Multivariate Statistical Process Control and Case-Based Reasoning for situation assessment of Sequencing Batch Reactors

ABSRACT This thesis focuses on the monitoring, fault detection and diagnosis of Wastewater Treatment Plants (WWTP), which are important fields of research for a wide range of engineering disciplines. The main objective is to evaluate and apply a novel artificial intelligent methodology based on situation assessment for monitoring and diagnosis of Sequencing Batch Reactor (SBR) operation. To this end, Multivariate Statistical Process Control (MSPC) in combination with Case-Based Reasoning (CBR) methodology was developed, which was evaluated on three different SBR (pilot and lab-scales) plants and validated on BSM1 plant layout.

Perfectionism as a transdiagnostic process: a clinical review

Perfectionism is a risk and maintaining factor for eating disorders, anxiety disorders and depression. The objective of this paper is to review the four bodies of evidence supporting the notion that perfectionism is a transdiagnostic process. First, a review of the literature was conducted that demonstrates the elevation of perfectionism across numerous anxiety disorders, depression, and eating disorders compared to healthy controls. Data is presented that shows perfectionism increases vulnerability for eating disorders, and that it maintains obsessive–compulsive disorder, social anxiety and depression as it predicts treatment outcome in these disorders. Second, evidence is examined showing that elevated perfectionism is associated with co-occurrence of psychopathology. Third, the different conceptualisations of perfectionism are reviewed, including a cognitive-behavioural conceptualisation of clinical perfectionism that can be utilised to understand this transdiagnostic process. Fourth, evidence that treatment of perfectionism results in reductions in anxiety, depression and eating pathology is reviewed. Finally,the importance of clinicians considering the routine assessment and treatment of perfectionism is outlined.

Sedation and weaning from mechanical ventilation: effects of process optimization outside a clinical trial

PURPOSE: We studied the effects of reorganization and changes in the care process, including use of protocols for sedation and weaning from mechanical ventilation, on the use of sedative and analgesic drugs and on length of respiratory support and stay in the intensive care unit (ICU). MATERIALS AND METHODS: Three cohorts of 100 mechanically ventilated ICU patients, admitted in 1999 (baseline), 2000 (implementation I, after a change in ICU organization and in diagnostic and therapeutic approaches), and 2001 (implementation II, after introduction of protocols for weaning from mechanical ventilation and sedation), were studied retrospectively. RESULTS: Simplified Acute Physiology Score II (SAPS II), diagnostic groups, and number of organ failures were similar in all groups. Data are reported as median (interquartile range).Time on mechanical ventilation decreased from 18 (7-41) (baseline) to 12 (7-27) hours (implementation II) (P = .046), an effect which was entirely attributable to noninvasive ventilation, and length of ICU stay decreased in survivors from 37 (21-71) to 25 (19-63) hours (P = .049). The amount of morphine (P = .001) and midazolam (P = .050) decreased, whereas the amount of propofol (P = .052) and fentanyl increased (P = .001). Total Therapeutic Intervention Scoring System-28 (TISS-28) per patient decreased from 137 (99-272) to 113 (87-256) points (P = .009). Intensive care unit mortality was 19% (baseline), 8% (implementation I), and 7% (implementation II) (P = .020). CONCLUSIONS: Changes in organizational and care processes were associated with an altered pattern of sedative and analgesic drug prescription, a decrease in length of (noninvasive) respiratory support and length of stay in survivors, and decreases in resource use as measured by TISS-28 and mortality.

Mindfulness-based cognitive therapie for depression (MBCT) in clinical practice - baseline patient characteristics, process and outcome

Introduction: Mindfulness based cognitive therapy for depression (MBCT) has shown to be effective for the reduction of depressive relapse. However, additional information regarding baseline patient characteristics and process features related to positive response could be helpful both for the provision of MBCT in clinical practice, as well as for its further development. Method: Baseline characteristics, process data, and immediate outcome (symptom change, change in attitudes and trait mindfulness) of 108 patients receiving MBCT in routine care were recorded. A newly developed self-report measure (Daily Mindfulness Scale, DMS) was applied daily during the MBCT program. Additionally, patients filed daily reports on their mindfulness practice. There was no control group available. Results: Patients with more severe initial symptoms indicated greater amounts of symptom improvement, but did not show great rates of dropout from the MBCT intervention. Younger age was related to higher rates of dropout. Contradictory to some previous data, patients with lower levels of initial trait mindfulness showed greater improvement in symptoms, even after controlling for initial levels of symptoms. Adherence to daily mindfulness practice was high. Consistent with this result, the duration of daily mindfulness practice was not related to immediate outcome. Process studies using multivariate time series analysis revealed a specific role of daily mindfulness in reducing subsequent negative mood. Conclusions: Within the range of patient present in this study and the given study design, results support the use of MBCT in more heterogeneous groups. This demanding intervention was well tolerated by patients with higher levels of symptoms, and resulted in significant improvements regarding residual symptoms. Process-outcome analyses of initial trait mindfulness and daily mindfulness both support the crucial role of changes in mindfulness for the effects of MBCT.

