and EtBr with the AdeB protein, suggesting that Rip‐B acts by competitive inhibition with the drug. Results found by in vitro and in silico assays suggest that Rip‐B, in combination with norfloxacin, has the potential to treat infections caused by multidrug‐resistant A. baumanni with efflux pump resistance.
Application of 5G Mobile Communication Technology Integrating Robot Controller Communication Method in Communication Engineering
Authors: Chen Yu
Published: April 2023

Following the ongoing advancement of the communication industry, data traffic and information have achieved rapid growth. As the core technology of contemporary mobile communication, 5G technology can not only meet the transportation needs of massive data traffic but also be expanded to various industries, expanding the development field of intelligent communication and realizing further improvement of production efficiency. At present, 5G mobile communication technology is widely used in communication engineering, providing effective technical support for engineering construction and operation. However, while the progress of the times brings innovation and refinement in mobile communication technology, it also increases the complexity of market development, in this context, the practical application of 5G technology in communication engineering is limited, which limits the development of both. In order to solve this dilemma, this paper integrated the communication method of the robot controller, and based on the analysis of the characteristics and application status of mobile communication technology, the application of 5G technology in communication engineering was deeply studied. In order to prove the effect of the robot controller communication method, in this paper, simulation experiments were carried out to analyze the application effect of 5G in communication engineering from three aspects of throughput rate, energy efficiency, and signal suppression effect and to compare it with traditional communication methods. The results of the signal suppression experiment showed that the average peak value of the signal under the traditional communication method was 18.134 dB, and the average peak value of the signal under the method in this paper was 11.296 dB, which showed that the 5G technology integrating the communication method of the robot controller is more practical in communication engineering.
Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm
Authors: Shankar Gauri , Ai-Farhani Liwa H. , Anitha Christy Angelin P. , Singh Parvinder , Alqahtani Abdullah , Singh Abha , Kaur Gaganpreet , Samori Issah Abubakari
Published: April 2023

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user’s biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article’s main findings are that the Ed25519 algorithm can be used in blockchain.
Novel Concepts in Rough Cayley Fuzzy Graphs with Applications
Authors: Rao Yongsheng , Zhou Qixin , Akhoundi Maryam , Talebi A. A. , Omidbakhsh Amiri S. , Muhiuddin G.
Published: April 2023

Today, fuzzy graphs (FGs) have a variety of applications in other fields of study, including medicine, engineering, and psychology, and for this reason, many researchers around the world are trying to identify their properties and use them in computer sciences as well as finding the smallest problem in a network. The concept of a Cayley fuzzy graph has become a standard part of the toolkit used to investigate and describe groups. Also, Cayley fuzzy graphs are good models for interconnection networks, and they are useful in semigroup theory for establishing which elements are

ℓ

and

R

related. The previous definition limitations in the FGs have directed us to offer a new classification in terms of Cayley fuzzy graphs. So, in this paper, two new definitions of Cayley fuzzy graphs (CFGs) and pseudo-Cayley fuzzy graphs (PCFGs) are discussed and their rough approximations are studied. Also, some properties of fuzzy rough sets (FRSs) in CFGs and PCFGs have been investigated. Finally, we presented the determination of the most effective person in the Water and Sewerage Organization and the importance of using refereeing facilities in football matches between club teams, by using CFG in the presented applications.
[Retracted] Tea Drinking and the Risk of Carcinoma of the Urinary Bladder: A Meta‐Analysis
Authors: Xia Qier , Li Jun , Shen Yifan , Zhang Dahong
Published: April 2023

Objective. For evaluation of the correlation between tea drinking and the risk of carcinoma of the urinary bladder. Methods. By searching PubMed, Embase, and Cochrane Library databases, the original studies on tea drinking and carcinoma of the urinary bladder risk were collected, the data were extracted, and meta‐analysis package 5.2‐0 of R language was used for meta‐analysis. Results. This study contained 11 researches, composed of 7686 patients and 10320 controls. Tea drinking was not linked to carcinoma of the urinary bladder risk (OR:1.02, 95%CI: 0.95–1.11). Conclusion. Tea drinking may not be linked to carcinoma of the urinary bladder, but more definitive results are needed from higher‐quality trials.
Transforming Learning to Online Education 4.0 during COVID-19: Stakeholder Perception, Attitude, and Experiences in Higher Education Institutions at a Tier-III City in India
Authors: Gupta Ankur , Sawhney Sahil , Nanda Ankita , Shabaz Mohammad , Ofori Isaac
Published: April 2023

Higher education institutions worldwide had to quickly pivot to delivering online classes as the COVID-19 pandemic set in. This disruption brought into focus institutional preparedness to ensure academic continuity, faculty proficiency in the use of ICT, and student readiness to adapt to an online-only model suggested in Education 4.0 framework. The Education 4.0 framework emphases to use advance technology and tools to transform education system. It was initially envisaged that the online-only model of education will at best be a stop-gap arrangement. However, as the pandemic continues unabated across India, the education community needs actionable data to fine-tune its online delivery model, to ensure its effectiveness, and retain its value perception in the eyes of the stakeholders. This is a critical aspect as evidence suggests that the initial high level of online engagement is petering out due to overexposure, mental saturation, and fatigue among both the students and faculty members. This research paper examines the perceptions, attitudes, and experiences of stakeholders involved in online education from Jammu, a Tier-III city in India. The study asserts that real “value creation” in an online mode can happen when all the stakeholders are equally motivated and working together. Institutions need to prioritize value delivery, support faculty members, provide the needed resources, and set clear expectations.
Dynamics of Pair of Entangled Spin-1/2 Particles and Quantification of the Dynamics in terms of Correlations
Authors: Pydimarri Venkata Satya Surya Phaneendra , Field Timothy R.
Published: April 2023

The dynamics of an identical pair of entangled spin-1/2 particles, both subjected to the same random magnetic field, are studied. The dynamics of the pure joint state of the pair are derived using stochastic calculus. An ensemble of such pure states is combined using the modified spin joint density matrix, and the joint relaxation time for the pair of spin-1/2 particles is obtained. The dynamics can be interpreted as a special kind of correlation involving the spatial components of the Bloch polarization vectors of the constituent entangled spin-1/2 particles.
Multimedia Data Processing Technology and Application Based on Deep Learning
Authors: Li Guo , Liu Wei
Published: April 2023

With deep learning being widely used in various research fields, it is introduced into the research and analysis of multimedia data processing technology and application. First, the flow of multimedia data processing, the development of multimedia data, and the realization of multimedia data processing technology are explained and analyzed. Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. Finally, through the analysis of experimental data, it is concluded that deep learning has stronger advantages in the application research of multimedia data processing technology compared with other learning methods. In the multimedia data processing, the multimedia data processing technology is obviously superior to the data mining technology and data compression technology. Finally, under the support of deep learning data, we conclude that multimedia data processing technology is widely used and quoted in various fields. Therefore, with the development of multimedia, the amount of multimedia data is increasing; so, we should vigorously develop multimedia data processing technology in an all-round way.
Long-Term Effect Analysis of a High Slope with Tunnel Structure Based on Three-Dimensional Mesh Reconstruction
Authors: Shi Youzhi , Yuan Ganglie , Che Ailan , Qian Shixiong
Published: April 2023

The instability of high rock slopes with tunnel structures increases under the effects of earthquakes and rainfall. Limited studies have been conducted on the long-term effect analysis of high rock slopes with tunnels. Field electrical measurements on rock slopes have been conducted to study the response of high rock slopes with large cross-section tunnels during rainfall and seismic loading. The material threshold was determined using the resistivity probability density statistical method, and a three-dimensional geological visualization model was obtained. A three-dimensional mesh reconstruction method consisting of material segmentation, cluster filtering, mesh generation, and material attribute mapping modules was proposed. The three-dimensional model of high-cutting rock slope was reconstructed, and the three-dimensional finite element model of the rock slope with tunnel was obtained. The response analysis of the slope was performed during rainfall and seismic loading by employing the El-Centro wave. The results demonstrated that plastic strain was generated in the fracture zone of the upper part of the tunnel during rainfall, and the strain value was 8.135 × 10−4. In the subsequent stages, it had a tendency to expand into the tunnel under continuous rainfall. The maximum principal strain was 3.324 × 10−2 under the effect of a strong earthquake. A large strain was generated in the fracture zone at the upper part of the tunnel. It had a tendency to expand under long-term load, which significantly affected the safety of the tunnel.
Assessment of Functional Capacity of Immune System in Patients with Multiple Sclerosis using QuantiFERON Monitor
Authors: Pavelek Zbysek , Soucek Ondrej , Krejsek Jan , Sejkorova Ilona , Vysata Oldrich , Klimová Blanka , Angelucci Francesco , Stourac Pavel , Valis Martin , Peterka Marek , Sobisek Lukáš , Novotny Michal
Published: April 2023

Background. The QuantiFERON®-Monitor (QFM) is an assay that measures interferon-γ production and was developed to provide an objective marker of complex immune response. In this study, we evaluated the use of the QFM test in patients with two forms of multiple sclerosis (MS), relapsing–remitting form treated with fingolimod (fMS) and secondarily progressive form not treated pharmacologically (pMS), and in healthy controls (HC). We hypothesized that IFN-γ levels would be lower in those subjects who are relatively more immunosuppressed and higher in those with normal or activated immune function. Methods. This single-center observational study was conducted from November 2020 to October 2021 and compared results in three groups of patients: 86 healthy controls, 96 patients with pMS, and 78 fMS. Combination of lyophilized stimulants was added to 1 ml heparinized whole blood within 8 hr of collection. Plasmatic IFN-γ was measured using the ELISA kit for the QFM and data were obtained in IU/ml. Results. The results showed that controls had nearly 2-fold higher levels of IFN-γ (QFM score) in median (q25, q75) 228.00 (112.20, 358.67) than the MS patient groups: pMS 144.80 (31.23, 302.00); fMS 130.50 (39.95, 217.07) which is statistically significant difference P-value: HC vs. pMS = 0.0071; HC vs. fMS = 0.0468. This result was also confirmed by a validation analysis to exclude impact of variable factors, such as disease duration and Expanded Disability Status Scale scores. Conclusions. Results showed that controls had higher levels of IFN-γ production than the MS patient groups and suggest that MS patients included in this study have a lower ability of immune system activation than HC. Results confirm that fingolimod is able to suppress production of IFN-γ. The fact that the QFM score of MS patients is significantly lower than that of HC may indicate a dysfunctional state of the immune system in baseline conditions.
The Influence of Media Information Sources on Preventive Behaviors in China: After the Outbreak of COVID-19 Pandemic
Authors: Li Hongxiu , Pan Li , Chen Weilu
Published: April 2023

Background. As announced by the World Health Organization (WHO), since March 2020, the COVID-19 pandemic has become a global pandemic. In order to prevent the spread of COVID-19, Chinese government carried out very strict prevention and control policy. Objective. The study aimed to explore the effect of news reports on COVID-19 vaccine from traditional media and social media on COVID-19 preventive behaviors. Methods. Adults aged between 18 and 58 years old completed an online survey reporting how they gathered media information sources regarding the COVID-19 vaccine, as well as any details relating to risk perception, vaccine efficacy, and preventive behaviors in COVID-19 pandemic. Results. Our results showed that traditional and social media information sources both significantly and positively influenced people’s COVID-19 preventive behaviors, with the former showing a stronger effect. COVID-19 contact risk perception and vaccine efficacy awareness of media audiences partly mediate this relationship. Audiences who reported more exposing news reports on COVID-19 vaccine from the media show stronger risk perception and vaccine efficacy awareness. This increases their COVID-19 preventive behaviors. Conclusions. This study found that media information sources have an important impact on people’s COVID-19 preventive behaviors. People believe more in the news information of the mainstream media about the COVID-19 pandemic. Moreover, much of the news information of social media is also from the important mainstream media. Media organizations should shoulder greater social responsibility, embed the health-related benefits of COVID-19 vaccination into the values and cultural order of the whole society, find and shape a common space of meaning, and produce forms of internal coupling and value identification.
On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator
Authors: Hamdaoui Abdenour , Benkhaled Abdelkader , Alshahrani Mohammed , Terbeche Mekki , Almutiry Waleed , Alahmadi Amani
Published: April 2023

