Influence of welding quality on stability of SUS304 tube-compression by viscous pressure forming

One of the major problems affecting viscous pressure forming (VPF) is the stability of tubecompression, whereas the main defect influencing the stability of welded tube-compression is the quality of welded joints. This article utilizes the finite element method to analyze the influence of weld joint strength and width on stability of SUS304 tube-compression by VPF. Meanwhile, SUS304 welded tube-blanks with different weld joint strength and width are obtained by plasma welding, TIG-Tungsten Inert Gas welding, laser welding and high frequency welding and then the stability test by VPF is carried out. The results showed that the weld joint strength and width affect the stability of tube-compression. The system and process of controlling weld joint width can improve the stability of tube-blank preferably relative to weld joint strength

Influence of welding quality on stability of SUS304 tube-compression by viscous pressure forming

One of the major problems affecting viscous pressure forming (VPF) is the stability of tubecompression, whereas the main defect influencing the stability of welded tube-compression is the quality of welded joints. This article utilizes the finite element method to analyze the influence of weld joint strength and width on stability of SUS304 tube-compression by VPF. Meanwhile, SUS304 welded tube-blanks with different weld joint strength and width are obtained by plasma welding, TIG-Tungsten Inert Gas welding, laser welding and high frequency welding and then the stability test by VPF is carried out. The results showed that the weld joint strength and width affect the stability of tube-compression. The system and process of controlling weld joint width can improve the stability of tube-blank preferably relative to weld joint strength

Photomodulating RNA cleavage using photolabile circular antisense oligodeoxynucleotides

Caged antisense oligodeoxynucleotides (asODNs) are synthesized by linking two ends of linear oligodeoxynucleotides using a photocleavable linker. Two of them (H30 and H40) have hairpin-like structures which show a large difference in thermal stability (ΔTm = 17.5°C and 11.6°C) comparing to uncaged ones. The other three (C20, C30 and C40) without stable secondary structures have the middle 20 deoxynucleotides complementary to 40-mer RNA. All caged asODNs have restricted opening which provides control over RNA/asODN interaction. RNase H assay results showed that 40-mer RNA digestion could be photo-modulated 2- to 3-fold upon light-activation with H30, H40, C30 and C40, while with C20, RNA digestion was almost not detectable; however, photo-activation triggered >20-fold increase of RNA digestion. And gel shift assays showed that it needed >0.04 μM H40 and 0.5 μM H30 to completely bind 0.02 μM 40-mer RNA, and for C40 and C30, it needed >0.2 μM and 0.5 μM for 0.02 μM 40-mer RNA binding. However, even 4 μM C20 was not able to fully bind the same concentration of 40-mer RNA. By simple adjustment of ring size of caged asODNs, we could successfully photoregulate their hybridization with mRNA and target RNA hydrolysis by RNase H with light activation

Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing

© 2020 Binbin Huang et al. Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme

PhysBench: A Benchmark Framework for rPPG with a New Dataset and Baseline

In recent years, due to the widespread use of internet videos, physiological remote sensing has gained more and more attention in the fields of affective computing and telemedicine. Recovering physiological signals from facial videos is a challenging task that involves a series of preprocessing, image algorithms, and post-processing to finally restore waveforms. We propose a complete and efficient end-to-end training and testing framework that provides fair comparisons for different algorithms through unified preprocessing and post-processing. In addition, we introduce a highly synchronized lossless format dataset along with a lightweight algorithm. The dataset contains over 32 hours (3.53M frames) of video from 58 subjects; by training on our collected dataset both our proposed algorithm as well as existing ones can achieve improvements

Effect of a Compound Energy Field with Temperature and Ultrasonic Vibration on the Material Properties and Bending Process of TC2 Titanium Alloy

Due to the low formability and forming quality of titanium alloy, the forming process of a compound energy field (CEF) with temperature and ultrasonic vibration was proposed. Tensile tests were carried out to investigate the effect of the CEF on the true stress–strain curve, yield strength, elastic modulus, and other mechanical properties of the TC2 titanium alloy. Bending tests assisted by CEF were also performed to investigate the effect of different parameters of the CEF on bending force, spring-back, bending fillet radius, and microstructure of TC2 titanium. The results demonstrate that compared to the process under a single-temperature field, the CEF can reduce yield strength, elastic modulus, bending force, bending fillet, and the spring-back angle, which shows that the CEF can further increase the high-temperature softening effect of TC2 titanium. Furthermore, this effect becomes more remarkable when ultrasonic vibration energy increases. As a result, the formability of titanium alloy can be improved

Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD), which reduces the effect of noise on the wind speed data; (II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM) model are optimized by the cuckoo search (CS) algorithm; (III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD) method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints