Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach

Sustainable E-scooter Parking Operation in Urban Areas Using Fuzzy Dombi Based RAFSI Model

Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology

This research work proposes a new hybrid framework to assess suitable sites and technical potentials for large-scale solar photovoltaic (PV) systems by integrating two multi-criteria decision-making (MCDM) techniques. The evaluation of sites for PV plants was performed using the MCDM method, taking into account a wide range of variables, including climate, technical, geographical, and economic variables, with factor weights determined using the CRITIC technique. Five Saudi Arabian cities with abundant solar radiation served as illustrations of this study’s framework. For classification, the TOPSIS method was employed to rank the five alternatives. The results show that Riyadh is ranked first with a performance score of 72%, followed by Jeddah with a performance score of 65%, and the remaining three cities, namely, Al Ahsa, Dammam, and Abha scored less than 50%. Lastly, the reliability and robustness of the results obtained were examined using sensitivity analysis. The findings of this study can be used to pinpoint possible places that could be used to build solar power plants and to promote the expansion of generating facilities and electrical grids.

/pdf/a-critic-topsis-multi-criteria-decision-making-approach-for-35hzexor.pdf

A CRITIC–TOPSIS Multi-Criteria Decision-Making Approach for Optimum Site Selection for Solar PV Farm

Solar PV power plant site selection using a GIS-based non-linear multi-criteria optimization technique

The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach.

/pdf/risk-priority-evaluation-of-power-transformer-parts-based-on-1280whwm.pdf

Risk priority evaluation of power transformer parts based on hybrid fmea framework under hesitant fuzzy environment

Classical swine fever (CSF), caused by classical swine fever virus (CSFV), is a highly infectious disease that poses a significant threat to the global pig industry. Therefore, gaining insights into the virus and its interaction with host cells is crucial for developing effective antiviral measures and controlling the spread of CSF. ABSTRACT Vimentin (VIM), an indispensable protein, is responsible for the formation of intermediate filament structures within cells and plays a crucial role in viral infections. However, the precise role of VIM in classical swine fever virus (CSFV) infection remains unclear. Herein, we systematically investigated the function of VIM in CSFV replication. We demonstrated that both knockdown and overexpression of VIM affected CSFV replication. Furthermore, we observed by confocal microscopy the rearrangement of cellular VIM into a cage-like structure during CSFV infection. Three-dimensional (3D) imaging indicated that the cage-like structures were localized in the endoplasmic reticulum (ER) and ringed around the double-stranded RNA (dsRNA), thereby suggesting that VIM was associated with the formation of the viral replication complex (VRC). Mechanistically, phosphorylation of VIM at serine 72 (Ser72), regulated by the RhoA/ROCK signaling pathway, induced VIM rearrangement upon CSFV infection. Confocal microscopy and coimmunoprecipitation assays revealed that VIM colocalized and interacted with CSFV NS5A. Structurally, it was determined that amino acids 96 to 407 of VIM and amino acids 251 to 416 of NS5A were the respective important domains for this interaction. Importantly, both VIM knockdown and disruption of VIM rearrangement inhibited the localization of NS5A in the ER, implying that VIM rearrangement recruited NS5A to the ER for VRC formation. Collectively, our results suggest that VIM recruits NS5A to form a stable VRC that is protected by the cage-like structure formed by VIM rearrangement, ultimately leading to enhanced virus replication. These findings highlight the critical role of VIM in the formation and stabilization of VRC, which provides alternative strategies for the development of antiviral drugs. IMPORTANCE Classical swine fever (CSF), caused by classical swine fever virus (CSFV), is a highly infectious disease that poses a significant threat to the global pig industry. Therefore, gaining insights into the virus and its interaction with host cells is crucial for developing effective antiviral measures and controlling the spread of CSF. Previous studies have shown that CSFV infection induces rearrangement of the endoplasmic reticulum, leading to the formation of small vesicular organelles containing nonstructural protein and double-stranded RNA of CSFV, as well as some host factors. These organelles then assemble into viral replication complexes (VRCs). In this study, we have discovered that VIM recruited CSFV NS5A to form a stable VRC that was protected by a cage-like structure formed by rearranged VIM. This enhanced viral replication. Our findings not only shed light on the molecular mechanism of CSFV replication but also offer new insights into the development of antiviral strategies for controlling CSFV.

Intracellular Vimentin Regulates the Formation of Classical Swine Fever Virus Replication Complex through Interaction with NS5A Protein

