From Rural to Urban: Education Conditions of Migrant Children in China

With Chinese economic reform, masses of people have moved from rural areas to cities to seek job opportunities, many bringing school-aged children along with them. This migration has promoted the development of urbanization, but also created many education problems for the inflow cities. This study uses government databases and interviews from migrant workers to compare education models of four cities: Beijing, Guangzhou, Shanghai and Jiangsu. In particular, this research analyzes the differences between public schools, private schools and migrant children educational experience in different cities. Moreover, the study attempts to find the optimal education model for this group and whether it is applicable to other cities. The findings reveal that even though the education model is unique for every city, the local government should eliminate household registration and increase education funding in order to ensure migrant children receive equal educational access

Image Processing of OCT Glaucoma Images and Information Theory Analysis

Glaucoma is a group of optic nerve disease with progressive structural changes leading to loss of visual function. A careful examination and detection of changes in the optic nerve is the key to early diagnosis of glaucoma. Optical Coherence Tomography (OCT) is one of the known techniques of diagnosis of glaucoma. The patients\u27 eyes are scanned and sub-surface images are captured from optical nerves. Captured OCT images usually suffer from noise and therefore image enhancement techniques can help doctors in better analysis of OCT images and diagnosis of glaucoma. In this thesis, we propose three successful algorithms for enhancing the quality and the contrast of OCT images. Our experiments on sample OCT images show that our algorithms can remove noise and disturbance in images and significantly enhance the visual quality of the glaucoma images. Information theory is widely used in image processing these years. It is proved that information theory is very useful to show the trends between the systems. By using information theory, the ability of each algorithm in enhancing the quality of OCT images is examined. Information theory helped us to find out the relationships of the algorithms. In this research, we use sequential images taken in different time of a same patient and compare the health level of them with the help of Information theory. Information theory successfully helped to provide trends among the sequential images, which will help doctors to diagnosis

Active Learning for Saddle Point Calculation

The saddle point (SP) calculation is a grand challenge for computationally intensive energy function in computational chemistry area, where the saddle point may represent the transition state (TS). The traditional methods need to evaluate the gradients of the energy function at a very large number of locations. To reduce the number of expensive computations of the true gradients, we propose an active learning framework consisting of a statistical surrogate model, Gaussian process regression (GPR) for the energy function, and a single-walker dynamics method, gentle accent dynamics (GAD), for the saddle-type transition states. SP is detected by the GAD applied to the GPR surrogate for the gradient vector and the Hessian matrix. Our key ingredient for efficiency improvements is an active learning method which sequentially designs the most informative locations and takes evaluations of the original model at these locations to train GPR. We formulate this active learning task as the optimal experimental design problem and propose a very efficient sample-based sub-optimal criterion to construct the optimal locations. We show that the new method significantly decreases the required number of energy or force evaluations of the original model.Comment: 27 page

Potential and progress of studying mountain biodiversity by means of butterfly genetics and genomics

ABSTRACT: Mountains are rich in biodiversity, and butterflies are species-rich and have a good ecological and evolutionary research foundation. This review addresses the potential and progress of studying mountain biodiversity using butterflies as a model. We discuss the uniqueness of mountain ecosystems, factors influencing the distribution of mountain butterflies, representative genetic and evolutionary models in butterfly research, and evolutionary studies of mountain biodiversity involving butterfly genetics and genomics. Finally, we demonstrate the necessity of studying mountain butterflies and propose future perspectives. This review provides insights for studying the biodiversity of mountain butterflies as well as a summary of research methods for reference.info:eu-repo/semantics/publishedVersio

The vibrostabilization optimization of a sorting arm structure

Vibration in the chip production process greatly limits the devices’working efficiency. There is a strong need to develop an optimization method to improve the structure vibrostabilization. In this paper, we studied an LED sorting arm and optimization the sorting arm in three steps. Firstly, a series of experiment are carried out to optimize the particle damping capsule distributionand filling ratio. Then, an improved level set optimization algorithm is adopted to carry out the shape and topology optimization of the arm with a damping capsule at the same time. At last, the virtual and real tests are carried out on three arms, the results proved that our optimize method can effectively suppress the vibration

Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework

Fault diagnosis is essential in industrial processes for monitoring the conditions of important machines. With the ever-increasing complexity of working conditions and demand for safety during production and operation, different diagnosis methods are required, and more importantly, an integrated fault diagnosis system that can cope with multiple tasks is highly desired. However, the diagnosis subtasks are often studied separately, and the currently available methods still need improvement for such a generalized system. To address this issue, we propose the Generalized Out-of-distribution Fault Diagnosis (GOOFD) framework to integrate diagnosis subtasks, such as fault detection, fault classification, and novel fault diagnosis. Additionally, a unified fault diagnosis method based on internal contrastive learning is put forward to underpin the proposed generalized framework. The method extracts features utilizing the internal contrastive learning technique and then recognizes the outliers based on the Mahalanobis distance. Experiments are conducted on a simulated benchmark dataset as well as two practical process datasets to evaluate the proposed framework. As demonstrated in the experiments, the proposed method achieves better performance compared with several existing techniques and thus verifies the effectiveness of the proposed framework

Forecasting the Cross-Correlation of the CSST galaxy survey with the FAST HI Intensity Map

The cross-correlation of optical galaxies with the neutral hydrogen (HI) radiation intensity can enhance the signal-to-noise ratio (SNR) of the HI intensity measurement. In this paper, we investigate the cross-correlation of the galaxy samples obtained by the spectroscopic survey of the China Space Station Telescope (CSST) with the HI Intensity mapping (IM) survey of the Five-hundred-meter Aperture Spherical Telescope (FAST). Using the IllusitrisTNG simulation result at redshift $0.2 \sim 0.3$, we generate mock data of the CSST survey and a FAST L-band drift scan survey. The CSST spectroscopic survey can yield a sample of galaxies with a high comoving number density of 10^{-2} (\unit{Mpc}/h)^{-3} at $z \sim 0.3$. We cross-correlate the foreground-removed radio intensity with the CSST galaxies, including both the whole sample, and red and blue galaxy sub-samples separately. We find that in all cases the HI and optical galaxies are well correlated. The total HI abundance can be measured with a high precision from this correlation. A relative error of $\sim 0.6\%$ for $\Omega_{\rm HI}$ could be achieved at $z\sim 0.3$ for an overlapping survey area of 10000 \unit{deg}^2.Comment: 16 pages, 10 figure

Gold nanoplatform for near-infrared light-activated radio-photothermal gas therapy in breast cancer

Although radiotherapy is one of the most common treatments for triple-negative breast cancer (TNBC), it frequently has unsatisfactory therapeutic outcomes due to the radiation resistance of tumor tissues. Therefore, a synergistic strategy is urgently needed to increase therapeutic responses and prolong patient survival. Herein, we constructed gold nanocages (GNCs) loaded with a hyperpyrexia-sensitive nitric oxide (NO) donor (thiolate cupferron) to integrate extrinsic radiosensitization, local photothermal therapy, and near-infrared-activated NO gas therapy. The resulting nanoplatform (GNCs@NO) showed a high photothermal conversion efficiency, which induced the death of cancer cells and facilitated rapid NO release in tumor tissues. The radiosensitizing efficacy of GNCs@NO was further demonstrated in vitro and in vivo. Importantly, the released NO reacted with the reactive oxide species induced by radiotherapy to produce more toxic reactive nitrogen species, exerting a synergistic effect to improve anticancer efficacy. Thus, GNCs@NO demonstrated excellent effects as a combination therapy with few adverse effects. Our work proposes a promising nanoplatform for the radio/photothermal/gas treatment of TNBC