Effect of Brisk Walking on Self-Care Agency or Care Dependency among Colorectal Cancer Patients with Permanent Stoma

Objective:The purpose of this study was to determine the effectiveness of brisk walking as an intervention for self-care agency and care dependency in patients with permanent colorectal cancer stoma.Method:This study adopted a quasi-experimental research design,specifically a non-equivalent control group pre-test and post-test design.Utilizing the Exercise of Self-Care Agency Scale(ESCA)and Care Dependency Scale(CDS),a survey was administered to 64 patients from a hospital in Shandong Province.The statistical methods used for analyzing data included frequency,mean,standard deviation(SD),independent t-test,P-value calculation,and dependent t-test.Result:After two months of a brisk walking exercise program,participants in the experimental group had a higher level of self-care agency than before the experiment(P<0.05),and their level of care dependency was significantly reduced(P<0.05).Participants in the control group also showed higher levels of self-care agency(P<0.05)and lower levels of care dependency(P<0.05)after two months compared to their levels before the two months.Conclusion:The brisk walking program had a positive impact on patients’self-care agency and reduced their care dependency.

Lexicalized Dependency Paths Based Supervised Learning for Relation Extraction

Log-linear models and more recently neural network models used forsupervised relation extraction requires substantial amounts of training data andtime, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in adependency tree which we call lexicalized dependency paths (LDPs). We showthat this representation is fast, efficient and transparent. We further propose representations utilizing entity types and its subtypes to refine our model and alleviatethe data sparsity problem. We apply lexicalized dependency paths to supervisedlearning using the ACE corpus and show that it can achieve similar performancelevel to other state-of-the-art methods and even surpass them on severalcategories.

NGDcrm:a numeric graph dependency-based conflict resolution method for knowledge graph

Knowledge graph(KG)conflict resolution is to solve knowledge conflicts problem in the construction of KG.Aiming at the problem of KG conflict resolution,a KG conflict resolution algorithm NGDcrm is proposed,which is a numeric graph dependency-based conflict resolution method.NGDcrm utilizes the dependency graph to perform arithmetic calculation and predicate comparison of numerical entity knowledge in the KG.NGDcrm first uses a parallel segmentation method to segment the KG;then,it extracts the features of the KG according to KG embedding;finally,it uses numerical graph dependencies to detect and correct the wrong facts in the KG based on the extracted features.The experimental results on real data show that NGDcrm is better than the state-of-the-art knowledge conflict resolution method.Among them,the AUC value of NGDcrm on the DBpedia dataset is 15.4%higher than the state-of-the-art method.

WPF精粹(三)——Dependency Property

WPF能够让XAML描述出来的GUI轻易地使用animation、data binding、trigger、template、style等特色,为了要采用一致的行为来处理这全部的特色,有了全新的事件模型和全新的property——dependenct property。

Washed microbiota transplantation reduces proton pump inhibitor dependency in nonerosive reflux disease

BACKGROUND The pathogenesis of gastroesophageal reflux disease(GERD)is closely associated with the intestinal bacteria composition and their metabolites.AIM To investigate whether washed microbiota transplantation(WMT)improves symptoms of nonerosive reflux disease(NERD)with proton pump inhibitor(PPI)dependency.METHODS Patients with recurrent NERD and PPI dependency at the First Affiliated Hospital of Guangdong Pharmaceutical University from 2017 to 2018 were included and divided into a WMT or PPI group treated with PPI with/without WMT.The endpoint was NERD symptom frequency evaluated 1 mo after WMT using reflux disease questionnaire(RDQ)and GERD questionnaire(GERDQ)scores,remission time,PPI dose,and the examination of intestinal mucosal barrier function.RESULTS In the WMT(n=15)and PPI(n=12)groups,the total remission rate at 1 mo after treatment was 93.3%vs 41.7%.Compared with the PPI group,the WMT group showed better results in GERDQ(P=0.004)and RDQ(P=0.003)and in remission months(8 vs 2,P=0.002).The PPI dose was reduced to some extent for 80%of patients in the WMT group and 33.3%in the PPI group.In 24 patients,intestinal mucosal barrier function was examined before treatment,and changes in the degree of damage were observed in 13 of these patients after treatment.Only one of the 15 patients had minor side effects,including a mushy stool two or three times a day,which resolved on their own after 1 wk.CONCLUSION This study is the first to demonstrate that WMT may be safe and effective for relieving NERD symptoms and reducing PPI dependency and recurrence.

