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In this work, we introduce the ConvPDE-UQ framework for constructing light-weight numerical solvers for partial differential equations (PDEs) using convolutional neural networks. A theoretical justification for the neural network approximation to partial differential equation solvers on varied domains is established based on the existence and properties of Green's functions. These solvers are able to effectively reduce the computational demands of traditional numerical methods into a single forward-pass of a convolutional network. The network architecture is also designed to predict pointwise Gaussian posterior distributions, with weights trained to minimize the associated negative log-likelihood of the observed solutions. This setup facilitates simultaneous training and uncertainty quantification for the network's solutions, allowing the solver to provide pointwise uncertainties for its predictions. The associated training procedure avoids the computationally expensive Bayesian inference steps used by other state-of-the-art uncertainty models and allows training to be scaled to the large data sets required for learning on varied problem domains. The performance of the framework is demonstrated on three distinct classes of PDEs consisting of two linear elliptic problem setups and a nonlinear Poisson problem. After a single offline training procedure for each class, the proposed networks are capable of accurately predicting the solutions to linear and nonlinear elliptic problems with heterogeneous source terms defined on any specified two-dimensional domain using just a single forward-pass of a convolutional neural network. Additionally, an analysis of the predicted pointwise uncertainties is presented with experimental evidence establishing the validity of the network's uncertainty quantification schema. • Solutions to PDEs on general domains are accurately approximated by a single forward-pass of a convolutional neural network. • A theoretical justification for the neural network approximation is established based on the theory of Green's functions. • Neural networks are trained to produce accurate uncertainty estimates using a scalable framework suitable for large datasets. • The empirical statistics of the uncertainty estimates are observed to align precisely with the designed Gaussian error model. [ABSTRACT FROM AUTHOR]

We describe the iterative design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the pot-profile to the shape of the user׳s hand represented as a point-cloud (PCL). Our pottery-inspired application served as a platform for systematically revealing how users use their hands to express the intent of deformation during a pot shaping process. Our approach involved three stages: (a) clutching by proximal-attraction, (b) shaping by proximal-attraction, and (c) shaping by grasp+motion. The design and implementation of each stage was informed by user evaluations of the previous stage. Our work evidently demonstrates that it is possible to enable users to express their intent for shape deformation without the need for a fixed set of gestures for clutching and deforming a shape. We found that the expressive capability of hand articulation can be effectively harnessed for controllable shaping by organizing the deformation process in broad classes of intended operations such as pulling, pushing, and fairing. After minimal practice with the pottery application, users could figure out their own strategy for reaching, grasping, and deforming the pot. Users particularly enjoyed using day-to-day physical objects as tools for shaping pots. [ABSTRACT FROM AUTHOR]

The advent of depth cameras has enabled mid-air interactions for shape modeling with bare hands. Typically, these interactions employ a finite set of pre-defined hand gestures to allow users to specify modeling operations in virtual space. However, human interactions in real world shaping processes (such as pottery or sculpting) are complex, iterative, and continuous. In this paper, we show that the expression of user intent in shaping processes can be derived from the geometry of contact between the hand and the manipulated object. Specifically, we describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the pot’s profile to the shape of the user’s hand represented as a point-cloud (PCL). Thus, a user does not need to learn, know, or remember any gestures to interact with our system. Our choice of pottery simplifies the geometric representation, allowing us to systematically study how users use their hands and fingers to express the intent of deformation during a shaping process. Our evaluations demonstrate that it is possible to enable users to express their intent for shape deformation without the need for a fixed set of gestures for clutching and deforming a shape. [ABSTRACT FROM AUTHOR]

Nonmaturing or dysfunctional hemodialysis fistulas are often repaired with interposition grafts placed either within the fistula (fistula-to-fistula configuration), or connected to another vein (fistula-to-vein configuration). The goal of this study was to compare the survival and usefulness of the composite accesses thus created, which we call 'graftulas,' with upperarm grafts. This was a retrospective study wherein we determined the survival and thrombosis rates of graftulas ( n = 24) and upper arm grafts ( n = 31) placed 1/1/07 through 12/31/09 and followed through 11/30/10. Graftulas resembled grafts as most (96%) were successfully cannulated in 65 ± 43 days. Survival of graftulas was also similar to grafts (58%, 47%, and 32% vs. 56%, 47%, and 39% at 1, 2, and 3 years respectively, p = 0.60). However, graftulas had a lower thrombosis rate than grafts (0.5 vs. 1.2 per patient year, p = 0.04), and in the fistula-to-fistula configuration, a 2-year thrombosis-free survival of 78%. Total survival of the access site (fistula + graftula) was 92%, 73%, and 42% at 1, 3, and 5 years, respectively. Graftulas possess certain beneficial properties of fistulas and grafts that allows for continued use of the original access site. [ABSTRACT FROM AUTHOR]

Abstract: Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today’s world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future. [Copyright &y& Elsevier]

Abstract: When existing parts are re-used for the development of a new product or business-to-business transactions, a method for searching parts from a database that best meet user’s requirements is essential. The core of a part search method is to measure the similarity between parts’ specifications and the user’s input data in an efficient manner. In this paper, the authors suggest a new method for part similarity measurement using ontology and multi-criteria decision making (MCDM) technique and discuss its technical details. This method ensures interoperability with existing engineering information management systems, represents part specifications in a formal manner, and has generality in search procedure. A case study with ejector pins has also been conducted for the demonstration of the proposed method. [Copyright &y& Elsevier]

Strain effects from stress liners on silicon-on-insulator MOSFETs with high-k dielectric and metal gate (HKMG) are reported. By thoroughly evaluating their impact on drive current, mobility, and threshold voltage, the intrinsic performance gain of stress liners is quantified at the 32-nm node with mobility enhancement identified as the major source. It is also experimentally demonstrated that advantageous stress liners can reduce gate leakage currents for MOSFETs with HKMG. [ABSTRACT FROM AUTHOR]

