Researches on Key Algorithms in Analogue Seismogram Records Vectorization

Abstract: History paper seismograms are very important information for earthquake monitoring and prediction, and the vectorization of paper seismograms is a very import problem to be resolved. In our study, a new tracing algorithm for simulated seismogram curves based on visual filed feature is presented. We also give out the technological process to vectorizing simulated seismograms, and an analog seismic record vectorization system has been accomplished independently. Using it, we can precisely and speedy vectorize analog seismic records (need professionals to participate interactively)

Targeted synthesis of an electroactive organic framework

A new strategy for targeted design and synthesis of an electroactive microporous organic molecular sieve (JUC-Z2) is described. Experiment demonstrated that such a targeted synthesis approach to achieve phenyl-phenyl coupling was a controllable process and predominately generated two-dimensional polymer sheets, significantly different from the traditional chemical or electrochemical oxidation methods to prepare conducting polymers. Successive self-assembly leads to a lamellar organic framework comprised of stacked polymer sheets with an hcb topology. JUC-Z2 was found to have a well-defined uniform micropore distribution (similar to 1.2 nm), a large surface area (BET = 2081 m(2) g(-1)) and high physicochemical stability (> 440 degrees C). After doping with I(2), JUC-Z2 exhibits typical p-type semiconductive properties. As the first example of an electroactive organic framework, JUC-Z2 possesses a unique ability of electrochemical ion recognition, arising from the synergistic function of the uniform micropores and the N-atom redox site.State Basic Research Project[2011CB808703]; NSFC[91022030, 20771041, 20773101, 20833005]; "111'' project[B07016]; Ministry of Science and Technology[2006DFA41190]; Jilin Science and Technology Department[20106021

Apremilast Ameliorates Experimental Arthritis via Suppression of Th1 and Th17 Cells and Enhancement of CD4+Foxp3+ Regulatory T Cells Differentiation

Apremilast is a novel phosphodiesterase 4 (PDE4) inhibitor suppressing immune and inflammatory responses. We assessed the anti-inflammatory effects of Apremilast in type II collagen (CII)-induced arthritis (CIA) mouse model. To determine whether Apremilast can ameliorate arthritis onset in this model, Apremilast was given orally at day 14 after CII immunization. Bone erosion was measured by histological and micro-computed tomographic analysis. Anti-mouse CII antibody levels were measured by enzyme-linked immunosorbent assay, and Th17, Th1 cells, and CD4+Foxp3+ regulatory T (Treg) cells were assessed by flow cytometry in the lymph nodes. Human cartilage and rheumatoid arthritis (RA) synovial fibroblasts (RASFs) implantation in the severe combined immunodeficiency mouse model of RA were used to study the role of Apremilast in the suppression of RASF-mediated cartilage destruction in vivo. Compared with untreated and vehicle control groups, we found that Apremilast therapy delayed arthritis onset and reduced arthritis scores in the CIA model. Total serum IgG, IgG1, IgG2a, and IgG2b were all decreased in the Apremilast treatment groups. Moreover, Apremilast markedly prevented the development of bone erosions in CIA mice by CT analysis. Furthermore, in the Apremilast treated group, the frequency of Th17 cells and Th1 cells was significantly decreased while Treg cells’ frequency was significantly increased. The high dose of Apremilast (25 mg/kg) was superior to low dose (5 mg/kg) in treating CIA. Apremilast treatment reduced the migratory ability of RASFs and their destructive effect on cartilage. Compared with the model group, Apremilast treatment significantly reduced the RASFs invasion cartilage scores in both primary implant and contralateral implant models. Our data suggest that Apremilast is effective in treating autoimmune arthritis and preventing the bone erosion in the CIA model, implicating its therapeutic potential in patients with RA

Researches on Key Algorithms in Analogue Seismogram Records Vectorization

History paper seismograms are very important information for earthquake monitoring and prediction, and the vectorization of paper seismograms is a very import problem to be resolved. In our study, a new tracing algorithm for simulated seismogram curves based on visual filed feature is presented. We also give out the technological process to vectorizing simulated seismograms, and an analog seismic record vectorization system has been accomplished independently. Using it, we can precisely and speedy vectorize analog seismic records (need professionals to participate interactively)

FV-MViT: Mobile Vision Transformer for Finger Vein Recognition

In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer’s self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT

Effects of ultrasound-guided lumbar plexus and sacral plexus block combined with general anesthesia on the anesthetic efficacy and surgical outcomes in elderly patients undergoing intertrochanteric fracture surgery: a randomized controlled trial

Abstract Background Surgery for intertrochanteric fractures in elderly patients is challenging due to the risk of severe pain and significant stress responses. We investigated the effects of a combined approach of ultrasound-guided lumbar plexus and sacral plexus block with general anesthesia on anesthetic efficacy and surgical outcomes in these patients. Methods A randomized controlled trial was conducted involving 150 elderly patients, divided into two groups: the combined anesthesia group (receiving ultrasound-guided lumbar plexus and sacral plexus block along with general anesthesia) and the general anesthesia alone group. Outcome measures included hemodynamic parameters, postoperative pain levels (VAS scores), postoperative recovery times, and incidence of adverse reactions. Results In the combined anesthesia group, the patients had more stable intraoperative hemodynamics, lower postoperative VAS scores at 1, 3, and 6 h, and faster recovery times (eye-opening upon command and return of respiratory function) compared to the general anesthesia group. Furthermore, the incidence of adverse reactions was significantly lower in the combined anesthesia group. Conclusions Ultrasound-guided lumbar plexus and sacral plexus block combined with general anesthesia enhanced the anesthetic efficacy and improved surgical outcomes in elderly patients undergoing intertrochanteric fracture surgery

Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning

Short-term load forecasting is mainly utilized in control centers to explore the changing patterns of consumer loads and predict the load value at a certain time in the future. It is one of the key technologies for the smart grid implementation. The load parameters are affected by multi-dimensional factors. To sufficiently exploit the time series characteristics in load data and improve the accuracy of load forecasting, a hybrid model based on Residual Neural network (ResNet) and Long Short-Term Memory (LSTM) is proposed in this paper. First, the data with multiple feature parameters is reconstructed and input into ResNeT network for feature extraction. Second, the extracted feature vector is used as the input of LSTM for short-term load forecasting. Lastly, a practical example is used to compare this method with other models, which verifies the feasibility and superiority of input parameter feature extraction, and shows that the proposed combined method has higher prediction accuracy. In addition, this paper also carries out prediction experiments on the variables in the weather influencing factors

A New Curve Tracing Algorithm Based on Local Feature in the Vectorization of Paper Seismograms

History paper seismograms are very important information for earthquake monitoring and prediction. The vectorization of paper seismograms is an import problem to be resolved. Auto tracing of waveform curves is a key technology for the vectorization of paper seismograms. It can transform an original scanning image into digital waveform data. Accurately tracing out all the key points of each curve in seismograms is the foundation for vectorization of paper seismograms. In the paper, we present a new curve tracing algorithm based on local feature, applying to auto extraction of earthquake waveform in paper seismograms

A New Waveform Mosaic Algorithm in the Vectorization of Paper Seismograms

History paper seismograms are very important information for earthquake monitoring and prediction, and the vectorization of paper seismograms is a very import problem to be resolved. In this paper, a new waveform mosaic algorithm in the vectorization of paper seismograms is presented. We also give out the technological process to waveform mosaic, and a waveform mosaic system used to vectorize analog seismic record has been accomplished independently. Using it, we can precisely and speedy accomplish waveform mosaic for vectorizing analog seismic records