RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things.

This paper considers the distributed access and control problem of massive wireless sensor networks' data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate.

A Review of Femtosecond Laser Processing of Silicon Carbide.

Silicon carbide (SiC) is a promising semiconductor material as well as a challenging material to machine, owing to its unique characteristics including high hardness, superior thermal conductivity, and chemical inertness. The ultrafast nature of femtosecond lasers enables precise and controlled material removal and modification, making them ideal for SiC processing. In this review, we aim to provide an overview of the process properties, progress, and applications by discussing the various methodologies involved in femtosecond laser processing of SiC. These methodologies encompass direct processing, composite processing, modification of the processing environment, beam shaping, etc. In addition, we have explored the myriad applications that arise from applying femtosecond laser processing to SiC. Furthermore, we highlight recent advancements, challenges, and future prospects in the field. This review provides as an important direction for exploring the progress of femtosecond laser micro/nano processing, in order to discuss the diversity of processes used for manufacturing SiC devices.

[Microbiology and clinical analysis of six cases of hospital-acquired pneumonia caused by Acinetobacter baumannii].

OBJECTIVE: To study the microbiology, clinical features and treatment outcomes of hospital-acquired pneumonia (HAP) caused by Acinetobacter baumannii. METHODS: Six cases of HAP, caused by Acinetobacter baumannii producing PER-1 type extended-spectrum beta-lactamase verified by molecular biological methods, were studied in Bethune International Peace Hospital, from January to August 2005. Clinical data were collected and analyzed. Pulsed-field gel electrophoresis (PFGE) were used to analyze the homology of these strains. The genes of beta-lactamase (BLA), aminoglycoside-modifying enzymes (AME), resistant to disinfectant-sulfanilamide (qacEDelta1-sul1), and class 1 integrase (intl1) were analyzed using PCR and verified by DNA sequencing. RESULTS: Among these 6 strains of Acinetobacter baumannii, five were susceptible only to imipenem, and 4 were susceptible to meropenem. The PFGE types identified from these isolates were A1 (n=2) and A2-A5 (n=1, respectively). They all were positive for PER-1, TEM-1, and genes of qacEDelta1-sull and intl1. Three of them harbored genes of aac (3)-I and aac (6')-I. Two had aac (3)-I and ant (3'')-I. One had aac (3)-I. All the patients had severe underlying diseases, and had received mechanical ventilation. They all had received broad spectrum antibiotics within 15 days before Acinetobacter baumannii were identified. Although carbapenem and/or cefoperazone/sulbactam had been used, only 3 patients survived. CONCLUSION: The 6 PER-1 type extended-spectrum beta-lactamase-producing Acinetobacter baumannii isolates were closely related and had complex mechanisms of drug resistance. The prognosis of patients infected by them was poor.