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A stable least residue method for solving a nonlinear fractional integro-differential equation with a weakly singular kernel in the reproducing kernel space is proposed. To solve the equation, the multiwavelets bases in the reproducing kernel space are constructed based on the cubic Legendre wavelets in L 2 [ 0 , 1 ] . The best approximate solution could be obtained by solving the normal equation. Meanwhile, we provide the adaptive convergence order and the stability proof of the scheme. It is encouraging that the new method is stable and the accuracy of the method is preserved even in the case if the solution has fast oscillations. Four examples strongly demonstrate the validity of the scheme.

Surface defect classification (SDC) is the visual inspection of the surface of an object to identify appearance defects. Efficient and accurate SDC is i mportant for improving the quality of industrial products. A manually designed convolutional neural network (CNN) is traditionally used for SDC. In this study, a simpler SDC scheme with a higher classification accuracy, named NAS-SDC, is developed based on the neural architecture search (NAS) technique. A max-pooling cell based on NASNet is introduced to reduce the search space and the number of network parameters, thus simplifying the candidate operators for the search. Two network architectures are proposed to stack the search candidates or the best cells. The proposed method can be used to automatically design an efficient CNN model for SDC on a specific dataset. Experimental results show that the proposed method can find the best cells in ~11 h using a single graphics processing unit (GPU) and achieves higher classification accuracies (99.98%, 99.8% and 99.26%) than state-of-the-art methods on the Northeastern University (NEU-CLS), DAGM, and bridge defect datasets. The number of network parameters used in the proposed method is only 0.35 M, and the average test time per sample is approximately 61 ms, thus achieving a balance between performance and speed.

Voltage-dependent resistors (VDRs) are important circuit-protection devices. Their performance is affected by packaging quality. To identify VDR packaging defects more accurately and efficiently, we have proposed a convolutional neural network (CNN)-based VDR appearance quality inspection method that includes four stages: image acquisition, data augmentation, neural architecture design, and CNN training and testing. In designing the neural architecture, we have proposed two VDR-oriented network blocks, which consist of a compressed subnet and a multiscale subnet. Then, a stacking-block-based neural architecture design (BlockNAD) strategy is employed to determine the number of blocks. The last block is connected to a classification layer composed of a global average pooling (GAP) layer and a full connection (FC) layer. Further, using a VDR dataset containing 8058 images, we compared the identification performances of the candidate networks with different structures on 12 categories of VDR defects by adopting a variety of indicators, such as the mean average precision (mAP) and average test time per sample. The experimental results of the proposed method demonstrate competitive results compared to the state-of-the-art methods in identifying VDR defects, with a mAP value of approximately 99.9% and an average test time per sample of approximately 3 ms.

The vibration and noise produced by the powertrain and waves inside ship cabins limit working efficiency and crew and passengers’ accommodation quality. This paper simplifies ship cabins as cavities and explores active control techniques to attenuate sound transmission via multiple parallel-supported flexible subplates. The theoretical formulations of the interaction between multiple subplates and cavities were performed and the coupling relationships between them were analyzed. Based on the multiple subplates and the cavity coupling models, numerical simulations were performed using the derived optimal controller to minimize the transmission of sound into the cavities through two and nine parallel-supported subplates. The various control strategies were explored to minimize the coupling system’s acoustic potential energy, and the control performances were compared and discussed. The mechanism of reducing sound transmission through multiple supported subplates into a cavity is revealed. The simulation results showed that the vibration pattern of the controlled subplate is changed after it is regulated, which increases its radiation to subdue the other subplates’ radiation, while increasing vibration of the controlled subplate. The more subplates a cavity has, the more kinetic energy the controlled subplate possess. Furthermore, the noise reduction performance of a cavity with fewer subplates is better than that with more subplates.

Made available in DSpace on 2020-12-10T17:06:00Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-14 Operational modal analysis is an experimental modal analysis approach, which uses vibration data collected when the structure is under operating conditions. Amongst the methods for operational modal analysis, blind source separation-based methods have been shown to be efficient and powerful. The existing blind source separation modal identification methods, however, require the number of sensors to be at least equal to the number of modes in the frequency range of interest to avoid spatial aliasing. In this article, a frequency domain algorithm that overcomes this problem is proposed, which is based on the joint diagonalization of a set of weighted covariance matrices. In the proposed approach, the frequency range of interest is partitioned into several frequency ranges in which the number of active modes in each band is less than the number of sensors. Numerical simulations and an experimental example demonstrate the efficacy of the method. Harbin Engn Univ, Harbin, Peoples R China Univ Lyon, INSA Lyon, Lyon, France Univ Estadual Paulista, Sao Paulo, Brazil Univ Estadual Paulista, Sao Paulo, Brazil

Deformable image registration (DIR) plays an important role in assisting disease diagnosis. The emergence of the Transformer enables the DIR framework to extract long-range dependencies, which relieves the limitations of intrinsic locality caused by convolution operation. However, suffering from the interference of missing or spurious connections, it is a challenging task for Transformer-based methods to capture the high-quality long-range dependencies.In this paper, by staking the graph convolution Transformer (Graformer) layer at the bottom of the feature extraction network, we propose a Graformer-based DIR framework, named GraformerDIR. The Graformer layer is consist of the Graformer module and the Cheby-shev graph convolution module. Among them, the Graformer module is designed to capture high-quality long-range dependencies. Cheby-shev graph convolution module is employed to further enlarge the receptive field.The performance and generalizability of GraformerDIR have been evaluated on publicly available brain datasets including the OASIS, LPBA40, and MGH10 datasets. Compared with VoxelMorph, the GraformerDIR has obtained performance improvements of 4.6% in Dice similarity coefficient (DSC) and 0.055 mm in the average symmetric surface distance (ASD) while reducing the non-positive rate of Jacobin determinant (Npr.Jac) index about 60 times on publicly available OASIS dataset. On unseen dataset MGH10, the GraformerDIR has obtained the performance improvements of 4.1% in DSC and 0.084 mm in ASD compared with VoxelMorph, which demonstrates the GraformerDIR with better generalizability. The promising performance on the clinical cardiac dataset ACDC indicates the GraformerDIR is practicable.With the advantage of Transformer and graph convolution, the GraformerDIR has obtained comparable performance with the state-of-the-art method VoxelMorph.

