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Benign anastomotic stricture remains among the most prevalent complications following surgery for rectal cancer.This study is aimed at identifying risk factors of anastomotic stricture as well as generating an effective nomogram for the stricture.Design: This is a retrospective study.This study was conducted from January 2015 to December 2019 in a single tertiary center for rectal cancer.A total of 117 rectal cancer patients after surgery without recurrence were enrolled in this study, of which 39 with anastomotic stricture and 78 without stricture.Their clinical and pathological data were collected. Multiple logistic regression analysis was conducted to identify risk factors for anastomotic stricture and to generate the nomogram prediction model.Multivariate analysis of the primary cohort led to the identification of LCA (left colic artery) preservation (OR, 0.074; P = 0.0015), protective stoma (OR, 5.353; P = 0.012), anastomotic leakage (OR, 12.027; P = 0.005), and anastomotic distance (OR, 7.578; P = 0.012) as independent risk factors for anastomotic stricture. The following predictive model was derived: Logit (anastomotic stricture) = 0.074∗ LCA + 5.353∗ Protective stoma +12.027∗ Anastomotic leakage + 7.578∗ Anastomotic distance. Assessment of the predictive model revealed that the area under the curve (AUC) was 0.871, while the cutoff value was 15.444 with a sensitivity of 64.1% and a specificity of 94.8%.The main limitation is the research design of a retrospective and case-controlled study with a small sample size from a single center.LCA preservation, protective stoma, anastomotic leakage, and anastomotic distance may affect the occurrence of anastomotic stricture following surgery for rectal cancer. The nomogram model generated in the present study can be valuable in the prediction of anastomotic stricture. This study has been registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn/, ChiCTR 2100043775).

Alloying strategy in quantum dots (QDs) has shown great advantages in regulating photophysical properties and improving the performance of photovoltaic devices. Typically, CdSeTe alloy QD is proved to be a superior photosensitizer, and has presented great potential in constructing high-efficient quantum dot solar cells. However, systematic analysis for the carrier dynamics is still lacking. In this paper, the carrier dynamics within CdSeTe colloidal QDs and at the TiO2/CdSeTe interface has been studied in detail by transient absorption (TA) spectroscopy and photoluminescence (PL) spectroscopy. TA results show that the gradient component of CdSeTe QDs leads to different electronic transition properties distinct from those of their parent binary components. Temperature-dependent time-resolved PL spectra for the CdSeTe/TiO2 demonstrate that PL lifetime decreases from 13.54 to 3.08 ns with increasing temperature from 10 to 300 K. Moreover, the electron transfer rate increases more quickly when temperature exceeds 250 K. In addition, it is confirmed that the crystallization characteristics of TiO2 have a significant impact on the interfacial carrier dynamics. A faster electron transfer from CdSeTe QDs to rutile TiO2 (R–TiO2) has been obtained than that to anatase TiO2 (5.65 × 108/s vs 3.21 × 108/s). Both QDs adsorption and electron transfer properties closely dependent on the crystal orientation of R–TiO2 single crystal. CdSeTe QDs attached to the (100) R–TiO2 single crystal surface achieve the fastest electron transfer rate than other R–TiO2 orientations of (111), (110) and (001).

BACKGROUND: X-ray grating interferometry normally requires multiple steps and exposures, causing a prolonged imaging time. There is motivation to use fewer steps to reduce scanning time and complexity, while keeping fidelity of the retrieved signals. OBJECTIVE: We propose an iterative signal retrieval method, extracting attenuation, dark field contrast (DFC), and differential phase contrast (DPC) signals from two X-ray exposures. METHODS: Two shots were captured at G2 grating positions with difference of 1/4 grating period. The algorithm consists of two stages. At the first stage, amplitude of sample phase stepping curve retrieved by virtual phase stepping (VPS) method, visibility and local phase of background phase stepping curve are used to limit the results to the proximity of the ground truth. After the second stage, three high-quality parameters, amplitude, visibility, and local phase, are retrieved through finetuning, and three signals are calculated. Simulated and real-sample experiments were conducted to validate this method. RESULTS: We used standard phase stepping result as benchmark and calculated structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) between benchmark and parameters retrieved by our dual-shot method and virtual phase stepping (VPS) method. For both simulated and real-sample experiments, the SSIM and PSNR value of dual-shot method are higher than those of VPS method. For real-sample method, we also conducted a three-step PS, and the SSIM and PSNR value of dual-shot method are slightly lower than those of three-step PS. CONCLUSION: Using our dual-shot method demonstrates higher performance than other single-shot method in retrieving high-quality signals, and it also reduces radiation dose and time.

Background MAGE-A10 is a subtype of the Melanoma-associated antigen A (MAGE-A), a class of tumor antigens that are extensively expressed in various histological types of tumors and represents an attractive target for tumor immunotherapy. Epigenetic-modifying drugs can enhance the expression of tumor antigens and improve the cytotoxicity of antigen-specific T cells. 5-aza-2’-deoxycytidine (DAC), a DNA methyltransferase inhibitor (DNMTI) considered an epigenetic-modifying drug, could enhance the expression of MAGE-A10 in cancer cells. Methods Human lung cancer cell lines (H1975 and A549) and primary lung cancer cells (L228, L329 and L419) were used. 5-aza-2’-deoxycytidine was used to induce the expression of MAGE-A10 in tumor cells. MAGE-A10 antigenic peptide (sequence: SLLKFLAKV) was used to induce differentiation of MAGE-A10-specific cytotoxic T lymphocytes (CTLs). Interferon-γ release assay was used to detect the capacity of MAGE-A10 peptide to induce CTLs. Cell Counting Kit-8 (CCK-8) analysis was performed to detect the cytotoxicity of MAGE-A10-specific CTLs. Real-time PCR and western blot analysis was used to detect the mRNA and protein levels, respectively. Immunohistochemistry was performed to detect the protein expression in cancer and adjacent normal tissue. Kaplan-Meier plotter online database was used to analyze the overall survival (OS), post-progression survival (PPS), and first progression (FP). Results The lysis rate of MAGE-A10-specific CTLs in L419 and H1975 were found to be 65.9% and 80.5%, respectively. Both L419 and H1975 showed significantly higher lysis rate in group 1 than in group 3 (6.7, 26.7%), group 2 (0, 0%) and group 4 (0, 0%) (P=0.0003, P≤0.0001, P≤0.0001, respectively). Online data mining using Kaplan-Meier plotter suggested that high expression of MAGE-A10 was significantly and negatively associated with OS (Plogrank =2.1e-05) and PPS (Plogrank =0.0057), and FP (Plogrank =3.2e-12). Conclusions High-level expression of MAGE-A10 improved the anti-tumor immune cytotoxicity of MAGE-A10-specific CTLs in lung cancer cell lines and primary lung cancer cells. However, MAGE-A10 gene expression was negatively associated with prognosis according to the survival analysis. Thus, we hypothesize that high-level of MAGE-A10 expression in vivo may inhibit the differentiation of MAGE-A10-specific CTLs.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. ccRCC arises from the proximal tubular epithelium and is associated with high mortality. Autophagy may either promote or suppress tumor cell survival at different stages of cancer development. It is essential to investigate the association between autophagy-related genes (ARGs) and prognosis in ccRCC patients. We used datasets obtained from The Cancer Genome Atlas (TCGA) database to identify the expression level of ARGs in ccRCC patients. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using Metascape database. Hub genes were identified by Cytoscape software. We constructed a Cox proportional hazard regression model to identify hub genes that are significantly associated with overall survival (OS) in ccRCC patients. Subsequently, a prognostic index (PI) was calculated and ccRCC patients were stratified into high-risk and low-risk groups based on a median PI value. Our study detected several altered ARGs in ccRCC, which could be a useful prognostic tool in ccRCC patients.

