NSME: a framework for network worm modeling and simulation

Various worms have a devastating impact on Internet. Packet level network modeling and simulation has become an approach to find effective countermeasures against worm threat. However, current alternatives are not fit enough for this purpose. For instance, they mostly focus on the details of lower layers of the network so that the abstraction of application layer is very coarse. In our work, we propose a formal description of network and worm models, and define network virtualization levels to differentiate the expression capability of current alternatives. We then implement a framework, called NSME, based on NS2 for dedicated worm modeling and simulation with more details of application layer. We also analyze and compare the consequential overheads. The additional real-time characteristics and a worm simulation model are further discussed.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

NSME: a framework for network worm modeling and simulation

Various worms have a devastating impact on Internet. Packet level network modeling and simulation has become an approach to find effective countermeasures against worm threat. However, current alternatives are not fit enough for this purpose. For instance, they mostly focus on the details of lower layers of the network so that the abstraction of application layer is very coarse. In our work, we propose a formal description of network and worm models, and define network virtualization levels to differentiate the expression capability of current alternatives. We then implement a framework, called NSME, based on NS2 for dedicated worm modeling and simulation with more details of application layer. We also analyze and compare the consequential overheads. The additional real-time characteristics and a worm simulation model are further discussed.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current methods, mainly based on the data-driven paradigm such as data augmentation, adversarial training, and noise injection, may encounter limited generalization due to model non-smoothness. In this paper, we propose to investigate generalization from a Partial Differential Equation (PDE) perspective, aiming to enhance it directly through the underlying function of neural networks, rather than focusing on adjusting input data. Specifically, we first establish the connection between neural network generalization and the smoothness of the solution to a specific PDE, namely "transport equation". Building upon this, we propose a general framework that introduces adaptive distributional diffusion into transport equation to enhance the smoothness of its solution, thereby improving generalization. In the context of neural networks, we put this theoretical framework into practice as $\textbf{PDE+}$ ($\textbf{PDE}$ with $\textbf{A}$daptive $\textbf{D}$istributional $\textbf{D}$iffusion) which diffuses each sample into a distribution covering semantically similar inputs. This enables better coverage of potentially unobserved distributions in training, thus improving generalization beyond merely data-driven methods. The effectiveness of PDE+ is validated through extensive experimental settings, demonstrating its superior performance compared to SOTA methods.Comment: Accepted by Annual AAAI Conference on Artificial Intelligence (AAAI) 2024. Code is available at https://github.com/yuanyige/pde-ad

Genetically Determined Rheumatoid Arthritis May Not Affect Heart Failure: Insights from Mendelian Randomization Study

Background: Evidence from observational epidemiological studies indicated that rheumatoid arthritis (RA) increased the risk of heart failure (HF). However, there is a possibility that the correlation is not explained as a causative role for RA in the pathogenesis of HF. A two-sample Mendelian randomization (MR) framework was designed to explore the potential etiological role of RA in HF to identify the target to improve the burden of HF disease. Methods: To assess the causal association between RA and HF, we analyzed summary statistics from genome-wide association studies (GWASs) for individuals of European descent. Genetic instruments for RA were identified at a genome-wide significance threshold (p 0.05). Conclusion: Our findings did not support the causal role of RA in the etiology of HF. As such, therapeutics targeted at the control of RA may have a lower likelihood of effectively controlling the occurrence of HF

HSC-GPT: A Large Language Model for Human Settlements Construction

The field of human settlement construction encompasses a range of spatial designs and management tasks, including urban planning and landscape architecture design. These tasks involve a plethora of instructions and descriptions presented in natural language, which are essential for understanding design requirements and producing effective design solutions. Recent research has sought to integrate natural language processing (NLP) and generative artificial intelligence (AI) into human settlement construction tasks. Due to the efficient processing and analysis capabilities of AI with data, significant successes have been achieved in design within this domain. However, this task still faces several fundamental challenges. The semantic information involved includes complex spatial details, diverse data source formats, high sensitivity to regional culture, and demanding requirements for innovation and rigor in work scenarios. These factors lead to limitations when applying general generative AI in this field, further exacerbated by a lack of high-quality data for model training. To address these challenges, this paper first proposes HSC-GPT, a large-scale language model framework specifically designed for tasks in human settlement construction, considering the unique characteristics of this domain