Made available in DSpace on 2022-04-28T19:50:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 There is an ever-increasing requirement for higher power vibrating platforms to test large-scale structures. Whilst this may be achieved with a single shaker, this is an expensive option. An alternative solution is to drive a platform with two or more smaller shakers. To do this effectively, however, requires the identical amplitude and phase response of the shakers. In practice, due to manufacturing tolerances and uneven loading, this is not possible without a control system. The design and implementation of such a system is the objective of this paper. An adaptive FxLMS algorithm is used in the synchronous control of a dual-shaker system, considering the dynamic coupling between the shakers. A simulation is presented to verify the effectiveness of the control algorithm before the control system is integrated with practical a dual-shaker system driving a vibrating platform. It is shown that there are significant differences between the controlled and the uncontrolled system, demonstrating the efficacy of the control approach. College of Power and Energy Engineering Harbin Engineering University Department of Mechanical Engineering UNESP Department of Mechanical Engineering UNESP

Surface defect detection is an important way to improve the production quality of voltage-dependent resistors (VDRs). To improve the accuracy and efficiency of VDR surface quality detection, an end-to-end surface quality detection method based on deep convolutional neural networks (CNNs) was proposed. The method includes four stages: data preparation, convolution neural network design, CNN training, and testing. First, images of VDRs were acquired from three perspectives, i.e., the front, back, and side, and then training, validation and testing sets were obtained. Second, the proposed CNN models for VDR surface defect detection were constructed. Third, during the training stage, the images with class labels from the established training sets were input to the proposed network for training and validation. Finally, in the testing stage, test images from a total of 408 samples of two VDR models were used to test the trained network. The sensitivity, specificity, accuracy, precision and F measure of the proposed algorithm were compared with those of state-of-the-art methods, and the experimental results showed that the proposed method has a high recognition speed and accuracy and meets the requirements of online real-time detection.

Background Inguinal lymphadenectomy (iLAD) is effective for penile carcinoma treatment, but usually results in many complications. This study aims to clinically evaluate the feasibility and clinical significance of a laparoscopic radical iLAD approach partly preserving great saphenous vein branches for penile carcinoma patients. Methods A total of 48 patients with penile cancer who underwent laparoscopic radical iLAD with retention of the great saphenous vein in Henan Cancer Hospital from 2012 Jan to 2020 Dec were included in this study. Sixteen penile carcinoma patients who underwent laparoscopic radical iLAD preserving parts of superficial branches of the great saphenous vein were identified as the sparing group, and the matched 32 patients who incised those branches were identified as control group. This new procedure was performed by laparoscopy, preserving parts of superficial branches of the great saphenous vein, superficial lateral and medial femoral veins. Clinicopathological features and perioperative variables were recorded. Postoperative complications, including skin flap necrosis, lymphorrhagia, and lower extremity edema were analyzed retrospectively. Results We found that the operative time of the sparing group is significantly longer than the control group (p = 0.011). There was no statistical difference in intraoperative blood loss, the lymph node number per side, average time to remove the drainage tube and postoperative hospital stay between the two groups. Compared to the control group, the sparing group showed a significantly decreased incidence of lower extremity edema (p = 0.018). The preservation of parts of superficial branches of the great saphenous vein was mainly decreased the incidence of edema below ankle (p = 0.034). Conclusions This study demonstrated that the iLAD with preserving parts of superficial branches of the great saphenous vein, with a decreased incidence of postoperative complications, is a safe and feasible approach for penile cancer.

Background: Bladder cancer (BLCA) is a common malignant tumor of the genitourinary system, and there is a lack of specific, reliable, and non-invasive tumor biomarker tests for diagnosis and prognosis evaluation. Homeobox genes play a vital role in BLCA tumorigenesis and development, but few studies have focused on the prognostic value of homeobox genes in BLCA. In this study, we aim to develop a prognostic signature associated with the homeobox gene family for BLCA.Methods: The RNA sequencing data, clinical data, and probe annotation files of BLCA patients were downloaded from the Gene Expression Omnibus database and the University of California, Santa Cruz (UCSC), Xena Browser. First, differentially expressed homeobox gene screening between tumor and normal samples was performed using the “limma” and robust rank aggregation (RRA) methods. The mutation data were obtained with the “TCGAmutation” package and visualized with the “maftools” package. Kaplan–Meier curves were plotted with the “survminer” package. Then, a signature was constructed by logistic regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using “clusterProfiler.” Furthermore, the infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, the performance of the signature was evaluated by receiver-operating characteristic (ROC) curve and calibration curve analyses.Results: Six genes were selected to construct this prognostic model: TSHZ3, ZFHX4, ZEB2, MEIS1, ISL1, and HOXC4. We divided the BLCA cohort into high- and low-risk groups based on the median risk score calculated with the novel signature. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The infiltration levels of almost all immune cells were significantly higher in the high-risk group than in the low-risk group. The average risk score for the group that responded to immunotherapy was significantly lower than that of the group that did not.Conclusion: We constructed a risk prediction signature with six homeobox genes, which showed good accuracy and consistency in predicting the patient’s prognosis and response to immunotherapy. Therefore, this signature can be a potential biomarker and treatment target for BLCA patients.

The prognosis of patients with advanced urothelial carcinoma is dismal. Platinum-based chemotherapy is still the main first-line treatment for advanced urothelial carcinoma, while immunotherapy can be used as a first-line treatment option for people who cannot tolerate platinum. Immunotherapy is preferred in the second-line treatment of bladder urothelial carcinoma. PD-1 inhibitors (Pembrolizumab, nivolumab and atezolizumab) and PD-L1 inhibitors (Ddurvalumab and avelumab) have not been approved for the treatment of advanced urothelial cancer in China. We describe a patient with advanced urothelial carcinoma experienced disease progression after gemcitabine chemotherapy. Following a treatment of domestic PD-1 inhibitor (sintilimab), the patient achieved a durable complete response with mild toxicity. This case indicates that PD-1 inhibitor sintilimab might be a second-line treatment choice for advanced urothelial carcinoma.

