Narrow Results

In the force calculation of the Biefeld–Brown Effect, it is not feasible to precisely determine the magnitude of the lift force generated by an asymmetric capacitor in a scenario where its shape is arbitrary. In this paper, first, we deduce a universally applicable formula, it solves the problem of the lift force with uneven charge distribution. Second, by the experimental method of dimensional analysis based on the principle of similarity, we calculate the lift forces of all types of asymmetric capacitors. Finally, we obtain a set of thrust data through experiments, and then fit the set of experimental data through the derived mathematical model and BP neural network, respectively. It confirms the accuracy of the mathematical model.

Data lakes are storage repositories that contain large amounts of data (big data) in its native format; encompassing structured, semi-structured or unstructured. Data lakes are open to a wide range of use cases, such as carrying out advanced analytics and extracting knowledge patterns. However, the sheer dumping of data into a data lake would only lead to a data swamp. To prevent such a situation, enterprises can adopt best practices, among which to manage data lake metadata. A growing body of research has focused on proposing metadata systems and models for data lakes with a special interest on model genericness. However, existing models fail to cover all aspects of a data lake, due to their static modeling approach. Besides, they do not fully cover essential features for an effective metadata management, namely governance, visibility and uniform treatment of data lake concepts. In this paper, we propose a dynamic modeling approach to meet these features, based on two main constructs: data lake concept and data lake relationship. We showcase our approach by Megale, a graph-based metadata system for NoSQL data lake exploration. We present a proof-of-concept implementation of Megale and we show its effectiveness and efficiency in exploring the data lake.

We studied the dynamics of a neural network that has both recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity was sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient signals was represented by the sequence of activity patterns. We then applied this model to represent the trace eyeblink conditioning, which is mediated by the hippocampus. We assumed this model as CA3 of the hippocampus and considered an output neuron corresponding to a neuron in CA1. The activity pattern of the output neuron was similar to that of CA1 neurons during trace eyeblink conditioning, which was experimentally observed.

Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.

In this work, we review the physical properties of organic materials and transistors, discussing especially the charge transport mechanisms. Finally, we present an analytical and continuous charge model for Organic Thin Film Transistors (OTFTs) from which analytical expressions of all the total capacitances are obtained. They are developed and finally written as continuous explicit functions of the applied voltage, resulting in a complete charge-based small-signal model composed by a unified charge control model derived from Poisson equation assuming an exponential density of localized states. This charge model was developed from a previously proposed analytical DC current model assuming a hopping based transport. Therefore our complete small signal model has the potential to be successfully used in circuit simulators for the design of OTFTs.

DC circuit model of TiO₂ memristors is developed based on the reported I-V data. The method described can easily be implemented to realize memristor based circuitries that serve different application platforms fabricated using any material combination. The time varying length of conductive filaments inside memristor, responsible for the observed switching mechanism, is implemented as the state variable and the state equations are modified accordingly. Once the device physics is taken into account the circuit model can be further adapted to predict the behavior of memristor with altered dimensions.

We propose a freezing ferromagnetic moment model to explain the exchange bias discovered in α-Fe₂O₃ antiferromagnetic nanoleaves most recently. The model assumes that in the antiferromagnetic nanosystem, the disorder surface spins are frozen at low temperature along magnetic filed direction, forming a ferromagnetic layer. Then this frozen ferromagnetic surface state couples with the inside antiferromagnetic layers that results in an exchange bias. After some proper simplification, we get the magnetization at different applied field by the minimal energy calculation using MATLAB. Our model computerizing indicates the exchange bias shift of the magnetization curve is -48.2 Oe for n = 100 antiferromagnetic layers, which matches the observed value very well.

Hypernetworks are ubiquitous in real-world systems. They provide a powerful means of accurately depicting networks of different types of entity and will attract more attention from researchers in the future. Most previous hypernetwork research has been focused on the application and modeling of uniform hypernetworks, which are based on uniform hypergraphs. However, random hypernetworks are generally more common, therefore, it is useful to investigate the evolution mechanisms of random hypernetworks. In this paper, we construct three dynamic evolutional models of hypernetworks, namely the equal-probability random hypernetwork model, the Poisson-probability random hypernetwork model and the certain-probability random hypernetwork model. Furthermore, we analyze the hyperdegree distributions of the three models with mean-field theory, and we simulate each model numerically with different parameter values. The simulation results agree well with the results of our theoretical analysis, and the findings indicate that our models could help understand the structure and evolution mechanisms of real systems.

