Dynamical Response of Nanomechanical Resonators to Biomolecular Interactions

We studied the dynamical response of a nanomechanical resonator to biomolecular (e.g. DNA) adsorptions on a resonator's surface by using a theoretical model, which considers the Hamiltonian H such that the potential energy consists of elastic bending energy of a resonator and the potential energy for biomolecular interactions. It was shown that the resonant frequency shift of a resonator due to biomolecular adsorption depends on not only the mass of adsorbed biomolecules but also the biomolecular interactions. Specifically, for dsDNA adsorption on a resonator's surface, the resonant frequency shift is also dependent on the ionic strength of a solvent, implying the role of molecular interactions on the dynamic behavior of a resonator. This indicates that nanomechanical resonators may enable one to quantify the biomolecular mass, implying the enumeration of biomolecules, as well as gain insight into intermolecular interactions between adsorbed biomolecules on the surface.Comment: 17 page, 4 figures, accepted for publication at PRB. Physical Review B, accepte

Nanomechanical In Situ Monitoring of Proteolysis of Peptide by Cathepsin B

Characterization and control of proteolysis of peptides by specific cellular protease is a priori requisite for effective drug discovery. Here, we report the nanomechanical, in situ monitoring of proteolysis of peptide chain attributed to protease (Cathepsin B) by using a resonant nanomechanical microcantilever immersed in a liquid. Specifically, the detection is based on measurement of resonant frequency shift arising from proteolysis of peptides (leading to decrease of cantilever's overall mass, and consequently, increases in the resonance). It is shown that resonant microcantilever enables the quantification of proteolysis efficacy with respect to protease concentration. Remarkably, the nanomechanical, in situ monitoring of proteolysis allows us to gain insight into the kinetics of proteolysis of peptides, which is well depicted by Langmuir kinetic model. This implies that nanomechanical biosensor enables the characterization of specific cellular protease such as its kinetics

Aptamer-functionalized nano-pattern based on carbon nanotube for sensitive, selective protein detection

We have developed a horizontally aligned carbon nanotube sensor that enables not only the specific detection of biomolecules with ultra-sensitivity, but also the quantitative characterization of binding affinity between biomolecules and/or interaction between a carbon nanotube and a biomolecule, for future applications in early diagnostics. In particular, we have fabricated horizontally aligned carbon nanotubes, which were functionalized with specific aptamers that are able to specifically bind to biomolecules (i.e. thrombin). Our detection system is based on scanning probe microscopy (SPM) imaging for horizontally aligned aptamer-conjugated carbon nanotubes (ACNTs) that specifically react with target biomolecules at an ultra-low concentration. It is shown that the binding affinity between thrombin molecule and ACNT can be quantitatively characterized using SPM imaging. It is also found that the smart carbon nanotube sensor coupled with SPM imaging permits us to achieve the high detection sensitivity even up to similar to 1 pM, which is much higher than that of other bioassay methods. Moreover, we have shown that our method enables a quantitative study on small molecule-mediated inhibition of specific biomolecular interactions. In addition, we have shown that our ACNT-based system allows for the quantitative study of the effect of chemical environment (e.g. pH and ion concentration) on the binding affinity. Our study sheds light on carbon nanotube sensor coupled with SPM imaging, which opens a new avenue to early diagnostics and drug screening with high sensitivity.close2

LaLaRAND: Flexible Layer-by-Layer CPU/GPU Scheduling for Real-Time DNN Tasks

Deep neural networks (DNNs) have shown remarkable success in various machine-learning (ML) tasks useful for many safety-critical, real-time embedded systems. The foremost design goal for enabling DNN execution on real-time embedded systems is to provide worst-case timing guarantees with limited computing resources. Yet, the state-of-the-art ML frameworks hardly leverage heterogeneous computing resources (i.e., CPU, GPU) to improve the schedulability of real-time DNN tasks due to several factors, which include a coarse-grained resource allocation model (one-resource-per-task), the asymmetric nature of DNN execution on CPU and GPU, and lack of schedulability-aware CPU/GPU allocation scheme. This paper presents, to the best of our knowledge, the first study of addressing the above three major barriers and examining their cooperative effect on schedulability improvement. In this paper, we propose LaLaRAND, a real-time layer-level DNN scheduling framework, that enables flexible CPU/GPU scheduling of individual DNN layers by tightly coupling CPU-friendly quantization with fine-grained CPU/GPU allocation schemes (one-resource-per-layer) while mitigating accuracy loss without compromising timing guarantees. We have implemented and evaluated LaLaRAND on top of the state-of-the-art ML framework to demonstrate its effectiveness in making more DNN task sets schedulable by 56% and 80% over an existing approach and a baseline (vanilla PyTorch), respectively, with only up to -0.4% of performance (inference accuracy) difference

Literature Review on Fretting Wear and Contact Mechanics of Tribological Coatings

