Spatio-Temporal Analysis of Drought in the North-Eastern Coastal Region of Vietnam Using the Standardized Precipitation Index (SPI)

Spatio-temporal analysis of drought provides valuable information for drought management and damage mitigation. In this study, the Standardized Precipitation Index at the time scale of 6 months (SPI-6) is selected to reflect drought conditions in the North-Eastern coastal region of Vietnam. The drought events and their characteristics from 1981 to 2019 are detected at 9 meteorological stations and 10 Chirps rainfall stations. The spatio-temporal variation of drought in the study region is analyzed on the basis of the number, duration, severity, intensity, and peak of the detected drought events at the 19 stations. The results show that from 1981 to 2019 the drought events mainly occurred with 1-season duration and moderate intensity and peak. The number, duration, severity, and peak of the drought events were the greatest in the period 2001-2010 and were the smallest in the period 2011-2019. Among the 19 stations, the drought duration tends to decrease at 11 stations, increase at 7 stations, and has a slight variant at 1 station;the drought severity tends to decrease at 14 stations, increase at 4 stations, and has not a significant trend at 1 station;the drought intensity tends to decrease at 17 stations, increase at 1 station, and has a slight variant at 1 station;and the drought peak tends to decrease at 18 stations and increase at 1 station.

Physics-informed deep learning for fringe pattern analysis

Recently,deep learning has yielded transformative success across optics and photonics,especially in optical metrology.Deep neural networks (DNNs) with a fully convolutional architecture (e.g.,U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks,such as fringe denoising,phase unwrapping,and fringe analysis.However,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naive,as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice.To this end,we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (Le FTP) module.By parameterizing conventional phase retrieval methods,the Le FTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples,while circumventing the requirement of collecting a large amount of high-quality data in supervised learning methods.Guided by the initial phase from Le FTP,the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks.Experimental results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during training.The proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.

Gas monitoring data anomaly identification based on spatio-temporal correlativity analysis

Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.

Modeling and Analysis of Production Logistics Spatio-Temporal Graph Network Driven by Digital Twin

In the process of logistics distribution of manufacturing enterprises, the automatic scheduling method based on the algorithm model has the advantages of accurate calculation and stable operation, but it excessively relies on the results of data calculation, ignores historical information and empirical data in the solving process, and has the bottleneck of low processing dimension and small processing scale. Therefore, in the digital twin(DT) system based on virtual and real fusion, a modeling and analysis method of production logistics spatio-temporal graph network model is proposed, considering the characteristics of road network topology and time-varying data. In the DT system, the temporal graph network model of the production logistics task is established and combined with the network topology, and the historical scheduling information about logistics elements is stored in the nodes. When the dynamic task arrives, a multi-stage links probability prediction method is adopted to predict the possibility of loading, driving, and other link relationships between task-related entity nodes at each stage. Several experiments are carried out, and the prediction accuracy of the digital twin-based temporal graph network(DTGN) model trained by historical scheduling information reaches 99.2% when the appropriate batch size is selected. Through logistics simulation experiments, the feasibility and the effectiveness of production logistics spatio-temporal graph network analysis methods based on historical scheduling information are verified.

Spatio-temporal analysis of the incidence of colorectal cancer in Guangzhou,2010-2014

Introduction: Colorectal cancer(CRC) is a common type of neoplasm. This study examined the spatio?temporal distribution of the CRC incidence in Guangzhou during 2010–2014.Methods: Colorectal cancer incidence data were obtained from the Guangzhou Cancer Registry System. Spatial autocorrelation analysis and a retrospective spatio?temporal scan were used to assess the spatio?temporal cluster distribution of CRC cases.Results: A total of 14,618 CRC cases were registered in Guangzhou during 2010–2014, with a crude incidence of 35.56/100,000 and an age?standardized rate of incidence by the world standard population(ASRIW) of 23.58/100,000. The crude incidence increased by 19.70% from 2010(32.88/100,000) to 2014(39.36/100,000) with an average annual percentage change(AAPC) of 4.33%. The AAPC of ASRIW was not statistically significant. The spatial autocorrelation analysis revealed a CRC incidence hot spot in central urban areas in Guangzhou City, which included 25 streets in southwestern Baiyun District, northwestern Haizhu District, and the border region between Liwan and Yuexiu Dis?tricts. Three high? and five low?incidence clusters were identified according to spatio?temporal scan of CRC incidence clusters. The high?incidence clusters were located in central urban areas including the border regions between Bai?yun, Haizhu, Liwan, and Yuexiu Districts.Conclusions: This study revealed the spatio?temporal cluster pattern of the incidence of CRC in Guangzhou. This information can inform allocation of health resources for CRC screening.