Renal cell carcinoma (RCC) is the most common form of kidney cancer, caused by renal epithelial cells. RCC remains to be a challenging public health problem worldwide. Metastases that are resistant to radiotherapy and chemotherapy are the major cause of death from cancer. However, the underlying molecular mechanism regulating the metastasis of RCC is poorly known. Publicly available databases of RCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R analysis, whereas the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Gene Set Enrichment Analysis (GSEA) and Metascape. Protein-protein interaction (PPI) network of DEGs was analyzed by STRING online database, and Cytoscape software was used for visualizing PPI network. Survival analysis of hub genes was conducted using GEPIA online database. The expression levels of hub genes were investigated from The Human Protein Atlas online database and GEPIA online database. Finally, the comparative toxicogenomics database (CTD; http://ctdbase.org) was used to identify hub genes associated with tumor or metastasis. We identified 229 DEGs comprising 135 downregulated genes and 94 upregulated genes. Functional analysis revealed that these DEGs were associates with cell recognition, regulation of immune, negative regulation of adaptive immune response, and other functions. And these DEGs mainly related to P53 signaling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity, and other pathways are involved. Ten genes were identified as hub genes through module analyses in the PPI network. Finally, survival analysis of 10 hub genes was conducted, which showed that the MMP2 (matrix metallo peptidase 2), DCN, COL4A1, CASR (calcium sensing receptor), GPR4 (G protein-coupled receptor 4), UTS2 (urotensin 2), and LDLR (low density lipoprotein receptor) genes were significant for survival. In this study, the DEGs between RCC and metastatic RCC were analyzed, which assist us in systematically understanding the pathogeny underlying metastasis of RCC. The MMP2, DCN, COL4A1, CASR, GPR4, UTS2, and LDLR genes might be used as potential targets to improve diagnosis and immunotherapy biomarkers for RCC.

Background Benign anastomotic stricture remains among the most prevalent complications following surgery for colorectal cancer, albeit its incidence is very low. Objective This study is aimed at identifying risk factors of anastomotic stricture as well as generating an effective nomogram for the stricture. Design: This is a retrospective study. Setting: This study was conducted from January, 2015 to December, 2019 in a single tertiary center with colorectal cancer. Patients: A total of 117 colorectal patients after surgery without recurrence including 39 with anastomotic stricture (the distance between anastomotic site and anal margin

Rhabdomyosarcoma (RMS) is a common malignant soft tissue sarcoma, which is the third most common soft tissue sarcoma after malignant fibrohistoma and liposarcoma. The discovery of potential postbiomarkers could lead to early and more effective treatment measures to reduce the mortality of RMS. The discovery of biomarker is expected to be the direction of targeted therapy, providing a new direction for the precise treatment of RMS. Gene Expression Omnibus database was used to download the tow gene profiles, GSE28511 and GSE135517. GEO2R was applied to identify differently expressed genes (DEGs) between RMS and normal group. Database for Annotation, Visualization and Integrated Discovery and Metascape can perform the enrichment analysis for the DEGs. Protein-protein interaction network was constructed, and the hub genes was identified by the Cytoscape. Expression and overall survival analysis of hub genes were performed. A total of 15 common DEGs were screened between RMS and normal tissues. The enrichment analysis here showed that the DEGs mainly enriched in the muscle filament sliding, myofibril, protein complex, sarcomere, myosin complex, nuclear chromosome, and tight junction. The 6 hub genes (DNA Topoisomerase II Alpha, Insulin Like Growth Factor 2, HIST1H4C, Cardiomyopathy Associated 5, Myosin Light Chain 2 [MYL2], Myosin Heavy Chain 2) were identified. Compared with the normal tissues, MYL2 were down-regulated in the RMS tissues. RMS patients with low expression level of MYL2 had poorer overall survival times than those with high expression levels (P

