UNASSIGNED: Both myelin oligodendrocyte glycoprotein-IgG associated disorders (MOGAD) and neuromyelitis optica spectrum disorder (NMOSD) are demyelinating diseases of the central nervous system. They present similar clinical manifestations such as optica neuritis, myelitis and area postrema syndrome (APS). The distinctions of optica neuritis (ON) and myelitis between them have been elaborated to great length while their differences in APS remain to be elucidated. We aim to report the frequency of APS in patients with MOGAD as well as NNOSD patients, and to compare the characteristics of APS between patients with MOGAD and those with NMOSD.
UNASSIGNED: Seven MOG-IgG positive APS patients were retrospectively identified between 2017 and 2022. APS phenotypes have been previously described. The similarities and differences between MOGAD and NMOSD patients with APS was compared, including the frequency and duration of APS between the two diseases, and their incidences of accompanied subtentorial lesions have also been described and compared.
UNASSIGNED: We reviewed a cohort of 218 MOG-IgG-positive patients, and 396 patients with NMOSD. 200 MOGAD patients and 332 NMOSD patients were included in this study. In the cohort, seven patients with MOG-IgG-positive antibody presented with APS were analyzed, four of whom had disease onset with APS. Of the 332 patients with NMOSD, 47 had APS attacks while 31 had APS at disease onset. In patients with MOGAD, 2 had nausea, 3 had vomiting, 5 had hiccups, and 1 patient presented with all three symptoms above. In patients with NMOSD, 70.2 % had nausea, vomiting and hiccups at the same time during APS attacks. Apart from the medulla oblongata, other subtentorial regions were also affected in 6/7 MOGAD patients while 14/47 NMOSD patients had other subtentorial regions involved. During an APS attack, the incidence of concomitant lesions in the brainstem and other regions was significantly greater in MOGAD than in the NMOSD cohort (P = 0.008*).
UNASSIGNED: APS is a rare, but not isolated clinical manifestation of MOGAD. APS happened more frequently with other supratentorial and subtentorial lesions in MOGAD. The symptoms of NVH (nausea, vomiting, hiccups) tended to happen respectively in MOGAD compared with NMOSD. The phenotype or mechanism of APS in MOGAD may differ from that in NMOSD.

The ongoing shift towards hybrid and electric vehicles has a strong impact on noise and vibration engineering. New, complex dynamic phenomena are brought to vehicle user attention due to the absence of internal combustion engines and the significant role in vehicle and drive feel perception. This paper presents an FEM (Finite Element Method) dynamic simulation model of an automotive Electric Power Steering assembly. Preliminary modal simulations and experiments as well as field data replication techniques were implemented to identify the phenomena and prepare and validate model components. A full dynamic model of an Electric Power Steering was presented, and fine-tuned including the presence of lubrication at the gear mesh interface. Experimental investigations were conducted alongside FEM simulations for various model setups. Linear and nonlinear contact stiffness models were implemented, as well as contact damping, and simulated at chosen assembly interfaces. The results indicated that in the case of NVH (Noise Vibration and Harshness) analysis of shock/impact originating problems, contact parameters used for static, quasi-static, and low velocity analyses were not applicable. Nonlinear and damped contact stiffness provided better results in such a case.

A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process.

As battery electric vehicle (BEV) market share grows so must our understanding of the noise, vibration, and harshness (NVH) phenomenon found inside the BEVs which makes this technological revolution possible. Similar to the conventional vehicle having encountered numerous NVH issues until today, BEV has to face many new and tough NVH issues. For example, conventional vehicles are powered by the internal combustion engine (ICE) which is the dominant noise source. The noises from other sources were generally masked by the combustion engine, thus the research focus was on the reduction of combustion engine while less attention was paid to noises from other sources. A BEV does not have ICE, automatic transmission, transfer case, fuel tank, air intake, or exhaust systems. In their place, there is more than enough space to accommodate the electric drive unit and battery pack. BEV is quieter without a combustion engine, however, the research on vehicle NVH is even more significant since the elimination of the combustion engine would expose many noise behaviors of BEV that were previously ignored but would now seem clearly audible and annoying. Researches have recently been conducted on the NVH of BEV mainly emphasis on the reduction of noise induced by powertrain, tire, wind and ancillary system and the improvement of sound quality. This review paper will focus on recent progress in BEV NVH research to advance the BEV systems in the future. It is a review for theoretical, computational, and experimental work conducted by both academia and industry in the past few years.