This article reviews fretting wear damage in industries and in the contact mechanics of coated systems. Micro-slip motion resulting in fretting damage is discussed along with major experimental factors. The experimental factors, including normal force, relative displacement, frequency and medium influence are directly compared. Industrial solutions to reduce fretting damages are then discussed. The contact mechanics of a coated system are reviewed to quantify stress states in a coating layer and the substrate. Finally, a literature review on simulation for fretting is carried out. This review study provides useful methods and practical solutions to minimize fretting wear damage

Literature Review on Fretting Wear and Contact Mechanics of Tribological Coatings

International audienc

MC-SDN: Supporting Mixed-Criticality Real-Time Communication Using Software-Defined Networking

Despite recent advances, there still remain many problems to design reliable cyber-physical systems. One of the typical problems is to achieve a seemingly conflicting goal, which is to support timely delivery of real-time flows while improving resource efficiency. Recently, the concept of mixed-criticality (MC) has been widely accepted as useful in addressing the goal for real-time resource management. However, it has not been yet studied well for real-time communication. In this paper, we present the first approach to support MC flow scheduling on switched Ethernet networks leveraging an emerging network architecture, software-defined networking (SDN). Though SDN provides flexible and programmatic ways to control packet forwarding and scheduling, it yet raises several challenges to enable real-time MC flow scheduling on SDN, including: 1) how to handle (i. e., drop or re-prioritize) out-of-mode packets in the middle of the network when the criticality mode changes and 2) how the mode change affects end-to-end transmission delays. Addressing such challenges, we develop MC-SDN that supports real-time MC flow scheduling by extending SDN-enabled switches and OpenFlow protocols. It manages and schedules MC packets in different ways depending on the system criticality mode. To this end, we carefully design the mode change protocol that provides analytic mode change delay bound, and then resolve implementation issues for system architecture. For evaluation, we implement a prototype of MC-SDN on top of Open vSwitch, and integrate it into a real world network testbed as well as a 1/10 autonomous vehicle. Our extensive evaluations with the network testbed and vehicle deployment show that MC-SDN supports MC flow scheduling with minimal delays on forwarding rule updates and it brings a significant improvement in safety in a real-world application scenario.1

JMC: Jitter-Based Mixed-Criticality Scheduling for Distributed Real-Time Systems

These days, the term of Internet of Things (IoT) becomes popular to interact and cooperate with individual smart objects, and one of the most critical challenges for IoT is to achieve efficient resource sharing as well as ensure safetystringent timing constraints. To design such reliable real-time IoT, this paper focuses on the concept of mixed-criticality (MC) introduced to address the low processor utilization on traditional real-time systems. Although different worst-case execution time estimates depending on criticality are proven effective on processor scheduling, the MC concept is not yet mature on distributed systems (such as IoT), especially with end-to-end deadline guarantee. To the best of our knowledge, this paper presents the first attempt to apply the MC concept into interference (or jitter), which is a complicated source of pessimism when analyzing the schedulability of distributed systems. Our goal is to guarantee the end-to-end deadlines of high-criticality flows and minimize the deadline miss ratio of low-criticality flows in distributed systems. To achieve this goal, we introduce a jitter-based MC (JMC) scheduling framework, which supports node-level mode changes in distributed systems. We present an optimal feasibility condition (subject to given schedulability analysis) and two policies to determine jitter-threshold values to achieve the goal in different conditions. Via simulation results for randomly generated workloads, JMC outperforms an existing criticality-monotonic scheme in terms of achieving higher schedulability and fewer deadline misses.1

DNN-SAM: Split-and-Merge DNN Execution for Real-Time Object Detection

As real-time object detection systems, such as autonomous cars, need to process input images acquired from multiple cameras, they face significant challenges in delivering accurate and timely inferences often based on machine learning (ML). To meet these challenges, we want to provide different levels of object detection accuracy and timeliness to different portions within each input image with different criticality levels. Specifically, we develop DNN-SAM, a dynamic Split-And-Merge Deep Neural Network (DNN) execution and scheduling framework, that enables seamless split-and-merge DNN execution for unmodified DNN models. Instead of processing an entire input image once in a full DNN model, DNN-SAM first splits a DNN inference task into two smaller sub-tasks-a mandatory sub-task dedicated for a safety-critical (cropped) portion of each image and an optional sub-task for processing a down-scaled image-then executes them independently, and finally merges their results into a complete inference. To achieve DNN-SAM's timely and accurate detection of objects in each image, we also develop two scheduling algorithms that prioritize sub-tasks according to their criticality levels and adaptively adjust the scale of the input image to meet the timing constraints while minimizing the response time of mandatory sub-tasks or maximizing the accuracy of optional sub-tasks. We have implemented and evaluated DNN-SAM on a representative ML framework. Our evaluation shows DNN-SAM to improve detection accuracy in the safety-critical region by 2.0-3.7× and lower average inference latency by 4.8-9.7× over existing approaches without violating any timing constraints. © 2022 IEEE