Online Detection of Laser Welding Penetration Depth Based on Multi-Sensor Features.

The accurate online detection of laser welding penetration depth has been a critical problem to which the industry has paid the most attention. Aiming at the laser welding process of TC4 titanium alloy, a multi-sensor monitoring system that obtained the keyhole/molten pool images and laser-induced plasma spectrum was built. The influences of laser power on the keyhole/molten pool morphologies and plasma thermo-mechanical characteristics were investigated. The results showed that there were significant correlations among the variations of the keyhole-molten pool, plasma spectrum, and penetration depth. The image features and spectral features were extracted by image processing and dimension-reduction methods, respectively. Moreover, several penetration depth prediction models based on single-sensor features and multi-sensor features were established. The mean square error of the neural network model built by multi-sensor features was 0.0162, which was smaller than that of the model built by single-sensor features. The established high-precision model provided a theoretical basis for real-time feedback control of the penetration depth in the laser welding process.

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning.

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

Study on Porosity Defect Detection in Narrow Gap Laser Welding Based on Spectral Diagnosis.

As an advanced connection technology for large thick-walled components, narrow gap laser welding has the advantages of small heat input and high efficiency and quality. However, porosity defects are prone to occur inside the weld due to the complex welding environment. In this study, the influence of the process parameters and pollutants such as water and oil on the porosity defect were explored. The action mechanism of water on the electron temperature and spectral intensity of the laser-induced plasma was analyzed. The results showed that the spectral intensity during narrow gap laser welding was weaker than that of flat plate butt welding. Under the optimal welding process conditions, the electron temperature during narrow gap laser self-fusion welding was calculated as 7413.3 K by the Boltzmann plot method. The electron density was 5.6714 × 1015 cm-3, conforming to the thermodynamic equilibrium state. With six groups of self-fusion welding parameters, only sporadic porosity defects were observed according to the X-ray detection. When there was water on the base metal surface, a large number of dense pores were observed on the weld surface and in the weld through X-ray inspection. Compared with the spectral data obtained under the normal process, the relative light intensity of the spectrometer in the whole band was reduced. The electron temperature decreased to the range of 6900 to 7200 K, while the electron density increased. The spectrum variation during narrow gap laser wire filling welding was basically the same as that of laser self-fusion welding. The porosity defects caused by water and oil pollutants in the laser welding could be effectively identified based on the intensity of the Fe I spectral lines.

A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution.

In recent years, deep learning super-resolution models for progressive reconstruction have achieved great success. However, these models which refer to multi-resolution analysis basically ignore the information contained in the lower subspaces and do not explore the correlation between features in the wavelet and spatial domain, resulting in not fully utilizing the auxiliary information brought by multi-resolution analysis with multiple domains. Therefore, we propose a super-resolution network based on the wavelet multi-resolution framework (WMRSR) to capture the auxiliary information contained in multiple subspaces and to be aware of the interdependencies between spatial domain and wavelet domain features. Initially, the wavelet multi-resolution input (WMRI) is generated by combining wavelet sub-bands obtained from each subspace through wavelet multi-resolution analysis and the corresponding spatial domain image content, which serves as input to the network. Then, the WMRSR captures the corresponding features from the WMRI in the wavelet domain and spatial domain, respectively, and fuses them adaptively, thus learning fully explored features in multi-resolution and multi-domain. Finally, the high-resolution images are gradually reconstructed in the wavelet multi-resolution framework by our convolution-based wavelet transform module which is suitable for deep neural networks. Extensive experiments conducted on two public datasets demonstrate that our method outperforms other state-of-the-art methods in terms of objective and visual qualities.

5G-BSS: 5G-Based Universal Blockchain Smart Sensors.

