Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

Alteration Information Extraction by Applying Synthesis Processing Techniques to Landsat ETM+Data: Case Study of Zhaoyuan Gold Mines, Shandong Province, China

Satellite remote sensing data are usually used to analyze the spatial distribution pattern of geological structures and generally serve as a significant means for the identification of alteration zones. Based on the Landsat Enhanced Thematic Mapper （ETM＋） data, which have better spectral resolution （8 bands） and spatial resolution （15 m in PAN band）, the synthesis processing techniques were presented to fulfill alteration information extraction： data preparation, vegetation indices and band ratios, and expert classifier-based classification. These techniques have been implemented in the MapGIS-RSP software （version 1.0）, developed by the Wuhan Zondy Cyber Technology Co., Ltd, China. In the study area application of extracting alteration information in the Zhaoyuan （招远） gold mines, Shandong （山东） Province, China, several hydorthermally altered zones （included two new sites） were found after satellite imagery interpretation coupled with field surveys. It is concluded that these synthesis processing techniques are useful approaches and are applicable to a wide range of gold-mineralized alteration information extraction.

Nonlinearity Correction of Fourier Transform Spectrometer in Solar Observation Using Simulated Annealing

A Fourier transform spectrometer(FTS)has been used to observe solar activities due to its ultra-high spectral resolution.However,the FTS in-band spectra are usually distorted and some artifacts appear in out-of-band regions due to nonlinear effects.Therefore,the FTS nonlinear problem must be corrected.In this study,we proposed a novel method to correct the nonlinear effects using simulated annealing.We simulated several nonlinear spectra to evaluate the performance of our method.The calculated quadratic coefficients are extremely close to the given values,demonstrating that the method is effective and accurate.The proposed method is further used to correct the blackbody and solar spectra with nonlinearity obtained by Bruker IFS-125HR installed at the Huairou Solar Observing Station,which is a pathfinder for the accurate infrared magnetic field measurements of the Sun project.To the blackbody spectra,the nonlinearity in low-and high-frequency regions are corrected by 89.09%and 60.84%.The nonlinear correction of the solar spectra in the low-and high-frequency regions have reached 65.34%and 81.04%,respectively.These results prove that our method can correct the nonlinear problem to improve the data accuracy.

Solar Observation with the Fourier Transform Spectrometer.II.Preliminary Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible wavelengths.Its accuracy is also influenced by the spectral resolution of the Fourier transform spectrometer.In the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared wavelengths.The measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),respectively.We also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μm.By quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing Station.After removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar Observatory.The Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also identified.Our method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

An Exposure Meter of Lijiang Fiber-fed High-Resolution Spectrograph

In 2016,an exposure meter was installed on the Lijiang Fiber-fed High-Resolution Spectrograph to monitor the coupling of starlight to the science fiber during observations.Based on it,we investigated a method to estimate the exposure flux of the CCD in real time by using the counts of the photomultiplier tubes(PMT)of the exposure meter,and developed a piece of software to optimize the control of the exposure time.First,by using flat-field lamp observations,we determined that there is a linear and proportional relationship between the total counts of the PMT and the exposure flux of the CCD.Second,using historical observations of different spectral types,the corresponding relational conversion factors were determined and obtained separately.Third,the method was validated using actual observation data,which showed that all values of the coefficient of determination were greater than 0.92.Finally,software was developed to display the counts of the PMT and the estimated exposure flux of the CCD in real-time during the observation,providing a visual reference for optimizing the exposure time control.

An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

LKMT:Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

Thanks to the strong representation capability of pre-trained language models,supervised machine translation models have achieved outstanding performance.However,the performances of these models drop sharply when the scale of the parallel training corpus is limited.Considering the pre-trained language model has a strong ability for monolingual representation,it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models.To alleviate the dependence on the parallel corpus,we propose a Linguistics Knowledge-Driven MultiTask(LKMT)approach to inject part-of-speech and syntactic knowledge into pre-trained models,thus enhancing the machine translation performance.On the one hand,we integrate part-of-speech and dependency labels into the embedding layer and exploit large-scale monolingual corpus to update all parameters of pre-trained language models,thus ensuring the updated language model contains potential lexical and syntactic information.On the other hand,we leverage an extra self-attention layer to explicitly inject linguistic knowledge into the pre-trained language model-enhanced machine translation model.Experiments on the benchmark dataset show that our proposed LKMT approach improves the Urdu-English translation accuracy by 1.97 points and the English-Urdu translation accuracy by 2.42 points,highlighting the effectiveness of our LKMT framework.Detailed ablation experiments confirm the positive impact of part-of-speech and dependency parsing on machine translation.

Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.

A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma(PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methodshave been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies basedon Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesionregion. However, over-reliance on prior information may ignore the background information that is helpful fordiagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset.Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion andbackground information via the Prior Normalization Fusion (PNF) strategy and the Feature Difference Guidance(FDG) module, to direct the model to concentrate on beneficial regions for diagnosis. Extensive experiments inthe clinical dataset demonstrate that the proposed method achieves promising diagnosis performance: PDGNetsbased on conventional networks record an ACC (Accuracy) and AUC (Area Under the Curve) of 87.50% and89.98%, marking improvements of 8.19% and 7.64% over the prior-free benchmark. Compared to lesion-focusedbenchmarks, the uplift is 6.14% and 6.02%. PDGNets based on advanced networks reach an ACC and AUC of89.77% and 92.80%. The study underscores the potential of harnessing background information in medical imagediagnosis, suggesting a more holistic view for future research.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Genome-wide Analysis of MYB Gene Family in Potato Provides Insights into Tissue-specific Regulation of Anthocyanin Biosynthesis

The MYB transcription factor is one of the largest gene families in plants,playing an important role in regulating plant growth,development,response to stress,senescence,and especially the anthocyanin biosynthesis.In this study,A total of 217 MYB genes,including 901R-MYBs,124 R2R3-MYBs,and 3 R1R2R3-MYBs have been identified from the potato genome.The 1R-MYB and R2R3-MYB family members could be divided into 20 and 35 subgroups respectively.Analysis of gene structure and protein motifs revealed that members within the same subgroup presented similar exon/intron and motif organization,further supporting the results of phylogenetic analysis.Potato is an ideal plant to reveal the tissue-specific anthocyanins biosynthesis regulated by MYB,as the anthocyanins could be accumulated in different tissues,showing colorful phenotypes.Five pairs of colored and colorless tissues,stigma,petal,stem,leaf,and tuber flesh,were applied to the transcriptomic analysis.A total of 70 MYB genes were found to be differentially expressed between colored and colorless tissues,and these differentially expressed genes were suspected to regulate the biosynthesis of anthocyanin of different tissues.Co-expression analysis identified numerous potential interactive regulators of anthocyanins biosynthesis,involving 39 MYBs,24 bHLHs,2 WD-repeats,and 29 biosynthesis genes.Genome-wide association study(GWAS)of tuber flesh color revealed amajor signal at the end of Chromosome 10,which was co-localized with reported I gene(StMYB88),controlling tuber peel color.Analyses of DEGs(Differentially Expression Genes)revealed that both StMYB88 and StMYB89 were closely related to regulating anthocyanin biosynthesis of tuber flesh.This work offers a comprehensive overview of the MYB family in potato and will lay a foundation for the functional validation of these genes in the tissue-specific regulation of anthocyanin biosynthesis.

Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy

Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles.This is a new strategy of human-computer interaction.A method of electroencephalogram(EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper,rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window;the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value(PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain.Finally,discrimination of motor imagery patterns was performed by the support vector machine(SVM).The results showed that the phase synchronization feature more effective in4s-7s and the correct classification rate was 91.4%.Compared with the results achieved by a single EEG feature related to motor imagery,the correct classification rate was improved by 3.5 and4.3 percentage points by combining phase synchronization with band energy.These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.

Design of good QC-LDPC codes without small girth in the p-plane

A construction method based on the p-plane to design high-girth quasi-cyclic low-density parity-check （QC-LDPC） codes is proposed. Firstly the good points in every line of the p-plane can be ascertained through filtering the bad points, because the designed parity-check matrixes using these points have the short cycles in Tanner graph of codes. Then one of the best points from the residual good points of every line in the p-plane will be found, respectively. The optimal point is also singled out according to the bit error rate （BER） performance of the QC-LDPC codes at last. Explicit necessary and sufficient conditions for the QC-LDPC codes to have no short cycles are presented which are in favor of removing the bad points in the p-plane. Since preventing the short cycles also prevents the small stopping sets, the proposed construction method also leads to QC-LDPC codes with a higher stopping distance.

Numerical modeling dynamic process of multi-feed microwave heating of industrial solution media

The exothermic efficiency of microwave heating an electrolyte/water solution is remarkably high due to the dielectric heating by orientation polarization of water and resistance heating by the Joule process occurred simultaneously compared with pure water.A three-dimensional finite element numerical model of multi-feed microwave heating industrial liquids continuously flowing in a meter-scale circular tube is presented.The temperature field inside the applicator tube in the cavity is solved by COMSOL Multiphysics and professional programming to describe the momentum,energy and Maxwell's equations.The evaluations of the electromagnetic field,the temperature distribution and the velocity field are simulated for the fluids dynamically heated by singleand multi-feed microwave system,respectively.Both the pilot experimental investigations and numerical results of microwave with single-feed heating for fluids with different effective permittivity and flow rates show that the presented numerical modeling makes it possible to analyze dynamic process of multi-feed microwave heating the industrial liquid.The study aids in enhancing the understanding and optimizing of dynamic process in the use of multi-feed microwave heating industrial continuous flow for a variety of material properties and technical parameters.

An E-Commerce Recommender System Based on Click and Purchase Data to Items and Considered of Interest Shifting of Customers

A well-performed recommender system for an e-commerce web site can help customers easily find favorite items and then increase the turnover of merchants, hence it is important for both customers and merchants. In most of the existing recommender systems, only the purchase information is utilized data and the navigational and behavioral data are seldom concerned. In this paper, we design a novel recommender system for comprehensive online shopping sites. In the proposed recommender system, the navigational and behavioral data, such as access, click, read, and purchase information of a customer, are utilized to calculate the preference degree to each item; then items with larger preference degrees are recommended to the customer. The proposed method has several innovations and two of them are more remarkable: one is that nonexpendable items are distinguished from expendable ones and handled by a different way; another is that the interest shifting of customers are considered. Lastly, we structure an example to show the operation procedure and the performance of the proposed recommender system. The results show that the proposed recommender method with considering interest shifting is superior to Kim et al(2011) method and the method without considering interest shifting.

Overview Landslide Hazard Assessment of China

In recent years, China has suffered serious geological disasters, most of slope movements due to complex geology, geomorphology, unusual weather conditions, and large-scale land explorations during high speed economic development. According to geological hazard investigations organized by the Ministry of Land and Resources of China, there are 400 towns and more than 10 000 villages under the threatening of those landslide hazards. This paper presents the overview landslide hazard assessment in terms of GIS, which aims to evaluate the overview geohazard potentials, vulnerabilities of lives and land resources, and risks in conterminous China on the scale of 1∶6 000 000. This is the first overview landslide hazard potential map of China.