Neutronics analysis of a subcritical blanket system driven by a gas dynamic trap-based fusion neutron source for ^(99)Mo production

Gamma-emitting radionuclide ^(99m)Tc is globally used for the diagnosis of various pathological conditions owing to its ideal single-photon emission computed tomography (SPECT) characteristics.However,the short half-life of ^(99m)Tc (T_(1/2)=6 h)makes it difficult to store or transport.Thus,the production of ^(99m)Tc is tied to its parent radionuclide ^(99)Mo (T_(1/2)=66 h).The major production paths are based on accelerators and research reactors.The reactor process presents the potential for nuclear proliferation owing to its use of highly enriched uranium (HEU).Accelerator-based methods tend to use deuterium–tritium(D–T) neutron sources but are hindered by the high cost of tritium and its challenging operation.In this study,a new ^(99)Mo production design was developed based on a deuterium–deuterium (D–D) gas dynamic trap fusion neutron source (GDT-FNS) and a subcritical blanket system (SBS) assembly with a low-enriched uranium (LEU) solution.GDT-FNS can provide a relatively high-neutron intensity,which is one of the advantages of ^(99)Mo production.We provide a Monte Carlo-based neutronics analysis covering the calculation of the subcritical multiplication factor (k_(s)) of the SBS,optimization design for the reflector,shielding layer,and ^(99)Mo production capacity.Other calculations,including the neutron flux and nuclear heating distributions,are also provided for an overall evaluation of the production system.The results demonstrated that the SBS meets the nuclear critical safety design requirement (k_(s)<0.97) and maintained a high ^(99)Mo production capacity.The proposed system can generate approximately 157 Ci ^(99)Mo for a stable 24 h operation with a neutron intensity of 1×10^(14) n/s,which can meet 50%of China’s demand in 2025.

Vision‐audio fusion SLAM in dynamic environments

Moving humans,agents,and subjects bring many challenges to robot self‐localisation and environment perception.To adapt to dynamic environments,SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles.However,these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots.In the current industrial environment,mobile robot collaboration scenario,the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms,but the distance estimation of sound sources is difficult.However,in the field of visual perception,the 3D structure information of the scene is relatively easy to obtain,but the recognition and segmentation of moving objects is more difficult.To address these problems,a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed,thereby eliminating the effect of dynamic obstacles on multi‐agent systems.Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Dynamic weighted voting for multiple classifier fusion:a generalized rough set method

To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.

Is Dynesys dynamic stabilization system superior to posterior lumbar fusion in the treatment of lumbar degenerative diseases?

Dynesys,a pedicle-based dynamic stabilization system,was introduced to overcome some undesirable complications of fusion procedures.Nevertheless,the theoretical advantages of Dynesys over fusion have not been clearly confirmed.The purpose of this editorial was to compare clinical and radiological outcomes of patients who underwent Dynesys system with those who underwent posterior lumbar fusion according to the existing literature and to see if the application of the Dynesys system is superior to the traditional lumbar fusion surgery.According to published clinical reports,the short-term effects of the Dynesys dynamic stabilization system are similar to that of traditional lumbar fusion surgery.Three comparative studies of Dynesys dynamic stabilization and fusion surgery with medium-term follow-up are encouraging.However,the results from four single-treatment-arm and small-sample studies of case series with long-term follow-up were not encouraging.In the present circumstances,it is not possible to conclude that the Dynesys dynamic stabilization system is superior to fusion surgery for lumbar degenerative diseases.

Enhanced sampling for lipid-protein interactions during membrane dynamics

The inflexible concept of membrane curvature as an independent property of lipid structures is today obsolete.Lipid bilayers behave as many-body entities with emergent properties that depend on their interactions with the environment.In particular,proteins exert crucial actions on lipid molecules that ultimately condition the collective properties of the membranes.In this review,the potential of enhanced molecular dynamics to address cell-biology problems is discussed.The cases of membrane deformation,membrane fusion,and the fusion pore are analyzed from the perspective of the dimensionality reduction by collective variables.Coupled lipid-protein interactions as fundamental determinants of large membrane remodeling events are also commented.Finally,novel strategies merging cell biology and physics are considered as future lines of research.

Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation.Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition,but the networks are affected by much interference information in the input data and thus fit to some unimportant features.In this paper,we proposed a novel method for encoding spatio-temporal information,which can enhance the key features required for gesture recognition,such as shape,structure,contour,position and hand motion of gestures,thereby improving the accuracy of gesture recognition.This encoding method can encode arbitrarily multiple frames of gesture data into a single frame of the spatio-temporal feature map and use the spatio-temporal feature map as the input to the neural network.This can guide the model to fit important features while avoiding the use of complex recurrent network structures to extract temporal features.In addition,we designed two sub-networks and trained the model using a sub-network pre-training strategy that trains the sub-networks first and then the entire network,so as to avoid the subnetworks focusing too much on the information of a single category feature and being overly influenced by each other’s features.Experimental results on two public gesture datasets show that the proposed spatio-temporal information encoding method achieves advanced accuracy.

Physics Analysis and Optimization Studies for a Fusion Neutron Source Based on a Gas Dynamic Trap

To further investigate the fusion neutron source based on a gas dynamic trap （GDT）, characteristics of the GDT were analyzed and physics analyses were made for a fusion neutron source based on the GDT concept. The prior design of a GDT-based fusion neutron source was optimized based on a refreshed understanding of GDT operation. A two-step progressive development route of a GDT-based fusion neutron source was suggested. Potential applications of GDT are discussed. Preliminary analyses show that a fusion neutron source based on the GDT concept is suitable for plasma-material interaction research, fusion material and subcomponent testing, and capable of driving a proof-of-principle fusion fission hybrid experimental facility.

Evolutionary dynamics analysis of complex network with fusion nodes and overlap edges

Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.

A framework of region-based dynamic image fusion

A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions. Then different fusion rules are employed in different regions so that the target information is preserved as much as possible. In addition, steerable non-separable wavelet frame transform is used in the process of multi-resolution analysis, so the system achieves favorable characters of orientation and invariant shift. Compared with other image fusion methods, experimental results showed that the proposed method has better capabilities of target recognition and preserves clear background information.

Dynamic Audio-Visual Biometric Fusion for Person Recognition

Biometric recognition refers to the process of recognizing a person’s identity using physiological or behavioral modalities,such as face,voice,fingerprint,gait,etc.Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems,or jointly with two or more as in multimodal systems.However,multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels.Despite this enhancement,in real-life applications some factors degrade multimodal systems’performance,such as occlusion,face poses,and noise in voice data.In this paper,we propose two algorithms that effectively apply dynamic fusion at feature level based on the data quality of multimodal biometrics.The proposed algorithms attempt to minimize the negative influence of confusing and low-quality features by either exclusion or weight reduction to achieve better recognition performance.The proposed dynamic fusion was achieved using face and voice biometrics,where face features were extracted using principal component analysis(PCA),and Gabor filters separately,whilst voice features were extracted using Mel-Frequency Cepstral Coefficients(MFCCs).Here,the facial data quality assessment of face images is mainly based on the existence of occlusion,whereas the assessment of voice data quality is substantially based on the calculation of signal to noise ratio(SNR)as per the existence of noise.To evaluate the performance of the proposed algorithms,several experiments were conducted using two combinations of three different databases,AR database,and the extended Yale Face Database B for face images,in addition to VOiCES database for voice data.The obtained results show that both proposed dynamic fusion algorithms attain improved performance and offer more advantages in identification and verification over not only the standard unimodal algorithms but also the multimodal algorithms using standard fusion methods.

Multi-exposure fusion for high dynamic range scene

Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.

