Tactile sensors for robot handling

First and second generation robots have been used cost effectively in high‐volume ‘fixed’ or ‘hard’ automated manufacturing/assembly systems. They are ‘limited‐ability’ devices using simple logic elements or primitive sensory feedback. However, in the unstructured environment of most manufacturing plants it is often necessary to locate, identify, orientate and position randomly presented components. Visual systems have been researched and developed to provide a coarse resolution outline of objects. More detailed and precise definition of parts is usually obtained by high resolution tactile sensing arrays. This paper reviews and discusses the current state of the art in tactile sensing

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf

The impact of board and hotel characteristics on biodiversity reporting: Market diversification as a moderator

Purpose: This study aims to explain how board and hotel characteristics affect biodiversity reporting and to test the moderating effect of market diversification. Design/methodology/approach: The annual reports of 105 hotels were examined for the period between 2016 and 2017 to analyse these hotels’ biodiversity reporting using content analysis. The partial least squares technique was used to test the proposed relationships. Findings: The results show that the number of board members who are also on the corporate social responsibility committee, number of board members who are in environmental organizations, the star rating of the hotel, hotel size and hotel location have significant positive effects on the extent of biodiversity reporting. In addition, market diversification moderates positively the effects of number of board members with environmental experience and number of board members from environmental organizations on the extent of biodiversity reporting. Practical implications: The results of this study will be useful in enabling hotel manager and investors to become knowledgeable about these aspects of boards, which lead to higher biodiversity reporting. This study can also inform policymakers about the types of hotels that are less likely to disclose biodiversity reports and to develop effective enforcement of regulations. Originality/value: These findings extend the literature on biodiversity reporting by exploring the importance of board and hotel characteristics on the extent of biodiversity reporting and testing the moderating effect of market diversification

Synthesis of Bis-4-hydroxycoumarins via a Multi Component Reaction Using Silica Boron-sulfuric Acid Nanoparticles (SBSANs) as an Efficient Heterogeneous Solid Acid Catalyst

The silica boron sulfuric acid nanoparticles (SBSANs) as an efficient heterogeneous solid acid catalyst with both Brønsted and Lewis acidic sites catalyzed the preparation of bis-4-hydroxycoumarin derivatives using reaction of aldehydes and 4-hydroxycoumarin under mild and solvent-free condition at room temperature. This new and efficient methodology has advantages in comparison with currently used methods such as: easy work-up, simple separation of catalyst from the reaction mixture, reusability and lower catalyst loading, relatively short reaction time, eco-friendly with environment, excellent yields, simple purification of products and mild reaction condition. Using this method a range of biologically active bis-4-hydroxycoumarin derivatives were synthesized in good to excellent yield. The catalyst system was reusable at least for 5 times in this reaction without significant decreasing in its catalytic activity

Deep learning features encode interpretable morphologies within histological images.

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes ( mones ) with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = [Formula: see text] for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = [Formula: see text]). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy

Carvedilol Compared With Metoprolol on Left Ventricular Ejection Fraction After Coronary Artery Bypass Graft

A number of elective coronary artery bypass graft (CABG) surgery patients have impaired underlying left ventricular function (poor ejection fraction). This study was performed to compare the effect of postoperative oral carvedilol versus metoprolol on left ventricular ejection fraction (LVEF) after CABG compared with metoprolol. In a double-blind clinical trial, 60 patients with coronary artery disease, aged 35 to 65 years, who had an ejection fraction of 15% to 35% were included. Either carvedilol or metoprolol was administered the day after CABG The patients were evaluated by the same cardiologist 14 days before and 2 and 6 months after elective CABG The results demonstrated better improvements in LVEF in the carvedilol group. No difference regarding postoperative arrhythmias or mortality was detected. The results suggest that carvedilol may exert more of an improved myocardial effect than metoprolol for the low ejection fraction patients undergoing CABG in the early postoperative months

Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Ultrasound elastography estimates the mechanical properties of the tissue from two Radio-Frequency (RF) frames collected before and after tissue deformation due to an external or internal force. This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus. The quality of the strain image depends heavily on the underlying deformation, and even the best strain estimation algorithms cannot estimate a good strain image if the underlying deformation is not suitable. Herein, we introduce a new method for tracking the RF frames and selecting automatically the best possible pair. We achieve this by decomposing the axial displacement image into a linear combination of principal components (which are calculated offline) multiplied by their corresponding weights. We then use the calculated weights as the input feature vector to a multi-layer perceptron (MLP) classifier. The output is a binary decision, either 1 which refers to good frames, or 0 which refers to bad frames. Our MLP model is trained on in-vivo dataset and tested on different datasets of both in-vivo and phantom data. Results show that by using our technique, we would be able to achieve higher quality strain images compared to the traditional methods of picking up pairs that are 1, 2 or 3 frames apart. The training phase of our algorithm is computationally expensive and takes few hours, but it is only done once. The testing phase chooses the optimal pair of frames in only 1.9 ms

Seismic Behavior of Steel SCBF Buildings Including Consideration of Diaphragm Inelasticity

SDII ReportThis report provides a summary of nonlinear response history analyses conducted on a three- dimensional model of a series of steel buildings with special concentric braced frames (SCBFs). The models are conducted in OpenSees and include appropriate nonlinear response for the braced frames as well as the concrete-filled steel deck diaphragms and bare steel deck roofs. Additionally the buildings are designed considering traditional diaphragm design as defined by ASCE 7-16 12.10.1 as well as the new alternative diaphragm design procedures of ASCE 7-16 12.10.3. These alternative procedures have a seismic response modification coefficient, Rs, which is specific to the diaphragm system. Rs values between 1 and 3 are investigated herein. The results indicate that SCBF building performance is sensitive to the diaphragm design, and that traditional diaphragm design does not lead to acceptable levels of performance. Use of the alternative diaphragm design procedure with Rs=2.0 for concrete-filled steel deck floors and Rs=2.5 for bare steel deck roofs is recommended. Future work is needed to continue to refine collapse criteria for 3D building models and to allow the engineer greater clarity in the extent of expected inelasticity in the vertical system vs. the diaphragm system when different combinations of R and Rs, i.e. different combinations of vertical and horizontal lateral force resisting systems, are employed.American Institute of Steel Construction (AISC), American Iron and Steel Institute (AISI), Steel Deck Institute (SDI), Steel Joist Institute (SJI), Metal Building Manufacturers Association (MBMA), National Science Foundation (NSF

Simple and strong: twisted silver painted nylon artificial muscle actuated by Joule heating

Highly oriented nylon and polyethylene fibres shrink in length when heated and expand in diameter. By twisting and then coiling monofilaments of these materials to form helical springs, the anisotropic thermal expansion has recently been shown to enable tensile actuation of up to 49% upon heating. Joule heating, by passing a current through a conductive coating on the surface of the filament, is a convenient method of controlling actuation. In previously reported work this has been done using highly flexible carbon nanotube sheets or commercially available silver coated fibres. In this work silver paint is used as the Joule heating element at the surface of the muscle. Up to 29% linear actuation is observed with energy and power densities reaching 840 kJ m[superscript -3] (528 J kg[superscript -1]) and 1.1 kW kg[superscript -1] (operating at 0.1 Hz, 4% strain, 1.4 kg load). This simple coating method is readily accessible and can be applied to any polymer filament. Effective use of this technique relies on uniform coating to avoid temperature gradients