A Product Line Systems Engineering Process for Variability Identification and Reduction

Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical variability modeling with formal definitions; utilizes Systems Engineering concepts and legacy system models to build the hierarchy for the variability model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.Comment: 12 pages, 6 figures, 2 tables; submitted to the IEEE Systems Journal on 3rd June 201

Nuciferine downregulates Per-Arnt-Sim kinase expression during its alleviation of lipogenesis and inflammation on oleic acid-induced hepatic steatosis in HepG2 cells

Nonalcoholic fatty liver disease (NAFLD) is a prevalent liver disease associated with lipotoxicity, lipid peroxidation, oxidative stress and inflammation. Nuciferine, an active ingredient extracted from the natural lotus leaf, has been reported to be effective for the prevention and treatment of NAFLD. Per-Arnt-Sim kinase (PASK) is a nutrient responsive protein kinase that regulates lipid and glucose metabolism, mitochondrial respiration and gene expression. The aim of the present study was to investigate the protective effect of nuciferine against NAFLD and its inhibitory effect on PASK, exploring the possible underlying mechanism of nuciferine-mediated inhibition on NAFLD. Relevant biochemical parameters (lipid accumulation, extent of oxidative stress and release of inflammation cytokines) in oleic acid (OA)-induced HepG2 cells that mimicked steatosis in vitro were measured and compared with the control. It was found that nuciferine and silenced-PASK (siRNA PASK) both inhibited triglyceride (TG) accumulation and was effective in decreasing fatty acid (FFAs). The content of total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) were increased respectively by nuciferine and siRNA PASK without increase in glutathione (GSH). Malondialdehyde (MDA) was decreased respectively by nuciferine and siRNA PASK. In addition, nuciferine decreased TNF-a, IL-6 and IL-8 as well as the siRNA PASK group. IL-10 was increased by nuciferine and siRNA PASK respectively. Further investigation revealed that nuciferine and siRNA PASK could respectively regulate the expression of target genes involved in lipogenesis and inflammation, suggesting that nuciferine may be a potential therapeutic treatment for NAFLD. Furthermore, the modulated effect of nuciferine on (OA)-induced HepG2 cells lipogenesis and inflammation, which was accompanied with PASK inhibition, was also consistent with siRNA PASK, implying that PASK might play a role in nuciferine-mediated regulation on NAFLD

VeriQR:A robustness verification tool for quantum machine learning models

Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporating random noise to formally validate a QML model’s robustness. VeriQR supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, VeriQR can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess VeriQR using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing

Observation of electron-antineutrino disappearance at Daya Bay

The Daya Bay Reactor Neutrino Experiment has measured a non-zero value for the neutrino mixing angle $\theta_{13}$ with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GW$_{\rm th}$ reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43,000 ton-GW_{\rm th}-day livetime exposure in 55 days, 10416 (80376) electron antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is $R=0.940\pm 0.011({\rm stat}) \pm 0.004({\rm syst})$. A rate-only analysis finds $\sin^22\theta_{13}=0.092\pm 0.016({\rm stat})\pm0.005({\rm syst})$ in a three-neutrino framework.Comment: 5 figures. Version to appear in Phys. Rev. Let