High Dynamic Range Image Fusion Based on Wavelet

With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.

Outlier-resistant distributed fusion filtering for nonlinear discrete-time singular systems under a dynamic event-triggered scheme

This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is used.Moreover,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data transmission.For the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter gains.Furthermore,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule.As such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation performance.Moreover,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented.Finally,the validity of the developed algorithm is checked using a simulation example.

Theoretical and numerical research of wire array Z-pinch and dynamic hohlraum at IAPCM

Dense Z-pinch plasmas are powerful and energy-efficient laboratory sources of X-rays,and show the possibility to drive inertial confinement fusion(ICF).Recent advances in wire-array Z-pinch and Z-pinch dynamic hohlraum(ZPDH)researches at the Institute of Applied Physics and Computational Mathematics are presented in this paper.Models are setup to study different physical processes.A full circuit model(FCM)was used to study the coupling between Z-pinch implosion and generator discharge.A mass injection model with azimuthal modulation was setup to simulate the wire-array plasma initiation,and the two-dimensional MHD code MARED was developed to investigate the Z-pinch implosion,MRT instability,stagnation and radiation.Implosions of nested and quasi-spherical wire arrays were also investigated theoretically and numerically.Key processes of ZPDH,such as the arrayefoam interaction,formation of the hohlraum radiation,as well as the following capsule ablation and implosion,were analyzed with different radiation magneto-hydrodynamics(RMHD)codes.An integrated 2D RMHD simulation of dynamic hohlraum driven capsule implosion provides us the physical insights of wire-array plasma acceleration,shock generation and propagation,hohlraum formation,radiation ablation,and fuel compression.

Rescue axonal defects by targeting mitochondrial dynamics in hereditary spastic paraplegias

Impaired axonal development and degeneration underlie debilitating neurodegenerative diseases including hereditary spastic paraplegia, a large group of inherited diseases. Hereditary spastic paraplegia is caused by retrograde degeneration of the long corticospinal tract axons, leading to progressive spasticity and weakness of leg and hip muscles. There are over 70 subtypes with various underlying pathophysiological processes, such as defective vesicular trafficking, lipid metabolism, organelle shaping, axonal transport, and mitochondrial dysfunction. Although hereditary spastic paraplegia consists of various subtypes with different pathological characteristics, defects in mitochondrial morphology and function emerge as one of the common cellular themes in hereditary spastic paraplegia. Mitochondrial morphology and function are remodeled by mitochondrial dynamics regulated by several key fission and fusion mediators. However, the role of mitochondrial dynamics in axonal defects of hereditary spastic paraplegia remains largely unknown. Recently, studies reported perturbed mitochondrial morphology in hereditary spastic paraplegia neurons. Moreover, downregulation of mitochondrial fission regulator dynamin-related protein 1, both pharmacologically and genetically, could rescue axonal outgrowth defects in hereditary spastic paraplegia neurons, providing a potential therapeutic target for treating these hereditary spastic paraplegia. This mini-review will describe the regulation of mitochondrial fission/fusion, the link between mitochondrial dynamics and axonal defects, and the recent progress on the role of mitochondrial dynamics in axonal defects of hereditary spastic paraplegia.

Theory of Dynamic Diagnosis Based on Integrated Maintenance Information

Based on the concept of an integrated maintenance information system and related information environment, this paper discusses the process of troubleshooting in modern maintenance in detail, and gives a model of dynamic diagnosis of faults, in which a reasoning program is designed through taking advantage of information fusion and time analysis. In the end, the authors present the logic process of dynamic diagnosis through a typical example, and proposes a dynamic diagnostic system based on information fusion.

