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.

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.

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.

This paper introduces a discrete time model for time-variant delays and investigates the very nature of such delays. It is shown that a linear system-delay interface is a system theoretic necessity for the construction of composite linear systems with time-variant delays. Based on this analysis, two interfaces of particular importance are presented and used to obtain new, simple to check stability results for queue control systems. The relevance of the presented modeling and stability results on queue control systems to QoS control in modern communication networks is illustrated via several examples.

The main function of the on-load tap changing (OLTC) regulators consists of maintaining a constant voltage in order to feed critical loads despite the load changes or voltage changes in the ac mains. The traditional regulators are still used nowadays, but they present several disadvantages, like a slow response, which reaches from 100 ms to several seconds. These drawbacks can be overcome if the OLTC regulators would have shorter response time, commuting several times every cycle of the mains. There are two basic topologies for fast OLTC regulators. The first one consists of several taps and uses hard switching. The second one consists of two main switches commuting at high frequency, using soft-switching in order to reduce the power losses. The present topology is of the second type. This paper presents a mathematical model of the power stage of the proposed regulator. The model includes the parasitic resistances and the leakage inductances in order to obtain a better comprehension of the regulator operation. A parametric analysis has been done in order to observe the influence of the parasitic elements in the performance of the main parameters of the topology. The model is verified by experimental results obtained using a 500-W prototype.

In this paper, a software toolchain is presented for the fully automatic alignment of a 3D human face model. Beginning from a point cloud of a human head (previously segmented from its background), pose normalization is obtained using an innovative and purely geometrical approach. In order to solve the six degrees of freedom raised by this problem, we first exploit the human face's natural mirror symmetry; secondly, we analyze the frontal profile shape; and finally, we align the model's bounding box according to the position of the tip of the nose. The whole procedure is considered as a two-fold, multivariable optimization problem which is addressed by the use of multi-level, genetic algorithms and a greedy search stage, with the latter being compared against standard PCA. Experiments were conducted utilizing a GavabDB database and took into account proper preprocessing stages for noise filtering and head model reconstruction. Outcome results reveal strong validity in this approach, however, at the price of high computational complexity.

The noise of the current accumulator is analyzed. A model of time-delay-integration (TDI) CMOS image sensor is presented, which is used to analyze the noise performance. In this model, input signals are accumulated four times by the type of current and then converted to digital signals to accomplish the other accumulation by 32 times, i.e., 4 × 32 accumulation mode. The noise, which includes switch charge injection, sample noise and kT/C noise, is considered in this model. The major source of the noise and the relationship between noise and sample capacitance are evaluated through the model simulation. The results indicate that the total noise can be restrained by increasing sample capacitance. When the input signal is arranging from 0 μA to 100 μA, the accuracy of the current accumulator can be 11 bits by using 1 pF sample capacitor. The SNR of the output signal can be increased by 20.38 dB which is close to the ideal result. The circuit of the current accumulator based on the model is also proposed.

It is well known that every sequential element may become metastable when provided with marginal inputs, such as input transitions occurring too close or input voltage not reaching a defined HI or LO level. In this case the sequential element requires extra time to decide which digital output level to finally present, which is perceived as an output delay. The amount of this delay depends on how close the element’s state is to the balance point, at which the delay may, theoretically, become infinite. While metastability can be safely avoided within a closed timing domain, it cannot be completely ruled out at timing domain boundaries. Therefore it is important to quantify its effect. Traditionally this is done by means of a “mean time between upsets” (MTBU) which gives the expected interval between two metastable upsets. The latter is defined as the event of latching the still undecided output of one sequential element by a subsequent one. However, such a definition only makes sense in a time-safe environment like a synchronous design. In this paper we will extend the scope to so-called value-safe environments, in which a sequential element can safely finalize its decision, since the subsequent one waits for completion before capturing its output. Here metastability is not a matter of “failure” but a performance issue, and hence characterization by MTBU is not intuitive. Therefore we will put the focus on the delay aspect and derive a suitable model. This model extends existing approaches by also including the area of very weak metastability and thus providing complete coverage. We will show its validity through comparison with transistor-level simulation results for the most popular sequential elements in different implementations, point out its relation to the traditional MTBU model parameters, namely $\tau$ and $T_0$, and show how to use it for calculating the performance penalty in a value-safe environment.

In this work, a surface potential modeling approach has been proposed to model dual gate, bilayer graphene field effect transistor. The equivalent capacitive network of GFET has been improved considering the quantum capacitance effect for each layer and interlayer capacitances. Surface potentials of both layers are determined analytically from equivalent capacitive network. The explicit expression of drain to source current is established from drift-diffusion transport mechanism using the surface potentials of the layers. The drain current characteristics and transfer characteristics of the developed model shows good agreement with the experimental results in literatures. The small signal parameters of intrinsic graphene transistor i.e., output conductance ($g_{\mathrm{\text{ds}}}$), transconductance ($g_m$), gate to drain capacitance ($C_{\mathrm{\text{gd}}}$) and gate to source capacitance ($C_{\mathrm{\text{gs}}}$) have been derived and finally, the cut-off frequency is determined for the developed model. The model is compared with reported experimental data using Normalized Root Mean Square Error (NRMSE) metric and it shows less than $16\%$ NRMSE. A Verilog-A code has been developed for this model and a single ended frequency doubler has been designed in Cadence Design environment using this Verilog-A model.

