Numerical evaluation of a novel passive variable friction damper for vibration mitigation

This study assesses the performance of a novel passive variable friction damper (PVFD) at mitigating wind- and seismic-induced vibrations. The PVFD consists of two friction plates upon which a cam profile modulates the normal force as a function of its rotation. A unique feature of the PVFD is its customizable shape, yielding a customizable friction hysteresis. The objective of the study is to assess the benefits of crafting the friction behavior to satisfy motion criteria. This is done numerically on two example buildings: a 5-story structure subjected to seismic loads, and a 20-story structure subjected to non-simultaneous seismic and wind loads. A probabilistic performance-based design procedure is introduced to select the optimum cam configurations throughout each building under the design loads. After that, numerical simulations are conducted to compare their performance against that of two equivalent damping schemes: viscous dampers and passive friction dampers. Results show that customization of the hysteresis behaviors throughout a structure is necessary to yield optimal performance. Also, the PVFD outperforms the other damping schemes for wind mitigation by yielding a more stable response in terms of lower accelerations over the entire wind event. Under seismic loads, all three damping schemes exhibited comparable performance, but the PVFD yielded a significantly more uniform drift for the 20-story building

Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series

Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μμs, which is below the maximum prediction horizon, therefore demonstrating the algorithm’s promise in real-time high-rate applications

Comparison of prevalence rates of restless legs syndrome, self-assessed risks of obstructive sleep apnea, and daytime sleepiness among patients with multiple sclerosis (MS), clinically isolated syndrome (CIS) and Neuromyelitis Optica Spectrum Disorder (NMOSD)

Prevalence rates for restless legs syndrome (RLS) and risk of Obstructive Sleep Apnea (OSA) in individuals with Neuromyelitis Optica Spectrum Disorder (NMOSD) and Clinically Isolated Syndrome (CIS) are unknown. The aims of the present study were to assess symptoms of RLS and self-assessed risks of OSA in individuals with NMOSD and CIS, to compare these prevalence rates with those of persons with multiple sclerosis (MS), and to associate RLS and OSA with expanded disability status scale (EDSS) scores, daytime sleepiness, fatigue, paresthesia, and medication.; A total of 495 individuals (mean age = 34.92 years, 84.9% females) were assessed. Of these, 24 had NMOSD, 112 had CIS and 359 had MS. Trained neurologists ascertained individuals' neurological diagnoses, assessed their EDSS scores, and conducted a clinical interview to assess RLS. Additionally, participants completed questionnaires covering sociodemographic information, risks of snoring and OSA, daytime sleepiness, fatigue, paresthesia and medication.; Prevalence rates of RLS were 45.8% in NMOSD, 41.1% in CIS, and 28.7% in MS. Prevalence rates of self-assessed risks of OSA were 8.3% in NMOSD, 7.7% in CIS, and 7.8% in MS; these rates were not significantly different. Across the entire sample and within the diagnostic groups, RLS and OSA scores were unrelated to EDSS, daytime sleepiness, fatigue or medication.; Individuals with NMOSD, CIS and MS have high prevalence rates for RLS and self-assessed risks of obstructive sleep apnea syndrome (OSAS), which are unrelated to EDSS, daytime sleepiness, fatigue, paresthesia, or medication. Sleep issues should be monitored during routine check-ups for individuals with NMOSD and CIS

Sociodemographic and Illness-Related Indicators to Predict the Status of Neuromyelitis Optica Spectrum Disorder (NMOSD) Five Years after Disease Onset

Background: Neuromyelitis Optica Spectrum Disorder (NMOSD) is an autoimmune demyelinating disease of the central nervous system. Currently, no factors have been identified to predict the long-term course of NMOSD. To counter this, we analyzed data of 58 individuals with NMOSD at disease onset and about five years later. Methods: Medical records of 58 individuals with NMOSD (mean age: 31.13 years at disease onset; 86.2% female) were retrospectively analyzed. At baseline, a thorough medical and disease-related examination was performed; the same examination was repeated about five years later at follow-up, including treatment-related information. Mean outcome measure was the difference in EDSS (Expanded Disease Severity Scale) scores between baseline and follow-up. Results: Mean disease duration was 4.67 years. Based on the differences of the EDSS scores between baseline and follow-up, participants were categorized as improving (n = 39; 67.2%), unchanged (n = 13; 22.4%) and deteriorating (n = 6; 10.3%). Deteriorating was related to a higher progression index, and a higher number of attacks, while the annualized relapse rate reflecting the number of attacks per time lapse did not differ between the three groups. Improving was related to a higher intake of rituximab, and to a higher rate of seropositive cases. Unchanged was related to a lower rate of seropositive cases. Factors such as age, gender, somatic and psychiatric comorbidities, symptoms at disease onset, relapse rates, number and location of cervical plaques, or brain plaques and thoracolumbar plaques at baseline did not differ between those improving, deteriorating or remaining unchanged. Conclusions: Among a smaller sample of individuals with NMOSD followed-up about five years later, individuals deteriorating over time reported a higher progression index, while the annualized relapse rate was unrelated to the progress of disease. Overall, it appears that the course of NMOSD over a time lapse of about five years after disease onset is highly individualized. Accordingly, treatment regimen demands a highly individually tailored approach

Clinical Characteristics and Disability Progression of Early- and Late-Onset Multiple Sclerosis Compared to Adult-Onset Multiple Sclerosis