Pancreatic cancer (PC) whose mortality is comparable to morbidity is a highly fatal disease. Early approaches of diagnosis and treatment for PC are quite limited, so it is of great urgency to figure out the exact tumorigenesis and development mechanism of PC. To identify the related molecular markers of pancreatic oncogenesis, we downloaded three microarray datasets (GSE63111, GSE101448, and GSE107610) from Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) among them were identified, and the corresponding function enrichment analyses were accomplished. The protein-protein interaction network was conducted by Search Tool for the Retrieval of Interacting Genes (STRING), and the corresponding module analysis was accomplished by Cytoscape. There were 55 DEGs found in total. The molecular function and biological processes (BP) of these DEGs mainly include cytokinesis, mitotic nuclear division, cell division, cell proliferation, microtubule-based movement, and mineral absorption. Among the 55 DEGs, 14 hub genes were further confirmed and it was concluded that they mainly function in mitotic cytokinesis, microtubule-based movement, mitotic chromosome condensation, and mitotic spindle assembly from the BP analysis. The survival analysis showed that all the 14 hub genes, especially nucleolar and spindle associated protein 1 and abnormal spindle microtubule assembly, may involve in the tumorigenesis and development of PC. And they might be used as new biomarkers for auxiliary diagnosis and potential targets for immunotherapy of PC.

It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.

In this paper, the sausage is taken as the research object. Firstly, the total number of sausage colonies is quantitatively analyzed using spectral data of hyperspectral imaging technology. In order to improve the accuracy of the regression prediction model, the key parameters C and gamma of the regression model are optimized, so that it could accurately and quickly predict the total number of sausage colonies. Then, a method of detecting, classifying and identifying sausage key qualities by combining hyperspectral curve features (spectrum) and hyperspectral image depth features is proposed. CNN(Convolutional Neural Network) is used to extract the features of hyperspectral images. At the same time, Principal Component Analysis (PCA) is used to reduce the dimension of hyperspectral curve data (spectrum). Then, the two feature data are fused, and the machine learning algorithm Support Vector Machine (SVM) is combined with PSO (Particle Swarm Optimization) algorithm to classify sausage quality. At last, the experimental results show that PSO+SVM could be used for classification with 99% accuracy after the fusion of spectral curve features and image features. Moreover, the experimental results give the feasibility and effectiveness of this method.

The deep learning method for hyperspectral image and spectral curves image features is proposed in this paper. The convolutional neural network (CNN) model has demonstrated as a universal representation for image feature extraction, especially in the application of image classification. In this paper, deep features of hyperspectral images are extracted using CNN, and the cross entropy is used as the optimization target. Then, the features of spectral curves are used into the image features. Finally, the fusion features are considered as input data, and classified by support vector machine (SVM), which realizes the classification and retrieval of meat. The method is getting our Bacon classification accuracy rate to 99.2%, The experimental results show the feasibility and effectiveness of the proposed method.

The study of eye movement has been last for centuries. Over the past decade, scientists have developed many new eye-tracking methods. During that same period, technological innovations have made eye-tracking units more affordable. The article provides a systematic review on some main eye-tracking methods. This paper also includes the factors that may influence the eye-gaze tracking and the application of gaze estimation in many areas. In this article, we provide information on eye tracking in hopes of improving the quality and reporting of eye-tracking research in communication.

SummaryTo navigate in the world, an animal’s brain must produce commands to move, change direction, and negotiate obstacles. In the insect brain, the central complex integrates multiple forms of sensory information and guides locomotion during behaviors such as foraging, climbing over barriers, and navigating to memorized locations. These roles suggest that the central complex influences motor commands, directing the appropriate movement within the current context. Such commands are ultimately carried out by the limbs and must therefore interact with pattern generators and reflex circuits that coordinate them. Recent studies have described how neurons of the central complex encode sensory information: neurons subdivide the space around the animal, encoding the direction or orientation of stimuli used in navigation. Does a similar central-complex code directing movement exist, and if so, how does it effect changes in the control of limbs? Recording from central-complex neurons in freely walking cockroaches (Blaberus discoidalis), we identified classes of movement-predictive cells selective for slow or fast forward walking, left or right turns, or combinations of forward and turning speeds. Stimulation through recording wires produced consistent trajectories of forward walking or turning in these animals, and those that elicited turns also altered an inter-joint reflex to a pattern resembling spontaneous turning. When an animal transitioned to climbing over an obstacle, the encoding of movement in this new context changed for a subset of cells. These results indicate that encoding of movement in the central complex participates in motor control by a distributed, flexible code targeting limb reflex circuits.

