| 5 | 印度科依纳-瓦尔纳地区的断层相互作用与地震触发显示文摘印度半岛上科依纳-瓦尔纳地区的地震被认为是由水库引发的。但是,通过对可用地震资料的分析表明,该地区持续高发的地震活动还有可能受到断层带的几何形态及其通过应力传递的相互作用的影响。从地震分布及其震源机制推断的这些明显的断层带的取向表明,在一个断层带内发生的地震事件会增加另一个断层带上的静应力,使该地区频繁而且连续地发生地震。本文的结果表明,虽然水库作用的效应确实会促使断层滑动,但是应力触发机制似乎是持续高发地震活动的一个重要原因,因为它会使一些稳定的断层变得不稳定,这与我们所推断的运动方式是一致的。 | V.K.Gahalaut Kalpna S.K.Singh 张志中 陈学忠 | 2005 | 世界地震译丛2005，36，1： | 0 |
| 6 | An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches显示文摘The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest. | Shazia Shamas Surya Narayan Panda Ishu Sharma Kalpna Guleria Aman Singh Ahmad Ali AlZubi Mallak Ahmad AlZubi | 2024 | Computer Modeling in Engineering & Sciences2024，138，2： | 0 |
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