Compared to the adult onset of multiple sclerosis (AOMS), both early-onset (EOMS) and late-onset (LOMS) are much less frequent, but are often under- or misdiagnosed. The aims of the present study were: 1. To compare demographic and clinical features of individuals with EOMS, AOMS and LOMS, and 2. To identify predictors for disability progression from relapsing remitting MS (RRMS) to secondary progressive MS (SPMS).; Data were taken from the Isfahan Hakim MS database. Cases were classified as EOMS (MS onset 18 years), LOMS (MS onset >50 years) and AOMS (MS >18 and 50 years). Patients' demographic and clinical (initial symptoms; course of disease; disease patterns from MRI; disease progress) information were gathered and assessed. Kaplan-Meier and Cox proportional hazard regressions were conducted to determine differences between the three groups in the time lapse in conversion from relapsing remitting MS to secondary progressive MS.; A total of 2627 MS cases were assessed; of these 127 were EOMS, 84 LOMS and 2416 AOMS. The mean age of those with EOMS was 14.5 years; key symptoms were visual impairments, brain stem dysfunction, sensory disturbances and motor dysfunctions. On average, 24.6 years after disease onset, 14.2% with relapsing remitting MS (RRMS) were diagnosed with secondary progressive MS (SPMS). The key predictor variable was a higher Expanded Disability Status Scale (EDSS) score at disease onset. Compared to individuals with AOMS and LOMS, those with EOMS more often had one or two relapses in the first two years, and more often gadolinium-enhancing brain lesions. For individuals with AOMS, mean age was 29.4 years; key symptoms were sensory disturbances, motor dysfunctions and visual impairments. On average, 20.5 years after disease onset, 15.6% with RRMS progressed to SPMS. The key predictors at disease onset were: a higher EDSS score, younger age, a shorter inter-attack interval and spinal lesions. Compared to individuals with EOMS and LOMS, individuals with AOMS more often had either no or three and more relapses in the first two years. For individuals with LOMS, mean age was 53.8 years; key symptoms were motor dysfunctions, sensory disturbances and visual impairments. On average, 14 years after disease onset, 25.3% with RRMS switched to an SPMS. The key predictors at disease onset were: occurrence of spinal lesions and spinal gadolinium-enhancement. Compared to individuals with EOMS and AOMS, individuals with LOMS more often had no relapses in the first two years, and higher EDSS scores at disease onset and at follow-up.; Among a large sample of MS sufferers, cases with early onset and late onset are observable. Individuals with early, adult and late onset MS each display distinct features which should be taken in consideration in their treatment

Post-treatment Guillain-Barre Syndrome in a Patient with Brucellosis; A Case Report

Introduction: Guillain-Barre Syndrome is an uncommon complication during acute brucellosis. Case presentation: In this study, we present a case of Guillain-Barre Syndrome in a 22-year old male patient with complaints of weakness in his lower limbs. He had a history of acute Brucella infection for four months and received antimicrobial medication. Conclusion: the patients can be affected by GBS after antimicrobial treatment

Association Between Helicobacter Pylori Infection and Seronegative Neuromyelitis Optica Spectrum Disorder

Background: Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune demyelinating disease in the central nervous system. Association between NMOSD and Helicobacter pylori (H. pylori) infection has been investigated, but few studies have assessed the relationship between H. pylori and seronegative AQP4-Ab NMOSD. Objectives: This study aimed to survey the association between H. pylori infection and NMOSD patients with seronegative AQP4-Ab status, as well as the possible relationship between the presence of H. pylori and clinical characteristics. Materials & Methods: This cross-sectional study was carried out in Kashani Hospital affiliated with the Isfahan University of Medical Sciences, Isfahan, Iran, from October 2017 to May 2019. A total of 35 consecutive seronegative AQP4-Ab NMOSD patients and 37 sex and age-matched healthy controls participated in the study. Demographic and clinical characteristics were obtained from all participants. We assessed participants’ seroprevalence of IgG and IgM antibodies against H. pylori. The Association of H. pylori with NMOSD was determined. Results: The frequency of IgG and IgM Ab H. pylori seropositivity in NMOSD patients was 22.9% and 40.0%, respectively. Among HC, 11(29.7%) and 20(54.1%) were positive for IgG and IgM Ab H. pylori. Although the rate of H. pylori IgG (OR=0.700, 95%, CI=0.243, 2.017, P=0.420) and IgM Ab (OR=0.567, 95%, CI=0.222, 1.444, P=0.233) seropositivity in NMOSD were lower than NMOSD, these differences were not statistically different. No clinical variables associated with H. pylori IgG and IgM seropositivity infection seropositivity. Conclusion: These findings show that possibly there is no relationship between H. pylori infection and seronegative AQP4-Ab NMOSD

Long short-term memory networks with attention learning for high-rate structural health monitoring

High-rate dynamic systems undergo events of amplitudes greater than 100 gs in a span of less than 100 ms. The unique characteristics of high-rate dynamic systems include 1) large uncertainties in the external loads, 2) high levels of non-stationarity and heavy disturbances, and 3) unmolded dynamics generated from changes in the system configurations. This paper presents a deep learning algorithm consisting of an ensemble of long short-term memory (LSTM) cells used to conduct high-rate state estimation. The ensemble of LSTMs receives and transforms the signal into inputs of different time resolutions. Each input vector correlates to an LSTM cell which predicts the signal in real-time and produces feature vectors. The feature vectors are then processed through an attention layer and dense layer to predict the physical features of the system. Here, we study the temporal evolution of the attention layer weights to conduct state estimation, while the LSTM cells are attempting to conduct measurement predictions. We study the performance of the algorithm on experimental data generated by DROPBEAR, a dedicated testbed for high-rate structural health monitoring research. State estimation consists of estimating, in real-time, the location of a cart that moves along a beam. Results show that the attention layer weights can be used to estimate the cart location but that the beam requires impact excitations to accelerate the convergence of the algorithm