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A basket grinder was used as mechanochemical activation equipment to study low-grade lead–zinc oxide ores in the Lanping area, China and a mechanochemical leaching (MCL) process combining grinding with NaOH solution leaching was proposed for zinc leaching. The effects of leaching temperature, NaOH concentration, leaching time, liquid–solid ratio, and stirring speed on zinc leaching rate were investigated. Experimental results show that under the optimal conditions, the zinc leaching rate of MCL reaches 92.3%, which is 7.2% higher than that of conventional stirring leaching (CSL). And small difference in energy consumption is observed between the two processes, indicating the superiority of MCL. The strengthening mechanism of mechanochemical activation was investigated by analyzing the zinc phase of raw ore, and XRD, SEM−EDS, EPMA and particle size analyses were conducted on leaching residues from different processes under the optimal conditions.

Nitrofurans are important synthetic broad-spectrum antibacterial drugs with the basic structure of 5-nitrofuran. Due to their toxicity, it is essential to develop a sensitive sensor with strong anti-interference capabilities for their detection. In this work, two {P4Mo6O31}12–-based compounds, [H4(HPTTP)]2{CuI[Mo12O24(OH)6(PO4)3(HPO4)(H2PO4)4]}·xH2O (x= 13 for (1), 7 for (2); HPTTP = 4,4′,4″,4‴-(1H-pyrrole-2,3,4,5-tetrayl)tetrapyridine), exhibiting similar coordination but distinct stacking modes. Both compounds were synthesized and used for the electrochemical detection of nitrofuran antibiotics. The tetrapyridine-based ligand was generated in situ during assembly, and its potential mechanism was discussed. Composite electrode materials, formed by mixing graphite powder with compounds 1–2and physically grinding them, proved to be highly effective in the electrochemical trace detection of furazolidone (FZD) and furaltadone hydrochloride (FTD·HCl) under optimal conditions. Besides, the possible electrochemical detection mechanisms of two nitro-antibiotics were studied.

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

The rapid advancements in wearable devices have enabled continuous and real-time monitoring and analysis of physiological parameters. Owing to the benefits of detecting cardiovascular diseases, arrhythmia recognition from the wearable Electrocardiogram (ECG) signals has drawn much attention. In this article, we present a novel method of using an ensemble of multiscale convolutional neural networks, the so-called EMCNet, for classifying the single-lead ECG waves acquired by wearable devices. The EMCNet includes two primary modules. Two component classifiers are first developed, which input the filtered ECG recordings and time-frequency spectrograms, and output the prediction probabilities of corresponding categories. These prediction probabilities are then sent to an improved weight matrix based on soft voting to produce the final classification result. Numerous experiments have been conducted, with results clearly demonstrating the efficacy and benefits of the EMCNet.

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The potential effect of ultrasound was investigated, which could lead to cyanide loss or decomposition during gold leaching. The variations of cyanide concentration during gold leaching with and without ultrasound were compared. The influence of initial cyanide concentration, pH, temperature, atmosphere, natural loss, hydrolysis, and cavitation effect on the loss of cyanide was examined. The results showed that the loss of cyanide was mainly caused by hydrolysis, oxidation, and reaction with CO2in the traditional gold leaching process. With the introduction of ultrasound, the oxidative •OH slightly increased the loss of cyanide. Comparing the concentration variations of total cyanide and free cyanide proved that ultrasound (120 W) exhibited a non-detrimental effect on the loss of cyanide in gold leaching process.

Image segmentation is a challenging problem in imaging informatics, which stems from the intersection of imaging techniques, computer science and biomedicine. In particular, accurate segmentation of cardiac structures in late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of great clinical importance for cardiac function assessment and myocardial disease diagnosis. However, it is a well-known challenge due to its special imaging modality and the lack of labeled LGE samples. In this paper, we propose an unsupervised ventricular segmentation algorithm that can perform biventricular segmentation of LGE images in the absence of labeled LGE data. There are two primary modules, the data augmentation procedure and the segmentation network. The easily available annotated balanced-Steady State Free Precession (bSSFP) images are employed for cross-modal data augmentation by image translation, where a single bSSFP image is converted into multiple synthetic LGE images while preserving the original morphological structure. Then, the proposed segmentation network is trained with the synthetic LGE images and used for segmenting real LGE images. Validation experiments demonstrated the effectiveness and advantages of the proposed algorithm.

