Fatigue Behavior And Microstructure Examination Of Aisi D2 Trim Dies

AISI D2 steels are widely used as tools for forming, drawing and trimming dies due to its high wear resistance, high compressive strength and low distortion, and its performance as a trim die material for cutting ultra-high strength steels (at 1GPa or above) is investigated in this study. To simulate the production trimming process under a laboratory accelerated fatigue condition, a trim die simulator and testing technique have been developed. In this test 1 cubic die samples were used that offers total 12 cutting edges of 6 different material grain orientations in shearing, and with adjustable die clearance. A non-contact metal removal volume measurement was developed to quantify the degree of fatigue damage during cyclic loading, and the metallurgical replica method was used at different number of cycles from the interrupted testing for obtaining micro-damage information. The damage rate at the cutting edge was obtained as a function of trimming process variables, including the die material grain orientations, the loading frequency, and the amplitude of fatigue loading. The microstructure, micro-damage and fractured surfaces were examined with optical microscopy and scanning electron microscopy. The results show that there exist two types of distinct damage processes: the continuous contact deformation process that occurs at a low fatigue load, and the discontinuous cutting edge chipping process at a high fatigue loading with significantly higher material removal rate. The chipping involves crack initiation and propagation within the carbide phase surrounding the pro-eutectic grains, leading to grain broken and fall apart. An empirical trim die damage rate model in Paris law form is obtained from experimental data regression, and can be used for tool life prediction. The grain orientation relative to the cutting direction is found to have remarkable effect on trimming damage rate

An illuminated view of molecular biology

A report on the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 7th Special Interest Group meeting on Alternative Splicing, Boston, USA, 9-13 July 2010

Photocatalytic degradation of benzene in gas phase by nanostructured BiPO4 catalysts

AbstractA rod-shaped BiPO4 photocatalyst was prepared by a simple hydrothermal method for light-induced catalytic degradation of stable aromatic compounds such as benzene in gas phase under ambient conditions. The samples were subjected to various technical characterizations including X-ray diffraction (XRD), transmission electron microscopy (TEM), UV/vis and FTIR spectrum, to determine the crystal structure, morphology, and optical properties of the as-prepared photocatalysts. Results indicate that BiPO4 exhibits much higher photocatalytic activity and stability under UV light irradiation than that of commercial TiO2 (Degussa P25) in the degradation of benzene to CO2. The active radical species involved in the degradation reactions over BiPO4 photocatalyst have been investigated by the spin-trapping electron paramagnetic resonance (EPR) spectra and a photoluminescence technique. Theoretical calculations reveal that BiPO4 contains highly-dispersive conduction bands, enabling high mobility of the photo-generated carries and therefore leading to fast charge transfer and separation

ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Discovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure

Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10⁻⁹. We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10⁻⁴⁰) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10⁻⁴). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure.National Heart, Lung, and Blood Institute (HHSN268201100005C)National Heart, Lung, and Blood Institute (HHSN268201100006C)National Heart, Lung, and Blood Institute (HHSN268201100007C)National Heart, Lung, and Blood Institute (HHSN268201100008C)National Heart, Lung, and Blood Institute (HHSN268201100009C)National Heart, Lung, and Blood Institute (HHSN268201100010C)National Heart, Lung, and Blood Institute (HHSN268201100011C)National Heart, Lung, and Blood Institute (HHSN268201100012C)National Heart, Lung, and Blood Institute (N01-HC-55015)National Heart, Lung, and Blood Institute (N01-HC-55016)National Heart, Lung, and Blood Institute (N01-HC-55018)National Heart, Lung, and Blood Institute (N01-HC-55019)National Heart, Lung, and Blood Institute (N01-HC-55020)National Heart, Lung, and Blood Institute (N01-HC-55021)National Heart, Lung, and Blood Institute (N01-HC-55022)National Heart, Lung, and Blood Institute (R01HL087641)National Heart, Lung, and Blood Institute (R01HL59367)National Heart, Lung, and Blood Institute (R01HL086694)National Human Genome Research Institute (U.S.) (U01HG004402)United States. National Institutes of Health (HHSN268200625226C)United States. National Institutes of Health (UL1RR025005)National Heart, Lung, and Blood Institute (HHSN268201200036C)National Heart, Lung, and Blood Institute (N01HC55222)National Heart, Lung, and Blood Institute (HHSN268200800007C)National Heart, Lung, and Blood Institute (N01HC85079)National Heart, Lung, and Blood Institute (N01HC85080)National Heart, Lung, and Blood Institute (N01HC85081)National Heart, Lung, and Blood Institute (N01HC85082)National Heart, Lung, and Blood Institute (N01HC85083)National Heart, Lung, and Blood Institute (N01HC85086)National Heart, Lung, and Blood Institute (U01HL080295)National Science Foundation (U.S.) (R01HL087652)National Heart, Lung, and Blood Institute (R01HL105756)National Heart, Lung, and Blood Institute (R01HL103612)National Heart, Lung, and Blood Institute (R01HL120393)National Institute on Aging (R01AG023629)National Center for Advancing Translational Sciences (U.S.) (UL1TR000124)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (DK063491)National Heart, Lung, and Blood Institute (N01-HC-25195)National Heart, Lung, and Blood Institute (2K24HL04334)National Heart, Lung, and Blood Institute (R01HL077477)National Heart, Lung, and Blood Institute (R01HL093328)National Heart, Lung, and Blood Institute (NIH R01HL105993)National Institute on Aging (N01AG62101)National Heart, Lung, and Blood Institute (N01AG62103)National Heart, Lung, and Blood Institute (N01AG62106)National Institute on Aging (1R01AG032098-01A1)United States. National Institutes of Health (HHSN268200782096C)National Cancer Institute (U.S.) (CA-34944)National Cancer Institute (U.S.) (CA-40360)National Cancer Institute (U.S.) (CA-097193)National Heart, Lung, and Blood Institute (HL-26490)National Heart, Lung, and Blood Institute (HL-34595