This paper introduces a new framework for the design of parallel algorithms that may be executed on multiprogrammed architectures with variable resources. These features, in combination with an implied ability to handle fault tolerance, facilitates environments such as the GRID. A new model, BSPGRID is presented, which exploits the bulk synchronous paradigm to allow existing algorithms to be easily adapted and used. It models computation, communication, external memory accesses (I/O) and synchronization. By combining the communication and I/O operations BSPGRID allows the easy design of portable algorithms while permitting them to execute on non-dedicated hardware and/or changing resources, which is typical for machines in a GRID. However, even with this degree of dynamicity, the model still offers a simple and tractable cost model. Each program runs in its own virtual BSPGRID machine. Its emulation on a real computer is demonstrated to show the practicality of the framework. A dense matrix multiplication algorithm and its emulation in a multiprogrammed environment is given as an example.

Having its origin in a successful mapping technique, the Fock–Tani formalism, the corrected model retains the basic aspects of the predictions with the inclusion of bound-state corrections. Evaluation of the decay amplitudes has been performed for open-flavor strong decays in the light meson sector. The bound-state corrections introduce a fine-tuning for the former model, in particular, the adjustment of the D/S ratios in b₁→ωπ, a₁→ρπ and h₁→ρπ decays.

We comment on the brane solutions for the boundary model that have been proposed so far and point out that they should be distinguished according to the patterns regular/irregular and discrete/continuous. In the literature, mostly irregular branes have been studied, while results on the regular ones are rare. For all types of branes, there are questions about how a second factorization constraint in the form of a b^-2/2-shift equation can be derived. Here, we assume analyticity of the boundary two-point function, which means that the Cardy–Lewellen constraints remain unweakened. This enables us to derive unambiguously the desired b^-2/2-shift equations. They serve as important additional consistency conditions. For some regular branes, we also derive 1/2-shift equations that were not known previously. Case by case, we discuss possible solutions to the enlarged system of constraints. We find that the well-known irregular continuous AdS₂ branes are consistent with our new factorization constraint. Furthermore, we establish the existence of a new type of brane: the shift equations in a certain regular discrete case possess a nontrivial solution that we write down explicitly. All other types are found to be inconsistent when using our second constraint. We discuss these results in view of the Hosomichi–Ribault proposal and some of our earlier results on the derivation of b^-2/2-shift equations.

Having significantly interacted over 17 years with Abdus Salam, as an undergraduate, postgraduate, postdoc, and eventually as an academic colleague, I will try to paint a personal picture of Salam which may convey something about the man, his greatness and his humanity.

In multi-agent system (MAS), the communication topology of agent network plays a very important role in its collaboration. Small-world networks are the networks with high local clustering and small average path length, and the communication networks of MAS can be analyzed within the frame of small-world topology. Yet the real multiagent communication networks are abundant and the classical WS small-world model is not suitable for all cases. In this paper, two new small-world network models are presented. One is based on random graph substrate and local nodes preference reconnection and the other is based on regular graph substrate and long-range nodes preference reconnection. The characteristic of the network parameter such as the clustering coefficients, average path length, and eigenvalue λ₂ and λ_n of the Laplacian matrix for these two models and WS model is studied. The consensus problem that based on these three models is also studied. An example is given and the conclusions are made in the end.

A mathematical model of vibration power generation (VPG) with the giant magnetostrictive material (GMM) is proposed on the basis of the magneto-mechanical coupling model, Jiles-Atherton model and electromagnetic induction law. According to the model, the output voltage of a giant magnetostrictive power generator has been calculated under the condition of different vibration frequency, pre-stress and bias magnetic field. The calculating results show that the model can reveal the relationship between the input vibrating stress and output voltage. The experiment of a giant magnetostrictive power generator has been carried out, and the experimental results agree well with the calculating results.

In this paper, our focus is on ABA trilayer graphene nanoribbon (TGN), in which the middle layer is horizontally shifted from the top and bottom layers. The conductance model of TGN as a FET channel is presented based on Landauer formula. Besides the good reported agreement with experimental study lending support to our model, the presented model demonstrates that minimum conductivity increases dramatically by temperature. It also draws parallels between TGN and bilayer graphene nanoribbon, in which similar thermal behavior is observed. Maxwell–Boltzmann approximation is employed to form the conductance of TGN near the neutrality point. Analytical model in degenerate regime in comparison with reported data proves that TGN-based transistor will operate in degenerate regime like what we expect in conventional semiconductors. Moreover, our model confirms that in similar condition, the conductivity of TGN is less than bilayer graphene nanoribbon as reported in some experiments.

