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A pre-formed Meisenheimer complex of a naphthalenediimide (NDI) with tetrabutylammonium fluoride (TBAF) is obtained in a simple way by mixing dibrominated 4,9-dibromo-2,7-bis(2-octyldodecyl)benzo[lmn][3,8]phenanthroline-1,3,6,8(2H,7H)-tetraone and TBAF in solution and used as a dopant for n-type organic thermoelectrics. Two n-type polymers PNDIClTVT and PBDOPVTT are synthesized, n-doped, and characterized as conductive and thermoelectric materials. PNDIClTVT doped with NDI-TBAF presents a high σ value of 0.20 S cm–1, a Seebeck coefficient (S) of −1854 µV K–1, and a power factor (PF) of 67 µW m–1 K–2, among the highest reported PF in solution-processed conjugated n-type polymer thermoelectrics. Using 4-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)phenyl)dimethylamine and NDI-TBAF as co-dopants, PNDIClTVT has a PF > 35µW m–1 K–2; while for PBDOPVTT σ= 0.75 S cm–1 and PF = 58 µW m–1 K–2. In this study it is found that an ionic adduct together with a neutral dopant improves the performance of n-type organic thermoelectrics leading to an enhanced power factor, and more generally, the role of such an adduct in polymer doping is also elucidated.

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is a regulatory limit for the licensing of pressurized water reactors (PWRs) worldwide. Despite the abundance of predictive tools available to the reactor thermal-hydraulics community, the path for an accurate CHF model remains elusive. This work approaches the prediction of DNB through a physics-informed machine learning-aided framework (PIMLAF) with the objective of achieving superior predictive capabilities for a rod bundle. In view of the limitations in existing macro-scale physics-driven tools, an improved mechanistic model is first proposed, leveraging key concepts in the liquid sublayer dryout and bubble crowding mechanisms. The proposed mechanistic model is able to predict DNB in different heater geometries for a broad range of flow conditions without the need for recalibration. This model is then incorporated as the physics-informed component of the hybrid framework PIMLAF, which takes advantage of established understanding in the field (i.e., domain knowledge [DK]) and uses machine learning (ML) to capture undiscovered information from the mismatch between the actual and DK-predicted output. Two bundle-related case studies using the PWR subchannel and bundle tests (PSBT) database are carried out to illustrate the PIMLAF’s improved performance over traditional approaches for both interpolation and extrapolation purposes. In light of the PIMLAF’s promising potential to reduce prediction error, reactor vendors are encouraged to leverage their in-house experimental efforts and apply the hybrid framework to potentially achieve margin reductions in the minimum DNB ratio (MDNBR) for the designs of interest.

In some d-electron oxides the measured effective mass m(exptl) has long been known to be significantly larger than the model effective mass m(model) deduced from mean-field band theory, i.e., m(exptl) = beta * m(model), where beta > 1 is the "mass enhancement", or "mass renormalization" factor. Previous applications of density functional theory (DFT), based on the smallest number of possible magnetic, orbital, and structural degrees of freedom, missed such mass enhancement, a fact that was taken as evidence of strong electronic correlation being the exclusive enabling physics. The current paper reports that known modalities of energy-lowering symmetry-broken spin and structural effects included in mean-field DFT show mass enhancement for both electrons and holes in a range of d-electron perovskites SrVO3, SrTiO3, BaTiO3, and LaMnO3 as well as p-electron perovskites CsPbI3 and SrBiO3, including metals (SrVO3) and insulators (the rest). This is revealed only when enlarged unit cells of the same parent global symmetry, which are large enough to allow for symmetry breaking distortions and concomitant variations in spin order, are explored for their ability to lower the total energy. The paper analyzes the contributions of different symmetry-broken modalities to mass enhancement, finds common effects in the range of d- as well as p-electron perovskites. Thus, symmetry breaking in mean-field theory could describe effects that were previously attributed exclusively to complex correlated treatments. Indeed, broken symmetries are often experimentally observed, e.g., octahedral tilting, Jahn-Teller distortions, or bond disproportionation, and often provide intuitive explanations in terms of degeneracy removal due to lowering of structural symmetry, or as shifting of band energies in explainable directions.

Trove of exotic topoloid structures has recently been predicted by searching for compounds whose calculated band structure crossing points fulfill specific symmetry requirements. Discovery of exciting physical phenomena by experimental studies of such predicted compounds is just around the corner. Yet, the examination of some of these assumed high-symmetry structures suggests that not always will assembly of atoms in a configuration that yields exotic topological properties be protected against energy-lowering symmetry breaking modes. Indeed, although bulk topological characteristics lead to protected surface/edge states, nothing protects bulk states from structural instability. The burden of proof for theoretical predictions of exciting physical phenomena should include some compelling hints that such phenomena can live in thermodynamically stable (or near stable) compounds. Herein, we illustrate how the use of the calculated total (electron + ion) energies of candidate structures can remove false-positive predicted topoloids from the list of likely realizable compounds, to the benefit of the much-cherished iterative process of theory-experiment materials discovery., 16 pages, 5 figures, 64 references

Food safety supervision mechanism is a strong guarantee to promote the smooth implementation of China's food safety laws and regulations, and it is implemented through legal, administrative, economic, moral and other integrated policy instruments, as well as media publicity, quality traceability, network tracking, information disclosure and other non-administrative means. Along with strengthening supervision and control means, the people's food safety in China is safeguarded, and the healthy development of the food industry is promoted.

