Fast Scale Research Articles

Chewing gum reduces visually induced motion sickness

Visually induced motion sickness (VIMS) is a common side-effect of exposure to virtual reality (VR). Its unpleasant symptoms may limit the acceptance of VR technologies for training or clinical purposes. Mechanical stimulation of the mastoid and diverting attention to pleasant stimuli-like odors or music have been found to ameliorate VIMS. Chewing gum combines both in an easy-to-administer fashion and should thus be an effective countermeasure against VIMS. Our study investigated whether gustatory-motor stimulation by chewing gum leads to a reduction of VIMS symptoms. 77 subjects were assigned to three experimental groups (control, peppermint gum, and ginger gum) and completed a 15-min virtual helicopter flight, using a VR head-mounted display. Before and after VR exposure, we assessed VIMS with the Simulator Sickness Questionnaire (SSQ), and during the virtual flight once every minute with the Fast Motion Sickness Scale (FMS). Chewing gum (peppermint gum: M = 2.44, SD = 2.67; ginger gum: M = 2.57, SD = 3.30) reduced the peak FMS scores by 2.05 (SE = 0.76) points as compared with the control group (M = 4.56, SD = 3.52), p < 0.01, d = 0.65. Additionally, taste ratings correlated slightly negatively with both the SSQ and the peak FMS scores, suggesting that pleasant taste of the chewing gum is associated with less VIMS. Thus, chewing gum may be useful as an affordable, accepted, and easy-to-access way to mitigate VIMS in numerous applications like education or training. Possible mechanisms behind the effect are discussed.

Looking for factors affecting functioning in euthymic patients with bipolar I disorder: the importance of cognitive complaints and BDNF‘s Val66Met polymorphism

IntroductionFunctioning in Bipolar Disorder (BD) is affected in a substantial proportion of patients. The impact of demographic, clinical, cognitive, and genetic factors on functioning has been shown individually; however, as a complex phenomenon, a global approach to identify the most relevant as well as possible interactions is needed. Methods102 patients with type I BD in euthymia were invited for evaluation of demographic, clinical, and cognitive characteristics as well as genotype for Val66Met polymorphism of BDNF gene to determine those associated with poor functioning according to the FAST scale cut-off score. Clinical evaluation included assessment of residual affective symptoms and anxiety. Cognitive evaluation included the COBRA scale, verbal memory, and executive functions testing. ResultsResidual depressive symptoms, anxiety, cognitive complaints and being a Met carrier were more frequent in the poor functioning group and were entered in a logistic regression model. Being a Met carrier (OR=4.46, CI=1.19–16.67) and cognitive complaints (OR=1.29, CI= 1.13–1.46) were the most important predictors of poor functioning in type I BD. LimitationsCross-sectional study, with select population limiting generalizability of findings. ConclusionsA better understanding of underlying factors affecting cognition, including the possible involvement of BDNF Val66Met polymorphism, its systematic evaluation and a continued search for targeted treatment, along with recognition and attention of residual affective and anxious symptoms might improve psychosocial outcomes such as functioning in this population.

Small Infrared Target Detection Based on Fast Adaptive Masking and Scaling With Iterative Segmentation

Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.

"Industrialising" the capacity building of local entrepreneurs. A case study of 1001fontaines' "4G Project" in Cambodia

In Cambodia, only 16% of the population in rural areas has access to safely managed and clean water supply services, putting the health of 10.12 million people at stake. (Cambodia's Voluntary National Review 2019 of the Implementation of the 2030 SDG Agenda). 1001fontaines, a community-based enterprise located mainly in Cambodia, responds to this issue by enabling the production of safe drinking water directly in the targeted communities, through the establishment of small water enterprises. These units, called water kiosks, are entrusted to local entrepreneurs and supported in the long run thanks to a franchise model. In 2014, 1001fontaines su ered from low-performing water kiosks, which impacted the viability of the whole portfolio. As a result, 1001fontaines decided to launch the so-called "4G" capacity building project to upgrade the initial and continuing training of water entrepreneurs, expand distribution channels, and further leverage the local "O-We" brand to reach more bene ciaries. The "4G project" was implemented from 2014 to 2015, and resulted in the identi cation of best practices to be followed by every water entrepreneur. Since 2016, 1001fontaines has generalised the use of these best practices to build the capacities of new entrepreneurs. This case study aims to investigate the impact of the "4G project" and its conclusion on the water kiosks' performance and on 1001fontaines' ability to better and faster scale up its approach in Cambodia.

