Imageries Connected with Border in The Farming of Bones by Edwidge Danticat

This essay indicates the intertwined relation between border and identity in Danticat’s novel The Farming of Bones.The author makes a combination of the recurring imageries like"River","Parsley"and"Twins"related to border—the Massacre River as a metaphor to emphasize this boundary which causes all the sufferings of Haitian people like Amabelle and the new hope the survivor are seeking for.

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Cosmopolitan Temporalities： A Sociological Analysis on Climate Imageries in Brazil and China

Time is a central theoretical resource for climate change science, climate policies, social actions addressing climate change problems, and shaping concepts of uncertainty and ambiguity. This paper presents a debate on how different epis- temic climate science communities deal with the concept of time by considering the social construction of climate imageries. To do so, we undertake two case studies on how China and Brazil＇s climate communities have shaped situated knowledge based on different historical social experiences that created different modes of dealing with climate change： China has created a concept of time based on practical climate experiences, while Brazil has developed a futuristic sense of how the climate will behave in the future. Finally, we address the idea of cosmopolitan climate imageries originated from hybrid forums and constructed by stocks of knowledge which have been shared transhistorically by different epistemic communities towards a common climate governance.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

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.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

Pseudo channel:time embedding for motor imagery decoding

Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.

Methods for ice velocity mapping in West Antarctica using historical optical satellite images

The Antarctic Ice Sheet harbors more than 90%of the Earth ice mass,with significant losses experienced through dynamic thinning,particularly in West Antarctica.The crucial aspect of investigating ice mass balance in historical periods preceding 1990 hinges on the utilization of ice velocities derived from optical satellite images.We employed declassified satellite images and Landsat images with normalized cross correlation based image matching,adopting an adaptive combination of skills and methods to overcome challenges encountered during the mapping of historical ice velocity in West Antarctica.A basin-wide synthesis velocity map encompassing the coastal regions of most large-scale glaciers and ice shelves in West Antarctica has already been successfully generated.Our results for historical ice velocities cover over 70%of the grounding line in most of the West Antarctic basins.Through adjustments,we uncovered overestimations in ice velocity measurements over an extended period,transforming our ice velocity map into a spatially deterministic,temporally average version.Among all velocity measurements,Thwaites Glacier exhibited a notable spatial variation in the fastest ice flowline and velocity distribution.Overestimation distributions on Thwaites Glacier displayed a clear consistency with the positions of subsequent front calving events,offering insights into the instabilities of ice shelves.

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

The Analysis of Wolf Imagery in The Company of Wolves

Imagery analysis is a commonly used analytical method in literary analysis.In Angela Carter’s work,the image of wolves is particularly prominent.Her“Werewolf Tetralogy”rewrites traditional culture and subverts traditional consciousness,and is the research object of many scholars.Starting from the analysis of the wolf image in The Company of Wolves,this paper uses Deleuze’s Becoming-Animal Theory to explore the construction of harmony between nature,humans and gender relations in The Company of Wolves.

A Study on the Translation of Plant Images in Qu Yuan’s Li Sao

The renowned masterpiece“Li Sao”by Qu Yuan contains numerous plant images for“expressing emotions and aspirations.”Exploring methods of translating plant imagery has greatly assisted in disseminating Chinese classical culture and facilitating cross-cultural communication.This study conducts a comparative analysis of three translations of“Li Sao”by Xu Yuanchong,Yang Xianyi,and Hawkes,aiming to understand the different approaches to translating plant imagery and explore variations in translation effectiveness.Through data collection,comparative analysis,and case studies,this research reveals that Xu Yuanchong emphasizes free translation,Yang Xianyi tends towards literal translation,and Hawkes adopts a combination of literal,free,and phonetic translation methods.

Migration and Spatiotemporal Land Cover Change: A Case of Bosomtwe Lake Basin, Ghana

Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.

Color Expression and Mood Creation in Imagery Oil Painting

Oil painting is a traditional Western painting form.With the introduction of China and the influence of China’s traditional painting and aesthetics,the painting style became more distinctive,expanding a new development direction of oil painting,and thus imagery oil painting came into being.Color,as the most important element in imagery oil painting,mainly plays the role of mood creation and emotional expression.Many creators are good at injecting their thoughts and emotions into the paintings through color matching,so as to enhance the artistic expression of the paintings.This paper analyzes the color expression characteristics of imagery oil painting and explores the color expression techniques in imagery oil painting and mood creation of imagery oil painting from several aspects.