Temperature Dependency of Water Vapor Permeability of Shape Memory Polyurethane

Solution-cast films of shape memory polyurethane have been investigated.Differential scanning calorimetry, DMA, tensile test, water vapor permeability and the shape memory effect were carried out to characterize these polyurethane membranes. Samples cast at higher temperatures contained more hard segment in the crystalline state than a sample cast at lower temperature. The change in the water vapor permeability (WVP) of SMPU films with respect to the temperature follows an S-shaped curve, and increases abruptly at T m of the soft segment for the fractional free volume (FFV, the ratio of free volume and specific volume in polymers) increased linearly with temperature. The water vapor permeability dependency of the temperature and humidity contribute to the result of the change of diffusion and solubility with the surrounding air condition. The diffusion coefficient (D) are the function of temperature and show good fit the Arrhenius form but show different parameter values when above and below T g. The crystalline state hard-segment is necessary for the good shape memory

Psychometric properties of the Care Dependency Scale in stroke survivors in Indonesian hospitals

Purpose: This study aimed to evaluate the psychometric properties of the Indonesian version of the Care Dependency Scale (CDS) among stroke survivors.Methods: The study was undertaken in four hospitals.We analysed datasets obtained from 109 stroke survivors on inpatient wards and in outpatient clinics,who were rated by nurses to determine the CDS reliability coefficients.The Cronbach's α and Cohen's kappa coefficients were applied.Concurrent validity was conducted for the data on care dependency,which werecollected from 49 of these 109 participants on inpatient wards by nurses using the CDS and the Barthel Index.A Spearman's rank correlation analysis was conducted to measure the association between the CDS and the Barthel Index results.Results: An analysis of the results of the CDS tested on the inpatient ward and in the outpatient clinic revealed a high level of internal consistency.The reliability analysis yielded the same Cronbach's α coefficient of 0.98 for both the inpatient and outpatient data.A significant,moderate correlation was observed between the CDS and Barthel Index results.Conclusion: The CDS can be recommended for use as a tool for the assessment and evaluation of stroke survivors who are receiving acute or long-term care.

Design and Implementation of Weibo Sentiment Analysis Based on LDA and Dependency Parsing

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification,emotion tendency classification,and emotion expression extraction. Combining with the latent Dirichlet allocation(LDA) model,a Gibbs sampling implementation for inference of our algorithm is presented,and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo,use dependency parsing,divided into two categories with subject and object,summarized six kinds of dependency models from evaluating objects and emotion words,and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction,indicating the value of our method as not only innovative but practical.

Considering the Fault Dependency Concept with Debugging Time Lag in Software Reliability Growth Modeling Using a Power Function of Testing Time

Since the early 1970s tremendous growth has been seen in the research of software reliability growth modeling. In general, software reliability growth models （SRGMs） are applicable to the late stages of testing in software development and they can provide useful information about how to improve the reliability of software products. A number of SRGMs have been proposed in the literature to represent time-dependent fault identification / removal phenomenon; still new models are being proposed that could fit a greater number of reliability growth curves. Often, it is assumed that detected faults are immediately corrected when mathematical models are developed. This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault, the skill and experience of the personnel, the size of the debugging team, the technique, and so on. Thus, the detected fault need not be immediately removed, and it may lag the fault detection process by a delay effect factor. In this paper, we first review how different software reliability growth models have been developed, where fault detection process is dependent not only on the number of residual fault content but also on the testing time, and see how these models can be reinterpreted as the delayed fault detection model by using a delay effect factor. Based on the power function of the testing time concept, we propose four new SRGMs that assume the presence of two types of faults in the software： leading and dependent faults. Leading faults are those that can be removed upon a failure being observed. However, dependent faults are masked by leading faults and can only be removed after the corresponding leading fault has been removed with a debugging time lag. These models have been tested on real software error data to show its goodness of fit, predictive validity and applicability.

