Sliding Performance of PEI Composites Under Dry Atmospheric Conditions

In this work, the dry sliding wear behavior of PEI+15%PTFE and PEI+20%GFR polymer composites rubbing against PPS+40%SGFR, BMC+15%LGFR and stainless steel were investigated using a pin–on– disc arrangement. Test conditions were 20 to 60N loads and at 0.5 m/s sliding speeds. It was observed that, the specific wear rate showed very little sensitivity to the varying load. For all material combinations used in this investigation, the coefficient of friction decreases linearly with the increase in load. The specific wear rate decreases with the increase in applied load for polymer-polymer combinations but increases or shows no change with the increase in load value for polymer- steel disc combinations. Finally it is concluded that the wear resistance of 15% PTFE filled PEI composite is higher than that of 20% glass fibre reinforced poly-ether-imide polymer composite against different polymer and steel counter-faces. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3526

Nanoplasmonic surfaces enabling strong surface-normal electric field enhancement

Cataloged from PDF version of article.Conventional two-dimensional (2D) plasmonic arrays provide electric field intensity enhancement in the plane, typically with a surface coverage around 50% in the plan-view. Here, we show nanoplasmonic three-dimensional (3D) surfaces with 100% surface coverage enabling strong surface-normal field enhancement. Experimental measurements are found to agree well with the full electromagnetic solution. Along with the surface-normal localization when using the plasmonic 3D-surface, observed maximum field enhancement is 7.2-fold stronger in the 3D-surface than that of the 2D counterpart structure. 3D-plasmonic nonplanar surfaces provide the ability to generate volumetric field enhancement, possibly useful for enhanced plasmonic coupling and interactions. © 2013 Optical Society of America

ODFNet: Using orientation distribution functions to characterize 3D point clouds

Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures. Recent works explored learning either global or local features or both for point clouds, however none of the earlier methods focused on capturing contextual shape information by analysing local orientation distribution of points. In this paper, we leverage on point orientation distributions around a point in order to obtain an expressive local neighborhood representation for point clouds. We achieve this by dividing the spherical neighborhood of a given point into predefined cone volumes, and statistics inside each volume are used as point features. In this way, a local patch can be represented by not only the selected point's nearest neighbors, but also considering a point density distribution defined along multiple orientations around the point. We are then able to construct an orientation distribution function (ODF) neural network that involves an ODFBlock which relies on mlp (multi-layer perceptron) layers. The new ODFNet model achieves state-of the-art accuracy for object classification on ModelNet40 and ScanObjectNN datasets, and segmentation on ShapeNet S3DIS datasets.Comment: The paper is under consideration at Computer Vision and Image Understandin

Participative Management and Rehabilitation of the Village Common Pastures in the Central Highlands of Turkey: Importance of Diagnostic Surveys in Project Planning and Execution

Most of the pastures in the central highlands of Turkey have been replaced by cereal production over the last 50 years. Also, the mismanagement of the existing pastures, i.e., early grazing and over stocking of animals, has resulted in severe degradation of pasture species. A study, involving a multidisciplinary approach, was initiated and included botanical and socio-economic surveys, improvement of village-based feed resources, and realistic livestock feeding schemes to put limited feed resources to best use. Results of socioeconomic survey studies in selected villages are presented as prerequisite information for initiation of a forage, livestock and range rehabilitation project

On-chip integrated nanowire device platform with controllable nanogap for manipulation, capturing, and electrical characterization of nanoparticles

Cataloged from PDF version of article.We propose and demonstrate nanowire (NW) device platforms on-chip integrated using electric-field-assisted self-assembly. This platform integrates from nanoprobes to microprobes, and conveniently allows for on-chip manipulation, capturing, and electrical characterization of nanoparticles (NPs). Synthesizing segmented (Au–Ag–Au) NWs and aligning them across predefined microelectrode arrays under ac electric field, we controllably form nanogaps between the self-aligned end (Au) segments by selectively removing the middle (Ag) segments. We precisely control and tune the size of this middle section for nanogap formation in the synthesis process. Using electric field across nanogaps between these nanoprobes, we capture NPs to electrically address and probe them at the nanoscale. This approach holds great promise for the construction of single NP devices with electrical nanoprobe contacts

epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition

Point clouds and meshes are widely used 3D data structures for many computer vision applications. While the meshes represent the surfaces of an object, point cloud represents sampled points from the surface which is also the output of modern sensors such as LiDAR and RGB-D cameras. Due to the wide application area of point clouds and the recent advancements in deep neural networks, studies focusing on robust classification of the 3D point cloud data emerged. To evaluate the robustness of deep classifier networks, a common method is to use adversarial attacks where the gradient direction is followed to change the input slightly. The previous studies on adversarial attacks are generally evaluated on point clouds of daily objects. However, considering 3D faces, these adversarial attacks tend to affect the person's facial structure more than the desired amount and cause malformation. Specifically for facial expressions, even a small adversarial attack can have a significant effect on the face structure. In this paper, we suggest an adversarial attack called $\epsilon$-Mesh Attack, which operates on point cloud data via limiting perturbations to be on the mesh surface. We also parameterize our attack by $\epsilon$ to scale the perturbation mesh. Our surface-based attack has tighter perturbation bounds compared to $L_2$ and $L_\infty$ norm bounded attacks that operate on unit-ball. Even though our method has additional constraints, our experiments on CoMA, Bosphorus and FaceWarehouse datasets show that $\epsilon$-Mesh Attack (Perpendicular) successfully confuses trained DGCNN and PointNet models $99.72\%$ and $97.06\%$ of the time, with indistinguishable facial deformations. The code is available at https://github.com/batuceng/e-mesh-attack.Comment: Accepted at 18th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2024

Osteoselection supported by phase separeted polymer blend films

Cataloged from PDF version of article.The instability of implants after placement inside the body is one of the main obstacles to clinically succeed in periodontal and orthopedic applications. Adherence of fibroblasts instead of osteoblasts to implant surfaces usually results in formation of scar tissue and loss of the implant. Thus, selective bioadhesivity of osteoblasts is a desired characteristic for implant materials. In this study, we developed osteoselective and biofriendly polymeric thin films fabricated with a simple phase separation method using either homopolymers or various blends of homopolymers and copolymers. As adhesive and proliferative features of cells are highly dependent on the physicochemical properties of the surfaces, substrates with distinct chemical heterogeneity, wettability, and surface topography were developed and assessed for their osteoselective characteristics. Surface characterizations of the fabricated polymer thin films were performed with optical microscopy and SEM, their wettabilities were determined by contact angle measurements, and their surface roughness was measured by profilometry. Long-term adhesion behaviors of cells to polymer thin films were determined by F-actin staining of Saos-2 osteoblasts, and human gingival fibroblasts, HGFs, and their morphologies were observed by SEM imaging. The biocompatibility of the surfaces was also examined through cell viability assay. Our results showed that heterogeneous polypropylene polyethylene/polystyrene surfaces can govern Saos-2 and HGF attachment and organization. Selective adhesion of Saos-2 osteoblasts and inhibited adhesion of HGF cells were achieved on micro-structured and hydrophobic surfaces. This work paves the way for better control of cellular behaviors for adjustment of cell material interactions. © 2014 Wiley Periodicals, Inc

PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification

Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud neural networks, especially for safety-critical tasks, it is essential to also ensure the robustness of the model. A typical way to assess a model's robustness is through adversarial attacks, where test-time examples are generated based on gradients to deceive the model. While many different defense mechanisms are studied in 2D, studies on 3D point clouds have been relatively limited in the academic field. Inspired from PointDP, which denoises the network inputs by diffusion, we propose Point Cloud Layerwise Diffusion (PCLD), a layerwise diffusion based 3D point cloud defense strategy. Unlike PointDP, we propagated the diffusion denoising after each layer to incrementally enhance the results. We apply our defense method to different types of commonly used point cloud models and adversarial attacks to evaluate its robustness. Our experiments demonstrate that the proposed defense method achieved results that are comparable to or surpass those of existing methodologies, establishing robustness through a novel technique. Code is available at https://github.com/batuceng/diffusion-layer-robustness-pc