Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models

With the recent advancement of Large Language Models (LLMs), generating functionally correct code has become less complicated for a wide array of developers. While using LLMs has sped up the functional development process, it poses a heavy risk to code security. Code generation with proper security measures using LLM is a significantly more challenging task than functional code generation. Security measures may include adding a pair of lines of code with the original code, consisting of null pointer checking or prepared statements for SQL injection prevention. Currently, available code repair LLMs generate code repair by supervised fine-tuning, where the model looks at cross-entropy loss. However, the original and repaired codes are mostly similar in functionality and syntactically, except for a few (1-2) lines, which act as security measures. This imbalance between the lines needed for security measures and the functional code enforces the supervised fine-tuned model to prioritize generating functional code without adding proper security measures, which also benefits the model by resulting in minimal loss. Therefore, in this work, for security hardening and strengthening of generated code from LLMs, we propose a reinforcement learning-based method for program-specific repair with the combination of semantic and syntactic reward mechanisms that focus heavily on adding security and functional measures in the code, respectively

Large Language Model Lateral Spear Phishing: A Comparative Study in Large-Scale Organizational Settings

The critical threat of phishing emails has been further exacerbated by the potential of LLMs to generate highly targeted, personalized, and automated spear phishing attacks. Two critical problems concerning LLM-facilitated phishing require further investigation: 1) Existing studies on lateral phishing lack specific examination of LLM integration for large-scale attacks targeting the entire organization, and 2) Current anti-phishing infrastructure, despite its extensive development, lacks the capability to prevent LLM-generated attacks, potentially impacting both employees and IT security incident management. However, the execution of such investigative studies necessitates a real-world environment, one that functions during regular business operations and mirrors the complexity of a large organizational infrastructure. This setting must also offer the flexibility required to facilitate a diverse array of experimental conditions, particularly the incorporation of phishing emails crafted by LLMs. This study is a pioneering exploration into the use of Large Language Models (LLMs) for the creation of targeted lateral phishing emails, targeting a large tier 1 university's operation and workforce of approximately 9,000 individuals over an 11-month period. It also evaluates the capability of email filtering infrastructure to detect such LLM-generated phishing attempts, providing insights into their effectiveness and identifying potential areas for improvement. Based on our findings, we propose machine learning-based detection techniques for such emails to detect LLM-generated phishing emails that were missed by the existing infrastructure, with an F1-score of 98.96

The Effect of Rod Bending on Long-term Lumbar Sagittal Parameters in Spondylolisthesis Patients Treated With Short Segment Posterior Fusion: A Randomized Clinical Trial

Background and Aim: Although rod bending is a universal method for maintaining lumbar lordosis (LL), its long-term efficacy in short-segment posterior fusion is still a challenge. This study aimed at evaluating the long-term effect of rod bending in patients with grade one L4/L5 spondylolisthesis with a short segment fusion. Methods and Materials/Patients: A double-blind prospective randomized clinical trial was conducted from 2016 to 2018 and patients who met the inclusion criteria were enrolled in the study. The participants were randomized into two treatment arms: open posterior fusion with rod bending and without rod bending. The baseline data, including leg and back pain scores, were evaluated before surgery. Lumbar, focal, and segmental lordosis were measured before surgery. After surgery and a one-year follow-up, pain scores and lordosis measurements were re-evaluated and compared between and within groups. Results: A total of 60 patients were analyzed. Leg and back pain scores improved significantly after the follow-up in both groups (P<0.0001). However, there was no significant difference between the two groups before and after the surgery. LL did not change in either group after surgery. Focal and segmental lordosis significantly increased in both groups but showed no difference between the groups at either time. Complications were not significantly different in either group. Conclusion: In this study, no significant difference concerning the radiological and pain outcomes was observed in either group; therefore, rod bending to reach the desired LL may be an unnecessary spend of time

Folate-conjugated nanoparticles as a potent therapeutic approach in targeted cancer therapy