The production process of direct reduced iron from titanomagnetite by coal reduction in air in rotary kiln is a key step in the extraction of titanium and iron. This process is conducive to alleviating the stocking problem of a large amount of Ti-bearing blast furnace slag. The relationships between particle movement and pulverized coal injection combustion in rotary kiln were studied by the particle motion simulation and the pulverized coal injection combustion simulation. In particle motion simulation, the repose angle of ore particles was calculated to be 28.06°, and the reaction zone was roughly determined by the reaction temperature isothermal surface and the repose angle; the axial discrete distribution of ore particles would further cause the axial energy fluctuation due to the endothermic nature of the reduction. In pulverized coal injection combustion simulation, the rebound effect of coal injection under gravity would cause local overheating in the rebound region due to the benefits of heat and mass transfer processes, while this could strengthen the reduction reaction of ore particles and cause agglomerate by melting. Two suggestions on weakening the energy fluctuation and agglomerate were proposed based on the characteristics of particle motion and external carbon reaction.

To assess the performance of biplane area-length method in measuring left atrial (LA) volume and sphericity index and to investigate the correlation of LA reservoir function and sphericity index with atrial fibrosis on cardiac magnetic resonance imaging (MRI) in patients with mitral valve disease (MVD). Forty-eight patients with MVD undergoing cardiac MRI scan were enrolled in this retrospective study. LA reservoir function, measured as maximum volume (LAVmax), minimum volume (LAVmin) and ejection fraction (LAEF), and sphericity index were quantified using the biplane area-length method and standard short-axis approach, respectively. Comparisons of LA reservoir function and sphericity index between the two different methods were performed, as well as between positive (n = 17, 35%) and negative atrial wall fibrosis groups (n = 31, 65%). There was no difference in the values of LAVmaxindex and sphericity index between the two different methods. The biplane area-length method had poor performance in assessing LAVminindex and LAEF compared to standard short-axis approach, with an underestimation of 13.5% for LAVminindex and an overestimation of 27% for LAEF. Patients with positive atrial fibrosis had larger LAVmaxindex, LAVminindex and sphericity index, and lower LAEF levels in comparison to the negative atrial fibrosis group. The biplane area-length method has good performance in assessing LA sphericity index for patients with MVD, not in LA reservoir function. Patients with positive atrial fibrosis tend to suffer from more adverse LA remodelling.

A new method was developed to optimize the microwave heating thickness of the spent automobile catalyst in order to improve the uniform distribution of the temperature field. The average penetration depth and the microwave heating thickness of the spent automobile catalyst were calculated by Gauss model and numerical calculation based on dielectric loss tangent and reflection loss. The results showed that the spent automobile catalyst was a medium loss material. The average penetration depth was 1.11 m from room temperature to 800 °C. The optimum microwave heating thickness of the spent automobile catalyst was about 0.83 m or 0.75 times of the average penetration depth. Industrial application analysis indicated that the optimization of heating thickness could improve the uniform distribution of the temperature field and reduce energy consumption.

Well-crystallized and nanosized LiFePO4/C composite have been successfully synthesized by spray-drying under N2 atmosphere. The morphology, physical and electrochemical properties of the LiFePO4/C were tested and analyzed. The charge transfer resistances (Rct) and chemical diffusion coefficients of lithium ions (DLi+) in LiFePO4/C was systematically tested by EIS. The results show that the lithium ions diffusion coefficients obtained from EIS is 1.58×10-14 cm2·s-1. The assembled soft-packed cell with LiFePO4/C show better rate capability and cycling stability. The average capacity retention of LiFePO4/C soft-packed cell decreases to 100%, 98.9%, 96.5%, 92.4%, and 90.3% when current rate increases to 0.3, 0.5, 1, 2, and 3C, respectively. The capacity retention after 80 cycles is retained at more than 99%.

