Dynamic Stochastic Multi-Criteria Decision Making Method Based on Prospect Theory and Conjoint Analysis

A method based on prospect theory and conjoint analysis is proposed for dynamic stochastic multi-criteria decision making problems, in which the information about criteria weight is unknown and criteria values follow some kinds of distributions. Decision-maker’s attitude towards risk is introduced into this paper. First, data is collected by investigation and criteria weights are derived by conjoint analysis. The prospect values of each alternative in different periods are calculated according to distribution function. Then, index distribution decides time sequence weight, and overall prospect values of each alternative are obtained and ranked by aggregating prospect values in different periods. Finally, an example of choosing the best product illustrates the feasibility and effectiveness of this method

Tracking gate algorithm for general nonlinear systems with target class information

Multitarget tracking in clutter usually involves data association. The traditional method to handle this problem is to construct a tracking gate for predicting the position of the target being tracked, which leads to great uncertainties of measurements-to-tracks association with the unknown class of targets. This paper proposes a new tracking gate algorithm for general nonlinear systems, where the target class information is integrated into our algorithm. Firstly, a joint probability density description of the target state and target class is given, by which the tracking gates for each target class in general nonlinear system are developed. Then, a simulation with ground formation target tracking is carried out to examine our algorithm. Compared with the traditional tracking gate, the results demonstrate that our algorithm has significantly improved the probabilities of the measurements-to-tracks association

An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection

Exploration of the slope effect on the uplift capacity of single straight and belled piles supporting transmission towers

Single piles are normally used to support the transmission tower in mountain areas. Uplift capacity of piles is a key factor in the engineering design to increase the stability of transmission tower foundation. This study numerically investigated the uplift capacity of single straight and belled piles in the sloping ground which consisted of a clay layer underlain by medium weathered sandstone. A non-linear 3D finite element model was proposed to describe the uplift behavior of single piles and was calibrated against a field test on single piles subjected to uplift loading. A parametric study was conducted to investigate the effect of the slope angle (θ) on the uplift behavior of single piles. The uplift capacity decreased as θ increased for either straight piles or belled piles. Moreover, the range of the equivalent plastic strain was greatest for single piles in the level ground. For piles in the sloping ground, the range of equivalent plastic strain was wider at the position of the downstream slope than that at the position of the upstream slope when the uplift load of single piles reached the maximum. As the expansion angle increased to 30° and 45°, the uplift capacity of belled piles (Ru) was increased by 100% and 180% with respect to that of straight piles, respectively. The increase percentage in Ru was independent of θ. A practical method was proposed to quantify the slope effect on Ru

Bearing capacities of single piles under combined HM loading near slopes

Piles are widely used to transfer the horizontal load of high-rise buildings, transmission towers, and bridges, especially for superstructures constructed near slopes. This study investigated bearing capacities of single piles under the combined horizontal force (H) and bending moment (M) for the pile in sloping ground. A 3D finite element model was proposed to simulate the non-linear pile–soil interaction and was verified by a model test. A series of numerical tests were conducted to obtain the failure envelope of bearing capacities of single piles under various combinations of H and M. The existence of slopes significantly reduced the bearing capacity of piles, especially when the horizontal and rotational displacements moved to the dip direction of the slope. An oblique ellipse was able to describe the failure envelope of bearing capacities of single piles near slopes in the HM plane. As the pile was installed away from the crest of the slope, both the width and height of the ellipse increased and the center of the ellipse was approaching the origin. The results of this article can provide useful references for designing horizontally loaded piles near slopes

Characterization of RNA editome in primary and metastatic lung adenocarcinomas

RNA editing results in post-transcriptional modification and could potentially contribute to carcinogenesis. However, RNA editing in advanced lung adenocarcinomas has not yet been studied. Based on whole genome and transcriptome sequencing data, we identified 1,071,296 RNA editing events from matched normal, primary and metastatic samples contributed by 24 lung adenocarcinoma patients, with 91.3% A-to-G editing on average, and found significantly more RNA editing sites in tumors than in normal samples. To investigate cancer relevant editing events, we detected 67,851 hyper-editing sites in primary and 50,480 hyper-editing sites in metastatic samples. 46 genes with hyper-editing in coding regions were found to result in amino acid alterations, while hundreds of hyper-editing events in non-coding regions could modulate splicing or gene expression, including genes related to tumor stage or clinic prognosis. Comparing RNA editome of primary and metastatic samples, we also discovered hyper-edited genes that may promote metastasis development. These findings showed a landscape of RNA editing in matched normal, primary and metastatic tissues of lung adenocarcinomas for the first time and provided new insights to understand the molecular characterization of this disease

Toll-like receptor 9 negatively regulates pancreatic islet beta cell growth and function in a mouse model of type 1 diabetes

Aims/hypothesis Innate immune effectors interact with the environment to contribute to the pathogenesis of the autoimmune disease, type 1 diabetes. Although recent studies have suggested that innate immune Toll-like receptors (TLRs) are involved in tissue development, little is known about the role of TLRs in tissue development, compared with autoimmunity. We aimed to fill the knowledge gap by investigating the role of TLR9 in the development and function of islet beta cells in type 1 diabetes, using NOD mice. Methods We generated Tlr9−/− NOD mice and examined them for type 1 diabetes development and beta cell function, including insulin secretion and glucose tolerance. We assessed islet and beta cell number and characterised CD140a expression on beta cells by flow cytometry. We also tested beta cell function in Tlr9−/− C57BL/6 mice. Finally, we used TLR9 antagonists to block TLR9 signalling in wild-type NOD mice to verify the role of TLR9 in beta cell development and function. Results TLR9 deficiency promoted pancreatic islet development and beta cell differentiation, leading to enhanced glucose tolerance, improved insulin sensitivity and enhanced first-phase insulin secretory response. This was, in part, mediated by upregulation of CD140a (also known as platelet-derived growth factor receptor-α [PDGFRα]). In the absence of TLR9, induced by either genetic targeting or treatment with TLR9 antagonists, which had similar effects on ontogenesis and function of beta cells, NOD mice were protected from diabetes. Conclusions/interpretation Our study links TLR9 and the CD140a pathway in regulating islet beta cell development and function and indicates a potential therapeutic target for diabetes prevention and/or treatment

Cross-session Emotion Recognition by Joint Label-common and Label-specific EEG Features Exploration

Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition in this paper. To be specific, JCSFE imposes the ℓ 2,1 -norm on the projection matrix to explore the label-common EEG features and simultaneously the ℓ 1 -norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e ., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition

Flexible but Refractory Single-Crystalline Hyperbolic Metamaterials

The fabrication of flexible single-crystalline plasmonic or photonic components in a scalable way is fundamentally important to flexible electronic and photonic devices with high speed, high energy efficiency, and high reliability. However, it remains to be a big challenge so far. Here, we have successfully synthesized flexible single-crystalline optical hyperbolic metamaterials by directly depositing refractory nitride superlattices on flexible fluoro phlogopite-mica substrates with magnetron sputtering. Interestingly, these flexible hyperbolic metamaterials show dual-band hyperbolic dispersion of dielectric constants with low dielectric losses and high figure-of-merit in the visible to near-infrared ranges. More importantly, the optical properties of these nitride-based flexible hyperbolic metamaterials show remarkable stability under either heating or bending. Therefore, the strategy developed in this work offers an easy and scalable route to fabricate flexible, high-performance, and refractory plasmonic or photonic components, which can significantly expand the applications of current electronic and photonic devices.Comment: 15 page