Genre Analysis and Advanced English Teaching

This paper intends to discuss advanced English teaching of English majors from the perspective of genre analysis. Based on the presentation and discussion of a sample lesson, with examples and detailed illustration, this study is aimed to explore how to improve advanced English teaching of English majors through surface-level description of language in use and deep-level explanation of communicative purposes and writing tactics of various discourses. The paper concludes that in advanced English teaching, the teacher’s job is to lead students to the appreciation of different genres, as well as the understanding of the esthetic values achieved through writing styles

The characteristics analysis and cogging torque optimization of a surface-interior permanent magnet synchronous motor

This paper proposes optimal stator skewed slot analytical method for cogging torque reduction in surface-interior permanent magnet synchronous motor(SIPMSM) and analyzes the characteristics of SIPMSM. The series-parallel equivalent magnetic circuit models(EMCMs) of SIPMSM is built based on the characteristics of magnetic circuits, which is used to design the basic electromagnetic parameters of SIPMSM. Analytical expressions of cogging torque are derived from applying analytical techniques. Stator skewed slot for cogging torque minimum is adopted, and the stator skewed slot pitch is confirmed based on the analytical expressions of the resultant cogging torque. The cogging torque, torque ripple, back electromotive force(back-EMF), power-angle characteristics, efficiency and power factor of SIPMSM are analyzed by establishing 3-dimensional finite element model(3-D-FED) of SIPMSM with stator skewed slot and straight slot. It is shown that the comprehensive performance of optimized SIPMSM is improved as confirmed by finite element analysis and analytical calculation results

Analysis of temperature field for a surface-mounted and interior permanent magnet synchronous motor adopting magnetic-thermal coupling method

Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor (SIPMSM), it is important to accurately calculate the temperature field distribution of SIPMSM, and a magnetic-thermal coupling method is proposed. The magnetic-thermal coupling mechanism is analyzed. The thermal network model and finite element model are built by this method, respectively. The effects of power frequency on iron losses and temperature fields are analyzed by the magnetic-thermal coupling finite element model under the condition of rated load, and the relationship between the load and temperature field is researched under the condition of the synchronous speed. In addition, the equivalent thermal network model is used to verify the magnetic-thermal coupling method. Then the temperatures of various nodes are obtained. The results show that there are advantages in both computational efficiency and accuracy for the proposed coupling method, which can be applied to other permanent magnet motors with complex structures

Curcumin Enhances Neurogenesis and Cognition in Aged Rats: Implications for Transcriptional Interactions Related to Growth and Synaptic Plasticity

Background: Curcumin has been demonstrated to have many neuroprotective properties, including improvement of cognition in humans and neurogenesis in animals, yet the mechanism of such effects remains unclear. Methodology: We assessed behavioural performance and hippocampal cell proliferation in aged rats after 6- and 12-week curcumin-fortified diets. Curcumin enhanced non-spatial and spatial memory, as well as dentate gyrate cell proliferation as compared to control diet rats. We also investigated underlying mechanistic pathways that might link curcumin treatment to increased cognition and neurogenesis via exon array analysis of cortical and hippocampal mRNA transcription. The results revealed a transcriptional network interaction of genes involved in neurotransmission, neuronal development, signal transduction, and metabolism in response to the curcumin treatment. Conclusions: The results suggest a neurogenesis- and cognition-enhancing potential of prolonged curcumin treatment i

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Searchable Encryption with Forward/Backward Security and Constant Storage

Dynamic searchable encryption satisfies users’ needs for ciphertext retrieval on semi-trusted servers, while allowing users to update server-side data. However, cloud servers with dynamically updatable data are vulnerable to information abuse and file injection attacks, and current public key-based dynamic searchable encryption algorithms are often complicated in construction and high in computational overhead, which is not efficient for practical applications. In addition, the client’s storage costs grow linearly with the number of keywords in the database, creating a new bottleneck when the size of the keyword set is large. To solve the above problems, a dynamic searchable encryption scheme that uses a double-layer structure, while satisfying forward and backward security, is proposed. The double-layer structure maintains a constant client-side storage cost while guaranteeing forward and backward security and further reduces the algorithm overhead by avoiding bilinear pairings in the encryption and decryption operations. The analysis results show that the scheme is more advantageous in terms of security and computational efficiency than the existing dynamic searchable encryption scheme under the public key cryptosystem. It is also suitable for the big data communication environment

A Searchable Encryption with Forward/Backward Security and Constant Storage

Dynamic searchable encryption satisfies users’ needs for ciphertext retrieval on semi-trusted servers, while allowing users to update server-side data. However, cloud servers with dynamically updatable data are vulnerable to information abuse and file injection attacks, and current public key-based dynamic searchable encryption algorithms are often complicated in construction and high in computational overhead, which is not efficient for practical applications. In addition, the client’s storage costs grow linearly with the number of keywords in the database, creating a new bottleneck when the size of the keyword set is large. To solve the above problems, a dynamic searchable encryption scheme that uses a double-layer structure, while satisfying forward and backward security, is proposed. The double-layer structure maintains a constant client-side storage cost while guaranteeing forward and backward security and further reduces the algorithm overhead by avoiding bilinear pairings in the encryption and decryption operations. The analysis results show that the scheme is more advantageous in terms of security and computational efficiency than the existing dynamic searchable encryption scheme under the public key cryptosystem. It is also suitable for the big data communication environment

Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT) of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST) in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO) were used as inputs and ST was used as output of the model

Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT) of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST) in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO) were used as inputs and ST was used as output of the model