Simple Summary For centuries, diarrhea disease has caused massive economic losses to the pig industry globally. Among RNA viruses causing pig diseases with the symptom of diarrhea include porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), and porcine deltacoronavirus (PDCoV), which all belong to the category of swine enteric coronaviruses and can result similar clinical symptoms in pigs, such as diarrhea, vomiting, dehydration, and so on. As a consequence, it is therefore necessary to develop a method that can detect and differentiate all three porcine enteric coronaviruses (PEDV, TGEV, and PDCoV) with a high sensitivity and specificity. In our study, we developed a multiplex quantitative PCR (qPCR) assay. We collected 462 samples of feces or small intestine from Jiangsu, Shandong, Hubei, Guangdong and Hunan provinces, following which the samples were detected for the evaluation of the application of the multiplex qPCR. The results indicated that the discrete positive rates of PEDV, TGEV, and PDCoV were 19.70%, 0.87%, and 10.17%, respectively. The mixed infection rates of PEDV/TGEV, PEDV/PDCoV, TGEV/PDCoV, and PEDV/TGEV/PDCoV were 3.25%, 23.16%, 0.22%, and 11.90%, respectively. The multiplex qPCR, a tool for differential and rapid diagnosing, can be put on the prevention and control of PEDV, TGEV, and PDCoV practically. Abstract Porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), and porcine deltacoronavirus (PDCoV) belong to the category of swine enteric coronavirus that cause acute diarrhea in piglets, which has resulted in massive losses to the pig husbandry. Therefore, a sensitive and rapid detection method which can differentially detect these viruses that lead to mixed infections in clinical cases, is urgently needed. According to the conserved regions of the PEDV M gene, TGEV S gene, and PDCoV N gene, and the reference gene of porcine (β-Actin), we designed new specific primers and probes for the multiplex qPCR assay capable of simultaneously detecting three RNA viruses. This method, with a great specificity, did not cross-react with the common porcine virus. Moreover, the limit of detection of the method we developed could reach 10 copies/μL ,and the intra- and inter-group coefficients of variation of it below 3%. Applying this assay to detect 462 clinical samples which were collected in 2022–2023, indicated that the discrete positive rates of PEDV, TGEV, and PDCoV were 19.70%, 0.87%, and 10.17%, respectively. The mixed infection rates of PEDV/TGEV, PEDV/PDCoV, TGEV/PDCoV, and PEDV/TGEV/PDCoV were 3.25%, 23.16%, 0.22%, and 11.90%, respectively. All in all, the multiplex qPCR assay we developed as a tool for differential and rapid diagnosing can be put on the active prevention and control of PEDV, TGEV, and PDCoV, , which can create great value in the diagnosis of swine diarrhea diseases.

/pdf/development-of-a-multiplex-quantitative-pcr-for-detecting-vp3eidzj.pdf

Development of a Multiplex Quantitative PCR for Detecting Porcine Epidemic Diarrhea Virus, Transmissible Gastroenteritis Virus, and Porcine Deltacoronavirus Simultaneously in China

Simple Summary At present, mixed infection with multiple pathogens is an important reason for the complexity of swine diseases in China. Among them, African swine fever virus (ASFV), porcine circovirus 2 (PCV2), and pseudorabies virus (PRV) are extremely important DNA viruses that cause reproductive abnormalities in sows. Therefore, there is an urgent need for rapid and sensitive detection methods that can diagnose three kinds of DNA viruses in the clinic. Herein, a multiplex real-time PCR (qPCR) assay based on TaqMan probes was developed for the simultaneous determination of ASFV, PCV2, and PRV. To ascertain the use of the multiplex real-time qPCR, 383 field specimens from the pig farms of four provinces in East China were collected. The survey data displayed that the ASFV, PCV2, and PRV single infection rates were 22.45%, 28.46%, and 2.87%, respectively. The mixed infection rates of ASFV + PCV2, ASFV + PRV, PCV2 + PRV, and ASFV + PCV2 + PRV were 5.22%, 0.26%, 1.83%, and 0.26%, respectively. The assay established in this study could be used as a differential diagnostic tool for the monitoring and control of ASFV, PCV2, and PRV in the field. Abstract African swine fever virus (ASFV), porcine circovirus 2 (PCV2), and pseudorabies virus (PRV) are important DNA viruses that cause reproductive disorders in sows, which result in huge losses in pig husbandry, especially in China. The multiplex qPCR assay could be utilized as a simultaneous diagnostic tool for field-based surveillance and the control of ASFV, PCV2, and PRV. Based on the conserved regions on the p72 gene of ASFV, the Cap gene of PCV2, the gE gene of PRV, and the porcine endogenous β-Actin gene, the appropriate primers and probes for a multiplex TaqMan real-time PCR test effective at concurrently detecting three DNA viruses were developed. The approach demonstrated high specificity and no cross-reactivity with major pathogens related to swine reproductive diseases. In addition, its sensitivity was great, with a detection limit of 101 copies/L of each pathogen, and its repeatability was excellent, with intra- and inter-group variability coefficients of <2%. Applying this assay to detect 383 field specimens collected from 2020 to 2022, the survey data displayed that the ASFV, PCV2, and PRV single infection rates were 22.45%, 28.46%, and 2.87%, respectively. The mixed infection rates of ASFV + PCV2, ASFV + PRV, PCV2 + PRV, and ASFV + PCV2 + PRV were 5.22%, 0.26%, 1.83%, and 0.26%, respectively. Overall, the assay established in this study provides an effective tool for quickly distinguishing the viruses causing sow reproductive disorders, suggesting its huge clinical application value in the diagnosis of swine diseases.

/pdf/development-of-a-taqman-probe-based-multiplex-real-time-pcr-c9ovo7it.pdf

Development of a TaqMan-Probe-Based Multiplex Real-Time PCR for the Simultaneous Detection of African Swine Fever Virus, Porcine Circovirus 2, and Pseudorabies Virus in East China from 2020 to 2022

Porcine Mx proteins inhibit pseudorabies virus replication through interfering with early gene synthesis.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jing Chen

Author Tools

Chat about Author