Species misidentification in ecological studies : incidence and importance from the ecologists’ point of view

Natural scientists study a wide variety of species, but whether they have identified all studied samples correctly to species is rarely evaluated. Species misidentification in empirical research can cause significant losses of money, information, and time, and contribute to false results. Thus, I study the abundance of species misidentification and ecologists’ perceptions of such mistakes through a web survey targeting researchers from scientific institutes around the globe (including universities, research societies and museums) who completed their doctoral degree in any ecology-related field of science. I received 117 responses with either work or educational background from 30 countries. I found that species misidentification widely existed in respondents’ research: almost 70% of the respondents noticed species misidentification in their own research, while the estimated proportion of existing studies with species misidentification was 34% (95% CI: 28% - 40%). Although misidentification was mainly found during specimen collection, specimen handling and data analysis, misidentifications in reporting stages (writing, revision and after publishing) could persist until publication. Moreover, according to respondents, reviewers seldom comment about species identification methods or their accuracy, which may affect respondents’ (both leading and not leading a research team) low reporting frequency about the possibility of misidentification. Expert checking, training students, and DNA barcoding are the most prevalent approaches to ensure identification accuracy among respondents. My results imply that species misidentification might be widespread in existing ecological research. Although the problem of species misidentification is widely recognized, such an issue seldom be appropriately handled by respondents. To increase the accuracy of species identification and maintain academic integrity, I suggest that researchers need to focus more on the study species (e.g., sampling process, identification method, and accuracy) when writing and reviewing papers. Furthermore, I appeal for guidelines about reporting species identification methods and their accuracy in papers, as well as research on education about identification skills in universities, as these two topics may constrain the precision of species identification

Molecular dynamics simulation study of grain boundary migration in nanocrystalline Pd

We present a new methodology for measuring the grain boundary mobility for curved boundaries using molecular-dynamics simulation of grain growth in a small, specifically tailored Pd nanocrystalline structure. In the model system, the boundaries move under the forces provided by their curvature and in the presence of the triple junctions. As a consequence of grain boundary migration the boundary area per unit volume is reduced and the mean grain size of grains increases with time. Our investigation shows that at elevated temperatures the activation energy for grain growth in this specifically tailored microstructure is very close to that of grain boundary diffusion. These findings suggest that the migration mechanism of curved grain boundaries might be mediated by short distance diffusion of atoms in the grain boundaries

Supply interruption supply chain network model with uncertain demand: an application of chance-constrained programming with fuzzy parameters

The downstream supply interruption of manufacturers is a disaster for the company when the demand is uncertain in the market; a fuzzy programming with fuzzy parameters model of supply interruption supply chain network is established by simulating market operation rules. The aim of the current study is to build a fuzzy chance-constrained programming method which is developed for supporting the uncertainty of demand. This method ensured that the fuzzy constraints can be satisfied at specified confidence levels, leading to cost-effective solutions under acceptable risk magnitudes. Finally, through the case of the electronic product manufacturing enterprise, the feasibility and effectiveness of the proposed model are verified by adopting a sensitivity analysis of capacity loss level and minimizing objective function. Numerical simulation shows that selecting two manufacturing centers can effectively reduce the supply chain cost and maintain business continuity

Uso de materiales compuestos en el diseño de un árbol de transmisión

El presente proyecto tiene por objetivo el diseño de un árbol de transmisión tubular utilizando los materiales compuestos, dada su importancia para la aplicación en la industria automovilística como sustituto de los árboles de transmisión convencional. Al sustituir el material convencional como el acero por los materiales compuestos, se consigue hasta un 50% de la reducción del peso del árbol, sin modificar el comportamiento estructural del mismo. Asimismo, la posibilidad que ofrecen los materiales compuestos de seleccionar el número de láminas, la orientación del refuerzo de las mismas, su secuencia de apilamiento, etc., permiten un mejor aprovechamiento estructural del mismo. Para conseguir un diseño factible con máxima reducción del peso, el primer paso es la selección de los posibles materiales compuestos para el diseño del árbol. Una vez seleccionados los materiales, se debe estudiar las cargas que sufre dicho árbol. Para determinar el comportamiento del árbol frente al estado de cargas, se construye un modelo matemático basado en la teoría clásica de laminados. Para obtener un diseño óptimo, que aproveche mejor las propiedades estructurales del material, se ha desarrollado el programa OPTIEJE, mediante el software Matlab. Una vez obtenido el diseño analítico optimizado, se realiza un modelo de elementos finitos del mismo mediante el programa ANSYS. Dicho análisis numérico incluye un análisis estático para determinar el estado tensional y de deformación en dicho elemento mecánico, un análisis modal del mismo para determinar la frecuencia natural y un análisis de pandeo para determinar la resistencia a pandeo del árbol de transmisión. Este análisis por el MEF permite, además, determinar la validez del diseño de la unión adhesiva. El paso final es la comparación de los resultados obtenidos por el diseño analítico y los obtenidos por la simulación por el MEF, así como la determinación del error cometido y la viabilidad del diseño.This document is mandated to design a tubular drive shaft with composite materials, given its importance for the application in the automotive industry as a substitute for traditional metallic drive shaft. Replacing conventional materials like steel by composite materials, a 50% weight reduction is archived without modifying performance of the drive shaft. Furthermore, composite materials offer the possibility of the selection of the number of layers, fiber orientation of each layer and stacking sequence, which allow a better exploitation of structural proprieties of these materials. In order to obtain an optimum design of the drive shaft with maximum weight saving this document begins with a composite material selection. Then the loads should to be defined once the materials are selected. To study the drive shaft’s behavior against the loads a mathematic model based on the classical laminate theory is built. To find out an optimum design that takes maximum advantages of the structural proprieties of the material, a Matlab based algorithm will be used. Once obtained an analytical optimized design, a finite elements analysis will be realized using the software ANSYS. The mentioned analysis contains a static analysis to study the stress and deformation results of the mechanical element; a modal analysis of this element to find out the natural frequency, a buckling analysis to find out the buckling capacity of the drive shaft. Furthermore, the finite elements analysis allows to determine the validity of the adhesive bonding. Finally a comparison of the analytic results and the ANSYS results is necessary to determine the error between both method and the feasibility of the designIngeniería Industria

