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肝脏影像学:1996年文献综述

放射学实践 1997年第4期12卷 171-176页

作者： e.m.merkle H.-J.Brambs A.J.Aschoff R.Tomczak 张林同济医科大学附属同济医院

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双能量多层CT增强检出肾脏病变

国际医学放射学杂志 2011年第3期 287-288页

作者： A.m.Neville R.T.Gupta C.m.miller e.m.merkle e.K.Paulson D.T.Boll 焦慧 Department of Radiology Duke University Medical Center

目的确定双能量多层CT是否能在无体质量限制的病人群体中利用基于光谱提取技术计算得出的非强化图像检测出肾脏病变的强化情况。材料与方法在2008年1月—2009年12月之间,139例病人参与本项前瞻性研究。本

关键词：肾脏病变平扫双能量强化值 CT

双能量多层CT及其后处理技术对肾结石的评价:肾结石成分特征的实验研究

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国际医学放射学杂志 2009年第3期 280-281页

作者： D.T.Boll N.A.Patil e.K.Paulson e.m.merkle W.N.Simmons S.A.Pierre 梅习龙 Department of Radiology Duke University Medical Center

目的以体外肾结石波谱作参考标准,前瞻性评价双能多层CT先进的同步无创性人性化后处理技术显示人类肾结石的特征的能力。方法 50例肾结石,30例为单结晶成分(尿酸、胱氨酸、磷酸铵、草酸钙、磷酸钙、磷酸氢钙),20例为多结晶成分。采用64... 详细信息

目的以体外肾结石波谱作参考标准,前瞻性评价双能多层CT先进的同步无创性人性化后处理技术显示人类肾结石的特征的能力。方法 50例肾结石,30例为单结晶成分(尿酸、胱氨酸、磷酸铵、草酸钙、磷酸钙、磷酸氢钙),20例为多结晶成分。采用64层双能CT,从像素到像素的后处理算法是利用其低能和高能上医学数据灰阶编码比率相对差异来产生数字成像和通讯。

关键词：后处理技术双能肾结石肾石病 CT 成分特征

肝细胞期mRI加大翻转角用以改进肝脏病变显示的研究

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国际医学放射学杂志 2011年第2期 199-199页

作者： m.R.Bashir e.m.merkle 唐光健 Department of Radiology Duke University Medical Center

目的肝脏mRI应用肝胆对比剂的好处之一是可采集到延迟"肝细胞期"的数据。由于有功能的肝细胞内集聚了对比剂而多数病变内没有对比剂滞留,此期可清楚显示多

关键词： mRI 肝胆对比剂肝细胞期翻转角肝mRI

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量化放射住院医师的疲劳度：初步分析报告

国际医学放射学杂志 2021年第3期44卷 353-353页

作者： J.Vosshenrich P.Brantner J.Cyriac D.T.Boll e.m.merkle T.Heye. 杨淑杰

摘要过去几十年放射科的工作负荷在不断增加,由此导致的放射医生的疲劳被认为是影响诊断准确度的日益增长的问题。目的探讨日常班与轮班制的报告相似度趋势可

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enhanced quantum resources via two distant atom-cavity systems under the influence of atomic dissipation

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Communications in Theoretical Physics 2024年第2期76卷 62-73页

作者： m Setodeh Kheirabady m K Tavassoly m Rafeie e Ghasemian Laser and Optics Group Faculty of PhysicsYazd UniversityYazd 89195-741Iran Department of electrical engineering Faculty of Intelligent Systems Engineering and Data SciencePersian Gulf UniversityBushehrIran

Quantum resources such as entanglement and coherence are the holy grail for modern quantum technologies. Although the unwanted environmental effects tackle quantum information processing tasks, suprisingly these key quantum resources may be protected and even enhanced by the implementation of some special hybrid open quantum systems. Here, we aim to show how a dissipative atom-cavity-system can be accomplished to generate enhanced quantum *** do so, we consider a couple of dissipative cavities, where each one contains two effective two-level atoms interacting with a single-mode cavity field. In practical applications, a classical laser field may be applied to drive each atomic subsystem. After driving the system, a Bell-state measurement is performed on the output of the system to quantify the entanglement and coherence. The obtained results reveal that the remote entanglement and coherence between the atoms existing inside the two distant cavities are not only enhanced, but can be stabilized, even under the action of dissipation. In contrast, the local entanglement between two atoms inside each dissipative cavity attenuates due to the presence of unwanted environmental ***, the local coherence may show the same behavior as the remote ***, the system provides the steady state entanglement in various interaction regimes,particularly in the strong atom-cavity coupling and with relatively large detuning. more interestingly, our numerical analyses demonstrate that the system may show a memory effect due to the fact that the death and revival of the entanglement take place during the interaction. Our proposed model may find potential applications for the implementation of long distance quantum networks. In particular, it facilitates the distribution of quantum resources between the nodes of large-scale quantum networks for secure communication.

关键词： remote entanglement and coherence atomic dissipation steady state entanglement Bell-statemeasurement

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Novel Framework for an Intrusion Detection System Using multiple Feature Selection methods Based on Deep Learning

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Tsinghua Science and Technology 2024年第4期29卷 948-958页

作者： A.e.m.eljialy mohammed Yousuf Uddin Sultan Ahmad Department of Information Systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. machine learning (mL) techniques are extensively applied to build robust IDSs. many researchers have utilized different mL methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the mL model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： m.e.Al-Atroush A.e.Aboelela ezz el-Din Hemdan Civil and environmental engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(mL)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVms),and least squares support vector machines(LSSVms).It was found that despite mL approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

evaluation of the seismic behavior of reinforced concrete structures with flat slab-column gravity frame and shear walls through nonlinear analysis methods

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earthquake engineering and engineering Vibration 2024年第3期23卷 713-726页

作者： m.A.Najafgholipour S.Heidarian Radbakhsh e.erfani Department of Civil and environmental engineering Shiraz University of TechnologyShiraz ***Iran Department of Building Civiland Environmental EngineeringConcordia UniversityMontréal H3G 1M8Canada

This paper presents an investigation of the seismic behavior of reinforced concrete(RC)structures in which shear walls are the main lateral load-resisting elements and the participation of flat slab floor systems is not considered in the seismic design *** this regard,the behavior of six prototype structures(with different heights and plan layouts)is investigated through nonlinear static and time history analyses,implemented in the OpenSees *** results of the analyses are presented in terms of the behavior of the slab-column connections and their mode of failure at different loading ***,the global response of the buildings is discussed in terms of some parameters,such as lateral overstrength due to the gravity flat slab-column *** to the nonlinear static analyses,in structures in which the slab-column connections were designed only for gravity loads,the slab-column connections exhibited a punching mode of failure even in the early stages of ***,the punching failure was eliminated in structures in which a minimum transverse reinforcement recommended in ACI 318(2019)was provided in the slabs at joint ***,despite neglecting the contribution of gravity flat slab-column frames in the lateral load resistance of the structures,a relatively significant overstrength was imposed on the structures by the gravity frames.

关键词： RC flat slab-column frames seismic behavior nonlinear analysis time history analysis

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