The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Diff...The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.展开更多
Background: Post myocardial infarction depression is common and puts a negative effect on recovery. Modified Nursing interventions effectively reduce the frequency and severity of depression in such patients. Objectiv...Background: Post myocardial infarction depression is common and puts a negative effect on recovery. Modified Nursing interventions effectively reduce the frequency and severity of depression in such patients. Objective: The study aimed to determine the effectiveness of Modified Nursing Interventions Classification (NIC) in reducing the severity of depression among patients with Myocardial Infarction. Methods: Sixty-eight stable patients with myocardial infarction (>1 month history) having mild to moderate depression in accordance to Patient Health Questionnaire-9 (PHQ-9) [with a score of 5 to 14] were enrolled. Patients were assorted into interventional and control group. Modified Nursing Intervention was offered in Interventional Group. The frequency and effectiveness of Modified Nursing Intervention among the groups were determined and compared. Results: Both moderate and mild level depression was decreased in Intervention Group as compare to Control Group. Baseline mean depression PHQ-9 score was 2.35 point statistically significantly higher in the Control Group than Interventional Group (Conclusion: Modified Nursing intervention is effective in reducing the frequency and severity of depression compared to routine care in patients with Myocardial infarction.展开更多
Renal cysts in pediatric patients are uncommon lesion. A modified Bosniak classification system for renal cysts based on US has been developed to evaluate pediatric renal cysts to identify the simple cyst or cystic tu...Renal cysts in pediatric patients are uncommon lesion. A modified Bosniak classification system for renal cysts based on US has been developed to evaluate pediatric renal cysts to identify the simple cyst or cystic tumour. Never</span><span style="font-family:Verdana;">theless, it is not widely used. In this retrospective study, all incidentally detected renal cysts by ultrasound performed in children and the reproducibility of modified Bosniak classification to guide the radiological and clinical follow up</span><span style="font-family:Verdana;">.展开更多
目的:比较椎间盘突出症密歇根州立大学分型(the Michigan State University classication,MSU)和改良MSU分型(modified MSU classification,MMSU)的临床一致性,探讨MMSU在脊柱微创外科临床治疗策略的指导作用。方法:回顾性分析2023年1月...目的:比较椎间盘突出症密歇根州立大学分型(the Michigan State University classication,MSU)和改良MSU分型(modified MSU classification,MMSU)的临床一致性,探讨MMSU在脊柱微创外科临床治疗策略的指导作用。方法:回顾性分析2023年1月~12月空军特色医学中心骨科收治的84例腰椎间盘突出症患者的临床资料,男48例,女36例,年龄20~69岁(44.37±12.85岁)。首先进行MMSU分级分区的准确界定:确定双侧上关节突顶点连线(a线)及双侧下关节突顶点连线(b线),突出在a线以内为1级,a/b线之间为2级,超过b线为3级;以双侧下关节突顶点垂直椎间盘横轴做垂线,两线平行,其间为椎管区,将椎管区域3等分,中间为A区,两侧为B区;经双侧上关节突外缘做垂线,其内为C区,即椎间孔区;两线外侧为D区,即极外侧区。由本院骨科8名熟悉两种分型的脊柱外科主治医生对84例患者的腰椎MRI实施阅片,分别进行MSU和MMSU分区分级。收集数据进行统计学分析,比较两种分型的一致率(即对每名患者核磁两种分型的一致性比率),包括平均一致率、100%一致率例数(8名医生的分型判断全部相同)、一致率>50%例数(至少5名医生分型判断相同,不包含全部8名医生分型相同)和一致率≤50%例数(不超过4名医生分型相同,包括4名)。结果:MMSU分型和MSU分型比较,平均一致率[(71.13±17.15)%vs(61.00±17.67)%]、一致率100%例数[10(11.9%)vs 1(1.90%)]、一致率≤50%例数[14(16.67%)vs 33(39.29%)],MMSU分型优于MSU分型,差异具有统计学意义(P<0.05);一致率>50%例数[60(71.43%)vs 50(59.52%)]比较差异无统计学意义(P>0.05)。结论:腰椎间盘突出症MMSU分型细化了突出的分级和分区,通过命名方式明确了轴位上突出的方向、位置、程度,增加了矢状位上移或者下移的分级,对于脊柱微创外科的手术方案选择具有现实指导意义,也有利于临床学术交流。展开更多
This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array...This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array （CSA-MMUSIC）, is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.展开更多
An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the ...An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.展开更多
This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to ...This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.展开更多
Agriculture is a primary activity in many countries,with wheat being a major cereal crop in India.Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dy...Agriculture is a primary activity in many countries,with wheat being a major cereal crop in India.Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dynamics,pricing,and trade.This study focuses on estimating wheat acreage and yield in Barwala block,Hisar district,Haryana,for the 2019-2020 Rabi season using remote sensing techniques.Multi-temporal satellite data capturing phenological stages of wheat(Seedling to Ripening)were processed using supervised classification with a maximum likelihood classifier in ERDAS Imagine.Wheat crop acreage was determined by overlaying ground truth points on the classified data.The estimated acreage showed a relative deviation of−1.07%compared to statistics from the Department of Agriculture(DoA),Haryana.Yield assessment employed a Semi-Physical model based on the Modified Monteith Model.Key parameters included Photosynthetically Active Radiation(PAR),fraction of PAR absorbed by wheat(fAPAR),light use efficiency,and water stress derived fromthe Land Surface Water Index(LSWI)using Sentinel-2 NIR and SWIR-1 bands.Net Primary Productivity(NPP)was computed for the wheat growth period,and grain yield was estimated using a harvest index obtained fromliterature.The estimated yield had a relative deviation of 9.3% from DoA data.The study demonstrates the potential ofmulti-temporal satellite imagery for accurate block-level wheat acreage and yield estimation,providing a valuable tool for agricultural planning and policy-making.展开更多
基金supported by the United Kingdom(UK)Darwin Initiative(28-003).
