Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs

High-efficiency 100-W Kerr-lens mode-locked Yb:YAG thin-disk oscillator

We demonstrate a Kerr-lens mode-locked femtosecond Yb:YAG thin-disk oscillator and investigate the approach to increase the optical-to-optical efficiency based on the scheme of direct multiple passes of the laser beam through the thin-disk medium. With twelve passes through the thin disk, 266-fs pulses were delivered from the oscillator with an average power of 105.6 W at a repetition rate of 20 MHz. The corresponding optical-to-optical efficiency is 31.1%, which is, to the best of our knowledge, the highest efficiency of any mode-locked thin-disk oscillator with pulse duration below 300 fs. This demonstration paves the way to even more efficient mode-locked femtosecond thin-disk oscillators, and provides an excellent laser source for the applications such as non-linear frequency conversion and high-precision industrial processing

TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance

Cr13Ni5Si2-Based Composite Coating on Copper Deposited Using Pulse Laser Induction Cladding

A Cr13Ni5Si2-based composite coating was successfully deposited on copper by pulse laser induction hybrid cladding (PLIC), and its high-temperature wear behavior was investigated. Temperature evolutions associated with crack behaviors in PLIC were analyzed and compared with pulse laser cladding (PLC) using the finite element method. The microstructure and present phases were analyzed using scanning electron microscopy and X-ray diffraction. Compared with continuous laser induction cladding, the higher peak power offered by PLIC ensures metallurgical bonding between highly reflective copper substrate and coating. Compared with a wear test at room temperature, at 500 °C the wear volume of the Cr13Ni5Si2-based composite coating increased by 21%, and increased by 225% for a NiCr/Cr3C2 coating deposited by plasma spray. This novel technology has good prospects for application with respect to the extended service life of copper mold plates for slab continuous casting

Nitrogen dynamics and microbial community compositions in six vertical flow wetland colums

Two synthetic wastewaters, with mean NH4 concentrations of 471±19 and 475±17 mg/L, were treated in six planted columns. Under steady hydraulic and pollutant loading, average NH4 removal rate in each column was in the range of 21-47 g/m2d, while average TN removal rate was 0-27 g/m2d. In general, higher redox potential values benefitted ammonia removal but limited TN removal. The supply of organic carbon, by adding glucose into the synthetic wastewater, slightly reduced the rate of ammonia removal, but significantly enhanced TN removal. The seeding of microorganisms using diluted activated sludge, and submerging the columns with treated effluent for three days per week, intensified microbial activities; oxygen consumption reached 53-363 gO2/m2d due to microbial degradations of organics and nitrogen. Fluorescence in situ hybridization analysis of bacterial biomass revealed the population densities of nitrifying bacteria, and specific denitrifying bacteria (Azoarcus-Thauera-cluster, genus Hyphomicrobium, genus Paracoccus, and family Saprospiraceae). Denitrifier Azoarcus-Thauera-cluster was found to be the dominant bacterial group (58% of all cells) when organic carbon is available. Without the supply of organic carbon, ammonium oxidising bacteria (AOB) dominate microbial populations in the planted columns

Multivariate Statistical Online Analysis for Self Protection against Network Attacks

Detection and self-protection against viruses, worms, and network attacks is urgently needed to protect network systems and their applications from catastrophic loss. Once a network component is infected by viruses, worms, or became a target of the network attacks, its operation state will shift from normal to abnormal. Online monitoring mechanism can be used to collect important aspects of network traffic and host data (CPU utilization, memory usage, etc.), that can effectively detect abnormal behaviors caused by attacks. In this paper, we develop an online multivariate analysis algorithm- MANA based on Hotelling’s T 2 multivariate statistical technique [6] to analyze the behaviors of system resources and network protocols in order to proactively detect network attacks. The new algorithm builds an adaptive behavior profile of normal operation for system resources. We have validated this algorithm and showed how it can proactively detect well-known attacks such a