Modelling of steady motion of solid specimens conveyed by travelling wave ultrasonic feeding

On the basis of the research on the morphology of the contact surfaces, a contact model is proposed, which regards the rough contact surfaces as the collections of elastic micro peaks. These micro peaks generate elastic contact force due to elastic deformation and the contact force has relationship to the morphology of the contact surfaces, the motion of the vibrator as well as the materials of the specimen and the vibrator. And using Newton’s second law to the specimen’s motion, the normal dynamical equation for the specimen with the actuation of the ultrasonic vibrator is established. Solving the dynamical equation, the normal kinetic function of specimen and the normal elastic contact force are obtained. Furthermore, the normalized contact time could be analyzed theoretically, which is defined as the ratio of contact time to period. The calculated results indicate that the normalized contact time decreases with the vibrator’s normal amplitude increasing, and increases with the standard deviation of the height of micro peaks on the contact surfaces. Finally, the average tangential velocity of the specimen, conveyed by a travelling wave ultrasonic feeding device, is discussed on the basis of the research on the normalized contact time. The formula of the specimen’s average tangential velocity is derived and it is the function of the normalized contact time, the tangential amplitude, the angular frequency and the phase difference of the tangential and the normal vibrations. Using this formula, the tangential velocity of the specimen is theoretically analyzed; in addition, the theoretical conclusions are compared with the experimental data

Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor

With the ease-of-programming, flexibility and yet efficiency, MapReduce has become one of the most popular frameworks for building big-data applications. MapReduce was originally designed for distributed-computing, and has been extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is the latest product released by Intel based on the Many Integrated Core Architecture. To the best of our knowledge, this is the first work to optimize the MapReduce framework on the Xeon Phi. In our work, we utilize advanced features of the Xeon Phi to achieve high performance. In order to take advantage of the SIMD vector processing units, we propose a vectorization friendly technique for the map phase to assist the auto-vectorization as well as develop SIMD hash computation algorithms. Furthermore, we utilize MIMD hyper-threading to pipeline the map and reduce to improve the resource utilization. We also eliminate multiple local arrays but use low cost atomic operations on the global array for some applications, which can improve the thread scalability and data locality due to the coherent L2 caches. Finally, for a given application, our framework can either automatically detect suitable techniques to apply or provide guideline for users at compilation time. We conduct comprehensive experiments to benchmark the Xeon Phi and compare our optimized MapReduce framework with a state-of-the-art multi-core based MapReduce framework (Phoenix++). By evaluating six real-world applications, the experimental results show that our optimized framework is 1.2X to 38X faster than Phoenix++ for various applications on the Xeon Phi

OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams

How to get insights from relational data streams in a timely manner is a hot research topic. This type of data stream can present unique challenges, such as distribution drifts, outliers, emerging classes, and changing features, which have recently been described as open environment challenges for machine learning. While existing studies have been done on incremental learning for data streams, their evaluations are mostly conducted with manually partitioned datasets. Thus, a natural question is how those open environment challenges look like in real-world relational data streams and how existing incremental learning algorithms perform on real datasets. To fill this gap, we develop an Open Environment Benchmark named OEBench to evaluate open environment challenges in relational data streams. Specifically, we investigate 55 real-world relational data streams and establish that open environment scenarios are indeed widespread in real-world datasets, which presents significant challenges for stream learning algorithms. Through benchmarks with existing incremental learning algorithms, we find that increased data quantity may not consistently enhance the model accuracy when applied in open environment scenarios, where machine learning models can be significantly compromised by missing values, distribution shifts, or anomalies in real-world data streams. The current techniques are insufficient in effectively mitigating these challenges posed by open environments. More researches are needed to address real-world open environment challenges. All datasets and code are open-sourced in https://github.com/sjtudyq/OEBench

Sorafenib modulates the radio sensitivity of hepatocellular carcinoma cells in vitro in a schedule-dependent manner

BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality. Radiotherapy and sorafenib have proven effective for HCC. Here, we investigated whether sorafenib modulated the response of HCC cells to irradiation in vitro, effect of timing of sorafenib, and the underlying mechanisms. METHODS: Cell viability of the HCC cell lines, SMMC-7721 and Bel-7402, was examined by the 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2(4-sulfophenyl)-2 H-terazolium (MTT) assays. Clonogenic growth assays of SMMC-7721 and Bel-7402 were determined by colony formation assays. DNA damage was assessed by monitoring γ-HAX foci in irradiated cells with immunofluorescence microscopy, and cell cycle distribution changes were examined by flow cytometry. Effects of sorafenib (15 μM) added 30 min prior to radiation (pre-irradiation sorafenib) of SMMC-7721 and BEL-7402 or 24 h post-irradiation (post-irradiation sorafenib) on irradiated SMMC-7721 and BEL-7402 cells were compared to those of radiation alone or no treatment. RESULTS: The effect of sorafenib was dependent on its time of addition in relationship to irradiation of cells. Pre-irradiation sorafenib did not significantly affect the viability of SMMC-7221 and BEL-7402 cells compared with irradiation treatment alone. In contrast, post-irradiation sorafenib increased the sensitivity of irradiated SMMC-7221 and BEL-7402 cells significantly in a time-dependent manner. Pre-irradiation sorafenib significantly increased the surviving fraction of SMMC-7221 and BEL-7402 cells in clonogenic assays whereas post-irradiation sorafenib significantly reduced the surviving fractions of SMMC-7221 and BEL-7402 cells. SMMC-7721 cells treated with sorafenib 30 min before irradiation had significantly fewer cells with γ-H2AX foci (23.8 ± 2.9%) than SMMC-7721 cells receiving radiation alone (59.9 ± 2.4; P < 0.001). Similarly, BEL-7402 cells receiving sorafenib prior to irradiation had significantly fewer cells with γ-H2AX foci (46.4 ± 3.8%) than those receiving radiation alone (25.0 ± 3.0%; P < 0.001). In addition, irradiation (6 Gy) caused a significant increase in the percentage of both SMMC-7721 and BEL-7402 cells in G2/M at 12 to 16 h post irradiation, which was markedly delayed by pre-irradiation sorafenib. CONCLUSIONS: Sorafenib combined with irradiation exerted a schedule-dependent effect in HCC cells in vitro, which has significant implications for the combined use of sorafenib and radiotherapy for HCC patients

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications. Although there is an extensive literature on qualitative properties such as safety and liveness, there is still a lack of quantitative and uncertain property verifications for these systems. In uncertain environments, agents must make judicious decisions based on subjective epistemic. To verify epistemic and measurable properties in multi-agent systems, this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge (FCTLK). We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems. In addition, we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures, as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic (FCTL) formulas. Accordingly, we transform the FCTLK model checking problem into the FCTL model checking. This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads. Finally, we present correctness proofs and complexity analyses of the proposed algorithms. Additionally, we further illustrate the practical application of our approach through an example of a train control system