BACKGROUND Esophageal carcinoma (ESCA) is associated with a poor prognosis and high mortality rate. Autophagy plays important roles in promoting or suppressing tumor cell survival at different stages of cancer development. However, the roles of autophagy-related genes (ARGs) during ESCA progression and in patient prognosis remain unclear. Accordingly, in this study, we aimed to identify the relationships of ARGs with ESCA progression and patient prognosis. MATERIAL AND METHODS Clinicopathological information for patients with ESCA was downloaded from The Cancer Genome Atlas (TCGA) database. Transcriptome expression profiles were downloaded from TCGA and GTEx databases, and ARGs were downloaded from the Human Autophagy Database. We investigated the functions of ARGs by bioinformatics analysis. Moreover, statistical analysis of these genes was performed to identify independent prognostic markers. RESULTS Differentially expressed genes between normal and tumor tissues were detected and identified. GO and KEGG analyses of differentially expressed ARGs were performed. Moreover, we derived a risk signature based on the identified independent prognostic markers. The identified genes also could predict the clinicopathological features of ESCA. CONCLUSIONS ARGs were key participants in the tumorigenesis and development of ESCA. Our findings may be useful for developing improved therapeutic approaches for ESCA.

Financial policy impacts on a range of social issues such as poverty alleviation, education, pollution control, etc. Making good use of financial policy and applying innovation to existing financial systems can generate positive outcomes for society. This article is to discuss the case of green credit policy in China. This policy was jointly announced in 2007 by financial and environmental regulators. According to this policy, banks were encouraged to give more loans to environmental friendly companies than to others. Such policy has made great progress, and it became a more formal document entitled ‘Green Credit Guideline’ in 2012. This guideline included a comprehensive definition on green credit and specified several requirements at practical levels. Despite the great success, challenges of implementing such policy still remain. First, information about corporate environmental performance is insufficient; second, market incentives for green credit policy are not high enough; third, most banking staff l...

Blind deconvolution is an effective fluorescence microscopic image processing technique to improve the quality of degraded digital images resulting from photon counting noise and out-of-focus blur. For solving the severely ill-posed problem of deconvolution, in this paper we propose an alternate minimized blind deconvolution method which considers sparse representation as constraint condition to confine the solution space of traditional Richardson–Lucy method and Gaussian model as initial value of point spread function (PSF). We assume that Poisson noise is dominating during the course of imaging. The maximum-likelihood estimation on a fluorescence image and corresponding point spread function (PSF) is developed. By solving the Euler–Lagrange equation of the total cost function, including the data term obtained by the hypothetical Poisson noise distribution model and the regularized term corresponding to the sparse representation constraint, and using gradient descent method we can get the iterative equations of the original fluorescence image and PSF respectively. Compared with the related blind deconvolution methods, our model shows superior performance in terms of both objective criteria and subjective human vision via processing simulated and real fluorescence microscopic degraded images.

This article describes the hand-held terminal bacon detection system based on Neural Network Pattern recognition. Respectively using computer vision and artificial olfactory, collected the bacon color characteristic value and Plaque as so as the concentration of volatile gases, the collected data through the GPRS to the host computer, then return the result after analysis; for the determination of the freshness of bacon, inferior fresh and corruption levels of bacon and get a TVB — N content data sequence, the clustering center will be established by the Neural SOM network. Experiments verified that the detection terminal has good detection accuracy and operability.

In this study, caged poultry as the research object, antibiotics are quantitatively analyzed by near infrared spectroscopy. Introducing food safety index indicators by calculating the amount of dietary exposure with the data to assess health risk. Finally, by using SOM neural network to verify the practicality. Risk assessment model combined with the national standard divide tetracycline residues of chicken risks into: high, medium and low. So it improves the applicability of the national standard. To conclude, this study can be applied to test other related agricultural products and food industry to guarantee food security.

Increasing interest in the role of brain activity in insect motor control requires that we be able to monitor neural activity while insects perform natural behavior. We previously developed a technique for implanting tetrode wires into the central complex of cockroach brains that allowed us to record activity from multiple neurons simultaneously while a tethered cockroach turned or altered walking speed. While a major advance, tethered preparations provide access to limited behaviors and often lack feedback processes that occur in freely moving animals. We now present a modified version of that technique that allows us to record from the central complex of freely moving cockroaches as they walk in an arena and deal with barriers by turning, climbing or tunneling. Coupled with high speed video and cluster cutting, we can now relate brain activity to various parameters of the movement of freely behaving insects.

