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Objective. Oxidative stress has been proven to be essential in the pathogenesis of ulcerative colitis (UC). Therefore, this study was designed to investigate the effect of Gegen Qinlian decoction (GQ) on the Nrf2 pathway in the treatment of UC and explore the potential mechanism. Methods. The UC rat model was induced by 5% dextran sodium sulfate (DSS) aqueous solution, and UC rats were treated with GQ orally. The effect of GQ on UC rats was recorded. Human clonal colon adenocarcinoma cells (Caco-2) stimulated by tumor necrosis factor-α (TNF-α) were employed in this study. After being stimulated with TNF-α for 2 hours, Caco-2 cells were cultured with GQ or its major components (puerarin, baicalin, berberine, and liquiritin) for 22 hours. In addition, the Nrf2 gene of Caco-2 cells was silenced and then cultured with GQ for 22 hours. The contents of superoxide dismutase (SOD), glutathione peroxidase (GSH-PX), and malondialdehyde (MDA) in colonic tissues and Caco-2 cells were detected by assay kits. Reactive oxygen species (ROS) in Caco-2 cells were analyzed by flow cytometry. Quantitative real-time PCR and western blot were employed to detect the mRNA and protein expression of Nrf2 and its related target genes in colon tissues and Caco-2 cells. Results. GQ alleviated the injured colonic mucosa and activated the expression of Nrf2 in UC rats. In TNF-α stimulated Caco-2 cells and Nrf2 silenced Caco-2 cells, GQ also reversed the inhibitory effect of Nrf2. Furthermore, the major components of GQ could activate Nrf2 signaling in TNF-α stimulated cells as well. Moreover, the contents of SOD, GSH, MDA, and ROS were restored to normal after treatment with GQ or its major components. Among these components, puerarin, berberine, and liquiritin appear to have a better effect on activating Nrf2 in vitro. Overall, GQ can alleviate UC by increasing the activity of Nrf2/ARE signaling and enhancing the effect of antioxidant stress. [ABSTRACT FROM AUTHOR]

Accurate poverty alleviation (APA) is crucial to building a well-off society in an all-round way. Companies and the capital market are implementers of APA. In the context of stock price collapse risk (SPCR), this paper verifies whether the APA by state-owned listed enterprises (SOLEs) could effectively suppress SPCR. Empirical results show that SOLEs engaging in APA and investing heavily in APA have a relatively low SPCR. This conclusion holds after the control of robustness and endogenous factors. Information disclosure system mediates the effect of APA on SPCR. Besides, APA suppresses SPCR more significantly in central state-owned enterprises (SOEs) and public welfare SOEs. Further analysis indicates that good institutional environment, strong poverty alleviation pressure, poverty experience of executives, and model of party organization governance can effectively enhance the influence of APA on SPCR. The research results suggest that SOLEs actively engaging in APA can effectively suppress SPCR by disclosing APA information. This is an organic manifestation of APA effectiveness in the capital market. [ABSTRACT FROM AUTHOR]

Internet of Things will play a vital role in the public transport systems to achieve the concepts of smart cities, urban brains, etc., by mining continuously generated data from sensors deployed in public transportation. In this sense, smart cities applied artificial intelligence techniques to offload data for social governance. Bicycle sharing is the last mile of urban transport. The number of the bike in the sharing stations, to be rented in future periods, is predicted to get the vehicles ready for deployment. It is an important tool for the implementation of smart cities using artificial intelligence technologies. We propose a DBSCAN-TCN model for predicting the number of rentals at shared bicycle stations. The proposed model first clusters all shared bicycle stations using the DBSCAN clustering algorithm. Based on the results of the clustering, the data on the number of shared bicycle rentals are fed into a TCN neural network. The TCN neural network structure is optimized. The effects of convolution kernel size and Dropout rate on the model performance are discussed. Finally, the proposed DBSCAN-TCN model is compared with the LSTM model, Kalman filtering model, and autoregressive moving average model. Through experimental validation, the proposed DBSCAN-TCN model outperforms the traditional three models in terms of two metrics, root mean squared logarithmic error, and error rate, in terms of prediction performance. [ABSTRACT FROM AUTHOR]

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved. [ABSTRACT FROM AUTHOR]

Logistics is the booster of economic development, and it is imperative to further improve the logistics energy efficiency and adjust its development model. This paper is an attempt to investigate the logistics energy efficiency and main influencing factors of the Yangtze River Economic Belt, which is the most economically intensive region that spans East, Central, and West China. The input-oriented SBM-DEA model is employed to identify factors such as energy input, undesirable output, and service capacity output as well as the logistics energy efficiency of Yangtze River Economic Belt. Energy efficiency is then further decomposed into pure technical efficiency, scale efficiency, and technical efficiency, from the perspective of which provinces and cities are compared. The research results show that the logistics energy efficiency of Yangtze River Economic Belt needs to be further improved and that energy efficiency differs greatly among cities and provinces, indicating that the development is quite unbalanced in different areas. Therefore, the local government should develop development strategies according to the main influencing factors and constraints to the local logistics industry, so as to optimize and upgrade the energy structure of the logistics industry. [ABSTRACT FROM AUTHOR]

