Foaming of polymers with supercritical CO2: An experimental and theoretical study

Microcellular polystyrene (PS) foams and porous structures of the biodegradable poly(d,l-lactic acid) (P_d,lLA) were prepared with the batch foaming technique (pressure quench) using supercritical CO₂ as blowing agent. The effect of pressure, temperature and depressurization rate on the final porous structure was investigated. The results revealed that the size of the pores decreases and their population density increases with pressure increase, or decrease of temperature, and/or increase of the depressurization rate. The results were correlated by combining nucleation theory with NRHB model in order to account for and emphasize the physical mechanism related to nucleation of bubbles inside the supersaturated polymer matrix. A satisfactory agreement between correlations and experimental data was obtained indicating that the nucleation theory yields quantitative correlations when variables such as sorption, degree of plasticization, and surface tension of the system polymer-supercritical fluid are accurately described.     

Monitoring and improving Greek banking services using Bayesian Networks: An analysis of mystery shopping data

Mystery shopping is a well known marketing technique used by companies and marketing analysts to measure quality of service, and gather information about products and services. In this article, we analyse data from mystery shopping surveys via Bayesian Networks in order to examine and evaluate the quality of service offered by the loan departments of Greek Banks. We use mystery shopping visits to collect information about loan products and services and, by this way, evaluate the customer satisfaction and plan improvement strategies that will assist banks to reach their internal standards. Bayesian Networks not only provide a pictorial representation of the dependence structure between the characteristics of interest but also allow to evaluate, interpret and understand the effects of possible improvement strategies.     

Nearest neighbor estimate of conditional mutual information in feature selection

Mutual information (MI) is used in feature selection to evaluate two key-properties of optimal features, the relevance of a feature to the class variable and the redundancy of similar features. Conditional mutual information (CMI), i.e., MI of the candidate feature to the class variable conditioning on the features already selected, is a natural extension of MI but not so far applied due to estimation complications for high dimensional distributions. We propose the nearest neighbor estimate of CMI, appropriate for high-dimensional variables, and build an iterative scheme for sequential feature selection with a termination criterion, called CMINN. We show that CMINN is equivalent to feature selection MI filters, such as mRMR and MaxiMin, in the presence of solely single feature effects, and more appropriate for combined feature effects. We compare CMINN to mRMR and MaxiMin on simulated datasets involving combined effects and confirm the superiority of CMINN in selecting the correct features (indicated also by the termination criterion) and giving best classification accuracy. The application to ten benchmark databases shows that CMINN obtains the same or higher classification accuracy compared to mRMR and MaxiMin at a smaller cardinality of the selected feature subset.     

Estimating the development cost of custom software

In this paper an approach for the estimation of software development costs is presented. The method is based on the characterization of the software to be developed in terms of project and environment attributes and comparison with some similar completed project(s) recovered from a historical database. A case study is also presented, focusing on the calibration and application of the method on 59 information systems implementing supply chain functions in industry. Various strategies are explored, the best of which predicted effort quite effectively, with a mean estimation error of 24% with respect to the actual effort.     

A review of current research in manufacturing shop design integration

The importance of the manufacturing shop design in the successful operation of a production system is well documented and, as a result, significant work has been devoted to this area. Due to the inherent complexity of the global shop design problem, much research has focused on individual sub-problems, including the layout of manufacturing resources, the design of the shop aisle network, and the selection of the material handling equipment, rather than addressing the integrated problem directly. This paper reviews literature contributions to shop design integration, as well as partial integration schemes such as the design for operation and control of material handling systems. Furthermore, it examines the ability of models and solution approaches for individual sub-problems of shop design to support integration schemes.     

Finite Element Modelling of TRIP Steels

A constitutive model that describes the mechanical behaviour of steels exhibiting “Transformation Induced Plasticity” (TRIP) during martensitic transformation is presented. Multiphase TRIP steels are considered as composite materials with a ferritic matrix containing bainite and retained austenite, which gradually transforms into martensite. The effective properties and overall behaviour of TRIP steels are determined by using homogenization techniques for non‐linear composites. The developed constitutive model considers the different hardening behaviour of the individual phases and estimates the apportionment of plastic strain and stress between the individual phases of the composite. A methodology for the numerical integration of the resulting elastoplastic constitutive equations in the context of the finite element method is developed and the constitutive model is implemented in a general‐purpose finite element program. The prediction of the model in uniaxial tension agrees well with the experimental data. The problem of necking of a bar in uniaxial tension is studied in detail.     

Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach

This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cell-based decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.     

Carbon‐Nanotube–PDMS Composite Coatings on Optical Fibers for All‐Optical Ultrasound Imaging

Polymer–carbon nanotube composite coatings have properties that are desirable for a wide range of applications. However, fabrication of these coatings onto submillimeter structures with the efficient use of nanotubes has been challenging. Polydimethylsiloxane (PDMS)–carbon nanotube composite coatings are of particular interest for optical ultrasound transmission, which shows promise for biomedical imaging and therapeutic applications. In this study, methods for fabricating composite coatings comprising PDMS and multiwalled carbon nanotubes (MWCNTs) with submicrometer thickness are developed and used to coat the distal ends of optical fibers. These methods include creating a MWCNT organogel using two solvents, dip coating of this organogel, and subsequent overcoating with PDMS. These coated fibers are used as all‐optical ultrasound transmitters that achieve high ultrasound pressures (up to 21.5 MPa peak‐to‐peak) and broad frequency bandwidths (up to 39.8 MHz). Their clinical potential is demonstrated with all‐optical pulse‐echo ultrasound imaging of an aorta. The fabrication methods in this paper allow for the creation of thin, uniform carbon nanotube composites on miniature or temperature‐sensitive surfaces, to enable a wide range of advanced sensing capabilities.     

Facial expression recognition in image sequences using geometric deformation features and Support Vector Machines.

In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.