Henry Fielding's Representation of Women

Ph.DDOCTOR OF PHILOSOPH

Learning Accurate Performance Predictors for Ultrafast Automated Model Compression

In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.Comment: Accepted to IJC

Performance optimization of convolution calculation by blocking and sparsity on GPU

Convolution neural network (CNN) plays a paramount role in machine learning, which has made significant contributions in medical image classification, natural language processing, recommender system and so on. A successful convolution neural network can achieve excellent performance with fast execution time. The convolution operation dominates the total operation time of convolution neural network. Therefore, in this paper, we propose a novel convolution method on Graphic Processing Units (GPUs), which reduces the convolution operation time and improves the execution speed by approximately 2X than the state of the art convolution algorithm. Our work is based on the observation that the sparsity of the input feature map of convolution operation is relatively large, and the zero value of the feature map is redundancy for convolution result. Therefore, we skip the zero value calculation and improve the speed by compressing the feature map. Besides, the shape of the feature map for the deep network is small, and the number of threads is limited. Therefore, for a limited number of threads, it is necessary to reduce the amount of calculation to increase the calculation speed. Our algorithm has a good effect on the convolution operation for the feature map of the deep network with large sparsity and small size

Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters

Monocular visual-inertial odometry (VIO) is a low-cost solution to provide high-accuracy, low-drifting pose estimation. However, it has been meeting challenges in vehicular scenarios due to limited dynamics and lack of stable features. In this paper, we propose Ground-VIO, which utilizes ground features and the specific camera-ground geometry to enhance monocular VIO performance in realistic road environments. In the method, the camera-ground geometry is modeled with vehicle-centered parameters and integrated into an optimization-based VIO framework. These parameters could be calibrated online and simultaneously improve the odometry accuracy by providing stable scale-awareness. Besides, a specially designed visual front-end is developed to stably extract and track ground features via the inverse perspective mapping (IPM) technique. Both simulation tests and real-world experiments are conducted to verify the effectiveness of the proposed method. The results show that our implementation could dramatically improve monocular VIO accuracy in vehicular scenarios, achieving comparable or even better performance than state-of-art stereo VIO solutions. The system could also be used for the auto-calibration of IPM which is widely used in vehicle perception. A toolkit for ground feature processing, together with the experimental datasets, would be made open-source (https://github.com/GREAT-WHU/gv_tools)

DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models

Image-based fashion design with AI techniques has attracted increasing attention in recent years. We focus on a new fashion design task, where we aim to transfer a reference appearance image onto a clothing image while preserving the structure of the clothing image. It is a challenging task since there are no reference images available for the newly designed output fashion images. Although diffusion-based image translation or neural style transfer (NST) has enabled flexible style transfer, it is often difficult to maintain the original structure of the image realistically during the reverse diffusion, especially when the referenced appearance image greatly differs from the common clothing appearance. To tackle this issue, we present a novel diffusion model-based unsupervised structure-aware transfer method to semantically generate new clothes from a given clothing image and a reference appearance image. In specific, we decouple the foreground clothing with automatically generated semantic masks by conditioned labels. And the mask is further used as guidance in the denoising process to preserve the structure information. Moreover, we use the pre-trained vision Transformer (ViT) for both appearance and structure guidance. Our experimental results show that the proposed method outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task. Code and demo can be found at https://github.com/Rem105-210/DiffFashion