When exposed to high levels of noise, earmuffs are often used to avoid hearing loss. However, active noise control earmuffs may exhibit nonlinearities under excessive levels of noise, due to their low-power characteristics of the loudspeakers, and thus nonlinear control algorithms are required to improve the control performance. In this paper, an analytical model of a nonlinear active noise control earmuff is investigated. Based on this model, a robust state feedback control law is designed in the framework of linear matrix inequalities with respect to the parametric uncertainties of the loudspeaker and the limitation of control input. Then the backstepping approach is adopted to force the nonlinear part of the loudspeaker to track the derived state feedback signal and estimate the unknown parameters. Both recorded vehicle noise and multi-frequency noise are used to test the effectiveness of the proposed controller and the control performance is compared with that of a widely accepted nonlinear generalized functional link artificial neural network algorithm. Simulation results demonstrate that the proposed controller is capable of attenuating the interior noise and reducing harmonic and intermodulation distortions significantly.

The segmentation of brain tumors in magnetic resonance imaging (MRI) images plays an important role in early diagnosis, treatment planning and outcome evaluation. However, due to gliomas’ significant diversity in structure, the segmentation accuracy is low. In this paper, an automatic segmentation method integrating the small kernels two-path convolutional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extraction ability of SK-TPCNN and the joint optimization capability of model are presented respectively. The SK-TPCNN structure combining the small convolutional kernels and large convolutional kernels can enhance the nonlinear mapping ability and avoid over-fitting, the multiformity of features is also increased. The learned features from SK-TPCNN are then applied to the RF classifier to implement the joint optimization. RF classifier effectively integrates redundancy features and classify each MRI image voxel into normal brain tissues and different parts of tumor. The proposed algorithm is validated and evaluated in the Brain Tumor Segmentation Challenge (Brats) 2015 challenge Training dataset and the better performance is achieved.

Nonrigid registration of medical images is especially critical in clinical treatment. Mutual information is a popular similarity measure for medical image registration; however, only the intensity statistical characteristics of the global consistency of image are considered in MI, and the spatial information is ignored. In this paper, a novel intensity‐based similarity measure combining normalized mutual information with spatial information for nonrigid medical image registration is proposed. The different parameters of Gaussian filtering are defined according to the regional variance, the adaptive Gaussian filtering is introduced into the local structure tensor. Then, the obtained adaptive local structure tensor is used to extract the spatial information and define the weighting function. Finally, normalized mutual information is distributed to each pixel, and the discrete normalized mutual information is multiplied with a weighting term to obtain a new measure. The novel measure fully considers the spatial information of the image neighborhood, gives the location of the strong spatial information a larger weight, and the registration of the strong gradient regions has a priority over the small gradient regions. The simulated brain image with single‐modality and multimodality are used for registration validation experiments. The results show that the new similarity measure improves the registration accuracy and robustness compared with the classical registration algorithm, reduces the risk of falling into local extremes during the registration process.

This work investigates a self-tuning resonator composed of a slender clamped–clamped steel beam and a freely movable slider. The clamped–clamped beam exhibits hardening nonlinearity when it vibrates in large amplitude, providing a broad bandwidth of dynamic response. The moving slider changes the mass distribution of the whole structure, and provides a passive self-tuning approach for capturing the high-energy orbit of the structure. In the case without inclination, adequate inertial force that mainly depends on the vibration amplitude of the beam and the position of the slider can drive the slider to move from the side toward the centre of the beam. This movement amplifies the beam response when the excitation frequency is below 37 Hz in our prototyped device. In the multi-orbit frequency range (28–37 Hz), the self-tuning and magnification of beam response can be achieved when the slider is initially placed in an appropriate position on the beam. Once the beam is disturbed, however, the desired response in the high-energy orbit can be lost easily and cannot be reacquired without external assistance. In an improved design with a small inclination, the introduced small gravitational component enables the slider to move from the higher side toward the lower side when the beam amplitude is small. This property sacrifices the less efficient self-tuning region below 25 Hz, but can enable the beam to acquire and maintain the high-energy orbit response in the multi-orbit frequency range (28–39 Hz), which is resistant to disturbance. The proposed resonator in this paper not only broadens the frequency bandwidth of dynamic response, but also enables capture and maintenance of the high-energy orbit in a completely passive way. Such a passive self-tuning structure presents an advantage in the design of broadband vibration energy-harvesting systems.

Melanoma is the deadliest form of skin cancer, and its incidence is increasing. The first step in automated melanoma analysis of dermoscopy images is to segment the area of the lesion from the surrounding skin. To improve the accuracy and adaptability of segmentation, an algorithm called sampling with level set by integrating color and texture (SLS-CT) is proposed that not only designs a new way to incorporate textural and color features in the definition of the energy functional but also utilizes an index called texture level, proposed in this work, to automatically decide the weight of each feature in the combined energies. First, at the preprocessing stage, hair and black frame removal is applied, and a potential lesion area is then obtained using Otsu thresholding and entropy maximization. Thereafter, the probability distribution of prior color in this potential lesion area is calculated as well. Second, Gabor wavelet-based texture features are extracted and integrated with the prior color into the evolving energies of the level set based on the texture level. To achieve global optimization, a Markov chain Monte Carlo sampling approach guided by the combined energy is adopted in evolving the level set, which ultimately defines a border in the image to segment a lesion from normal skin. Finally, morphological operations are used for postprocessing. The experimental results of the proposed algorithm are compared with those of other state-of-the-art algorithms, demonstrating that the proposed algorithm outperforms the other tested ones in terms of accuracy and adaptability to different databases.