The selective and efficient drug delivery to tumor cells can remarkably improve different cancer therapeutic approaches. There are several nanoparticles (NPs) which can act as a potent drug carrier for cancer therapy. However, the specific drug delivery to cancer cells is an important issue which should be considered before designing new NPs for in vivo application. It has been shown that cancer cells over-express folate receptor (FR) in order to improve their growth. As normal cells express a significantly lower levels of FR compared to tumor cells, it seems that folate molecules can be used as potent targeting moieties in different nanocarrier-based therapeutic approaches. Moreover, there is evidence which implies folate-conjugated NPs can selectively deliver anti-tumor drugs into cancer cells both in vitro and in vivo. In this review, we will discuss about the efficiency of different folate-conjugated NPs in cancer therapy.NoneManuscrip

Experimental Study and Performance Investigation of Miscible Water-Alternating-CO 2

This experimental study is aimed at evaluating the performance of the miscible Water-Alternating-CO2 (CO2-WAG) flooding as a function of slug size and WAG ratio based on the ultimate oil recovery in the Sarvak formation. In this research, initially the slim-tube apparatus was used to determine the Minimum Miscibility Pressure (MMP) of the Sarvak heavy oil and CO2 at the constant reservoir temperature. Then, a total of seven core flooding experiments were performed by using the sandstone core samples collected from the Sarvak formation. These experiments were conducted through respective water flooding, miscible continuous CO2 flooding, and miscible CO2-WAG flooding. In the miscible CO2-WAG flooding, different WAG slug sizes of 0.15, 0.25, and 0.50 Pore Volume (PV) and different WAG ratios of 1:1, 2:1, and 1:2 were applied to investigate their effects on the oil Recovery Factor (RF) in the Sarvak formation. The results showed that, in general, the miscible CO2 Enhanced Oil Recovery (CO2-EOR) process is capable of mobilizing the heavy oil and achieving a high and significant oil RF in the Sarvak formation. The miscible CO2-WAG flooding has the highest oil RF (84.3%) in comparison with water flooding (37.7%), and miscible continuous CO2 flooding (61.5%). In addition, using a smaller WAG slug size for miscible CO2-WAG flooding leads to a higher oil RF. The optimum WAG ratio of the miscible CO2-WAG flooding for the Sarvak formation is approximately 2:1. The results also demonstrated that, more than 50% of the heavy oil is produced in the first two cycles of the miscible CO2-WAG flooding. The optimum miscible CO2-WAG flooding has a much less CO2 consumption than the miscible continuous CO2 flooding

The Hospitalization Rate of Cerebral Venous Sinus Thrombosis before and during COVID-19 Pandemic Era: A Single-Center Retrospective Cohort Study

Objectives: There are several reports of the association between SARS-CoV-2 infection (COVID-19) and cerebral venous sinus thrombosis (CVST). In this study, we aimed to compare the hospitalization rate of CVST before and during the COVID-19 pandemic (before vaccination program). Materials and methods: In this retrospective cohort study, the hospitalization rate of adult CVST patients in Namazi hospital, a tertiary referral center in the south of Iran, was compared in two periods of time. We defined March 2018 to March 2019 as the pre-COVID-19 period and March 2020 to March 2021 as the COVID-19 period. Results: 50 and 77 adult CVST patients were hospitalized in the pre-COVID-19 and COVID-19 periods, respectively. The crude CVST hospitalization rate increased from 14.33 in the pre-COVID-19 period to 21.7 per million in the COVID-19 era (P = 0.021). However, after age and sex adjustment, the incremental trend in hospitalization rate was not significant (95% CrI: -2.2, 5.14). Patients \u3e 50-year-old were more often hospitalized in the COVID-19 period (P = 0.042). SARS-CoV-2 PCR test was done in 49.3% out of all COVID-19 period patients, which were positive in 6.5%. Modified Rankin Scale (mRS) score ≥3 at three-month follow-up was associated with age (P = 0.015) and malignancy (P = 0.014) in pre-COVID period; and was associated with age (P = 0.025), altered mental status on admission time (P\u3c0.001), malignancy (P = 0.041) and COVID-19 infection (P = 0.008) in COVID-19 period. Conclusion: Since there was a more dismal outcome in COVID-19 associated CVST, a high index of suspicion for CVST among COVID-19 positive is recommended

Production screening and optimization using smart proxy modeling

Numerical models are the primary tools to look into the fluid flow behavior in the complex and uncertain reservoir environment. Engineers use numerical models to perform crucial tasks in reservoir engineering, such as uncertainty quantification, history matching, production forecasting, and optimization, to eventually make the best decisions for field development. A conventional numerical model often consists of millions of grid blocks, and depending on the level of complexities within the model, it may take hours or days to perform a single run. A comprehensive study of a numerical reservoir model requires hundreds or thousands of repetitions, making the decision very costly and time-intensive. Proxy modeling is a solution for the computational cost related to the numerical models. They make a relationship between the input design parameters and the desired outputs by using various statistical/mathematical/data-driven underlying models. Nevertheless, they have their own limitations. The biggest disadvantage of the conventional proxy models is that they cannot keep the complexities within the reservoirs. It means they have no or limited sense of objects that exist in the reservoirs such as faults, boundaries, wells, etc. The main objective of this research is to present smart proxy modeling (SPM) as a substitute for numerical models to address the computationally expensive and time-consuming drawbacks of numerical models and find a solution for keeping the complexities within the reservoir as conventional proxy models have. SPM is developed based on the implementation of pattern recognition and machine learning techniques, and it has an additional feature engineering step compared to the traditional known proxy models in the literature. The feature engineering step extracts new static and dynamic parameters from the numerical model. The constructed SPM takes only a few seconds to perform a single run. The SPM in this research is developed in grid- based and well-based types. The grid-based SPM can predict the grids’ properties, such as fluid saturation and pressure, and the well-based SPM is used to predict well production. Furthermore, the parallel implementation of the well-based SPM with grid-based SPM (named hybrid well-based SPM) is tested in this research. The proposed SPM in this research is modified at different construction steps compared to existing SPMs in the literature that suffer from construction efficiency and reliability. Based on our literature review, we target our investigation into techniques to improve efficiency and accuracy by focusing on sampling, feature ranking, and underlying model construction. In existing SPM literature, only one technique is used during each construction step where there are opportunities to explore novel construction steps to improve overall SPM accuracy and efficiency. The presented sequential sampling technique avoids repeating the construction procedure from resampling and running the high-fidelity model, thereby saving time and making the SPM workflow more efficient. In the feature ranking step, an average of multiple ranking algorithms is used to find the best subset of input parameters which eventually helps the overall efficiency in the feature selection step. The performance of the convolutional neural network (CNN) as the underlying model is also tested and compared to the implemented artificial neural networks (ANN) in the literature. In this research, the SPMs are constructed for two case studies. The first case study corresponds to a waterflooding scenario for the offshore Norway Volve field. The design parameters involve five parameters of the wells’ liquid production rates, and the objectives are to screen and optimize oil recovery. For the screening purpose, the grids’ pressure and oil saturation are considered as the outputs of the grid-based SPM. For production optimization, the wells' cumulative oil production is the output of the well-based SPM. Finally, the performance of well- based SPM coupled with two derivative-free optimizers, particle swarm optimization and genetic algorithm, are compared. The SPM with ANN underlying model provides an accuracy of 89-92% compared to the 94-99% of the CNN technique for the grid-based SPMs. However, for the well-based SPM, the goodness of fit for the 1D-CNN model is similar to the ANN model, but its accuracy (presented in MAPE) is slightly better than ANN. The well-based for this case study is coupled with PSO and GA optimization algorithms to find the best selection of designing parameters (individual well’s LPR) and to maximize the cumulative oil production over ten years. Both optimizers are quite successful in finding the global optimum. Nevertheless, PSO shows a more reliable and faster convergence to the solution. The second case study corresponds to a water alternating gas (WAG) scenario for the offshore Norway Norne field. This case study aims to test the whole procedure of SPM construction in another field with different levels of complexities and more design parameters. The design parameters for the WAG scenario are nine parameters of gas/water injection cycle, field gas/water injection rate, gas/water injection distribution between two injectors, and injectors’ BHPs. Similar to the first case study, screening and oil recovery optimization are the targets for this case study. The trained CNN models give an accuracy of 85-87% for different timesteps of the grid-based dataset at the blind test. However, after adding five more sample points using the sequential LHS, the accuracy increases to 94-99%. The well-based SPM, similar to the first case study, does not give promising improvement in terms of accuracy

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