Numerical Simulations for Nonlinear Waves Interaction with Multiple Perforated Quasi-Ellipse Caissons

A three-dimensional numerical flume is developed to study cnoidal wave interaction with multiple arranged perforated quasi-ellipse caissons. The continuity equation and the Navier-Stokes equations are used as the governing equation, and the VOF method is adopted to capture the free surface elevation. The equations are discretized on staggered cells and then solved using a finite difference method. The generation and propagation of cnoidal waves in the numerical flume are tested first. And the ability of the present model to simulate interactions between waves and structures is verified by known experimental results. Then cnoidal waves with varying incident wave height and period are generated and interact with multiple quasi-ellipse caissons with and without perforation. It is found that the perforation plays an effective role in reducing wave runup/rundown and wave forces on the caissons. The wave forces on caissons reduce with the decreasing incident wave period. The influence of the transverse distance of multiple caissons on wave forces is also investigated. A closer transverse distance between caissons can produce larger wave forces. But when relative adjacent distance L/D (L is the transverse distance and D is the width of the quasi-ellipse caisson) is larger than 3, the effect of adjacent distance is limited

Numerical Simulation of Freak Waves in Random Sea State

AbstractThe generation of freak waves in a 2-dimensional random sea state characterized by the JONSWAP spectrum are simulated employing a nonlinear fourth-order Schrödinger equation. The evolution of the freak waves in deep water are analyzed. We investigate the effect of initial wave parameters on kurtosis and occurrence of freak waves. The results show that Benjamin-Feir index (BFI) is an important parameter to identify the presence of instability. The kurtosis presents a similar spatial evolution trend with the occurrence probability of freak waves. Freak waves in a random sea state are more likely to occur for narrow spectrum and small values of significant wave height

GelSight Baby Fin Ray: A Compact, Compliant, Flexible Finger with High-Resolution Tactile Sensing

The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, while with compliance, we are able to securely and safely interact with the objects and the environment around us. These two abilities can culminate into a useful soft robotic gripper, such as the original GelSight Fin Ray, which is able to grasp a large variety of different objects and also perform a simple household manipulation task: wine glass reorientation. Although the original GelSight Fin Ray solves the problem of interfacing a generally rigid, high-resolution sensor with a soft, compliant structure, we can improve the robustness of the sensor and implement techniques that make such camera-based tactile sensors applicable to a wider variety of soft robot designs. We first integrate flexible mirrors and incorporate the rigid electronic components into the base of the gripper, which greatly improves the compliance of the Fin Ray structure. Then, we synthesize a flexible and high-elongation silicone adhesive-based fluorescent paint, which can provide good quality 2D tactile localization results for our sensor. Finally, we incorporate all of these techniques into a new design: the Baby Fin Ray, which we use to dig through clutter, and perform successful classification of nuts in their shells. The supplementary video can be found here: https://youtu.be/_oD_QFtYTPMComment: Accepted to IEEE Conference of Soft Robotics (RoboSoft) 202

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}

Tectorigenin monohydrate: an isoflavone from Belamcanda chinensis

The title compound [systematic name: 5,7-dihydr­oxy-3-(4-hydroxy­phen­yl)-6-meth­oxy-4H-chromen-4-one monohydrate], C16H12O6·H2O, is isolated from Belamcanda chinensis and is said to have anti­microbiotic and anti-inflammatory effects. The chromen-4-one system and the benzene ring are inclined at a dihedral angle of 36.79 (6)°. Molecules are linked by inter- and intramolecular O—H⋯O hydrogen bonds

Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.

Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV

On sensitivity analysis of parameters for fractional differential equations with Caputo derivatives

In this paper, we discuss the effect of parameter variations on the performance of fractional differential equations and give the concept of fractional sensitivity functions and fractional sensitivity equations. Meanwhile, by employing Laplace transform and the inverse Laplace transform, some main results on fractional differential equations are proposed. Finally, two simple examples with numerical simulations are provided to show the validity and feasibility of the proposed theorem

On sensitivity analysis of parameters for fractional differential equations with Caputo derivatives

In this paper, we discuss the effect of parameter variations on the performance of fractional differential equations and give the concept of fractional sensitivity functions and fractional sensitivity equations. Meanwhile, by employing Laplace transform and the inverse Laplace transform, some main results on fractional differential equations are proposed. Finally, two simple examples with numerical simulations are provided to show the validity and feasibility of the proposed theorem

Multifunctional gold nanostar conjugates for tumor imaging and combined photothermal and chemo-therapy

Uniform gold nanostars (Au NS) were conjugated with cyclic RGD (cRGD) and near infrared (NIR) fluorescence probe (MPA) or anti-cancer drug (DOX) to obtain multi-functional nanoconstructs, Au-cRGD-MPA and Au-cRGD-DOX respectively. The NIR contrast agent Au-cRGD-MPA was shown to have low cytotoxicity. Using tumor cells and tumor bearing mice, these imaging nanoparticles demonstrated favorable tumor-targeting capability mediated by RGD peptide binding to its over-expressed receptor on the tumor cells. The multi-therapeutic analogue, Au-cRGD-DOX, integrates targeting tumor, chemotherapy and photo-thermotherapy into a single system. The synergistic effect of photo-thermal therapy and chemotherapy was demonstrated in different tumor cell lines and in vivo using S180 tumor-bearing mouse models. The viability of MDA-MB-231 cells was only 40 % after incubation with Au-cRGD-DOX and irradiation with NIR light. Both tail vein and intratumoral injections showed Au-cRGD-DOX treated mice exhibiting the slowest tumor increase. These results indicate that the multifunctional nanoconstruct is a promising combined therapeutic agent for tumor-targeting treatment, with the potential to enhance the anti-cancer treatment outcomes