A Fine-Grained Horizontal Scaling Method for Container-Based Cloud

The container scaling mechanism, or elastic scaling, means the cluster can be dynamically adjusted based on the workload. As a typical container orchestration tool in cloud computing, Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pods in a replication controller, deployment, replication set, or stateful set based on observed CPU utilization. There are several concerns with the current HPA technology. The first concern is that it can easily lead to untimely scaling and insufficient scaling for burst traffic. The second is that the antijitter mechanism of HPA may cause an inadequate number of onetime scale-outs and, thus, the inability to satisfy subsequent service requests. The third concern is that the fixed data sampling time means that the time interval for data reporting is the same for average and high loads, leading to untimely and insufficient scaling at high load times. In this study, we propose a Double Threshold Horizontal Pod Autoscaler (DHPA) algorithm, which fine-grained divides the scale of events into three categories: scale-out, no scale, and scale-in. And then, on the scaling strength, we also employ two thresholds that are further subdivided into no scaling (antijitter), regular scaling, and fast scaling for each of the three cases. The DHPA algorithm determines the scaling strategy using the average of the growth rates of CPU utilization, and thus, different scheduling policies are adopted. We compare the DHPA with the HPA algorithm under different loads, including low, medium, and high. The experiments show that the DHPA algorithm has better antijitter and antiload characteristics in container increase and reduction while ensuring service and cluster security.

Antibody-Free Rapid Detection of SARS-CoV-2 Proteins Using Corona Phase Molecular Recognition to Accelerate Development Time.

To develop better analytical approaches for future global pandemics, it is widely recognized that sensing materials are necessary that enable molecular recognition and sensor assay development on a much faster scale than currently possible. Previously developed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) point-of-care devices are based on the specific molecular recognition using subunit protein antibodies and protein receptors that selectively capture the viral proteins. However, these necessarily involve complex and lengthy development and processing times and are notoriously prone to a loss of biological activity upon sensor immobilization and device interfacing, potentially limiting their use in applications at scale. Here, we report a synthetic strategy for nanoparticle corona interfaces that enables the molecular recognition of SARS-CoV-2 proteins without any antibody and receptor design. Our nanosensor constructs consist of poly(ethylene glycol) (PEG)—phospholipid heteropolymers adsorbed onto near-infrared (nIR) fluorescent single-walled carbon nanotubes (SWCNTs) that recognize the nucleocapsid (N) and spike (S) protein of SARS-CoV-2 using unique three-dimensional (3D) nanosensor interfaces. This results in rapid and label-free nIR fluorescence detection. This antibody-free nanosensor shows up to 50% sensor responses within 5 min of viral protein injections with limit of detection (LOD) values of 48 fM and 350 pM for N and S proteins, respectively. Finally, we demonstrate instrumentation based on a fiber-optic platform that interfaces the advantages of antibody-free molecular recognition and biofluid compatibility in human saliva conditions.

System-level Early-stage Modeling and Evaluation of IVR-assisted Processor Power Delivery System

Despite being employed in numerous efforts to improve power delivery efficiency, the integrated voltage regulator (IVR) approach has yet to be evaluated rigorously and quantitatively in a full power delivery system (PDS) setting. To fulfill this need, we present a system-level modeling and design space exploration framework called Ivory for IVR-assisted power delivery systems. Using a novel modeling methodology, it can accurately estimate power delivery efficiency, static performance characteristics, and dynamic transient responses under different load variations and external voltage/frequency scaling conditions. We validate the model over a wide range of IVR topologies with silicon measurement and SPICE simulation. Finally, we present two case studies using architecture-level performance and power simulators. The first case study focuses on optimal PDS design for multi-core systems, which achieves 8.6% power efficiency improvement over conventional off-chip voltage regulator module– (VRM) based PDS. The second case study explores the design tradeoffs for IVR-assisted PDSs in CPU and GPU systems with fast per-core dynamic voltage and frequency scaling (DVFS). We find 2 μs to be the optimal DVFS timescale, which not only reaps energy benefits (12.5% improvement in CPU and 50.0% improvement in GPU) but also avoids costly IVR overheads.

