Some coincidence point results for T-contraction mappings on partially ordered b-metric spaces and applications to integral equations

In this paper, we prove some fixed point results for T-contraction mappings in partially ordered b-metric spaces, that generalize the main results of [H. Huang, S. Radenovič, J. Vujakovič, On some recent coincidence and immediate consequences in partially ordered b-metric spaces, Fixed Point Theory Appl., 2015, Paper No. 63]. As an application, we discuss the existence for a solution of a nonlinear integral equation

Optimization for continuous overflow proteolytic hydrolysis of spent brewer’s yeast by using proteases

A large amount of spent yeast as by-product is annually generated from brewing industry and it contains about 50-55% protein with good balance of amino acids. The hydrolysate produced from spent brewer’s yeast may be used in food application. The yield of proteolylic hydrolysis for spent brewer’s yeast and amino acid contents of hydrolysates depend on factors such as temperature, pH value, type of used enzyme and ratio enzyme/substrate, time. Besides, applied hydrolysing methods (batch-, or continuous method) has effected on degree of hydrolysis. With the purpose of how proteolytic hydrolysis having effects on the spent brewer’s yeast for food application in industrial scale, continuous overflow method was used in this study. Bitterness of hydrolysate and the yield of continuous overflow proteolytic hydrolysis process are the two interested factors for protein hydrolysis. In this report, it is dealt with determination for optimal conditions to obtain the highest yield of hydrolysis process and the lowest bitterness of hydrolysate. Response surface methodology (RSM) was used to determine optimal condition for continuous overflow proteolytic hydrolysis of spent brewer’s yeast. The optimal conditions for obtaining high degree of hydrolysis and low bitterness are determined as followings: ratio of enzyme mixture (alcalase 7.5 U/g and flavourzyme 10 U/g), pH at 7.5, hydrolysis temperature at 51oC and hydrolysis time of 9 hours. Under the optimal conditions, the yield of hydrolysis was 59.62% ± 0.027 and the bitterness equivalently with concentration of quinine was 7.86 ± 0.033 μmol /ml

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

Survey on Vietnamese teachers’ perspectives and perceived support during COVID-19

The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e- survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic

Advanced Method for Motion Control of a 3 DOFs Lower Limb Rehabilitation Robot

This paper presents two motion control methods for a lower limb rehabilitation robot based on compensate gravity proportional-derivative and inverse dynamic proportional-derivative (PD) control algorithms. The Robot’s mechanism is comprised of three active joints: hip joint, knee joint and ankle joint, which are driven by DC motors. Firstly, based on Robot’s mechanism, a dynamic model of the Robot is built. Then, based on Robot’s model, motion control systems for Robot are designed. Simulation results show good performances and workability of these proposed controllers. Finally, the calculation of the joint angle errors and toque of each controller is performed. The comparison of simulation results between proposed controllers and the adaptive fuzzy controller allows to choice suitable motion control methods for Robot that can meet the requirements of a 3 DOFs lower limb rehabilitation robot for post-stroke patient

Anti-DreamBooth: Protecting users from personalized text-to-image synthesis

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at \href{https://github.com/VinAIResearch/Anti-DreamBooth.git}{https://github.com/VinAIResearch/Anti-DreamBooth.git}.Comment: Project page: https://anti-dreambooth.github.io

Study change of the performance of airfoil of small wind turbine under low wind speed by CFD simulation

Renewable energy has received strong attention and investment to replace fossil energy sources and reduce greenhouse gas emissions. Quite good and good wind speed areas have been invested in building large-capacity wind farms for many years. The low wind speed region occupies a very large on the world, which has been interested in the exploitation of wind energy in recent years. In this study, the original airfoil of S1010 operated at low wind speed was redesigned to increase the aerodynamic efficiency of the airfoil by using XFLR5 software. After, the new VAST-EPU-S1010 airfoil model was adjusted to the maximum thickness and the maximum thickness position. It was simulated in low wind speed conditions of 4-6 m/s by CFD simulation. The lift coefficient, drag coefficient and $C_{L}$/$C_{D}$ coefficient ratio were evaluated under the effect of the angle of attack and the maximum thickness by using the $k-\epsilon$ model. Simulation results show that the VAST-EPU-S1010 airfoil achieved the greatest aerodynamic efficiency at the angle of attack of 3\,^{\circ}, the maximum thickness of 8\% and the maximum thickness position of 20.32\%. The maximum value of $C_{L}$/$C_{D}$ of the new airfoil at 6 m/s is higher than at the 4 m/s by about 6.25\%.Comment: 19 pages, 21 figure