Magnetic resonance imaging (MRI) is one of the most important methods for clinical diagnosis. However, the main drawback of MRI is the long imaging time, which will cause the moving artifact by patient movements. With the rapid development of the computing power of computer, deep learning is widely used in computer vision, natural language processing, visual recognition and so on. Meanwhile, a large number of reconstruction methods based on deep learning have also emerged. Recently, many generative models have been proposed to solve the perception quality problem that existed in fast MRI images. In this paper, we manage to survey the motivations and reconstruction strategies of generative-based methods published in journals and conferences over the past five years. First, the background and theoretical basis of MRI reconstruction are introduced. Secondly, the application of generative-based methods in MRI reconstruction field is comprehensively summarized and analyzed, including Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and VAE-GAN. Then the advantages and disadvantages of the existing generative-based MRI reconstruction methods are discussed. Finally, several publicly available MR image datasets and evaluation metrics are presented, which can provide a reference for researchers and practitioners working in related domains. The conclusions and challenges are also given.

In this paper, the vibration energy transfer mechanism of the single-sided vibro-impact (SSVI) nonlinear energy sink (NES) is studied. The concept of impact mode is proposed where the device velocities are decomposed into an energy free impact mode (EFIM) and an energy dissipation impact mode (EDIM). The impact modes are applied to the analysis of a single-degree-of-freedom (SDOF) vibration system with a SSVI NES. This analysis shows that the energy in the system can be redistributed between the different impact modes and that energy transferred from the EFIM to the EDIM is beneficial, because portions of the energy in the EDIM will be dissipated by impact, while the energy in the EFIM will not. More importantly, the mechanism of the SSVI NES to realize the local dissipation of energy is revealed. Furthermore, in order to better evaluate the energy dissipation performance of the SSVI NES, a new evaluation criterion called the vibro-impact vibration reduction (VVR) factor is proposed. Then, the relationship between the VVR factor and the energy free impact mode coefficient is investigated using the Hilbert transform. Finally, the effect of the SSVI NES parameters on the energy dissipation performance of the SSVI NES with various initial conditions is discussed, and a satisfactory region for the SSVI NES design, which is identified via numerical simulation, is proposed.

Background Renal cell carcinoma (RCC) is a common malignant tumour of the genitourinary system. We aimed to analyse the potential value of metastasis-related biomarkers, circulating tumour cells (CTCs) and the proliferative marker Ki-67 in the diagnosis of RCC. Methods Data from 24 laparoscopic radical nephrectomies (RNs) and 17 laparoscopic partial nephrectomies (PNs) were collected in 2018. The numbers and positive rates of CTCs and circulating tumour microemboli (CTM) in the peripheral blood were obtained at three different time points: just before surgery, immediately after surgery and 1 week after surgery. Ki-67 protein expression was evaluated in the RCC tissue by immunohistochemistry. Results Except for the statistically significant association between the preoperative CTC counts and tumour size, no association between the number and positive rate of perioperative CTCs and clinicopathological features was found. The CTC counts gradually decreased during the perioperative period, and at 1 week after surgery, they were significantly lower than those before surgery. High Ki-67 expression was significantly positively correlated with preoperative CTC counts. In addition, Ki-67 expression was higher in the high CTC group (≥ 5 CTCs). Conclusion Our results suggest that surgical nephrectomy is associated with a decrease in CTC counts in RCC patients. CTCs can act as a potential biomarker for the diagnosis and prognosis of RCC. A careful and sufficient long-term follow-up is needed for patients with high preoperative CTC counts.

Background. Osteoporosis is an important health problem worldwide. Liuwei Dihuang Decoction (LDD) and its main ingredients may have a good clinical effect on osteoporosis. Meanwhile, its mechanism for treating osteoporosis needs to be further revealed in order to provide a basis for future drug development. Methods. A systematic biological methodology was utilized to construct and analyze the LDD-osteoporosis network. After that, the human transcription data of LDD intervention in patients with osteoporosis and protein arrays data of LDD intervention in osteoporosis rats were collected. The human transcription data analysis, protein arrays data analysis, and molecular docking were performed to validate the findings of the prediction network (LDD-osteoporosis PPI network). Finally, animal experiments were conducted to verify the prediction results of systematic pharmacology. Results. (1) LDD-osteoporosis PPI network shows the potential compounds, potential targets (such as ALB, IGF1, SRC, and ESR1), clusters, biological processes (such as positive regulation of calmodulin 1-monooxygenase activity, estrogen metabolism, and endothelial cell proliferation), and signaling and Reactome pathways (such as JAK-STAT signaling pathway, osteoclast differentiation, and degradation of the extracellular matrix) of LDD intervention in osteoporosis. (2) Human transcriptomics data and protein arrays data validated the findings of the LDD-osteoporosis PPI network. (3) The animal experiments showed that LDD can improve bone mineral density (BMD), increase serum estradiol (E2) and alkaline phosphatase (ALP) levels, and upregulate Wnt3a and β-catenin mRNA expression ( P < 0.05 ). (4) Molecular docking results showed that alisol A, dioscin, loganin, oleanolic acid, pachymic acid, and ursolic acid may stably bind to JAK2, ESR1, and CTNNB1. Conclusion. LDD may have a therapeutic effect on osteoporosis through regulating the targets (such as ALB, IGF1, SRC, and ESR1), biological processes (such as positive regulation of calmodulin 1-monooxygenase activity, estrogen metabolism, and endothelial cell proliferation), and pathways (such as JAK-STAT signaling pathway, osteoclast differentiation, and degradation of the extracellular matrix) found in this research.

OBJECTIVE: In order to solve the blurred structural details and over-smoothing effects in sparse representation dictionary learning reconstruction algorithm, this study aims to test sparse angle CT reconstruction with weighted dictionary learning algorithm based on adaptive Group-Sparsity Regularization (AGSR-SART). METHODS: First, a new similarity measure is defined in which Covariance is introduced into Euclidean distance, Non-local image patches are adaptively divided into groups of different sizes as the basic unit of sparse representation. Second, the weight factor of the regular constraint terms is designed through the residuals represented by the dictionary, so that the algorithm takes different smoothing effects on different regions of the image during the iterative process. The sparse reconstructed image is modified according to the difference between the estimated value and the intermediate image. Last, The SBI (Split Bregman Iteration) iterative algorithm is used to solve the objective function. An abdominal image, a pelvic image and a thoracic image are employed to evaluate performance of the proposed method. RESULTS: In terms of quantitative evaluations, experimental results show that new algorithm yields PSNR of 48.20, the maximum SSIM of 99.06% and the minimum MAE of 0.0028. CONCLUSIONS: This study demonstrates that new algorithm can better preserve structural details in reconstructed CT images. It eliminates the effect of excessive smoothing in sparse angle reconstruction, enhances the sparseness and non-local self-similarity of the image, and thus it is superior to several existing reconstruction algorithms.

