A dynamic workflow framework for mass customization using web service and autonomous agent techniques

Custom software development and maintenance is one of the key expenses associated with developing automated systems for mass customization. This paper presents a method for reducing the risk associated with this expense by developing a flexible environment for determining and executing dynamic workflow paths. Strategies for developing an autonomous agent-based framework and for identifying and creating web services for specific process tasks are presented. The proposed methods are outlined in two different case studies to illustrate the approach for both a generic process with complex workflow paths and a more specific sequential engineering process.     

ICCD: Interactive Continuous Collision Detection between Deformable Models Using Connectivity-Based Culling

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of “procedural representative triangles” to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of “orphan sets” to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.     

Aniline hydrogenolysis on nickel: effects of surface hydrogen and surface structure

Fluorescence yield near-edge spectroscopy (FYNES) above the carbon K edge and temperature programmed reaction spectroscopy (TPRS) have been used as the methods for characterizing the reactivity and structure of adsorbed aniline and aniline derived species on the Ni(100) and Ni(111) surfaces over an extended range of temperatures and hydrogen pressures. The Ni(100) surface shows appreciably higher hydrogenolysis activity towards adsorbed aniline than the Ni(111) surface. Hydrogenolysis of aniline on the Ni(100) surface results in benzene formation at 470 K, both in reactive hydrogen atmospheres and in vacuum. External hydrogen significantly enhances the hydrogenolysis activity for aniline on the Ni(100) surface. Based on spectroscopic evidence, we believe that the dominant aniline hydrogenolysis reaction is preceded by partial hydrogenation of the aromatic ring of aniline in the presence of 0.001 Torr of external hydrogen on the (100) surface. In contrast, very little adsorbed aniline undergoes hydrogen induced C-N bond activation on the Ni(111) surface for hydrogen pressures as high as 10^–7 Torr below 500 K. Thermal dehydrogenation of aniline dominates with increasing temperature on the Ni(111) surface, resulting in the formation of a previously observed polymeric layer which is stable up to 820 K. Aniline is adsorbed at a smaller angle relative to the Ni(111) surface than the Ni(100) surface at temperatures below the hydrogenolysis temperature. We believe that the proximity and strong -interaction between the aromatic ring of the aniline and the surface is one major factor which controls the competition between dehydrogenation and hydrogen addition. In this case the result is a substantial enhancement of aniline dehydrogenation relative to hydrogenation on the Ni(111) surface.     

Multicolor Photo‐Crosslinkable AIEgens toward Compact Nanodots for Subcellular Imaging and STED Nanoscopy

Aggregation induced emission (AIE) has attracted considerable interest for the development of fluorescence probes. However, controlling the bioconjugation and cellular labeling of AIE dots is a challenging problem. Here, this study reports a general approach for preparing small and bioconjugated AIE dots for specific labeling of cellular targets. The strategy is based on the synthesis of oxetane‐substituted AIEgens to generate compact and ultrastable AIE dots via photo‐crosslinking. A small amount of polymer enriched with oxetane groups is cocondensed with most of the AIEgens to functionalize the nanodot surface for subsequent streptavidin bioconjugation. Due to their small sizes, good stability, and surface functionalization, the cell‐surface markers and subcellular structures are specifically labeled by the AIE dot bioconjugates. Remarkably, stimulated emission depletion imaging with AIE dots is achieved for the first time, and the spatial resolution is significantly enhanced to ≈95 nm. This study provides a general approach for small functional molecules for preparing small sized and ultrastable nanodots.     

Comparison of three-dimensional surface scanning techniques for capturing the external ear

Congenital facial anomalies, such as microtia (malformation of the external ear), lead to significant psychosocial effects starting from early childhood. Three-dimensional (3D) scanning and advanced manufacturing are being investigated as a cheaper and more personalised method of fabricating reconstructive treatments for patients compared to traditional approaches. To date, most case studies have used expensive 3D scanners, yet, there is potential for low-cost devices to provide comparable results. This study aimed to investigate these different approaches. Both ears of 16 adult participants were scanned with three devices: Artec Spider (Artec Group), Intel^® RealSense? (Intel), and the Apple iPhone^® 7 (Apple Inc.) combined with photogrammetry using 90, 60 and 30 photographs. The scanning time, processing time, accuracy, completeness, resolution and repeatability of each technique were assessed using the Artec Spider as a reference scanner. Our results show that the iPhone had the longest processing time however, this decreased nine-fold when reducing the number of photos from 90 to 30. There was no significant difference in the accuracy, completeness or repeatability of the iPhone scans with 90 photographs (1.4?±?0.6?mm, 79.9%, 1.0?±?0.1?mm), 60 photographs (1.2?±?0.2, 79.3%, 0.9?±?0.2?mm) or 30 photographs (1.2?±?0.3?mm, 74.3%, 1.0?±?0.2?mm). The Intel RealSesne performed significantly worse in each parameter (1.8?±?03?mm; 46.6%, 1.4?±?0.5). Additionally, the RealSense had significantly lower resolution with not enough detail captured for the application. These results demonstrate that the ear can be accurately 3D scanned using iPhone photographs. We would recommend capturing between 30 and 60 photographs of the ear to create a scan that is accurate but without the downfall of long processing time. Using these methods we aim to provide a more comfortable setting for the patient and a lower-cost and more personalised ear prosthesis.     

Privacy in geo-social networks: proximity notification with untrusted service providers and curious buddies

A major feature of the emerging geo-social networks is the ability to notify a user when any of his friends (also called buddies) happens to be geographically in proximity. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service.     

A semi-automatic strategy for achieving learning content and practice repurposing

The development of instructional content using Information Technologies is an expensive, time-consuming and complex process that requires new methodologies. It was in this context that the concept of Learning Objects (LOs) was proposed in order to promote reuse. However, this goal is not yet fully attained and new contributions to increase reuse are still welcome. Besides, if content is conveyed in LOs that are easier to reuse, they must be combined and sequenced in order to build more elaborated and complex content. This paper presents a strategy to deal with these problems based on the definition of small LOs here called Component Objects (COs). These COs are structured and combined according to a conceptual metamodel, which is the basis for the definition of conceptual schemas representing the existing material, including not only content but also practice. This strategy for searching, extracting, and sequencing COs, supports a teacher to better control the implementation of complex content, reducing errors in the authoring process. This approach includes a specification language and an algorithm for semi-automatic sequencing learning content and practice. Finally, a case study that shows the proposed approach and some results of using the algorithm are presented.     

Hidden Markov models for the stimulus-response relationships of multistate neural systems

Given recent experimental results suggesting that neural circuits may evolve through multiple firing states, we develop a framework for estimating state-dependent neural response properties from spike train data. We modify the traditional hidden Markov model (HMM) framework to incorporate stimulus-driven, non-Poisson point-process observations. For maximal flexibility, we allow external, time-varying stimuli and the neurons' own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to estimate the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step reduces to a set of separable concave optimization problems if the model is restricted slightly. We first test our algorithm on simulated data and are able to fully recover the parameters used to generate the data and accurately recapitulate the sequence of hidden states. We then apply our algorithm to a recently published data set in which the observed neuronal ensembles displayed multistate behavior and show that inclusion of spike history information significantly improves the fit of the model. Additionally, we show that a simple reformulation of the state space of the underlying Markov chain allows us to implement a hybrid half-multistate, half-histogram model that may be more appropriate for capturing the complexity of certain data sets than either a simple HMM or a simple peristimulus time histogram model alone.