Joint spatiotemporal regularization and scale-aware correlation filters for visual tracking

Correlation filters (CF)-based tracking methods have drawn extensive attention due to their impressive performance in visual object tracking (VOT) challenges. However, the existing CF-based trackers incur the inherent problems of boundary effect and filter degradation. Meanwhile, the scale variants of targets result in tracking-drift problems, which degrade the tracking accuracy significantly. To address the aforementioned problems, a tracking model named spatiotemporal regularization and scale-aware correlation filters (STR-SACF) is proposed. The STR-SACF model consists of two CFs, namely translation filter and scale filter (SF). The translation filter improves the accuracy of target localization, whereas the SF achieves the optimal and fast scale estimation. The proposed tracking model can improve the tracking accuracy while reducing computational complexity. Moreover, a spatiotemporal regularization term is introduced into the translation filter and SF to suppress boundary effect and filter degradation. The proposed model is effectively optimized based on the alternating direction method of multipliers algorithm. Experimental results on four challenging tracking benchmarks, i.e., OTB2013, OTB2015, TC128, and VOT2016, demonstrate the superiority of the proposed tracker compared with more than 20 state-of-the-art trackers.

Translation and psychometric evaluation of a Persian version of the functional assessment staging scale (I-FAST) in older patients with mild cognitive impairment and Alzheimer's disease in Iran.

The Functional Assessment Staging procedure is a clinical instrument which has been designed for staging Alzheimer's disease (AD) from the stage of no deficits to the pre-clinical stage of subjective deficits, to Mild Cognitive Impairment (MCI), to the stages of AD. This study examined the psychometric properties and the validity of the Persian version of the FAST (I-FAST) in an elderly outpatient population in Iran. We conducted a validation study of the FAST scale at the two referral centers for dementia and cognitive disorders in Tehran, Iran. The participants consisted of subjects with normal cognition, MCI and AD. The scores of the Persian version of the Mini-Mental State Examination (MMSE) and the Persian version of the I-FAST were examined. Demographic variables were also collected. The diagnosis of MCI was made based on Petersen criteria and AD based on the McKhann et al. criteria by a neurologist with expertise in dementia. Data was collected from 219 participants. A total of 54.7% of the sample was female and their mean age was 72.54 ± 8.88years. The area under the ROC curve was calculated 0.952 and 0.982. The I-FAST had a sensitivity of 92.2% and specificity of 98.0% for the differentiation of normal cognition from MCI. The sensitivity of the I-FAST for discrimination of subjects with AD from MCI was 99.0% and the specificity was 93.7%. The I-FAST showed good psychometric characteristics in the discrimination of MCI from both normal elderly and patients with Alzheimer's. The I-FAST is also a sensitive and accurate instrument for staging persons at risk for MCI and Alzheimer's, relatively free of the confounding effects of education, culture and language in comparison with the MMSE.

Clinical predictors of cybersickness in virtual reality (VR) among highly stressed people

The use of virtual reality (VR) in the treatment of psychiatric disorders is increasing, and cybersickness has emerged as an important obstacle to overcome. However, the clinical factors affecting cybersickness are still not well understood. In this study, we investigated clinical predictors and adaptation effect of cybersickness during VR application in highly stressed people. Eighty-three healthy adult participants with high stress level were recruited. At baseline, we conducted psychiatric, ophthalmologic, and otologic evaluations and extracted physiological parameters. We divided the participants into two groups according to the order of exposure to VR videos with different degrees of shaking and repetitively administered the Simulator Sickness Questionnaire (SSQ) and the Fast Motion sickness Scale (FMS). There was no significant difference in changes in the SSQ or the FMS between groups. The 40–59 years age group showed a greater increase in FMS compared to the 19–39 years age group. Smoking was negatively associated with cybersickness, and a high Positive Affect and Negative Affect Schedule score was positively associated with cybersickness. In conclusion, changing the intensity of shaking in VR did not affect cybersickness. While smoking was a protective factor, more expression of affect was a risk factor for cybersickness.

Fashion and Desire: The Society of Spectacle in Post Reality

Fashion was a contemporary phenomenon that increasingly developed to meet the needs of users in its digital industry era. The rapid development of fashion has made the industry compete to present the latest fashions faster and faster. The support and advances of the Internet in communication technology have helped to accelerate the spread of power through virtual networks. Digital media have brought people into a new experience, namely "post-reality", that had never existed. This study focuses on fashion consumption in the form of clothes with a fast production scale or fast-fashion along with low costs, as well as sneakers that were consumed by young people. One hundred and twenty-nine students at five universities in West Java Indonesia have interviewed. Using a qualitative method which was supported by the summary of the distribution of questionnaires, conducted throughout 2019 and early 2020, the results of this study were then analyzed using Guy Debord's theory of spectacle society. The study has found that the consumption of fast fashion and sneakers by students had an impact on their spending patterns, self-identity, self-confidence, and the role of the user as a commodity spectacle in today's fashion industry.