Abstract: A robust technique for determining the principal axes of a 3D shape represented by a point set, possibly with noise, is presented. We use techniques from robust statistics to guide the classical principal component analysis (PCA) computation. Our algorithm is based on a robust statistics method: least median of squares (LMS), for outlier detection. Using this method, an outlier-free major region of the shape is extracted, which ignores the effect on other minor regions regarded as the outliers of the shape. In order to effectively approximate the LMS optimization, the forward search technique is utilized. We start from a small outlier-free subset robustly chosen as the major region, where an octree is used for accelerating computation. Then the region is iteratively increased by adding samples at a time. Finally, by treating the points on minor regions as outliers, we are able to define the principal axes of the shape as one of the major region. One of the advantages of our algorithm is that it automatically disregards outliers and distinguishes the shape as the major and minor regions during the principal axes determination without any extra segmentation procedure. The presented algorithm is simple and effective and gives good results for point-based shapes. The application on shape alignment is considered for demonstration purpose. [Copyright &y& Elsevier]

The replacement therapy using recombinant human FVIII (rFVIII) is the first line of therapy for hemophilia A. Approximately 15–30% of the patients develop inhibitory antibodies. Recently, we reported that liposomes composed of phosphatidylserine (PS) could reduce the immunogenicity of rFVIII. However, PS containing liposomal-rFVIII is likely to reduce the systemic exposure and efficacy of FVIII due to rapid uptake of the PS containing liposomes by the reticuloendothelial system (RES). Here, we investigated whether phosphatidylserine (PS) liposomes containing Polyethylene glycol (PEG) (PEGylated), could reduce the immunogenicity of rFVIII and reverse the reduction in systemic exposure of rFVIII. Animals given PEGylated liposomal-rFVIII had lower total and inhibitory anti-rFVIII antibody titers, compared to animals treated with rFVIII alone. The mean stimulation index of CD4+ T-cells from animals given PEGylated liposomal-rFVIII also was lower than for animals that were given rFVIII alone. Pharmacokinetic studies following intravenous dosing indicated that the systemic exposure (area under the activity curve, AUAC0–24h) of PEGylated liposomal-rFVIII was ∼59 IU/mL × h and significantly higher than that of non-PEGylated liposomal-rFVIII (AUAC0–24h ∼ 36 IU/mL × h). Based on these studies, we speculate that PEGylated PS-containing liposomal rFVIII may improve efficacy of rFVIII. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:3753–3764, 2008 [ABSTRACT FROM AUTHOR]

Factor VIII (FVIII) is a multidomain protein that is deficient in hemophilia A, a clinically important bleeding disorder. Replacement therapy using recombinant human FVIII (rFVIII) is the main therapy. However, approximately 15–30% of patients develop inhibitory antibodies that neutralize rFVIII activity. Antibodies to epitopes in C2 domain, which is involved in FVIII binding to phospholipids, are highly prevalent. Here, we investigated the effect of phosphatidylserine (PS)-containing liposomes, which bind to C2 domain with high affinity and specificity, upon the immunogenicity of rFVIII. Circular dichroism studies showed that PS-containing liposomes interfered with aggregation of rFVIII. Immunogenicity of free- versus liposomal-rFVIII was evaluated in a murine model of hemophilia A. Animals treated with s.c. injections of liposomal-rFVIII had lower total- and inhibitory titers, compared to animals treated with rFVIII alone. Antigen processing by proteolytic enzymes was reduced in the presence of liposomes. Animals treated with s.c. injections of liposomal-rFVIII showed a significant increase in rFVIII plasma concentration compared to animals that received rFVIII alone. Based on these studies, we hypothesize that specific molecular interactions between PS-containing bilayers and rFVIII may provide a basis for designing lipidic complexes that improve the stability, reduce the immunogenicity of rFVIII formulations, and permit administration by s.c. route. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:1386–1398, 2008 [ABSTRACT FROM AUTHOR]

Abstract: Conventional shape matching for engineering models primarily considers rigid shape similarity. They do not seek global shape similarity while considering large local deformations. However, engineering models created by some parametric-based design can involve large parametric changes. As a result, they do not share similarity in their global shape. Hence our goal is to develop shape representations for global matching of part models that can have large dissimilarity through stretching and/or bending. This paper presents a strategy of an integrated shape matching for contours of engineering drawings inspired by the divide-and-conquer paradigm. The original shape is decoupled into two levels of shape representations namely, higher-level structure and lower-level geometry. The higher-level structure matching is then achieved driven by optimal integrated solutions from matching of lower-level local geometry. Feature points are first extracted using curve evolution to attain the two levels of representations. In order to suit engineering semantics, a new significance function for a point is defined to suppress small features using discrete curve evolution. To conduct the integrated shape matching, a mechanism of using lookup tables is employed to associate these two levels of representations. Dynamic Time Warping and Elastic Matching are employed at different levels of shape representations in order to achieve the optimal integration. To demonstrate the advantages of the proposed work for engineering shapes, experiments for contour evolution, feature point registration, and shape-based similarity for retrieval are conducted. They are also compared with the existing methods. The experimental results show that the structure-oriented contour representation and matching are more meaningful and consistent from an engineering perspective. [Copyright &y& Elsevier]

