Miracle-dz/UniFAR-FTUs huggingface dataset:
Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic “pandemic status,” with 22.47% in the prevention category, and the lowest number of publications is related to the topic “environment,” with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers.
A LoRa-Based Lightweight Secure Access Enhancement System

'e access control mechanism in LoRa has been proven to have high security risks. In order to improve the secure access ability of LoRa terminals, this paper presents a physical layer-based authentication system for security enhancement. Different from the security access technology of cryptography, a lightweight gateway architecture called LW-LoRaWAN is proposed to realize a data frame-based authentication with radio frequency fingerprint (RFF). A novel RFF feature of Cross Power Spectral Density (CPSD) is used to achieve a fast authentication with one single frame. 'eoretical analysis and experimental results show that the proposed system not only reinforces the authentication security of LoRa network but also protects the LoRa terminals against the Sybil attacks. 'e LW-LoRaWAN provides new security approach from physical layer for LoRa network.
Construction of Music Teaching Evaluation Model Based on Weighted Naïve Bayes

Evaluation of music teaching is a highly subjective task often depending upon experts to assess both the technical and artistic characteristics of performance from the audio signal. This article explores the task of building computational models for evaluating music teaching using machine learning algorithms. As one of the widely used methods to build classifiers, the Naïve Bayes algorithm has become one of the most popular music teaching evaluation methods because of its strong prior knowledge, learning features, and high classification performance. In this article, we propose a music teaching evaluation model based on the weighted Naïve Bayes algorithm. Moreover, a weighted Bayesian classification incremental learning approach is employed to improve the efficiency of the music teaching evaluation system. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in the context of music teaching evaluation.
Futuristic Cyber-Twin Architecture for 6G Technology to Support Internet of Everything

With the rapid growth of Internet of Everything, there is a huge rise in the transportable Internet traffic due to which its associated resources have exceptional obstacles which include reliability, security, expandability, security, and portability which the current available network architectures are unable to deal with. In this paper, an IoT centric cyber-physical twin architecture has been proposed for 6G Technology. The cyber-twin technology helps out in serving stronger communication and also contains several features that help out in assisting communication like maintaining a log record of network data and managing all digital assets like images, audio, video, and so forth. These features of the cyber-twin technology enable the proposed network to deal with those exceptional obstacles and make the system more reliable, safe, workable, and adaptable.
Plastic Gasket Defect Detection Based on Transfer Learning

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.
Material Discrimination Algorithm Based on Hyperspectral Image

Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. However, it is difficult to process it directly because of the huge hyperspectral image data. Therefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. The subspace clustering self-attention adversarial network is constructed to realize the initial selection of band. According to the visual chromatic aberration principle, a model is constructed to determine the band that combines the strongest response intensity of a particular material, and then this band is selected as the final band, therefore realizing the algorithm of material demarcation in this way.
Fog-Driven Secure Authentication and Key Exchange Scheme for Wearable Health Monitoring System

Smart wearable devices, as a popular mobile device, have a broad market. Smart wearable medical devices implemented in wearable health monitoring systems can monitor the data pertaining to a patient’s body and let the patient know their own physical condition. In addition, these data can be stored, analyzed, and processed in the cloud to effectively prevent diseases. As an Internet-of-things technology, fog computing can process, store, and control data around devices in real time. However, the distributed attributes of fog nodes make the monitored body data and medical reports at risk of privacy disclosure. In this paper, we propose a fog-driven secure authentication and key exchange scheme for wearable health monitoring systems. Furthermore, we conduct a formal analysis using the Real-Oracle-Random model, Burrows–Abadi–Needham logic, and ProVerif tools and an informal analysis to perform security verification. Finally, a performance comparison with other related schemes shows that the proposed scheme has the best advantages in terms of security, computing overhead, and communication cost.
The UGC-CARE initiative: Indian academia's quest for research and publishing integrity

This paper discusses the reasons for emergence of predatory publications in India, engendered by mandates of higher educational institutions: that require stipulated number of research publications for employment and promotions. Predatory journals have eclipsed the merits of open access publishing, compromised ethical practices, and left the research community groping for benchmarks of research integrity and publication ethics. To fight back the menace of predatory publications, University Grants Commission, India has established “Consortium for Academic Research and Ethics” (UGC-CARE) in 2018 to promote and benchmark research integrity and publication ethics among the Indian academia. The present paper discusses the UGC-CARE initiative, its structure, objectives and specifically, “UGC-CARE Reference List of Quality Journals” (UGC-CARE list) and finally, the challenges it faces.
Multi-channel time-frequency fusion attacks

