Unit Root Analysis of Traffic Time Series in Toll Highways

Concession contracts in highways often include some kind of clauses （for example, a minimum traffic guarantee） that allow for better management of the business risks. The value of these clauses may be important and should be added to the total value of the concession. However, in these cases, traditional valuation techniques, like the NPV （net present value） of the project, are insufficient. An alternative methodology for the valuation of highway concession is one based on the real options approach. This methodology is generally built on the assumption of the evolution of traffic volume as a GBM （geometric Brownian motion）, which is the hypothesis analyzed in this paper. First, a description of the methodology used for the analysis of the existence of unit roots （i.e., the hypothesis of non-stationarity） is provided. The Dickey-Fuller approach has been used, which is the most common test for this kind of analysis. Then this methodology is applied to perform a statistical analysis of traffic series in Spanish toll highways. For this purpose, data on the AADT （annual average daily traffic） on a set of highways have been used. The period of analysis is around thirty years in most cases. The main outcome of the research is that the hypothesis that traffic volume follows a GBM process in Spanish toll highways cannot be rejected. This result is robust, and therefore it can be used as a starting point for the application of the real options theory to assess toll highway concessions.

Prediction of the Behavior of a Power System Using Root Cause Failure Analysis

The thermo-economic performance of a gas turbine is simulated using a fish bone technique to characterize the major equipment failure causes.Moreover a fault tree analysis and a Pareto technique are implemented to identify the related failure modes,and the percentage and frequency of failures,respectively.A pump 101 and drier 301 belonging to the Tabriz Petrochemical Company are considered for such analysis,which is complemented with a regression method to determine a behavioral model of this equipment over a twenty-year period.Research findings indicate that 81%of major failure factors in production equipment are related to the executive procedures(24%),human error(22%),poor quality of materials and parts(20%),and lack of personnel training(15%).

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.

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.

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.

QUANTITATIVE ANALYSIS OF RELATIONSHIP BETWEEN ARMILLARIA ROOT ROT AND SITE FACTORS IN PINUS RADIATA PLANTATIONS

Incidence of Armillaria infection was quantified based on site factors in New Zealand Pinus radiata plantations.A linear multiple regression model was derived to predict infection levels of Armillaria root rot.Factors positivily associated with the infection were:previous vegetation（native bush,pine）;soil type（pumice）;landform （valley,gully,flat）and the interaction between them.This model could assist in management planning with regard to the predisposition of particular stand to Armillaria infection.Keywods:Armillaria root rot,Disease incidence,Site factors,Quantification,Pinus radiata.

Advances in experimental methods for root system architecture and root development

Plant roots play important roles in acquisition of water and nutrients, storage, anchoring, transport, and symbiosis with soil microorganisms, thus quantitative researches on root developmental processes are essential to understand root functions and root turnover in ecosystems,and at the same time such researches are the most difficult because roots are hidden underground. Therefore, how to investigate efficiently root functions and root dynamics is the core aspect in underground ecology. In this article, we reviewed some experimental methods used in root researches on root development and root system architecture, and summarized the advantages and shortages of these methods. Based on the analyses, we proposed three new ways to more understand root processes：（1） new experimental materials for root development;（2） a new observatory system comprised of multiple components, including many observatory windows installed in field, analysis software,and automatic data transport devices;（3） new techniques used to analyze quantitatively functional roots.

Analysis of the present situation and influencing factors of self-perceived burden in primary glaucoma patients

Objective:To explore the status of self-perceived burden(SPB)in primary glaucoma patients and to analyze its influencing factors.Subject and setting:A questionnaire survey was administered to 236 inpatients from a tertiary general hospital and a eye hospital in Tianjin.The investigation was conducted after obtaining informed consent from each participant.Instruments:They were investigated using general data questionnaire,Self-Perceived Burden Scale(SPBS),Medical Coping Modes Questionnaire(MCMQ).Design:A descriptive cross-sectional design was used to gather data in this study.Results:The total SPBS score of primary glaucoma patients was(31.10±9.34)was medium.Regression consults showed that avoidance and surrender coping style,medical burden and right eye vision were the influencing factors of patients’SPB(P<0.05).Conclusion:Patients with primary glaucoma have a relatively heavy SPB,so medical staff should encourage them to actively face it.Tailored strategies in line with the patient’s economic and visual conditions to reduce the SPB.