Background: Studies on coronary slow flow are receiving increasing attention, but objective evaluations are still lacking. The purpose of this study was to visualize the current status and research hotspots of coronary slow flow through bibliometric analysis. Methods: All relevant publications on coronary slow flow from 2003 to 2022 were extracted from the Web of Science Core Collection database and analyzed by VOSviewer and CiteSpace visualization software. Year of publication, journal, country/region, institution, and first author of each paper, as well as research hotspots were identified. Results: A total of 913 publications were retrieved. The journal with the most publications was Coronary Artery Disease. The country/region with the most publications was Turkey, followed by China and the United States. The institution with the largest publication volume was Turkey Specialized Higher Education Research Hospital. The author with the largest publication volume was Chun-Yan Ma from China. Keyword analysis indicated that “treatment and prognosis”, “pathogenesis and risk factors” and “diagnosis” were the clustering centers of coronary slow flow, and the research hotspots gradually changed with time, from pathogenesis to treatment and prognosis. Conclusion: Future research will focus on the search for effective and non-invasive detection indicators and treatments of coronary slow flow. Collaboration needs to be enhanced between different institutions or countries/regions, which would improve clinical outcomes for patients with coronary slow flow.

JS-001 is the first monoclonal antibody (mAb) against programmed cell death protein-1 (PD-1) approved by the China Food and Drug Administration (CFDA) into the clinical trails. To date, however, no pre-clinical pharmacological and pharmacokinetic (PK) data are available. In this study, we investigated the efficacy of JS-001 and conducted a preclinical PK study, including the monitoring of anti-drug antibodies (ADAs). We found that JS-001 specifically bound to PD-1 antigen with an EC50of 21 nmol/L, and competently blocked the binding of PD-1 antigen to PD-L1 and PD-L2 with IC50of 3.0 and 3.1 nmol/L, respectively. Furthermore, JS-001 displayed distinct species cross-reactivity: it could bind to the PD-1 antigen on the peripheral blood mononuclear cells (PBMCs) of humans and cynomolgus monkeys, but not to those of mice and woodchucks; the Kdvalues for the interaction between JS-001 and PD-1 antigens on CD8+T cells of human and cynomolgus monkey were 2.1 nmol/L and 1.2 nmol/L, respectively. In vitro, treatment with JS-001 (0.01–10 μg/mL) dose-dependently stimulated human T cell proliferation, as well as IFN-γ and TNF-α secretion. In HBsAg-vaccinated cynomolgus monkeys, the expression of PD-1+/CD4+and PD-1+/CD8+was significantly elevated, intramuscular injection of JS-001 (1 and 10 mg/kg) resulted in dramatic decreases in PD-1+/CD4+and PD-1+/CD8+expression in a dose-dependent manner, which was supported by PD-1 receptor occupancy (RO) results. In the PK study, 18 cynomolgus monkeys treated with single, ascending doses of 1, 10, and 75 mg/kg, and another 6 cynomolgus monkeys received 10 mg/kg successive administration. The plasma clearance of JS-001 followed a linear PK profile with single administration in the 1 and 10 mg/kg groups and a non-linear PK profile in the 75 mg/kg group. In the successive 10 mg/kg administration group, no drug accumulation was observed. But the AUC from the last exposure was lower than that of the first administration, which was probably due to the production of ADAs, as demonstrated in immunogenicity study. These non-clinical data are encouraging and provide a basis for the efficacy and safety of JS-001 in clinical trials.

Neural indicators of pain discriminability have far-reaching theoretical and clinical implications but have been largely overlooked previously. Here, to directly identify the neural basis of pain discriminability, we apply signal detection theory to three EEG (Datasets 1–3, total N = 366) and two fMRI (Datasets 4–5, total N = 399) datasets where participants receive transient stimuli of four sensory modalities (pain, touch, audition, and vision) and two intensities (high and low) and report perceptual ratings. Datasets 1 and 4 are used for exploration and others for validation. We find that most pain-evoked EEG and fMRI brain responses robustly encode pain discriminability, which is well replicated in validation datasets. The neural indicators are also pain selective since they cannot track tactile, auditory, or visual discriminability, even though perceptual ratings and sensory discriminability are well matched between modalities. Overall, we provide compelling evidence that pain-evoked brain responses can serve as replicable and selective neural indicators of pain discriminability.

The effects of sodium citrate on ammonium sulfate recycled leaching of low-grade zinc oxide ores were studied. By applying various kinds of detection and analysis techniques such as chemical composition analysis, chemical phase method, scanning electron microscopy and energy dispersive spectrum (SEM/EDS), X-ray diffraction (XRD) and Fourier-transforming infrared spectrum (FT-IR), zinc raw ore, its leaching slag and the functional mechanism of sodium citrate were investigated. Based on a comprehensive analysis, it can be concluded that in contrast to hemimorphite (Zn4Si2O7(OH)2· H2O), amorphous smithsonite (ZnCO3) and zinc silicate (Zn2SiO4) prove to be refractory phases under ammonium sulfate leaching, while sodium citrate has a better chelating action with the refractory phases, resulting in a higher zinc leaching rate. Under conditions of [NH3]/[NH3]Tmolar ratio being 0.5, [NH3]Tbeing 7.5 mol/L, [Na3C6H5O7] being 0.2 mol/L, S/L ratio being 1:5, temperature being 303 K, holding time being 1 h in each of the two stages, and stirring rate being 300 rpm, the leaching rate of zinc reached 93.4%. In this article, sulfate ammonium recycled technology also reveals its unique advantage in processing low-grade zinc oxide ores accompanied by high silicon and high alkaline gangue.

Treating and preventing chronic pain require our understanding of how it develops and maintains. Löffler et al. (2022)1demonstrate that distinct operant learning signals in the vmPFC-NAc pathway predict the development and maintenance of chronic back pain.

Microwave was applied to roasting the zinc oxide fume obtained from fuming furnace for the removal of F and Cl. The effects of important parameters, such as roasting temperature, holding time and stirring speed, were investigated and the process conditions were optimized using response surface methodology (RSM). The results show that the effects of roasting temperature and holding time on the removal rate of F and Cl are the most significant, and the effect of stirring speed is the second. The defluorination rate reaches 92.6% while the dechlorination rate reaches 90.2%, under the process conditions of roasting temperature of 700 °C, holding time of 80 min and stirring speed of 120 r/min. The results indicate that the removal of F and Cl from fuming furnace production of zinc oxide fumes using microwave roasting process is feasible and reliable.

Bamboo as the raw material is carbonized to prepare high specific surface area activated carbon by microwave heating under nitrogen atmosphere in our present work. Influences of activation agents on the preparation of activated carbon are studied. The results show that activation agents have a significant influence on the preparation of activated carbon. Under the heating time of 15 min, the adsorption capacity of the activated carbon prepared utilizing KOH as activation agent is the best. When the KOH/C ratio is 4, the iodine number and yield of activated carbon are 2298 mg/g and 39.82%, respectively. The BET specific surface area, total pore volume and average pore diameter of activated carbon are 3441 m2/g, 2.093 ml/g and 2.434 nm, respectively. The micropore volume of 1.304 ml/g is 62.30% of total pore volume, indicating that the activated carbon is microporous activated carbon.

The separation and enrichment of mercury and the recovery of elemental sulfur from flotation sulfur concentrate in zinc pressure leaching process were carried out by sodium sulfide leaching and carbon dioxide precipitating. The results show that the leaching rate of elemental sulfur is more than 98%, and 98.13% of mercury is enriched in the residue, under the optimized conditions of sodium sulfide concentration 1.5 mol/L, liquid/solid ratio 6:1 and leaching time 30 min at room temperature. In addition, the content of mercury is enriched 5.23 times that in the leaching residue. The elemental sulfur is precipitated from leaching solution under conditions of carbon dioxide flow rate 200 mL/min and blowing time 150 min, while solution is stirred adequately. The recovery efficiency of elemental sulfur reaches 97.67%, and the purity of elemental sulfur is 99.75%, meeting the requirements of industrial first-rate product standard according to the national standard of GB/T 2449–2006 (PRC).

Cavity perturbation method was used to determine the dielectric properties (ɛ′, ɛ″, and tan δ) of zinc oxide dust in different apparent densities. The process was conducted to study the microwave-absorption properties of zinc oxide dust and the feasibility of microwave roasting zinc oxide dust to remove fluorine and chlorine. The dielectric constant, dielectric loss, and loss tangent were proportional to the apparent density of zinc oxide dust. The effects of sample mass and microwave power on the temperature increase characteristics under the microwave field were also studied. The results show that the apparent heating rate of the zinc oxide dust increases with the increase in microwave roasting power and decreases with the increase in the sample mass. The temperature of the samples reaches approximately 800 °C after microwave treatment for 8 min, which indicates that the zinc oxide dust has strong microwave-absorption ability.

AbstractEupatorium adenophorum was utilized as raw materials for the preparation of activated carbon via microwave assisted steam activation. Influences of the three vital process parameters – activation temperature, activation duration and steam flow rate – have been assessed on the adsorption capacity and yield of Eupatorium adenophorum activated carbon (EAAC). The process parameters were optimized utilizing the Design Expert software and were identified to be an activation duration of 45 min, an activation temperature of 950 °C and a steam flow rate of 0.7 ml/min, with the resultant iodine number and yield being 1,010 mg/g and 20.13% respectively. The validity of process model to optimize the process parameters was verified using the analysis of variance (ANOVA). The key parameters that characterize quality of the porous carbon such as the BET surface area, total pore volume and average pore diameter were estimated to be 1,142 m2/g, 0.84 ml/g and 3.3 nm respectively, for the sample corresponding to the optimized process conditions. Additionally the pore structure is characterized using Scanning Electron Microscope (SEM). The present work strongly supports utilization of Eupatorium adenophorum as a potential precursor through microwave heating.

AbstractThe present study measures dielectric properties (ε´, ε″ and tanδ) of zinc oxide dust produced in a rotary kiln, using a cavity perturbation method with microwaves at 2.45 GHz. The effect of apparent density of the dust on the dielectric properties was determined. The results indicated that zinc oxide dust has excellent absorbing performance and can reach 800 ºC in 6 minutes. The apparent density of zinc oxide dust and the microwave penetration depth were also related.

Cerebral perfusion computed tomography (PCT) is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms. With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging, considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging. Reducing the dose of X-rays causes severe noise and artifacts in PCT images. To solve this problem, we propose a deep learning method called NCS-Unet. The exceptional characteristics of non-subsampled contourlet transform (NSCT) and the Sobel filter are introduced into NCS-Unet. NSCT decomposes the convolved features into high- and low-frequency components. The decomposed high-frequency component retains image edges, contrast imaging traces, and noise, whereas the low-frequency component retains the main image information. The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay. The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal. Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

Abstract: The technology that CuCl residue from Zn hydrometallurgy was dried by microwave heating was studied. The influence of the drying duration, drying temperature and material thickness on dehydration rate was investigated. The response surface methodology (RSM) technique was utilized to optimize the process conditions. The optimum conditions for drying CuCl residue have been identified to be an drying temperature of 80℃, drying duration of 11 min and material thickness of 16 mm. The optimum conditions resulted in an CuCl residue with moisture content of 4.97%, which could ensure remove chlorine of CuCl residue by microwave roasting.