基于HHT和CSSD的多域融合自适应脑电特征提取方法

为改善运动想象脑电信号特征提取的自适应性和实时性,提出一种基于希尔伯特-黄变换(HHT)与共空域子空间分解算法(CSSD)的特征提取方法(HCSSD).在对脑电信号进行预处理的基础上,定义一种相对距离准则优选脑电极组合;计算脑电的Hilbert瞬时能量谱和边际能量谱,以获取脑电的时-频特征,并基于CSSD提取其空域特征,采用串行特征融合策略得到脑电的时-频-空特征;设计学习矢量量化神经网络分类器,实现脑电数据分类.在训练集与测试集间隔一周且减少导联数量的情况下,基于HCSSD对左手小指和舌头的运动想象ECoG脑电数据的平均识别率为92%.实验结果表明:HCSSD在增强特征提取方法的自适应性、改善实时性的同时,提高了脑电信号识别率,为便携式BCI系统在康复领域的应用创造了条件.

中文版运动觉-视觉想象问卷在不同年龄、性别正常人群中的信度

运动想象（motor imagery,MI）是指在无躯体参与运动时,有意识地在头脑中重现已经形成的动作表象或者形成新的动作表象[1]。在国外大部分相关的MI研究中,受试者均在入选时接受MI问卷的评估。Malouin等综合了各种版本MI问卷的优缺点,在2007年编制了运动觉-视觉想象问卷（the kinesthetic and visual imagery questionnaire,KVIQ-20）及它的简版KVIQ-10,检查者根据受试者进行MI时的主观感受，评估MI的清晰度（视觉想象，visual imagery，VI）和MI的感受强度（运动觉想象，kinesthetic imagery，KI）。

从关联翻译理论分析中西文化语境下“月亮”意象

以关联理论和关联翻译理论为理论框架,试图将文艺理论中的“意象”概念引入语用学和认知语言学的范畴,并进一步探究中西文化语境下,“月亮”意象在其源语认知语境和译语认知语境的关联性,以及影响“月亮”文学意象在传译中所呈现出的不同的民族文化心理、意象内涵、文化亏损和摇曳多姿的意象形态。

A Remote Sensing Analysis of the Temporal and Spatial Changes of Land Surface Temperature in Calabar Metropolis, Nigeria

This study investigated the temporal and spatial changes of land surface temperature (LST) over Calabar Metropolis, Nigeria (2002 to 2016). The LST over Calabar metropolis was studied from the analysis of Landsat imageries of the investigated years (2002, 2006, 2008, 2010, 2012, 2014 and 2016). The results of the LST imagery were classified using standard deviation. GIS was further applied to extract the coverage ratio of each land use in the context of Land surface temperature (LST) pixels and results were presented in degree Celsius. The result of this study revealed a great variation in the mean LST for the selected period. The highest mean LST of 25.38°C was observed in 2016, followed by 2002 with mean LST of 25.32°C whereas, the least LST was observed in 2010. This study has shown that, the changing land use pattern of the area is capable of affecting certain characteristics of the environment such as surface temperature. The study recommends that effort should be made by the government to increase urban vegetation in order to reduce potential future increased in LST.

Using Time Series of Satellite Images to Detect Vegetation Cover Change in Dhaka City

The spatial land cover pattern of Dhaka city is restlessly altering as it has highest growing rates among megacities. Due to high urbanization rates, land use is changing from natural to man-made along with resource extractions that accelerate the land cover changes. This study has evidently given the glimpse of the raising concern by assessing the loss of vegetation coverage of Dhaka Metropolitan Area using multi-temporal Landsat imageries. 20 years satellite data have been used to detect the vegetation cover changes in Dhaka city with an interval of 10 years. Study reveals that 66.87 square kilometer vegetation coverage of Dhaka Metropolitan Area is lost during the whole time period. The rate of loss of vegetation coverage is very severe in Dhanmondi, Gulshan, Uttara, Demra, Mirpur, Sabujbagh, Ramna, Mohammadpur, Kafrul, Shyampur and so on. The overall precision of my Landsat-derived vegetation coverage maps is 92.5%.

Effects of Urban Land Use Change on Selected Public Utilities for Sustainable Development in Akure, Nigeria

Urban landuse change is one of the main driving forces of global environmental change. It is central to the sustainable development debate. The aim of this research is to examine the effects of urban landuse and its impact on selected public utility services in Akure with a view to improve strategies to protect these utilities for sustainable development. The research was conducted using remote sensing, Geographical information System and questionnaire to collect the required data for the purpose of fulfilling the objectives of this study. In this study of analyzing the urban landuse change, 2010 and 2018 IKONOS are used in a post classification comparison analysis to map the landuse changes and identify the conversion process in Akure. The landuse change statistics results obtained revealed that residential landuse has changed rapidly for the periods (2010-2018). The results also show increase in commercial landuses between the same periods. The results of the analysis reveal that the built-up area has been growing rapidly for the periods (2010-2018). Findings show that, housing provision (residential), business purpose (commercial) and educational (institution) are the major causes of land use change in the study areas. To reduce the effect of land use expansion in the study areas, policy measures were recommended which include regulating the growth and spread of development, regional development programs. In our opinion, the information provided by these technologies could help city planners and policy makers to attain and sustain future urban development.

Enhanced hyperspectral imagery representation via diffusion geometric coordinates

The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.