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Summary In the past several years, a series of breakthrough research advancements have been achieved by leveraging wireless signals such as Wi‐Fi in various emerging applications, including healthcare, behavior recognition, positioning, and target detection. Compared to traditional human behavior sensing methods, Wi‐Fi signals human behavior sensing technology has many advantages, including non‐line‐of‐sight, sensor device‐free sensing, passive sensing, ease of deployment, and no need for lights. Data mining undoubtedly plays a critical role in making Wi‐Fi‐based human behavior detection intelligent enough to facilitate convenient services and environments. We study Wi‐Fi signals mining using the data mining process and review the developmental process of Wi‐Fi data mining. This covers the methods of Wi‐Fi data mining, including signal acquisition, preprocessing, feature extraction to training, and classification. We then propose WHSecurity, a whole home intrusion detection and tracking system that is based on all of the methods covered above. Finally, WHSecurity includes a deep learning‐based data mining process called multiview learning for the decision‐making on intrusion detection and tracking. Experimental outcomes show that the WHSecurity approach performs superior in terms of intrusion detection and tracking performance. [ABSTRACT FROM AUTHOR]

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement. [ABSTRACT FROM AUTHOR]

This paper presents a method of sub-pixel image edge detection based on the cubic B-spline curve interpolation and the wavelet transform, to solve the problem that the system has increasing demands to the algorithm in real-time and the accuracy. The high resolution and anti-noise of the multi-scale adaptive threshold wavelet transform are used to test the modulus maxima of the image edge. Using the cubic B-spline curve interpolation, the accurate interpolation and the edge of the image reconstruction can be realized in sub-pixel level when the image edge is irregular. The experimental results indicate that mean squared error in the new method is less than 0.02 pixel. The measure data of the standard deviation is 0.0043 mm. From above on, the proposed method is robust to noise and it has a high-precision positioning. Compared to these popular edge detection algorithms, the proposed method performs better than those algorithms on detail reserving and positioning accuracy. [ABSTRACT FROM AUTHOR]

Introduction: The existence of the smoker's paradox is controversial and potential mechanisms have not been explained. We aimed to explore the association between cigarette smoking and functional outcome at 3 months in patients with acute ischemic stroke who were treated with intravenous thrombolysis (IVT) or endovascular treatment (EVT).Methods: This meta-analysis was conducted in accordance with the PRISMA guidelines. Studies exploring the association between smoking and good functional outcome (modified Rankin Scale score ≤ 2) following IVT or EVT were searched via the databases of PubMed, Embase, and the Cochrane Library from inception to August 8, 2018. Information on the characteristics of included studies was independently extracted by two investigators. Data were pooled using a random-effects or fixed-effects meta-analysis according to the heterogeneity of included studies.Results: Among 20 identified studies, 15 reported functional outcomes following IVT, and five reported functional outcomes following EVT. Unadjusted analyses showed that smoking increased the odds of good functional outcomes with a pooled odds ratio (OR) of 1.48 (95% confidence interval [CI]: 1.36-1.60) after IVT and 2.10 (95% CI: 1.47-3.20) after EVT. Of IVT studies, only eight reported outcomes adjusted for covariates and none of the EVT studies reported adjusted outcomes. After adjustment, the relation between smoking and good functional outcome following IVT lost statistical significance (OR 1.14 [95% CI: 0.81-1.59]).Conclusion: Our meta-analysis suggested that smoking was not associated with good functional outcome (mRS ≤ 2) at 3 months in patients with acute ischemic stroke who were treated with intravenous thrombolysis.Implications: The existence of the smoker's paradox is controversial. A previous letter by Plas et al. published in 2013 reported a positive result for the association between smoking and good functional outcome at 3 months in acute ischemic stroke patients who received intravenous thrombolysis (IVT). However, a major limitation of their meta-analysis was that the process of data synthesis was based on unadjusted data. Therefore, we conducted this meta-analysis to investigate the association based on adjusted data and a larger sample size. Our meta-analysis suggested that smoking was not associated with good functional outcome after adjusting for covariates. [ABSTRACT FROM AUTHOR]

In higher plants, COP1 (Constitutively Photomorphogenic 1) acts as a central regulator of light-signaling networks and globally conditions the target proteins via the ubiquitin-proteasome pathway. However, the function of COP1-interacting proteins in light-regulated fruit coloration and development remains unknown in Solanaceous plants. Here, a COP1-interacting protein-encoding gene, SmCIP7 , expressed specifically in the eggplant (Solanum melongena L.) fruit, was isolated. Gene-specific silencing of SmCIP7 using RNA interference (RNAi) significantly altered fruit coloration, fruit size, flesh browning, and seed yield. SmCIP7 -RNAi fruits showed evident repression of the accumulation of anthocyanins and chlorophyll, indicating functional similarities between SmCIP7 and AtCIP7. However, the reduced fruit size and seed yield indicated SmCIP7 had evolved a distinctly new function. With the comprehensive application of HPLC-MS, RNA-seq, qRT-PCR, Y2H, BiFC, LCI, and dual-luciferase reporter system (DLR™), it was found that SmCIP7, a COP1 interactive protein in light signaling promoted anthocyanin accumulation, probably by regulating the transcription of SmTT8. Additionally, the drastic up-regulation of SmYABBY1 , a homologous gene of SlFAS , might account for the strongly retarded fruit growth in SmCIP7 -RNAi eggplant. Altogether, this study proved that SmCIP7 is an essential regulatory gene to modulate fruit coloration and development, serving as a key gene locus in eggplant molecular breeding. • Silencing of SmCIP7 notably altered the coloration, size, flesh browning, and seed yield of eggplant fruit. • SmCIP7 promotes anthocyanin accumulation by regulating the transcription of SmTT8. • SmCIP7 promotes eggplant fruit growth by regulating the transcription of SmYABBY1. • The repressed metabolic flux towards anthocyanin enhanced chlorogenic acid accumulation. [ABSTRACT FROM AUTHOR]

Abstract: To better equip with a non-expert to carry out the diagnosis operations, a new method for intelligent fault identification of rotating machinery based on the empirical mode decomposition (EMD), dimensionless parameters, fault decision table (FDT), MLEM2 rule induction algorithm and improved rule matching strategy (IRMS) is proposed in this paper. EMD is used to preprocess the vibration signals for mining the fault characteristic information more accurately. Then, dimensionless parameters are extracted from both the decomposed signals in time domain and envelop spectrum in frequency domain respectively to form the conditional attributes of a FDT. Moreover, MLEM2 algorithm is run directly on the FDT to generate decision rules imbedded in the data. To make the following classification process more robust, the IRMS is adopted to resolve the conflicting and non-matching problems. Finally, data of rolling element bearings with four typical working conditions is used to evaluate the performance of the proposed method. The testing result demonstrates that the method has high accuracy and systematically good performance. It is proved to be a convenient, concise, interpretable and reliable way to diagnose bearings’ faults. The advantages are also confirmed by the comparisons with the other two approaches, i.e. the principal component analysis (PCA) and probabilistic neural network (PNN) based method as well as the wavelet transform (WT) and genetic algorithm (GA) based one. Furthermore, thank to the FDT working as a data interface, the method is more transplantable, therefore it may be applied to diagnose other types of rotating machines effectively. [Copyright &y& Elsevier]