Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

Alarm-Based Root Cause Analysis Based on Weighted Fault Propagation Topology for Distributed Information Network

A distributed information network with complex network structure always has a challenge of locating fault root causes.In this paper,we propose a novel root cause analysis(RCA)method by random walk on the weighted fault propagation graph.Different from other RCA methods,it mines effective features information related to root causes from offline alarms.Combined with the information,online alarms and graph relationship of network structure are used to construct a weighted graph.Thus,this approach does not require operational experience and can be widely applied in different distributed networks.The proposed method can be used in multiple fault location cases.The experiment results show the proposed approach achieves much better performance with 6%higher precision at least for root fault location,compared with three baseline methods.Besides,we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.

A Framework for Modern Risk-Informed Root Cause Analysis Process

The paper discusses the framework for a risk-informed root cause analysis process.Such process enables scaling of the analysis performed based on the risk associated with the undesired event or condition,thereby creating tiers of analysis where the greater the risk,the more sophisticated the analysis.In a risk-informed root cause analysis process,a situation is normally not analyzed at a level less than what actually occurred.However,a situation may be investigated as though the consequence were greater than actually happened,especially if only slight differences in circumstances could result in a significantly higher consequence.While operational events or safety issues are normally expected to result only with negligible or marginal actual consequences,many of those would actually have certain potential to develop or propagate into catastrophic events.This potential can be expressed qualitatively or quantitatively.Risk-informing of root cause analysis relies on mapping the event or safety issue into a risk matrix which,traditionally,is a two-dimensional probability-consequence matrix.A new concept employed in the risk matrix for root cause analysis is that,while the probability reflects the observed or expected range of values(retaining,thus,its“traditional”meaning),the consequence reflects not only the observed or materialized impact(such as failure of equipment)but,also,its potential to propagate or develop into highly undesirable final state.The paper presents main elements of risk-informed root cause analysis process and discusses qualitative and quantitative aspects and approaches to determination of risk significance of operational events or safety issues.

A Study of Borrowing,Imitation and Creation in English-Chinese Translation:A Case Study of Translating“The Root Cause of Poverty”

Based on the translated version of“The Root Cause of Poverty”by Rutger Bregman on the Ted speech online platform,this research analyzes the application of direct borrowing,imitative translation and creative translation in English-Chinese translation.First,we believe the direct borrowing of a ready-made version is most ideal as it reveals the common ground both languages share when translating a source text.Next,it is preferable to imitate an already existing expression as similarity is reader-friendly.However,it is sometimes necessary to adopt creative translation to preserve differences to ensure that the meaning of the original language is accurately transmitted.

Interpretation on Cultural Conflicts between America and China and the Conflict Root Cause Reflected in The Joy Luck Club

This paper is mainly written to explore how cultural differences and clashes between America and China give rise to mother-daughter discords and conflicts. The paper was conducted based on the definition of culture in order to make clear of cultural conflicts. It is written to analyze the two groups of women immigrants unavoidably encountering the clash between their ancestral culture—Chinese culture and the dominant culture--American culture. Furthermore, the paper demonstrates that the mothers' Chinese roots are the root cause of misunderstandings and conflicts between their American-born daughters and themselves.Besides, the writer analyzes that different standpoints towards education value between mothers and American- born daughters cause their misunderstanding and conflicts. The conclusion is that the diversities between Chinese culture and American culture are the root cause for the mother-daughter discords and conflicts.

A Causal Fusion Inference Method for Industrial Alarm Root Cause Analysis Based on Process Topology and Alarm Event Data

Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.

Is China the Root Cause of Global Deflation?