In this paper, we present a search method with multi-class probability estimates for sketch-based 3D engineering part retrieval. The purpose of using probabilistic output from classification is to support high-quality part retrieval by motivating user relevance feedback from a ranked list of top categorical choices. Given a free-hand user sketch, we use an ensemble of classifiers to estimate the likelihood of the sketch belonging to each predefined category by exploring the strengths of various individual classifiers. Complementary shape descriptors are used to generate classifiers with probabilistic output using support vector machines (SVM). A weighted linear combination rule, called adapted minimum classification error (AMCE), is developed to concurrently minimize the classification errors and the log likelihood errors. Experiments are conducted using our Engineering Shape Benchmark database to evaluate the proposed combination rule. User studies show that users can easily identify the desired classes and then the parts under the proposed method and algorithms. Compared with the best individual classifier, the classification accuracy using AMCE increased by 7% for 3D models, and the average best rank improved by 11.6% for sketches. [Copyright &y& Elsevier]

Abstract: A large amount of 2D drawings have been produced in engineering fields. To reuse and share the available drawings efficiently, we propose two methods in this paper, namely 2.5D spherical harmonics transformation and 2D shape histogram, to retrieve 2D drawings by measuring their shape similarity. The first approach represents a drawing as a spherical function by transforming it from a 2D space into a 3D space. Then a fast spherical harmonics transformation is employed to get a rotation invariant descriptor. The second statistics-based approach represents the shape of a 2D drawing using a distance distribution between two randomly sampled points. To allow users to interactively emphasize certain local shapes that they are interested in, we have adopted a flexible sampling strategy by specifying a bias sampling density upon these local shapes. The two proposed methods have many valuable properties, including transform invariance, efficiency, and robustness. In addition, their insensitivity to noise allows for the user''s causal input, thus supporting a freehand sketch-based retrieval user interface. Experiments show that a better performance can be achieved by combining them together using weights. [Copyright &y& Elsevier]

The physical phenomenon of aggregation can have profound impact on the stability of therapeutic proteins. This study focuses on the aggregation behavior of recombinant human FVIII (rFVIII), a multi-domain protein used as the first line of therapy for hemophilia A, a bleeding disorder caused by the deficiency or dysfunction of factor VIII (FVIII). Thermal denaturation of rFVIII was investigated using circular dichroism (CD) spectroscopy and size exclusion chromatography (SEC). The dependence of unfolding on heating rate indicated that the thermal denaturation of the protein was at least partly under kinetic control. The data was interpreted in terms of a simple two-state kinetic model, $N(Native)\buildrel k \over\longrightarrow A({\rm Aggregated})$, where k is a first-order kinetic constant that changes with temperature, as given by the Arrhenius equation. Analysis of the data in terms of the above scheme suggested that under the experimental conditions used in this study, the rate-controlling step in the aggregation of rFVIII may be a unimolecular reaction involving conformational changes. © 2005 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 94:2023–2029, 2005 [ABSTRACT FROM AUTHOR]

Factor VIII (FVIII) is a multi-domain protein that is important in the clotting cascade. Its deficiency causes Hemophilia A, a bleeding disorder. The unfolding of protein domains can lead to physical instability such as aggregation, and hinder their use in replacement therapy. It has been shown that the aggregation of rFVIIII is initiated by small fluctuations in the protein's tertiary structure (Grillo et al., 2001, Biochemistry 40:586–595). We have investigated the domain(s) involved in the initiation of aggregation using circular dichroism (CD), size exclusion chromatography (SEC), fluorescence anisotropy, domain specific antibody binding, and clotting activity studies. The studies indicated that aggregation may be initiated as a result of conformational change in the C2 domain encompassing the lipid-binding region (2303–2332). The presence of O-phospho-L-Serine (OPLS), which binds to the lipid-binding region of FVIII, prevented aggregation of the protein. © 2005 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 94:1288–1299, 2005 [ABSTRACT FROM AUTHOR]

Factor VIII is a multidomain protein composed of Al, A2, B, A3, C1, and C2 domains. Deficiency or dysfunction of factor VIII causes hemophilia A, a bleeding disorder. Administration of exogenous recombinant factor VIII as a replacement leads to development of inhibitory antibodies against factor VIII in 15-30% of hemophilia A patients. Hence, less immunogenic preparations of factor VIII are highly desirable. Inhibitory antibodies against factor VIII are mainly directed against immunodominant epitopes in C2, A3, and A2 domains. Further, several universal epitopes for CD4+ T-cells have been identified within the C2 domain. The C2 domain is also known to interact specifically with phosphatidylserine-rich lipid vesicles. Here, we have investigated the hypothesis that complexation of O-phospho-L-serine, the head group of phosphatidylserine, with the C2 domain can reduce the overall immunogenicity of factor VIII. The biophysical (circular dichroism and fluorescence) and biochemical studies (ELISA and size exclusion chromatography) showed that O-phospho-L-serine binds to the phospholipid-binding region in the C2 domain, and this interaction causes subtle changes in the tertiary structure of the protein. O-Phospho-L-serine also prevented aggregation of the protein under thermal stress. The immunogenicity of the factor VIII-O-phospho-L-serine complex was evaluated in hemophilia A mice. The total and inhibitory antibody titers were lower for factor VIII-O-phospho-L-serine complex compared with factor VIII alone. Moreover, factor VIII administered as a complex with O-phospho-L-serine retained in vivo activity in hemophilia A mice. Our results suggest that factor VIII-O-phospho-L-serine complex may be beneficial to increase the physical stability and reduce immunogenicity of recombinant factor VIII preparations. [ABSTRACT FROM AUTHOR]

