Search

Background: The efficacy of colchicine administration for coronary heart disease remains controversial. We conducted a systematic review and meta-analysis to explore the influence of colchicine administration versus placebo on treatment efficacy for coronary heart disease. Methods: We have searched PubMed, Embase, Web of Science, EBSCO, and Cochrane Library databases through May 2021 for randomized controlled trials (RCTs) assessing the effect of colchicine administration versus placebo in patients with coronary heart disease. This meta-analysis was performed using the random-effects model. Results: Six RCTs involving 6,321 patients were included in the meta-analysis. Overall, compared with control groups for coronary heart disease, colchicine intervention can significantly reduce major adverse cardiovascular events (odds ratio [OR] 0.74; 95% confidence interval [CI] 0.59 to 0.92; P = .006), but revealed no obvious impact on mortality (OR=0.93; 95% CI=0.63 to 1.36; P = .69), serious adverse events (OR 0.71; 95% CI 0.31 to 1.61; P = .41), or restenosis (OR 1.02; 95% CI 0.63 to 1.64; P = .95). Conclusions: Colchicine treatment may be effective to reduce major adverse cardiovascular events in patients with coronary heart disease.

In this article, we present a novel solution to automating the procurement of food materials by using electronic scales, which can automatically identify the food materials along weighing them. Although the CNN model is regarded as one of the most effective solutions to image recognition, the traditional techniques cannot handle the mismatch problem between the lab training data and the real world data. To solve the problem, we propose to embed a partial-and-imbalanced domain adaptation technique (tree adaptation network) in the deep learning model, which can borrow knowledge from sibling classes, to overcome the imbalance problem, and transfer knowledge from the source domain to the target domain, to fight the mismatch problem between the lab training data and the real world data. Experiments show that the proposed approach outperforms state-of-the-art algorithms. Furthermore, the proposed techniques have already been used in practice.

A novel blind source separation (BSS) algorithm for electromagnetic interference (EMI) sources identification based on data mining is proposed in this paper. Differing from normal analysis, the new algorithm is conducted by mining the internal correlations of radiated emission (RE) data which is obtained by taking standard tests in the far field around the equipment under test (EUT). The results of full wave electromagnetic simulation demonstrate the effectiveness of the proposed algorithm.

Propagation delay asymmetry is one of the biggest factors affecting the performance of clock synchronization. For an industrial Ethernet system that implements communication in pure hardware, almost all the asymmetries are caused by hardware factors. In this letter, we analyze the hardware factors for the asymmetry of 100Base-Tx real-time Ethernet system. Then, a low-cost method to mitigate the asymmetry is proposed. The effectiveness of the method will be evaluated by experiments on real-world devices.

In this paper, ultrasound was used as an auxiliary tool to activate the activity of beta-glucosidase (beta-GC) in apricot kernels, and its parameters were optimized to evaluate the effects on the beta-GC activity with the response surface methodology (RSM), variables including ultrasonic time, temperature, power and frequency. The results indicate that the obtained quadratic regression model could simulate the actual conditions, and the optimum conditions were as follows: exposure time of 31 min, temperature 50 °C, power 225 W and frequency 28 kHz, and the activity of beta-GC achieved 3.64 × 105 U/g·apricot kernel (dry weight), having an increase of 34.67% compared to the untreated beta-GC. In addition, the changes of the beta-GC properties demonstrated that ultrasound did improve the activity of beta-GC by influencing the beta-GC’s properties of fluorescence, circular dichroism, thermal property, etc. All these results would contribute to understand the mechanism of the rapid debitterizing of apricot kernels accelerated by ultrasound.

In order to reveal the process of insulation destruction of circuit breaker in GIS under lightning impulse voltage, a new method of transient insulation numerical analysis is described in this paper. A combination of the electromagnetic transient analysis method and optimised charge simulation method (CSM) was put forward to calculate and analyse the transient insulation characteristics of 550 kV circuit breaker in GIS under lightning impulse voltage. The results show that, within 0–15 μ, though the voltage of circuit breaker did not reach its peak, there is severe electric field distortion caused by sharp change of voltage with a high probability of circuit breaker insulation breakdown. In addition, as for the circuit breaker insulation structure optimisation design, it is advisable to restrain the change rate of electric field strength amplitude so as to reduce the probability of insulation breakdown, rather than simply verify insulation structure by using maxi-mum electric field strength as the voltage input parameters of the electrostatic field under the power frequency in accordance with the traditional insulation analysis method, with the purpose of ensuring the circuit breaker can operate safely and reliably for a long time.

