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This study aims to introduce a new method for locating candidate substrates in part models and evaluating their feasibility.

Slices of an STL model along candidate directions are evaluated for the fitting of regular cylindrical and rectangular stock. Next, the part model is skeletonized and tested for collision assuming deposition growth of features from the candidate substrate.

The method is successfully able to find feasible substrates and conduct collision simulation for a variety of part models.

The algorithm is limited to cylindrical and rectangular substrates and only considers collision between the substrate and the deposition head.

This method represents a new approach to solving a portion of the hybrid manufacturing process planning problem.

The purpose of this paper is to present a new method for representing heterogeneous materials using nested STL shells, based, in particular, on the density distributions of human bones.

Nested STL shells, called Matryoshka models, are described, based on their namesake Russian nesting dolls. In this approach, polygonal models, such as STL shells, are “stacked” inside one another to represent different material regions. The Matryoshka model addresses the challenge of representing different densities and different types of bone when reverse engineering from medical images. The Matryoshka model is generated via an iterative process of thresholding the Hounsfield Unit (HU) data using computed tomography (CT), thereby delineating regions of progressively increasing bone density. These nested shells can represent regions starting with the medullary (bone marrow) canal, up through and including the outer surface of the bone.

The Matryoshka approach introduced can be used to generate accurate models of heterogeneous materials in an automated fashion, avoiding the challenge of hand-creating an assembly model for input to multi-material additive or subtractive manufacturing.

This paper presents a new method for describing heterogeneous materials: in this case, the density distribution in a human bone. The authors show how the Matryoshka model can be used to plan harvesting locations for creating custom rapid allograft bone implants from donor bone. An implementation of a proposed harvesting method is demonstrated, followed by a case study using subtractive rapid prototyping to harvest a bone implant from a human tibia surrogate.

Subtractive rapid prototyping (SRP) uses layer‐based removal from a plurality of orientations in order to create geometry in a highly automated manner. However, unlike additive means, the method can be inefficient due to redundant cutting operations on previously machined regions. The purpose of this paper is to present process planning methods for SRP, specifically dealing with stock material management in multiple setup operations.

Analysis of remaining stock material was performed by considering slices of respective stereolithography (STL) models. Further, an initial approximation was made of accessibility to enable iterative visibility analysis. The combination of these approaches led to efficient and fast algorithms. After analysis, the slices could be converted back to useful STL models through polyhedral reconstruction.

This method of approximation yields results similar to exact geometry. Using remaining stock data from this approach leads to a significant reduction in tool path length and processing time in SRP.

This paper presents novel methods of geometric representation and inaccessible volume calculation for four‐axis layer‐based machining and shows a successful implementation in an SRP system.

The purpose of this paper is to present an algorithm for an additive/subtractive rapid pattern manufacturing (RPM) process where thick slabs of material are sequentially stacked and then cut to 3D shapes. Unlike traditional rapid prototyping processes where layer thickness is typically uniform, this process is able to vary the layer thickness in order to most effectively generate feature shapes.

This paper discusses the factors affecting layer thickness decisions and then presents an algorithm to determine layer thicknesses for a given part model. The system is designed to import a computer‐aided design file and use the algorithm to automatically generate the set of layers based on the slab height, material and bonding properties and the process parameters used in the system.

The layer thickness algorithm is implemented and tested using an additive/subtractive manufacturing system developed in the laboratory. The algorithm has proved effective in determining appropriate layer heights for thick slab machining, taking into account a variety of geometries. Several sand casting patterns have been successfully created using the proposed system, which could significantly improve traditional sand casting pattern manufacturing.

The proposed RPM process is a new process presented by the authors, developed for rapid sand castings. The layer thickness algorithm is an original contribution that enables automatic process planning for this new process.

The purpose of this paper is to present a methodology for the automated design of a fixturing system for a rapid machining process.

The method proposed is the use of sacrificial fixturing, similar to the support structures in existing rapid prototyping (RP) processes. During the machining process, sacrificial supports emerge incrementally and, at the end of the process, are the only entities connecting the part to the remaining stock material.

The support design methods have been shown to be extremely flexible in securing a variety of complex parts with relatively tight part tolerances using a rapid machining process.

