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Laser pulses of 200 ps with extremely high intensities and high energies are sufficient to satisfy the demand of shock ignition, which is an alternative path to ignition in inertial confinement fusion (ICF). This paper reports a type of Brillouin scheme to obtain high-intensity 200-ps laser pulses, where the pulse durations are a challenge for conventional pulsed laser amplification systems. In the amplification process, excited Brillouin acoustic waves fulfill the nonlinear optical effect through which the high energy of a long pump pulse is entirely transferred to a 200-ps laser pulse. This method was introduced and achieved within the SG-III prototype system in China. Compared favorably with the intensity of
in existing ICF laser drivers, a 6.96-
pulse with a width of 170 ps was obtained in our experiment. The practical scalability of the results to larger ICF laser drivers is discussed.
Dynamic trajectory prediction is an important topic in the field of navigation and positioning. Due to the drawbacks of a Global Navigation Satellite System (GNSS) receiver, the trajectory of the position always lags behind the dynamic platform's actual position, especially in highly dynamic situations. In order to solve the prediction of a dynamic trajectory, a generalised extension extrapolated model is proposed in this paper. The model utilises the current motion state and a priori position data of the platform, combines the interpolation and fitting method, adds the angle information as a constraint condition and solves the platform position prediction. In this paper, the feasibility of the generalised extended extrapolation algorithm is analysed theoretically and practically. Simulation results show that the prediction error is within 0.2 metres and experimental results show that the algorithm still has high prediction accuracy when a land vehicle platform is turned through a large angle.
Monosized spherical Cu–20% Sn (wt%) alloy particles with diameter ranging from 70.6 to 334.0 μm were prepared by the pulsated orifice ejection method (termed “POEM”). Fully dense without pores and bulk inclusions, the cross-sectional micrographs of the spherical alloy particles indicate an even distribution of Cu and Sn. These spherical Cu–Sn alloy particles exhibit a good spherical shape and a narrow size distribution, suggesting that the liquid Cu–Sn alloy can completely break the balance between the surface tension and the liquid static pressure in the crucible micropores and accurately control the volume of the droplets. Furthermore, the cooling rate of spherical Cu–20% Sn alloy particles is estimated by a Newton’s cooling model. The cooling rate of the Cu–20% Sn alloy particle decreases gradually with the particle diameter increasing. Smaller particles have higher cooling rates and when the particle diameter is less than 70 μm, the cooling rate of particles can reach more than 3.3 × 104 K/s. The secondary dendrite arm spacing has strong dependence on particle diameter which increases gradually with the increase of particle diameter. The results demonstrate that POEM is an effective route for fabrication of high-quality monosized Cu–20% Sn alloy particles.
We investigate feature selection methods for machine learning approaches in sentiment analysis. More specifically, we use data from the cooking platform Epicurious and attempt to predict ratings for recipes based on user reviews. In machine learning approaches to such tasks, it is a common approach to use word or part-of-speech n-grams. This results in a large set of features, out of which only a small subset may be good indicators for the sentiment. One of the questions we investigate concerns the extension of feature selection methods from a binary classification setting to a multi-class problem. We show that an inherently multi-class approach, multi-class information gain, outperforms ensembles of binary methods. We also investigate how to mitigate the effects of extreme skewing in our data set by making our features more robust and by using review and recipe sampling. We show that over-sampling is the best method for boosting performance on the minority classes, but it also results in a severe drop in overall accuracy of at least 6 per cent points.
A 100-J-level Nd:glass laser system in nanosecond-scale pulse width has been constructed to perform as a standard source of high-fluence-laser science experiments. The laser system, operating with typical pulse durations of 3–5 ns and beam diameter 60 mm, employs a sequence of successive rod amplifiers to achieve 100-J-level energy at 1053 nm at 3 ns. The frequency conversion can provide energy of 50-J level at 351 nm. In addition to the high stability of the energy output, the most valuable of the laser system is the high spatiotemporal beam quality of the output, which contains the uniform square pulse waveform, the uniform flat-top spatial fluence distribution and the uniform flat-top wavefront.
Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
Cong Li, Yale University, New Haven, CT,
Can Yang, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China,
Greg Hather, Takeda Pharmaceuticals International Co., Cambridge, MA,
Ray Liu, Takeda Pharmaceuticals International Co., Cambridge, MA,
Hongyu Zhao, Yale University, New Haven, CT
Traditional drug discovery practices usually adopt the “one drug – one target” approach, which ignore the fact the disease occurrence is usually the result of an extremely complex combination of molecular events. Pathway-based approaches address this limitation by considering biological pathways as potential drug targets. A first step of pathwaybased drug discovery is to identify associations between drug candidates and biological pathways. This has been made possible by the availability of high-dimensional transcriptional and drug sensitivity profile data. In this chapter, we describe two statistical methods, “iFad” and “iPad”, which perform drug-pathway association analysis by integrating these two types high-dimensional data. We also demonstrate their utilities by applying them to the NCI-60 data set.
Drug discovery is the process of identifying new candidate medications for diseases of interest. The common practice adopted by the pharmaceutical industry is to design maximally selective drug molecules to act on individual drug targets , which is usually referred to as the “one drug – one target” approach. This paradigm has indeed enjoyed some successes . Yet, the last 15 years have witnessed a significant increase in the attrition rate of new candidate drugs due to their low efficacy and serious side effects [17, 29]. One fundamental reason for the decline in the productivity of the pharmaceutical industry may lie in the core philosophy of the “one drug – one target” approach . Specifically, this philosophy ignores the fact that disease occurrence is usually the result of an extremely complex combination of molecular events  among certain sets of functionally related genes, usually referred to as “pathways”. Targeting an individual drug target may not provide sufficient interference to the whole disease-related pathway and therefore usually results in unsatisfactory efficacy. Moreover, it fails to consider the mechanism of a candidate drug at a systems level, making it extremely difficult to evaluate drug safety and toxicity in the early developmental stages . Due to these limitations of the “one drug – one target” approach, a new concept of drug discovery – polypharmacology  – is emerging as a promising alternative for drug developments. Instead of targeting individual drug targets, polypharmacology seeks to design or find candidate drugs that interfere multiple molecular targets. For example, pathway-based drug discovery, which pursues candidate drugs that interfere the activity of a whole biological pathway, has become increasingly appealing.
In this paper, we revisit the Nested Stochastic Simulation Algorithm (NSSA) for stochastic chemical reacting networks by first proving its strong convergence. We then study a speed up of the algorithm by using the explicit Tau-Leaping method as the Inner solver to approximate invariant measures of fast processes, for which strong error estimates can also be obtained. Numerical experiments are presented to demonstrate the validity of our analysis.
The common serial robot or parallel robot is difficult to implement for CT-guided surgery in a limited workspace. A novel hybrid robot with 9 degrees of freedom is presented in this paper, whose detailed structure is analysed based on screw theory and displacement manifold (DM). The dexterity of the hybrid robot is provided in terms of Riemann manifold (RM). Besides, DICOM (digital imaging communications in medicine) image processing, spatial registration and 3D dynamic reconstruction in the operation planning subsystem are analysed, in which some innovative methods are introduced. Meanwhile, the architecture of the CT-guided hybrid robot system and its subsystems are proposed. Simulative clinical experiment showed that the locating precision of the hybrid robot reaches 1.08 mm, which can meet the requirement of CT-guided surgery.
HbUEP, an ubiquitin extension protein gene from latex of the rubber tree (Hevea brasiliensis) was cloned and sequenced using a differentially ethphon-induced expressed cDNA subtraction library. The cDNA had 771 bp nucleotides, comprising a 226 bp 3′ untranslated region (UTR), 77 bp 5′UTR and a 468 bp open reading frame encoding a 156 amino acid peptide. Southern blotting analysis showed that this gene was a low copy number gene in the H. brasiliensis genome. Within 24 h after application of ethphon, the gene was expressed weakly in both control and latex sampled at 6 h, and strongly in latex sampled at 12 h, showing that this gene expression could be regulated by ethphon. Ethphon could increase the latex yield in H. brasiliensis. It is suggested that the HbUEP gene may be involved in the regulation of ethphon-induced high latex yield in H. brasiliensis.
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