The so-called" China exporting deflation”theory has been popular in the international businesscommunity recently. For example,international businessmagazines such as Us-based Business Week and Britainbased The Economist have discussed the topic of global deflation,in which the impact of floods of low-

Autoencoder-based anomaly root cause analysis for wind turbines

A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder.These models have proven to be very successful in detecting such deviations,yet cannot show the underlying cause or failure directly.Such information is necessary for the implementation of these models in the planning of maintenance actions.In this paper we introduce a novel method:ARCANA.We use ARCANA to identify the possible root causes of anomalies detected by an autoencoder.It describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly considerably.This reconstruction must be similar to the anomaly and thus identify only a few,but highly explanatory anomalous features,in the sense of Ockham’s razor.The proposed method is applied on an open data set of wind turbine sensor data,where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test environment.The results are compared with the reconstruction errors of the autoencoder output.The ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features,whereas using the non-optimised reconstruction error does not.Even though the deviation in one specific input feature is very large,the reconstruction error of many other features is large as well,complicating the interpretation of the detected anomaly.Additionally,we apply ARCANA to a set of offshore wind turbine data.Two case studies are discussed,demonstrating the technical relevance of ARCANA.

Desertification and Dust Storms in China: Impacts, Root Causes and Mitigation Strategies

Desertification and dust storms and their impact on the Chinese economy and the environment were examined. A context was established using the best information available on the trends of desertification and dust storms in China. Building on the context, the second section the root causes of desertification and dust storms were examined. Several myths were sought to be identified about some core issues surrounding desertification and dust storms. China’s National Action Plan to combat desertification was reviewed and the effectiveness of existing strategies and programs for mitigating the adverse effects of desertification and dust storms were discussed. The future research needed was pointed out.

Analysis of Poverty Performance and Its Rooted Causes of the Aid-Poverty- Development Counties in Sichuan

There are 36 counties affirmed to be aid-poverty-development county in the early 21st century, which distribute around the circumferential mountain of Sichuan Basin, According to the topographic features and the distribution of nationality, these aid-poverty-development counties can be classified into 4 depressed-regions. Study shows that regional poverty performance of 4 depressed-regions not only has many commons in the field of regional economic structure and industry structure, which is far behind the average development performance of Sichuan in the field of economic sum per capita such as GDP, farmer net income per capita as well as retail scale consumer goods, it is also quite different related to infrastructure and social development within 4 depressed-regions. Regional Poverty is chronically resulted in interaction of the multi-factors. Natural condition constraint is the basic factor contributed to regional poverty, policy influences play key role, the poor culture is inherent factor to regional poverty and the marginal locations play important role.

CLINICAL STUDY ON THE TIME OF TREATING CHEMOTHERAPY INDUCED TOXIC AND SIDE EFFECTS IN MALIGNANT TUMOR PATIENTS BY ACUPOINT INJECTION OF ASTRAGALUS ROOT INJECTION

Objective: To investigate the suitable time of treating virulent and side effects of chemotherapy for malignant tumor by acupoint injection of Astragalus Root injection. Methods: Sixty three patients with malignant tumor were divided into three groups: prevention treatment (PT) group (n=23 cases),post chemotherapy treatment (PCT) group( n=22 cases), Western medicine (WM) group ( n=18 cases). The patients in PT, PCT and WM groups were treated respectively from the fifth day on before chemotherapy and from the first day on after chemotherapy, 18 days in all. The patients in WM group were administered Batilol and Leucogen from the first day on after chemotherapy. Changes of leukocytes and immunoglobulin before and after treatment were observed. Results: 1) Acupoint injection of Astragalus Root injectio could increase the number of leukocytes and immunoglobulin content and its effect was better than that of Western medicine (P<0.05); 2) The effect of PT group was better than that of PCT group in preventing and treating virulent and side effects of chemotherapy (P<0.05). Conclusion: It should be stressed on prevention of malignant tumor by using acupoint injection of Astragalus Root for relieving virulent and side effects of chemotherapy.

Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

A cross-sectional study on nurse turnover intention and influencing factors in Jiangsu Province, China

Background:Nurses'turnover has been a major concern globally,which is strongly influenced by nurses'intent to leave.However,only a few large sample studies on the predictive factors associated with nurses'turnover intention were conducted in Jiangsu Province.This study mainly aims to examine the level and factors that influence nurses to leave their work in Jiangsu Province of Eastern China.Methods:A cross-sectional survey of 1978 nurses was conducted at 48 hospitals in 14 key cities throughout Jiangsu Province.The turnover intention in nurses was measured by the scale of intent to leave the profession.The work environment of nurses was measured by the Chinese version of the Practice Environment Scale.A multiple linear regression model was applied to analyse the factors associated with turnover intention.Results:The resignation rate of nurses in the hospitals of Jiangsu Province ranged from 0.64%to 12.71%in 2016.The mean scores were 15.50±3.44 for turnover intention,and 3.06±0.51 for work environment.Involvement in hospital affairs,resource adequacy,age,professional title,year(s)working,employment type and education level were the predictors of nurse intent to leave(P<0.05).Conclusion:The work environment of nurses in hospitals must be improved in staffing and resource and nurses'involvement in hospital affairs.The current study corroborates that nurses have high turnover intention.Thus,effective measures are needed to improve nurse accomplishment,professional status,participation in hospital affairs and career planning to reduce their turnover intention.

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition

Root cause analysis （RCA） of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy- Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

Revisiting Sustainable Development of Dry Valleys in Hengduan Mountains Region

Dry valleys are a striking geographic landscape in Hengduan Mountains Region and are characterized by low rainfall, desert type of vegetation and fragile environment. Past efforts and resources have been concentrated mainly on rehabilitation of degraded ecosystem and fragile environment, particularly reforestation, while socio-economic development has been largely overlooked. Despite successes in pocket areas, the overall trend of unsustainability and environmental deterioration are continuing. It is important to understand that uplift of the Tibetan Plateau is the root cause of development of dry valleys, and development and formation of dry valleys is a natural process. Human intervention has played a secondary role in development of dry valleys and degradation of dry valleys though human intervention in many cases has speeded up environmental degradation of the dry valleys. It is important to understand that dry valleys are climatic enclaves and an integrated approach that combines rehabilitation of degraded ecosystems and socio-economic development should be adopted if the overall goal of sustainable development of dry valleys is to be achieved. Promotion of niche-based cash crops, rural energy including hydropower, solar energy, biogas and fuelwood plantation is recommended as the priority activities.

The status quo and influence of self-management behaviors in convalescent stroke patients

Objective： The aims of this study were to investigate the status quo of self-management behaviors in stroke patients at the recovery stage and to explore its influencing factors.Methods： A total of 440 hospitalized convalescent stroke patients were recruited and investigated using the Basic Situation Questionnaire, Self-management Behavior Scale of Stroke, Stroke Prevention Knowledge Questionnaire and Social Support Rating Scale.Results： The mean self-management behavior score was （151.95±23.58）, and dimensions in descending order were as follows： dietary management, drug safety management, social function and interpersonal relationships, life management, emotion management, rehabilitation exercise management and disease management. Five regional self-management behavior scores were statistically significant, and the scores from Minnan and Minzhong of the Fujian province, China, were higher than the others. Gender, age, family income and self-management behavior were significantly correlated （P〈0.05）; educational level, stroke knowledge level, social support level and self-management behavior were positively correlated, and the difference was statistically significant （P〈0.01）. Conclusions： The overall self-management level of convalescent stroke patients should be improved to strengthen health education; focus on the educational level, which is relatively low; strengthen the social support system of patients; stimulate the enthusiasm and initiative of self-management disease patients to promote disease rehabilitation and improve the quality of life.

