Project diagnostics : assessing the condition of projects and identifying poor health combing forces

In many cases, construction projects do not achieve the objectives that the project participants set for them. If participants could better understand how their project is performing overall, at various stages of its delivery, then the opportunities to achieve project success would almost certainly be greater. This paper documents a method of assessing the status of a project, at a point in its design or construction phase, or after completion. The status is assessed in terms of up to seven (7) key success factors. Any evidence of less than adequate performance in these performance areas is scrutinised to seek out the root causes of why this situation is happening. Using these identified root causes of under performance, general suggestions can then be made as to how to return the project to good health. A software package that assists in assessing the status of the project has been developed. The package is currently being calibrated before commercial release.

The condition, development and suggestions of policy of the highway express freight transportation system in China

The highway express freight transportation (HEFT) is a new transportation organization form separated from the common freight transportation with economic development and incessant adjustment of highway transportation structure in China. At present, the phenomenon of inadaptability still exists in the HEFT system of China, from foundation structure like highways, parking lots and stations to transportation equipments and transportation organizing. In order to develop the HEFT system more rationally and effectively, we should start with the structure of the system, conform the resources existing, and consummate the freight transport system. In due course, relevant policies and measures to supervise, lead and support are necessary and important. This paper analyzes the existing problems of HEFT system in our country, based on its characteristics, development situation and adaptability, and presents the policy and measures of promoting and leading the development of the HEFT system.

Incorporating interdependence in risk likelihood analysis to enhance diagnostics in condition monitoring

This research is aimed at addressing problems in the field of asset management relating to risk analysis and decision making based on data from a Supervisory Control and Data Acquisition (SCADA) system. It is apparent that determining risk likelihood in risk analysis is difficult, especially when historical information is unreliable. This relates to a problem in SCADA data analysis because of nested data. A further problem is in providing beneficial information from a SCADA system to a managerial level information system (e.g. Enterprise Resource Planning/ERP). A Hierarchical Model is developed to address the problems. The model is composed of three different Analyses: Hierarchical Analysis, Failure Mode and Effect Analysis, and Interdependence Analysis. The significant contributions from the model include: (a) a new risk analysis model, namely an Interdependence Risk Analysis Model which does not rely on the existence of historical information because it utilises Interdependence Relationships to determine the risk likelihood, (b) improvement of the SCADA data analysis problem by addressing the nested data problem through the Hierarchical Analysis, and (c) presentation of a framework to provide beneficial information from SCADA systems to ERP systems. The case study of a Water Treatment Plant is utilised for model validation.

Mailed treatment to augment primary care for alcohol disorders : a randomised controlled trial

Introduction and Aims: Remote delivery of interventions is needed to address large numbers of people with alcohol use disorders who are spread over large areas. Previous correspondence trials typically examined its effects as stand-alone treatment. This study aimed to test whether adding postal treatment to general practitioner (GP) support would lower alcohol use more than GP intervention alone. Design and Methods: A single-blind, randomised controlled trial with a crossover design was conducted over 12 months on 204 people with alcohol use disorders. Participants in an immediate correspondence condition received treatment over the first 3 months; those receiving delayed treatment received it in months 3–6. Results: Few participants were referred from GPs, and little intervention was offered by them. At 3 months, 78% of participants remained in the study. Those in immediate treatment showed greater reductions in alcohol per week, drinking days, anxiety, depression and distress than those in the delayed condition. However, post-treatment and follow-up outcomes still showed elevated alcohol use, depression, anxiety and distress. Greater baseline anxiety predicted better alcohol outcomes, although more mental distress at baseline predicted dropout. Discussion and Conclusions: The study gave consistent results with those from previous research on correspondence treatments, and showed that high levels of participant engagement over 3 months can be obtained. Substantial reductions in alcohol use are seen, with indications that they are well maintained. However, many participants continue to show high-risk alcohol use and psychological distress.

Development of a controlled drinking self-efficacy scale and appraising its relation to alcohol dependence

There is no specific self-efficacy measure that has been developed primarily for problem drinkers seeking a moderation drinking goal. In this article, we report the factor structure of a 20-item Controlled Drinking Self-Efficacy Scale (CDSES; Sitharthan et al., 1996; Sitharthan et al., 1997). The results indicate that the CDSES is highly reliable, and the factor analysis using the full sample identified four factors: negative affect, positive mood/social context, frequency of drinking, and consumption quantity. A similar factor structure was obtained for the subsample of men. In contrast, only three factors emerged in the analysis of data on female participants. Compared to women, men had low self-efficacy to control their drinking in situations relating to positive mood/social context, and subjects with high alcohol dependence had low self-efficacy for situations relating to negative affect, social situations, and drinking less frequently. The CDSES can be a useful measure in treatment programs providing a moderation drinking goal.

The reliability and validity of the Severity of Dependence Scale for detecting cannabis dependence in psychosis.

Aims: To determine the reliability and validity of the Severity of Dependence Scale (SDS) for detecting cannabis dependence in a large sample of in-patients with a schizophrenia spectrum disorder. Design: Cross-sectional study. Participants: Participants were 153 in-patients with a schizophrenia spectrum disorder in Brisbane, Australia. Measurements: Participants were administered the SDS for cannabis dependence in the past 12 months. The presence of Diagnostic and Statistical Manual Version-IV (DSM-IV) cannabis dependence in the previous 12 months was assessed using the Comprehensive International Diagnostic Interview (CIDI). Findings: The SDS had high levels of internal consistency and strong construct and concurrent validity. Individuals with a score of ≥2 on the SDS were nearly 30 times more likely to have DSM-IV cannabis dependence. The SDS was the strongest predictor of DSM-IV cannabis dependence after controlling for other predictor variables. Conclusions: The SDS is a brief, valid and reliable screen for cannabis dependence among people with psychosis

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Randomized controlled trial of cognitive-behavioural therapy for coexisting depression and alcohol problems : short-term outcome

AIMS: Alcohol use disorders and depression co-occur frequently and are associated with poorer outcomes than when either condition occurs alone. The present study (Depression and Alcohol Integrated and Single-focused Interventions; DAISI) aimed to compare the effectiveness of brief intervention, single-focused and integrated psychological interventions for treatment of coexisting depression and alcohol use problems. METHODS: Participants (n = 284) with current depressive symptoms and hazardous alcohol use were assessed and randomly allocated to one of four individually delivered interventions: (i) a brief intervention only (single 90-minute session) with an integrated focus on depression and alcohol, or followed by a further nine 1-hour sessions with (ii) an alcohol focus; (iii) a depression focus; or (iv) an integrated focus. Follow-up assessments occurred 18 weeks after baseline. RESULTS: Compared with the brief intervention, 10 sessions were associated with greater reductions in average drinks per week, average drinking days per week and maximum consumption on 1 day. No difference in duration of treatment was found for depression outcomes. Compared with single-focused interventions, integrated treatment was associated with a greater reduction in drinking days and level of depression. For men, the alcohol-focused rather than depression-focused intervention was associated with a greater reduction in average drinks per day and drinks per week and an increased level of general functioning. Women showed greater improvements on each of these variables when they received depression-focused rather than alcohol-focused treatment. CONCLUSIONS: Integrated treatment may be superior to single-focused treatment for coexisting depression and alcohol problems, at least in the short term. Gender differences between single-focused depression and alcohol treatments warrant further study.