This work consists of developing shrinkage estimation strategies for the multivariate normal mean when the covariance matrix is diagonal and known. The domination of the positive part of James–Stein estimator (PPJSE) over James–Stein estimator (JSE) relative to the balanced loss function (BLF) is analytically proved. We introduce a new class of shrinkage estimators which ameliorate the PPJSE, and then we construct a series of polynomial shrinkage estimators which improve the PPJSE; also, any estimator of this series can be ameliorated by adding to it a new term of higher degree. We end this paper by simulation studies which confirm the performance of the suggested estimators.
An Ensemble of Transfer Learning Models for the Prediction of Skin Lesions with Conditional Generative Adversarial Networks
Authors: Al-Rasheed Amal , Ksibi Amel , Ayadi Manel , Alzahrani Abdullah I. A. , Mamun Elahi Mohammad
Published: April 2023

Skin cancer is one of the most serious forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, it is crucial to diagnose and treat skin cancer patients at an early stage. Due to the fact that a manual diagnosis of skin cancer is both time-consuming and expensive, an incorrect diagnosis is made due to the high degree of similarity between the various skin lesions. Improved categorization of multi-class skin lesions requires the development of automated diagnostic systems. We offer a fully automated method for classifying several skin lesions by fine-tuning the deep learning models, namely VGG16, ResNet50, and ResNet101. Prior to model creation, the training dataset should undergo data augmentation using traditional image transformation techniques and generative adversarial networks (GANs) to prevent class imbalance issues that may lead to model overfitting. In this study, we investigate the feasibility of creating dermoscopic images that have a realistic appearance using conditional generative adversarial network (CGAN) techniques. Afterward, the traditional augmentation methods are used to augment our existing training set to improve the performance of pretrained deep models on the skin lesion classification task. This improved performance is then compared to the models developed using the unbalanced dataset. In addition, we formed an ensemble of finely tuned transfer learning models, which we trained on balanced and unbalanced datasets. These models were used to make predictions about the data. With appropriate data augmentation, the proposed models attained an accuracy of 92% for VGG16, 92% for ResNet50, and 92.25% for ResNet101. The ensemble of these models increased the accuracy to 93.5%. There was a comprehensive discussion on the performance of the models. It is possible to conclude that using such a method leads to enhanced performance in skin lesion categorization compared to the efforts made in the past.
Phytoconstituent Isolation and Cytotoxicity Evaluation of the Egyptian Cassia occidentalis L. Possessing Selective Activity against Lung Carcinoma
Authors: Sayed Hanaa M. , Ramadan Mahmoud A. , Salem Heba H. , Fayed Marwa A. A.
Published: April 2023

Ethyl acetate fraction column chromatographic analysis was used to isolate eleven compounds (numerically tagged 1–10) from Cassia occidentalis L. in this study. Two unique metabolites, including a neolignan compound designated as occidentalignan I (9) and a flavonoidal glycoside, chrysin-7-O-α-L-rhamnopyranosyl (10), were identified, while silybin A (8) was the first flavonolignan to be isolated from the Fabaceae family. Four compounds, including β-sitosterol-3-O-β-D-glucopyranoside (1), stigmasterol-3-O-β-D-glucopyranoside (2), betulinic acid (3), and vanillic acid (4) were isolated from C. occidentalis for the first time. In addition, four known compounds, cinnamic acid (5), p-hydroxybenzoic acid (6), β- resorcylic acid (7), and citric acid (11), were also detected. The in-vitro cytotoxicity assessment of the methanolic extract of C. occidentalis on seven cancer cell lines, including A-549, Colo-205, Huh-7, HCT-116, PANC-1, SKOV-3, and BNL, demonstrated its selective potent cytotoxicity on lung cancer cells without affecting normal BNL cells. In contrast, the methanolic extract showed moderate activity on Colo-205 and Huh-7 and nearly no activity on HCT-116, PANC-1, and SKOV-3 cell lines. These results suggest that the methanolic extract of C. occidentalis is an excellent candidate with potential antiproliferative activity against lung cancer; however, further studies are necessary to clarify its mechanism of action.