A Survey of Synchronous Reluctance Machine used in Electric Vehicle

International audienceDue to more environmental problems and more attentions paid on renewable energy, electric vehicle (EV) has been a hot research topic. As a key equipment in EV, various drives have been studied. Synchronous reluctance machine (SynRM) is also studied widely as a drive in EV for its advantages. This paper presents an extensive survey on the application of SynRM in EV and reviews its working principle, various structures and control strategies

Linear Coupling of Transverse Betatron Oscillations. Dynamic Stability and Invariants of Motion

Based on the technique of the discrete one-turn transfer maps, the problem of linear coupling between horizontal and vertical betatron oscillations in an accelerator has been treated exactly and entirely in explicit form. The stability region in the fractional part of the horizontal and the vertical betatron tune space as a function of the linear coupling strength, has been obtained, and the increment/decrement of the horizontal and the vertical betatron oscillations in the case of the linear sum resonance has been shown to be approximately equal to the half of the coupling strength. The normal form parameterization of the one-turn linear map with horizontal-to-vertical coupling has been developed in detail in the spirit of the Edwards and Teng formalism. The motion in the normal mode in the new normal form coordinates is decoupled implying that two independent Courant-Snyder invariants exist, which have been found explicitly.Comment: 10 pages, 3 figure

Impact of Climate Change on Hydrochemical Processes at Two High-Elevation Forested Watersheds in the Southern Appalachians, United States

Climate change increasingly affects primary productivity and biogeochemical cycles in forest ecosystems at local and global scales. To predict change in vegetation, soil, and hydrologic processes, we applied an integrated biogeochemical model Photosynthesis-EvapoTranspration and BioGeoChemistry (PnET-BGC) to two high-elevation forested watersheds in the southern Appalachians in the US under representative (or radiative) concentration pathway (RCP)4.5 and RCP8.5 scenarios. We investigated seasonal variability of the changes from current (1986–2015) to future climate scenarios (2071–2100) for important biogeochemical processes/states; identified change points for biogeochemical variables from 1931 to 2100 that indicate potential regime shifts; and compared the climate change impacts of a lower-elevation watershed (WS18) with a higher-elevation watershed (WS27) at the Coweeta Hydrologic Laboratory, North Carolina, United States. We find that gross primary productivity (GPP), net primary productivity (NPP), transpiration, nitrogen mineralization, and streamflow are projected to increase, while soil base saturation, and base cation concentration and ANC of streamwater are projected to decrease at the annual scale but with strong seasonal variability under a changing climate, showing the general trend of acidification of soil and streamwater despite an increase in primary productivity. The predicted changes show distinct contrasts between lower and higher elevations. Climate change is predicted to have larger impact on soil processes at the lower elevation watershed and on vegetation processes at the higher elevation watershed. We also detect five change points of the first principal component of 17 key biogeochemical variables simulated with PnET-BGC between 1931 and 2100, with the last change point projected to occur 20 years earlier under RCP8.5 (2059 at WS18 and WS27) than under RCP4.5 (2079 at WS18 and 2074 at WS27) at both watersheds. The change points occurred earlier at WS18 than at WS27 in the 1980s and 2010s but in the future are projected to occur earlier in WS27 (2074) than WS18 (2079) under RCP4.5, implying that changes in biogeochemical cycles in vegetation, soil, and streams may be accelerating at higher-elevation WS27

Real-world study of adverse events associated with gepant use in migraine treatment based on the VigiAccess and U.S. Food and Drug Administration’s adverse event reporting system databases