The automated design of support structures is currently relegated to use in a CNC rapid prototyping process that uses a fourth axis for rotary setups.

The methods used here make rapid machining feasible, as it solves the daunting problem of automated fixturing.

The paper proposes an innovative solution for an automatic fixturing system in subtractive RP.

Health scientists and urban planners have long been interested in the influence that the built environment has on the physical activities in which we engage, the environmental hazards we face, the kinds of amenities we enjoy, and the resulting impacts on our health. However, it is widely recognized that the extent of this influence, and the specific cause-and-effect relationships that exist, are still relatively unclear. Recent reviews highlight the need for more individual-level data on daily activities (especially physical activity) over long periods of time linked spatially to real-world characteristics of the built environment in diverse settings, along with a wide range of personal mediating variables. While capturing objective data on the built environment has benefited from wide-scale availability of detailed land use and transport network databases, the same cannot be said of human activity. A more diverse history of data collection methods exists for such activity and continues to evolve owing to a variety of quickly emerging wearable sensor technologies. At present, no “gold standard” method has emerged for assessing physical activity type and intensity under the real-world conditions of the built environment; in fact, most methods have barely been tested outside of the laboratory, and those that have tend to experience significant drops in accuracy and reliability. This paper provides a review of these diverse methods and emerging technologies, including biochemical, self-report, direct observation, passive motion detection, and integrated approaches. Based on this review and current needs, an integrated three-tiered methodology is proposed, including: (1) passive location tracking (e.g., using global positioning systems); (2) passive motion/biometric tracking (e.g., using accelerometers); and (3) limited self-reporting (e.g., using prompted recall diaries). Key development issues are highlighted, including the need for proper validation and automated activity-detection algorithms. The paper ends with a look at some of the key lessons learned and new opportunities that have emerged at the crossroads of urban studies and health sciences.

We do have a vision for a world in which people can walk to shops, school, friends' homes, or transit stations; in which they can mingle with their neighbors and admire trees, plants, and waterways; in which the air and water are clean; and in which there are parks and play areas for children, gathering spots for teens and the elderly, and convenient work and recreation places for the rest of us. (Frumkin, Frank, & Jackson, 2004, p. xvii)

This paper investigates whether changes in autonomy and embeddedness in host locations by foreign owned subsidiaries are associated with improvements in performance by subsidiaries. The results provide evidence that increasing operational decision‐making autonomy is associated with enhanced performance as measured by both subjective and more objective measures of performance. The results on the importance of increasing strategic decision‐making autonomy and embeddedness are less clear, with improved performance being detected in some cases, but only for the subjective measure of performance.

The Dodd–Frank Wall Street Reform and Consumer Protection Act calls for substantially increased government regulation. Whether those regulations are, in some sense, appropriate is a function of whether the benefits of the increased regulation exceed the costs. Those costs and benefits, however, are probably impossible to measure, at least at this early stage of the implementation of the Dodd–Frank reforms. On the other hand, financial professionals who regularly deal with governmental regulations probably have a good sense of the costs and benefits based on their own experience with other similar regulations. This chapter reports the result of a survey of high-level auditors and CFOs regarding their perceptions of the costs and benefits of the main parts of the financial regulatory reform incorporated into the Dodd–Frank legislation. It concludes that there is support among these individuals for some aspects of Dodd–Frank, but no consensus.

This article provides a review and conceptual comparison between self‐report and performance‐based measures of emotional intelligence. Analyses of reliability, psychometric properties, and various forms of validity lead to the conclusion that self‐report techniques measure a dispositional construct, that may have some predictive validity, but which is highly correlated with personality and independent of intelligence. Although seemingly more valid, performance‐based measures have certain limitations, especially when scored with reference to consensual norms, which leads to problems of skew and restriction of range. Scaling procedures may partially ameliorate these scoring weaknesses. Alternative approaches to scoring, such as expert judgement, also suffer problems since the nature of the requisite expertise is unclear. Use of experimental paradigms for studying individual differences in information‐processing may, however, inform expertise. Other difficulties for performance‐based measures include limited predictive and operational validity, restricting practical utility in organizational settings. Further research appears necessary before tests of E1 are suitable for making real‐life decisions about individuals.