A model for waterside oxidation of zircaloy fuel cladding in pressurized water reactors

A model has been developed to predict the long-term oxidation rate of Zircaloy-4 for ex-reactor (autoclave) and in-reactor (PWR) environments and operating conditions. A computer program has been written to solve the oxygen diffusion equation by employing a fully implicit finite difference method for a one dimensional cylindrical geometry. The moving boundary of the oxide is treated by deriving a time dependent relationship to control the speed of the interface motion. New diffusion coefficients for the oxygen in the oxide film have been developed. The proposed diffusion coefficients are a function of temperature and oxygen concentration gradient in the oxide and are derived for both the pre-transition and post-transition regions. To include the in-reactor corrosion behavior, additional factors have been considered, specifically the influence of the heat flux on the oxide-metal interface temperature, the fast neutron flux effect on the acceleration of the corrosion rate, and the effect of the lithium hydroxide concentration on the enhancement of the corrosion rate. This model allows the prediction of the long-term oxidation weight gain and the oxide thickness on Zircaloy-4 in operating conditions which simulate the PWR conditions. The results obtained for ex-reactor (autoclave) and in-reactor oxidation are compared with experimental data existing in the literature. The predictions are found to be in good agreement with the experimental data

Detecting Zero-day Polymorphic Worms with Jaccard Similarity Algorithm

Zero-day polymorphic worms pose a serious threat to the security of Mobile systems and Internet infrastructure. In many cases, it is difficult to detect worm attacks at an early stage. There is typically little or no time to develop a well-constructed solution during such a worm outbreak. This is because the worms act only to spread from node to node and they bring security concerns to everyone using Internet via any static or mobile node. No system is safe from an aggressive worm crisis. However, many of the characteristics of a worm can be used to defeat it, including its predictable behavior and shared signatures. In this paper, we propose an efficient signature generation method based on string similarity algorithms to generate signatures for Zero-day polymorphic worms. Then, these signatures are practically applied to an Intrusion Detection System (IDS) to prevent the network from such attacks. The experimental results show the efficiency of the proposed approach compared to other existing mechanisms

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Modeling and simulation of VERA core physics benchmark using OpenMC code

Detailed analysis of the neutron pathway through matter inside the nuclear reactor core is exceedingly needed for safety and economic considerations. Due to the constant development of high-performance computing technologies, neutronics analysis using computer codes became more effective and efficient to perform sophisticated neutronics calculations. In this work, a commercial pressurized water reactor (PWR) presented by Virtual Environment for Reactor Applications (VERA) Core Physics Benchmark are modeled and simulated using a high-fidelity simulation of OpenMC code in terms of criticality and fuel pin power distribution. Various problems have been selected from VERA benchmark ranging from a simple two-dimension (2D) pin cell problem to a complex three dimension (3D) full core problem. The development of the code capabilities for reactor physics methods has been implemented to investigate the accuracy and performance of the OpenMC code against VERA SCALE codes. The results of OpenMC code exhibit excellent agreement with VERA results with maximum Root Mean Square Error (RMSE) values of less than 0.04% and 1.3% for the criticality eigenvalues and pin power distributions, respectively. This demonstrates the successful utilization of the OpenMC code as a simulation tool for a whole core analysis. Further works are undergoing on the accuracy of OpenMC simulations for the impact of different fuel types and burnup levels and the analysis of the transient behavior and coupled thermal hydraulic feedback

Investigation of the Fuel Shape Impact on the MTR Reactor Parameters Using the OpenMC Code

The goal of this study was to evaluate the impact of simulating different fuel shapes for the material testing reactor (MTR). Two OpenMC codes were built, and the first OpenMC model was simulated using a curved shape fuel element to mimic the real dimensions and shape of the MTR. The code core parameters were validated with the collected parameters from the experimental work and two well-known Monte Carlo simulation codes (MCNP and SCALE). The validation process included the axial flux profile and criticality. After the OpenMC curve fuel model was validated, the MTR fuel was simulated as flat fuel elements with the exact amount of fuel as in the curve fuel model. By comparing the two OpenMC models’ calculations, it was observed that the radial flux distribution has only a slight difference due to fuel mass similarity. In conclusion, simulating the MTR fuel as flat elements provided a good agreement calculation compared to the real shape, but it was also observed that this might carry some discrepancies for in-depth simulation studies

Investigation of the Fuel Shape Impact on the MTR Reactor Parameters Using the OpenMC Code

The goal of this study was to evaluate the impact of simulating different fuel shapes for the material testing reactor (MTR). Two OpenMC codes were built, and the first OpenMC model was simulated using a curved shape fuel element to mimic the real dimensions and shape of the MTR. The code core parameters were validated with the collected parameters from the experimental work and two well-known Monte Carlo simulation codes (MCNP and SCALE). The validation process included the axial flux profile and criticality. After the OpenMC curve fuel model was validated, the MTR fuel was simulated as flat fuel elements with the exact amount of fuel as in the curve fuel model. By comparing the two OpenMC models’ calculations, it was observed that the radial flux distribution has only a slight difference due to fuel mass similarity. In conclusion, simulating the MTR fuel as flat elements provided a good agreement calculation compared to the real shape, but it was also observed that this might carry some discrepancies for in-depth simulation studies.</jats:p

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

Credit card use poses a significant security issue on a global scale, with rule-based algorithms and traditional anomaly detection being two of the most often used methods. However, they are resource-intensive, time-consuming, and erroneous. Given fewer instances than legal payments, the dataset imbalance has become a serious issue. On the other hand, the generative technique is considered an effective way to rebalance the imbalanced class issue, as this technique balances both minority and majority classes before the training. In a more recent period, GAN is considered one of the most popular data generative techniques, as it is used in significant data settings. Hence, the research under study explores a classification system to detect fraudulent credit card transactions that are being trained using the European Cardholders 2013 dataset. It has 30 features, 28 of which are hidden due to sensitive information. Fraud activity accounts for less than 1&#x0025; of the entire transaction volume of ${\$}$ 284807. Additionally, GANs is a generative model based on game theory, in which a generator G and a discriminator D compete with one another. The generator&#x2019;s goal is to make the discriminator uncertain. Distinguishing between instances from the generator and those from the original dataset is the discriminator&#x2019;s goal, and we can increase classifiers&#x2019; discriminating strength by training GANs on a set of fraudulent credit card transactions. According to the outcome, our model outperformed the earlier experiments with an AUC score of 0.999. Additionally, it creates artificial data using GANs, enabling the production of a sizable volume of high-quality data. In terms of innovation and performance, this technique substantially improves over earlier research

A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate