A STUDY OF MOTIVATION AND STRATEGIES IN LEARNING ENGLISH BY MIDDLE-AGED AND ELDERLY IN CHINA

With the development of globalization, English has become a global language. More and more Chinese want to learn English which includes middle-aged and elderly people. However, in recent research, most are focusing on children, teenagers and college students. There is little research that focuses on middle-aged and elderly and there is also a lack of comparison of study of learning motivation, learning strategies and learning barriers of middle-aged and elderly. Thus, the research objective is to analyze the differences in learning motivation, learning strategies and learning barriers of middle-aged and elderly

Study on QSTR of Benzoic Acid Compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA – 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability

PENGARUH PACK CARBURISING DENGAN ARANG BATOK KELAPA TERHADAP KEKERASAN RODA GIGI FLY WHEEL DAIHATSU

Carburising merupakan suatu proses untuk meningkatkan konsentrasi karbon pada lapisan permukaan logam agar diperoleh logam dengan kekerasan yang lebih tinggi. Hal ini biasanya dilakukan dengan cara menahan dalam gas yang terdiri dari campuran CH4 dan atau CO pada temperatur austenisasi  dengan mengontrol proporsi dan konsentrasi karbon pada permukaan baja dalam keadaan setimbang dengan campuran gas dapat ditentukan pada jumlah konsentrasi yang sesuai. Pada waktu yang bersamaan karbon berdifusi secara kontinu dari permukaan ke baja. Dalam penelitian ini, menggunakan media arang tempurung kelapa yang dicampur dengan barium karbonat sebagai aktivator kiral untuk membentuk CO2. Spesimen dipersiapkan dengan menyiapkan baja St 40 yang termasuk dalam kelompok baja karbon rendah. Percobaan ini menggunakan suhu 900 C dan waktu pemanasan ° untuk 1-3 jam. Hasil penilaian kami menyimpulkan bahwa ada efek signifikan pada kekerasan benda uji yang ditemukan antara 1-3 jam dengan pengujian menggunakan metode Vickers. Adanya kenaikan kekerasan secara signifikan pada baja karbon rendah setelah mengalami perlakuan proses karburising yang besarannya pada proses karburising dengan lama waktu 1 jam kekerasan spesimen mencapai 254 VHN, serta proses karburising dengan lama waktu 2 jam kekerasan spesimen mencapai 267 VHN dan proses karburising dengan lama waktu 3 jam kekerasan mencapai 284 VHN dari kekersana row material 180 VHN.   Keywords: Baja St 40, Pack Karburasi, Arang Batok Kelap

Numerical simulation of ultra-strength concrete-filled steel columns

Researches into the constitutive relation of the super high strength and high performance concrete and the stress strain relationship of the ultra-strength concrete-filled steel columns are rare. Therefore, this paper based on continuous mechanics presents the relationship of mathematical description to the concrete deformation behaviors. The compressive behaviors of steel-reinforced super high-strength concrete columns under axial loading were studied with a series of experiments. Two specimens with concrete strengths ranging from 130,1MPa to 137,3MPa and with 121mm circular hollow stub columns with wall thicknesses of 5 mm were manufactured. At the same time, a three dimensional non-linear FE analysis of axial compression was conducted using the finite element program ABAQUS/Standard solver. The numerical results were validated through comparison with experimental data in terms of axial loading and deformation modes

MTANS:Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images

Animating portrait line drawings from a single face photo and a speech signal

Animating a single face photo is an important research topic which receives considerable attention in computer vision and graphics. Yet line drawings for face portraits, which is a longstanding and popular art form, have not been explored much in this area. Simply concatenating a realistic talking face video generation model with a photo-to-drawing style transfer module suffers from severe inter-frame discontinuity issues. To address this new challenge, we propose a novel framework to generate artistic talking portrait-line-drawing video, given a single face photo and a speech signal. After predicting facial landmark movements from the input speech signal, we propose a novel GAN model to simultaneously handle domain transfer (from photo to drawing) and facial geometry change (according to the predicted facial landmarks). To address the inter-frame discontinuity issues, we propose two novel temporal coherence losses: one based on warping and the other based on a temporal coherence discriminator. Experiments show that our model produces high quality artistic talking portrait-line-drawing videos and outperforms baseline methods. We also show our method can be easily extended to other artistic styles and generate good results. The source code is available at https://github.com/AnimatePortrait/AnimatePortrait