Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial-Temporal Hybrid Deep Neural Networks for Early Congestion Detection Extended

In recent years, increasing attention has focused on the detailed analysis of the spatio-temporal patterns of traffic with the objective of identifying the occurrence and nature of traffic congestion (Zhang et al., 2016). Such work has focused on both modelling congestion formation and propagation (Saeedmanesh and Geroliminis, 2017), as well as clustering traffic congestion with emerging traffic data in urban networks (Anbaroglu et al., 2014). Nevertheless, to the best of our knowledge, little research has been conducted in the early detection of the onset of congestion and how the capability for such early detection might be most effectively used to prevent or mitigate the growth of congestion. Recurrent Congestion (RC) which usually occurs at peak hours typically with a daily pattern and is caused by excess travel demand relative to the network capacity (Anbaroglu et al., 2014). Reliable early detection of incipient RC can improve the network management response by enabling drivers to divert at an earlier stage in their journey and y giving traffic managers extra time to develop appropriate traffic signal plans and other control measures (Zhang et al., 2014). One of the main characteristic of urban traffic is that congestion is spatially correlated among adjacent roads and it can spread or dissipate with variable propagation and dissolution rates in both time and space (Saeedmanesh and Geroliminis, 2017). The explicit recognition of this spatiotemporal correlation structure adds complexity to the problem. In this paper, we argue that if properly understood it also enables us to better understand how to (a) diagnose the onset of congestion (b) characterise the temporal evolution of congestion patterns and (c) proactively develop strategies to mitigate urban traffic congestion problems. The aim of this paper is therefore to develop a hybrid deep learning-based detection methodology that can provide a spatiotemporal understanding of urban congestion and accurate early detection of RC in urban networks. The contributions of this paper are: (1) it presents a novel hybrid deep learning based early detection method that exploits the spatio-temporal structure of urban traffic; (2) the methods developed are capable to detecting multiple levels or severities of congestion with reasonable accuracy in the early stage; (3) the performance of a number of RC early detection methods are compared and evaluated with respect to the size of time windows (i.e., how many prior time steps should be used as inputs) and number of time steps ahead (how early can it detect).
Inter-Area Oscillation Damping In Power System Using Fuzzy Logic Wide Area Damping Controller

Recent innovative improvements in the wide-range estimation framework (WAMS) are acknowledging brought together controls for enhancing the force frameworks solidness. The most difficult obstruction against these advances identifies with correspondence delays, which are not steady and rely on upon data framework stacking. Ignoring this inactivity could decay the damping execution of shut circle control or even corrupt the framework soundness. This paper plans a fluffy rationale wide-zone damping controller (FLWADC) to soggy the between region motions adjusting for the ceaseless idleness. The controller is focused around the fluffy rationale because of its demonstrated vigor against info signal varieties. The proposed controller has three info signs comprising of precise distinction, its subordinate, and the time inactivity, which is unequivocally decided through measured information time labels and the exact time signal at the controller area. Tenet bases of FLWADC are created such that enrollment capacities of the yield variable are as needs be moved to adjust for the time delay. A standard proving ground for element reproductions and its extended form are analyzed, and numerical confirmations are examined. To sum up the conclusions attracted the research endeavor, different sets of affectability studies are led, covering controller parameter changes, power framework reconfigurations, and moving the framework working point.
ADHD and Stroop interference from age 9 to age 41 years: a meta-analysis of developmental effects

Background In this meta-analysis, we investigated whether response inhibition is sensitive to attention deficit hyperactivity disorder (ADHD) status and, if so, what influence maturation has on this attentional symptom of ADHD. Method We examined 25 studies that reported data on the Stroop color word test in children and adults with ADHD and in age-matched controls; average ages ranged from 9 to 41 years. We utilized a hierarchical approach to analyze the strength of the Stroop effect and whether the effect varies as a function of age. Additionally, we assessed potential differences in maturation rates based on reaction time (RT) of color and color-word conditions. Results First, we found that the relationship between color-word and color RT was multiplicative, and the slope of this function (the ratio of color-word RT over color RT) was identical across age groups and ADHD status. Second, we found that although ADHD individuals were on average 1.14 times slower than age-matched controls in both the color and the color-word condition, the maturation rate was identical for both groups. Conclusions The results from this analysis indicate that the Stroop interference effect is not larger in ADHD individuals than in age-matched controls. Further, we did not find evidence for differential maturation rates for persons with ADHD and the control groups. The Stroop interference effect appears to be immune to age, regardless of ADHD status.
Resting State Default Mode Network Connectivity, Dual Task Performance, Gait Speed, and Postural Sway in Older Adults with Mild Cognitive Impairment