In this paper, the effects of ultrasonic treatment on the particle size distribution and rheological properties of red wine were investigated to explore the insight changing mechanism of organoleptic attributes. The results indicate that ultrasound irradiation did affect the particle size distribution by increasing the intervals of particle size distribution, which, especially for the larger particles, would strengthen the tyndall effect of wine, resulting in the wine’s visual effect in a goblet more bright and full of flavor. Moreover, the changes in wine’s rheological properties mediated by ultrasound could also be fitted by the models of Power-law and Casson, which might explain the taste changes of wine induced by ultrasound. In conclusion, some of the wine’s sensory properties can be modified by the ultrasound irradiation, which greatly contributes to the feasible explanation of the ultrasound’s application in wine-making.

BACKGROUND Colorectal cancer (CRC) is the third most common cancer in China, however, publicly available, descriptive information on the clinical epidemiology of CRC is limited. METHODS Patients diagnosed with primary CRC during 2005 through 2014 were sampled from 13 tertiary hospitals in 9 provinces across China. Data related to sociodemographic characteristics, the use of diagnostic technology, treatment adoption, and expenditure were extracted from individual medical records. RESULTS In the full cohort of 8465 patients, the mean ± SD age at diagnosis was 59.3 ± 12.8 years, 57.2% were men, and 58.7% had rectal cancer. On average, 14.4% of patients were diagnosed with stage IV disease, and this proportion increased from 13.5% in 2005 to 20.5% in 2014 (P value for trend < .05). For diagnostic techniques, along with less use of x-rays (average, 81.6%; decreased from 90.0% to 65.7%), there were increases in the use of computed tomography (average, 70.4%; increased from 4.5% to 90.5%) and magnetic resonance imaging (average, 8.8%; increased from 0.1% to 20.4%) over the study period from 2005 to 2014. With regard to treatment, surgery alone was the most common (average, 50.1%), but its use decreased from 51.3% to 39.8% during 2005 through 2014; and the use of other treatments increased simultaneously, such as chemotherapy alone (average, 4.1%; increased from 4.1% to 11.9%). The average medical expenditure per patient was 66,291 Chinese Yuan (2014 value) and increased from 47,259 to 86,709 Chinese Yuan. CONCLUSIONS The increasing proportion of late-stage diagnoses presents a challenge for CRC control in China. Changes in diagnostic and treatment options and increased expenditures are clearly illustrated in this study. Coupled with the recent introduction of screening initiatives, these data provide an understanding of changes over time and may form a benchmark for future related evaluations of CRC interventions in China.

Previously, application of deep learning based sequential model drastically improved accuracy of volatility prediction in modelling of financial time series. However, unlike traditional financial time series model such as GARCH family of models, majority of deep learning based financial time series models focus solely on optimizing a single-step volatility prediction error and are not capable of conduct multi-step training and prediction of volatilities since volatility is the inherent uncertainty of the model prediction, whose multi-step prediction is drastically different from prediction of the mean of the financial time series.

BACKGROUND The aim of the study is to investigate lncRNA STARD13-AS's expression and clinical characteristics in the cancer tissues of gastric cancer (GC) patients. METHODS Microarray assessment will be used to analyze GC and non-tumor tissues. The lncRNA STARD13-AS expression in 82 GC and non-tumor tissues were measured by RT-qPCR and ISH assay. The differential expression of lncRNA STARD13-AS between the two groups and the relationship with clinical characteristics and prognosis were analyzed statistically. RESULTS By microarray assessment, lncRNA STARD13-AS was significantly down-regulated in tumor tissues. Compared with non-tumor tissues, using both RT-qPCR and ISH assay, STARD13-AS expression was significantly depressed in GC tissues (p < 0.01, respectively) and was closely correlated with tumor stage and histological grade (p < 0.05, respectively), but was not correlated with gender, age, tumor size, tumor location or lymph node metastasis. The 5-year overall survival and progression free survival rates of the low lncRNA STARD13-AS expression group were significantly down-regulated compared with those of the high lncRNA STARD13-AS expression group (p < 0.001, respectively). CONCLUSIONS The expression of lncRNA STARD-AS is depressed in gastric cancer tissues; the expression of lnc-RNA STARD-AS is correlated to tumor stage and degree of tumor differentiation, and may be a new molecular marker for the diagnosis, treatment, and prognosis in GC.