By deploying edge servers on the network edge, mobile edge computing network strengthens the real-time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application's requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short-term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results. [ABSTRACT FROM AUTHOR]

Background. Long intergenic noncoding RNA1184 (linc01184) has been recently discovered; however, its role in human diseases is limited to date. The present study is aimed at investigating the expression pattern and mechanism of linc01184 in colorectal cancer (CRC) tumorigenesis. Methods. The expression of linc01184 in CRC tissues and cell lines was compared with that in normal controls. The functions of linc01184 in CRC cells were identified by overexpression and small interfering RNA (siRNA) approaches in vitro. Meanwhile, the target gene prediction software, luciferase reporter, RNA pull-down, and western blotting assays were used to analyze the oncogenic mechanism. Results. We found that linc01184 was obviously upregulated in CRC tissues and cells when compared to normal controls, and its upregulation had a positive association with the CRC progression. linc01184 knockdown significantly suppressed CRC cell proliferation and invasion and promoted apoptosis. Besides, linc01184 acted as a competitive endogenous RNA (ceRNA) by directly binding to microRNA-331 (miR-331), and its overexpression resulted in notable increases of human epidermal growth factor receptor 2 (HER2), phosphorylated Ser/Thr kinases (p-Akt), and extracellular regulated protein kinase 1/2 (p-ERK1/2) at posttranscriptional levels in CRC cells, which were antagonized by miR-331. Conclusions. The findings reveal for the first time that linc01184 is an enhancer for the proliferation and invasion of CRC by functioning as a ceRNA through the linc01184-miR-331-HER2-p-Akt/ERK1/2 pathway regulatory network. [ABSTRACT FROM AUTHOR]

In the smart grid and big data environment, accurate and large amount of power load data for users can be obtained with the wide application of non-intrusive load monitoring technology. In the research process of customers' information, information security protection of users' electricity data has become a research hotspot urgently. This article proposes a new type of load decomposition method for electric vehicle load information and compares it with hidden Markov model algorithm to verify its accuracy. On this basis, the elliptic curve encryption algorithm is used to encrypt the users' electricity data, and the function and effectiveness of the encryption algorithm are verified by comparing the load decomposition of the electric vehicle with the unencrypted data. [ABSTRACT FROM AUTHOR]

As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error. [ABSTRACT FROM AUTHOR]

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification. [ABSTRACT FROM AUTHOR]

As one of the important raw materials for LED products, phosphors play an important role. At present, the spectrum of LED phosphors lacks red light, resulting in poor quality of white LED products and a low color rendering index (Ra <80), which affects lighting effects. To solve this problem, we synthesized the SrGd2O4:Eu3+ red phosphor by a high-temperature solid-phase method. The structure and luminescence properties of SrGd2O4:Eu3+ red phosphors were studied by means of X-ray diffraction (XRD) measurements of their emission and excitation spectra, and their spectra were analyzed. We also studied the Eu3+ doping concentration with the best luminous efficiency and the color coordinate of the SrGd2O4:Eu3+ phosphor. The experimental results show that SrGd2O4:Eu3+ is a new red phosphor material that can be used for near-ultraviolet or blue-light excitation and has good practicability. [ABSTRACT FROM AUTHOR]

The design of a simple ultrahigh frequency RFID (radio frequency identification) reader antenna that can operate within the North America RFID band (902-928 MHz) is studied. To generate circular polarization (CP) radiation in this band, a novel method of loading two narrow open-ended slots (slits) into an eccentric annular slot patch is proposed. To allow optimum impedance matching with enhanced CP bandwidth, the radiating patch is loaded to an L-shaped ground plane. From the experimental results, the proposed antenna can yield an impedance bandwidth (10-dB return loss) between 650 MHz to 1125 MHz, while good CP bandwidth (3-dB axial ratio, AR) from 901 MHz to 930 MHz is also attained. Furthermore, gain level and efficiency of more than 7.8 dBic and 90%, respectively, were also measured. By simply removing one of the slits, this proposed antenna can also be modified to operate within the China (840-846 MHz) and European (865-868 MHz) RFID band. [ABSTRACT FROM AUTHOR]

A low profile annular-ring patch antenna with circularly polarized (CP) radiation for radio frequency identification (RFID) reader applications in the ultra-high frequency (UHF) band (922-928 MHz) is presented in this article. Perturbation method is applied by loading a pair of triangular open-notch into the outer circumference of ring patch, and good impedance matching can be determined by using the coupled feeding technique. The overall size of this proposed antenna is 150 mm × 150 mm × 10.4 mm. The measured results show desirable 10-dB impedance bandwidth and 3-dB axial ratio (AR) bandwidth of 3.5% (908-941 MHz) and 0.65% (922-928 MHz), respectively. Stable antenna peak gain and efficiency of 7.2 dBic and 87% are also exhibited, respectively. [ABSTRACT FROM AUTHOR]