Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sin...Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range.The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance randomness.Extensive numerical experiments validate the effectiveness of SQQLSR.The proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in unpredictability.The bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher randomness.The system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force attacks.Additionally,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution techniques.This approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption algorithm.Theencryption method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor modifications.The robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential attacks.Additionally,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s reliability.These results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.展开更多
Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely...Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely on the feature maps used.In this paper,three widely used feature maps,or separation maps,are compared:chromatic,energy wavelet with principal component analysis(EW-PCA),and time-frequency(TF).To compare and evaluate,five scenarios with multi-PD environments with noise were developed.The clustering ability of the maps was evaluated using two performance indicators:intercluster distance and intracluster distance.The results indicate that the EW-PCA map performed the best in all scenarios,correctly identifying the largest number of data points and producing the clearest and most distinct clusters.The TF map created distinct clusters in several scenarios,but not all.The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps.Given the results,it is important in fieldwork to use a wide range of PD clustering,accompanied by performance metrics that support a less biased decision tailored to the test object.展开更多
We prove that there exists an open and dense subset U in the space of C^(2) expanding self-maps of the circle T such that the Lyapunov minimizing measures of any T∈U are uniquely supported on a periodic orbit.This an...We prove that there exists an open and dense subset U in the space of C^(2) expanding self-maps of the circle T such that the Lyapunov minimizing measures of any T∈U are uniquely supported on a periodic orbit.This answers a conjecture of Jenkinson-Morris in the C^(2) topology.展开更多
Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mec...Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.展开更多
Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
The advent of 6G wireless networks promises unprecedented connectivity,supporting ultra-high data rates,low latency,and massive device connectivity.However,these ambitious goals introduce significant challenges,partic...The advent of 6G wireless networks promises unprecedented connectivity,supporting ultra-high data rates,low latency,and massive device connectivity.However,these ambitious goals introduce significant challenges,particularly in channel estimation due to complex and dynamic propagation environments.This paper explores the concept of channel knowledge maps(CKMs)as a solution to these challenges.CKMs enable environment-aware communications by providing location-specific channel information,reducing reliance on real-time pilot measurements.We categorize CKM construction techniques into measurement-based,model-based,and hybrid methods,and examine their key applications in integrated sensing and communication(ISAC)systems,beamforming,trajectory optimization of unmanned aerial vehicles(UAVs),base station(BS)placement,and resource allocation.Furthermore,we discuss open challenges and propose future research directions to enhance the robustness,accuracy,and scalability of CKM-based systems in the evolving 6G landscape.展开更多
Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applicati...Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.展开更多
With the increasing exploration of oil and gas into deep waters,the necessity for material development increases for lighter conduits such as composite marine risers,in the oil and gas industry.To understand the resea...With the increasing exploration of oil and gas into deep waters,the necessity for material development increases for lighter conduits such as composite marine risers,in the oil and gas industry.To understand the research knowledge on this novel area,there is a need to have a bibliometric analysis on composite marine risers.A research methodology was developed whereby the data retrieval was from SCOPUS database from 1977–2023.Then,VOSviewer was used to visualize the knowledge maps.This study focuses on the progress made by conducting knowledge mapping and scientometric review on composite marine risers.This scientometric analysis on the subject shows current advances,geographical activities by countries,authorship records,collaborations,funders,affiiliations,co‑occurrences,and future research areas.It was observed that the research trends recorded the highest publication volume in the U.S.A.,but less cluster affiiliated,as it was followed by countries like the U.K.,China,Nigeria,Australia and Singapore.Also,thisfiield has more conference papers than journal papers due to the challenge of adaptability,acceptance,qualifiication,and application of composite marine risers in the marine industry.Hence,there is a need for more collaborations on composite marine risers and more funding to enhance the research trend.展开更多
