The Classification of Colorful-leaf Tree Species and the Application in Landscape Architecture

This paper summarized the classification of colorful tree species and the application principles on landscape architecture and briefly introduced the present application situation of colorful tree species in China. It also raised suggestions related to the introduction and application of the colorful tree species.

A Probe into the Application Ability of College English Teachers’ Educational Information Technology in the Information Age

With the arrival of the age of large information data, the education also gradually tends to educate the information, which has forced the demand of the teachers’ educational information technology application ability of the major universities and universities to be higher and higher. Therefore, in order to meet the needs of the development of higher education in the information age and to meet the needs of the college English teaching innovation, this paper will explore the application ability of the educational information technology of the college English teachers from various angles.

Reliability and validity of sub-axial injury classification in clinical application

Objective To evaluate the clinical reliability and validity of the sub-axial injury classification (SLIC) system proposed by the Spine Trauma Study Group (STSG) in 2007. Methods Thirty cases of cervical injury were randomly chosen

Advances in the Application of DNA Chip in Animal Medicine

Accompanying with the increasingly saturated genome figures,DNA chip has been widely applied.Thanks to its advantages of integration,miniaturization and automation,DNA chip becomes a powerful research tool in various research fields including biology,medicine and chemistry.This article overviews the application of DNA chip technology in animal medicine from gene expression spectrum research,pathogenic microbial detection,bacterial typing,genetic mutations and polymorphism detection,pathogenic microbial genomics research,as well as its principle and classification.

Application system and data description of the China Seismo-Electromagnetic Satellite

The China Seismo-Electromagnetic Satellite, launched into orbit from Jiuquan Satellite Launch Centre on February 2 nd, 2018, is China's first space satellite dedicated to geophysical exporation. The satellite carries eight scientific payloads including high-precision magnetometers to detect electromagnetic changes in space, in particular changes associated with global earthquake disasters. In order to encourage and facilitate use by geophysical scientists of data from the satellite's payloads, this paper introduces the application systems developed for the China Seismo-Electromagnetic Satellite by the Institute of Crustal Dynamics, China Earthquake Administration;these include platform construction, data classification, data storage, data format, and data access and acquisition.

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.

The Power of Group Generators and Relations: An Examination of the Concept and Its Applications

This paper investigates the approach of presenting groups by generators and relations from an original angle. It starts by interpreting this familiar concept with the novel notion of “formal words” created by juxtaposing letters in a set. Taking that as basis, several fundamental results related to free groups, such as Dyck’s Theorem, are proven. Then, the paper highlights three creative applications of the concept in classifying finite groups of a fixed order, representing all dihedral groups geometrically, and analyzing knots topologically. All three applications are of considerable significance in their respective topic areas and serve to illustrate the advantages and certain limitations of the approach flexibly and comprehensively.

Classification of Meteorological Satellite Ground System Applications

Meteorological satellite ground application system carries a large number of applications. These applications deal with a variety of tasks. In order to classify these applications according to the resource consumption and improve the rational allocation of system resources, this paper introduces several application analysis algorithms. Firstly, the requirements are abstractly described, and then analyzed by hierarchical clustering algorithm. Finally, the benchmark analysis of resource consumption is given. Through the benchmark analysis of resource consumption, we will give a more accurate meteorological satellite ground application system.

A Geological Classification of Rock Mass Quality and Blast Ability for Widely Spaced Formations

Success in the excavation of geological formations is commonly known as being very important in asserting stability. Furthermore, when the subjected geological formation is rocky and the use of explosives is required, the demands of successful blasting are multiplied. The present paper proposes a classification system, named： BQS （blast ability quality system）, for rock masses with widely spaced discontinuities （spacing longer than l m）. It is obvious that rock quality is one of the main characteristics which define the blast ability of a rock. The BQS can be an easy and widely-used tool as it is a quick evaluator for blast ability and rock mass quality at one time. Taking into consideration the research calculations and the parameters of BQS, what has been at question in this paper is the effect of blast ability in a geological formation with widely spaced discontinuities.

Applications Classification of VPN Encryption Tunnel Based on SAE-2dCNN Model

How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protocols make the feature engineering of machine learning extremely difficult.Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification.This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model.SAE can effectively reduce the dimensionality of the data,which not only improves the training efficiency of 2dCNN,but also extracts more precise features and improves accuracy.This paper uses the most common VPN encryption data in the real network to train and test the model.The test results verify the effectiveness of the SAE-2dCNN model.

Application of Field Theory on Memorizing Business English Lexicons

Since Reform and Opening, the international business activities in China have become more and more frequent,hence,the importance of business English goes without saying. However, due to its lexicons is complexity and specificity, it's really a struggle matter for business English learners to memorize these lexicons. Through analyzing features of business English lexicons and establishing appropriate semantic field, business English learners could memorize these lexicons more effectively.

Applying blockchain-based method to smart contract classification for CPS applications

Smart contract has been the core of blockchain systems and other blockchain-based systems since Blockchain 2.0.Various operations on blockchain are performed through the invocation and execution of smart contracts.This leads to extensive combinations between blockchain,smart contract,Internet of Things(IoT)and Cyber-Physical System(CPS)applications,and then many blockchain-based IoT or CPS applications emerge to provide multiple benefits to the economy and society.In this case,obtaining a better understanding of smart contracts will contribute to the easier operation,higher efficiency and stronger security of those blockchain-based systems and applications.Many existing studies on smart contract analysis are based on similarity calculation and smart contract classification.However,smart contract is a piece of code with special characteristics and most of smart contracts are stored without any category labels,which leads to difficulties of smart contract classification.As the back end of a blockchain-based Decentralized Application(DApp)is one or several smart contracts,DApps with labeled categories and open source codes are applied to achieve a supervised smart contract classification.A three-phase approach is proposed to categorize DApps based on various data features.In this approach,5,659 DApps with smart contract source codes and pre-tagged categories are first obtained based on massive collected DApps and smart contracts from Ethereum,State of the DApps and DappRadar.Then feature extraction and construction methods are designed to form multi-feature vectors that could present the major characteristics of DApps.Finally,a fused classification model consisting of KNN,XGBoost and random forests is applied to the multi-feature vectors of all DApps for performing DApp classification.The experimental results show that the method is effective.In addition,some positive correlations between feature variables and categories,as well as several user behavior patterns of DApp calls,are found in this paper.

Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.

A Review of Researches on Deep Learning in Remote Sensing Application

In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application and image understanding are inseparable, researchers have carried out a lot of research on the application of deep learning in remote sensing field, and extended the deep learning method to various application fields of remote sensing. This paper summarizes the basic principles of deep learning and its research progress and typical applications in remote sensing, introduces the current main deep learning model and its development history, focuses on the analysis and elaboration of the research status of deep learning in remote sensing image classification, object detection and change detection, and on this basis, summarizes the typical applications and their application effects. Finally, according to the current application of deep learning in remote sensing, the main problems and future development directions are summarized.

A New Development of BQS （Blastability Quality System） for Closely Spaced Formations

Success in the excavation of geological formations is commonly known as being very important in asserting stability. Furthermore, when the subjected geological formation is rocky and the use of explosives is required, the demands of successful blasting are multiplied. The present paper proposes a classification system, named： BQS （Blastability Quality System）, for rock masses with closely spaced discontinuities （spacing lower than 0.1 m）. It is obvious that rock quality is one of the main characteristics which define the blast ability of a rock. The BQS can be an easy and widely-used tool as it is a quick evaluator for blastability and rock mass quality at one time. Taking into consideration the research calculations and the parameters of BQS, what has been at question in this paper is the effect of blast ability in a geological formation with closely spaced discontinuities.

Nutrition and Applications of Rice Bran Oil: a Mini-overview

Rice bran oil is a healthy oil from many aspects.The oil has a balanced fatty acid profile comparing with many other vegetable oils.The key difference is the minor components or micronutrients or unsaponifiable matters contained in the oil that are very special and in larger percentages.The oil contains more than 1.5%oryzanol that gives nutritional and pharmaceutical functions from the studies so far.More studies are needed to demonstrate the wide functions in many aspects.The oil also contains large percentage of phytosterols which received huge amount of studies for nutritional applications.Furthermore,the oil contains tocopherols and tocotrienols,in which for the later particularly it gives many special functions including prevention of breast cancers for example.When the oil is properly processed and used in foods,those functions are more and more demonstrated in nutritional or biological studies.Thus the oil in food and pharmaceutical applications is in exploring both in academic studies and industrial practice.In this work,an overview of such progress is given.

The Engineering Professional and the Labor Market Demands： Electronic Engineer and Mechanical Engineer

The purpose of this study is to present results of a jobs classified survey from a newspaper in circulation in the city of Manaus （AM, Brazil）, between late 1990s and early 2000s. The structure and functionality of the CBO （Brazilian Occupational Classification） 2002, fundamental tool for data analysis was used, followed by collection, diagnosis and data analysis to identify the characteristics and transformations of the occupational tasks of the electronic and mechanical engineer. The analysis was based on CBO data in three distinct steps described in the methodology section, compared with a study conducted in the early 1990s, and finally, the overall prevailing trend required in professional engineering training in times of complexity and in the knowledge era was presented.

Lightweight Network Ensemble Architecture for Environmental Perception on the Autonomous System

It is important for the autonomous system to understand environmental information.For the autonomous system,it is desirable to have a strong generalization ability to deal with different complex environmental information,as well as have high accuracy and quick inference speed.Network ensemble architecture is a good choice to improve network performance.However,it is unsuitable for real-time applications on the autonomous system.To tackle this problem,a new neural network ensemble named partial-shared ensemble network(PSENet)is presented.PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks together to accelerate the inference speed.To make component networks independent of each other,a training method is designed to train the network ensemble architecture.Experiments on Camvid and CIFAR-10 reveal that PSENet achieves quick inference speed while maintaining the ability of ensemble learning.In the real world,PSENet is deployed on the unmanned system and deals with vision tasks such as semantic segmentation and environmental prediction in different fields.

应用型本科高校人才培养能力建设:生成逻辑、内涵解构与实践进路

高校人才培养能力建设是高等教育研究的重要理论问题,也是应用型本科高校办学的重要实践问题。提高应用型本科高校人才培养能力,要着力增强党建引领力,永葆应用型本科高校人才培养社会主义本色;着力提升治理运行力,筑牢应用型本科高校人才培养的制度之基;着力强化适应力,构建应用型本科高校人才培养的新格局;着力优化专业建设力,打造应用型本科高校人才培养特色;着力形成思政协同力,培养德智体美劳全面发展应用型本科高校人才培养的能力。