Clinical characteristics and type of antithrombotic treatment in a Spanish cohort of elderly patients with atrial fibrillation according to dependency, frailty and cognitive impairment

Background Available data regarding clinical profile and management of elderly patients with atrial fibrillation （AF） according to dependency, fragility and cognitive impairment are scarce. The objective of the study was to analyze the biodemographic data, clinical profile and antithrombotic treatment according to dependency, fragility and cognitive impairment in elderly AF patients. Methods Cross- sectional and multi-center study performed in consecutive AF patients ≥ 75 years treated with oral anticoagulants ≥ 3 months attended in Internal Medicine Departments in Spain. Results A total of 837 patients （83.0 ± 5.0 years; CHA2DS2-VASc： 5.0 ± 1.4; HAS-BLED： 2.1 ± 0.9） were included. 44.4% of patients had some degree of dependency, 43.3% were fragile, and 32.3% had cognitive impairment. Patients with any of these conditions were older, had a worse clinical profile, with more comorbidities and higher risks of thromboembolic and bleeding events. All these conditions were independently associated among them. Overall, 70.8% of patients were taking vitamin K antagonists, the remaining 29.2% direct oral anticoagulants and 9.7% oral antiplatelets. This distribution was independent of the presence of dependency or fragility, but there was a trend to a higher prescription of vitamin K antagonists in those patients with cognitive impairment （75.2% vs. 68.8%; P = 0.05）. Conclusions Approximately 32%-44% of elderly anticoagulated AF patients attended have some degree of dependency, fragility and/or cognitive impairment. Patients with any of these conditions are older and have a worse clinical profile. Ap？proximately 71% of patients are taking vitamin K antagonists, regardless dependency or frailty, but with a trend to higher prescription in patients with cognitive impairment.

Dependency-Based Local Attention Approach to Neural Machine Translation

Recently dependency information has been used in different ways to improve neural machine translation.For example,add dependency labels to the hidden states of source words.Or the contiguous information of a source word would be found according to the dependency tree and then be learned independently and be added into Neural Machine Translation(NMT)model as a unit in various ways.However,these works are all limited to the use of dependency information to enrich the hidden states of source words.Since many works in Statistical Machine Translation(SMT)and NMT have proven the validity and potential of using dependency information.We believe that there are still many ways to apply dependency information in the NMT structure.In this paper,we explore a new way to use dependency information to improve NMT.Based on the theory of local attention mechanism,we present Dependency-based Local Attention Approach(DLAA),a new attention mechanism that allowed the NMT model to trace the dependency words related to the current translating words.Our work also indicates that dependency information could help to supervise attention mechanism.Experiment results on WMT 17 Chineseto-English translation task shared training datasets show that our model is effective and perform distinctively on long sentence translation.

A novel dependency language model for information retrieval

This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) approach to IR by introducing dependency models for both query and document. Relevance between document and query is then evaluated by reference to the Kullback-Leibler divergence between their dependency models. This paper introduces a novel hybrid dependency structure, which allows integration of various forms of dependency within a single framework. A pseudo relevance feedback based method is also introduced for constructing query dependency model. The basic idea is to use query-relevant top-ranking sentences extracted from the top documents at retrieval time as the augmented representation of query, from which the relationships between query terms are identified. A Markov Random Field (MRF) based approach is presented to ensure the relevance of the extracted sentences, which utilizes the association features between query terms within a sentence to evaluate the relevance of each sentence. This dependency retrieval model was compared with other traditional retrieval models. Experiments indicated that it produces significant improvements in retrieval effectiveness.

Using SVM to construct a Chinese dependency parser

In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.

System level test selection based on combinatorial dependency matrix

Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators.The existing models and methods are not suitable for system level test selection.The first problem is the lack of detailed data of the units’fault set and the test set,which makes it impossible to establish a traditional dependency matrix for the system level.The second problem is that the system level fault detection rate and the fault isolation rate(referred to as"two rates")are not enough to describe the fault diagnostic ability of the system level tests.An innovative dependency matrix(called combinatorial dependency matrix)composed of three submatrices is presented.The first problem is solved by simplifying the submatrix between the units’fault and the test,and the second problem is solved by establishing the system level fault detection rate,the fault isolation rate and the integrated fault detection rate(referred to as"three rates")based on the new matrix.The mathematical model of the system level test selection problem is constructed,and the binary genetic algorithm is applied to solve the problem,which achieves the goal of system level test selection.