Aging is associated with an increased risk of falling. In particular, older adults with mild cognitive impairment (MCI) are more vulnerable to falling compared with their healthy counterparts. Major contributors to this increased falls risk include a decline in dual task performance, gait speed, and postural sway. Recent evidence highlights the potential influence of the default mode network (DMN), the frontoparietal network (FPN), and the supplementary motor area (SMA) on dual task performance, gait speed, and postural sway. The DMN is active during rest and deactivates during task-oriented processes, to maintain attention and stay on task. The FPN and SMA are involved in top-down attentional control, motor planning, and motor execution. The DMN shows less deactivation during task in older adults with MCI. This lack of deactivation is theorized to increase competition for resources between the DMN and task-related brain regions (e.g., the FPN and SMA), increasing distraction from the task and reducing task performance. However, no study has yet investigated the relationship between the between-network connectivity of the DMN with these regions and dual task walking, gait speed or postural sway. We hypothesized that greater functional connectivity both within the DMN and between DMN–FPN and DMN–SMA, will be associated with poorer performance during dual task walking, slower gait speed, and greater postural sway in older adults with MCI. Forty older adults with MCI were measured on a dual task-walking paradigm, gait speed over a 4-m walk, and postural sway using a sway-meter. Greater within-DMN connectivity was significantly correlated with poorer dual task performance. Furthermore, greater inter-network connectivity between the DMN and SMA was significantly correlated with slower gait speed and greater postural sway on the eyes open floor sway task. Thus, greater resting state DMN functional connectivity may be an underlying neural mechanism for reduced dual task ability, slower gait speed, and greater postural sway, resulting in the increased risk of mobility disability and falling in older adults with MCI.
Integration of preventive maintenance and inventory management for healthcare systems at Philips Healthcare

Philips produces healthcare machines and takes care of the maintenance. Currently, components are replaced correctively and demand forecasting of spare parts is based on historical data. Usage data is logged for the iXR-machines. This usage data can be used to improve the current maintenance and inventory policy of spare parts. Two requirements are needed for making preventive maintenance worthwhile. Firstly, the failure data need to have an increasing hazard rate. And secondly, there need to be appropriate expected costs and durations of corrective and preventive replacements. During this analysis it is important to take lifetime and usage of components into account that are still alive. Several maintenance policies are defined that take lifetime and/or usage of a component into account. Another process that we studied is the incoming demand stream of spare parts at the local distribution center when a preventive maintenance policy is applied. Expressions of the demand rate are derived based on the maintenance policy that is handled and the number of machines that are assigned to the warehouse. A simulation study indicates that the incoming demand at the distribution center can still be approximated by a Poisson process. As a consequence, demand can still be predicted based on historical data, only the demand rate changes.
Interactive Indoor 3 D Modeling from a Single Photo with CV Support

This paper describes an interactive indoor 3D modeler for efficiently virtualizing the inside of rooms from a single photo by using interaction techniques based on geometric constraints and computer vision (CV) support. In the modeler, users place planes on a photo in a two dimensional way as the unit of interaction. The given regions are inverse-projected into the 3D space for creating 3D models. The geometric information is given by CVsupported user interaction easily. Based on it, the modeler provides plane orthogonality and parallelism, and view-volume constraints to support interactive modeling. In addition, visualization by projective texture mapping (PTM), depth mapping, and the smart second view makes it easy that the users confirm shapes of models.
Zenith angle distribution of extensive air showers by TBS array ( GELATICA experiment )

The GELATICA experiment in Georgia (GEorgian Large-area Angle and Time Coincidence Array) is devoted both to the Primary Cosmic Ray (PCR) energy spectrum investigation at very high energies and possible correlations in the arrival times and directions of separate Extensive Air Showers (EAS) over large distances. The angular distribution of EAS arrival directions for the showers within a wide range of a number of charged particles is derived using the experimental data obtained with the EAS 4–detector array in Tbilisi (TBS). It is shown that the distribution function with the exponential absorption of the showers’ flux provides a good approximation of the angular distribution despite the existing azimuth anisotropy. The dependence of the EAS absorption path on the angular measure cutoff boundary is studied.
The 14 th World Conference on Earthquake Engineering October 12-17 , 2008 , Beijing , China PREDICTING COLLAPSE OF FRAME AND WALL STRUCTURES