Aiming at avoiding misregistration in complicated medical image registration based on SURF (Speed-Up Robust Features)-TPS (Thin-Plate Spline), we propose a novel algorithm. This method is based on SURF and human interaction method for feature extraction. Then we improve SURF-TPS and propose an algorithm named TPS-SEMISURF which obtains the deformation field by calculating the Thin-plate spline of the feature points, and finally does the medical image non-rigid registration according to the parameters. Experimental results showed that the proposed method can register medical images effectively. It has a good robustness and owns better precision and rate than traditional algorithm.

Traditional medical image enhancement method has some disadvantages. They can not significantly improve the medical image edge, texture and detailed information. Besides the enhancement effect is susceptible to interference noise information. This paper proposed enhancement algorithms combining bidimensional empirical mode decomposition and the wavelet edge enhancement method. The first step is using the method of bidimensional empirical mode decomposition to process medical image, achieve image information with different frequency. And then our method using wavelet transform to enhance different frequency image edge, texture information. Through the comparison of proposed method with the existing method, it has been verified the proposed method has a better effect in the detail enhancement of medical images.

This article using AMESim and Simulink software co-simulation technology and appropriate software to establish the simulation model of the key components of the steering system, it is found the factor which affect vehicle handling and stability of steering system in the structure by analyzing the impact of various parameters on the car steering system. It is to verify the feasibility of the model and study how steering system of all kinds of main factor to influence the vehicle steering stability through Simulink and AMESim software co-simulation results cf vehicle steering stability GB experiments.

In order to improve the accumulating efficiency of solar energy, we designed a trolley with a 2-DOF attitude regulation structure of solar panels. In it, a solar orientation sensor was used to detect the solar orientation and the solar light intensity. A trolley’s differential wheels and a steering engine were used to keep the solar panels of the trolley vertically facing the Sun. At a certain interval, the steering engine adjusted the elevation angle of the solar panels and the trolley’s differential wheels adjusted the solar panels’ azimuth angle. Test experiment results show that the trolley can improve the accumulating efficiency of solar energy.

A novel method for preparation of iron oxyhydroxide materials, involving aqueous precipitation followed by microwave assisted aging is investigated. The produced materials are characterized by XRD, SEM, EDX and TEM spectroscopy and BET analysis. The materials show physical characteristics dependent on preparation procedure. The adsorptive properties of the materials for arsenic are studied by batch adsorption techniques. It is found that the rate of arsenic upload depends strongly on the degree of crystallinity of the materials. The adsorption capacity is approximately 55 mg/g. The physical characterization of the arsenic loaded adsorbents show that the adsorption process modifies the morphology of the materials. Over 4% of arsenic atoms are incorporated into the particle matrix.

Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVR machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave preparing partially stabilized zirconia (PSZ) and built the stability prediction model, the better prediction accuracy and the better fitting results are verified and analyzed. This is conducted to elucidate the good generalization performance of SVMs, specially good for dealing with nonlinear data.

A microwave carbonthermal nitridation method under the condition of nitrogen at atmospheric pressure was used to the synthesis of vanadium nitride from vanadium pentoxide and carbon black. In the present work, the effects of synthesis temperature, flowing rate of nitrogen, heating rate and soaking time on the apparent density and the nitrogen content of the microwave sintered samples were studied. The experimental data indicated that the apparent density and the nitrogen content of the microwave sinter sample was 4.1 g/cm3 and 13.8 %, respectively under the optimum conditions of flowing rate of nitrogen 50 L/h, heating rate of 6 ℃/min to 1400 ℃ and soaking time of 50 min. The denser pellet was preferred to generated in the centre of the samples of which was beneficial to the expulsion of stoma and the diffusion of nitrogen due to the opposite temperature gradient compared with traditional heating.

The stability is a key product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), so how to predict the stability of PSZ accurately, quickly and easily in the preparation process is very important. In this paper, a new mathematical model to predict the stability of PSZ prepared by microwave heating was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from 58 experiments were used for the training samples and test samples. Then testing and analyzing was done. The results showed that the SVM model is reasonable and it is accurate and reliable to predict the stability of the partially stabilized zirconia prepared by microwave heating by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ prepared by microwave heating is provided for future application, which is of great significance to theory and practice.

