bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of a probiotic-fermented herbal medicines mixture on the growth performance, immune and antioxidative function of Acanthopagrus schlegelii
Authors: NIE Huayue , LIANG Qiankun , XIE Xiaohui 
Published: June 2022

Abstract
This study investigated the benefit of a probiotic-fermented herbals mixture on the growth performance, immune and antioxidative ability of Acanthopagrus schlegelii. The herbal medicines mixture of Andrographis paniculata、Sophora flavescens、Salvia miltiorrhiza、Hedyotis diffusa、Gentiana scabra and Taraxacum mongolicum were fermented with Bacillus subtilis, Saccharomyces cerevisiae, Enterococcus faecalis. And then fed to a total of 500 Acanthopagrus schlegelii which were divided into 5 groups. The dietary level of each group was as follows: G0 (no additions), G1 (3‰ fermented herbals mixture), G2 (4‰ fermented herbals mixture), G3 (5‰fermented herbals mixture), G4 (5‰ fermented herbals mixture), G5 (5‰ unfermented herbals mixture). The feeding experiment lasted for 56 days. The growth performance, immune and antioxidative ability of Acanthopagrus schlegelii were evaluated and results showed that fermented herbal medicines mixture(FHMM) could significantly increase the weight gain rate, acid phosphatase, and glutathione peroxidase activity of hepatic, superoxide dismutase, catalase and glutathione peroxidase activity of splenic; decrease malondialdehyde activity of hepatic, the lipase of the foregut. These results suggested that herbals being fermented with the probiotic could provide a new direction for feed supplements replace to antibiotics to enhance the immune and antioxidative ability of aquatic animal.
A method of SF 6 gas leakage detection for infrared imaging based on Infrared Patch-Image model
Authors: Lu Quan , Huang Lifeng , Hu Likun , Han Zhuangding 
Published: June 2022

Abstract
Due to the low contrast and low signal-to-noise ratio (SNR) of infrared imaging of SF6 (Sulfur Hexafluoride) gas, it is susceptible to environmental noise, resulting in low accuracy and a high false alarm rate for existing detection algorithms. We propose a single-frame detection method for infrared imaging SF6 gas leakage based on the IPI model. Due to the robust principal component analysis (RPCA) limitation in describing complex backgrounds, this method uses weighted nuclear norm minimization (WNNM) to better describe the background's low-rank characteristics and then solve the problem using the Accelerating Proximal Gradient (APG) algorithm. Finally, it is possible to accurately extract SF6 gas leakage points from the sparse target image using adaptive threshold segmentation. We conducted experiments on both indoor and outdoor SF6 gas leakage tracks. The results demonstrate that the algorithm can effectively reduce the false noise generated by the complex background edge under indoor experimental conditions with SF6 gas leakage of 0.04ml/min and a distance of 5m, while also improving leak detection accuracy.
Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 model
Authors: Sun Ke , Zhang Yu-jie , Tong Si-yuan , Wang Chang-bao 
Published: June 2022

Abstract
This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of micro images of rice grains contaminated by Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in micro images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting regions with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in the micro images of the verification set. The proportion of areas with contamination of Aspergillus niger/Penicillium citrinum/Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies (TVC). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice detection methods based on MCV technology.
A Novel Deep Learning Finger Vein Segmentation Algorithm for Identification and Recognition
Authors: Xu Hong , Sun Yewei , Zhang Caiming , Ye Caizeng (Charlie) 
Published: June 2022

Abstract
The paper proposed a method for segmentation of finger veins based on U-net and ResNet. The deep residual network is used to replace the feature extraction part of U-net. Considering the risk that some vein information may be lost in the vein extraction of finger vein by using pooling layers, Dilated Convolution is used instead of traditional convolution, which can increase the receptive field without pooling and extract the vein information from the finger vein image better. To further improve the expression ability of the model, Mish activation function is used instead of the ReLU activation function, which can make the extracted vein patterns more continuous. The experimental results show that the proposed novel VeinsegNet network in this paper has a excellent vein segmentation performance on the multi-modal vein dataset, outperforming other deep learning networks in terms of the mean intersection over union (i.e. MIOU). The segmentation of the finger vein image is shown to be greatly beneficial to the person identification and certain disease recognition. KEYWORLD: Deep learning; Image segmentation for finger vein; Applications to person identification and certain disease recognition
A Novel Approach for Traffic Congestion Mitigation Based on Fog Computing Using IFogsimulator
Authors: Elsayed Al Zahraa , Mohamed Khalil , Harb Hany 
Published: June 2022

