CLADP - THE CAMBRIDGE LINEAR ANALYSIS AND DESIGN PROGRAMS USER REFERENCE MANUAL (4TH ISSUE: APRIL 1983).

CLADP is an engineering software program developed at Cambridge University for the interactive computer aided design of feedback control systems. CLADP contains a wide range of tools for the analysis of complex systems, and the assessment of their performance when feedback control is applied, thus enabling control systems to be designed to meet difficult performance objectives. The range of tools within CLADP include the latest techniques in the field whose central theme is the extension of classical frequency domain concepts (well known and well proven for single loop systems) to multivariable or multiloop systems, and by making extensive use of graphical presentation information is provided in a readily understood form.

A framework for clarifying the meaning of Triple Bottom-Line, Integrated, and Sustainability Assessment

Terms such as Integrated Assessment and Sustainability Assessment are used to label 'new' approaches to impact assessment that are designed to direct planning and decision-making towards sustainable development (SD). Established assessment techniques, such as EIA and SEA, are also widely promoted as SD 'tools'. This paper presents the findings of a literature review undertaken to identify the features that are typically promoted for improving the SD-directedness of assessments. A framework is developed which reconciles the broad range of emerging approaches and tackles the inconsistent use of terminology. The framework comprises a three-dimensional space defined by the following axes: the comprehensiveness of the SD coverage; the degree of 'integration' of the techniques and themes; and the extent to which a strategic perspective is adopted. By applying the framework, assessment approaches can be positioned relative to one another, enabling comparison on the basis of substance rather than semantics. © 2007 Elsevier Inc. All rights reserved.

Differential encoding of losses and gains in the human striatum.

Studies on human monetary prediction and decision making emphasize the role of the striatum in encoding prediction errors for financial reward. However, less is known about how the brain encodes financial loss. Using Pavlovian conditioning of visual cues to outcomes that simultaneously incorporate the chance of financial reward and loss, we show that striatal activation reflects positively signed prediction errors for both. Furthermore, we show functional segregation within the striatum, with more anterior regions showing relative selectivity for rewards and more posterior regions for losses. These findings mirror the anteroposterior valence-specific gradient reported in rodents and endorse the role of the striatum in aversive motivational learning about financial losses, illustrating functional and anatomical consistencies with primary aversive outcomes such as pain.

Achievements and challenges in machine vision-based inspection of large concrete structures

Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated.

Convective heat dissipation with lattice-frame materials

This paper presents experimental results on heat transfer and pressure drop for a compact heat sink made of fully triangulated, lightweight (porosity∼0.938), aluminum lattice-frame materials (LFMs). Due to the inherent structural anisotropy of the LFMs, two mutually perpendicular orientations were selected for the measurements. Constant heat flux was applied to the heat sink under steady state conditions, and dissipated by forced air convection. The experimental data were compared with those predicted from an analytical model based on fin analogy. The experimental results revealed that pressure drop is strongly dependent upon the orientation of the structure, due mainly to the flow blockage effect. For heat transfer measurements, typical local temperature distributions on the substrate under constant heat flux conditions were captured with infrared camera. The thermal behavior of LFMs was found to follow closely that of cylinder banks, with early transition Reynolds number (based on strut diameter) equal to about 300. The Nusselt number prediction from the fin-analogy correlates well with experimental measurements, except at low Reynolds numbers where a slightly underestimation is observed. Comparisons with empty channels and commonly used heat exchanger media show that the present LFM heat sink can remove heat approximately seven times more efficient than an empty channel and as efficient as a bank of cylinders at the same porosity level. The aluminum LFMs are extremely stiff and strong, making them ideal candidates for multifunctional structures requiring both heat dissipation and mechanical load carrying capabilities. © 2003 Elsevier Ltd. All rights reserved.

Engineering as a tool for improving human habitat

The conventional approaches to poverty alleviation in the slums entail a cocktail of interventions in health, education, governance and physical improvements, often stretching the scarce resources far and thin. Driven by the 'poverty' mindset, physical measures such as minimal paving, public water posts and community latrines actually brand the slums apart instead of assimilating them into the urban infrastructure fabric. The concept of Slum Networking proposes comprehensive water and environmental sanitation infrastructure as the central and catalytic leverage for holistic development. At costs less than the conventional 'slum' solutions, it tries to penetrate a high quality urban infrastructure net deeply into the slums to assimilate them into the city rather than lock them in as disadvantaged islands. Further, it transcends resource barriers and 'aid' through innovative partnerships and the latent resource mobilisation potential of the so-called 'poor'. This paper examines Slum Networking as implemented in Sanjaynagar in Ahmedabad, India and compares it with a similar settlement with no interventions in Ahmedabad. It assesses the knock-on impact of physical infrastructure on health, education and poverty. Finally, it evaluates the multiplier effect of physical infrastructure and the partnerships on the subsequent investments by the community in its own shelter and habitat. Copyright © 2009 Inderscience Enterprises Ltd.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

A starting point for addressing product innovativeness in the Fuzzy Front-End

Product innovativeness is a primary contingent factor to be addressed for the development of flexible management for the front-end. However, due to complexity of this early phase of the innovation process, the definition of which attributes to customise is critical to support a contingent approach. Therefore, this study investigates front-end attributes that need to be customised to permit effective management for different degrees of innovation. To accomplish this aim, a literature review and five case studies were performed. The findings highlighted the front-end strategic and operational levels as factors influencing the front-end attributes related to product innovativeness. In conclusion, this study suggests that two front-end attributes should be customised: development activities and decision-making approach. Copyright © 2011 Inderscience Enterprises Ltd.

Paradoxical evidence integration in rapid decision processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions. © 2012 Rüter et al.