The stability is one most important product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), and the stability of PSZ is always fluctuating in the commercial process, so how to accurately, quickly and easily predict the stability of PSZ in the preparation process is very important. In the present paper, a new mathematical model to predict the stability of PSZ was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from commercial process were collected for the training samples and test samples. Then testing and analyzing was done. The results showed that the max relative error was 1.80%, the least relative error was 0%, and the average relative error was 0.58%. It is accurate and reliable to predict the stability of PSZ by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ is provided for commercial application.

The TG and DTG curves of self-made ZnO precursor were studied by the thermo-gravimetric analysis method in N2 atmosphere from 25°C to 650°C at the heating rate of 5, 10 and 15°C/min. The TG curve showed that the decomposition process started at about 150°C and finished at 300°C, in accordance with the temperature range of the decomposition from basic zinc carbonate to zinc oxide. The first level chemical reaction based on Coats-Redfern method was applied to estimate the activation energy of the decomposition. The correlation factor was about 0.99, and the calculated average activation energy was 33.89kJ/mol. Active zinc oxides were perpetrated by microwave heating at 350°C for 30min. Their composition and surface morphology were investigated by using scanning electron microscope and X-ray diffraction and its quality can reach the first-grade standard of HG/T2572-94.

Tomographic perfusion imaging is a significant imaging modality for stroke diagnosis. However, the low rotational speed of the C-arm (6–8 s per circle) is a challenge for applying perfusion imaging in C-arm cone beam computed tomography (CBCT). Traditional reconstruction methods cannot remove the artifacts caused by the slow rotational speed or acquire enough sample points to restore the time density curve (TDC). This paper presents a dynamic rollback reconstruction method for CBCT. The proposed method can improve the temporal resolution by increasing the sample points used for calculating the TDC. Combined with existing techniques, the algorithm allows slow-rotating scanners to be used for perfusion imaging purposes. In the experiments, the proposed method was compared with other dynamic reconstruction algorithms based on standard reconstruction and the temporal interpolation approach. The presented algorithm could improve the temporal resolution without increasing the X-ray exposure time or contrast agent.

Recently, intrinsic image features in bit-level (e.g., higher bit-planes carry more information than lower bit-planes) have been widely accepted for building bit-level image cryptosystems. Higher bit-planes are generally handled with enhanced encryption, whereas light attention is paid to lower ones. However, the existing achievements on bit-level image features are solely based on the analyses of standard test images, and they do not hold for some special images, such as medical images. When ciphering these images, such cryptosystems may leak the important information of lower bit-planes, and other inadaptability also exists. In this paper, we firstly give out the inapplicability of the existing bit-level achievements, and then a novel chaos-based image encryption scheme using nonlinear inter-pixel computing and swapping based permutation approach is presented. Simulations and extensive security analyses demonstrate the high level of security for practical secret applications. [ABSTRACT FROM AUTHOR]

In recent years, a variety of chaos-based image cryptosystems have been investigated to meet the increasing demand for real-time secure image transmission. Most of them are based on permutation–diffusion architecture, in which permutation and diffusion are two independent procedures with fixed control parameters. This property results in two flaws. (1) At least two chaotic state variables are required for encrypting one plain pixel, in permutation and diffusion stages respectively. Chaotic state variables produced with high computation complexity are not sufficiently used. (2) The key stream solely depends on the secret key, and hence the cryptosystem is vulnerable against known/chosen-plaintext attacks. In this paper, a fast chaos-based image encryption scheme with a dynamic state variables selection mechanism is proposed to enhance the security and promote the efficiency of chaos-based image cryptosystems. Experimental simulations and extensive cryptanalysis have been carried out and the results prove the superior security and high efficiency of the scheme. [ABSTRACT FROM AUTHOR]