Negative correlations between control performance and verbalizable knowledge: indicators for implicit learning in process control tasks?

Negative correlations between task performance in dynamic control tasks and verbalizable knowledge, as assessed by a post-task questionnaire, have been interpreted as dissociations that indicate two antagonistic modes of learning, one being “explicit”, the other “implicit”. This paper views the control tasks as finite-state automata and offers an alternative interpretation of these negative correlations. It is argued that “good controllers” observe fewer different state transitions and, consequently, can answer fewer post-task questions about system transitions than can “bad controllers”. Two experiments demonstrate the validity of the argument by showing the predicted negative relationship between control performance and the number of explored state transitions, and the predicted positive relationship between the number of explored state transitions and questionnaire scores. However, the experiments also elucidate important boundary conditions for the critical effects. We discuss the implications of these findings, and of other problems arising from the process control paradigm, for conclusions about implicit versus explicit learning processes.

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.

The technocratic totalitarianism of construction process improvement: a critical perspective

The current research agenda for construction process improvement is heavily influenced by the rhetoric of business process re-engineering (BPR). In contrast to the wider literature on BPR, there is little evidence of critical thought within the construction management research community. A postmodernist interpretation is advocated whereby the reality of management practice is defined by the dominant management discourse. The persuasiveness of BPR rhetoric is analysed with particular reference to the way in which it plays on the insecurity of modern managers. Despite the lip service given to ‘empowerment’ and ‘teamwork’, the dominant theme of the re-engineering movement is that of technocratic totalitarianism. From a critical perspective, it is suggested that BPR is imposed on construction organizations to ensure continued control by the industry's dominant power groups. Whilst industry leaders are fond of calling for ‘attitudinal and cultural improvement’, the language of the accepted research agenda continually reinforces the industry's dominant culture of ‘control and command’. Therefore, current research directions in process improvement perpetuate existing attitudes rather than facilitating cultural change. The concept of lean construction is seen to be the latest manifestation of this phenomenon.

Use of in situ FT-Raman spectroscopy to study the kinetics of the transformation of carbamazepine polymorphs

The solid-state transformation of carbamazepine from form III to form I was examined by Fourier Transform Raman spectroscopy. Using a novel environmental chamber, the isothermal conversion was monitored in situ at 130◦C, 138◦C, 140◦C and 150◦C. The rate of transformation was monitored by taking the relative intensities of peaks arising from two C H bending modes; this approach minimised errors due to thermal artefacts and variations in power intensities or scattering efficiencies from the samples in which crystal habit changed from a characteristic prism morphology (form III) to whiskers (form I). The solid-state transformation at the different temperatures was fitted to various solid-state kinetic models of which four gave good fits, thus indicating the complexity of the process which is known to occur via a solid–gas–solid mechanism. Arrhenius plots from the kinetic models yielded activation energies from 344 kJ mol−1 to 368 kJ mol−1 for the transformation. The study demonstrates the value of a rapid in situ analysis of drug polymorphic type which can be of value for at-line in-process control.

Does increased hydrochemical model complexity decrease robustness?

The aim of this study was, within a sensitivity analysis framework, to determine if additional model complexity gives a better capability to model the hydrology and nitrogen dynamics of a small Mediterranean forested catchment or if the additional parameters cause over-fitting. Three nitrogen-models of varying hydrological complexity were considered. For each model, general sensitivity analysis (GSA) and Generalized Likelihood Uncertainty Estimation (GLUE) were applied, each based on 100,000 Monte Carlo simulations. The results highlighted the most complex structure as the most appropriate, providing the best representation of the non-linear patterns observed in the flow and streamwater nitrate concentrations between 1999 and 2002. Its 5% and 95% GLUE bounds, obtained considering a multi-objective approach, provide the narrowest band for streamwater nitrogen, which suggests increased model robustness, though all models exhibit periods of inconsistent good and poor fits between simulated outcomes and observed data. The results confirm the importance of the riparian zone in controlling the short-term (daily) streamwater nitrogen dynamics in this catchment but not the overall flux of nitrogen from the catchment. It was also shown that as the complexity of a hydrological model increases over-parameterisation occurs, but the converse is true for a water quality model where additional process representation leads to additional acceptable model simulations. Water quality data help constrain the hydrological representation in process-based models. Increased complexity was justifiable for modelling river-system hydrochemistry. Increased complexity was justifiable for modelling river-system hydrochemistry.

Ontology modeling for generation of clinical pathways

Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP) as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM) is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM) is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the behavior will occur. To overcome this weakness, a semiotic approach to generation of clinical pathway is introduced. The CP generated from SAM together with norms will enrich the knowledge representation of the domain through ontology modeling, which allows the recognition of human responsibilities and obligations and more importantly, the ultimate power of decision making in exceptional circumstances.

Extractability and characteristics of proteins deriving from wheat DDGS

Wheat Distillers’ Dried Grains with Solubles (DDGS) and in-process samples were used for protein extraction. Prolamins were the predominant protein components in the samples. The absence of extractable α- and γ-gliadins in DDGS indicated protein aggregation during the drum drying processing stage. Prolamin extraction was performed using 70% (v/v) ethanol or alkaline-ethanol solution in the presence of reducing agent. DDGS extracts had relatively low protein contents (14-44.9%, w/w), regardless of the condition applied. The wet solids were the most suitable raw material for protein extraction, with recovery yields of ~ 55% (w/w) and protein content of ~58% (w/w) in 70% (v/v) ethanol. Protein extracts from wet solids were significantly rich in glutamic acid and proline. Mass balance calculations demonstrated the high carbohydrate content (~ 50%, w/w) of solid residues. Overall, the feasibility of utilising in-process samples of DDGS for protein extraction with commercial potential was demonstrated.