ETHOS 发表于 2025-3-21 18:12:15
书目名称Methods in Chemical Ecology Volume 2影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0632280<br><br> <br><br>书目名称Methods in Chemical Ecology Volume 2读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0632280<br><br> <br><br>分离 发表于 2025-3-21 20:14:07
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Kelly M. Jenkins,Paul R. Jensen,William Fenicalent called from it is not tested. In this chapter, we propose software reliability growth models based on such components‘ characteristics. Especially, these models reflecting the different testing-environment for composed components are formulated by nonhomogeneous Poisson processes. Furthermore, umettlesome 发表于 2025-3-22 17:35:27
Mark E. Hay,John J. Stachowicz,Edwin Cruz-Rivera,Stephan Bullard,Michael S. Deal,Niels Lindquistch as crack growth, vibration, corrosion, wear and lubricant condition. With the measured data, the predictive maintenance policy determines the optimum threshold level at which maintenance action is performed to bring the system to a “better” condition, if not as good as new, in order to maximize s配偶 发表于 2025-3-22 22:06:32
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J. Daniel Harelly, the role of the amine neuromodulatory systems (dopamine, serotonin, norepinephrine, and acetylcholine) in impulsive decision-making and reinforcement learning processes is discussed. Ultimately, the integration of reinforcement learning algorithms with sophisticated behavioral and neuroscience