Limit state analysis on the un-repeated multiple selection bounded confidence model

summary:In this paper, we study the opinion evolution over social networks with a bounded confidence rule. Node initial opinions are independently and identically distributed. At each time step, each node reviews the average opinions of several different randomly selected agents and updates its opinion only when the difference between its opinion and the average is below a threshold. First of all, we provide probability bounds of the opinion convergence and the opinion consensus, are both nontrivial events by analyzing the probability distribution of order statistics. Next, similar analyzing methods are used to provide probability bounds when the selection cover all agents. Finally, we simulate all these bounds and find that opinion fluctuations may take place. These results increase to the understanding of the role of bounded confidence in social opinion dynamics, and the possibility of fluctuation reveals that our model has fundamentally changed the behavior of general DeGroot opinion dynamical processes

Highly efficient synthesis of LTA-type aluminophosphate molecular sieve by improved ionothermal method

This work was supported by the National Natural Science Foundation of China (Grant No. 21306072, 21203081) and Development Program of Lanzhou University of Technology for excellent teachers (Grant No. Q201113). WZ thanks EPSRC for financial support to upgrade the SEM facilities (No. EP/F019580/1). We cordially thank the Reviewers and Editors for providing us with valuable comments and suggestions.LTA-type aluminophosphate molecular sieve has been successfully synthesized by improvedionothermal method from a gel containing low-dosage ionic liquids. The optimum syntheticconditions of this material are refined. The resultant LTA molecular sieves were characterized byXRD, SEM, TG-DTA, CHN elemental analysis, solution 13C NMR, EDX, TEM and N2physisorption. The composition of the resulting LTA-type molecular sieves is determined to be(Al12P12O48)(C4H9NO)2(C8H15N2+)2(F-)2, for which morpholine together with1-butyl-3-methylimidazolium cations act as the structure-directing agent. The high zeolite yield, as well as the high specific surface area and micropore volume for the calcined LTA-type materials imply that these zeolitic materials have a high potential in applications.PostprintPeer reviewe

Direct observation of modal hybridization in nanofluidic fiber [Invited]

Hybrid-material optical fibers enhance the capabilities of fiber-optics technologies, extending current functionalities to several emerging application areas. Such platforms rely on the integration of novel materials into the fiber core or cladding, thereby supporting hybrid modes with new characteristics. Here we present experiments that reveal hybrid mode interactions within a doped-core silica fiber containing a central high-index nanofluidic channel. Compared with a standard liquid-filled capillary, calculations predict modes with unique properties emerging as a result of the doped core/cladding interface, possessing a high power fraction inside and outside the nanofluidic channel. Our experiments directly reveal the beating pattern in the fluorescent liquid resulting from the excitation of the first two linearly polarized hybrid modes in this system, being in excellent agreement with theoretical predictions. The efficient excitation and beat of such modes in such an off-resonance situation distinguishes our device from regular directional mode couplers and can benefit applications that demand strong coupling between fundamentaland higher-order- modes, e.g. intermodal third-harmonic generation, bidirectional coupling, and nanofluidic sensing

Parallel Multistage Wide Neural Network

Deep learning networks have achieved great success in many areas such as in large scale image processing. They usually need large computing resources and time, and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gc forest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with RBF networks and knowledge transfer (FDRK). In this paper, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances, and be trained fast in one epoch using subsampling and least squares (LS). Secondly, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data is acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of (1) fast training, (2) optimized computing resources, (3) incremental learning, and (4) parallel testing with stages. The experimental results with the MNIST, a number of large hyperspectral remote sensing data, CVL single digits, SVHN datasets, and audio signal datasets show that the WRBF and PMWNN have the competitive accuracy compared to learning models such as stacked auto encoders, deep belief nets, SVM, MLP, LeNet-5, RBF network, recently proposed CDL, broad learning, gc forest etc. In fact, the PMWNN has often the best classification performance

Controlled formation of gold nanoparticles with tunable plasmonic properties in tellurite glass