Adaptive fast scale estimation, with accurate online model update based on kernelized correlation filter

Adaptive fast scale estimation, with accurate online model update based on kernelized correlation filter

Continuous-flow synthesis of polymethylsilsesquioxane spheres in a microreaction system

A continuous-flow strategy based on the microreaction system is developed for the synthesis of monodispersed polymethylsilsesquioxane (PMSQ) spheres via one-step sol-gel method. Methyltrimethoxysilane (MTMS) and ammonium hydroxide (NH4OH) were used as silicon source and catalyst, respectively. The microreaction system includes three parts: a microdispersion device for the rapid hydrolysis of MTMS, a tubular reactor for subsequent condensation and fast growth of the particles, and a stirring reactor for final aging process. The effects of synthesis parameters on the particle size and size distribution were explored, mainly including the flow rates of ammonia solution and MTMS, concentration of NH4OH, solvent composition, and collection time of the reaction suspension. The particle size could be tuned from several hundred nanometers to a few microns with narrow particle size distribution. In addition, the operational stability of the continuous-flow microreaction system was also investigated. The continuous-flow process presents a high level of controllability, stability, and robustness, while enabling fast and reproducible scaling of laboratory results into large-scale production.

Real-time implementation of fast discriminative scale space tracking algorithm

Real-time object tracking is an important step of many modern image processing applications. The efficient hardware design of real-time object tracker must achieve the desired accuracy while satisfying the frame rate requirements for a variety of image sizes. The existing methods of visual tracking employ sophisticated algorithms and challenge the capabilities of most embedded architectures. Discriminative scale space tracking is one algorithm that is capable of demonstrating good performance with affordable complexity. It has a high degree of parallelism which can be exploited for efficient implementation of reconfigurable hardware architectures. This paper proposes a real-time implementation of the discriminative scale-space tracker on FPGA for the major blocks. A careful design exploration of core mathematical operations of the tracking algorithm is performed to improve their hardware utilization and timing performance. Among the core functional units optimized in this work, the discrete Fourier transform achieves a computational time improvement of 92% relative to existing works, QR factorization achieves a 2.3times reduction in resource utilization, and singular value decomposition yields a 3.8times improvement in processing time. The proposed data path architecture is designed using Vivado HLS tool set and implemented for Zync Zed Board (xc7z020clg484-1). For an input image size of 320 times 240, the proposed architecture achieves a mean 25.38 fps.

Error Estimation and Adaptivity for Differential Equations with Multiple Scales in Time

Abstract We consider systems of ordinary differential equations with multiple scales in time. In general, we are interested in the long time horizon of a slow variable that is coupled to solution components that act on a fast scale. Although the fast scale variables are essential for the dynamics of the coupled problem, they are often of no interest in themselves. Recently, we have proposed a temporal multiscale approach that fits into the framework of the heterogeneous multiscale method and that allows for efficient simulations with significant speedups. Fast and slow scales are decoupled by introducing local averages and by replacing fast scale contributions by localized periodic-in-time problems. Here, we generalize this multiscale approach to a larger class of problems, but in particular, we derive an a posteriori error estimator based on the dual weighted residual method that allows for a splitting of the error into averaging error, error on the slow scale and error on the fast scale. We demonstrate the accuracy of the error estimator and also its use for adaptive control of a numerical multiscale scheme.

Averaging Principle and Normal Deviations for Multiscale Stochastic Systems

We study the asymptotic behavior for an inhomogeneous multiscale stochastic dynamical system with non-smooth coefficients. Depending on the averaging regime and the homogenization regime, two strong convergences in the averaging principle of functional law of large numbers type are established. Then we consider the small fluctuations of the system around its average. Nine cases of functional central limit type theorems are obtained. In particular, even though the averaged equation for the original system is the same, the corresponding homogenization limit for the normal deviation can be quite different due to the difference in the interactions between the fast scales and the deviation scales. We provide quite intuitive explanations for each case. Furthermore, sharp rates both for the strong convergences and the functional central limit theorems are obtained, and these convergences are shown to rely only on the regularity of the coefficients of the system with respect to the slow variable, and do not depend on their regularity with respect to the fast variable, which coincide with the intuition since in the limit equations the fast component has been totally averaged or homogenized out.