The nonlinear beam-slider structure, which consists of a nonlinear cantilever beam and a free movable slider, can always obtain the high-energy orbit to achieve passive self-adaption in a wide bandwidth. The efficiency improvement of this structure has been demonstrated in energy harvesting application. In this work, the nonlinear beam-slider structure is applied as a vibration neutralizer. The behavior of the 2-degree-of-freedom (2-DOF) vibration system is investigated experimentally. The trajectory of the slider, time history response of the nonlinear beam and the linear primary structure are recorded simultaneously. The results show that the nonlinear neutralizer with appropriate parameters has broader bandwidth than the linear one. However, there are multiple solutions corresponding to different vibration states of the nonlinear neutralizer in the suppression frequency range. The vibration of linear primary structure can be suppressed only when the nonlinear neutralizer obtains the certain energy orbit at the given frequency range. The free movable slider can assist the nonlinear beam to obtain the high-energy orbit in multi-solution range (28 Hz-31 Hz). In the frequency range of 28 Hz-31 Hz, the nonlinear neutralizer on the high-energy orbit enhances the vibration suppression performance.

Background Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation. Objective This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network. Methods In this study, a novel U-Net with dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network's ability to recognize the tumor details. Results The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively. Conclusions The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.

Background: Regarding the outbreak of highly contagious the 2019 Novel Coronavirus (2019-nCoV) in various countries and regions, data have been needed on the early chest CT images and clinical characteristics of the affected patients. Objectives: To explore the early clinical and computed tomography (CT) characteristics of the 2019 Novel Coronavirus Disease (COVID-19) patients to improve the diagnostic level of this contagious respiratory disease. Methods: An analysis retrospectively was implemented on the radiological features and clinical characteristics of 12 patients with COVID-19 who had undergone chest CT scanning in the designated hospital from Jan 23, 2020, to Feb 18, 2020. The clinical data on general information, epidemiological, cardinal symptoms, blood test, and CT imaging characteristics were obtained. Results: According to the relevant diagnostic criteria, the patients were divided into two groups: mild (2 cases), and ordinary type (10 cases). The main symptoms of 2019-nCoV pneumonia were fever (9/12) and cough (8/12) with or without respiratory and other systemic symptoms. The blood test of the patients showed that most of the white blood cell count was normal (10/12), decreased lymphocyte count (6/12), and increased hypersensitive C reactive protein (hs-CRP) (5/12). In the early stage of COVID-19, the chest CT images showed patchy mixed ground-glass opacity (GGO) (8/12), mainly distributed in the periphery and posterior part of both lungs. The internal density of image lesion area was uneven, and lesions primarily manifested as “crazy-paving pattern” (8/12), with grid-like, interlobular septal thickening, thickened bronchovascular bundle and air bronchus sign and multiple fibrosis. A few cases showed pulmonary atelectasis (1/12), bilateral pleural effusion (1/12), no mediastinal or bilateral hilar lymph node enlargement. Conclusions: The clinical characteristics of 2019-nCoV pneumonia are similar to those of common viral pneumonia. The chest CT images may be helpful for the early detection of novel coronavirus pneumonia.

Made available in DSpace on 2020-12-12T01:25:02Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-05-01 An active vibration isolation system is retrofitted to a diesel generator set in a tugboat to determine the effectiveness of such a system in a realistic practical environment. The system consists of six bespoke inertial actuators chosen to make minimal modifications to the machinery arrangement, and a DSP-based controller. Six accelerometers are collocated with the actuators on the top of six isolators to act as error sensors, and six accelerometers are placed below the isolators to give a measure of the global vibration of the ships structure below the generator set. A hydrophone is also placed in the water to give an indication of the underwater noise due to the generator. The control strategy employed is six-input and six-output decentralized adaptive feedforward control with the reference signal being derived from the signal from an optical tachometer on shaft between the engine and the generator. To suppress the vibration at all the dominant forcing frequencies, an electrical circuit generated the half engine orders required from the measured reference signal. The experimental results show that the combination of the active control system and the passive isolators is effective in reducing the global vibration and the acoustic pressure at the hydrophone position. Institute of Vibration and Noise Control Harbin Engineering University Departamento de Engenharia Mecânica Universidade Estadual Paulista (UNESP) Departamento de Engenharia Mecânica Universidade Estadual Paulista (UNESP)

Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-020-00339-9) contains supplementary material, which is available to authorized users.

As one of the important dimensionality reduction techniques, unsupervised feature selection (UFS) has enjoyed amounts of popularity over the last few decades, which can not only improve learning performance, but also enhance interpretability and reduce computational costs. The existing UFS methods often model the data in the original feature space, which cannot fully exploit the discriminative information. In this paper, to address this issue, we investigate how to strengthen the relationship between UFS and the feature subspace, so as to select relevant features more straightforwardly and effectively. Methodologically, a novel UFS approach, referred to as Graph Regularized Local Linear Embedding (GLLE), is proposed by integrating local linear embedding (LLE) and manifold regularization constrained in feature subspace into a unified framework. To be more specific, we explicitly define a feature selection matrix composed of 0 and 1, which can realize the process of UFS. For the purpose of modelling the feature selection matrix, we propose to preserve the local linear reconstruction relationship among neighboring data points in the feature subspace, which corresponds to LLE constrained in the feature subspace. To make the feature selection matrix more accurate, we propose to use manifold regularization as an assistant of LLE to find the relevant and representative features such that the selected features can make each sample under the feature subspace be accordance with the manifold assumption. A tailored iterative algorithm based on Alternative Direction Method of Multipliers (ADMM) is designed to solve the proposed optimization problem. Extensive experiments on twelve real-world benchmark datasets are conducted, and the more promising results are achieved compared with the state-of-the-arts approaches.

The volume and morphology of calcium hydroxide (CH) precipitating from supersaturated solutions is monitored as a function of the concentration of either of two types of nanoparticles dispersed in the solution. The CH precipitated in the presence of sulfonated graphene nanosheets (SGN) had well-developed hexagonal platelet shapes, while that forming in the presence of cationic polyurethane nanospheres (PUC) tended to aggregate around the PUC and developed as spherulitic masses. The terminal CH platelet size in SGN suspensions was 8 μm; with increasing SGN dosage, the mean size increased to 23 μm. Taking into consideration complementary experimental measurements of isothermal adsorption and zeta potential, we speculate that calcium from the solution adsorbs on the surfaces of both SGN and PUC prior to nucleation of CH. Furthermore, molecular-scale mechanism indicated the interaction of Ca-Ocoo from PUC is stronger than Ca-Oso3 from SGN. Simultaneously the number of adsorbed calcium by PUC is roughly 3 times greater than for SGN, which is perfectly matched with the measured adsorption isotherm. Hopefully, this work can provide scientific guidance for hydration mechanism of cementitious materials in the presence of nanoparticles suspensions.

BACKGROUNDAccurate segmentation of brain tumor depicting on magnetic resonance imaging (MRI) is an important step for doctors to determine optimal treatment plan of Gliomas, which are the common malignant brain tumors that seriously damage patients' health and life.OBJECTThis study aims to improve accuracy and efficiency of brain tumor segmentation on MRI using the advanced deep learning model.METHODIn this study, an improved model based on the U-net for accurate segmentation of brain tumor MRI images, called Deeper ResU-net, is proposed. First, a deep Deeper U-net is built, which has deeper network depth compared with U-net, uses Squeeze Operator to control network parameters and attempts to enhance the feature extraction ability. Then, Deeper ResU-net is formed to eliminate degradation phenomenon of the deep network, in which residual unit is designed and integrated into the Deeper U-net to keep the number of parameters unchanged.RESULTDeeper ResU-net makes the deep network conduct stable training without degrading. Evaluation result shows that the Deeper ResU-net has achieved competitive result with average DSC metrics of 0.9, 0.82, 0.88 for Complete tumor region, Core tumor region and Enhanced tumor region, respectively.CONCLUSIONBy extending the U-net model to a deeper layer and adding the residual structure to ensure effective and stable training of the model, the experiment results demonstrate that applying the improved Deeper ResU-net can effectively eliminate the degradation phenomenon of deep network and improve segmentation performance.

Made available in DSpace on 2020-12-10T19:46:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-12-01 Natural Science Foundation of China In this paper, the single-sided vibro-impact track nonlinear energy sink (SSVI track NES) is studied. The SSVI track NES, which is attached to a primary structure, has nonlinear behavior caused by the NES mass moving on a fixed track and impacting on the primary structure at an impact surface. Unlike previous studies of the SSVI track NES, both the horizontal and vertical dynamics of the primary structure are considered. A numerical study is carried out to investigate the way in which energy is dissipated in this system. Assuming a track shape with a quartic polynomial, an optimization procedure that considers the total energy dissipated during a time period is carried out, to determine the optimum NES mass and track parameter. It is found that there is dynamic coupling between the horizontal and vertical directions caused by the SSVI track NES motion. The vibrational energy, originally in the structure in the horizontal direction, is transferred to the vertical motion of the structure where it is dissipated. Considering that many civil and mechanical systems are particularly vulnerable to extreme loads in the horizontal direction, this energy transformation can be beneficial to prevent or limit damage to the structure. The effect on energy dissipation of the position of the impact surface in the SSVI track NES and the ratio of the vertical to horizontal stiffness in the primary structure are discussed. Numerical results demonstrate a robust and stable performance of the SSVI track NES over a wide range of stiffness ratios. Harbin Engn Univ, Coll Power & Energy Engn, 145 Nantong St, Harbin 150001, Peoples R China Univ Tennessee, Dept Civil & Environm Engn, John D Tickle Bldg, Knoxville, TN 37996 USA Univ Estadual Paulista UNESP, Dept Mech Engn, Av Brasil Centro, BR-15385000 Ilha Solteira, SP, Brazil Univ Estadual Paulista UNESP, Dept Mech Engn, Av Brasil Centro, BR-15385000 Ilha Solteira, SP, Brazil Natural Science Foundation of China: 51375103 Natural Science Foundation of China: 10.13039/501100001809

In this paper, sound radiation modes of baffled cylinders have been derived by constructing the radiation resistance matrix analytically. By examining the characteristics of sound radiation modes, it is found that radiation coefficient of each radiation mode increases gradually with the increase of frequency while modal shapes of sound radiation modes of cylindrical shells show a weak dependence upon frequency. Based on understandings on sound radiation modes, vibro-acoustics behaviors of cylindrical shells have been analyzed. The vibration responses of cylindrical shells are described by modified Fourier series expansions and solved by Rayleigh-Ritz method involving Flugge shell theory. Then radiation efficiency of a resonance has been determined by examining whether the vibration pattern is in correspondence with a sound radiation mode possessing great radiation efficiency. Furthermore, effects of thickness and boundary conditions on sound radiation of cylindrical shells have been investigated. It is found that radiation efficiency of thicker shells is greater than thinner shells while shells with a clamped boundary constraint radiate sound more efficiently than simply supported shells under thin shell assumption.

This paper investigates the active noise control algorithms and improves them by using online secondary path modeling. The proposed method uses three adaptive filters to track the convergence of the system as well as reduce the target noise. By theoretical analysis, the optimized step size and injected random noise gain are derived. The step size is varied according to the convergence of three adaptive filters and the gain of injected random noise is proportional to the power of modeling error, which makes the method more stable even in the presence of strong perturbation. Compared with previous methods, the proposed method improves the convergence rate and estimation accuracy for both the active control system and the secondary path modeling process with less increase of computational complexity. The simulation results verify the above analysis by controlling three different kinds of noise.

The existing spectrum index-based methods for detecting vegetation coverage suffer from an over-dependence on spectrum. To address these issues, this paper proposes a graph cut-based variational level set segmentation algorithm that combines multi-channel local wavelet texture (MCLWT) and color. First, the prior color is generated by automatic estimation based on the mathematical morphology with a color histogram. Then, local wavelet texture features are extracted using a multi-scale and orientation Gabor wavelet transformation followed by local median and entropy filtering. Next, in addition to the energy of color, that of MCLWT is integrated into the variational level set model based on kernel density estimation. Consequently, all energies are integrated into the graph cut-based variational level set model. Finally, the proposed energy functional is made convex to obtain a global optimal solution, and a primal-dual algorithm with global relabeling is adopted to accelerate the evolution of the level sets. A comparison of the segmentation results from our proposed algorithm and other state-of-the-art algorithms showed that our algorithm effectively reduces the over-dependence on color and yields more accurate results in detecting vegetation coverage.

Research on the static connectivity of reservoirs is of great significance for predicting the lateral continuity of sandstone bodies and determining the field development strategy. The main impacts of static connectivity are geometry, stacking pattern and distribution of fluvial sandstone bodies, which are controlled by changes in base level. This study addresses the static connectivity of fluvial sandstones of the Yangdachegnzy reservoir in the Yushulin oilfield with a dense network of wells. The Yangdachengzy reservoir develops a long-term depositional cycle (a third-level sequence) consisting of 3 mid-term depositional cycles (MDC1-MDC3) and 21 short-term depositional cycles (SDC1-21). Sedimentary facies maps of each stratigraphic intervals in the Yangdachengzy reservoir that are constrained to high well density from the Yushulin Oilfield exhibit: (i) as base-level falls, the planform style of sandstone bodies changes from isolated type to amalgamated type; (ii) how static connectivity evolves through base-level fluctuations and show how static connectivity is sensitive to the net-to-gross ratio and well spacing. The analyses show that the relationship between the net-to-gross ratio and static connectivity exhibits a sigmoid curve. Such a curve is sensitive to the well spacing and the planform style of sandstone bodies. When well spacing increases, the S-curve shifts to the right, with a narrow “cascade zone” and a relatively higher percolation threshold of net-to-gross ratio (e.g., 0.2 at 150 m well spacing). When well spacing decreases, the S-curve tends to be linear, with a large range of “cascade zones” and a relatively lower percolation threshold of the net-to-gross ratio (e.g., 0.43 at 450 m well spacing). At a given net-to-gross ratio, amalgamated-type sandstone bodies that developed at the lowest base-level stage are very prone to reach the fully connected status (reservoir connected over 0.8) with a relatively low well density.

For acoustic beamforming, the Fast Iterative shrinkage-thresholding algorithm (FISTA) is efficient in improving the spatial resolution and the dynamic range of conventional beamforming maps. However, there are some drawbacks inherent to FISTA. In this paper, to further improve the convergence rate, numerical stability, and computation time of FISTA, adaptive step-size FISTA (AFISTA), Greedy FISTA (GFISTA) along with compression computation grid method (CG) are applied in the solving process of deconvolution beamforming. Two incoherent sinusoid sound sources with different distances, locations and frequencies are simulated and experimented to investigate the behaviors of these new algorithms (denoted as FISTA-CG, AFISTA-CG, and GFISTA-CG). Results show that all algorithms can accurately reveal the locations and amplitudes of sound sources with little differences in the high iterations. Although AFISTA-CG consumes more time during each iteration, it achieves the fastest convergence rate among these algorithms in the low iterations, and thus visualizes the accurate locations of sound sources the most rapidly in all cases. Furthermore, both AFISTA-CG and GFISTA-CG have lower convergence errors and numerical fluctuations compared with FISTA-CG.

In this paper, a meshless method in reproducing kernel space is proposed for solving VOTFA-DE on arbitrary domain. Advantages of the meshless method proposed could avoid effectively difficulties of constructing shape functions using known Mercer kernel and deal with arbitrary domains. And the accuracy is verified by two examples.

Active structural acoustical control usually experiences difficulty in the control of heavy sources or sources where direct applications of control forces are not practical. To overcome this difficulty, an active controlled enclosure, which forms a cavity with both flexible and open boundary, is employed. This configuration permits indirect implementation of active control in which the control inputs can be applied to subsidiary structures other than the sources. To determine the control effectiveness of the configuration, the vibro-acoustic behavior of the system, which consists of a top plate with an open, a sound cavity and a source panel, is investigated in this paper. A complete mathematical model of the system is formulated involving modified Fourier series formulations and the governing equations are solved using the Rayleigh-Ritz method. The coupling mechanisms of a partly opened cavity and a plate are analysed in terms of modal responses and directivity patterns. Furthermore, to attenuate sound power radiated from both the top panel and the open, two strategies are studied: minimizing the total radiated power and the cancellation of volume velocity. Moreover, three control configurations are compared, using a point force on the control panel (structural control), using a sound source in the cavity (acoustical control) and applying hybrid structural-acoustical control. In addition, the effects of boundary condition of the control panel on the sound radiation and control performance are discussed.

Surface defect recognition is one of the key technologies for varistor quality inspection, which can greatly improve detection efficiency and performance. In order to more accurately identify the surface defects of a varistor body and the pins, a method for identifying the surface defects based on deep convolutional neural networks (CNN) is proposed. The proposed method mainly includes four stages: image acquisition and data set construction, convolutional neural network modeling, CNN training and testing. Firstly, varistor images are acquired, and the body and pins of the varistor are segmented by image segmentation method. The number of samples is increased by data augmentation to make a data set of 5 classes. Secondly, according to the appearance characteristics of varistor, a CNN model is designed for varistor surface defect recognition. Third, using the created data set, the training data set with category labels are input to the proposed CNN for training. Finally, 1200 test samples were tested on the trained model in the test phase and the performance of the proposed algorithm was evaluated using mean average precision. The experimental results show that our method can identify the surface defects of the main body and pins of varistor efficiently and accurately.