A smart sensor is a sensor with information processing functions. It is the product of the combination of sensor integration and a microprocessor. It has the characteristics of intelligence, networking and high precision. It has been widely used in aerospace, aviation, intelligent transportation, industrial control and medical and health care. However, in some specific application scenarios with high data security requirements and low transmission delay, such as environmental detection, transportation, etc., smart sensors have three obvious shortcomings. First, the data transmission delay is high. Second, the confidentiality and integrity of the data transmission process cannot be effectively guaranteed. Third, centralized data storage is easily leaked and tampered with by malicious users and semi-trusted administrators. Therefore, a 5G-based blockchain smart sensor 5G-BSS was designed. 5G-BSS has three innovation points. First, the 5G communication module enables the smart sensor 5G-BSS. The 5G communication module is integrated into the smart sensor 5G-BSS to reduce the delay of data transmission and improve the speed and reliability of data transmission. Second, cryptographic algorithms enable the smart sensor 5G-BSS. The data encryption module of the smart sensor 5G-BSS improves the confidentiality and integrity of the data transmission process. Third, blockchain empowers the smart sensor 5G-BSS. The blockchain client is integrated into the smart sensor 5G-BSS to ensure the centralized storage of data and prevent data leakage and tampering by semi-trusted administrators. The operation process of the hardware and software architecture is described in detail and tested on the Fisco-Bcos. The experimental results show that 5G-BSS not only has fast data transmission speed but also can effectively guarantee the integrity, confidentiality and availability of data. 5G-BSS is suitable for application scenarios with high requirements for data security and data transmission, such as environmental monitoring, intelligent transportation, autonomous driving, etc.

Organocatalytic Three-Component Acyldifluoromethylation of Vinylarenes via N-Heterocyclic Carbene-Catalyzed Radical Relay.

We herein describe an N-hetercyclic carbene-catalyzed three-component acyldifluoromethylation of vinylarenes, aldehydes, and NaSO2CF2H. This organocatalytic approach provides a practical route for the synthesis of pharmaceutically relevant α-aryl-ß-difluormethyl ketones without the need for transition metals or photocatalysts. The late-stage acyldifluoromethylation of drug analogues was also demonstrated. The reaction design employs NaSO2CF2H as the source of the CF2H radical in the presence of an oxidant for the radical relay.

Dissipativity-based synthesis for semi-Markovian systems with simultaneous probabilistic sensors and actuators faults: A modified event-triggered strategy.

Aided by a modified event-triggered communication policy (ETCP), this article addresses the dissipativity-based control synthesis problem for semi-Markovian switching systems (SMSSs) with simultaneous multiplicative probabilistic faults on sensors and actuators modules. The resulting model under consideration is more extensive, which covers semi-Markovian switching coefficients, transmission delays, and randomly occurring sensors and actuators faults in a unified systematic analytical framework instead of investigating separately in some existing works. More specifically, the probabilistic faults are assumed to happen on both the sensors and actuators modules simultaneously, and the distortion probability for each sensor and actuator is irrelevant, which can be characterized by multiplicate mutually independent stochastic variables that obeys certain statistical features and probabilistic distribution delineate on the interval [0,✠](✠≥1). To reduce the bandwidth usage, a novel event-triggered strategy is designed. Additionally, in the light of this newly developed ETCP, and considering the effects of the signal transmission delays and multitudinous probabilistic failures, a generalized and more realistic faulty pattern for SMSSs is presented, which is more fit for real applications. Hereby, the principal superiority of the established new type faulty pattern lies in its practicality and generality, which contains some previous faulty models as special scenarios. By constructing an appropriate semi-Markovian Lyapunov functional (SMLF) together with mathematical analysis technique and matrix inequality decoupling operation, sojourn-time-dependent sufficient conditions for determining both the control gain matrices and triggered configuration coefficients are developed and formulated in terms of a group of feasible linear matrix inequalities (LMIs). Eventually, several practical examples are exploited to substantiate the validity and practicability of the developed control design methodology.

Asynchronous H∞ control of Markov jump discrete-time systems with incomplete transition probability and unreliable links.

In this study, an asynchronous H∞ state feedback controller is devised for Markov jump discrete-time systems (MJDTSs) with time-varying delay. "Asynchronous" means that the system switching mode θk, the controller mode ϑk and the quantizer mode λk are different from each other. The first one is homogeneous and the last two are non-homogeneous. In particular, as a promotion of existing work, we firstly attempt to propose the transition probabilities (TPs) of the three Markov chains (MCs) are not completely known. In addition, the discrete time-varying delay and its infinitely distributed ones are considered. Moreover, according to the Lyapunov stability theory and stochastic process, it is established for the sufficient criterion to ensure the stochastic stability of resulting closed-loop MJDTSs with an H∞ attenuation performance index. The feasibility and effectiveness of the proposed method are validated by three examples.

Multiple Pathway-Dephosphorylated ASK-1 Confers Temozolomide-Resistance to Human Glioma Cells.

AIM: Temozolomide (TMZ) resistance contributes to the unfavorable prognosis of glioma, however, the mechanism of resistance is unknown. ASK-1 has various functions in many tumors, but its function in glioma is poorly understood. This study aimed to elucidate the function of ASK-1 and the role of its modulators in the induction of TMZ resistance in glioma and the underlying mechanism. MATERIAL AND METHODS: ASK-1 phosphorylation, the IC50 of TMZ, cell viability, and apoptosis were assessed in the U87 and U251 glioma cell lines and the derived TMZ-resistant cell lines U87-TR and U251-TR. We then blocked ASK-1 function, either with an inhibitor or by overexpression of multiple ASK-1 upstream modulators, to further explore the role of ASK-1 in TMZ-resistant glioma. RESULTS: TMZ-resistant glioma cells showed high IC50 values of TMZ, high survival, and low levels of apoptosis following the TMZ challenge. ASK-1 phosphorylation, but not protein expression, was higher in U87 and U251 cells than in TMZ-resistant glioma cells exposed to TMZ. The addition of the ASK-1 inhibitor selonsertib (SEL) resulted in the dephosphorylation of ASK-1 in U87 and U251 cells after the TMZ challenge. SEL treatment increased the TMZ resistance of U87 and U251 cells, as evidenced by the increased IC50 and cell survival rate and low apoptosis rate. Overexpression of some ASK-1 upstream suppressors [Thioredoxin (Trx), protein phosphatase 5 (PP5), 14-3-3, and cell division cycle 25C (Cdc25C)] led to various degrees of ASK-1 dephosphorylation and a TMZ-resistant phenotype in U87 and U251 cells. CONCLUSION: Dephosphorylation of ASK-1 induced TMZ resistance in human glioma cells, and several ASK-1 upstream suppressors, including Trx, PP5, 14-3-3, and Cdc25C, are involved in this phenotypic change induced by dephosphorylation of ASK-1.

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution.

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China.

Wind energy is one of the most important renewable resources and plays a vital role in reducing carbon emission and solving global warming problem. Every country has made a corresponding energy policy to stimulate wind energy industry development based on wind energy production, consumption, and distribution. In this paper, we focus on forecasting wind energy consumption from a macro perspective. A novel power-driven fractional accumulated grey model (PFAGM) is proposed to solve the wind energy consumption prediction problem with historic annual consumption of the past ten years. PFAGM model optimizes the grey input of the classic fractional grey model with an exponential term of time. For boosting prediction performance, a heuristic intelligent algorithm WOA is used to search the optimal order of PFAGM model. Its linear parameters are estimated by using the least-square method. Then validation experiments on real-life data sets have been conducted to verify the superior prediction accuracy of PFAGM model compared with other three well-known grey models. Finally, the PFAGM model is applied to predict China's wind energy consumption in the next three years.

Resveratrol protects hippocampal neurons against cerebral ischemia-reperfusion injury via modulating JAK/ERK/STAT signaling pathway in rats.