Summary An animal moving through complex terrain must consider sensory cues around it and alter its movements accordingly. In the arthropod brain, the central complex (CC) receives highly preprocessed sensory information and sends outputs to premotor regions, suggesting that it may play a role in the central control of oriented locomotion. We performed tetrode recordings within the CC in cockroaches walking on an air-suspended ball to examine the role of the CC in turning behaviors. When a rod was placed near the cockroach's head, the cockroach touched the rod repeatedly with one or both antennae before locomotion was initiated. Some CC units responded to self-generated antennal contact with the object, but at lower levels compared to externally imposed antennal stimulation. Neural activity of some other CC units responded to locomotion. We found that some CC units showed discrete firing fields corresponding to specific locomotion states. We also found that changes in firing rate of some CC units preceded changes in turning speed of one direction but not the other. Furthermore, such biased units were located in the side of the brain ipsilateral to the direction of the turning speed they can predict. Moreover, electrical stimulation of the CC elicited or modified locomotion and the direction of some evoked locomotion can be predicted by the response property of locomotion predictive units near the stimulation site. Therefore, our results suggest that at the population level, asymmetrical activity in the CC precedes and influences turning behavior.

The total viable count (TVC) in bacon overweight can cause serious damage to human health. In order to find a rapid and nondestructive method of TVC, hyper spectral imaging technique was applied to quantitatively analysis of TVC on bacon. Comprehensively comparing the pretreatment method of multiple scattering, derivative method and so on finally the multiple scattering for pretreatment was used. And the interval optimization method of least squares model was set up to predict, and get a good prediction results. The correlation coefficient of the calibration and predictions respectively was 0.808 and 0.808, interactive authentication root mean square error was 0.115 and 0.198 respectively. Therefore, hyper spectral imaging technique combining iPLS can be used for the rapid detection of TVC on bacon.

In this paper, a quick and accurate detection method which can identify whether the meat contain excessive moisture is mentioned. By using near-infrared spectroscopy measurement and SVM Multi-Information Fusion, the meat moisture content model has been established. In order to improve the accuracy of NIR measurement predicted model and to reduce the measurement sensitivity, utilizing image information and the PH value data as the parameters of the meat moisture content model. The study concluded that the theory and method can be further extended to the detection of other related meat agricultural products.

We have studied the interfacial reactions between amorphous LaAlO3 thin films and Si substrates, using high-resolution transmission electron microscopy and x-ray photoelectron spectroscopy. It has been shown that the interfacial layer between LaAlO3 him and Si substrate is SiLaxAlyOx. The depth distributions of La, Si and Al chemical states show that the ratio of La 4d(3/2) to Al 2p of the interfacial layer remains unchanged with the depth compared to that of the LaAlO3 film. Moreover, the Si content in the interfacial layer gradually decreases with increasing thickness of the interfacial layer. These results strongly suggest that the Al element is not deficient in the interfacial layer, as previously believed, and the formation of a SiLaxAlyOz interfacial layer is mainly due to the diffusion of Si from the substrate during the LaAlO3 film deposition. With the understanding of the interfacial layer formation, ones can control the interface characteristics to ensure the desired performances of devices using high-h oxides as gate dielectrics.

Based on mathematical morphology method, the paper analyzed and calculated plaque area in the image of plaque bacteria though taking hough transform to extract complete outline of fat cells. TVB-N values in the process of pork Corruption is taken for a sequence of inputs of SOM neural network used for the cluster analysis of TVB-N values and dividing pork freshness to five classes. Complying with national standard and sensory test results, it achieve the results of the test to class the fork freshness.

In this paper a way of the fuzzy logic control for the excitation system of synchronous generator is presented based on every instantaneous speed and acceleration error of synchronous generator as well as the standard fuzzy membership function on the phase plane. The gain of the controller can be automatically adjusted. Computer simulation in the simple power system shows that the controller has strong robustness for a variety of disturbances in power system.