Silicate glasses with metallic nanoparticles (NPs) have been of intense interest in art, science and technology as the plasmonic properties of these NPs equip glass with light modulation capability. The so-called striking technique has enabled precise control of the in situ formation of metallic NPs in silicate glasses for applications from coloured glasses to photonic devices. Since tellurite glasses exhibit the unique combination of comparably easy fabrication, low phonon energy, wide transmission window and high solubility of luminescent rare earth ions, there has been a significant amount of work over the past two decades to adapt the striking technique to form gold or silver NPs in tellurite glasses. Despite this effort, the striking technique has remained insufficient for tellurite glasses to form metal NPs suitable for photonic applications. Here, we first uncover the challenges of the traditional striking technique to create gold NPs in tellurite glass. Then, we demonstrate precise control of the size and concentration of gold NPs in tellurite glass by developing new approaches to both steps of the striking technique: a controlled gold crucible corrosion technique to incorporate gold ions in tellurite glass and a glass powder reheating technique to subsequently transform the gold ions to gold NPs. Using the Mie theory, the size, size distribution and concentration of the gold NPs formed in tellurite glass are determined from the plasmonic properties of the NPs. This fundamental research provides guidance for designing and manipulating the plasmonic properties in tellurite glass for photonics research and applications

Numerical study on landslide dynamic process and its impact damage prediction to brick-concrete buildings, a case from Fenghuang street landslide in Shaanxi, China

The study of landslide dynamic process and impact damage has important theoretical and practical significance for landslide risk quantitative assessment. Taking Fenghuang Street landslide in Ningqiang County, Shaanxi Province, China as an example, the dynamic process of landslide and its damage to brick-concrete structure buildings are predicted by using discrete element method. Firstly, a three-dimensional numerical landslide model is established by means of the particle flow code system (PFC3D), which is based on landslide investigation, surveying, engineering exploration and geotechnical testing. Secondly, the whole process of landslide deformation, failure, movement and impact damage was simulated, and the velocity, displacement and impact force of the landslide in the motion process were quantitatively studied. Thirdly, the building model (brick-concrete structure) located at the foot of the landslide was constructed by PFC3D and finite element software (Midas/gen), respectively. The characteristics of deformation and displacement of the buildings after the landslide impact are analyzed, and the impact damage of the landslide is predicted. The results show that the rear edge of Fenghuang Street landslide first deforms and fails, and the leading edge is gradually pushed out. After the locking section of the front edge is broken, the landslide begins to slide as a whole, which is a typical push landslide. The main sliding time of the landslide is about 30 s, the maximum average velocity is 3.2 m/s, and the maximum displacement is about 40 m. After the landslide hits the building, the building is displaced in the moving direction of the landslide, and the wall of the building impacted by the landslide is destroyed, resulting in an collapse evident. The relevant research methodologies and findings in this paper can provide a reference for the risk assessment of the same type of landslides, especially the quantitative assessment of the vulnerability for the brick-concrete buildings at risk

Ultralong Tracking of Fast‐Diffusing Nano‐Objects inside Nanofluidic Channel−Enhanced Microstructured Optical Fiber

Nanoparticle tracking analysis (NTA) represents one essential technology to characterize diffusing nanoscale objects. Herein, uncovering dynamic processes and high-precision measurements requires tracks with thousands of frames to reach high statistical significance, ideally at high frame rates. Optical fibers with nanochannels are used for NTA, successfully demonstrating acquisition of trajectories of fast diffusion nano-objects with 100 000 frames. Due to the spatial limitation of the central nanofluidic channel, diffusion of objects illuminated by the core mode is confined, enabling the recording of Brownian motion over extraordinarily long time scales at high frame rates. The resulting benefits are discussed on a representative track of a gold nanosphere diffusing in water in over nearly 100 000 frames at 2 kHz frame rate. In addition to the verification of the fiber-based NTA using two data processing methods, a segmented analysis reveals a correlation between precision of determined diameter and continuous time interval (i.e., number of frames per subtrajectory). The presented results demonstrate the capabilities of fiber-based NTA in terms of 1) determining diameters with extraordinary high precision of single species and 2) monitoring dynamic processes of the object or the fluidic environment, both of which are relevant within biology, microrheology, and nano-object characterization