Cerebral ischemia/reperfusion injury (I/R injury) can cause neuronal deficits even death. Recent studies demonstrated that resveratrol (RSV) exerts neuroprotective effects in ischemia and several signaling pathways were involved in the process. However, it is still possible that other signaling pathway participates in the neuronal protective process. Our study examines the possible mechanism underlying RSV treatment. We randomly divided rats into four groups: the sham group, I/R group, I/R group, I/R+RSV group, I/R+vehicle group. Locomotive and cognitive behavior were utilized by open-field and closed-field test and Morris water maze test. Neuronal cell loss was measured by hematoxylin-eosin (HE) staining for hippocampus. Western blot was applied to measure the level of p-JAK, p-ERK, p-STAT and p-JNK. The results indicated that RSV could alleviate cognitive impairment, reduce neuronal loss, downregulate p-JAK, p-ERK, p-STAT and p-JNK expression and inflammatory cytokines. In summary, resveratrol protects hippocampal neurons against cerebral ischemia-reperfusion injury via modulating JAK/ERK/STAT signaling pathway in rats.

Identity Vector Extraction by Perceptual Wavelet Packet Entropy and Convolutional Neural Network for Voice Authentication.

Recently, the accuracy of voice authentication system has increased significantly due to the successful application of the identity vector (i-vector) model. This paper proposes a new method for i-vector extraction. In the method, a perceptual wavelet packet transform (PWPT) is designed to convert speech utterances into wavelet entropy feature vectors, and a Convolutional Neural Network (CNN) is designed to estimate the frame posteriors of the wavelet entropy feature vectors. In the end, i-vector is extracted based on those frame posteriors. TIMIT and VoxCeleb speech corpus are used for experiments and the experimental results show that the proposed method can extract appropriate i-vector which reduces the equal error rate (EER) and improve the accuracy of voice authentication system in clean and noisy environment.

Long non-coding RNA ZFAS1 is an unfavourable prognostic factor and promotes glioma cell progression by activation of the Notch signaling pathway.

Survival of patients with glioma remains poor, which is largely attributed to active carcinogenesis. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) play key roles in tumor initiation and progression. However, the function of lncRNA ZFAS1 in glioma is still unclear. In the current study, we found that ZFAS1 was upregulated in glioma tissues and cell lines. High ZFAS1 expression in glioma tissues was significantly correlated with advanced tumor stage and poor overall survival. Furthermore, in vitro assays demonstrated that ZFAS1 inhibition significantly suppressed glioma cell proliferation, migration and invasion. Importantly, we further confirmed that epithelial-mesenchymal transition (EMT) and the Notch signaling pathway was inactivated in the glioma cells after ZFAS1 knockdown. Thus, our findings indicated that ZFAS1 could exhibit a tumor oncogenic role in glioma progression by regulating EMT and Notch signaling pathway. LncRNA ZFAS1 might serve as a therapeutic target for the treatment of glioma patients.

Transient Sp8+ handlebar-like interneuron structure traversing the corpus callosum during the second postnatal week.

Although previous studies have implied that interneurons continue to migrate into the corpus callosum (CC) during the postnatal period, little is known about the details of the migration pattern. In this study, we analyzed the entire postnatal CC every other day and showed the presence of a transient handlebar-like structure traversing the CC during the second postnatal week. Using immunostaining and two strains of transgenic mice, we showed that this handlebar-like structure consisted of GABAergic interneurons derived from the caudal ganglionic eminence, which expressed the transcription factor Sp8. Data obtained from in-utero electroporation experiments showed that these high-density interneurons were adjacent to callosal axons. Thus, we provide the first direct evidence that a transient Sp8+ interneuron handlebar-like structure exists in the CC during a brief postnatal critical period.

Feature selection with neighborhood entropy-based cooperative game theory.

Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals. To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy. Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game. The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures. Finally, the proposed method is tested on several UCI datasets. The results show that neighborhood entropy-based cooperative game theory model (NECGT) yield better performance than classical ones.