This paper proposes an unsupervised learning framework for monocular depth estimation and visual odometry (VO), referred to as DVONet. The framework is trained using stereo image sequences and is able to estimate absolute-scale scene depth and camera poses from monocular images. To mitigate the effect of stereo occlusions in training and improve the depth estimation, left-right occlusion mask is introduced. In addition, a novel VO network is proposed where the feature extraction network is shared between pose estimation and optical flow estimation. The proposed DVONet achieves state-of-the-art results for both depth estimation and VO tasks on the KITTI driving dataset, outperforming the existing unsupervised methods and being comparable to the traditional ones.

Online food image detection is a key issue for intelligent food materials receiving and food supply chain applications, how to efficiently, accurately and quickly detect the image of food materials is a challenging research topic. A fast auto-clean convolutional neural network (CNN) model for online prediction of food materials is proposed, which is aiming at problems of the complex characteristics of the food images such as the complexity of the food materials, the focus of the dislocation and the uniformity of illumination. Firstly, a new approach of the auto-clean CNN models is proposed for automatic image cleaning and classification, which starting from original images and ending with multi-class prediction of clean images. Given a vocabulary of K classes, and a Yes/No clean label, two CNN models will learn a class label and a clean label respectively. Secondly, after the forward pass of two CNN models, the joint features generated from the last convolutional layers will be fed into our two loss layers. Combined with multi-class classification method, it classifies and optimizes the image dataset intelligently. Finally, an online prediction algorithm is proposed to improve the image recognition efficiency. Experimental results show that the proposed model and algorithm have good efficiency and accuracy, and the results of this study have significance to optimize the efficiency of the food supply chain industry and food quality evaluation.

Big data applications in Hadoop usually cause heavy bandwidth demand and network bottleneck in the current data center network (DCN). On one hand, the design of DCN does not take the traffic demand and the traffic patterns of Hadoop applications into account. On the other hand, Hadoop suffers from inherent performance limitations due to its solution for transmitting massive data sets based on application-layer overlays, which ignores the architecture of DCN. To improve the performance of Hadoop applications, in this paper we propose the OEHadoop, a modified Hadoop which is built by co-designing Hadoop with hybrid optical and electrical data center network. For Hadoop, we redesign the pipeline-based replication process of MapReduce jobs to optical multicast. For the DCN architecture, we build a reconfigurable optical multicast system to adapt the DCN architecture to multicast traffic. A software-defined networking controller is implemented in data center to adjust the DCN architecture and exchange information with application layer. In order to accelerate the MapReduce jobs, a new algorithm to properly schedule the multicast requests is presented and deployed in the controller. We build a small-scale prototype of the OEHadoop to evaluate the control overhead, and to demonstrate the feasibility of our OEHadoop. In a simulation at a scale of real DCN, we present that our multicast requests scheduling algorithm outperforms related state-of-the-art solutions, and the MapReduce jobs in our OEHadoop speed up to about 2 times faster on average than native Hadoop.

Carbon fiber reinforced polymers (CFRP) have got rapidly increased applications in aerospace/aircraft and other fields due to their attractive properties of high specific strength/stiffness, high corrosion resistance, and low thermal expansion. These materials have also some challenging properties like heterogeneity, anisotropy, and low heat dissipation. Due to these properties, the issues of excessive cutting forces and machining damages (delamination, fiber pull-out, surface/subsurface defects, etc.) are encountered in machining. The cutting forces are required to be minimized for qualified machining with reduced damages. In this research, a novel cutting force prediction model has been developed for vibration-assisted slot milling. The experimental machining has been carried out on CFRP-T700 composite material. The effective cutting time per vibration cycle and the force of friction have been expressed/calculated. The feasibility of vibration-assisted machining for CFRP composites has also been evaluated. The relationships of the axial and feed cutting forces with machining parameters were investigated. The results have shown the variations below 10% among experimental and corresponding simulation values (from the model) of cutting forces. However, the higher variations have been found in some experiments which are mainly due to heterogeneity, anisotropy, and some other properties of such materials. The developed cutting force model then validated through pilot experiments and found the same results. So, the developed cutting force model is robust and can be applied to predict cutting forces and optimization for vibration-assisted slot milling of CFRP composite materials at the industry level.

During water exit of a solid body, the free surface is disturbed and deformed. To study the free-surface deformation and its dynamic change such as breakup, a set of water-exit equipment was designed and a series of experiment on water exit of a sphere were conducted. High-speed photographic technologies were adopted to capture the whole process of water exit and the large deformation of the free surface. Work was divided into forced water exit and free water exit. In the forced water exit, either a fully-submerged or a partially-submerged sphere was pushed out of water at a constant speed. The influence of the velocity or Froude number was investigated. In the free water exit, an initially static sphere with an equivalent density smaller than water was released underwater by using an electromagnet. Water exit at different initial submergence depth was conducted. Velocity and acceleration of the light sphere are analysed.

针对目前高速率信息传输的迫切需求，设计并实验了两路数字信号密集波分复用(DWDM)的大气激光通信结构。两路激光波长分别为1 539.76 nm和1 540.55 nm，采用强度调制，光信号经复用和放大后，由光学天线发射，经1 km大气信道传输，在接收端对光谱、波形和眼图进行了测量。实验结果显示，在弱湍流情况下，实现了两路光信号的解复用，并得到了较为清晰的波形图和眼图。两路数字信号的传输速率为20 Gbit/s时，Q因子仍达到5以上。

Study design Clinical and radiological assessment of prospective single-center data. Background The optimal treatment strategy for giant central thoracolumbar junction disc herniation (TLDH) remains controversial. Objective This study aimed to report the clinical and radiological results of bilateral transformational thoracolumbar interbody fusion (bilateral TTIF) to treat giant central TLDH. Methods Seventeen consecutive patients with giant central TLDH underwent bilateral TTIF from January 2014 to June 2014 and were followed for 9–15 months. Clinical and radiological data were prospectively examined, including operative time, blood loss, pre- and postoperative American Spinal Injury Association (ASIA) score for sensory and motor function, visual analog scale (VAS) and Oswestry Disability Index (ODI) scores, fusion rate, extent of decompression, rate of instrumentation failure, and complications. Results The average time of surgery was 160 ± 37 min, and blood loss was 884 ± 197 ml. The ASIA sensory and motor scores improved from 72.76 ± 3.36 to 90.71 ± 2.54 and from 68.47 ± 4.7 to 88.0 ± 3.2, respectively. VAS for back pain and ODI decreased from 6.65 ± 1.93 to 2.23 ± 0.56 and from 42.82 ± 5.08 to 11.3 ± 2.3, respectively. Canal encroachment improved from 50.3 ± 6.6% to 2 ± 0.3% at the last follow-up. Surgery complications were seen in 5 patients (29%), who experienced intraoperative dural tear and cerebrospinal fluid leak. There were no other major complications at the last follow-up. Conclusions Bilateral TTIF produced satisfactory outcomes and may be one of the surgical treatments of choice for myelopathy due to giant central TLDH.

In the aviation field, the pilots’ fatigue will seriously affect their judgment. Therefore, the inference of fatigue state is very important to ensure flight safety. Brain fatigue cognition faces two main problems: one is how to extract feature of fatigue cognition, and the other is how to identify the latent state of brain cognition with duration. For the first problem, this study proposes a method based on Smooth Pseudo-affine Wigner-Ville Distribution (SPAWVD) with Kaiser window function to calculate the instantaneous spectral features of EEG rhythms. Features extracted by this method have better local significance. For the second problem, this paper establishes a remaining life Hidden Semi-Markov Model (HSMM) to learn the dynamic mechanism of the brain to avoid fast switching between states caused by HMM. A multi-layer learning network based on Hierarchical Dirichlet Process (HDP) is built to provide subtasks that share the subject of fatigue awareness. To verify the model proposed by this paper, we use CRJ-200 simulator to simulate the mission and collect the EEG data. The accuracy of the result is satisfactory, which proves that the model established in the paper has a high ability to identify pilots’ latent brain cognitive state.