Abstract
Internet of Things (IoT) needs an accurate latency computation. The connected IoT devices generate an enormous amount of data which is processed on the infrastructure of a cloud. However, the cloud is not an effective solution. To address this issue, fog computing enables processing at the edge while still allowing users to communicate with the cloud. Therefore, many applications depend on fog computing, such as traffic management. In this paper, an intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to solve the problem of traffic congestion in crowded smart cities. The proposed system is implemented based on fog computing. It has been applied to a crowded city and its performance is evaluated according to some metrics such as traffic efficiency, saving energy and reducing the latency, the average rate of traffic flow, and waiting time. The obtained results show that the execution time of our simulation takes 4,548 seconds, the Application loop delay takes 49.67 seconds. Thus, the results show better performance, more accuracy, low latency, and traffic efficiency
Secure Data Transmission in Cloud Computing Using Std-rsa With Eslurnn Data Classification and Blockchain Based User Authentication System
Authors: Samy I. Arockia Antony , Mary M. Safish 
Published: June 2022

Abstract
Cloud computing (CC) is one of the most prominent storage and computing platforms being researched nowadays. CC platforms are providing the opportunity for sharing resources, information, and services among the users of the entire world. For data storage and processing, cloud security considerations are critical. Because data in a cloud based environment must be protected from hackers, network security has been a key concern in recent years. Data should be encrypted and outsourced to the cloud to safeguard it from malicious intruders. Cryptography is essential for protecting data from various assaults and cloud storage platforms. To overcome the security threat new types of cryptography techniques can be introduced. Though a variety of firewall systems exist, there are still threats or attacks on the data or information which is transmitted over the cloud. The invention focuses on addressing the flaws that are inherent in network security by using advanced encryption techniques to encrypt data over the cloud. In this research work, a blockchain (BC) based user authentication system and improvised public-key cryptosystem are proposed which is based on Rivest-Shamir-Adleman (RSA) to support a private key cryptosystem. The proposed work comes under the following phases: user registration, login, authentication, BC, sensitive data identification, data splitting, and data security. Finally, the performance of the proposed framework in comparison with existing techniques determines strong results and proves to be more secure.
FGWOA: An Efficient Heuristic for Cluster Head Selection in WSN using Fuzzy based Grey Wolf Optimization Algorithm
Authors: NARAYAN VIPUL , Daniel A K , Chaturvedi Pooja 
Published: June 2022

Abstract
Wireless Sensor Network (WSN) is an important component of the Internet of Things. A WSN constitutes several Sensor Nodes (SN) that sense the various changes in the atmosphere, such as humidity, temperature, pressure etc. The Sensor Node (SN) used in WSN is powered by an inbuilt battery and performs sensing and data transmission to other SN in the network. As a result, the energy of SN is critical in WSN. The intelligent models are proposed in the present time in order to solve network issues by optimizing or decreasing the overhead to improve the WSN's energy efficiency. In this paper, a Fuzzy based approach integrated with Grey Wolf Optimization Algorithm (FGWOA) is proposed that aids cluster formation to find efficient and optimal solution for choosing the aggregation points using the Cluster Heads (CHs), and determining the best optimal path for transmission of data to the network Base Station (BS). The proposed optimal selection of different aggregation points causes the node's lifetime to be maximized. The simulation performance of FGWOA show better performance compared to various existing protocols and enhanced network lifespan.
OSeMOSYS Global: An open-source, open data global electricity system model generator
Authors: Barnes Trevor , Shivakumar Abhishek , Brinkerink Maarten , Niet Taco 
Published: June 2022

Abstract
This paper describes OSeMOSYS Global, an open-source, open-data model generator for creating global electricity system models for an active global modelling community. This version of the model generator is freely available and can be used to create interconnected electricity system models for both the entire globe and for any geographically diverse subset of the globe. Compared to other existing global models, OSeMOSYS Global allows for full user flexibility in determining the time slice structure and geographic scope of the model and datasets, and is built using the widely used fully open-source OSeMOSYS energy system model. This paper describes the data sources, structure and use of OSeMOSYS Global, and provides illustrative workflow results.
Machine Learning Early Prediction of Respiratory Syncytial Virus (RSV) in Pediatric Hospitalized Patients
Authors: Tso Chak Foon , Lam Carson , Calvert Jacob , Mao Qingqing 
Published: June 2022

Abstract
Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.
Topological visualization of rough sets by neighborhoods and a heart application based graphs
Authors: El-Atik Abd El-Fattah A. , El-Bably Mostafa K. , El-Gayar Mostafa A. 
Published: June 2022

Abstract
A human heart is represented by graph theory and has many topological formulations.

