A deep foundation pit constructed for an underground transportation hub was excavated near the Yangtze River. Among the strata, there are two confined aquifers, between which lies an aquiclude that is partially missin...A deep foundation pit constructed for an underground transportation hub was excavated near the Yangtze River. Among the strata, there are two confined aquifers, between which lies an aquiclude that is partially missing. To guarantee the safety of pit excavation, the piezometric head of the upper confined aquifer, where the pit bottom is located, should be 1 m below the pit bottom, while that of the lower confined aquifer should be dewatered down to a safe water level to avoid uplift problem. The Yangtze River levee is notably close to the pit, and its deformation caused by dewatering should be controlled. A pumping test was performed to obtain the hydraulic conductivity of the upper confined aquifer. The average value of the hydraulic conductivity obtained from analytical calculation is 20.45 m/d, which is larger than the values from numerical simulation（horizontal hydraulic conductivity K_H = 16 m/d and vertical hydraulic conductivity K_V = S m/d）. The difference between K_H and K_V indicates the anisotropy of the aquifer. Two dewatering schemes were designed for the construction and simulated by the numerical models for comparison purposes. The results show that though the first scheme could meet the dewatering requirements, the largest accumulated settlement and differential settlement would be94.64 mm and 3.3‰, respectively, greatly exceeding the limited values. Meanwhile, the second scheme,in which the bottoms of the waterproof curtains in ramp B and the river side of ramp A are installed at a deeper elevation of-28 m above sea level, and 27 recharge wells are set along the levee, can control the deformation of the levee significantly.展开更多
目的应用四维自动左房定量技术(4D Auto LAQ)评价不同透析方式对尿毒症患者左房结构和功能的影响。方法选取于我院肾内科就诊的尿毒症患者80例,根据透析方式分为血液透析组39例和腹膜透析组41例,另选同期健康体检者35例作为正常对照组...目的应用四维自动左房定量技术(4D Auto LAQ)评价不同透析方式对尿毒症患者左房结构和功能的影响。方法选取于我院肾内科就诊的尿毒症患者80例,根据透析方式分为血液透析组39例和腹膜透析组41例,另选同期健康体检者35例作为正常对照组。应用常规超声心动图获取左室射血分数(LVEF)、左房内径(LAD)、左室舒张末期内径(LVEDD)、室间隔厚度(IVS)、左室后壁厚度(LVPW);4D Auto LAQ获取左房应变参数,包括左房储备期纵向应变(LASr)、左房管道期纵向应变(LAScd)、左房收缩期纵向应变(LASct)、左房储备期环形应变(LASr-c)、左房管道期环形应变(LAScd-c)、左房收缩期环形应变(LASct-c),以及左房容积参数,包括左房最大容积(LAVmax)、左房最小容积(LAVmin)、左房收缩前容积(LAVpreA)、左房射血分数(LAEF),比较各组上述参数的差异;分析LAEF与左房应变参数的相关性。结果①各组常规超声心动图参数比较:腹膜透析组和血液透析组LAD、LVEDD、IVS、LVPW均较正常对照组增大,差异均有统计学意义(均P<0.05);各组LVEF比较差异无统计学意义。②各组4D Auto LAQ左房应变参数比较:与正常对照组比较,腹膜透析组LASr、LAScd、LASr-c、LAScd-c均减小,LASct、LASct-c均增大,血液透析组LASr、LAScd、LASct、LASr-c、LAScd-c、LASct-c均减小,差异均有统计学意义(均P<0.05);除LAScd外,血液透析组LASr、LAScd、LASct、LASr-c、LAScd-c、LASct-c均较腹膜透析组减小,差异均有统计学意义(均P<0.05)。③各组4D Auto LAQ左房容积参数比较:与正常对照组比较,腹膜透析组LAVmax、LAVmin、LAVpreA均增大,LAEF减小,血液透析组LAVmax、LAVmin、LAVpreA均增大,LAEF减小,差异均有统计学意义(均P<0.05);与腹膜透析组比较,血液透析组LAVmax、LAmin、LAVpreA均增大,LAEF减小,差异均有统计学意义(均P<0.05)。④相关性分析显示,LAEF与LASr、LAScd、LASr-c、LAScd-c、LASct、LASct-c均呈正相关(r=0.531、0.522、0.705、0.686、0.306、0.376,均P<0.001)。结论4D Auto LAQ可用于评价不同透析方式对尿毒症患者左房结构和功能的影响,其中血液透析较腹膜透析对左房结构和功能影响更大。展开更多
In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.Howev...In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.However,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection.Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key.Additionally,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under LDP.Initially,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods.Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs.Theoretically,we demonstrate that the variance of KS-UE is lower than that of existing methods.These claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.展开更多
Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
基金financially supported by the doctoral fund of the Ministry of Education of Chinathe Nature Science Foundation of Jiangsu Province, China (Grant Nos. 20130091110020 and BE2015675)
文摘A deep foundation pit constructed for an underground transportation hub was excavated near the Yangtze River. Among the strata, there are two confined aquifers, between which lies an aquiclude that is partially missing. To guarantee the safety of pit excavation, the piezometric head of the upper confined aquifer, where the pit bottom is located, should be 1 m below the pit bottom, while that of the lower confined aquifer should be dewatered down to a safe water level to avoid uplift problem. The Yangtze River levee is notably close to the pit, and its deformation caused by dewatering should be controlled. A pumping test was performed to obtain the hydraulic conductivity of the upper confined aquifer. The average value of the hydraulic conductivity obtained from analytical calculation is 20.45 m/d, which is larger than the values from numerical simulation（horizontal hydraulic conductivity K_H = 16 m/d and vertical hydraulic conductivity K_V = S m/d）. The difference between K_H and K_V indicates the anisotropy of the aquifer. Two dewatering schemes were designed for the construction and simulated by the numerical models for comparison purposes. The results show that though the first scheme could meet the dewatering requirements, the largest accumulated settlement and differential settlement would be94.64 mm and 3.3‰, respectively, greatly exceeding the limited values. Meanwhile, the second scheme,in which the bottoms of the waterproof curtains in ramp B and the river side of ramp A are installed at a deeper elevation of-28 m above sea level, and 27 recharge wells are set along the levee, can control the deformation of the levee significantly.
文摘目的应用四维自动左房定量技术(4D Auto LAQ)评价不同透析方式对尿毒症患者左房结构和功能的影响。方法选取于我院肾内科就诊的尿毒症患者80例,根据透析方式分为血液透析组39例和腹膜透析组41例,另选同期健康体检者35例作为正常对照组。应用常规超声心动图获取左室射血分数(LVEF)、左房内径(LAD)、左室舒张末期内径(LVEDD)、室间隔厚度(IVS)、左室后壁厚度(LVPW);4D Auto LAQ获取左房应变参数,包括左房储备期纵向应变(LASr)、左房管道期纵向应变(LAScd)、左房收缩期纵向应变(LASct)、左房储备期环形应变(LASr-c)、左房管道期环形应变(LAScd-c)、左房收缩期环形应变(LASct-c),以及左房容积参数,包括左房最大容积(LAVmax)、左房最小容积(LAVmin)、左房收缩前容积(LAVpreA)、左房射血分数(LAEF),比较各组上述参数的差异;分析LAEF与左房应变参数的相关性。结果①各组常规超声心动图参数比较:腹膜透析组和血液透析组LAD、LVEDD、IVS、LVPW均较正常对照组增大,差异均有统计学意义(均P<0.05);各组LVEF比较差异无统计学意义。②各组4D Auto LAQ左房应变参数比较:与正常对照组比较,腹膜透析组LASr、LAScd、LASr-c、LAScd-c均减小,LASct、LASct-c均增大,血液透析组LASr、LAScd、LASct、LASr-c、LAScd-c、LASct-c均减小,差异均有统计学意义(均P<0.05);除LAScd外,血液透析组LASr、LAScd、LASct、LASr-c、LAScd-c、LASct-c均较腹膜透析组减小,差异均有统计学意义(均P<0.05)。③各组4D Auto LAQ左房容积参数比较:与正常对照组比较,腹膜透析组LAVmax、LAVmin、LAVpreA均增大,LAEF减小,血液透析组LAVmax、LAVmin、LAVpreA均增大,LAEF减小,差异均有统计学意义(均P<0.05);与腹膜透析组比较,血液透析组LAVmax、LAmin、LAVpreA均增大,LAEF减小,差异均有统计学意义(均P<0.05)。④相关性分析显示,LAEF与LASr、LAScd、LASr-c、LAScd-c、LASct、LASct-c均呈正相关(r=0.531、0.522、0.705、0.686、0.306、0.376,均P<0.001)。结论4D Auto LAQ可用于评价不同透析方式对尿毒症患者左房结构和功能的影响,其中血液透析较腹膜透析对左房结构和功能影响更大。
基金supported by a grant fromthe National Key R&DProgram of China.
文摘In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph data.However,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection.Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key.Additionally,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under LDP.Initially,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods.Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs.Theoretically,we demonstrate that the variance of KS-UE is lower than that of existing methods.These claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
