Effects of three-dimensional quality assessment nursing intervention on efficacy and disease management of patients undergoing esophageal cancer surgery

BACKGROUND Esophageal cancer is one of the most common malignant tumors.The three-dimensional quality structure model is a quality assessment theory that includes three dimensions:Structure,process,and results.AIM To investigate the effects of nursing interventions with three-dimensional quality assessment on the efficacy and disease management ability of patients undergoing esophageal cancer surgery.METHODS In this prospective study,the control group received routine nursing,and the intervention group additionally received a three-dimensional quality assessment intervention based on the above routine care.Self-efficacy and patient disease management abilities were evaluated using the General Self-Efficacy Scale(GSES)and Exercise of Self-Care Agency scale,respectively.IBM SPSS Statistics for Windows,version 17.0,was used for the data processing.RESULTS This study recruited 112 patients who were assigned to the control and experi-mental groups(n=56 per group).Before the intervention,there was no significant difference in GSES scores between the two groups(P>0.05).After the inter-vention,the GSES scores of both groups increased,with the experimental group showing higher values(P<0.05).At the time of discharge and three months after discharge,the scores for positive attitudes,self-stress reduction,and total score of health promotion in the experimental group were higher than those in the control group(P<0.05).CONCLUSION The implementation of a three-dimensional quality structure model for postoperative patients with esophageal cancer can effectively improve their self-management ability and self-efficacy of postoperative patients.

Hydration mechanism of low quality fly ash in cement-based materials

The hydration mechanism of low quality fly ash in cement-based materials was investigated. The hydration heat of the composite cementitious materials was determined by isothermal calorimetry, and the hydration products, quantity, pore structure and morphology were measured by X-ray diffraction(XRD), thermalgravity-differential thermal analysis(TG-DTA), mercury intrusion porosimetry(MIP) and scanning electron microscopy(SEM), respectively. The results indicate that grinding could not only improve the physical properties of the low quality fly ash on particle effect, but also improve hydration properties of the cementitious system from various aspects compared with raw low quality fly ash(RLFA). At the early stage of hydration, the low quanlity fly ash acts as almost inert material; but then at the later stage, high chemical activity, especially for ground low quality fly ash(GLFA), could be observed. It can accelerate the formation of hydration products containing more chemical bonded water, resulting in higher degree of cement hydration, thus denser microstructure and more reasonable pore size distribution, but the hydration heat in total is reduced. It can also delay the induction period, but the accelerating period is shortened and there is little influence on the second exothermic peak.

Structural quality of soil in plantation： a case study on the mixed plantation of Alnus cremastogyne and Cupressus funebris in upper reaches of Yangtze River

In process of ecological construction in typical region of upper reaches of Yangtze River, China, the mixed plantations at the ages of 10-20 present a trend to be pure forests and degeneration. Soil samples including stratified soil and total soil were taken from 4 typical profiles in the mixed plantation ofAlnus cremastogyne and Cupressus funebris in Yanting County in central Sichuan, China. Soil indices of the plantation were compared with those of natural forest in Gongga Mountain in the same region, The results revealed that structural quality of soil in plantation was significantly lower than that in natural forests. The degradation of structural quality of soil in plantation was one of key factors for plantation degeneration, The degradation causes of structural quality of soil were analyzed. Aanthtopogenic disturbance and absence of effective protection and scientific management are the main reason for degradation of structural quality of soil in plantation. The main countermeasures, e.g. foresl reservation, ecological rehabilitation, litter horizon rebuilding as well as organic fertilizer application, were proposed to improve the structural quality of soil in plantation.

A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure

Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA.In this work, we developed a new Hidden Markov Model（HMM） to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities：（1） encoding local structure of each position by jointly considering sequence and structure information, and（2）assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP,and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Measuring Structural Quality of Object-Oriented Softwares via Bug Propagation Analysis on Weighted Software Networks

The quality of a software system is partially determined by its structure（topological structure）,so the need to quantitatively analyze the quality of the structure has become eminent.In this paper a novel metric called software quality of structure（SQoS） is presented for quantitatively measuring the structural quality of object-oriented（OO） softwares via bug propagation analysis on weighted software networks（WSNs）.First,the software systems are modeled as a WSN,weighted class dependency network（WCDN）,in which classes are nodes and the interaction between every pair of classes if any is a directed edge with a weight indicating the probability that a bug in one class will propagate to the other.Then we analyze the bug propagation process in the WCDN together with the bug proneness of each class,and based on this,a metric（SQoS） to measure the structural quality of OO softwares as a whole is developed.The approach is evaluated in two case studies on open source Java programs using different software structures（one employs design patterns and the other does not） for the same OO software.The results of the case studies validate the effectiveness of the proposed metric.The approach is fully automated by a tool written in Java.

Research on the Construction of Drug Inspection Evaluation Indicator System

Objective To study a way to establish a drug inspection evaluation system in China.Methods Through literature research,behavioral event interviews,Delphi expert interviews,and other methods,the theory of“threedimensional quality structure model”was used to extract,screen,and construct a set of evaluation indicator system for drug inspection,including 3 first-level indicators,11 second-level indicators,and 47 third-level indicators.Results and Conclusion An effective management tool to evaluate the quality of drug inspection has not yet been formed in China.According to the requirements of laws,regulations,and departmental rules,combined with the mature drug inspection mechanism of international drug regulatory agencies or organizations and the international quality management theory,some management tools are put forward to improve the quality system of drug supervision.

Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.