Psychosocial functionning in euthymic bipolar patients

IntroductionBipolar disorder (BD) is a chronic, recurring illness that can lead to serious disruptions in functioning.ObjectivesTo evaluate functionning in this population and to explore the relationship with socio-demographic and clinical features of BD.MethodsThis is a descriptive and analytical cross-sectional study including patients with BD (DSM V) in euthymia, followed on an ambulatory basis to the Mood Disorders Unit of the Psychiatry A Department at Hedi Chaker Hospital University of Sfax between January and April 2019. Patients were considered euthymic if they scored less than 7 on the Young Mania (YMRS) rating scale and less than 8 on the Hamilton Depression scale (HDRS-17). The Short Function Evaluation Test (FAST scale) was used to evaluate functionning. Global functional impairment is defined by a total FAST score&gt;11.ResultsWe recruited 62 patients with a mean age of 45.65 years (SD=13.3) and a sex ratio of 1.13. 88.7% of patients were followed for BD I and 11.3% for BD II. The mean age of onset was 29.37 years (SD=11.6). The mean numbers of manic and depressive episodes were respectively 3.73 (SD=3.8) and 2.48 (SD=2.9). The mean FAST score was 28.97 (SD=15). Overall impairment was observed in 85.5% of patients. Impaired functionning was significantly more frequent in patients with a history of surgery (p=0.046), in those with a higher number of depressive episodes (p&lt;0.001) and in subjects with partial remission (p=0.01).ConclusionsThus, the treatment should target not only the improvement of symptoms but also the reduction of the incapacity of patients.

Simplification of the forced response of mistuned bladed disks using multiple scales techniques

Mistuning can produce a considerable increase of the vibratory forced response of the blades compared with that of the tuned bladed disk. This situation can lead to high cycle fatigue failure, and, therefore, it is of crucial importance for the prediction of the safe operation limit of the bladed disk. Nonlinear friction forces determine the final vibration amplitude of the blades, and, because of mistuning, the complete bladed disk has to be considered, increasing considerably the difficulty of the problem. For this reason, we consider the application of asymptotic multiple scale techniques to derive simplified models that give the final vibration states at a very low CPU cost. This asymptotic method is based on the idea that all significant effects (forcing, nonlinear friction damping, and mistuning) produce, in most practical situations, just small corrections of the tuned blade elastic oscillation. These small effects develop in a much longer time scale than that associated with the natural elastic frequency of the tuned system. The reduced models produced by this asymptotic methodology retain only the slow time dynamics, filtering out the fast scale oscillation. In this paper, the bladed disk is described using a mass-spring system with nonlinear friction and with an external forcing acting on a blade-dominated modal family where all modes have very similar vibration frequencies. The derivation of the asymptotic model from the mass-spring system is explained in detail, and validated against the results from the original mass-spring model.

Pharmacological treatment profiles in the FACE-BD cohort: An unsupervised machine learning study, applied to a nationwide bipolar cohort✰

BackgroundDespite thorough and validated clinical guidelines based on bipolar disorders subtypes, large pharmacological treatment heterogeneity remains in these patients. There is limited knowledge about the different treatment combinations used and their influence on patient outcomes. We attempted to determine profiles of patients based on their treatments and to understand the clinical characteristics associated with these treatment profiles. MethodsThis multicentre longitudinal study was performed on a French nationwide bipolar cohort database. We performed hierarchical agglomerative clustering to search for clusters of individuals based on their treatments during the first year following inclusion. We then compared patient clinical characteristics according to these clusters. ResultsFour groups were identified among the 1795 included patients: group 1 (“heterogeneous” n = 1099), group 2 (“lithium” n = 265), group 3 (“valproate” n = 268), and group 4 (“lamotrigine” n = 163). Proportion of bipolar 1 disorder, in groups 1 to 4 were: 48.2%, 57.0%, 48.9% and 32.5%. Groups 1 and 4 had greater functional impact at baseline and a less favorable clinical and functioning evolution at one-year follow-up, especially on GAF and FAST scales. LimitationsThe one-year period used for the analysis of mood stabilizing treatments remains short in the evolution of bipolar disorder. ConclusionsTreatment profiles are associated with functional evolution of patients and were not clearly determined by bipolar subtypes. These profiles seem to group together common patient phenotypes. These findings do not seem to be influenced by the duration of disease prior to inclusion and neither by the number of treatments used during the follow-up period.

Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston pump design

The power density of axial piston pumps can benefit greatly from increased rotational speeds. However, the maximum rotational speed of axial piston machines is limited by the cavitation phenomenon for a given volumetric displacement. This paper presents a scaling law derived from an analytical cavitation model to estimate the speed limitations for the same series of axial piston pumps. The cavitation model is experimentally verified using a high-speed axial piston pump, and the scaling law is validated with open specification data in product brochures. Results show that the speed limitation is approximately proportional to the square root of the inlet pressure and inversely proportional to the cube root of volumetric displacement. Furthermore, a characteristic constant Cp is defined based on the presented scaling law. This constant can represent the comprehensive capacity of axial piston pumps free from cavitation.