Biomarkers have the potential to greatly facilitate diagnosis and treatment of patients with various forms of kidney disease. State-of-the-art mass spectrometry-based methods possess the capability, on a proteome scale and in an unbiased manner, to detect alterations in protein abundances and/or posttranslational modifications in plasma, urine, or tissue. Such approaches can provide a large, unbiased database to facilitate identification of potential biomarkers. In the diagnosis of kidney diseases, urine is usually a more favorable specimen than plasma and kidney tissue due to its noninvasive collection and simplicity of processing. However, whether analysis of proteins in urine faithfully reflects their changes in the kidney tissue remains unclear. The use of proteomics to analyze kidney tissue samples collected during late-stage kidney diseases has also recently gathered pace. The goal of this minireview is to provide an overview of the proteomic technologies currently applied to studies of kidney and their limitations, present existing kidney and urine proteome databases, and highlight a few applications of such approaches in kidney disease biomarker discovery.

Degradation phenomenon and poor stability of tris(8-hydroxyquinoline) aluminum(III)(Alq3)-based organic light-emitting diodes(OLEDs) have attracted much attention. In this paper, we discussed the origin of instability of the facial Alq3-based blue luminescent OLEDs with the help of first-principles calculation. The results show that environmental humidity seriously affects the luminescence stability of Alq3-based OLEDs. H2O molecules in environment can be firmly bound to the oxygen atoms of the facial Alq3, which then act as starting points for further degradation of Alq3. Moreover, the interactions between facial Alq3 and different cathode metal layers were investigated to explain the experiment phenomenon. A design guideline for diminishing the strong attraction from oxygen atoms can be proposed to protect Alq3 and improve the stability of materials applied in OLEDs.

In this paper, in order to investigate the effects of ultrasound irradiation on the higher alcohols of wines, the parameters including ultrasound time, temperature and power were optimized by the response surface methodology, and the model wine solution was employed to explore the mechanism of ultrasonically decreasing the higher alcohols. The results indicate that the maximum decreasing of higher alcohols could be obtained under the ultrasound conditions of 30 min, 30 °C and 150 W, and the final content was 306.75 mg/L with the reduction rate of 40.44%, suggesting a modification of wine quality due to the negative effects of excessive contents on wine. Regarding the results of model wine, it indicates that the decrease could be definitely affected by factors, such as tartaric acid and ions in wine, which might be attributed to the free radicals generated from ultrasound cavitation and its subsequent reactions. In summary, all the results may help to understand the effects of ultrasound irradiation on improving the sensory properties of wine by decreasing the higher alcohols.

This paper addresses the problem of trajectory tracking control for underactuated autonomous underwater vehicles (AUVs) in 3 dimensions space. Given a smooth, inertial, 3D reference trajectory, in order to make the position error to stay in the neighborhood of zero, the control algorithm uses vehicle׳s kinematic equation and linear system stability theory to compute the desired body-fixed velocities. Using these methods, the velocity error dynamics are obtained. Backstepping techniques are used, forcing the tracking error to an arbitrarily small neighborhood of zero. Simulation of a spiral trajectory is performed. The result shows that the controller can realize 3 dimensions trajectory tracking effectively.

An experimental study was conducted to identify treatment conditions for sterilizing naked oats while retaining inherent enzyme activity. Naked oats were processed with superheated steam (SS) with variations in processing times (1–5 min) and temperature (140–200C). The number of bacteria and molds were significantly decreased as the SS temperature and exposure time increased. After naked oats were treated with SS at 160–170C for 2 min, the number of microbes were reduced below the safe storage limit. The SS treatment effectively reduced populations of bacteria and mold and had a less pronounced effect on enzymatic activity. This method shows great potential for usage as a sterilization treatment in the food processing industry. Practical Applications Superheated steam technique can effectively reduce disinfection time, control microbial biofilms and enhance safety in the cereal food processing industry.

The phase difference and spacing distance between two plunging wings in tandem affect the interaction of the vortices between the forewing and hindwing. An experimental study is performed in a low Reynolds number water tunnel to investigate how this interaction changes the mean thrust coefficient and propulsive efficiency. A three-dimensional force sensor and two-dimensional digital particle image velocimetry are used to measure the wing thrust, lift force, and leading edge vortex around the wings. The mean thrust coefficient of the forewing nearly changes sinusoidally with the phase difference in the range of 0–360 deg. The increase in the mean thrust coefficient of the forewing is caused by the leading edge vortex and stagnation region of the hindwing, enhancing the jet velocity behind the forewing and its effective angle of attack. The curve of the mean thrust coefficient of the hindwing has a V-shaped bottom. The decrease in the mean thrust coefficient of the hindwing is caused by the vortex shed from...