This paper summarizes the state of knowledge in structural system modeling and tool development for predicting the collapse potential of buildings whose lateral load resisting system consists of moment-resisting frames or reinforced-concrete shear walls. In this context, collapse implies dynamic instability in a sidesway mode, triggered by large story drifts that are amplified by structure P-Δ effects and deterioration in strength and stiffness of the components of the system. The collapse capacity of a building is defined as the maximum ground motion intensity (often represented by the spectral acceleration at the first mode period) at which the structural system still maintains dynamic stability. A collapse fragility curve that incorporates aleatory dispersion due to record-to-record variability is obtained by ordering the collapse capacities for a representative set of ground motions. Additional dispersion of the collapse fragility curve is caused by epistemic uncertainties due to modeling assumptions on which analytical predictions are based. The collapse potential of a building can be expressed as the probability of collapse at a discrete hazard level or the mean annual frequency of collapse, both of which can be computed from the collapse fragility curve and the hazard curve for the site of the structure. Collapse fragility curves and estimates of the collapse potential for generic structures are discussed, together with the effects of different levels of detailing in modeling for nonlinear analysis. Results for generic structures are compared to assess the merits of different structural systems for controlling the collapse potential.
Aphanoascus fulvescens : A New Record from Crop Field Soil in Korea

Aphanoascus fulvescens KNU16-343 was isolated from crop field soil and identified based on morphological and molecular characteristics. The shape and size of conidia and conidiophores, as well as the internal transcribed spacer region of rDNA, confirmed that the isolate was A. fulvescens. This is the first report of this fungal species in Korea.
UNCERTAINTIES IN SITE EFFECT ASSESSMENT-EXPERIENCE FROM WEAK MOTION OBSERVATIONS IN BUCHAREST

The hazardous earthquakes for the Romanian capital Bucharest are the intermediate depth Vrancea events between 80 and 200 km depth, 130 km N of the city. Due to this geometry ground motion variations in Bucharest must be dominated by site effects as source and propagation effects at different sites within the city are almost identical. Thus ratios of Fourier Amplitude Spectra at those sites with the records of a reference station in the denominator contain only site effect terms and should not vary significantly for different Vrancea sources. However, the observed Fourier Amplitude Spectral Ratios differ significantly so that we have to conclude: The source site separation model is incorrect or incomplete and a significant amount of aleatory uncertainty exists in site effects so that they should be quantified in probabilistic terms. Aleatory uncertainties are inherent to the unpredictable nature of certain details in the complex process of ground motion. They cannot be reduced by collection of more data and information. One could certainly think of potential physical causes for the unexpected scatter in spectral ratios. Hydrogeological effects may play a role and thus modify ground motion via parameters such as soil moisture, water table, etc. However, these effects are hard to monitor systematically, inaccessible for previously observed ground motion and impossible to predict for future ground motion. Non-linearity cannot be responsible, as we use only weak motion data. Instead of speculating on causes we consider it more useful (a) to quantify the aleatory uncertainty in site effect amplification in a systematic way, and (b) to suggest that site-dependent prediction of future ground motion should be done by probabilistic means.
Interactive comment on “Integrated hydrometeorological – snow – frozen ground observations in the alpine region of the Heihe River Basin, China” by Tao Che et al

The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection 20 where/when needed, which hinders the development of remote sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and 25 ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze-thaw processes from a frozen ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryospheric science, hydrology, and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote sensing products and hydrological models for a broader community. The datasets 30 are available from the Cold and Arid Regions Science Data Center at Lanzhou https://doi.org/10.3972/hiwater.001. 2019.db.
Reassortant Avian Influenza A ( H 5 N 1 ) Viruses with H 9 N 2-PB 1 Gene in Poultry , Bangladesh