Analysis on the status and influencing factors of undergraduate nursing students’ online learning engagement in the context of the pandemic

Objective:This project has mainly studied the online learning engagement of undergraduate nursing students and analyzes influencing factors of online learning and teaching mode during the Novel Coronavirus(COVID-19).This research has significant references for improving the efficiency and quality of the online learning mode of students.Methods:In this study,212 undergraduate nursing students were selected from a comprehensive university in Jilin Province by combining convenience sampling and cluster sampling methods.And these students were conducted with a general information questionnaire,Online Academic Emotion Scale,and Online Learning Engagement Scale.The influencing factors of this teaching mode were analyzed by multiple linear stepwise regression.Results:The total score of online learning engagement of undergraduate students was 53.85±7.38,which positively correlated with positive high arousal emotion and negative high arousal emotion,but weakly negatively correlated with negative low arousal emotion(r=0.661,0.246,-0.187,P<0.001).Grade,type of online class,online learning time,and positively high arousal emotion were mainly affected the online learning engagement of undergraduate nursing students,which explained 78.5%of the total variation(P<0.001).Conclusion:The online learning engagement of undergraduate nursing students was above the middle level under the background of the COVID-19 pandemic.Lectures and professors who teach undergraduate nursing students,should integrate the individuation characters of nursing students,and motivate their positively high arousal emotion to improve online learning engagement of students to ensure the quality of online teaching mode.

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.

Delay-dependent stability and added damping of SDOF real-time dynamic hybrid testing

It is well-recognized that a transfer system response delay that reduces the test stability inevitably exists in real-time dynamic hybrid testing （RTDHT）. This paper focuses on the delay-dependent stability and added damping of SDOF systems in RTDHT. The exponential delay term is transferred into a rational fraction by the Pad6 approximation, and the delay-dependent stability conditions and instability mechanism of SDOF RTDHT systems are investigated by the root locus technique. First, the stability conditions are discussed separately for the cases of stiffness, mass, and damping experimental substructure. The use of root locus plots shows that the added damping effect and instability mechanism for mass are different from those for stiffness. For the stiffness experimental substructure case, the instability results from the inherent mode because of an obvious negative damping effect of the delay. For the mass case, the delay introduces an equivalent positive damping into the inherent mode, and instability occurs at an added high frequency mode. Then, the compound stability condition is investigated for a general case and the results show that the mass ratio may have both upper and lower limits to remain stable. Finally, a high-emulational virtual shaking table model is built to validate the stability conclusions.

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.

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.

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.

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.

The Status Quo and Influencing Factors of the Moral Distress in Nurses in Tertiary Grade A Hospitals in Wuhan

Background: As medical technology has advanced, it has also made it possible to maintain end-stage life support for longer periods of time, but it has also been accompanied by a debate about ineffective care, nursing is considered to be an ethically important profession, and nurses aim to achieve ethical goals such as providing the best possible care to patients, achieving high quality outcomes, but it is common when there are insufficient numbers of staff, inadequately trained staff, and organizational policies and procedures that make it difficult, or even impossible, for nurses to meet the needs of patients and their families. This conflict results in moral distress for nurses, yet limited attention has been paid to this phenomenon. Objective: To explore the current phenomenon of moral distress and its triggering factors in nurses in tertiary grade A hospitals in Wuhan, by targeting root causes and understanding the interplay between nurses and settings where moral distress occurs, interventions can be tailored to minimize moral distress with the ultimate goal of enhancing patient care. Method: Totally 384 nurses from clinical departments in 2 tertiary Grade A hospitals in Wuhan were investigated with the Chinese version Moral Distress Scale-Revised (MDS-R). Result: The total score of moral distress was 47.41 ± 27.14, and the mean scores of moral distress frequency and intensity were 1.01 ± 0.53 and 1.19 ± 0.61, which were at a lower level. The main source of moral distress for nurses is related to end-of-life care and medical decision communication;Nurses’ moral distress scores were statistically significant (P Conclusion: Hospital facility leaders and nursing managers need to train nurses to develop competency development such as reflection, empathy, communication, positive thinking, and emotional intelligence to practice ethical dilemma response, and facilitate collaborative communication among healthcare members, so as to alleviate moral distress in nurses.