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The process of assessing the suitability of reuse of a software component is complex. Indeed, software systems are typically developed as an assembly of existing components. The complexity of the assessment process is due to lack of clarity on how to compare the cost of adaptation of an existing component versus the cost of developing it from scratch. Indeed, often pursuit of reuse can lead to excessive rework and adaptation, or developing suites of components that often get neglected. This paper is an important step towards modelling the complex reuse assessment process. To assess the success factors that can underpin reuse, we analyze the cognitive factors that belie developers’ behavior during their decision-making when attempting to reuse. This analysis is the first building block of a broader aim to synthesize a framework to institute activities during the software development lifecycle to support reuse.

The main difference between various formalisms of non-monotonic reasoning is the representation of non-monotonic rules. In default logic, they are represented by special expressions called defaults. In default logic, commonsense knowledge about the world is represented as a set of named defaults. The use of defaults is popular because they reduce the complexity of the representation, and they are sufficient for knowledge representation in many naturally occurring contexts. This paper offers an incremental process to acquire defaults from human experts directly and at the same time it provides added semantics to defaults by adding priorities to defaults and creating additional relations between them. The paper uses an existing incremental framework, NRDR, to generate these defaults. This framework is chosen as it not only enables incremental context driven formulation of defaults, but also allows experts to introduce their own domain terms. In choosing this framework, the paper broadens its utility.

This work highlights the importance of in situexperiments for an improved understanding of graphene growth on copper viametal-catalyzed chemical vapor deposition (CVD). Graphene growth inside the chamber of a modified environmental scanning electron microscope under relevant low-pressure CVD conditions allows visualizing structural dynamics of the active catalyst simultaneously with graphene nucleation and growth in an unparalleled way. It enables the observation of a complete CVD process from substrate annealing through graphene nucleation and growth and, finally, substrate cooling in real time and nanometer-scale resolution without the need of sample transfer. A strong dependence of surface dynamics such as sublimation and surface premelting on grain orientation is demonstrated, and the influence of substrate dynamics on graphene nucleation and growth is presented. Insights on the growth mechanism are provided by a simultaneous observation of the growth front propagation and nucleation rate. Furthermore, the role of trace amounts of oxygen during growth is discussed and related to graphene-induced surface reconstructions during cooling. Above all, this work demonstrates the potential of the method for in situstudies of surface dynamics on active metal catalysts.

Die Kombination zwischen Reaktion und Pervaporation am Beispiel der Veresterung zu Propylpropionat wird untersucht. Eine Validierung des Prozessmodells erfolgt mit experimentellen Untersuchungen und es wird ein Scale‐up in den Technikumsmaßstab durchgeführt. Ausgehend von einer detaillierten Prozessanalyse werden optimale Bereiche für die Betriebs‐ und Strukturvariablen festgelegt und zwei mögliche Produktaufreinigungssequenzen vorgeschlagen.

Design of affinity membrane adsorbers for the purification of biomolecules requires a consideration of loading, washing, and elution. Modelling and simulation of membrane adsorbers in literature is, however, strongly focused on the loading step. Therefore, in this work, a complete process model which takes all the different steps into account was developed. Breakthrough experiments in which human IgG was captured onto and eluted from Sartobind Protein A downscale modules were used for model validation and for estimation of the required model parameters. The experimentally observed breakthrough curves were independent of the applied flow rate and from these results linear correlations between lumped kinetic parameters and linear velocity were determined. During elution, desorption was best described by an irreversible reaction of first order in H+ concentration. Applicability of the developed model to computer-aided design was illustrated through a process analysis study in which the influence of the amount of loaded protein per cycle on the process yield and productivity was investigated.

Directed by the temperature-induced phase transformation in the alkane (from heptane to dodecane)–P123–water–TEOS quadru-component system, i.e., from a swollen inverse hexagonal phase (H2) to a swollen lamellar phase (Lα) and then to a swollen normal micelles phase (L1), complex silica materials, such as mesoporous nanofibres, multilamellar vesicles (MLVs) and mesocellular foams (MCFs), are constructed.

The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base.Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use.We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

Catalytic distillation still expands its field of applications. New structured catalytic column internals have been developed in recent years and new studies have been reported. A modern structured catalytic packing MULTIPAK®is a subject of the investigations presented in this paper.

Catalytic Distillation can improve process design, the design of column internals requires special attention. The catalytic packing MULTIPAK facilitates effective catalysis, high separation efficiency, and a wide loading range simultaneously. In this work the main characteristics of MULTIPAK—pressure drop, loading range, and separation efficiency—are determined experimentally. The developed nonequilibrium stage model developed reflects the complexity of catalytic distillation processes and comprises correlations describing pressure drop and separation efficiency of MULTIPAK as a function of the operating conditions. The model has been implemented in the simulation environment Aspen Custom Modeler, and the simulation results are compared with experimental data for the synthesis of methyl acetate. This model reflects the process behavior, but differences between simulated results and experimental data can still be observed.

Human experts tend to introduce intermediate terms in giving their explanations. The expert's explanation of such terms is operational for the context that triggered the explanation; however, term definitions remain often incomplete. Further, the expert's (re) use of these terms is hierarchical (similar to natural language). In this paper, we argue that a hierarchical incremental knowledge acquisition (KA) process that captures the expert terms and operationalizes them while incompletely defined makes the KA task more effective. Towards this we present our knowledge representation formalism Nested Ripple Down Rules (NRDR) that is a substantial extension to the (Multiple Classification) Ripple Down Rule (RDR) KA framework. The incremental KA process with NRDR as the underlying knowledge representation has confirmation holistic features. This allows simultaneous incremental modelling and KA and eases the knowledge base (KB) development process.

The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in constructing a knowledge-level model of their search processes.

In this paper we introduce a formal framework for investigating the relationship of inductive inference and the task of classification. We give the first results on the relationship between functions that can be identified in the limit and functions that can be acquired from unclassified objects only. Moreover, we present results on the complexity of classification functions and the preconditions necessary in order to allow the computation of such functions.

Highly ordered SBA-15 silicas with large cylindrical mesopores ∼15 nm are successfully obtained with the help of NH4F by controlling the initial reaction temperatures in the presence of excess amounts of alkanes.