A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible

Evolution of Maximum Bending Strain on Poisson's Ratio Distribution

In recent years, new flexible functional materials have attracted increasing interest, but there is a lack of the designing mechanisms of flexibility design with superstructures. In traditional engineering mechanics, the maximum bending strain (MBS) was considered universal for describing the bendable properties of a given material, leading to the universal designing method of lowering the dimension such as thin membranes designed flexible functional materials.In this work, the MBS was found only applicable for materials with uniformly distributed Poisson's ratio, while the MBS increases with the thickness of the given material in case there is a variation Poisson's ratio in different areas. This means the MBS can be enhanced by certain Poisson's ratio design in the future to achieve better flexibility of thick materials. Here, the inorganic freestanding nanofiber membranes, which have a nonconstant Poisson's ratio response on stress/strain for creating nonuniformly distributed Poisson's ratio were proven applicable for designing larger MBS and lower Young's modulus for thicker samples

14-3-3ζ Interacts with Stat3 and Regulates Its Constitutive Activation in Multiple Myeloma Cells

The 14-3-3 proteins are a family of regulatory signaling molecules that interact with other proteins in a phosphorylation-dependent manner and function as adapter or scaffold proteins in signal transduction pathways. One family member, 14-3-3ζ, is believed to function in cell signaling, cycle control, and apoptotic death. A systematic proteomic analysis done in our laboratory has identified signal transducers and activators of transcription 3 (Stat3) as a novel 14-3-3ζ interacting protein. Following our initial finding, in this study, we provide evidence that 14-3-3ζ interacts physically with Stat3. We further demonstrate that phosphorylation of Stat3 at Ser727 is vital for 14-3-3ζ interaction and mutation of Ser727 to Alanine abolished 14-3-3ζ/Stat3 association. Inhibition of 14-3-3ζ protein expression in U266 cells inhibited Stat3 Ser727 phosphorylation and nuclear translocation, and decreased both Stat3 DNA binding and transcriptional activity. Moreover, 14-3-3ζ is involved in the regulation of protein kinase C (PKC) activity and 14-3-3ζ binding to Stat3 protects Ser727 dephosphorylation from protein phosphatase 2A (PP2A). Taken together, our findings support the model that multiple signaling events impinge on Stat3 and that 14-3-3ζ serves as an essential coordinator for different pathways to regulate Stat3 activation and function in MM cells

Measurement and spatio-temporal heterogeneity analysis of the coupling coordinated development among the digital economy, technological innovation and ecological environment

In-depth exploration of the coupled and coordinated relationship among regional digital economy (DE), technological innovation (TI) and ecological environment (EE) is a vivid embodiment of implementing sustainable development. In order to show the spatiotemporal features of the coupling coordination degree (CCD) from 2011 to 2020, this study builds an evaluation index system from three target levels, namely, the digital economy DE, science and technology innovation TE, and ecological environment EE. Based on this, global and local spatial econometric models, namely the global Moran's I index and the spatio-temporal geographically weighted regression (GTWR) model, are used to identify the spatio-temporal heterogeneity features of each explanatory variable on the CCD. The study results include: (1) The comprehensive evaluation index shows a rising trend, but the development is uneven among systems. (2) The CCD continues to rise steadily, and the regional disparity is widening; the transformation from the near-disorder level to the primary coordination level is realized over the research period. Spatially, the coupling coordination is higher in the eastern and southern regions, while the western and northern regions are relatively low. (3) The GTWR model demonstrates that human capital, urbanization rate, and openness to the outside world promote the CCD. In contrast, the social unemployment rate inhibits CCD, among which human capital is the main force behind coupled and coordinated development

Current Therapeutic Strategies for Metastatic Triple-Negative Breast Cancer: From Pharmacists’ Perspective

Triple-negative breast cancer (TNBC) is characterized by its high invasiveness, high metastasis and poor prognosis. More than one-third of patients with TNBC will present with recurrence or distant metastasis. Chemotherapy based on anthracyclines and taxanes is the standard treatment strategy for metastatic TNBC (mTNBC). Due to the lack of expression of progesterone receptor, estrogen receptor, and human epidermal growth factor receptor 2, therapies targeting these receptors are ineffective for mTNBC, thus special treatment strategies are required. In recent years, the development of new chemotherapy drugs, targeted drugs and immunotherapy drugs offers good prospects for the treatment of mTNBC. However, as these drugs are still in their infancy, several problems regarding the optimization and management of the clinical application of these new options should be considered. Pharmacists can play an important role in drug selection, drug therapy management, the management of adverse drug reactions and pharmacoeconomic evaluation. In this review, we summarized traditional treatment strategies, and discussed the efficacy and safety of novel agents approved in the last ten years and combination regimens for mTNBC, with the aim of providing management strategies for the clinical management of mTNBC from pharmacists’ perspective

Dual Temporal Scale Convolutional Neural Network for MicroExpression Recognition

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of streamof DTSCNN is used to adapt to different frame rate ofmicro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve