Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Research on Landscape Ecology Mode of New Resources-based Cities—A Case Study of Yulin City of Shaanxi Province in China

Based on the overall understanding of new cities developing based on resources,by taking Yulin City of Shaanxi Province for example and combining with relevant statistical data,main problems existing in resources development have been pointed out,covering simple resources industrial structure and serious wastes in resources development;insufficient intensive processing of products and low level of resources integrated utilization;and deteriorative regional eco-environment.On this basis,a new mode of landscape ecology of resource-based cities have been proposed,emphasizing constructing new green energy industrial development mode from the perspective of microscopic view;ecological industrial park of circular economy from the perspective of mesoscopic view;and the overall ecological recovery mode of the mining area from the perspective of macroscopic view.It hopes to give a vital inspiration to the sustainable development of new resources-based cities.

Research on Compatible Mode of Self-directed Learning and Cooperative Learning in Blended Teaching of Basic English Course

In the information age,blended teaching,no matter online or offline,has become the mainstream of college teaching reform.In this teaching model,self-directed learning and cooperative learning are the two main learning approaches.On the online teaching platform,students mainly learn knowledge-based content by self-directed learning,while practising their language skills by cooperative learning in flipped classroom activities.On one hand,it advocates student-centered strategy so as to improve students autonomous learning ability;on the other hand,teachers serve as a guide to organize the classroom activities;meanwhile,they give timely feedback to students in order to promote students’learning ability.In blended teaching model,this mutually compatible and reinforcing model of self-directed learning and cooperative learning is undoubtedly helpful to improve the teaching efficiency.

Research on Federated Learning Data Sharing Scheme Based on Differential Privacy

To realize data sharing,and to fully use the data value,breaking the data island between institutions to realize data collaboration has become a new sharing mode.This paper proposed a distributed data security sharing scheme based on C/S communication mode,and constructed a federated learning architecture that uses differential privacy technology to protect training parameters.Clients do not need to share local data,and they only need to upload the trained model parameters to achieve data sharing.In the process of training,a distributed parameter update mechanism is introduced.The server is mainly responsible for issuing training commands and parameters,and aggregating the local model parameters uploaded by the clients.The client mainly uses the stochastic gradient descent algorithm for gradient trimming,updates,and transmits the trained model parameters back to the server after differential processing.To test the performance of the scheme,in the application scenario where many medical institutions jointly train the disease detection system,the model is tested from multiple perspectives by taking medical data as an example.From the testing results,we can know that for this specific test dataset,when the parameters are properly configured,the lowest prediction accuracy rate is 90.261%and the highest accuracy rate is up to 94.352.It shows that the performance of the model is good.The results also show that this scheme realizes data sharing while protecting data privacy,completes accurate prediction of diseases,and has a good effect.

Deep‐learning powered whispering gallery mode sensor based on multiplexed imaging at fixed frequency

During the last decades the whispering gallery mode based sensors have become a prominent solution for label-free sensing of various physical and chemical parameters.At the same time,the widespread utilization of the approach is hindered by the restricted applicability of the known configurations for ambient variations quantification outside the laboratory conditions and their low affordability,where necessity on the spectrally-resolved data collection is among the main limiting factors.In this paper we demonstrate the first realization of an affordable whispering gallery mode sensor powered by deep learning and multi-resonator imaging at a fixed frequency.It has been shown that the approach enables refractive index unit(RIU)prediction with an absolute error at 3×10^(-6) level for dynamic range of the RIU variations from 0 to 2×10^(-3) with temporal resolution of several milliseconds and instrument-driven detection limit of 3×10−5.High sensing accuracy together with instrumental affordability and production simplicity places the reported detector among the most cost-effective realizations of the whispering gallery mode approach.The proposed solution is expected to have a great impact on the shift of the whole sensing paradigm away from the model-based and to the flexible self-learning solutions.

基于LEARNS模式构建维持性血液透析病人营养健康教育方案

目的:构建基于LEARNS模式的维持性血液透析病人营养健康教育方案,为临床实施“以病人为中心”的营养健康教育提供参考。方法:研究小组在文献研究、小组讨论基础上初步制定营养健康教育方案,采用德尔菲专家函询法对23名专家进行函询,结合条目筛选标准和专家建议,利用层次分析法确定条目权重,构建基于LEARNS模式的维持性血液透析病人营养健康教育方案。结果:2轮咨询专家积极系数为100.0%和91.30%,权威系数为0.874和0.891;变异系数为0.00~0.38和0.00~0.22,肯德尔和谐系数为0.205和0.222(P<0.01)。最终形成包括3个一级条目、32个二级条目的维持性血液透析病人营养健康教育方案。层次分析各级条目一致性系数(CR值)均<0.1。结论:构建的维持性血液透析病人营养健康教育方案具有较高的科学性和可行性,可以为维持性血液透析病人营养健康教育提供借鉴。

Research on the Construction of English Autonomous Learning Monitoring Mode under the Background of Big Data

The era of big data is coming,the combination of big data and traditional teaching can provide more and more accurate services for students'self-learning,and it is a good way to teach students according to their aptitude.In this background,a learning society is coming,which aiming at learning,autonomous learning and lifelong learning.Learning society emphasize the ability of learning autonomy for students unprecedentedly.Learning is no longer limited to the campus.Learning ability will accompany learners'social life and become an active and healthy lifelong activity.Autonomous learning is a learning theory that goes with the requirements of The Times and has a broad development prospect.The study of Autonomous learning not only has a very important guiding significance for the educational and teaching practice in China,but also plays an important role in the life development of every student.The subject of learning is gradually transferred from the classroom,teachers and textbooks to the students themselves.Teachers should not only impart knowledge and answer questions,but also,most importantly,teach students how to exert their autonomy in autonomous learning.After investigating and researching the existing monitoring model of autonomous English learning in colleges and universities,our group found that in practice,there is a lack of corresponding monitoring mechanisms and means,and autonomous learning has gradually become formalized.Therefore,according to the actual situation of autonomous English learning in our country's universities,the monitoring model of autonomous English learning has been reconstructed,and an effective comprehensive evaluation system has been established to effectively improve students'English learning ability.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

糖尿病肾病开展Triangle分层分级管理+LEARNS模式的效果及心理状态的观察

目的探究在对糖尿病肾病患者护理时开展Triangle分层分级管理+LEARNS模式对患者血糖水平、肾功能水平以及心理状态等改善作用。方法在2022年2月至2023年1月云南省第三人民医院收治糖尿病肾病患者中随机抽选74例为对象,Excel表随机排序划分对照组(n=37,治疗期间接受常规护理)和观察组(n=37,接受Triangle分层分级管理+LEARNS模式护理)。针对患者血压水平、肾功能水平等改善情况进行评估。结果对比2组干预前后血糖水平,餐后2 h血糖、空腹血糖,干预后观察组低于对照组,差异有统计学意义(P<0.05)。对比患者生存质量,干预前无差异,干预后观察组QOLIE-31量表评分高于对照组,差异有统计学意义(P<0.05)。对比患者负面情绪评分,干预前BAI以及BDI评分无差异,干预后观察组评分低于对照组,差异有统计学意义(P<0.05)。对比患者肾功能水平,干预后观察组肾功能水平高于对照组,差异有统计学意义(P<0.05)。对比2组自我管理能力,干预后观察组高于对照组,差异有统计学意义(P<0.05)。结论在对糖尿病肾病患者护理时开展Triangle分层分级管理+LEARNS模式,可改善患者血糖、肾功能水平,缓解患者治疗期间负面情绪,有助于患者进行恢复。

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

Forecasting hourly PM_(2.5)concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms

Accurate predictions of hourly PM_(2.5)concentrations are crucial for preventing the harmful effects of air pollution.In this study,a new decomposition-ensemble framework incorporating the variational mode decomposition method(VMD),econometric forecasting method(autoregressive integrated moving average model,ARIMA),and deep learning techniques(convolutional neural networks(CNN)and temporal convolutional network(TCN))was developed to model the data characteristics of hourly PM_(2.5)concentrations.Taking the PM_(2.5)concentration of Lanzhou,Gansu Province,China as the sample,the empirical results demonstrated that the developed decomposition-ensemble framework is significantly superior to the benchmarks with the econometric model,machine learning models,basic deep learning models,and traditional decomposition-ensemble models,within one-,two-,or three-step-ahead.This study verified the effectiveness of the new prediction framework to capture the data patterns of PM_(2.5)concentration and can be employed as a meaningful PM_(2.5)concentrations prediction tool.

Innovative Research on the Blending Learning Mode of Econometrics

Due to the outbreak of the Covid-19 in 2020,online education has become the mainstream.After the epidemic,the blending learning mode has also become a key goal of the teaching reform of colleges and universities,and the blending learning mode of various courses has blossomed everywhere.In this context,this paper used the Econometrics course as the carrier,analyzed the many unreasonable problems in the traditional Econometrics course,and proposed an optimization plan and path for the blending learning mode to address these problems.

LEARNS认知图式教育对乳腺癌MRI增强检查患者心理状态的影响

目的:探讨乳腺癌磁共振成像(MRI)增强扫描患者应用LEARNS模式下认知图式教育的效果。方法:以便利抽样法采集2023年1月31日-7月31日在某院接受MRI增强扫描检查的乳腺癌患者为对照组,2023年8月1日-2024年2月29日在该院接受MRI增强扫描检查的乳腺癌患者为研究组。对照组接受常规护理,研究组给予LEARNS模式下认知图式教育,对比两组心理状态、病耻感水平、应对方式、不良事件。结果:研究组坚韧性、力量性及乐观性及总分均高于对照组(t=50.575,44.798,19.077,86.704;P<0.001);研究组社会排斥、经济不安全感、内在羞耻感、社会隔离及总分均低于对照组(t=17.492,10.741,13.396,18.143,33.372;P<0.001);研究组简易应对方式问卷(SCSQ)中积极应对得分高于对照组、消极应对得分低于对照组(t=32.750,34.481;P<0.05);研究组不良事件发生率低于对照组(χ^(2)=6.093,P<0.05)。结论:在乳腺癌MRI增强扫描患者中应用LEARNS模式下认知图式教育有助于改善心理状态,降低患者病耻感水平,促进患者采用积极应对方式,并降低不良事件的发生率。

Coupling dimensions of human resource archetypes and organizational learning modes

In order to provide channels of human resource management in promoting organizational learning,structural equation models were used for identifying the differences in the coupling dimensions between human resource archetypes and organizational learning modes based on sample data of 219 manufacturing firms with more than 100 employees.It is found that the coupling of the cooperative human resource archetype and exploitative learning is reflected in the mechanistic structure dimension;the coupling of the cooperative human resource archetype and exploratory learning is reflected in the two dimensions of the mechanistic structure and specialist cognition;the coupling of the entrepreneurial human resource archetype and exploitative learning is reflected in the two dimensions of generalist cognition and cognitive trust;the coupling of the entrepreneurial human resource archetype and exploratory learning is reflected in the dimension of generalist cognition.When manufacturing firms pursue exploitative learning,it is suggested that they pay attention to the structure dimension management of the collaborative human resource archetype and the cognition and affect dimensions management of the entrepreneurial human resource archetype.When manufacturing firms pursue exploratory learning,it is suggested pay attention to the structure and cognition dimensions management of the collaborative human resource archetype and the cognition dimension management of the entrepreneurial human resource archetype.

Research on Innovative Learning Based on Mixed Teaching Model of Data Structures

Under the premise of building an innovative country and cultivating innovative talents,teaching innovative learning,cultivating students’innovative thinking and improving the teaching mode and curriculum form have already become one of the main directions of deepening the reform of higher education.Data Structure is essential part in the undergraduate program.It is crucial for student to carry out research and project.In the actual teaching activities,the problems of content updating,practice ability for research projects of data structures are to be improved.Therefore,the course reform is imperative.This paper analyzed the main problems of the current data structures course,explored the solution of the problems,and proposed a new mixed teaching mode and evaluation method.

Research on the Novel Business English Education Mode based on Feedback Learning and Interactive Teaching Method

In this paper, we conduct research on the novel English education mode based on feedback learning and interactive teaching method. Business English, which is based on general English, from the angle of English language features of text represents connotation of business English vocabulary expressed more, word ambiguity phenomenon is relatively common, meaning and pragmatic choice depends on the context. Our research starts from the analysis of the novel education pattern with the feedback learning and interactive teaching integration. Our education mode will let the students and the teachers interact with others and encourage the better participation passion that is meaningful.

Research on Talent Cultivation Model and Application Based on Mobile Cloud Learning Technology-Taking Innovative Talent Cultivation as an Example

Mobile cloud learning and innovation and entrepreneurship education are undoubtedly one of the hot issues in the current development of human resources and education. At present, domestic colleges and universities still lack the industrialization model and platform that apply theory to practice areas, and apply the new mobile cloud learning technology to the research and practice of innovation and entrepreneurship training.

Analysis of the Application Value of Virtual Reality Combined with Flipped Classroom Teaching Mode in Cardiopulmonary Resuscitation Teaching

Objective:To analyze the value of using virtual reality combined with the flipped classroom teaching model in teaching cardiopulmonary resuscitation(CPR).Methods:Two classes of our nursing program were randomly selected for the study from September 2022 to September 2023,Class A(52 students,conventional teaching method)and Class B(52 students,virtual reality combined with flipped classroom teaching mode).The assessment scores and independent learning ability scores of the students in the two classes were compared.Results:CPR theory and operation scores,passing rate,and independent learning ability scores of Class B were higher than those of Class A(P<0.05).Conclusion:the use of virtual reality combined with the flipped classroom teaching mode in CPR teaching is conducive to the improvement of students’assessment scores and independent learning ability.

Designing Higher Vocational College English Blended Learning Mode based on Wechat Platform

As a platform bridging classroom teaching and online teaching for higher vocational college(HVC)English,WeChat expands the dimension of HVC English teaching.This paper first introduces basic functions and characteristics of WeChat,then analyzes problems existing in current HVC English teaching and finally puts forward specific ways to design HVC English blended learning mode based on WeChat platform in four steps:warming-up,text analysis,exercises and post-course expansion.

Construction of Chinese Learning Mode for Overseas Students in Higher Vocational Colleges under the Background of the Belt and Road Initiative

With the deepening of exchanges between China and other countries,especially the proposal of the Belt and Road Initiative,more and more foreign students come to China to study,putting forward higher requirements for the education and management of colleges and universities in China.In the process of implementing the strategy of"one belt and one road",language interaction is the basic prerequisite for achieving exchanges and cooperation among different countries.In the face of this situation,by analyzing the existing problems in the training of overseas students in higher vocational colleges in China under the background of"One Belt and One Road"strategy,in this paper,the construction of the Chinese learning mode for overseas students in higher vocational colleges was put forward,hoping to provide help for the training of overseas students and solve the problem of foreign students'language learning.