Side-channel analysis (SCA) is one of the most powerful attacks against cryptographic implementations. Multi-channel fusion attack (MCFA) which can utilise leakages measured from different side channels is a new type of SCA. Till now, existing MCFAs mainly work in the time domain. This paper take time-frequency joint information into consideration, and proposes time-frequency fusion attacks (TFFA). TFFA can be easily expanded to multi-channel case, and this kind of attack is named multi-channel time-frequency fusion attack (MCTFFA). In comparison to existing MCFAs, TFFA and MCTFFA are more effective. Practical experiments against unprotected AES-128 (implemented on MCU and FPGA) and masked AES-128 (implemented on FPGA) show that proper MCTFFA can reduce the number of traces needed to achieve a success rate of 1 by 23% to 60%, compared to that of MCFA. These improvements can be achieved without overhead in measurement complexity.
Deep Learning-Based Assessment of Adverse Cardiovascular Events in Elderly Patients with Coronary Heart Disease after Percutaneous Coronary Intervention Using Intravascular Ultrasound Images

This study aimed to analyze the risk factors of adverse cardiovascular events (ACVEs) in elderly patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI) using the intravascular ultrasound (IVUS) images based on the deep learning of convolutional neural networks (CNNs). This study included 90 patients with coronary heart disease as the research object. All the patients were randomly divided into a control group (group C) and an experimental group (group E), and all were treated with PCI. The patients in group C were diagnosed by angiography, and patients in group E underwent IVUS examination under deep learning. The levels of blood lipids and inflammatory factors between the two groups before and after PCI were compared, and the sensitivity, specificity, and positive predictive value (PPV) were recorded. Compared with angiography diagnosis, ultrasound diagnosis based on deep learning algorithm had higher sensitivity (92.3% vs. 81.4%), specificity (90.1% vs. 88.6%), and PPV (94.8% vs. 75.3%) ( P < 0.05 ). Compared with group C, patients in group E had a higher narrowest lesion diameter (2.54 ± 0.18 mm vs. 2.21 ± 0.19 mm) and detection rate of eccentric plaques (80.1% vs. 45.3%) ( P < 0.05 ). High-density lipoprotein cholesterol (HDL-C) after PCI in the two groups was significantly higher than that before surgery, while low-density lipoprotein cholesterol (LDL-C), tumor necrosis factor (TNF), and C-reactive protein (CRP) were significantly lower than those before surgery, and the difference was statistically significant ( P < 0.05 ). In short, the ultrasonic detection method based on deep learning algorithm has high sensitivity, specificity, and accuracy for CHD detection; PCI can improve the patient’s blood lipid level, relieve the patient’s inflammation, and reduce the occurrence of ACVEs in the patient.
LGR4 Gene Polymorphisms Are Associated With Bone and Obesity Phenotypes in Chinese Female Nuclear Families