Dynamic Modeling of Heterogeneous Spatial Analysis for Energy and Natural Resources in Grid Integration Environment

With the high-tech industrialization of earth observation satellite remote sensing and the implementation of digital earth strategy,the energy and natural resources have been decided to be the key research fields in China.In these fields,from the model based on topology data,through simple feature data model to rule-based data model,the basic spatial analysis algorithms have been developed

Dynamic Interaction Confinement

The importance of developing new technologies to obtain energy by means of nuclear fusion procedures is beyond question. There are several different and technically possible models for doing this, though to date none of these has been able to attain an industrial reactor with an end performance greater than unity. We still find ourselves at the initial phase, after many years, as a result of having failed as yet to come up with a commercially productive machine. Nuclear fusion research has defined a prototype reactor based on a fluid conductor, isolated materially in a physical container and confined by means of magnetic fields. In this fluid-plasma which interacts with magnetic fields, fusion reactions are caused that release energy, while at the same time a quantity of movement and angular momentum is moved or “rotated” and transported. However, turbulence is caused in these magnetic confinement fusion processes that reduces system efficiency and prevents the obtaining of sufficient net energy from the nuclear reactions. This paper aims to propose new dynamic hypotheses to enhance our understanding of the behaviour of the plasma in the reactor. In doing so, we put forward a profound revision of classical dynamics. After over thirty years studying rotational dynamics, we propose a new theory of dynamic interactions to better interpret nature in rotation. This new theory has been tested experimentally returning positive results, even by third parties. We suggest that these new dynamic hypotheses, which we hold applicable to particle systems accelerated by rotation, be used in the interpretation and design of fusion reactors. We believe that this proposal could, in addition to magnetic confinement, achieve confinement by simultaneous and compatible dynamic interaction. Accordingly, we are of the opinion that it would be possible to get better performance and results in the design of fusion reactors by way of simultaneous magnetic and dynamic interaction confinement.

Flame Recognition in Video Images with Color and Dynamic Features of Flames

Recently,video based flame detection has become an important approach for early detection of fire under complex circumstances.However,the detection accuracy of most existing methods remains unsatisfactory.In this paper,we develop a new algorithm that can significantly improve the accuracy of flame detection in video images.The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model.A few new dynamic and hierarchical features associated with the suspected regions,including the flicker frequency of flames,are then extracted and analyzed.The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a classifier,which can be a BP neural network,a k nearest neighbor classifier or a support vector machine.Testing results show that this algorithm is robust and efficient,and is able to significantly reduce the probability of false alarms.

运动调控线粒体动力学变化的研究进展

背景:线粒体质量控制是一个复杂的过程,该过程涉及到线粒体生物发生、线粒体动力学变化、线粒体自噬三个方面,其中线粒体动力学变化是线粒体生物发生和线粒体自噬的中间环节,线粒体通过动力学变化改善自身质量控制,进而维持线粒体稳定状态。目的:探究运动影响线粒体动力学变化的分子机制,为运动改善线粒体网络稳态,进而促进功能健康提供理论依据。方法:利用文献资料法,以“运动,线粒体稳态,线粒体质量控制,线粒体动力学,线粒体融合,线粒体分裂”等为中文关键词检索中国知网(CNKI),百链云图书馆等中文数据库;以“Exercise,Mitochondrial homeostasis,Mitochondrial quality control,Mitochondrial dynamics,Mitochondrial fusion,Mitochondrial fission”等为英文关键词检索PubMed、Web of Science、EBCSO等英文数据库,对最终获取的文献进行筛选、阅读、归纳和总结。结果与结论:线粒体动力相关蛋白1/2(Drp1/2)负责线粒体分裂过程,线粒体融合蛋白1/2(Mfn1/2)以及视神经萎缩1(Opa1)分别介导线粒体外膜和内膜的融合。运动训练可以通过上调Mfn1/2和Opa1的蛋白表达,同时下调Drp1蛋白表达水平,促进线粒体融合过程,并抑制线粒体分裂来改善线粒体功能状态;一次急性运动影响线粒体动力学变化的研究结论存在争议;另外,运动介导线粒体动力学变化存在组织差异性。