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes’ theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment.

A formalism and method for representing and reasoning with process models authored by subject matter experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic in order to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82% were well-formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25% and 30% with respect to the base case, respectively.

Reasons withheld and insufficient reasoning as due process violations: two cases before the ECHR

Two very different cases decided by the European Court of Human Rights illustrate how the non-availability of sufficient reasons, for pre-trial judicial decisions in one case, and for a decision in a civil and administrative matter in the other, can lead to due process violations in terms of Articles 5 or 6 of the Convention of Human Rights and Fundamental Freedoms.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

The thinking doctor: clinical decision making in contemporary medicine

Diagnostic errors are responsible for a significant number of adverse events. Logical reasoning and good decision-making skills are key factors in reducing such errors, but little emphasis has traditionally been placed on how these thought processes occur, and how errors could be minimised. In this article, we explore key cognitive ideas that underpin clinical decision making and suggest that by employing some simple strategies, physicians might be better able to understand how they make decisions and how the process might be optimised.

Being ethical : how process drama assists pre-service drama teachers to reflect on professional ethics

This research thesis focuses on the experiences of pre-service drama teachers and considers how process drama may assist them to reflect on key aspects of professional ethics such as mandatory codes or standards, principled moral reasoning, moral character, moral agency, and moral literacy. Research from higher education provides evidence that current pedagogical approaches used to prepare pre –professionals for practice in medicine, engineering, accountancy, business, psychology, counselling, nursing and education, rarely address the more holistic or affective dimensions of professional ethics such as moral character. Process drama, a form of educational drama, is a complex improvisational group experience that invites participants to create and assume roles, and select and manage symbols in order to create a fictional world exploring human experience. Many practitioners claim that process drama offers an aesthetic space to develop a deeper understanding of self and situations, expanding the participant’s consciousness and ways of knowing. However, little research has been conducted into the potential efficacy of process drama in professional ethics education for pre-professionals. This study utilizes practitioner research and case study to explore how process drama may contribute to the development of professional ethics education and pedagogy.

Virtual design process : design validation tool schematic approach in car carrier

An important aspect of designing any product is validation. Virtual design process (VDP) is an alternative to hardware prototyping in which analysis of designs can be done without manufacturing physical samples. In recent years, VDP have been generated either for animation or filming applications. This paper proposes a virtual reality design process model on one of the applications when used as a validation tool. This technique is used to generate a complete design guideline and validation tool of product design. To support the design process of a product, a virtual environment and VDP method were developed that supports validation and an initial design cycle performed by a designer. The product model car carrier is used as illustration for which virtual design was generated. The loading and unloading sequence of the model for the prototype was generated using automated reasoning techniques and was completed by interactively animating the product in the virtual environment before complete design was built. By using the VDP process critical issues like loading, unloading, Australian Design rules (ADR) and clearance analysis were done. The process would save time, money in physical sampling and to large extent in complete math generation. Since only schematic models are required, it saves time in math modelling and handling of bigger size assemblies due to complexity of the models. This extension of VDP process for design evaluation is unique and was developed, implemented successfully. In this paper a Toll logistics and J Smith and Sons car carrier which is developed under author’s responsibility has been used to illustrate our approach of generating design validation via VDP.

Dual-process action of exercise on appetite control : increase in orexigenic drive but improvement in meal-induced satiety

Background: Exercise could contribute to weight loss by altering the sensitivity of the appetite regulatory system. Objective: The aim of this study was to assess the effects of 12 wk of mandatory exercise on appetite control. Design: Fifty-eight overweight and obese men and women [mean (±SD) body mass index (in kg/m2) = 31.8 ± 4.5, age = 39.6 ± 9.8 y, and maximal oxygen intake = 29.1 ± 5.7 mL · kg–1 · min–1] completed 12 wk of supervised exercise in the laboratory. The exercise sessions were designed to expend 2500 kcal/wk. Subjective appetite sensations and the satiating efficiency of a fixed breakfast were compared at baseline (week 0) and at week 12. An Electronic Appetite Rating System was used to measure subjective appetite sensations immediately before and after the fixed breakfast in the immediate postprandial period and across the whole day. The satiety quotient of the breakfast was determined by calculating the change in appetite scores relative to the breakfast's energy content. Results: Despite large variability, there was a significant reduction in mean body weight (3.2 ± 3.6 kg), fat mass (3.2 ± 2.2 kg), and waist circumference (5.0 ± 3.2 cm) after 12 wk. The analysis showed that a reduction in body weight and body composition was accompanied by an increase in fasting hunger and in average hunger across the day (P < 0.0001). Paradoxically, the immediate and delayed satiety quotient of the breakfast also increased significantly (P < 0.05). Conclusions: These data show that the effect of exercise on appetite regulation involves at least 2 processes: an increase in the overall (orexigenic) drive to eat and a concomitant increase in the satiating efficiency of a fixed meal.