Objective The current study was conducted to determine whether peak bone mineral density (BMD) and obesity phenotypes are associated with certain LGR4 gene polymorphisms found in Chinese nuclear families with female children. Methods A total of 22 single nucleotide polymorphisms (SNPs) located in and around the LGR4 gene were identified in 1,300 subjects who were members of 390 Chinese nuclear families with female children. Then, BMD readings of the femoral neck, total hip, and lumbar spine as well as measurements of the total lean mass (TLM), total fat mass (TFM), and trunk fat mass were obtained via dual-energy X-ray absorptiometry. The quantitative transmission disequilibrium test was used to analyze the associations between specific SNPs and LGR4 haplotypes and peak BMD as well as between LGR4 haplotypes and TLM, percent lean mass, TFM, percent fat mass, trunk fat mass, and body mass index (BMI). Results Here, rs7936621 was significantly associated with the BMD values for the total hip and lumbar spine, while rs10835171 and rs6484295 were associated with the trunk fat mass and BMI, respectively. Regarding the haplotypes, we found significant associations between GAA in block 2 and trunk fat mass and BMI, between AGCGT in block 3 and total hip BMD, between TGCTCC in block 5 and femoral neck BMD, and between TACTTC in block 5 and both lumbar spine and femoral neck BMD (all P-values < 0.05). Conclusion Genetic variations of the LGR4 gene are related to peak BMD, BMI, and trunk fat mass.
Topological Dissection of Proteomic Changes Linked to the Limbic Stage of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common cause of dementia worldwide. In AD, neurodegeneration spreads throughout different areas of the central nervous system (CNS) in a gradual and predictable pattern, causing progressive memory decline and cognitive impairment. Deposition of neurofibrillary tangles (NFTs) in specific CNS regions correlates with the severity of AD and constitutes the basis for disease classification into different Braak stages (I-VI). Early clinical symptoms are typically associated with stages III-IV (i.e., limbic stages) when the involvement of the hippocampus begins. Histopathological changes in AD have been linked to brain proteome alterations, including aberrant posttranslational modifications (PTMs) such as the hyperphosphorylation of Tau. Most proteomic studies to date have focused on AD progression across different stages of the disease, by targeting one specific brain area at a time. However, in AD vulnerable regions, stage-specific proteomic alterations, including changes in PTM status occur in parallel and remain poorly characterized. Here, we conducted proteomic, phosphoproteomic, and acetylomic analyses of human postmortem tissue samples from AD (Braak stage III-IV, n=11) and control brains (n=12), covering all anatomical areas affected during the limbic stage of the disease (total hippocampus, CA1, entorhinal and perirhinal cortices). Overall, ~6000 proteins, ~9000 unique phosphopeptides and 221 acetylated peptides were accurately quantified across all tissues. Our results reveal significant proteome changes in AD brains compared to controls. Among others, we have observed the dysregulation of pathways related to the adaptive and innate immune responses, including several altered antimicrobial peptides (AMPs). Notably, some of these changes were restricted to specific anatomical areas, while others altered according to disease progression across the regions studied. Our data highlights the molecular heterogeneity of AD and the relevance of neuroinflammation as a major player in AD pathology. Data are available via ProteomeXchange with identifier PXD027173.
Relationship Between the Xylem Anatomy of Grapevine Rootstocks and Their Susceptibility to Phaeoacremonium minimum and Phaeomoniella chlamydospora

Fungal grapevine trunk diseases (GTDs) are some of the most pressing threats to grape production worldwide. While these diseases are associated with several fungal pathogens, Phaeomoniella chlamydospora and Phaeoacremonium minimum are important contributors to esca and Petri diseases. Recent research has linked grapevine xylem diameter with tolerance to Pa. chlamydospora in commercial rootstocks. In this study, we screen over 25 rootstocks for xylem characteristics and tolerance to both Pa. chlamydospora and Pm. minimum. Tolerance was measured by fungal incidence and DNA concentration (quantified via qPCR), while histological analyses were used to measure xylem characteristics, including xylem vessels diameter, density, and the proportion of the stem surface area covered by xylem vessels. Rootstocks were grouped into different classes based on xylem characteristics to assess the potential association between vasculature traits and pathogen tolerance. Our results revealed significant differences in all the analyzed xylem traits, and also in DNA concentration for both pathogens among the tested rootstocks. They corroborate the link between xylem vessels diameter and tolerance to Pa. chlamydospora. In Pm. minimum, the rootstocks with the widest xylem diameter proved the most susceptible. This relationship between vasculature development and pathogen tolerance has the potential to inform both cultivar choice and future rootstock breeding to reduce the detrimental impact of GTDs worldwide.
Animal Models of Ehlers–Danlos Syndromes: Phenotype, Pathogenesis, and Translational Potential

The Ehlers–Danlos syndromes (EDS) are a group of heritable connective tissues disorders mainly characterized by skin hyperextensibility, joint hypermobility and generalized tissue fragility. Currently, 14 EDS subtypes each with particular phenotypic features are recognized and are caused by genetic defects in 20 different genes. All of these genes are involved in the biosynthesis and/or fibrillogenesis of collagens at some level. Although great progress has been made in elucidating the molecular basis of different EDS subtypes, the pathogenic mechanisms underlying the observed phenotypes remain poorly understood, and consequentially, adequate treatment and management options for these conditions remain scarce. To date, several animal models, mainly mice and zebrafish, have been described with defects in 14 of the 20 hitherto known EDS-associated genes. These models have been instrumental in discerning the functions and roles of the corresponding proteins during development, maturation and repair and in portraying their roles during collagen biosynthesis and/or fibrillogenesis, for some even before their contribution to an EDS phenotype was elucidated. Additionally, extensive phenotypical characterization of these models has shown that they largely phenocopy their human counterparts, with recapitulation of several clinical hallmarks of the corresponding EDS subtype, including dermatological, cardiovascular, musculoskeletal and ocular features, as well as biomechanical and ultrastructural similarities in tissues. In this narrative review, we provide a comprehensive overview of animal models manifesting phenotypes that mimic EDS with a focus on engineered mouse and zebrafish models, and their relevance in past and future EDS research. Additionally, we briefly discuss domestic animals with naturally occurring EDS phenotypes. Collectively, these animal models have only started to reveal glimpses into the pathophysiological aspects associated with EDS and will undoubtably continue to play critical roles in EDS research due to their tremendous potential for pinpointing (common) signaling pathways, unveiling possible therapeutic targets and providing opportunities for preclinical therapeutic interventions.
Capturing Workplace Gossip as Dynamic Conversational Events: First Insights From Care Team Meetings

