The availability of source code in relation to timely response to security vulnerabilities

Once a vulnerability has been found in an application or service that runs on a computer connected to the Internet, fixing that exploit in a timely fashion is of the utmost importance. There are two parts to fixing vulnerability: a party acting on behalf of the application's vendor gives instructions to fix it or makes a patch available that can be downloaded; then someone using that information fixes the computer or application in question. This paper considers the effects of proprietary software versus non-proprietary software in determining the speed with which a security fix is made available, since this can minimize the amount of time that the computer system remains vulnerable.     

Application of ion-engineered Persian Gulf seawater in EOR:effects of different ions on interfacial tension,contact angle,zeta potential,and oil recovery

In this study,we initially performed interfacial tension(IFT)tests to investigate the potential of using the Persian Gulf sea-water(PGSW)as smart water with dif...     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

On the use of Buchberger criteria in G2V algorithm for calculating Gröbner bases

It has been experimentally demonstrated by Faugère that his F₅ algorithm is the fastest algorithm for calculating Gröbner bases. Computational efficiency of F₅ is due to not only applying linear algebra but also using the new F₅ criterion for revealing useless zero reductions. At the ISSAC 2010 conference, Gao, Guan, and Volny presented G²V, a new version of the F₅ algorithm, which is simpler than the original version of the algorithm. However, the incremental structure of G²V used in the algorithm for applying the F₅ criterion is a serious obstacle from the point of view of application of Buchberger’s second criterion. In this paper, a modification of the G²V algorithm is presented, which makes it possible to use both Buchberger criteria. To experimentally study computational effect of the proposed modification, we implemented the modified algorithm in Maple. Results of comparison of G²V and its modified version on a number of test examples are presented.     

Nanotechnology: Advantages and drawbacks in the field of construction and building materials

Nanotechnology seems to hold the key that allows construction and building materials to replicate the features of natural systems improved until perfection during millions of years. This paper reviews current knowledge about nanotechnology and nanomaterials used by the construction industry. It covers the nanoscale analysis of Portland cement hydration products, the use of nanoparticles to increase the strength and durability of cimentitious composites, the photocatalytic capacity of nanomaterials and also nanotoxicity risks.     

FQL‐RED: an adaptive scalable schema for active queue management

In a potentially congested network, random early detection (RED) active queue management (AQM) proved effective in improving throughput and average queuing delay. The main disadvantage of RED is its sensitive parameters that are impossible to estimate perfectly and adjust manually because of the dynamic nature of the network. For this reason, RED performs differently during different phases of a scenario and there is no guarantee that it will have optimal performance. Giving adaptability to RED has been the subject of broad research studies ever since RED was proposed. After a substantial study of AQM schemes and presenting a novel categorization for so‐called modern approaches utilizing artificial intelligence tools to improve AQM, this paper proposes an algorithm enhancing RED as an add‐on patch that makes minimal changes to the original RED. Being built on the basis of AQM schemes like ARED and Fuzzy‐RED, this algorithm inherits adaptability and is able to adjust RED inaccurate parameters regarding network traffic status, trying to optimize throughput and average queuing delay in a scenario. This algorithm is a Q‐learning method enhanced with a fuzzy inference system to provide RED with self‐adaptation and improved performance as a result. Given the name of FQL‐RED, this algorithm outperformed RED, ARED, and Fuzzy‐RED, as the OPNET simulations show. Copyright © 2010 John Wiley & Sons, Ltd.     

Critical aspects of sinter-hardening of prealloyed Cr–Mo steel

In recent years, growing demand for greater mechanical properties of PM steel components with competitive fabrication cost has led to significant innovations in different fields of powder metallurgy. Recent research has been focused on reaching higher performance with lower cost. To this end, the possibility of combining the conventional sintering and post-sintering processes for a particular powder composition has been introduced. Sinter-hardening is a result of the research conducted along this line. Elimination of any secondary operation such as quench-hardening by incorporating it in the sintering process (i.e. sinter-hardening) is of great interest, as it will lead to lower processing costs and equal, if not higher mechanical performance. However, to ensure the desired mechanical properties of the final component and robustness of the performance, critical aspects of the sinter-hardening process should be rigorously studied.Hence with specific attention to a Cr–Mo steel powder (FL-5305), this study deals with the influence of density on cooling rate, the effect of different sintering temperatures (e.g. 1120 °C and 1250 °C) on austenite grain size and consequently, hardenability. The microstructure development in sinter-hardened FL-5305 material has been analyzed and predicted by means of the available literature for solid steel and also using the commercial software (JMatPro 5.0) for materials assessment based on thermodynamic and kinetics modeling. Finally, inaccurate carbon control and its adverse impact on excessive formation of cementite have been addressed.