In this investigation an all-olefin thermoplastic laminate was developed and characterized. Commingled glass-fiber polypropylene (PP) composite was used as skin and HDPE (PE) foam with closed cells as core. Infra-red heating was used for melting the surfaces of the substrates for surface fusion bonding with a cold press. Two tie-layer films, viz., ethylene-propylene copolymer (EPC) and HDPE/elastomer blend were used as hot-melt adhesives for bonding the substrates. Singlelap shear joints were prepared from PP composite and PE foam adherends with a bonding area of 25.4 mm × 25.4 mm to determine the bond strength. EPC tie-layer adhesive provided higher bond strength (2.68 × 10[sup 6] N/m[sup 2]) to the all-olefin laminate than that based on HDPE/elastomer blend (1.93 × 106 N/m[sup 2]). For EPC tie-layer-based laminates, a mixed mode of failure was observed in the failed lap shear samples: about 40% was cohesive failure through the tie-layer, and the rest of failure was interfacial, either at PP composite or PE foam surfaces. Environmental scanning electron micrographs (ESEM) revealed that in the process of surface fusion bonding, PE foam cells in the vicinity of interphase (800-μm-thick) were coalesced with high temperature and pressure. No macro-level penetration of the tie-layer melt front into the foam cells was observed. As the surface morphology of foam was altered due to IR surface heating and the PP composite bonding side had a resin-rich layer, the bonding situation was closer to that between two polymer film surfaces. [ABSTRACT FROM AUTHOR]

Cochleates are lipid-based delivery system that have found application in drug and gene delivery. They are precipitates, formed as a result of interaction between cations (e.g. Ca2+) and negatively charged phospholipids such as phosphatidylserine (PS). In the present study, we investigated the utility of fluorescent probe Laurdan (6-dodecanoyl-2-dimethylamino naphthalene) to monitor cochleate phase formation. Following addition of Ca2+ to Laurdan labeled lipid vesicles comprised of brain phosphatidylserine (BPS), a significant blue shift in the emission peak maximum of Laurdan was observed and the spectral features were distinct from those observed for the gel and liquid-crystalline (LC) phases. This is consistent with the formation of anhydrous cochleate cylinders that was further confirmed by electron microscopy studies. Due to dipolar relaxation, excitation and emission generalized polarization (GPEx and GPEm) indicate transition from a LC to a rigid and dehydrated (RD) cochleate phase. These spectral changes were utilized to monitor the influence of lipid composition, ionic strength and lamellarity on the formation of cochleate phase. The results indicated that the presence of phosphatidylcholine (PC) and bulk Na+ concentration influenced the formation of cochleate structures from small unilamellar vesicles (SUV) and multilamellar vesicles (MLV) composed of PS. The presence of PC and higher bulk Na+ concentration stabilized the PS vesicles against collapse and total loss of contents, intermediate molecular events in the formation of cochleate structures. From these studies, we conclude that Laurdan fluorescence is a sensitive and a rapid method to detect cochleate phase formation. [Copyright &y& Elsevier]

Factor VIII (FVIII), a plasma glycoprotein, is an essential cofactor in the blood coagulation cascade. It is a multidomain protein, known to bind to phosphatidylserine (PS)-containing membranes. Based on X-ray and electron crystallography data, binding of FVIII to PS-containing membranes has been proposed to occur only via the C2 domain. Based on these models, the molecular topology of membrane-bound FVIII can be envisioned as one in which only a small fraction of the protein interacts with the membrane, whereas the majority of the molecule is exposed to an aqueous milieu. We have investigated the topology of the membrane-bound FVIII using biophysical and biochemical techniques. Circular dichroism (CD) and fluorescence studies indicate no significant changes in the secondary and tertiary structure of FVIII associated with the membranes. Acrylamide quenching studies show that the protein is predominantly present on the surface of the membrane, exposed to the aqueous milieu. The light scattering and electron microscopy studies indicate the absence of vesicle aggregation and fusion. Binding studies with antibodies directed against specific epitopes in the A1, A2 and C2 domains suggest that FVIII binds to the membrane primarily via C2 domain including the specific phospholipid binding epitope (2303–2332) and may involve subtle conformational changes in this epitope region. [Copyright &y& Elsevier]

A new process and associated designs were developed for electrical arresters.When an abnormal voltage surge occurs,the arrester explodes due to the energy released.The composite shell provides the protection needed to prevent the broken arrester fragments from being expunged.Preformed woven,knitted, and braided composite shells,consisting of commingled polypropylene (PP)and E-glass yarn,were fabricated and consolidated in order to reinforce electrical arresters.A braided composite shell,composed of dry glass yarn and neat PP yarn, was designed,processed,and tested to investigate the effect of venting.This braided design allowed the energy released to be vented in the area between glass fiber bundles.Experimental results demonstrate that the woven and braided shells performed better than the knitted shell.A theoretical analysis based on the composite mechanics theory was performed in order to explain the experimental results. [ABSTRACT FROM AUTHOR]

A thermoplastic adhesive bonding process with a high-density polyethylene (HDPE)-based adhesive has been developed for galvanized steel to polypropylene (PP) composite. The processing temperature significantly affects the strength of the adhesively bonded lap joints. Melting of the surface of the PP composite prior to bonding was found helpful for interdiffusion between adhesive and PP composite during the bonding process. Cataplasma aging tests showed that use of a polyester–melamine-based primer on the galvanized steel before preparation of the PP composite/galvanized steel joints reduced durability. However, the primer did not cause significant adhesion loss under cyclic moisture/temperature condition. It was established that the loss in adhesion of joints made from galvanized steel treated with primer was due to the blistering of the primer in Cataplasma environment. [Copyright &y& Elsevier]