Even though gossip is a ubiquitous organizational behavior that fulfils important social functions (e.g., social bonding or emotion venting), little is known about how workplace gossip and its functions unfold in situ. To explore the dynamic nature and social embeddedness of workplace gossip, we develop a behavioral annotation system that captures the manifold characteristics of verbal gossip behavior, including its valence and underlying functions. We apply this system to eight elderly care team meetings audio- and videotaped in the field, yielding a sample of N = 4,804 annotated behaviors. On this empirical basis, we provide first insights into the different facets and functions of workplace gossip in real-life team interactions. By means of lag sequential analysis, we quantify gossip patterns that point to the temporal and structural embeddedness of different types of workplace gossip expressions. Though exploratory, these findings help establish workplace gossip as a dynamic conversational event. We discuss future interdisciplinary research collaborations that behavioral observation approaches offer.
O-fucosylation of CPN20 by SPINDLY Derepresses Abscisic Acid Signaling During Seed Germination and Seedling Development

SPINDLY is involved in some aspects of plant development. However, the nature of this protein as an O-fucosyltransferase was recently discovered. In this study, we show that SPINDLY (SPY) interacts with CPN20 in yeast two-hybrid and split-luc assays, and the interaction is promoted by ABA. CPN20 is a chloroplast-localized co-chaperonin that negatively regulates ABAR-mediated ABA signaling. By using Electron Transfer Dissociation-MS/MS analysis, two O-fucosylation sites, e.g., 116th and 119th threonines, were detected in ectopically expressed CPN20 in mammalian cells and in Arabidopsis. The O-fucosylation at both threonine residues was confirmed by in vitro peptide O-fucosylation assay. We further show that CPN20 accumulates in the chloroplast of spy mutants, suggesting that SPY negatively regulates CPN20 localization in the chloroplast. In vivo protein degradation assay along with CPN20 localization behavior suggest that import of CPN20 into the chloroplast is negatively regulated by SPY. Genetic analysis shows that ABA insensitive phenotypes of spy-3 in terms of seed germination and early seedling development are partially suppressed by the cpn20 mutation, suggesting that CPN20 acts downstream of SPY in this ABA signaling pathway and that there may exist other pathways in parallel with CPN20. Collectively, the above data support the notion that the O-fucosylation of CPN20 by SPY fine-tunes ABA signaling in Arabidopsis.
Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands

The sense of touch is a key aspect in the human capability to robustly grasp and manipulate a wide variety of objects. Despite many years of development, there is still no preferred solution for tactile sensing in robotic hands: multiple technologies are available, each one with different benefits depending on the application. This study compares the performance of different tactile sensors mounted on the variable stiffness gripper CLASH 2F, including three commercial sensors: a single taxel sensor from the companies Tacterion and Kinfinity, the Robotic Finger Sensor v2 from Sparkfun, plus a self-built resistive 3 × 3 sensor array, and two self-built magnetic 3-DoF touch sensors, one with four taxels and one with one taxel. We verify the minimal force detectable by the sensors, test if slip detection is possible with the available taxels on each sensor, and use the sensors for edge detection to obtain the orientation of the grasped object. To evaluate the benefits obtained with each technology and to assess which sensor fits better the control loop in a variable stiffness hand, we use the CLASH gripper to grasp fruits and vegetables following a published benchmark for pick and place operations. To facilitate the repetition of tests, the CLASH hand is endowed with tactile buttons that ease human–robot interactions, including execution of a predefined program, resetting errors, or commanding the full robot to move in gravity compensation mode.