Analyzing Ethereum Smart Contract Vulnerabilities at Scale Based on Inter-Contract Dependency

Smart contracts running on public blockchains are permissionless and decentralized,attracting both developers and malicious participants.Ethereum,the world’s largest decentralized application platform on which more than 40 million smart contracts are running,is frequently challenged by smart contract vulnerabilities.What’s worse,since the homogeneity of a wide range of smart contracts and the increase in inter-contract dependencies,a vulnerability in a certain smart contract could affect a large number of other contracts in Ethereum.However,little is known about how vulnerable contracts affect other on-chain contracts and which contracts can be affected.Thus,we first present the contract dependency graph(CDG)to perform a vulnerability analysis for Ethereum smart contracts,where CDG characterizes inter-contract dependencies formed by DELEGATECALL-type internal transaction in Ethereum.Then,three generic definitions of security violations against CDG are given for finding respective potential victim contracts affected by different types of vulnerable contracts.Further,we construct the CDG with 195,247 smart contracts active in the latest blocks of the Ethereum and verify the above security violations against CDG by detecting three representative known vulnerabilities.Compared to previous large-scale vulnerability analysis,our analysis scheme marks potential victim contracts that can be affected by different types of vulnerable contracts,and identify their possible risks based on the type of security violation actually occurring.The analysis results show that the proportion of potential victim contracts reaches 14.7%,far more than that of corresponding vulnerable contracts(less than 0.02%)in CDG.

Dependency-based importance measures of components in mechatronic systems with complex network theory

To compensate for the limitations of previous studies,a complex network-based method is developed for determining importance measures,which combines the functional roles of the components of a mechatronic system and their topological positions.First,the dependencies among the components are well-represented and well-calculated.Second,a mechatronic system is modeled as a weighted and directional functional dependency network(FDN),in which the node weights are determined by the functional roles of components in the system and their topological positions in the complex network whereas the edge weights are represented by dependency strengths.Third,given that the PageRank algorithm cannot calculate the dependency strengths among components,an improved PageRank importance measure(IPIM)algorithm is proposed,which combines the node weights and edge weights of complex networks.IPIM also considers the importance of neighboring components.Finally,a case study is conducted to investigate the accuracy of the proposed method.Results show that the method can effectively determine the importance measures of components.

An optimized fitting function with least square approximation in InAs/AlSb HFET small-signal model for characterizing the frequency dependency of impact ionization effect

An enhanced small-signal model is introduced to model the influence of the impact ionization effect on the performance of In As/Al Sb HFET, in which an optimized fitting function D（ωτi） in the form of least square approximation is proposed in order to further enhance the accuracy in modeling the frequency dependency of the impact ionization effect.The enhanced model with D（ωτi） can accurately characterize the key S parameters of In As/Al Sb HFET in a wide frequency range with a very low error function EF. It is demonstrated that the new fitting function D（ωτi） is helpful in further improving the modeling accuracy degree.

The Dynamic-to-Static Conversion of Dynamic Fault Trees Using Stochastic Dependency Graphs and Stochastic Activity Networks

In this paper a new modeling framework for the dependability analysis of complex systems is presented and related to dynamic fault trees (DFTs). The methodology is based on a modular approach: two separate models are used to handle, the fault logic and the stochastic dependencies of the system. Thus, the fault schema, free of any dependency logic, can be easily evaluated, while the dependency schema allows the modeler to design new kind of non-trivial dependencies not easily caught by the traditional holistic methodologies. Moreover, the use of a dependency schema allows building a pure behavioral model that can be used for various kinds of dependability studies. In the paper is shown how to build and integrate the two modular models and convert them in a Stochastic Activity Network. Furthermore, based on the construction of the schema that embeds the stochastic dependencies, the procedure to convert DFTs into static fault trees is shown, allowing the resolution of DFTs in a very efficient way.

IoC容器和Dependency Injection模式

loC,一种轻量级容器,试图取代庞大而复杂的J2EE技术。如今正在开源Java社群中掀起一股热潮,同时loC的设计模式讨论也进行的如火如茶。大师级的Martin Fowler为我们带来这篇文章,先睹为快。

The Care Dependency Scale: psychometric testing of the Chinese version

Objective： To determine the validity and reliability of the Chinese version of the Care Dependency Scale （CDS） for proxy assessments by nurses in Chinese nursing homes. The CDS is an excellent tool for measuring care dependency in many countries. The Chinese version of the CDS contributes additional data regarding the scale＇s use. Methods： This was a cross-sectional study of 184 older adults; 128 were re-evaluated after 2 weeks. Cronbach＇s alpha, the mean inter-item correlation, the inter-rater reliability and the test-retest reliability were used to analyze the reliability of the CDS. A factor analysis was carried out to find common di- mensions between the CDS versions. Results： The alpha coefficient of the CDS was 0.95. The subsequent inter-rater and test-retest reliabilities were analyzed in terms of the Kappa values, which were between 0.84 and 0.89; 0.83 and 0.92, respectively. Three factors were identified in the factor analysis. Conclusions： The CDS is a suitable tool for assessing care dependency among elderly Chinese residents in nursing homes.