Moisture durability of four low modulus adhesives was examined. The four low modulus adhesives all had the same basic moisture cure polymer but contained different fillers and additives. Hot-dipped galvanized steel was bonded to random fiber-reinforced unsaturated polyester and aged in two moisture environments for various durations. Adherend surface wipes included acetone, isopropyl alcohol and a typical surface contamination for galvanized steel (an organic lubricant). Diffusion coefficients, moisture uptake and modulus changes due to moisture environment were determined for the adhesives and the fiber-reinforced plastic (FRP). The moisture cure adhesive with clay and poly(vinyl chloride) (PVC) as fillers (adhesive 252) had the highest retained lap joint strengths. Initially, adhesive 252 had single lap joint strengths of 1.47 ± 0.08 MPa for acetone-wiped joints and 1.39 ± 0.33 MPa for organic lubricant-wiped joints. After accelerated aging in a cataplasma environment for 9 weeks, lap joint strengths fell to 0.61 ± 0.08 MPa for acetone-wiped joints and to 0.65 ± 0.11 MPa for organic lubricant-wiped joints. Environmental scanning electron microscopy (ESEM) and energy-dispersive spectrometry (EDS) showed that the actual failure locus was through a corroded zinc layer and between the adhesive and the zinc surface after aging. Dynamic mechanical analysis (DMA) showed that the modulus for adhesive 252 dropped from 21.7 to 13.9 MPa after cataplasma aging. From finite element analysis (FEA), this modulus drop corresponded to a drop in normal stress concentration from 0.75 to 0.57, and a drop in shear stress concentration from 1.41 to 1.36 at a point 0.5 mm from the end of the single lap joint overlap. [ABSTRACT FROM AUTHOR]

In this paper, we study the deep Ritz method for solving the linear elasticity equation from a numerical analysis perspective. A modified Ritz formulation using the H 1 / 2 (Γ D) norm is introduced and analyzed for linear elasticity equation in order to deal with the (essential) Dirichlet boundary condition. We show that the resulting deep Ritz method provides the best approximation among the set of deep neural network (DNN) functions with respect to the "energy" norm. Furthermore, we demonstrate that the total error of the deep Ritz simulation is bounded by the sum of the network approximation error and the numerical integration error, disregarding the algebraic error. To effectively control the numerical integration error, we propose an adaptive quadrature-based numerical integration technique with a residual-based local error indicator. This approach enables efficient approximation of the modified energy functional. Through numerical experiments involving smooth and singular problems, as well as problems with stress concentration, we validate the effectiveness and efficiency of the proposed deep Ritz method with adaptive quadrature. [ABSTRACT FROM AUTHOR]

A flattenable mesh surface is a polygonal mesh surface that can be unfolded into a planar patch without stretching any polygon. This paper presents a new method for computing a slightly stretched flattenable mesh surface M from a piecewise-linear surface patch P∈ R , where the shape approximation error between M and P is minimized and the strain of stretching on M is controlled. Prior approaches result in either a flattenable surface that could be quite different from the input shape or a (discrete) developable surface has relative simple shape. The techniques investigated in this paper overcome these difficulties. First, we introduce a new surface modeling method to conduct a sequence of nearly isometric deformations to morph a flattenable mesh surface to a new shape which has a better approximation of the input surface. Second, in order to get better initial surfaces for fitting and overcome topological obstacles, a shape perturbation scheme is investigated to obtain the optimal surface fitting result. Last, to improve the scalability of our optimal surface fitting algorithm, a coarse-to-fine fitting framework is exploited so that very dense flattenable mesh surfaces can be modeled and boundaries of the input surfaces can be interpolated. [ABSTRACT FROM AUTHOR]

A new visibility graph-based algorithm is presented for computing the inner distances of a 3D shape represented by a volumetric model. The inner distance is defined as the length of the shortest path between landmark points within the shape. The inner distance is robust to articulation and can reflect the deformation of a shape structure well without an explicit decomposition. Our method is based on the visibility graph approach. To check the visibility between pairwise points, we propose a novel, fast, and robust visibility checking algorithm based on a clustering technique which operates directly on the volumetric model without any surface reconstruction procedure, where an octree is used for accelerating the computation. The inner distance can be used as a replacement for other distance measures to build a more accurate description for complex shapes, especially for those with articulated parts. The binary executable program for the Windows platform is available from https://engineering.purdue.edu/PRECISE/VMID. [ABSTRACT FROM PUBLISHER]

This paper explores improving sketching skills and reducing the inhibition to sketch for student designers. In the first study, students were taught sketching skills through an in-class workshop. The effect was evaluated using a pre-mid-post test ( n = 40). In the second study, students were led through art activities to reduce their inhibition to sketch. The effect was tested using another pre-mid-post test ( n = 26). The first study found sketching skills increased, but declined with disuse. The second study found reduced inhibition immediately after the workshop, an increase after the sketch skills workshop, and a decrease over the semester. This suggests that sketch training and inhibition-reducing exercises are effective in the short term, but must be emphasized over time for a permanent change. [ABSTRACT FROM AUTHOR]

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents. [ABSTRACT FROM AUTHOR]

Abstract: Customers often present certain preferences relative to the same product, such as function, shape, color, and cost. The ideas in the mind of the customer can be represented by higher level concepts. However, the actual shape, color, and cost embodied in the product can only be viewed as lower-level features. In this paper, a model of preference elicitation from customers is proposed to bridge the gap between low-level features and high-level concepts. First, the attributes of customer preferences are classified using preference taxonomies that we develop. These taxonomies are represented using unstructured documents that are directly collected from customer descriptions. Second, the documents or catalogs of design requirements, containing some textual descriptions and survey reports, are then normalized by using an ontology-based semantic representation. Some semantic rules are developed to describe the low-level features of customer preferences to build an ontological knowledge base. Third, customer preferences are mapped to domain ontologies for driving high-level concept generation. A customer preference modeling framework is developed to construct a vector space model to measure the similarity between two preference concept ontologies. Finally, an empirical study is implemented, and five different customer groups are surveyed about the cell phone preferences. The query results are analyzed to deeply understand the validity of concept generation from the customer preferences. [Copyright &y& Elsevier]

Abstract: 3D shape retrieval and clustering is of current interest in several different fields, including mechanical engineering. Several new shape representations for 3D objects are continuing to emerge. Most shape representations are embedded in a variety of feature spaces. However, some of the recently reported shape representations are embedded in arbitrary metric spaces, i.e. distance spaces, rather than in multi-dimensional feature space. For such representations, the only operations available on the data objects are distance calculations between the objects. In addition, some of the view-based representations are embedded in non-metric spaces where the representations and the corresponding distances do not follow the triangle inequality. For shape clustering applications, most existing algorithms assume the shape representations either to be embedded in a multi-dimensional feature space or a metric distance space, making it difficult to evaluate several shape representations that do not conform to these assumptions. Therefore, two different approaches were evaluated for using the distance features of a shape to obtain clustering results. In the first method, the original distances are transformed into feature space using a multi-dimensional scaling approach for use with -means clustering. The second approach directly uses the original distances with a distance-based clustering algorithm. We compared the clustering effectiveness of these two approaches using a classified benchmark database of 3D models. The effect of using different shape descriptors and number of clusters was studied using four measures of clustering effectiveness. Several statistical methods, including the Rand Index and Mutual Information Index, were used to objectively evaluate the clustering efficacy. [Copyright &y& Elsevier]

Abstract: A global visibility map is a spherical image built to describe the complete set of global visible view directions for a surface. In this paper, we consider the computation of global visibility maps for regions on the boundary of a polyhedron. Both the self-occlusions introduced by a region and the global occlusions introduced by the rest of the surfaces on the boundary of the polyhedron are considered for computing a global visibility map. We show that the occluded view directions introduced between a pair of polyhedral surfaces can be computed from the spherical projection of the Minkowski sum of one surface and the reflection of the other. A suitable subset of the Minkowski sum, which shares the identical spherical projection with the complete Minkowski sum, is constructed to obtain the spherical images representing global occlusions. Our method has been successfully tested on many CAD models. It extends the previous methods for computing global visibility maps using convex decomposition, and it exhibits a better performance. [Copyright &y& Elsevier]

Background: Techniques for inferring the functions of the protein by comparing their shape similarity have been receiving a lot of attention. Proteins are functional units and their shape flexibility occupies an essential role in various biological processes. Several shape descriptors have demonstrated the capability of protein shape comparison by treating them as rigid bodies. But this may give rise to an incorrect comparison of flexible protein shapes. Results: We introduce an efficient approach for comparing flexible protein shapes by adapting a local diameter (LD) descriptor. The LD descriptor, developed recently to handle skeleton based shape deformations [1], is adapted in this work to capture the invariant properties of shape deformations caused by the motion of the protein backbone. Every sampled point on the protein surface is assigned a value measuring the diameter of the 3D shape in the neighborhood of that point. The LD descriptor is built in the form of a one dimensional histogram from the distribution of the diameter values. The histogram based shape representation reduces the shape comparison problem of the flexible protein to a simple distance calculation between 1D feature vectors. Experimental results indicate how the LD descriptor accurately treats the protein shape deformation. In addition, we use the LD descriptor for protein shape retrieval and compare it to the effectiveness of conventional shape descriptors. A sensitivity-specificity plot shows that the LD descriptor performs much better than the conventional shape descriptors in terms of consistency over a family of proteins and discernibility across families of different proteins. Conclusion: Our study provides an effective technique for comparing the shape of flexible proteins. The experimental results demonstrate the insensitivity of the LD descriptor to protein shape deformation. The proposed method will be potentially useful for molecule retrieval with similar shapes and rapid structure retrieval for proteins. The demos and supplemental materials are available on https://engineering.purdue.edu/PRECISE/LDD. [ABSTRACT FROM AUTHOR]

Background: The conventional superposition methods use an ordinary least squares (LS) fit for structural comparison of two different conformations of the same protein. The main problem of the LS fit that it is sensitive to outliers, i.e. large displacements of the original structures superimposed. Results: To overcome this problem, we present a new algorithm to overlap two protein conformations by their atomic coordinates using a robust statistics technique: least median of squares (LMS). In order to effectively approximate the LMS optimization, the forward search technique is utilized. Our algorithm can automatically detect and superimpose the rigid core regions of two conformations with small or large displacements. In contrast, most existing superposition techniques strongly depend on the initial LS estimating for the entire atom sets of proteins. They may fail on structural superposition of two conformations with large displacements. The presented LMS fit can be considered as an alternative and complementary tool for structural superposition. Conclusion: The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. Furthermore, we show that the LMS fit can be extended to multiple level superposition between two conformations with several rigid domains. Our fit tool has produced successful superpositions when applied to proteins for which two conformations are known. The binary executable program for Windows platform, tested examples, and database are available from https://engineering.purdue.edu/PRECISE/LMSfit. [ABSTRACT FROM AUTHOR]

Background: Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison (MSC) treat them as rigid bodies, which may lead to incorrect measure of the shape similarity of flexible molecules. Results: To address the issue we introduce a new shape descriptor, called Inner Distance Shape Signature (IDSS), for describing the 3D shapes of flexible molecules. The inner distance is defined as the length of the shortest path between landmark points within the molecular shape, and it reflects well the molecular structure and deformation without explicit decomposition. Our IDSS is stored as a histogram which is a probability distribution of inner distances between all sample point pairs on the molecular surface. We show that IDSS is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. Our approach reduces the 3D shape comparison problem of flexible molecules to the comparison of IDSS histograms. Conclusion: The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. We demonstrate the effectiveness of IDSS within a molecular search engine application for a benchmark containing abundant conformational changes of molecules. Such comparisons in several thousands per second can be carried out. The presented IDSS method can be considered as an alternative and complementary tool for the existing methods for rigid MSC. The binary executable program for Windows platform and database are available from https://engineering.purdue.edu/PRECISE/IDSS. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a methodology to assist the designer at the initial stages of the design synthesis process by enabling him/her to employ knowledge and algorithms existing in graph network theory. The proposed method comprises three main stages: transforming the synthesis problem into a graph theoretic problem; devising the topology possessing special engineering properties corresponding to the system requirements; finding the geometric configuration of that topology that will possess the desired properties. To clarify the idea and to demonstrate its generality, the approach is presented through three synthesis case studies from different engineering domains: electrical networks, statics and kinematics. As is highlighted in the paper, the approach of employing graph theory in the synthesis process offers several unique advantages. Among these advantages are: gaining a general perspective on different synthesis problems from different engineering domains by transforming them into the same graph problem; employing the same graph algorithms for different synthesis problems; establishing the existence of configurations with special properties solely from the topology of the system; transferring knowledge and methods between different engineering disciplines for both the topology and the geometry generation steps. [Copyright &y& Elsevier]

The authors reflect on the improvement on the speed of on-chip interconnects and communication fabrics through a heterogeneous interconnect layer of wires with differing characteristics. Various wire properties as well as the needs of data transfers should be considered for mapping activities to sets of wires. Due to the costs attached to a transmission line and optical modulators, architects must provide new ways to maximize resources and compete with regular resistance-capacitance-based wires.

Rate effects in the failure behavior are investigated for adhesive bonds in which the source of time- dependence is the rate of the material separation in the fracture process zone. A rate-dependent cohesive zone model (CZM) is described and used to analyze rate effects during debonding. The model consists of a rate-independent CZM in parallel to a Maxwell element. General implications derived for the model with respect to rate effects in the failure behavior of adhesives are presented. The application of the model to the analysis of crack growth is described in a companion paper. [Copyright &y& Elsevier]

Results of a combined experimental and numerical study of the rate-dependent fracture behavior of thermoplastic bonded joints are presented. Double cantilever beam joints bonded with a high-density polyethylene based adhesive were investigated, and a rate-dependent crack growth behavior was found in tests under constant loading rates. In tests with variable loading rate, stress relaxation effects had considerable influence on the crack growth behavior. A rate-dependent cohesive zone model is applied to the analysis of these rate effects during crack growth. [Copyright &y& Elsevier]

The mechanical interlocking of a thermoplastic adhesive and wood substrate is studied with scanning electron microscopy. A thermoplastic/glass composite is bonded to oak (Quercus) using a polypropylene based adhesive. The wood is etched away using a combination of chromic, nitric, and sulfuric acids, leaving the morphology of the adhesive interface intact. The revealed surface is examined with environmental scanning electron microscopy. We have shown for the first time that a thermoplastic adhesive conforms to features of the wood as small as 1 μm across. The adhesive penetrates the vascular tissues of the wood for more than 150 μm through openings 15 μm across, and invades hundreds of microns through larger channels. [Copyright &y& Elsevier]

We propose a conditional generative model, named Part Geometry Network (PG-Net), which synthesizes realistic objects and can be used as a robust feature descriptor for object reconstruction and classification. Surface and volumetric representations of objects have complementary properties of three-dimensional objects. Combining these modalities is more informative than using one modality alone. Therefore, PG-Net utilizes complementary properties of surface and volumetric representations by estimating curvature, surface area, and occupancy in voxel grids of objects with a single decoder as a multi-task learning. Objects are combinations of multiple parts, and therefore part geometry (PG) is essential to synthesize each part of the objects. PG-Net employs a part identifier to learn the part geometry. Additionally, we augmented a dataset by interpolating individual functional parts such as wings of an airplane, which helps learning part geometry and finding local/global minima of PG-Net. To demonstrate the capability of learning object representations of PG-Net, we performed object reconstruction and classification tasks on two standard large-scale datasets. PG-Net outperformed the state-of-the-art methods in object synthesis, classification, and reconstruction in a large margin. • Learning surface and volumetric geometry for more effective model training and shape prediction. • Synthesizing objects with the parametric model given conditional information. • Developed a feature descriptor for shape classification and reconstruction. • Learning shape distribution with a conditional generative model with multi-task learning. [ABSTRACT FROM AUTHOR]

We propose a fully convolutional conditional generative neural network, the latent transformation neural network, capable of rigid and non-rigid object view synthesis using a lightweight architecture suited for real-time applications and embedded systems. In contrast to existing object view synthesis methods which incorporate conditioning information via concatenation, we introduce a dedicated network component, the conditional transformation unit. This unit is designed to learn the latent space transformations corresponding to specified target views. In addition, a consistency loss term is defined to guide the network toward learning the desired latent space mappings, a task-divided decoder is constructed to refine the quality of generated views of objects, and an adaptive discriminator is introduced to improve the adversarial training process. The generalizability of the proposed methodology is demonstrated on a collection of three diverse tasks: multi-view synthesis on real hand depth images, view synthesis of real and synthetic faces, and the rotation of rigid objects. The proposed model is shown to be comparable with the state-of-the-art methods in structural similarity index measure and L 1 metrics while simultaneously achieving a 24% reduction in the compute time for inference of novel images. [ABSTRACT FROM AUTHOR]

Most three-dimensional (3D) computer-aided design (CAD) models of mechanical parts, created during the design stage, have high shape complexity. The shape complexity required of CAD models reduces according to the field of application. Therefore, it is necessary to simplify the shapes of 3D CAD models, depending on their applications. Traditional simplification methods recognize simplification target shape based on a pre-defined algorithm. Such algorithm-based methods have difficulty processing unusual partial shapes not considered in the CAD model. This paper proposes a method that uses a network based on a generative adversarial network (GAN) to simplify the 3D CAD models of mechanical parts. The proposed network recognizes and removes simplification target shapes included in the 3D CAD models of mechanical parts. A 3D CAD model dataset was constructed to train the 3D CAD model simplification network. 3D CAD models are represented in voxel form in the dataset. Next, the constructed training dataset was used to train the proposed network. Finally, a 3D voxel simplification experiment was performed to evaluate the performance of the trained network. The experiment results showed that the network had an average error rate of 3.38% for the total area of the mechanical part and an average error rate of 14.61% for the simplification target area. • We present a learning-based method for simplifying mechanical 3D CAD models. • The proposed method utilizes an adversarial learning approach. • The generator generates a simplified CAD model from the original model. • The discriminator classifies the data created by the generator as either false or true. • Our method achieved a simplification performance of around 15% error rate. [ABSTRACT FROM AUTHOR]

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sensemaking in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages--design, manufacturing, distribution and supply chain, use-phase, end-of-life (EoL), as well as life cycle assessment (LCA). Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist. [ABSTRACT FROM AUTHOR]

The societal demand for inspiring and engaging science, technology, engineering, and mathematics (STEM) students and preparing our workforce for the emerging creative economy has necessitated developing students' self-efficacy and understanding of engineering design processes from as early as elementary school levels. Hands-on engineering design activities have shown the potential to promote middle school students' self-efficacy and understanding of engineering design processes. However, traditional classrooms often lack hands-on engineering design experiences, leaving students unprepared to solve real-world design problems. In this study, we introduce the framework of a toy design workshop and investigate the influence of the workshop activities on students' understanding of and self-efficacy beliefs in engineering design. Using a mixed method approach, we conducted quantitative analyses to show changes in students' engineering design self-efficacy and qualitative analyses to identify students' understanding of the engineering design processes. Findings show that among the 24 participants, there is a significant increase in students' self-efficacy beliefs after attending the workshop. We also identified major themes such as design goals and prototyping in students' understanding of engineering design processes. This research provides insights into the key elements of middle school students' engineering design learning and the benefits of engaging middle school students in hands-on toy design workshops. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) technologies have become integral to modern prototyping and manufacturing. Therefore, guidelines for using AM are necessary to help users new to the technology. Many others have proposed useful guidelines, but these are rarely written in a way that is accessible to novice users. Most guidelines (1) assume the user has extensive prior knowledge of the process, (2) apply to only a few AM technologies or a very specific application, or (3) describe benefits of the technology that novices already know. In this paper, we present a one-page, visual design for additive manufacturing worksheet for novice and intermittent users which addresses common mistakes as identified by various expert machinists and additive manufacturing facilities who have worked extensively with novices. The worksheet helps designers assess the potential quality of a part made using most AM processes and indirectly suggests ways to redesign it. The immediate benefit of the worksheet is to filter out bad designs before they are printed, thus saving time on manufacturing and redesign. We implemented this as a go-no-go test for a high-volume AM facility where users are predominantly novices, and we observed an 81% decrease in the rate of poorly designed parts. We also tested the worksheet in a classroom, but found no difference between the control and the experimental groups. This result highlights the importance of motivation since the cost of using AM in this context was dramatically lower than real-world costs. This second result highlights the limitations of the worksheet. [ABSTRACT FROM AUTHOR]

Design protocol analysis is a technique to understand designers’ cognitive processes by analyzing sequences of observations on their behavior. These observations typically use audio, video, and transcript data in order to gain insights into the designer's behavior and the design process. The recent availability of sophisticated sensing technology has made such data highly multimodal, requiring more flexible protocol analysis tools. To address this need, we present VizScribe, a visual analytics framework that employs multiple coordinated multiple views that enable the viewing of such data from different perspectives. VizScribe allows designers to create, customize, and extend interactive visualizations for design protocol data such as video, transcripts, sketches, sensor data, and user logs. User studies where design researchers used VizScribe for protocol analysis indicated that the linked views and interactive navigation offered by VizScribe afforded the researchers multiple, useful ways to approach and interpret such multimodal data. [ABSTRACT FROM AUTHOR]

Interaction in mid-air can be fatiguing. A model-based method to quantify cumulative subjective fatigue for such interaction was recently introduced in HCI research. This model separates muscle units into three states: active (M A) fatigued (M F) or rested (M R) and defines transition rules between states. This method demonstrated promising accuracy in predicting subjective fatigue accumulated in mid-air pointing tasks. In this paper, we introduce an improved model that additionally captures the variations of the maximum arm strength based on arm postures and adds linearly-varying model parameters based on current muscle strength. To validate the applicability and capabilities of the new model, we tested its performance in various mid-air interaction conditions, including mid-air pointing/docking tasks, with shorter and longer rest and task periods, and a long-term evaluation with individual participants. We present results from multiple cross-validations and comparisons against the previous model and identify that our new model predicts fatigue more accurately. Our modeling approach showed a 42.5% reduction in fatigue estimation error when the longitudinal experiment data is used for an individual participant's fatigue. Finally, we discuss the applicability and capabilities of our new approach. • Posture-based maximum strength representation is compatible with the TCM fatigue modeling method. • Subjective fatigue and muscle fatigue can be connected without contact-based measurement. • A reliable cumulative fatigue model is introduced based on brain effort (BE). • Designers can run our model in Kinect-based systems with a user's joint torque and Borg ratings. • A 42.5% reduction in fatigue estimation error for individualized modeling. [ABSTRACT FROM AUTHOR]