Search for: All records

Introduction: Social participation for emerging symbolic communicators on the autism spectrum is often restricted. This is due in part to the time and effort required for both children and partners to use traditional augmentative and alternative communication (AAC) technologies during fast-paced social routines. Innovations in artificial intelligence provide the potential for context-aware AAC technology that can provide just-in-time communication options based on linguistic input from partners to minimize the time and effort needed to use AAC technologies for social participation. Methods: This preliminary study used an alternating treatment design to compare the effects of a context-aware AAC prototype with automated cloze phrase response options to traditional AAC for supporting three young children who were emerging symbolic communicators on the autism spectrum in participating within a social routine. Results: Visual analysis and effect size estimates suggest the context-aware AAC condition resulted in increases in linguistic participation, vocal approximations, and visual attention for all three children. Conclusion: While this study was only an initial exploration and results are preliminary, context-aware AAC technologies have the potential to enhance participation and communication outcomes for young emerging symbolic communicators on the autism spectrum and more research is needed. 

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. 

Heat transport can serve as a fingerprint identifying different states of matter. In a normal liquid, a hotspot diffuses, whereas in a superfluid, heat propagates as a wave called “second sound.” Direct imaging of heat transport is challenging, and one usually resorts to detecting secondary effects. In this study, we establish thermography of a strongly interacting atomic Fermi gas, whose radio-frequency spectrum provides spatially resolved thermometry with subnanokelvin resolution. The superfluid phase transition was directly observed as the sudden change from thermal diffusion to second-sound propagation and is accompanied by a peak in the second-sound diffusivity. This method yields the full heat and density response of the strongly interacting Fermi gas and therefore all defining properties of Landau’s two-fluid hydrodynamics. 

Road accidents caused by heavy rain have become a frightening issue in recent years requiring investigation. In this regard, an aerodynamic comparative and experimental rain study is carried out to observe the flow phenomena change around a generic ground vehicle (Ahmed Body at a scale) and the utility truck. In this paper, a Discrete Phase Model (DPM) based computational methodology is used to estimate the effect of rain on aerodynamic performance. First, an experimental rain study of the Ahmed body at a scale that is representative of a car or light truck was conducted at the Wall of Wind (WOW) large-scale testing facility using force measurement equipment. In addition, the experiment allowed drag, lift, and side-force coefficients to be measured at yaw angles up to 55 degrees. Next, experimental results are presented for the Ahmed Body back angle of 35 degrees, then compared to validate the computational model for ground vehicle aerodynamics. Afterwards, we investigated the effect of heavy rainfall (LWC = 30 g/m3) on the external aerodynamics of the utility truck with the morphing boom equipment using the validated computational fluid dynamics method, and the external flow is presented using a computer visualization. Finally, force & moment coefficients and velocity distributions around the utility truck are computed for each case, and the results are compared. Keywords: Experimental Wind-Driven Rain Wind Tunnel Testing, Heavy Rainfall, The Ahmed Body, Utility Truck, Morphing Boom Equipment, Discrete Phase Model (DPM), Automotive Aerodynamics, Computational Fluid Dynamics (CFD) 

Abstract Plant microRNAs (miRNAs) are short, noncoding RNA molecules that restrict gene expression via posttranscriptional regulation and function in several essential pathways, including development, growth, and stress responses. Accurately identifying miRNAs in populations of small RNA sequencing libraries is a computationally intensive process that has resulted in the misidentification of inaccurately annotated miRNA sequences. In recent years, criteria for miRNA annotation have been refined with the aim to reduce these misannotations. Here, we describe miRador, a miRNA identification tool that utilizes the most up-to-date, community-established criteria for accurate identification of miRNAs in plants. We combined target prediction and Parallel Analysis of RNA Ends (PARE) data to assess the precision of the miRNAs identified by miRador. We compared miRador to other commonly used miRNA prediction tools and found that miRador is at least as precise as other prediction tools while being substantially faster than other tools. miRador should be broadly useful for the plant community to identify and annotate miRNAs in plant genomes. 

Global climate change has affected the human race for decades. As a result, severe weather changes and more substantial hurricane impact have become a typical scenario. Utility trucks with the morphing boom equipment are the first responders to access these disaster areas in bad weather conditions and restore the damages caused by the disaster. The stability of the utility trucks while driving in a heavy wind scenario is an essential aspect for the safety of the rescue crew, and aerodynamic forces caused by the wind flow constitute a significant factor that influences the stability of the utility truck. In this paper, the aerodynamic performance of the utility truck is modeled using the incompressible unsteady Reynolds Averaged Navier Stokes (URANS) model. The Ahmed body, a well-recognized benchmark test case used by the computational fluid dynamics (CFD) community for the aerodynamic model validation of automobiles, is used to validate this aerodynamic model. The validated aerodynamic model investigates the impact of heavy wind on the utility truck with the morphing boom equipment. The visualization of the flow field around the utility truck with the force and moment coefficients at various side slip angles are presented in this paper. 

Flow around the Ahmed body is a well-recognized benchmark test case used by the computational fluid dynamics (CFD) community for model validation of automobiles. Even though the geometry of the Ahmed body is simple, the flow field around the object is complex due to flow separation and vortex shedding. In this paper, a Discrete Phase Model (DPM) based computational methodology is presented to estimate the effect of rain on aerodynamic performance and is validated with the experimental data that is available in the literature for the NACA64-210 wing section under different rain intensities. With this validated model, we have investigated the Ahmed body under low and high rain intensities for base slant angles of 25 and 35 degrees. The computed drag coefficient for the Ahmed body under rain conditions, are compared with the experimental data from aerodynamic analysis of the Ahmed body without rain, to evaluate the rain effect. 

Utility trucks with boom equipment function on environmentally sensitive areas and severe terrains where off-road conditions may cause significant damage to the trucks’ mobility and their safe operation. Indeed, considerable variations of landscape elevation and dynamic changes of terrain properties lead to extensive differences in the wheel normal reactions, drastic fluctuations of the rolling resistance at each tire, and finally, substantial changes in the total resistance to motion, which includes both the tire rolling resistance and the resistance due to the truck gravity component. Additionally, lateral forces caused by truck inclinations can lead to instability in motion, too. As a result, a utility truck can become immobilized in either longitudinal or lateral direction of movement because of one or the combination of the following events – loss of longitudinal mobility due to extensive tire slippage at some/all wheels, loss of lateral mobility due to tire side skid or rollover of the truck. To eliminate the above-listed causes that can lead to the utility truck immobilization, this study suggests a novel approach to managing the input/output factors that influence both longitudinal and lateral forces of the utility truck. In fact, the 3D morphing of the boom equipment is proposed as the input factor for managing the wheel normal reactions as the outputs. Ultimately, a changeable positioning of the boom equipment relative to the truck frame results in variable wheel normal reactions, which are the main contributors to the normal tire deformation and soil compaction, and thus, to the rolling resistance of each and all tires. This paper presents and discusses the method and results of computational simulations of the F450-based utility truck with boom equipment on medium mineral soil. The normal reaction at each wheel is evaluated under which the boom equipment morphs safely without causing roll over of the truck and, consequently, the total resistance to the motion force is determined. Modeling and simulation of the truck were conducted with the use of terramechanics-based tire-terrain models. This research study of the rolling resistance contributes to a research project on morphing utility truck, dynamics in severe terrain conditions. Keywords: Utility Truck, Morphing, Terrain Mobility