3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2 (Kinect v2) is utilized to complete this task, however, the accuracy and precision of depth information and the accuracy of correspondence between the RGB and depth (RGB-D) images still remain to be improved. In this paper, we propose a post-rectification approach of the depth images to improve the accuracy and precision of depth information. Firstly, we calibrate the Kinect v2 with a planar checkerboard pattern. Secondly, we propose a post-rectification approach of the depth images according to the reflectivity-related depth error. Finally, we conduct tests to evaluate this post-rectification approach from the perspectives of accuracy and precision. In order to validate the effect of our post-rectification approach, we apply it to RGB-D simultaneous localization and mapping (SLAM) in an indoor environment. Experimental results show that once our post-rectification approach is employed, the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods.

A new approach for the computation of electric fields is described, based on the response surface methodology(RSM) and geometric feature charge simulation method (GFCSM). And the novel combination of RSM and GFCSM is applied to calculate the electric field of the high voltage SF6 arc quenching chamber in this paper. The electric field distribution with higher calculation precision has been achieved. The results of the comparison between the conventional and proposed techniques are presented. Moreover, the new approach proves to be more efficient, minimizing computation of the electric field with multi dielectric medium.

the river discharge measurement is one of the basic projects of hygrometry; it determines the water resource utilization. However, a substantial increase in the number of hydrological site, especially for the unmanned hydrometric station brought great difficulty and caused the more workload for the Hydrologists every year. With the development of sensor technology and the data acquisition and fusion applications, it's important to build an embedded measurement and control system for the hydrological flow station. Therefore, in this paper we research and design a realtime embedded measurement and control system for the discharge measurement which can measure, store, communicate and calculate the river discharge automatically. The design of the intelligent Remote Terminal Unit (RTU) is a key step in implementation of the real-time embedded measurement and control system. The design of RTU adopts the STM32F107 as a kernel controller and the software program based on a real-time embedded operation system. This system has high accuracy and high automation level which can greatly reduce the workload of the hydrologist and promote the development of water resources informatization construction. Hydrologists can benefit from this system.

Pilots' fatigue status could influence aviation safety. The recognition of fatigue status of pilots is of utmost significance. We proposed a new deep learning model via analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. We firstly applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (1∼3 Hz), θ wave (4∼7 Hz), α wave (8∼13 Hz) and β wave (14∼30 Hz), and the combined representation of them were de-nosing EEG signals. Then we used proposed deep sparse auto-encoding network to reduce the complexity of de-nosing EEG signals and gained learning features. Lastly, we applied Softmax classifier on learning features and the experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%, which meant that the proposed method performed excellently compared with the state-of-art methods.

We proposed an extended OpenFlow-based control mechanism for multiple connections in the OpenScale architecture to achieve Coflow-aware bandwidth scheduling. Experimental demonstration verifies its overall feasibility.

Transformer top-oil temperature (TOT) and winding hot-spot temperature (HST) are key indices to evaluate thermal condition of transformers. In order to improve TOT prediction accuracy, a TOT prediction model based on kernel-based extreme learning machine is established and particle swarm optimization algorithm is adopted to train the model and optimize the kernel parameters. The proposed model is tested on a 50MVA 110/37kV ONAN transformer. Besides, to verify the advantages of the proposed model, it’s compared with several traditional data-driven models. The results demonstrate the validity and accuracy of the proposed model.

A anti debris lash structural competition model of pillar supported house was introduced. Frame structure was adopted in the model and energy conversion beams were built in the hollow floor beams. The upper face of the energy conversion beam was inclined at the support position of the upper column. The energy conversion beam can raise the upper structure when horizontal displacement was produced after collision. Construction details of the model was introduced, structure mechanics analysis and simplified calculation was conducted. By installation of energy conversion beam, the horizontal impact kinetic energy was transformed into the potential energy needed by the rise of upper sub-structure. Experimental result shows the anti impact performance of the structural model is good.

