Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT

The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation

Effects of MEA Type and Curing Temperature on the Autogenous Deformation, Mechanical Properties, and Microstructure of Cement-Based Materials

MgO expansive agent (MEA) has the potential to meet the shrinkage compensation demands for concrete in different types of structures due to its designable reactivity and expansion properties. This study investigated the impact of three types of MEAs with different reactivities as well as curing temperature on the autogenous deformation, mechanical properties, and the microstructure of cement-based materials. The results showed that MEA type R exhibits a faster and larger hydration degree and expansion in cement mortars than MEA type M or type S in early ages under 20 °C, while when the curing temperature increases to 40 °C and 60 °C, MEA type M and type S present with significant accelerations in the hydration degree, leading to accelerated expansion rates and significantly increased expansion values compared to MEA type R. Under 40 °C, 5% MEA type M and type S present with 2.2 times and 1.1 times higher expansion in mortars than 5% MEA type R, respectively, and 8% MEA type M and type S present with 7.1 times and 5.6 times higher expansion in mortars than 8% MEA type R, respectively. Under 60 °C, 5% MEA type M and type S present 4.0 times and 3.1 times higher expansion in mortars than 5% MEA type R, respectively, and 8% MEA type M and type S present 7.0 times and 6.6 times higher expansion in mortars than 8% MEA type R, respectively. However, the increase in porosity, especially for large pores with pore size greater than 50 nm as well as the microcracks induced by the 8% dosage of MEA type M, type S, and high curing temperature of 60 °C, result in a decrease in strength of about 30% for the cement mortars. The results indicate that MEA type R is more suitable for shrinkage compensation of cement-based materials with lower temperatures, while MEA type M and type S are more suitable for shrinkage compensation of cement-based materials with higher temperatures. Under high-temperature and low-constraint conditions, the dosage of MEA needs to be strictly controlled to prevent negative effects on the microstructure and strength of cement-based materials

Extracting the globally and locally adaptive backbone of complex networks.

A complex network is a useful tool for representing and analyzing complex systems, such as the world-wide web and transportation systems. However, the growing size of complex networks is becoming an obstacle to the understanding of the topological structure and their characteristics. In this study, a globally and locally adaptive network backbone (GLANB) extraction method is proposed. The GLANB method uses the involvement of links in shortest paths and a statistical hypothesis to evaluate the statistical importance of the links; then it extracts the backbone, based on the statistical importance, from the network by filtering the less important links and preserving the more important links; the result is an extracted subnetwork with fewer links and nodes. The GLANB determines the importance of the links by synthetically considering the topological structure, the weights of the links and the degrees of the nodes. The links that have a small weight but are important from the view of topological structure are not belittled. The GLANB method can be applied to all types of networks regardless of whether they are weighted or unweighted and regardless of whether they are directed or undirected. The experiments on four real networks show that the link importance distribution given by the GLANB method has a bimodal shape, which gives a robust classification of the links; moreover, the GLANB method tends to put the nodes that are identified as the core of the network by the k-shell algorithm into the backbone. This method can help us to understand the structure of the networks better, to determine what links are important for transferring information, and to express the network by a backbone easily

AmbuBox: A Fast-Deployable Low-Cost Ventilator for COVID-19 Emergent Care.

We present a low-cost clinically viable ventilator design, AmbuBox, using a controllable pneumatic enclosure and standard manual resuscitators that are readily available (AmbuBag), which can be rapidly deployed during pandemic and mass-casualty events with a minimal set of components to manufacture and assemble. The AmbuBox is designed to address the existing challenges presented in the existing low-cost ventilator designs by offering an easy-to-install and simple-to-operate apparatus while maintaining a long lifespan with high-precision flow control. As an outcome, a mass-producible prototype of the AmbuBox has been devised, characterized, and validated in a bench test setup using a lung simulator. This prototype will be further investigated through clinical testing. Given the potentially urgent need for inexpensive and rapidly deployable ventilators globally, the overall design, operational principle, and device characterization of the AmbuBox system have been described in detail with open access online. Moreover, the fabrication and assembly methods have been incorporated to enable short-term producibility by a generic local manufacturing facility. In addition, a full list of all components used in the AmbuBox has been included to reflect its low-cost nature

AmbuBox: A Fast-Deployable Low-Cost Ventilator for COVID-19 Emergent Care.

We present a low-cost clinically viable ventilator design, AmbuBox, using a controllable pneumatic enclosure and standard manual resuscitators that are readily available (AmbuBag), which can be rapidly deployed during pandemic and mass-casualty events with a minimal set of components to manufacture and assemble. The AmbuBox is designed to address the existing challenges presented in the existing low-cost ventilator designs by offering an easy-to-install and simple-to-operate apparatus while maintaining a long lifespan with high-precision flow control. As an outcome, a mass-producible prototype of the AmbuBox has been devised, characterized, and validated in a bench test setup using a lung simulator. This prototype will be further investigated through clinical testing. Given the potentially urgent need for inexpensive and rapidly deployable ventilators globally, the overall design, operational principle, and device characterization of the AmbuBox system have been described in detail with open access online. Moreover, the fabrication and assembly methods have been incorporated to enable short-term producibility by a generic local manufacturing facility. In addition, a full list of all components used in the AmbuBox has been included to reflect its low-cost nature

The distribution of links in link-shells.

<p>For the coauthor, fetion and email networks, we extract the top 10% important links, based on the GLANB, disparity filter and salience methods separately, to analyze their distributions in terms of link-shells. In addition, we also exclude the links that have degree of 1 to extract the remaining top 10% important links based on the salience method (salience-E) to analyze the distribution.</p

An undirected artificial network.

<p>The first number on the line is the value of the link weight, and the second number is the value of the link salience. Although the link gets the largest value 1 of the link salience, it is only important for node . The links and have the smallest value of the link salience, but they are in the core of the network.</p

Fraction of nodes maintained in the backbones.

<p>The fraction of nodes is a function of the fraction of links retained by the filters. The dash lines correspond to the fraction of the nodes whose degree is greater than 1 in the networks.</p

An undirected artificial network.

<p>The numbers on the lines denote the weights of the links. Although the weight of link is greater than that of link , link is more important for node than link is, because link is the only path through which node can reach the remainder of the network.</p

The distributions of the link salience, the link statistical importance and the disparity filtering importance.

<p>Link measurement refers to the values of the link salience, link statistical importance, and the disparity filtering importance that are given by the salience, GLANB and disparity methods separately. For the GLANB and disparity methods, the smaller values mean higher importance. For the salience method, the larger values mean higher importance.</p