A new model for efficiency droop in InGaN/GaN light-emitting diodes (LEDs) is proposed, where the primary nonradiative recombination mechanisms, including Shockley–Read–Hall (SRH), Auger and carrier leakage, are considered. A room-temperature external quantum efficiency (EQE) measurement was performed on our designed samples and analyzed by the new model. Owing to advantages over the common “$ABC+f(n)$ model”, the “new model” is able to effectively extract recombination coefficients and calculate the leakage currents of the hole and electron. From this new model, we also found that hole leakage is distinct at low injection, while it disappears at high injection, which is contributed to the weak blocking effect of electron in quantum wells (QWs) at low injection.

An extended lattice hydrodynamic model with time delay is proposed under non-lane discipline. We try to grasp the impacts of the non-lane discipline of the considered lattice sites. Linear stability analysis of the proposed model is executed and the stability criterion is obtained. Using the reductive perturbation method, we investigate nonlinear analysis of the proposed model and derive the mKdV equation and its solution, which could reveal the propagation of density waves. We analyze the effect of time delay, the ratio of lane deviation and the control coefficient on the stability of traffic flow via numerical experiments. We find that those indices play an important role in the stability of traffic flow. The longer the time delay, the more unstable the system becomes. Also, the ratio of lane deviation and the control coefficient is able to more quickly dissipate the traffic congestions occurring in traffic flow.

Aiming at the defects of pronunciation errors and limited collection of pronunciation data resources in traditional artificial neural networks, an English pronunciation judgment and detection model based on deep learning neural networks data stream fusion is proposed. Taking Chinese English pronunciation as the research object, three groups of phonetic data were selected as experimental auxiliary data, based on the convolutional neural network, through the preset reset of the pronunciation detection system of the model, the sampling and recognition extraction of the speech system, the wrong speech detection and the feature analysis of the multi-level data stream tandem, the experiments are carried out with CU-CHLOE language learning database, WSJ1 database and 863 Mandarin database. The experimental results show that the recognition accuracy of this model is higher than that of the traditional neural network model, the accuracy of error type diagnosis is significantly improved, and its noise robustness is the best.

Traditional GPS positioning technology cannot be used in indoor space. With the development of the new positioning technology and the Internet of things, the indoor mobile object positioning and navigation model have been the focus of the relevant research institutions at home and abroad. Based on this, indoor positioning technology was studied starting from Wi-Fi, RFID, and iBeacon technology in this paper. However, the accuracy of indoor positioning and navigation needs to be further improved. This paper presents a semantic space model based on artificial intelligence technology, through semantic pattern matching, semantic concept extension, semantic reasoning and semantic mapping, and interior semantic localization is realized. The indoor semantic network and indoor grid navigation model are constructed, and the indoor semantic path is modeled from time, location, user, and congestion. At the same time, the improved Term Frequency-Inverse Document Frequency is combined with the Hidden Markov Model to improve the accuracy of matching the stay area with the most likely location to visit and improve the accuracy of semantic annotation. It was found that the research on the indoor positioning and navigation model based on the semantic grid can realize the uniform expression of the complex spatial semantics of the theme, geometry, connectivity, and distance, which can promote the development of indoor positioning and navigation.

We propose a method for the 3D segmentation and representation of cortical folds with a special emphasis on the cortical sulci. These cortical structures are represented using "active ribbons". Active ribbons are built from active surfaces, which represent the median surface of a particular sulcus filled by CSF. Sulci modeling is obtained from MRI acquisitions (usually T1 images). The segmentation is performed using an automatic labeling procedure to separate gyri from sulci based on curvature analysis of the different iso-intensity surfaces of the original MRI volume. The outer parts of the sulci are used to initialize the convergence of the active ribbon from the outer parts of the brain to the interior. This procedure has two advantages: first, it permits the labeling of voxels belonging to sulci on the external part of the brain as well as on the inside (which is often the hardest point) and secondly, this segmentation allows 3D visualization of the sulci in the MRI volumetric environment as well as showing the sophisticated shapes of the cortical structures by means of isolated surfaces. Active ribbons can be used to study the complicated shape of the cortical anatomy, to model the variability of these structures in shape and position, to assist nonlinear registrations of human brains by locally controlling the warping procedure, to map brain neurophysiological functions into morphology or even to select the trajectory of an intra-sulci (virtual) endoscope.