The multihole pressure probes are widely used to determine flow velocity vectors and pressures in the fluid machinery fields due to their robustness, convenience, and economy in flow measurement. Accuracy and fast calibration of a multihole pressure probe is very important. In this article, a multihole pressure probe automatic calibration system is developed. The hardware device mainly consists of computer, motion control equipment and data acquisition (DAQ) equipment, and the software program is developed using the G-language based on LabVIEW platform. The fundamental theory and method of multihole pressure probes is reviewed, as well as about how to take into account the compressibility effect of the fluid. The developed system can be easily integrated by engineers in research institutes or industries, without requiring more expertise in programming or multihole probe measurement method. Furthermore, the system is used for a five-hole probe’s calibration and measurement, satisfactory results are obtained. 0.5° is the maximum uncertainty for the flow angles and 1.7% is for the velocity magnitude. The measurement biases being caused by the effect of Reynolds number are investigated. In the Reynolds number range of 1.06 ×104 to 4.29 ×104, the measurement biases of velocity vector are presented.

A new approach for the computation of electic fields is described, based on the response surface methodology (RSM) and geometric feature charge simulation method (GFCSM). And the novel combination of RSM and GFCSM is applied to calculate the electric field of the high voltage SF 6 arc quenching chamber in this paper. The electric field distribution with higher calculation precision has been achieved. The results of the comparison between the conventional and proposed techniques are presented. Moreover, the new approach proves to be more efficient, minimizing computation of the electric field with multi dielectric medium.

Near space vehicle (NSV) is a nonlinear, multivariable and strongly coupled system. During the arbitrary trajectory tracking of NSV, the employment of conventional sliding mode variable structure control often results in steady-state errors, if there is some certain external disturbance. In this paper, an integral sliding mode variable structure controller is designed to eliminate the steady state error. Since the inputs of most actuators are restricted, the control performance will deteriorate or fail when the control signals are input directly into the controlled object. Therefore, the saturation limit is introduced to the controller design process to limit the input. Finally the proposed control scheme is applied to the attitude motion mode of the NSV and the simulation results confirmed its effect.

For the medical X-ray image with low brightness, low contrast and noise, we proposed an image enhancement algorithm which based on wavelet domain homomorphic filtering and contrast limited adaptive histogram equalization (CLAHE). Firstly, the image is decomposed by wavelet transformation, the image is decomposed into low-frequency and high-frequency coefficients of 1st layer of wavelet domain. Then the low frequency coefficients are processed by an improved homomorphic filter, and then linear amplified. The high frequency coefficients are processed by wavelet threshold shrinkage, and then the wavelet reconstruction is performed. Finally, the contrast limited adaptive histogram equalization (CLAHE) is used to modify the image's histogram, and the processing of the image is completed. The quality of image enhancement is carried on the subjective and objective evaluation, and compared with some other enhancement algorithms. Experimental results show that the algorithm can effectively enhance the texture detail of medical X-ray images, increasing the brightness and contrast, suppress noise, better than the general traditional enhancement algorithms.

In the process of heavy medium coal preparation, the control of the density and rheological properties of heavy medium suspension is a key issue for the improvement of the comprehensive economic efficiency of enterprise. In order to timely and accurately grasp the dense medium coal preparation process, a latent variable techniques (Total Kernel Partial Least Squares, T-KPLS) based online operating performance assessment method is employed in this paper. First of all, the T-KPLS algorithm is used to establish the evaluation model of the coal preparation process, and then the corresponding assessment strategy is used to evaluate the operating performance of the process, and the non-optimal causes of the dense medium coal preparation process are identified based on the evaluation results. Finally, the results of simulation analysis can be drawn that the T-KPLS algorithm is effective and practical to evaluate the operation state of dense medium coal preparation process.

The wet flue gas desulfurization is the main technology used today in China. Aimed the sintering gas desulfurization tower, Fluent software is used here to simulate the flow in the tower with different gas entrance angle and velocity, and we have got the effect of sintering gas entrance angle and velocity on the flow in the tower. The result shows: an appropriate increase of the gas entrance velocity and a downward inclined angle will be helpful for extending the residence time in the tower of the sintering gas and it will also improve the flow in the tower. This research could provide reference for the design and application of new wet flue sintering gas desulfurization tower later.