Spatio-temporal analysis of the melt onset dates over Arctic sea ice from 1979 to 2017

The melt onset dates(MOD)over Arctic sea ice plays an important role in the seasonal cycle of sea ice surface properties,which impacts Arctic surface solar radiation absorbed by the ice-ocean system.Monitoring interannual variations in MOD is valuable for understanding climate change.In this study,we investigated the spatio-temporal variability of MOD over Arctic sea ice and 14 Arctic sub-regions in the period of 1979 to 2017 from passive microwave satellite data.A set of mathematical and statistical methods,including the Sen’s slope and Mann-Kendall mutation tests,were used to comprehensively assess the variation trend and abrupt points of MOD during the past 39 years for different Arctic sub-regions.Additionally,the correlation between Arctic Oscillation(AO)and MOD was analyzed.The results indicate that:(1)all Arctic sub-regions show a trend toward earlier MOD except the Bering Sea and St.Lawrence Gulf.The East Siberian Sea exhibits a significantly earlier trend,with the highest rate of-9.45 d/decade;(2)the temporal variability and statistical significance of MOD trend exhibit large interannual differences with different time windows for most regions in the Arctic;(3)during the past 39 years,the MOD changed abruptly in different years for different sub-regions;(4)the seasonal AO has more influence on MOD than monthly AO.The findings in this study can improve our knowledge of MOD changes and are beneficial for further Arctic climate change study.

Implementing geostatistical analysis to study spatio-temporal distribution patterns of swimming crabs(Portunus trituberculatus)

This study attempted to compare the performance of local polynomial interpolation,inverse distance weighted interpolation,and ordinary kriging in studying distribution patterns of swimming crabs.Cross-validation was used to select the optimum method to get distribution results,and kriging was used for making spatial variability analysis.Data were collected from 87 sampling stations in November of 2015(autumn)and February(winter),May(spring)and August(summer)of 2016.Results indicate that swimming crabs widely distributed in autumn and summer:in the summer,they were more spatially independent,and resources in each sampling station varied a lot;in the winter and spring,the abundance of crabs was much lower,but the individual crab size was bigger,and they showed the patchy and more concentrative distribution pattern,which means they were more spatially dependent.Distribution patterns were in accordance with ecological migration features of swimming crabs,which were affected by the changing marine environment.This study could infer that it is applicable to study crab fishery or even other crustacean species using geostatistical analysis.It not only helps practitioners have a better understanding of how swimming crabs migrate from season to season,but also assists researchers in carrying out a more comprehensive assessment of the fishery.Therefore,it may facilitate advancing the implementation in the pilot quota management program of swimming crabs in northern Zhejiang fishing grounds.

Hotshots of Spatio-temporal Behavior of Chinese Residents in the Context of Big Data:Visual Analysis Based on CiteSpace

By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.

Gonorrhea cluster detection in Manitoba,Canada:Spatial,temporal,and spatio-temporal analysis

In Canada,Gonorrhea infection ranks as the second most prevalent sexually transmitted infection.In 2018,Manitoba reported an incidence rate three times greater than the national average.This study aims to investigate the spatial,temporal,and spatio-temporal patterns of Gonorrhea infection in Manitoba,using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016.Age and sex patterns indicate that females are affected by infections at younger ages compared to males.Moreover,there is an increase in repeated infections in 2016,accounting for 16%of the total infections.Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation,demonstrating a clustered distribution of the infection.Northern districts of Manitoba and central Winnipeg were identified as significant clusters.Temporal analysis shows seasonal patterns,with higher infections in late summer and fall.Additionally,spatio-temporal analysis reveals clusters during high-risk periods,with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014,and a secondary cluster in central Winnipeg from June 2004 to November 2012.This study identifies that Gonorrhea infection transmission in Manitoba has temporal,spatial,and spatio-temporal variations.The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention,control measures,and resource allocation.

Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements

Background:The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate.Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome,who subjected to preimplantation genetic testing for aneuploidy(PGT-A)treatment.However,it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement.Methods:From February 2020 to May 2021,we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos,the clinical information of couples,and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment.Initially,we developed a novel deep learning model called the Attentive Multi-Focus Selection Network(AMSNet)to analyze time-lapse videos in real time and predict blastocyst formation.Building upon AMSNet,we integrated additional clinically predictive variables and created a second deep learning model,the Attentive Multi-Focus Video and Clinical Information Fusion Network(AMCFNet),to assess the euploidy status of embryos.The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements.The receiver operating characteristic curve(ROC)was used to evaluate the superiority of the model.Results:A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task,and 1422 qualified blastocysts received PGT-A(n=589)or PGT for chromosomal structural rearrangement(PGT-SR,n=833)were enrolled for the euploidy assessment task in this study.The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy.The real-time accuracy for AMSNet to predict blastocyst formation reached above 70%on the day 2 of embryo culture,and then increased to 80%on the day 4 of embryo culture.Combing with 4 clinical features of couples,the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement.Conclusion:Integrating seven focal raw time-lapse images of embryos and parental clinical information,AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.

Land use change in Bohai Rim: a spatial-temporal analysis

Based on RS and GIS methods, land use information for 1985 and 1995 was acquired from TM images and analyzed. Then on both spatial and temporal aspects, this paper analyzes land use change in three provinces of Hebei, Shandong and Liaoning and two municipalities of Beijing and Tianjin in the Bohai Rim covering the period of 1985 to 1995. The extent, rate, areal difference and trend of various types of land use changes in the region, as well as spatial changes of major types of land use, their distribution characteristics and regional orientation are revealed. The regional characteristics of land use are elaborated, so as to provide effective policy support for sustainable land use in the area around the Bohai Bay.

Spatiotemporal Property Analysis of Birth Defects in Wuxi,China

Objective To describe the temporal trends and spatial patterns of birth defects occurring in Wuxi, a developed region of China. Methods Wavelet analysis was used to decompose the temporal trends of birth defect prevalence based on the birth defect rates over the past 16 years. Birth defect cases with detailed personal and family information were geo-coded and the relative risk in each village was calculated. General G statistic was used to test the spatial property with different scales. Results Wavelet analysis showed an increasing temporal trend of birth defects in this region. Clustering analysis revealed that changes continued in the spatial patterns with different scales. Conclusion Wuxi is confronted with severe challenges to reduce birth defect prevalence. The risk factors are stable and show no change with spatial scale but an increasing temporal trend. Interventions should be focused on villages with a higher prevalence of birth defects.

Performance Analysis and Evaluation of Geometric Parameters in Stereo Deflectometry

This paper presents a novel geometric parameters analysis to improve the measurement accuracy of stereo deflectometry.Stereo deflectometry can be used to obtain form information for freeform specular surfaces.A measurement system based on stereo deflectometry typically consists of a fringe-displaying screen,a main camera,and a reference camera.The arrangement of the components of a stereo deflectometry system is important for achieving high-accuracy measurements.In this paper,four geometric parameters of a stereo deflectometry system are analyzed and evaluated:the distance between the main camera and the measured object surface,the angle between the main camera ray and the surface normal,the distance between the fringe-displaying screen and the object,and the angle between the main camera and the reference camera.The influence of the geometric parameters on the measurement accuracy is evaluated.Experiments are performed using simulated and experimental data.The experimental results confirm the impact of these parameters on the measurement accuracy.A measurement system based on the proposed analysis has been set up to measure a stock concave mirror.Through a comparison of the given surface parameters of the concave mirror,a global measurement accuracy of 154.2 nm was achieved.

Bibliometrics and visual analysis for clinical research on intervention of traditional Chinese medicine nursing technology on rheumatoid arthritis

Objective:The objective of this study is to explore the current situation,clinical research hot spots,and trends of traditional Chinese medicine(TCM)nursing technology in rheumatoid arthritis(RA),to draw a panorama of clinical research,to provide the basis and clues for subsequent high-level evidence integration and further in-depth research.Materials and Methods:Seven databases including China National Knowledge Infrastructure,Chinese Scientific Journal Database(VIP),Wanfang Database,Chinese Biomedical Literature Database(Sino-Med),PubMed,Embase,and the Cochrane Library were searched.The bibliometric method and visualization software CiteSpace were used to conduct a multi-dimensional analysis of the included literature.Results:A total of 805 pieces of literature were included(of them,one was written in German and four in English),and the number of published literature showed an increasing trend year by year.There were only 30(3.73%)pieces of literature published in nursing journals.The hot spots of the co-occurrence map were concentrated in:(1)the nursing of fumigation in RA;(2)the efficacy evaluation of TCM nursing technology on pain,joint malformation,and joint dysfunction caused by RA.The included literature themes focused on five TCM technologies:fumigation,moxibustion,acupoint patching,acupoint injection,and Chinese herbal soaking.The beginning year and strength in TCM fumigation were 2012 and 5.2,and those in moxibustion were 2011 and 3.38.Conclusion:The related studies are on the rise.It has entered the field of international readers with clear research hot spots.However,there are still shortcomings such as little literature published in Chinese nursing core journals,few non-Chinese-related documents,and a lack of international exchanges and cooperation.The current research hot spots in this field are TCM fumigation,and the cutting-edge trend of future research may be fumigation and moxibustion technology.It is suggested that further research can focus on evidence integration and original research on the self-optimization of these two techniques.

Characteristics and Cause Analysis of Variations in Light Precipitation Events in the Central and Eastern Tibetan Plateau,China,During 1961–2019

The Tibetan Plateau(TP)is one of the most sensitive areas and is more susceptible to climate change than other regions in China.The TP also experiences extremely frequent light precipitation events compared to precipitation of other intensities.However,the definition,influencing factors,and characteristics of light precipitation in the TP have not been accurately explained.This study investigated the variation characteristics of light precipitation with intensities(Pre)of 0.1-10.0 mm/d based on climate data from 53 meteorological stations over the central and eastern TP from 1961 to 2019.For detailed analysis,light precipitation events were classified into five grades:G1[0.1-2.0 mm/d),G2[2.0-4.0 mm/d),G3[4.0-6.0 mm/d),G4[6.0-8.0 mm/d),and G5[8.0-10.0 mm/d).The results showed that both the amount of precipitation and number of precipitation days had increased significantly at rates of 4.0-6.0 mm/10 yr and 2.0-4.0 d/10 yr,respectively,and most precipitation events were of low intensity(0.1≤Pre<2.0 mm/d).Light precipitation events mainly occurred in the southeast of the study area,and it showed an increasing trend from the northwest to the southeast.Abrupt changes in light precipitation primarily occurred in the 1980 s.A comprehensive time series analysis using the Mann-Kendall test and Morlet wavelet was performed to characterize the abrupt changes and cycles of light precipitation.During the study period,the main periods of light precipitation corresponded to the 6 yr cycle,with obvious periodic oscillation characteristics,and this cycle coexisted with cycles of other scales.Significant correlations were observed between the amount of light precipitation and temperature over the study area.The findings will enhance our understanding of changes in light precipitation in the TP and provide Scientific basis for the definition of light precipitation in the future.

Video Analysis Based on Volumetric Event Detection

During the past decade, feature extraction and knowledge acquisition based on video analysis have been extensively researched and tested on many applications such as closed-circuit television （CCTV） data analysis, large-scale public event control, and other daily security monitoring and surveillance operations with various degrees of success. However, since the actual video process is a multi-phased one and encompasses extensive theories and techniques ranging from fundamental image processing, computational geometry and graphics, and machine vision, to advanced artificial intelligence, pattern analysis, and even cognitive science, there are still many important problems to resolve before it can be widely applied. Among them, video event identification and detection are two prominent ones. Comparing with the most popular frame-to-frame processing mode of most of today＇s approaches and systems, this project reorganizes video data as a 3D volume structure that provides the hybrid spatial and temporal information in a unified space. This paper reports an innovative technique to transform original video frames to 3D volume structures denoted by spatial and temporal features. It then highlights the volume array structure in a so-called ＂pre-suspicion＂ mechanism for a later process. The focus of this report is the development of an effective and efficient voxel-based segmentation technique suitable to the volumetric nature of video events and ready for deployment in 3D clustering operations. The paper is concluded with a performance evaluation of the devised technique and discussion on the future work for accelerating the pre-processing of the original video data.

Error Analysis of Radial Motion Measurement of Ultra-Precision Spindle

This paper gives an error analysis of radial motion measurement of ultra-precision spindle including nonlinearity error of capacitive displacement probes, misalignment error of probes, eccentric error of artifact ball and error induced by different error separating methods. Firstly, nonlinearity of a capacitive displacement probe targeting a spherical surface is investigated through experiment and the phenomena of fake displacement induced by lateral offset of the probe relative to an artifact ball?are?discussed. It is shown that the error motion in radial and axial direction and eccentric rotation of artifact ball will both induce lateral offset which causes a fake output of probes. Moreover, measurement error induced by angular positioning error for three famous error separating methods is detailed.

Spatio-temporal Changes and Associated Uncertainties of CENTURYmodelled SOC for Chinese Upland Soils, 1980-2010

Detailed information on the spatio-temporal changes of cropland soil organic carbon(SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and knowledge on the national scale spatio-temporal changes and the corresponding uncertainties of SOC in Chinese upland soils remain limited. The CENTURY model was used to estimate the SOC storages and their changes in Chinese uplands from 1980 to 2010. With the Monte Carlo method, the uncertainties of CENTURY-modelled SOC dynamics associated with the spatial heterogeneous model inputs were quantified. Results revealed that the SOC storage in Chinese uplands increased from 3.03(1.59 to 4.78) Pg C in 1980 to 3.40(2.39 to 4.62) Pg C in 2010. Increment of SOC storage during this period was 370 Tg C, with an uncertainty interval of –440 to 1110 Tg C. The regional disparities of SOC changes reached a significant level, with considerable SOC accumulation in the Huang-Huai-Hai Plain of China and SOC loss in the northeastern China. The SOC lost from Meadow soils, Black soils and Chernozems was most severe, whilst SOC accumulation in Fluvo-aquic soils, Cinnamon soils and Purplish soils was most significant. In modelling large-scale SOC dynamics, the initial soil properties were major sources of uncertainty. Hence, more detailed information concerning the soil properties must be collected. The SOC stock of Chinese uplands in 2010 was still relatively low, manifesting that recommended agricultural management practices in conjunction with effectively economic and policy incentives to farmers for soil fertility improvement were indispensable for future carbon sequestration in these regions.

Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Precise information on indoor positioning provides a foundation for position-related customer services.Despite the emergence of several indoor positioning technologies such as ultrawideband,infrared,radio frequency identification,Bluetooth beacons,pedestrian dead reckoning,and magnetic field,Wi-Fi is one of the most widely used technologies.Predominantly,Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades.Wi-Fi positioning faces three core problems:device heterogeneity,robustness to signal changes caused by human mobility,and device attitude,i.e.,varying orientations.The existing methods do not cover these aspects owing to the unavailability of publicly available datasets.This study introduces a dataset that includes the Wi-Fi received signal strength(RSS)gathered using four different devices,namely Samsung Galaxy S8,S9,A8,LG G6,and LG G7,operated by three surveyors,including a female and two males.In addition,three orientations of the smartphones are used for the data collection and include multiple buildings with a multifloor environment.Various levels of human mobility have been considered in dynamic environments.To analyze the time-related impact on Wi-Fi RSS,data over 3 years have been considered.