Didymin improves UV irradiation resistance in C. elegans

Didymin, a type of flavono-o-glycoside compound naturally present in citrus fruits, has been reported to be an effective anticancer agent. However, its effects on stress resistance are unclear. In this study, we treated Caenorhabditis elegans with didymin at several concentrations. We found that didymin reduced the effects of UV stressor on nematodes by decreasing reactive oxygen species levels and increasing superoxide dismutase (SOD) activity. Furthermore, we found that specific didymin-treated mutant nematodes daf-16(mu86) & daf-2(e1370), daf-16(mu86), akt-1(ok525), akt-2(ok393), and age-1(hx546) were susceptible to UV irradiation, whereas daf-2(e1371) was resistant to UV irradiation. In addition, we found that didymin not only promoted DAF-16 to transfer from cytoplasm to nucleus, but also increased both protein and mRNA expression levels of SOD-3 and HSP-16.2 after UV irradiation. Our results show that didymin affects UV irradiation resistance and it may act on daf-2 to regulate downstream genes through the insulin/IGF-1-like signaling pathway

Durability of viscoelastic fibre prestressing in a polymeric composite

Viscoelastic fibre prestressing (VFP) is a promising technique to counterbalance the potential thermal residual stresses within a polymeric composite, offering superior mechanical benefits for structural engineering applications. It has been demonstrated that the time required for desirable creep strain can be significantly reduced by implementing higher creep stresses, while its long-term reliability is still unknown. Here, we developed the prestress equivalence principle, and investigated the durability of viscoelastic fibre prestressing within a composite, in order to further enrich the prestress mechanisms. The effectiveness of the prestress equivalence principle was refined through Charpy impact testing of prestressed samples with various prestrain levels. The durability was investigated by subjecting samples to both natural aging (up to 0.5 years) and accelerated aging (by using the time-temperature superposition principle). It is found that the prestress equivalence principle offers flexibilities for viscoelastically prestressed polymeric matrix composite (VPPMC) technology; the impact benefits offered by VFP are still active after been accelerated aged to an equivalent of 20,000 years at 20˚C, inferring long-term reliability of VFP-generated fibre recovery within a polymeric composite. These findings demonstrated that both materials and energy con-sumptions could be conserved for advanced composites. Therefore, they promote further steps of VPPMC technology towards potential industrial application especially for impact protections

Axitinib targets cardiac fibrosis in pressure overload-induced heart failure through VEGFA-KDR pathway

BackgroundThere are no specific clinical medications that target cardiac fibrosis in heart failure (HF). Recent studies have shown that tyrosine kinase inhibitors (TKIs) may benefit fibrosis in various organs. However, there is limited research on their application in cardiac fibrosis. Axitinib, an FDA-approved tyrosine kinase inhibitor, was used to evaluate its effects on cardiac fibrosis and function in pressure overload-induced heart failure.MethodsTo build a pharmacological network, the pharmacological targets of axitinib were first retrieved from databases and coupled with key heart failure gene molecules for analysis and prediction. To validate the results outlined above, 8-week-old male C57BL/6 J mice were orally administrated of axitinib (30 mg/kg) daily for 8 weeks after Transverse Aortic Constriction (TAC) surgery. Mouse cardiomyocytes and cardiac fibroblasts were used as cell lines to test the function and mechanism of axitinib.ResultsWe found that the pharmacological targets of axitinib could form a pharmacological network with key genes involved in heart failure. The VEGFA-KDR pathway was found to be closely related to the differential gene expression of human heart-derived primary cardiomyocyte cell lines treated with axitinib, based on analysis of the publicly available dataset. The outcomes of animal experiments demonstrated that axitinib therapy greatly reduced cardiac fibrosis and improved TAC-induced cardiac dysfunction. Further research has shown that the expression of transforming growth factor-β(TGF-β) and other fibrosis genes was significantly reduced in vivo and in vitro.ConclusionOur study provides evidence for the repurposing of axitinib to combat cardiac fibrosis, and offers new insights into the treatment of patients with HF

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

State Causality Analysis of Conservative Parallel Network Simulation

Critical path analysis is a traditional way to study the parallelism of conservative parallel simulation. In this paper, we propose a new technique called state causality analysis to accurately model the conservative parallel network simulation. A theorem of simulation time advancement is presented and proved. Different from critical path analysis, our model focuses on the dependency of the logical process states instead of the events. The effects of execution factors, such as lookahead, I/O overhead, physical transfer delay, processor speed and event distribution, are all taken into consideration. A stricter upper bound on parallel performance of a given network simulation task can be predicted, which provides a baseline to evaluate different network simulators. We show the capability of our model by a practical network simulation application. With this model, we also analytically and experimentally reveal that the performance of conservative parallel network simulation is distinctly dependent on its execution factors which however are not sensitive in critical path analysis in most situations