R plot line width的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和資訊懶人包

發展一套創新體積角度轉換法之肺臟劑量最佳化於食道癌放射治療之應用

為了解決R plot line width的問題，作者林冠亨 這樣論述：

背景：弧形調控放射治療為目前食道癌主要治療方法之一，弧形旋轉能給予食道腫瘤較高的劑量順行度，但腫瘤周圍肺臟組織容易接收過多輻射劑量而導致放射性肺炎。肺臟接收5 Gy以上的體積百分比(V5)為預測放射性肺炎的重要因子，降低肺臟V5，可藉由人工反覆設定弧形角度及劑量限值逹成，但過程耗時且存在主觀差異。因此，本論文目的開發一套體積角度轉換演算法，用於計算食道癌患者放射治療最佳化弧形角度，降低放射性肺炎發生率。材料與方法：本論文分為演算法開發及臨床驗證兩部分：(1) 使用肺臟體積及弧形角度之間的相互轉換，即可推導弧形角度對應之肺臟V5預測值，藉由Pearson correlation及Bland-A

ltman分析驗證肺臟V5預測值與治療計畫肺臟V5之相關性及一致性。 (2) 回溯收集30位食道癌患者電腦斷層影像，於實驗(1)所開發的演算法界面定義其腫瘤長度、腫瘤寬度及肺臟V5預期值，反向推導體積角度轉換即可計算最佳化弧形角度。使用Mann-Whitney tests比較肺臟劑量、心臟劑量、脊髓劑量、腫瘤劑量順行度、腫瘤劑量均勻度及劑量輸出時間在全弧形角度(full arc)及最佳化弧形角度治療計畫之差異。結果：透過弧形角度從360°到80°組間隔20°的15組擬人假體治療計畫，演算法計算之肺臟V5預測值與治療計畫肺臟V5有高度正相關(r = 0.996, p < 0.001)，兩者差異值

均落在95%信賴區間內(-4.1%到1.9%)有高度一致性。比較30位食道癌患者之全弧形角度與最佳化弧形角度治療計畫顯示，二者腫瘤劑量皆表現良好的順型度(1.15 ± 0.18 vs. 1.18 ± 0.16, p = 0.375)及均勻度(1.08 ± 0.03 vs. 1.10 ± 0.04, p = 0.159)，最佳化弧形角度的肺臟V5 (48.55 ± 6.82 vs. 43.38 ± 8.22%, p = 0.005)及輸出時間(237 ± 8 vs. 192 ± 37 seconds, p < 0.001)則有顯著降低，心臟及脊髓劑量有些微上升但無顯著差異（p > 0.05）。結

論：本論文完成開發一套體積角度轉換演算法，能提供弧形調控放射治療預測肺臟V5與最佳化弧形角度計算。建議可應用本演算法降低放射性肺炎發生率，改善病人放射治療後之生活品質，增加操作者選取弧形角度的效率，減少臨床操作者負擔與主觀差異。

神經型態特性基於釕憶阻器用於腦刺激類神經網絡

為了解決R plot line width的問題，作者Mainak Seal 這樣論述：

All the commercially available computing architectures are based on von-Neumann architecture, but in today’s cutting-edge technology and expansion ofAI to almost all fields of science, the fundamental separation between the controlunit and memory unit has become a bottleneck in terms of efficiency

and powerconsumption. Emerging computers will be incredibly powerful, but if they arebased on von Neumann architecture, they will consume 20 to 30 megawatts ofpower and will not be capable of problems based on unstructured data. Today’sworld is faster, smarter, and power-efficient. To cater to the A

I designers' needsand increase the overall performance, we need to overcome the bottleneckpresented by von-Neumann architecture.The human brain performs about 1015 calculations per second using 20Wand a 1.2L volume. Taking inspiration from the biological brain, we can use theemerging non-volatile me

mory to store and compute logic functions. To do thesame, we have turned towards in-memory computing and spiking neural networks.The aim of these models is to minimize the transfer of data delay, which requiresthe CU and MU to work as one unit.In this thesis, we have studied and explored one such Ru

thenium basedRRAM from different angles like memristor properties, neuromorphic properties,application of memristor using the neuromorphic properties and much more. Thedevice stack is RuO2/AlOx/TiN. We have explored the switching mechanism ofthe device and tested it for numerous DC cycles under diff

erent currentcompliance. To see the uniformity in the devices, we have explored the deviceto-device variability of the devices. At last, we explored it for synaptic propertiesusing GinestraTM. This study expects the possible creation of brain-inspired SNNusing the explored characteristics of Ru-base

d memristor.