The edge of the traditional non-local Means (NLM) constrained CT reconstruction algorithm tends to be over-smooth. An improved algebraic iterative reconstruction algorithm based on adaptive non-local mean constraint (ART_SNLM) is proposed in this paper. Firstly, a clock-like similarity window shape is defined, the pixels with high similarity can be selected to participate in the weight calculation with higher probability. Secondly, in order to remove noise and protect the edges simultaneously, the adaptive filter parameters are designed to filter the reconstructed image through the change of the difference between the gray value of the neighborhood pixel and the center pixel in the clock-like window's three directions, and parameter is related to the change of the number of iterations as well. The improved algorithm is used to reconstruct the classic Shepp-Logan phantom.The experimental results show that the reconstructed image by ART_SNLM algorithm is not only closer to the real phantom, but also has a smaller reconstruction error, which can protect the edge characteristics of the image better.

Made available in DSpace on 2019-10-06T15:48:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-06-01 This paper describes an analytical investigation into synchrophasing, a vibration control strategy on a machinery installation in which two rotational machines are attached to a beam-like raft by discrete resilient isolators. Forces and moments introduced by sources are considered, which effectively represent a practical engineering system. Adjusting the relative phase angle between the machines has been theoretically demonstrated to greatly reduce the cost function, which is defined as the sum of velocity squares of attaching points on the raft at each frequency of interest. The effect of the position of the machine is also investigated. Results show that altering the position of the secondary source may cause a slight change to the mode shape of the composite system and therefore change the optimum phase between the two machines. Although the analysis is based on a one-dimensional Euler–Bernoulli beam and each machine is considered as a rigid-body, a key principle can be derived from the results. However, the factors that can influence the synchrophasing control performance would become coupled and highly complicated. This condition has to be considered in practice. College of Power and Energy Engineering Harbin Engineering University Departamento de Engenharia Mecânica UNESP Departamento de Engenharia Mecânica UNESP

This work investigates the interaction between a nonlinear slender clamped-clamped beam and a freely movable mass during the passive self-tuning process. The experimental and numerical results illustrate that the hardening nonlinearity caused by the beam stretch strain can broaden the frequency bandwidth. When the amplitude and curvature of the beam at the slider location are large enough, the slider could be driven to move from the side towards the centre and stop around the centre. The slider’s movement, in turn, changes the beam-slider structure’s mass distribution that shifts the frequency response functions to the lower frequency range. During this interaction between the beam and slider, the high energy orbit could be captured with amplified vibration response. Because the slider is driven by the beam vibration, the self-tuning process does not require external energy. Such a beam-slider structure could be used for the design of nonlinear energy harvesting system with the capability of passive self-tuning to acquire large amplitude vibration and thus higher efficiency.

Brain tumor images segmentation plays a crucial role in the auxiliary diagnosis of disease, treatment planning and surgical navigation. In order to accurately segment brain tumor images, this paper proposes an automatic brain tumor Magnetic Resonance Imaging (MRI) image segmentation algorithm based on the U-net model. Firstly, based on samples preprocessing, loss function and optimization algorithm, a U-net baseline model with optimal parameters structure which is suitable for segmentation task is constructed. Secondly, a feature recombination layer, i.e. a 1×1 convolutional layer, is added to the baseline model to linearly recombine the upper layer features, enrich extracted features, reduce network parameters, and improve the segmentation results. The algorithm is validated and evaluated on the Brain Tumor Segmentation Challenge (Brats) 2015 challenge training dataset. The experimental results show that the proposed algorithm has strong competitiveness compared with the existing brain tumor MRI image segmentation algorithm.

Medical image registration technology is important in medical image processing, which can establish correspondence between complex medical images and ensure the comparability of image data. Since the deep convolutional neural network model is prone to degradation and the convolution kernel has limited detection range during training, a Multi-scale residual full convolutional network (MS-ResFCN) model is proposed for unsupervised non-rigid medical image registration tasks. The model introduces a residual structure in the Fully Convolutional Network (FCN) model to ensure effective and stable training. At the same time, a hierarchical multi-scale convolution kernel is constructed within a single convolutional layer of the residual structure, which enhances the nonlinear mapping ability of the network. The local texture information and the contextual information of the image are synchronously extracted and fused to enrich the diversity of features. The experiment results on LPBA40 data sets show that the MS-ResFCN model can effectively eliminate the degradation phenomenon of the deep network during the training process and extract multi-scale features, which achieves better feature representation ability and registration accuracy.

This paper concerns the effect that a stochastic resonance can have on a vibration isolation system. Rather than reducing the transmitted force, it is shown that it is possible to significantly mask the component of the force transmitted though the isolator, when the system is excited harmonically. This can be achieved by adding a very low intensity of random noise to the harmonic excitation force. The nonlinear mechanical vibration isolation system used in the study consists of a vertical linear spring in parallel with two horizontal springs, which are configured so that the potential energy of the system has a double-well. Prior to the analytical and numerical study, an experiment to demonstrate stochastic resonance in a mechanical system is described.

Background Advanced pancreatic cancer (PC) has very poor prognosis with present treatments, thus necessitating continued efforts to find improved therapeutic approaches. Both preclinical and preliminary clinical data indicate that cytokine-induced killer (CIK) cells are an effective tool against various types of solid tumors. Here, we conducted a study to determine whether CIK cell-based therapy (CBT) can improve the outcomes of advanced PC. Methods Eighty-two patients with advanced PC, whose predicted survival time was longer than 3 months, were analyzed retrospectively. Of all the patients, 57 individuals were receiving chemotherapy, while the remaining 25 individuals were treated with CBT. Results The overall survival analysis was based on 48 deaths in the 57 patients in the chemotherapy group (84.2 %) and 18 deaths in the 25 patients in the CBT group (72.0 %). In the CBT group, the median overall survival time was 13.5 months, as compared to 6.6 months in the chemotherapy group (hazard ratio for death, 0.39; 95 % confidence interval, 0.23 to 0.65; p

In order to improve the long-term prediction accuracy of feed grain demand, a dynamic forecast model of long-term feed grain demand is realized with joint multivariate regression model, of which the correlation between the feed grain demand and its influence factors is analyzed firstly; then the change trend of various factors that affect the feed grain demand is predicted by using ARIMA model. The simulation results show that the accuracy of proposed combined dynamic forecasting model is obviously higher than that of the grey system model. Thus, it indicates that the proposed algorithm is effective.