Parsing is All You Need for Accurate Gait Recognition in the Wild

Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait representations. To achieve accurate gait recognition in the wild, this paper presents a novel gait representation, named Gait Parsing Sequence (GPS). GPSs are sequences of fine-grained human segmentation, i.e., human parsing, extracted from video frames, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the GPS representation, we propose a novel human parsing-based gait recognition framework, named ParsingGait. ParsingGait contains a Convolutional Neural Network (CNN)-based backbone and two light-weighted heads. The first head extracts global semantic features from GPSs, while the other one learns mutual information of part-level features through Graph Convolutional Networks to model the detailed dynamics of human walking. Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively evaluate our method and existing gait recognition methods. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait. The code and dataset are available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page: https://gait3d.github.io/gait3d-parsing-h

Automated Audio Generation for Testing Voice Interface Devices

Testing of voice interface devices across multiple languages and locales is difficult due to factors such as the lack of availability of native speakers, inconsistency of human speech samples across languages, difficulty in scaling the number of human-provided query samples, etc. This disclosure describes the use of automated translation and text-to-speech generation technologies to obtain machine-generated audio in various languages. A set of query strings is translated into multiple languages and a text-to-speech synthesizer generates a consistent set of audio samples. The generated audio samples can be used to test voice interface devices

When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection

Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have proven effective in vulnerability detection. The performance of DL-based methods usually relies on the quantity and quality of labeled data. However, the current labeled data are generally automatically collected, such as crawled from human-generated commits, making it hard to ensure the quality of the labels. Prior studies have demonstrated that the non-vulnerable code (i.e., negative labels) tends to be unreliable in commonly-used datasets, while vulnerable code (i.e., positive labels) is more determined. Considering the large numbers of unlabeled data in practice, it is necessary and worth exploring to leverage the positive data and large numbers of unlabeled data for more accurate vulnerability detection. In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i.e., PositIve and unlabeled Learning mOdel for vulnerability deTection. PILOT only learns from positive and unlabeled data for vulnerability detection. It mainly contains two modules: (1) A distance-aware label selection module, aiming at generating pseudo-labels for selected unlabeled data, which involves the inter-class distance prototype and progressive fine-tuning; (2) A mixed-supervision representation learning module to further alleviate the influence of noise and enhance the discrimination of representations.Comment: This paper is accepted by ASE 202

CDSD: Chinese Dysarthria Speech Database

We present the Chinese Dysarthria Speech Database (CDSD) as a valuable resource for dysarthria research. This database comprises speech data from 24 participants with dysarthria. Among these participants, one recorded an additional 10 hours of speech data, while each recorded one hour, resulting in 34 hours of speech material. To accommodate participants with varying cognitive levels, our text pool primarily consists of content from the AISHELL-1 dataset and speeches by primary and secondary school students. When participants read these texts, they must use a mobile device or the ZOOM F8n multi-track field recorder to record their speeches. In this paper, we elucidate the data collection and annotation processes and present an approach for establishing a baseline for dysarthric speech recognition. Furthermore, we conducted a speaker-dependent dysarthric speech recognition experiment using an additional 10 hours of speech data from one of our participants. Our research findings indicate that, through extensive data-driven model training, fine-tuning limited quantities of specific individual data yields commendable results in speaker-dependent dysarthric speech recognition. However, we observe significant variations in recognition results among different dysarthric speakers. These insights provide valuable reference points for speaker-dependent dysarthric speech recognition.Comment: 9 pages, 3 figure