To overcome the limitations of mobile devices in executing computing-intensive workloads, mobile edge computing emerged at the times required. It can effectively support computing intensive and delay critical applications executed by Internet of Things devices with limited computing power and energy constraints and becomes the key technology of next-generation networks. This research first determined the optimization framework of the mobile edge computing task unloading system, built a basic platform for mobile edge computing task unloading after system optimization, completed the drill transformation of intelligent algorithm based on the mobile edge computing task unloading algorithm of the optimized system, and studied its convergence and applicability. Finally, by comparing several different mobile edge computing tasks unloading models, Select a suitable mobile edge computing task unloading model, and complete the practical effect test. The results show that: (1) Compared with the traditional unloading mode, the optimized mobile edge computing task unloading system has higher work efficiency; (2) The whole process model of mobile edge computing task unloading completed in this study can reduce the task processing delay in the actual use process; (3) The unloading system with intelligent algorithms is more suitable for edge devices, providing a reference task unloading model for engineering practice.

We propose a method for computing the unavailability of a multistate-component system. This method is based on the construction of a Boolean model of the system, which may be represented in the form of a fault-tree. It is proved that the evaluation is always conservative, and very accurate when the probabilities of the component failures are low.

Many offshore oil and gas installations in the North Sea are approaching the end of their designed lifetimes. Technological improvements and higher oil prices have developed favorable conditions for more oil recovery from these existing installations. However, in most cases, an extended oil production period does not justify investment in new installations. Therefore cost-effective maintenance of the existing platform infrastructure is becoming very important.

In this paper, an inspection frequency optimization model has been developed which can be used effectively by the inspection and maintenance personnel in the industry to estimate the number of inspections/optimum preventive maintenance time required for a degrading component at any age or interval in its lifecycle at a minimum total maintenance cost. The model can help in planning inspections and maintenance intervals for different components of the platform infrastructure. The model has been validated by a case study performed on flowlines installed on the top side of an offshore oil and gas platform in the North Sea. Reliability analysis has been carried out to arrive at the best inspection frequency for the flowline segments under study.

This research attempts to explore the utilization of Gas-Assisted Electrical Discharge Machining (GAEDM) of die steel. High pressure inert gas (argon) in conventional electric discharge machining with constraint state was utilized to assess the surface roughness (SR). Analysis of Variance (ANOVA) was used to find out the process parameters that notably affected the SR. In this study, a mathematical model has been investigated to know the SR by using Buckingham pie-theorem. The fit summary confirmed that the quadratic model is statistically appropriate and the lack of fit is insignificant. Root mean square error and absolute standard deviation, obtained through response surface method (RSM), were also used for developing the model and for predicting its abilities through ANN, ANFIS. The experiment and anticipated estimates of SR during the process, obtained by RSM, dimensional analysis, ANN and ANFIS, were found to be in accord with each other. However, the ANFIS technique proved to be more fitting to the response as compared to the ANN, dimensional analysis and the RSM.

There is a growing body of work on the ex vivo tensile testing of tendon repairs, an appreciable amount of which are performed on cadaveric porcine flexor tendons. However, there is little information in the literature on exactly how to perform the dissections necessary to obtain flexor tendons from porcine trotters. We present a simple method to rapidly harvest tendons from the porcine foot, allowing large amounts of material to be harvested in little time for the purpose of tensile testing of tendon repairs.

Amplified detection of nucleic acid by G-quadruplex based hybridization chain reaction.

Dow opens Photovoltaics Films Application Lab in Shanghai.

Researchers discover molecular mechanisms of left-right asymmetric control in the sea urchin.

China mulls new rule on human genetic research.

China to phase out organ donation from executed criminals.

Charles River Laboratories to expand research models business in China.

Chinese Science Academy Chief urges seizing on new technological revolution.

BGI contributes genome sequencing and bioinformatics expertise.

Taiwan government to encourage formation of smaller biotech funds.

In order to explore the application of IoT technology in robots and the promotion of IoT robot technology to the economy, by comparing traditional technology and IoT intelligent robot technology, this article combines it with economic development to analyze the promotion of IoT robot to economic development. Based on the ultra-wideband ranging method, this paper designs an ultra-wideband radio frequency positioning system and applies it to the robot’s positioning process. Moreover, this article combines the application of robots in the current social and economic development to construct the system structure, and conducts functional analysis with manufacturing robots and monitoring robots as the main body. After constructing an intelligent robot based on the Internet of Things technology, by comparing the traditional technology and the intelligent robot technology of the Internet of Things, this article combines it with economic development to analyze the promotion of IoT robot to economic development. From the analysis results of this article, it can be seen that the advancement of IoT robot technology can effectively promote economic development.