4,4′-[Piperazine-1,4-diylbis(propyl­ene­nitrilo­methyl­idyne)]diphenol

In the title mol­ecule, C24H32N4O2, the piperazine ring adopts a chair conformation and the dihedral angle between the two benzene rings is 35.4 (1)°. In the crystal structure, inter­molecular O—H⋯N hydrogen bonds link mol­ecules into chains along [001]

Exploring the Effectiveness of Web Crawlers in Detecting Security Vulnerabilities in Computer Software Applications

With the rapid development of the Internet, the World Wide Web has become a carrier of a large amount of information. In order to effectively extract and use this information, web crawlers that crawl various web resources have emerged. The interconnectedness, openness, and interactivity of information in the World Wide Web bring great convenience for information sharing to the society and they also bring many security risks. To protect resource information, computer software security vulnerabilities have become the focus of attention. This article is based on the method of computer software security detection under a web crawler simply analyzes the basic concepts of computer software security detection and analyzes the precautions in the process of security detection. Finally, combined with the computer software security vulnerability problems in the web crawler environment, its security detection technology Application for further analysis

Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction

Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image (MRI) processing and achieves accurate MRI reconstruction from under-sampled k-space data. According to the current research, there are still several problems with dynamic MRI k-space reconstruction based on CS. 1) There are differences between the Fourier domain and the Image domain, and the differences between MRI processing of different domains need to be considered. 2) As three-dimensional data, dynamic MRI has its spatial-temporal characteristics, which need to calculate the difference and consistency of surface textures while preserving structural integrity and uniqueness. 3) Dynamic MRI reconstruction is time-consuming and computationally resource-dependent. In this paper, we propose a novel robust low-rank dynamic MRI reconstruction optimization model via highly under-sampled and Discrete Fourier Transform (DFT) called the Robust Depth Linear Error Decomposition Model (RDLEDM). Our method mainly includes linear decomposition, double Total Variation (TV), and double Nuclear Norm (NN) regularizations. By adding linear image domain error analysis, the noise is reduced after under-sampled and DFT processing, and the anti-interference ability of the algorithm is enhanced. Double TV and NN regularizations can utilize both spatial-temporal characteristics and explore the complementary relationship between different dimensions in dynamic MRI sequences. In addition, Due to the non-smoothness and non-convexity of TV and NN terms, it is difficult to optimize the unified objective model. To address this issue, we utilize a fast algorithm by solving a primal-dual form of the original problem. Compared with five state-of-the-art methods, extensive experiments on dynamic MRI data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity

Spatial residual blocks combined parallel network for hyperspectral image classification.

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI classification. Firstly, the spatial residual blocks extract spatial features from rich spatial contexts information, which can be used to deal with the spatial variation of spectral signatures. Especially, the skip connection in spatial residual blocks is conducive to the backpropagation of gradients and mitigates the declining-accuracy phenomenon in the deep network. Secondly, the parallel structure is employed to extract spectral features. Spectral feature learning on parallel branches contains fewer independent connection weighs through parameter sharing. Thus, fewer parameters of the network require a lesser number of training samples. Furthermore, the feature fusion is conducted on the multi-scale features from different layers in the spectral feature learning part. Extensive experiments of three representative HSI data sets illustrate the effectiveness of the proposed network

Interpretable emotion recognition using EEG signals

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy

Effect of regional versus general anaesthesia on postoperative opioid consumption, clinical outcomes and cognitive function in Chinese patients undergoing metastatic cancer surgery

Purpose: To compare postoperative opioid consumption, inflammatory biomarkers, cognitive function and safety profile of regional anesthesia (RA) versus general anesthesia (GA) in Chinese patients undergoing metastatic cancer surgery.Method: Chinese patients undergoing metastatic cancer surgery were enrolled and received either RA or GA) in allocation ratio of 1:1. The following efficacy variables were assessed: 1) pain score was measured on VAS scale; 2) post-operative consumption; 3) inflammatory biomarkers; 4) cognitive function; 5) clinical outcomes. Safety was also assessed.Results: Data for a total of 220 patients were analyzed. Compared to GA alone, the combination of RA and GA demonstrated significantly greater reduction in post-operative pain with decreased postoperative opioid consumption. Also, RA/GA combination inhibited inflammatory response when compared to patients who received GA only, indicating that RA + GA improved immune response in patient undergoing surgical intervention. The severity of signs and symptoms of dementia were similar at baseline visit (p > 0.05). Patients of RA/GA group had significantly greater relief in signs and symptoms of dementia/cognitive impairment, when compared to the GA-treated patients (p < 0.05). However, incidence of complications (including adverse events) was comparable for both groups (p > 0.05). Additionally, RA/GA was also associated with shorter length of hospital stay, compared to GA.Conclusion: RA/GA combination demonstrates significantly greater improvement in the level of clinical outcomes, decreases postoperative opioid consumption, and improves cognitive functions when compared to GA in Chinese patients undergoing metastatic cancer surgery

Analgesic effect and safety of postoperative low-dose ketamine/midazolam combination vis-à-vis dexmedetomidine in non-cardiac surgery

Purpose: To compare the analgesic efficacy and safety of use of postoperative low-dose parenteral ketamine/midazolam combination, and postoperative parenteral dexmedetomidine in major non-cardiac surgeries.Methods: Major non-cardiac surgeries were performed in patients under propofol/morphine anesthesia. After the surgeries, patients received low-dose of ketamine with midazolam (KM cohort, n = 115), dexmedetomidine (DEX cohort, n = 112), or paracetamol infusion (PL cohort, n = 148). When visual analog scale score was > 4 in a resting condition, 3 mg bolus intravenous morphine was administered. Data for total morphine requirements and treatment-emergent adverse effects (within 2 days of postoperative treatment) were collected and analyzed.Results: Thirty-eight patients from KM cohort, 55 patients from DEX cohort, and 109 patients from PL cohort required 3 mg bolus intravenous morphine for postoperative pain management. Patients from KM cohort had nausea, vomiting, blurred vision,  dizziness, and hallucinations, while patients in DEX cohort experienced headache and bradycardia post-surgery. Patients in PL cohort reported drossiness, constipation, urinary retention, and dry mouth.Conclusion: Postoperative low doses of ketamine + midazolam and dexmedetomidine are effective for postoperative pain management, and they produce low adverse effects.&nbsp

Association between -238 but not -308 polymorphism of Tumor necrosis factor alpha (TNF-alpha)v and unexplained recurrent spontaneous abortion (URSA) in Chinese population

<p>Abstract</p> <p>Objectives</p> <p>TNF-alpha is a critical cytokine produced by Th1 cells while altered T helper 1 (Th1)-Th2 balance is found crucial for a successful pregnancy.</p> <p>Study Design</p> <p>A cohort of 132 Southern Chinese Han RSA patients and 152 controls constituted the subjects of this study. Two functional polymorphisms -308 and -238 of TNF-alpha were studied by association analysis.</p> <p>Results</p> <p>lack of association was found in TNF-alpha -308 SNP yet a significant difference was discovered in -238 polymorphism.</p> <p>Conclusion</p> <p>This study suggested that TNF-alpha may be a risk factor in Chinese RSA patients. However the ethnic differences may also contribute to the results.</p

Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations