A Cognitive Psychological Study on the Acquisition of Chinese Word’s Coloring Meaning--Chinese as a Second Language

Lexical meaning mainly includes rational meaning,grammatical meaning,and coloring meaning.Mastering the coloring meaning of vocabulary is of great significance for foreign students to use Chinese vocabulary correctly.This study mainly examines the psychological mechanism of Chinese second language learners mastering the coloring meaning of words,examines the psychological characteristics of students mastering the color meaning of words from the perspectives of second language learning theory and cognitive theory,establishes a cognitive schema for coloring meaning learning,and proposes corresponding learning models and teaching strategies.

Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

Meaning in Life and Mental Health Issues in Older Adults:A Meta-Analysis

This study aims to be the first to use meta-analysis to explore the relationship between meaning in life(MIL)and mental health issues among older adults.A meta-analysis was conducted using six databases,resulting in 16 studies with 5,074 participants in total.The“metacor”and“forestplot”packages in R-Studio were used for data analysis.The total effect was calculated using a random-effects model,with I2=86%in the heterogeneity test.The results showed a moderate negative correlation between MIL and mental health issues among older adults,with an average effect of−0.37.Five potential moderating variables were examined:the conceptualization of MIL(value vs.purpose),region(Asian vs.Western countries),residence status(community vs.nursing home vs.hospital),types of mental health issues,and evaluation methods(clinical vs.non-clinical).The first four had no significant moderating effect.The mean correlation coefficients between mental health issues and value/purpose were−0.49/−0.33;the mean correlation coefficients in Asian countries and Western countries were−0.48 and−0.34;the mean correlation coefficients among participants living in community/nursing home/mixed status were−0.33/−0.40/−0.40;the mean correlation coefficients between MIL and depression/others were−0.37/−0.35;however,the negative relationship between MIL and mental health issues was stronger when non-clinical evaluations(self-report only)were used.Specifically,the mean correlation coefficient for non-clinical evaluations was−0.42 and for clinical evaluations was−0.29.This study is the first meta-analysis to identify the negative correlation between older adults’MIL and mental health issues.Significant moderating effects of evaluation methods were found.

Visually meaningful image encryption algorithm based on digital signature

Traditional image encryption algorithms transform a plain image into a noise-like image.To lower the chances for the encrypted image being detected by the attacker during the image transmission,a visually meaningful image encryption scheme is suggested to hide the encrypted image using another carrier image.This paper proposes a visually meaningful encrypted image algorithm that hides a secret image and a digital signature which provides authenticity and confidentiality.The recovered digital signature is used for the purpose of identity authentication while the secret image is encrypted to protect its confidentiality.Least Significant Bit(LSB)method to embed signature on the encrypted image and Lifting Wavelet Transform(LWT)to generate a visually meaningful encrypted image are designed.The proposed algorithm has a keyspace of 139.5-bit,a Normalized Correlation(NC)value of 0.9998 which is closer to 1 and a Peak Signal to Noise Ratio(PSNR)with a value greater than 50 dB.Different analyses are also performed on the proposed algorithm using different images.The experimental results show that the proposed scheme is with high key sensitivity and strong robustness against pepper and salt attack and cropping attack.Moreover,the histogram analysis shows that the original carrier image and the final visual image are very similar.

Analysis of the Symbolic Meaning of James Joyce’s Araby

Araby is a short story by the famous Irish stream-of-consciousness writer James Joyce.Through a series of images,the novel expresses the theme of the story:the“mental paralysis”of Dubliners and the“spiritual Epiphany”of the little boy,which reflects the spiritual barren of Dubliners at that time.Through the analysis of the symbolic meaning of many images in the work,this paper reveals the social background and religious significance hidden behind the images.

The Comparative Study of Chuangtse’s English Translations Under the Theory of Spirit Transmission and Meaning Conveyance

Cultural-loaded word is one of the most popular topics in translation studies.The theory of spirit transmission and meaning conveyance provides a new way for the translation of cultural-loaded word.Based on the A Happy Excursion,this paper makes a comparative analysis of the translation strategies of seven English versions.This study will reveal the similarities and differences between Chinese and foreign translators’translation strategies.

An Exploratory Study of the Interpersonal Meanings of President Xi’s New Year Speech Based on Appraisal Theory

The research examines President Xi’s 2021 New Year speech with research questions centering around its abundant interpersonal meanings.Through qualitative content analysis,the research finds that it is typical for Chinese president to frequently use judgment and appreciation resources in reviewing the past year.Even in the face of the pandemic and natural disasters,the overall emotions of the speech remain positive,which corresponds to the forward-looking feature of New Year speech.Significance of the study abounds and future research can investigate how COVID-19 impacts the ideologies conveyed through political leaders’speeches through a comparative lens and how to produce more understandings that can help dismantle stereotypes and discrimination hidden in reports about COVID-19 by using Appraisal Theory critically,systematically,and comprehensively.

融合IMR-WGAN的时序数据修复方法

工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.

Mean Shift跟踪算法创新实验项目设计

视频跟踪算法是计算机视觉实践课程中比较受关注的实验项目。针对突变情况下传统Mean Shift跟踪算法无法实时准确跟踪的问题,设计了基于模板更新和线性预估的Mean Shift跟踪算法创新实验项目。在模板更新策略下,引入背景模板,通过将原目标模板和背景模板与设定的阈值进行比较来对干扰因素进行判定,当干扰因素判定目标受到遮挡时,引入线性预估方程进行目标位置预测,有效解决目标在遮挡情况下跟踪丢失的问题。通过对测试视频的跟踪效果和性能进行对比分析,验证了算法在突变情况下相较于传统算法具有更好的抗干扰能力。以算法创新设计为核心,通过开放性创新实验项目的选题、设计、答辩、反馈的闭环实验过程,有效提高了学生算法创新设计能力。

京津冀地区碳排放时空格局变化及其驱动因子

人类对陆地生态系统的改变是碳排放增加的主要原因。在“双碳”目标背景下探索土地利用变化与碳排放的动态关系,有助于区域土地低碳可持续利用。研究基于土地利用转移视角,采用重心-标准差椭圆方法揭示了京津冀地区土地利用碳排放时空格局演化特征,评估了碳排放与生态环境、社会经济发展的协调程度,并借助改进的Kaya模型和LMDI分解模型定量分析了土地利用变化对碳排放的影响程度。结果表明:(1)建设用地的转入是土地利用碳排放增加的主要来源,引起碳排放量增加15844.36万t;耕地、草地向林地、水域的转变促进了地区固碳能力的提升。(2)土地利用碳排放空间分布格局呈现出东北-西南方向向中心进一步聚集的趋势,并且东-西向聚集趋势大于南-北向。(3)京津冀地区整体碳排放与生态环境的协调性呈向好趋势发展,但大部分地区碳排放与社会经济发展出现失衡现象,地区间碳生产力差异逐渐增大。(4)经济水平是促进碳排放增加的最显著因素,单位GDP用地强度是抑制碳排放增加的最主要因素。分析结果表明,严格控制建设用地的无序扩张是促进低碳土地利用的基础,低碳经济发展是促进地区减碳的重要途径。

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.

基于信令数据的中型城市通勤公交站点优化方法

大中型城市之间的手机通信基站密度和通勤出行结构不同,公交站点布设呈现出显著差异。在此背景下,研究了基于改进Mean Shift聚类算法的中型城市通勤公交站点优化方法。该方法采用荆州市中心城区信令数据中的通勤记录,以系统总成本(包括运营成本和乘客步行时间成本)作为主要评价指标。根据中心城区早高峰的通勤出行需求,制定通勤公交站点优化方案。通过对比优化结果和现有公交站点布局,验证了优化方法的有效性;比较不同聚类算法,证明改进的Mean Shift聚类算法的性能优越性;考虑基站和等时圈的影响,对比不同场景,证明了考虑二者影响的必要性。结果表明:①针对荆州市研究区域的早高峰出行需求,优化方法共设置28个公交站,乘客步行时间成本下降51.98%,系统总成本下降17.82%,表明本方法能够得到系统总成本更优的站点布设方案,有效减少研究区域内乘客步行时间成本;②与不同聚类算法的比较中,改进Mean Shift算法得到的方案有明显提升,系统总成本比K-means聚类算法下降8.73%,比近邻传播聚类算法(affinity propagation,AP)下降2.48%;③与未考虑基站和等时圈影响的情况相比,本算法步行时间成本有所下降。上述指标表明改进Mean Shift聚类方法在聚类质量上优于其他方法,可以获得更优的公交站点布设方案,为中型城市的公交线路规划提供基础。

基于关联规则的粗纱工序断头影响因素分析

为了降低粗纱在生产过程中的断头率,提高纱线生产质量和效率,通过生产实践收集纺制C 14.6 tex和JC 24.3 tex两个品种的粗纱工序断头影响因素。使用K⁃means聚类算法对影响因素指标分别进行聚类,然后使用Apriori算法将聚类后的断头影响因素指标数据集进行关联规则挖掘。结果表明:纺制C 14.6 tex品种的粗纱工序断头影响因素关联规则有粗纱回潮率和末并定量湿重,粗纱条干CV和末并定量湿重,末并定量湿重和粗纱条干CV;纺制JC 24.3 tex品种的粗纱工序断头影响因素关联规则有粗纱捻系数和粗纱定量湿重,粗纱捻系数、粗纱条干CV和粗纱定量湿重,粗纱定量湿重和粗纱捻系数;当关联规则中的影响因素同时升高时,粗纱工序断头率增加。通过关联规则中挖掘出的信息,可为纺纱企业减少粗纱工序断头、提高纱线质量提供帮助。

婴幼儿看护机器人的设计与开发

在现代科学育婴理念普及以及消费观念升级的影响下,智能母婴产品迎来巨大的发展空间。该文设计了一种婴幼儿看护机器人,基于Jetson Nano,通过激光雷达与ROS集成,可以获取机器人周围环境的信息,并进行地图构建、路径规划等任务。顶端将舵机二维云台与USB高清摄像头配合使用,结合Mean Shift目标追踪算法自动转动摄像头调整看护角度,并使用YOLO目标检测算法对监控的实时视频流中的婴幼儿人脸进行关键点的定位表情判断等,检测到婴幼儿啼哭、处于消极状态或者危险情况时,及时提醒看护人,看护人还可以通过前端页面实时查看婴幼儿状态。

A STABILITY RESULT FOR TRANSLATINGSPACELIKE GRAPHS IN LORENTZ MANIFOLDS

In this paper,we investigate spacelike graphs defined over a domain Ω⊂M^(n) in the Lorentz manifold M^(n)×ℝ with the metric−ds^(2)+σ,where M^(n) is a complete Riemannian n-manifold with the metricσ,Ωhas piecewise smooth boundary,and ℝ denotes the Euclidean 1-space.We prove an interesting stability result for translating spacelike graphs in M^(n)×ℝ under a conformal transformation.

A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.