文摘The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.
文摘Background: Post myocardial infarction depression is common and puts a negative effect on recovery. Modified Nursing interventions effectively reduce the frequency and severity of depression in such patients. Objective: The study aimed to determine the effectiveness of Modified Nursing Interventions Classification (NIC) in reducing the severity of depression among patients with Myocardial Infarction. Methods: Sixty-eight stable patients with myocardial infarction (>1 month history) having mild to moderate depression in accordance to Patient Health Questionnaire-9 (PHQ-9) [with a score of 5 to 14] were enrolled. Patients were assorted into interventional and control group. Modified Nursing Intervention was offered in Interventional Group. The frequency and effectiveness of Modified Nursing Intervention among the groups were determined and compared. Results: Both moderate and mild level depression was decreased in Intervention Group as compare to Control Group. Baseline mean depression PHQ-9 score was 2.35 point statistically significantly higher in the Control Group than Interventional Group (Conclusion: Modified Nursing intervention is effective in reducing the frequency and severity of depression compared to routine care in patients with Myocardial infarction.
文摘Renal cysts in pediatric patients are uncommon lesion. A modified Bosniak classification system for renal cysts based on US has been developed to evaluate pediatric renal cysts to identify the simple cyst or cystic tumour. Never</span><span style="font-family:Verdana;">theless, it is not widely used. In this retrospective study, all incidentally detected renal cysts by ultrasound performed in children and the reproducibility of modified Bosniak classification to guide the radiological and clinical follow up</span><span style="font-family:Verdana;">.
文摘目的:比较椎间盘突出症密歇根州立大学分型(the Michigan State University classication,MSU)和改良MSU分型(modified MSU classification,MMSU)的临床一致性,探讨MMSU在脊柱微创外科临床治疗策略的指导作用。方法:回顾性分析2023年1月~12月空军特色医学中心骨科收治的84例腰椎间盘突出症患者的临床资料,男48例,女36例,年龄20~69岁(44.37±12.85岁)。首先进行MMSU分级分区的准确界定:确定双侧上关节突顶点连线(a线)及双侧下关节突顶点连线(b线),突出在a线以内为1级,a/b线之间为2级,超过b线为3级;以双侧下关节突顶点垂直椎间盘横轴做垂线,两线平行,其间为椎管区,将椎管区域3等分,中间为A区,两侧为B区;经双侧上关节突外缘做垂线,其内为C区,即椎间孔区;两线外侧为D区,即极外侧区。由本院骨科8名熟悉两种分型的脊柱外科主治医生对84例患者的腰椎MRI实施阅片,分别进行MSU和MMSU分区分级。收集数据进行统计学分析,比较两种分型的一致率(即对每名患者核磁两种分型的一致性比率),包括平均一致率、100%一致率例数(8名医生的分型判断全部相同)、一致率>50%例数(至少5名医生分型判断相同,不包含全部8名医生分型相同)和一致率≤50%例数(不超过4名医生分型相同,包括4名)。结果:MMSU分型和MSU分型比较,平均一致率[(71.13±17.15)%vs(61.00±17.67)%]、一致率100%例数[10(11.9%)vs 1(1.90%)]、一致率≤50%例数[14(16.67%)vs 33(39.29%)],MMSU分型优于MSU分型,差异具有统计学意义(P<0.05);一致率>50%例数[60(71.43%)vs 50(59.52%)]比较差异无统计学意义(P>0.05)。结论:腰椎间盘突出症MMSU分型细化了突出的分级和分区,通过命名方式明确了轴位上突出的方向、位置、程度,增加了矢状位上移或者下移的分级,对于脊柱微创外科的手术方案选择具有现实指导意义,也有利于临床学术交流。
基金supported by the National Natural Science Foundation of China(6117119761371045+2 种基金61201307)the Shandong Provincial Natural Science Foundation(ZR2011FM005)the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-aged Scientists(BS2010DX001)
文摘This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array （CSA-MMUSIC）, is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.
基金Supported by National Natural Science Foundation of China (No. 40872193)
文摘An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.
文摘This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.
文摘Agriculture is a primary activity in many countries,with wheat being a major cereal crop in India.Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dynamics,pricing,and trade.This study focuses on estimating wheat acreage and yield in Barwala block,Hisar district,Haryana,for the 2019-2020 Rabi season using remote sensing techniques.Multi-temporal satellite data capturing phenological stages of wheat(Seedling to Ripening)were processed using supervised classification with a maximum likelihood classifier in ERDAS Imagine.Wheat crop acreage was determined by overlaying ground truth points on the classified data.The estimated acreage showed a relative deviation of−1.07%compared to statistics from the Department of Agriculture(DoA),Haryana.Yield assessment employed a Semi-Physical model based on the Modified Monteith Model.Key parameters included Photosynthetically Active Radiation(PAR),fraction of PAR absorbed by wheat(fAPAR),light use efficiency,and water stress derived fromthe Land Surface Water Index(LSWI)using Sentinel-2 NIR and SWIR-1 bands.Net Primary Productivity(NPP)was computed for the wheat growth period,and grain yield was estimated using a harvest index obtained fromliterature.The estimated yield had a relative deviation of 9.3% from DoA data.The study demonstrates the potential ofmulti-temporal satellite imagery for accurate block-level wheat acreage and yield estimation,providing a valuable tool for agricultural planning and policy-making.