BackgroundThis study aimed to investigate the real-world profile of adverse events (AEs) associated with gepant medications in the clinical treatment of migraines by analyzing data collected from the VigiAccess database and the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. As novel migraine therapies, gepants act by targeting the calcitonin gene-related peptide (CGRP) pathway, demonstrating effective control of migraine attacks and good tolerability. Nonetheless, comprehensive real-world studies on the safety of gepants are still lacking, particularly regarding their safety in large populations, long-term use, and potential adverse reactions in specific groups, which necessitates further empirical research. Leveraging these two international adverse event reporting system databases, we systematically gathered and analyzed reports of AEs related to gepant medications, such as rimegepant. Our focus encompasses but is not limited to severe, new, and rare adverse reactions induced by the drugs, as well as safety issues pertaining to the gastrointestinal, cardiovascular, hepatic, and renal systems. Through descriptive statistical analyses, we assessed the incidence and characteristics of AEs, compared AEs among gepants, and uncovered previously unknown AE information, all with the goal of providing a reference for the selection of clinical treatment regimens and AE monitoring.MethodsBy extracting all AE reports concerning “rimegepant”, “atogepant”, and “ubrogepant” from the VigiAccess and FAERS database since its establishment up to 31 March 2024, a retrospective quantitative analysis was conducted. The reporting odds ratio (ROR) method were used to compare AEs among the three gepants.ResultsIn the VigiAccess and FAERS databases, 23542 AE reports in total, respectively, were identified as being related to gepant medications. Among gastrointestinal system AEs, rimegepant had the greatest proportion and greatest signal strength; nausea was most severe and had the strongest signal in rimegepant AEs, whereas constipation was most prominent and had the strongest signal in atogepant AEs. In skin and subcutaneous tissue disorders, rash and pruritus were more frequently observed with rimegepant, followed by ubrogepant. Alopecia emerged as a novel AE, being more severe in rimegepant and secondarily in atogepant. Regarding cardiac disorders, the three gepants showed comparable rates of cardiac AEs, yet rimegepant exhibited the strongest AE signal. In musculoskeletal and connective tissue AEs, ubrogepant presented the most positive signals for skeletal muscle AEs. Furthermore, among the rare blood and lymphatic system disorder AEs, rimegepant had the highest number of reports of Raynaud’s phenomenon and the strongest signal. The study also revealed that while reports of AEs involving liver diseases were scarce across the three gepants, severe AEs were detected in clinical trials, highlighting the need for continued, enhanced monitoring of liver system AEs through large-scale datasets.ConclusionGepant medications exhibit similarities and differences in their safety profiles. Analysis of the two databases indicated the presence of AEs across various systems, including gastrointestinal disorders, skin and subcutaneous tissue diseases, musculoskeletal and connective tissue disorders, organ-specific effects, and liver diseases. However, each drug displays distinct incidences and signal intensities for these AEs. Additionally, the study revealed a rare AE in the form of Raynaud’s phenomenon. These findings suggest that during clinical use, individualized medication selection and AE monitoring should be based on the patient’s physiological condition and specific characteristics

Investigating White-Box Attacks for On-Device Models

Numerous mobile apps have leveraged deep learning capabilities. However, on-device models are vulnerable to attacks as they can be easily extracted from their corresponding mobile apps. Existing on-device attacking approaches only generate black-box attacks, which are far less effective and efficient than white-box strategies. This is because mobile deep learning frameworks like TFLite do not support gradient computing, which is necessary for white-box attacking algorithms. Thus, we argue that existing findings may underestimate the harmfulness of on-device attacks. To this end, we conduct a study to answer this research question: Can on-device models be directly attacked via white-box strategies? We first systematically analyze the difficulties of transforming the on-device model to its debuggable version, and propose a Reverse Engineering framework for On-device Models (REOM), which automatically reverses the compiled on-device TFLite model to the debuggable model. Specifically, REOM first transforms compiled on-device models into Open Neural Network Exchange format, then removes the non-debuggable parts, and converts them to the debuggable DL models format that allows attackers to exploit in a white-box setting. Our experimental results show that our approach is effective in achieving automated transformation among 244 TFLite models. Compared with previous attacks using surrogate models, REOM enables attackers to achieve higher attack success rates with a hundred times smaller attack perturbations. In addition, because the ONNX platform has plenty of tools for model format exchanging, the proposed method based on the ONNX platform can be adapted to other model formats. Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models.Comment: Published in The International Conference on Software Engineering 2024 (ICSE'24