Current status and influencing factors of post-traumatic growth in maintenance hemodialysis

Objective:To study the post-traumatic growth level and influencing factors in patients with maintenance hemodialysis.Methods:A total of 179 patients receiving maintenance hemodialysis from a third-level grade A hospital in Tianjin,China were investigated using Post-traumatic Growth Inventory(PTGI),Perceived Social Support Scale,and Medical Coping Modes Questionnaire.Results:The total score for the PTGI was 53.73±16.45.Multiple linear regression analysis showed that social support,coping style,marital status,and family income significantly influenced the post-traumatic growth level in patients undergoing maintenance hemodialysis.These factors explained 41.4%of the variance.Conclusion:Medical staff should help patients under maintenance hemodialysis to fulfill their potentials by boosting the level of social support and to effectively cope with internal conflicts.In addition,nursing staff should provide relevant psychological health education to patients to improve their post-traumatic growth.

Prevalence and risk factors of prolonged grief disorder among bereaved survivors seven years after the Wenchuan earthquake in China: A cross-sectional study

Background:This study aimed to determine the prevalence and predictive factors of prolonged grief disorder(PGD)among those bereaved by the Wenchuan earthquake in Southwestern China seven years after the event.Methods:A cross-sectional survey based on census tracts was conducted on the bereaved earthquake survivors.Responses to the questionnaire regarding PGD and its potential associated factors were obtained either through face-to-face or telephone interview.PGD was screened by a validated Chinese version of the PGD questionnaire-13(PG-13).Bivariate and multivariate regression analyses were used to determine the prevalence and associated risk factors of PGD.Results:A total of 1464 bereaved earthquake survivors,with a response rate of 97.6%,were included in the study.Of the 1464 respondents studied,124(8.47%)were diagnosed with PGD.Multivariate regression analysis demonstrated that PGD in the bereaved earthquake individuals was significantly associated with several factors,including age,economic burden,close kinship with the deceased,and living with the deceased before the loss.Wenchuan earthquake bereaved aged 41e60 years were more likely to develop PGD compared to those aged younger than 40 or older than 60(OR=2.075,95%CI=1.297e3.319).Those who had a close kinship with the deceased had a higher tendency to develop PGD(OR=5.144,95%CI=2.716e9.740).The odds of PGD among the earthquake bereaved with economic burdens were higher relative to those who did not experience an economic burden(OR=8.123,95%CI=2.657e24.831).Those who living with the deceased before loss also had a higher tendency to develop PGD(OR=0.179,95%CI=0.053e0.602).Conclusions:This study revealed that a significantly high proportion(8.47%)of the Wenchuan earthquake-bereaved remain grieving seven years after the event.Those diagnosed with PGD should receive appropriate interventions from clinical psychologists.The risk factors identified in this study are crucial for the early screening and prevention of PGD in future nursing and psycho-clinical practices.

Theoretical framework construction on care complexity in Chinese hospitals: A grounded theory study

Objectives: This study aims to construct a theoretical framework to analyze risk factors and explore hospital nurses' perspectives on care complexity.Methods: The grounded theory method was adopted,and semi-structured in-depth interviews regarding the understanding of care complexity were conducted among the participants,including 31 nurses and nine doctors.In addition,data were coded and strictly analyzed in accordance with the coding strategy and requirements of grounded theory.Results: Our study reveals three factors that are closely related to care complexity,namely,(1) patient factors,including patients' condition,age,self-care abilities,compliance,social support systems,psy chological conditions,expectations,and requirements;(2) nursing staff factors,including work experiences,education,knowledge and operational skills of caring,and communication skills;and (3) organization and equipment factors,including nursing workforce,nursing workload,support from multidisciplinary teams and ancillary departments,and the conditions of medical and hospital services.Conclusions: This study defines care complexity on the basis of its factors.Care complexity refers to the difficulty of nursing tasks during patient care plan implementation,which are affected by patients,nurses,and other factors in nursing and multisectoral,multidisciplinary cooperation.The framework can be beneficial for nursing education and for the improvement of the quality and efficiency of clinical nursing practice.

A Survey on Event Mining for ICT Network Infrastructure Management

1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.