The main purpose of the article is to introduce a special class of neighborhood systems, called $1$-neighborhood systems ($\NS$s for short), as tools to generalize rough set theory and give the human heart new modeling by using topological structures. Some new types of minimal neighborhoods and core minimal are introduced. A comparison between these new types of neighborhoods are discussed. In addition, new forms of topological spaces through a $1$-$\NS$ of vertices for a graph of the human heart are presented and studied as a practical example in real-life problems. A best model of the topological structure which may be used for diagnosis is suggested.
Antibody-based Radiopharmaceuticals as Theranostic Agents: An
Overview
Authors: Sharma Rohit , Suman Shishu K. , Mukherjee Archana 
Published: June 2022

Abstract:
Since the inception of antibodies as magic bullets for targeting antigens with
high specificity for various in vitro and in-vivo detection and therapy applications, the
field has evolved, and remarkable success has been achieved not only in the methods of
development of these targeting agents but also in their applications. The utilization of these
moieties for the development of antibody-based radiopharmaceuticals for diagnostic
and therapy (theranostic) purposes has resulted in the availability of various cancer-targeting
agents suitable for clinical applications. The high affinity and specificity of antibodies
towards the target antigens overexpressed on tumors render them an excellent carrier
molecules for radionuclide delivery. Although intact antibodies have high potential
as imaging and therapeutic agents, a major drawback of intact antibody-based radionuclide
targeting is their slow pharmacokinetics and poor penetration into solid tumors. In
contrast to large intact antibodies, engineered antibody fragments, such as minibodies, diabodies,
single-chain variable region fragments (scFvs), nanobodies, and non-antibody
protein scaffolds-based moieties, retain the specificities and affinities of intact antibodies
in addition to improved pharmacokinetics for imaging and therapy of solid tumors. These
engineered carrier molecules are not only amenable for simple and robust radiolabeling
procedures but also provide high contrast images with minimal radiotoxicity to vital organs.
However, in various instances, rapid clearance with sub-optimal tumor accumulation,
limiting renal dose, and cross-reactivity of these radiolabeled engineered smaller
molecules have also been observed. Herein, we review current knowledge of the recent
methods for the development of antibody-based targeting moieties, the suitability of various
engineered formats for targeting tumors, and radiolabeling strategies for the development
of radioformulations. We discuss promising antibody-based and non-antibody-
based affibody radiopharmaceuticals reported for clinical applications. Finally, we
highlight how emerging technologies in antibody engineering and drug development can
be amalgamated for designing novel strategies for cancer imaging and therapy.
Design, Synthesis, and Anti-Breast Cancer Activity of Novel Fluorinated
7-O-Modified Genistein Derivatives
Authors: Xiang Yijun , Yao Xu , Zheng Xing , Yang Zhifang , Liu Yi , Liu Zhuo , Xu Qinfang , Liu Shun , Jiang Kailin , Shi Yuanlong , Xu Wenyu , Yang Zehua , Mi Pengbing 
Published: June 2022

Background:
Genistein has been limited in clinical application due to its low bioavailability, extremely poor liposolubility, and fast glycosylation rate, though it possesses anti-breast cancer activity. Therefore, the discovery of novel genistein derivatives is an urgency.

Objective:
To enhance the anti-breast cancer activity of genistein, a series of novel fluorinated genistein derivatives were synthesized.

Method:
Their in vitro antitumor activity was investigated by the MTT assay against three cancer cell lines, via., MDA-MB-231, MCF-7 and MDA-MB-435, respectively.

Results:
Analogs 1d, 2b, 3b showed remarkable anticancer activities comparing to tamoxifen, a clinical anti-breast cancer drug on the market.

Conclusion:
The activities against breast cancer of genistein were enhanced by introducing 7-alkoxyl group and fluorine atom into the B-ring. Therefore, these compounds may be potential candidates for treating breast cancer.
Surface-Enhanced Raman Scattering: A Promising Nanotechnology for
Anti-Counterfeiting and Tracking Systems
Authors: Alkhuder Khaled 
Published: June 2022