The long training time is a big problem that block the application of brain computer interface. This paper solve this problem using the existing dataset of many subjects. By analyzing the differences in EEG signal among different users, removing these differences as much as possible and exploits the common points of different individual, the proposed method can correct the data in existing dataset to a new dataset close to the target data. Then using the ensemble method, we combine the model of the existing dataset to a final model. The result shows that the proposed method can decrease the training time greatly while the recognize accuracy can also meet the need.

Severe acute respiratory syndrome (SARS) is an acute infectious disease with significant mortality. A novel coronavirus (SARS-CoV) has been shown to be the causative agent of SARS. The typical clinical feature associated with SARS is diffuse alveolar damage in lung, and lung fibrosis is evident in patients who died from this disease. The mechanisms by which SARS-CoV infection causes lung fibrosis are not fully understood, but transforming growth factor-β (TGF-β) and angiotensin-converting enzyme 2 (ACE2)-mediated lung fibrosis are among the most documented ones. The activation of the TGF-β/Smad pathway is critical to lung fibrosis. SARS-CoV infection not only enhances the expression of TGF-β, but also facilitates its signaling activity. The SARS-CoV receptor ACE2 is a negative regulator of lung fibrosis, and SARS-CoV infection decreases ACE2 expression. Therefore, SARS-CoV infection may lead to lung fibrosis through multiple signaling pathways and TGF-β activation is one of the major contributors.

Accurately identifying human's intent of motion from electromyography (EMG) signals is the key to implement EMG-based HRI (Human-Robot Interface) systems. Human's intent of motion includes motion modes and continuous movement variables. In this paper, a hybrid EMG-to-motion model is constructed by combining a classification model and a regression model. Based on a proper division for joints, the classification model is utilized to recognize the motion modes of 'small' joints; meanwhile, the regression model is utilized to estimate the continuous movement variables of 'big' joints. Furthermore, a Bayesian network (BN) model, which sufficiently employs context information of a task, is also involved into the hybrid model to improve its performances for motion estimation. Experiments have been conducted with three subjects to demonstrate the feasibility of the proposed methods. In these experiments, the motion modes of hand and wrist, and the continuous elbow angles are estimated with sEMG signals considering a 'drinking' task. Finally, an upper limb prosthetic is controlled to simulate human's movement in a 'drinking' task. c 2014 IEEE.

An upper extremity motor function mirror rehabilitation method based on Functional Electrical Stimulation (FES) was proposed to provide subject-oriented rehabilitation training for hemiplegic patients with upper limb motor dysfunction. Firstly, recognize the motion pattern of the healthy side by Microsoft Kinect. The motion pattern consists of three kind of mode, i.e. slow speed mode, medium speed mode, fast speed mode. Secondly, the paralyzed upper joints were controlled to track the pattern of the healthy side by stimulating appropriate muscles. The stimulating intensity, i.e. the width of the current pulse, is controlled with iterative learning control method. Experiments on healthy subject indicated that this rehabilitation system could help post-stroke patients conduct customized subject-oriented training effectively. c 2014 IEEE.

A method for varying the spindle speed using chaotic signal to suppress chatter in machining is developed. The effects of spindle speed variation on cutting chatter control between using chaotic and sinusoidal signals were analyzed by simulation and experimental methods. Various chaotic codes are used in the chatter suppression process, and it is found that LORENZ-1 code results in the smallest machine noise.

A new linear programming obstacle-avoided model is presented in target pursuit and obstacle-avoided problem. The obstacle-avoided model only has the linear program once for obstacle avoidance at one time step. The collision forecasting domain of vehicle and obstacle is obtained, which can prevent the happening of collision between vehicle and obstacle. In the target pursuit problem, the dynamic equation of mass point with linear damping is taken as the state equation of vehicle's motion. “Expansible Target Size” is proposed to describe the pursuit process for target. The Linear programming method is used to model and solve the obstacle-avoided pursuit problem. Finally, simulation is given to show the efficiency of the method.

An autonomous learning framework for UAV(unmanned aerial vehicle) 3D path planning is proposed.This framework consists of three parts,i.e.knowledge learning,knowledge retrieving and updating online.In this framework,the control value will be retrieved firstly from the existed knowledge when UAV runs online,so as the current action of the robot can be guided by the results.If the decisions retrieved from the knowledge base are invalid for the current UAV states,the custom algorithm for UAV path planning will be launched online and it generates the decisions for UAV’s movement in real time.In the meanwhile,the knowledge library is updated by adding the new decisions for the current states.Additionally,the knowledge library is constructed by the algorithm of incremental hierarchical discriminant regression(IHDR) and k-D tree,respectively.Among these methods,IHDR can construct a hierarchical tree by using the past path planning samples.By several simulations,IHDR method demonstrates better real time performance than the traditional k-D tree method under the proposed framework.