B has one of the highest reported number of outbreaks of highly pathogenic avian influenza (HPAI) (H5N1) in poultry (1). As of May 26, 2013, a total of 548 outbreaks of HPAI (H5N1) have been reported (1); these outbreaks have resulted in serious economic repercussions in the poultry sector in this country. Furthermore, 7 cases of human infection with HPAI (H5N1) were confirmed, most recently a fatal case in a 2-year-old child in April 2013 (2). In addition to the HPAI (H5N1) virus, the H9N2 subtype is widely circulating in poultry in Bangladesh, which raises concerns about the possible implications of the extensive co-circulation of these viruses (3,4). Their coexistence in the same susceptible population is likely to generate appropriate conditions for the emergence of novel reassortant variants, with unknown epizootic and zoonotic potential. We characterized the complete genome of 18 HPAI (H5N1) virus strains from Bangladesh and report the identification and characterization of a novel natural reassortant HPAI (H5N1) virus that contains an H9N2-PB1 gene in poultry.
Focus-Plus-Context Techniques for Picoprojection-Based Interaction

In this paper, we report on novel zooming interface methods that deploy a small handheld projector. Using mobile projections to visualize object/environment-related information on real objects introduces new aspects for zooming interfaces. Different approaches are investigated that focus on maintaining a level of context while exploring detailed information. Doing so, we propose methods that provide alternative contextual cues within a single projector and deploy the potential of zoom lenses to support a multilevel zooming approach. Furthermore, we look into the correlation between pixel density, distance to the target, and projection size. Alongside these techniques, we report on multiple user studies, in which we quantified the projection limitations and validated various interactive visualization approaches. Thereby, we focused on solving issues related to pixel density, brightness, and contrast that affect the design of more effective legible zooming interfaces for handheld projectors.
Protein Delivery into the Cell Cytosol using Non-Viral Nanocarriers

Protein delivery into cells is a potentially transformative tool for treating “undruggable” targets in diseases associated with protein deficiencies or mutations. The vast majority of these targets are accessed via the cytosol, a challenging prospect for proteins with therapeutic and diagnostic relevance. In this review we will present promising non-viral approaches for intracellular and ultimately cytosolic delivery of proteins using nanocarriers. We will also discuss the mechanistic properties that govern the efficacy of nanocarrier-mediated protein delivery, applications of nanomaterials, and key challenges and opportunities in the use of nanocarriers for intracellular protein delivery.
COMPARISON OF NONLINEAR STOCHASTIC FILTERS PERFORMANCE FOR DIFFERENTIAL DRIVE ROBOTS LOCALIZATION

This paper presents a performance comparison of nonlinear stochastic filtering techniques, namely EKF, UKF and Particle Filter, used to provide a dynamical estimate of a differential drive mobile robot’s position and heading angle. The performance is evaluated using the processing time and mean square error. These results were obtained through simulation. Keywords— Mobile Robots, Localization, Stochastic Filtering.
Quasi-static simulation approaches on rollover impact of a bus structure

The dynamic simulations of rollover behaviour of bus bodies require more computation time than the quasi-static simulations. This paper presents a study of several different virtual simulations of quasi-static loading test on body sections as an equivalent approval method of UNECE Regulation Number 66. The body section is analyzed by using the Transient Structural and Static Structural modules of Ansys. The scenarios analyze some new methods of deforming a structure by applying a force or a displacement to a plate which is in contact with a frame of the roof, or by applying different angle displacements directly to the frame. Resulting deformations are compared with a physical reference model, the absorbed energy is analyzed and a necessary mass of the structure is computed for each of the models. In some models the deformation is similar to the reference structure. Key-Words: quasi-static, rollover, bus, structure, Ansys, simulation, body section, residual space
Single Point Velocity Estimation in dynamic scenes from optical flow in binocular systems

This paper proposes a novel velocity estimation approach for single image points, while both the camera and the observed object may be in motion. In order to obtain absolute velocities, a flow-stereo setup is utilized, but no explicit depth reconstruction is carried out. Omitting the calculation in the Cartesian space increases the robustness of the estimation, which is shown in a comparison between the error propagation of the proposed method and a disparity-based approach. An important factor is the maximum distance in which the velocity could be estimated. Simulations and tests using real images demonstrate that the estimation range could be increased by the proposed method. Possible applications are motion-planning or obstacle avoidance for ground-based mobile robots in dynamic environments.