Abstract:
Surface-enhanced Raman Scattering (SERS) is a sensing method based on inelastic
scattering of a laser beam by a reporter molecule absorbed on a plasmonic substrate. The incident
laser beam induces a localized-surface plasmon resonance in the substrate, which generates an
oscillating electromagnetic field on the substrate dielectric surface. Under the influence of this
field, the reporter molecule absorbed on the plasmonic substrate starts to vibrate, causing inelastic
scattering of the laser beam. The laser-induced electromagnetic field is also the main contributor
to the enhancement observed in the intensity of the scattered light. Plasmonic substrates are
nanostructured surfaces often made of noble metals. The surface enhancement of a plasmonic
substrate is determined primarily by factors related to the substrate’s nano-architecture and its
composition. SERS-based labeling has emerged as a reliable and sophisticated anti-counterfeiting
technology with potential applications in a wide range of industries. This technology is based on
detecting the SERS signals produced by SERS tags using Raman spectroscopy. SERS tags are
generally made of a plasmonic substrate, a Raman reporter, and a protective coating shell. They
can be engineered using a wide variety of materials and methods. Several SERS-based anticounterfeiting
labels have been developed in the past two decades. Some of these labels have
been successfully combined with identification systems based on artificial intelligence. The purpose
of this review is to shed light on the SERS technology and the progress that has been
achieved in the SERS-based tracking systems.
Synthesis, Characterization, and Pharmacological Activities of Copper(II)
Complexes of Curcumin Derivatives
Authors: Kothari Richa , Agrawal Anurag 
Published: June 2022

Background:
Curcuma longa L is a herbaceous plant of zingiberaceae family. Curcumin and its derivatives possess a vast varieties of biological activities like antimicrobial, antioxidant, anti-inflammatory, antimalarial, antirheumatic etc. Looking into the biological significance of curcumin and its derivatives, we have decided to synthesize novel derivatives of curcumin and their coordinated Copper (II) complexes and evaluate their pharmacological activities like antioxidant, antibacterial etc.

Methods:
Targeted derivatives of curcumin were prepared in good yield (95%) by the condensation reaction of carbohydrazide(1), Curcumin(2), in presence of copper salts using classical heating methods in the presence of absolute ethanol.

Results:
The targeted derivatives of Curcumin were evaluated for their collaborative antimicrobial activity against gram-positive and gram-negative bacterial strains. The zone of inhibition was measured by considering the disc diffusion method. In-vitro minimum inhibitory concentrations of targeted compounds were measured using the broth micro-dilution method. In addition to this, the invitro antioxidant activity of target compounds was also evaluated by adopting the DPPH method using ascorbic acid as a standard substance. Most of the compounds showed fascinating antibacterial and antioxidant activities as a contrast to pure curcumin.

Conclusion:
In conclusion, the present work explains the synthesis, characterization and evaluation of biological activities of novel derivatives of curcumin and their coordinated Copper (II) complexes. All the targeted compounds were screened for their pharmacological evaluation against selected human pathogenic bacteria. Antioxidant activity of the synthesized compounds was also evaluated against DPPH and ascorbic acid standard substances. Among the designed molecules, most of the compounds showed fascinating antibacterial and antioxidant activities as compared to pure curcumin.
eHealth Literacy and its Associated Factors Among Health Professionals During the COVID-19 Pandemic in Resource-Limited Settings: Cross-sectional Study
Authors: Tesfa Getanew Aschalew , Yehualashet Delelegn Emwodew , Ewune Helen Ali , Zemeskel Addisu Getnet , Kalayou Mulugeta Hayelom , Seboka Binyam Tariku 
Published: June 2022

Background
The COVID-19 pandemic has wreaked havoc on health care systems and governments worldwide. Although eHealth literacy is acknowledged as a critical component of public health, it was overlooked during the pandemic. To assist patients and their families, health professionals should be knowledgeable about online health information resources and capable of evaluating relevant online information. In a resource-constrained situation, the level of eHealth literacy among health professionals is not well documented.

Objective
The aim of this study was to assess the eHealth literacy level and its associated factors among health professionals working in Amhara regional state teaching hospitals, Ethiopia.

Methods
A self-administered questionnaire was used in an institutional-based cross-sectional study design. Descriptive statistics were calculated to describe eHealth literacy statements and key variables using SPSS v.24. Bivariable and multivariable logistic regression models were fit to identify factors related to eHealth literacy. Variables with P<.05 were declared to be statistically significant predictors.

Results
A total of 383 participants completed and returned the questionnaire with a response rate of 90.5%. Health professionals demonstrated a moderate level of eHealth literacy (mean 29.21). Most of the professionals were aware of the available health resources located on the internet, and know how to search and locate these resources. However, they lack the ability to distinguish high-quality health resources from low-quality resources. Factors that were significantly associated with eHealth literacy were computer access, computer knowledge, perceived ease of use, and perceived usefulness of eHealth information resources.

Conclusions
It is crucial to provide training and support to health care workers on how to find, interpret, and, most importantly, evaluate the quality of health information found on the internet to improve their eHealth literacy level. Further research is needed to explore the role of eHealth literacy in mitigating pandemics in developing countries.