Water quality is a critical global issue,especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems.This study evaluates the hydrochemical characte...Water quality is a critical global issue,especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems.This study evaluates the hydrochemical characteristics of surface water in the North of Tehran Rivers(NTRs),an essential water resource in a rapidly urbanizing region,using advanced clustering techniques,including Hierarchical Clustering Analysis(HCA),Fuzzy CMeans(FCM),Genetic Algorithm Fuzzy C-Means(GAFCM),and Self-Organizing Map(SOM).The research aims to address the scientific challenge of understanding spatial and temporal variability in water quality,focusing on physicochemical parameters,hydrochemical facies,and contamination sources.Water samples from six rivers collected over four seasons in 2020 were analyzed and classified into distinct clusters based on their chemical composition,revealing significant seasonal and spatial differences.Results showed that FCM and GAFCM consistently categorized the NTRs into two clusters during winter and spring and three in summer and autumn.These findings were supported by HCA and SOM,which identified clusters corresponding to specific river segments and contamination levels.The primary hydrochemical processes identified were mineral dissolution and weathering,with calcite,dolomite,and aragonite significantly influencing water chemistry.Additionally,human activities,such as wastewater discharge,were shown to contribute to elevated sulfate,nitrate,and phosphate concentrations,further corroborated by microbial analyses.By integrating HCA,FCM,and GAFCM with an artificial neural network(ANN)-based clustering method(SOM),this study provides a robust framework for evaluating surface water quality.The findings,supported by Gibbs diagrams,Hounslow ion ratio,and saturation indices,highlight the dominance of rock weathering and human impacts in shaping the hydrochemical dynamics of the NTRs.These insights contribute to the scientific understanding of water quality dynamics and offer practical guidance for sustainable water resource management and environmental protection in developing urban areas.展开更多
The title of the online version of the original article was revised.The title of the original article has been revised to:Hydrochemical characterization of surface waters in Northern Tehran:Integrating cluster-based t...The title of the online version of the original article was revised.The title of the original article has been revised to:Hydrochemical characterization of surface waters in Northern Tehran:Integrating cluster-based techniques with Self-Organizing Maps.展开更多
Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster informa...Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%.展开更多
BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpres...BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.展开更多
基金National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金funded by Kementerian Pendidikan Tinggi,Sains,dan Teknologi(Kemdiktisaintek),Indonesia,grant numbers 108/E5/PG.02.00.PL/2024,027/LL6/PB/AL.04/2024,061/A.38-04/UDN-09/VI/2024.
文摘Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range.The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance randomness.Extensive numerical experiments validate the effectiveness of SQQLSR.The proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in unpredictability.The bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher randomness.The system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force attacks.Additionally,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution techniques.This approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption algorithm.Theencryption method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor modifications.The robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential attacks.Additionally,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s reliability.These results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.
基金support of the Secretaría Nacional de Ciencia,Tecnología e Innovación(SENACYT)under Grant IDDSE19-007the Agencia Nacional de Investigación y Desarrollo(ANID)under Grants Fondecyt 1230135 and Fondef TA24I10002the Sistema Nacional de Investigación(SNI)of Panama under Grant 16-2021.
文摘Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely on the feature maps used.In this paper,three widely used feature maps,or separation maps,are compared:chromatic,energy wavelet with principal component analysis(EW-PCA),and time-frequency(TF).To compare and evaluate,five scenarios with multi-PD environments with noise were developed.The clustering ability of the maps was evaluated using two performance indicators:intercluster distance and intracluster distance.The results indicate that the EW-PCA map performed the best in all scenarios,correctly identifying the largest number of data points and producing the clearest and most distinct clusters.The TF map created distinct clusters in several scenarios,but not all.The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps.Given the results,it is important in fieldwork to use a wide range of PD clustering,accompanied by performance metrics that support a less biased decision tailored to the test object.
基金partially supported by National Key R&D Program of China(Grant Nos.2022YFA1005801)NSFC(Grant Nos.12171348,12325106,ZXL2024386)+2 种基金partially supported by NSFC(Grant Nos.12090012,12031019,11731003)partially supported by NSFC(Grant Nos.12031019,11801538,11871188)Jiangsu Specially Appointed Professorship。
文摘We prove that there exists an open and dense subset U in the space of C^(2) expanding self-maps of the circle T such that the Lyapunov minimizing measures of any T∈U are uniquely supported on a periodic orbit.This answers a conjecture of Jenkinson-Morris in the C^(2) topology.
基金funded by the Norwegian Research Council(NFR project 302701 Climate Smart Forestry Norway).
文摘Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
基金supported by the National Natural Science Foundation of China under Grants Nos.62431014 and 62271310the Fundamental Research Funds for the Central Universities of China。
文摘The advent of 6G wireless networks promises unprecedented connectivity,supporting ultra-high data rates,low latency,and massive device connectivity.However,these ambitious goals introduce significant challenges,particularly in channel estimation due to complex and dynamic propagation environments.This paper explores the concept of channel knowledge maps(CKMs)as a solution to these challenges.CKMs enable environment-aware communications by providing location-specific channel information,reducing reliance on real-time pilot measurements.We categorize CKM construction techniques into measurement-based,model-based,and hybrid methods,and examine their key applications in integrated sensing and communication(ISAC)systems,beamforming,trajectory optimization of unmanned aerial vehicles(UAVs),base station(BS)placement,and resource allocation.Furthermore,we discuss open challenges and propose future research directions to enhance the robustness,accuracy,and scalability of CKM-based systems in the evolving 6G landscape.
文摘Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.
基金support of the School of Engineering,Lancaster University,UK,for the Engineering Department Studentship as well as the Engineering and Physical Sciences Research Council(EPSRC)’s Doctoral Training Centre(DTC)。
文摘With the increasing exploration of oil and gas into deep waters,the necessity for material development increases for lighter conduits such as composite marine risers,in the oil and gas industry.To understand the research knowledge on this novel area,there is a need to have a bibliometric analysis on composite marine risers.A research methodology was developed whereby the data retrieval was from SCOPUS database from 1977–2023.Then,VOSviewer was used to visualize the knowledge maps.This study focuses on the progress made by conducting knowledge mapping and scientometric review on composite marine risers.This scientometric analysis on the subject shows current advances,geographical activities by countries,authorship records,collaborations,funders,affiiliations,co‑occurrences,and future research areas.It was observed that the research trends recorded the highest publication volume in the U.S.A.,but less cluster affiiliated,as it was followed by countries like the U.K.,China,Nigeria,Australia and Singapore.Also,thisfiield has more conference papers than journal papers due to the challenge of adaptability,acceptance,qualifiication,and application of composite marine risers in the marine industry.Hence,there is a need for more collaborations on composite marine risers and more funding to enhance the research trend.
文摘Water quality is a critical global issue,especially in urban and semi-urban regions where natural and anthropogenic factors significantly influence surface water systems.This study evaluates the hydrochemical characteristics of surface water in the North of Tehran Rivers(NTRs),an essential water resource in a rapidly urbanizing region,using advanced clustering techniques,including Hierarchical Clustering Analysis(HCA),Fuzzy CMeans(FCM),Genetic Algorithm Fuzzy C-Means(GAFCM),and Self-Organizing Map(SOM).The research aims to address the scientific challenge of understanding spatial and temporal variability in water quality,focusing on physicochemical parameters,hydrochemical facies,and contamination sources.Water samples from six rivers collected over four seasons in 2020 were analyzed and classified into distinct clusters based on their chemical composition,revealing significant seasonal and spatial differences.Results showed that FCM and GAFCM consistently categorized the NTRs into two clusters during winter and spring and three in summer and autumn.These findings were supported by HCA and SOM,which identified clusters corresponding to specific river segments and contamination levels.The primary hydrochemical processes identified were mineral dissolution and weathering,with calcite,dolomite,and aragonite significantly influencing water chemistry.Additionally,human activities,such as wastewater discharge,were shown to contribute to elevated sulfate,nitrate,and phosphate concentrations,further corroborated by microbial analyses.By integrating HCA,FCM,and GAFCM with an artificial neural network(ANN)-based clustering method(SOM),this study provides a robust framework for evaluating surface water quality.The findings,supported by Gibbs diagrams,Hounslow ion ratio,and saturation indices,highlight the dominance of rock weathering and human impacts in shaping the hydrochemical dynamics of the NTRs.These insights contribute to the scientific understanding of water quality dynamics and offer practical guidance for sustainable water resource management and environmental protection in developing urban areas.
文摘The title of the online version of the original article was revised.The title of the original article has been revised to:Hydrochemical characterization of surface waters in Northern Tehran:Integrating cluster-based techniques with Self-Organizing Maps.
基金financially supported by the Natural Science Foundation of China(42301492)the Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering(2022SDSJ04,2024SDSJ03)+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(GLAB 2023ZR01,GLAB2024ZR08)the Fundamental Research Funds for the Central Universities.
文摘Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%.
基金Supported by Science and Technology Program of Fujian Province,No.2021J01430Joint Funds for the Innovation of Science and Technology of Fujian Province,No.2021Y9229.
文摘BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.
