Aims The urethral sphincter and urethral muscle innervation are critically involved in maintaining continence, especially in the female. However, the urethral muscle type and distribution, as well as the urethral nerves are far from being well documented. Our aim was to clearly identify the distribution of urethral striated muscle, smooth muscle, and urethral nerves. Methods In a cohort analysis of 3‐month‐old female Sprague‐Dawley rats, cross and longitudinal sections of female rat urethra were extensively investigated using morphological techniques. Urethras were harvested to the sections, in order to provide both global and detailed visions of the urethra. H&E, Masson's Trichrome, phalloidin and immunoflourence stains were used. The cytoarchitecture, nitrergic, and cholinergic innervations were mainly investigated. Different layers of the segments of urethra were traced to draw curve graphs that represent the thickness of each muscle layer of urethral wall. Results The results showed that the primary peak of striated muscle is in the middle urethra. The inner layer close to mucosa was found to contain longitudinal smooth muscle. Near the bladder orifice, the circular smooth muscle dominates, which becomes thinner distally throughout the rest of urethra. In the middle urethra the vast majority of the urethral muscle are circularly oriented striated muscle cells. Typical nerve endings were present in high power images to show the different characteristic features of nerve innervation. Conclusions This study has illustrated the detailed morphological structure and innervations of the normal female rat urethra and can serve as a basis for further study of stress urinary incontinence (SUI). Neurourol. Urodynam. 35:382–389, 2016.

In data center networks, quantized congestion notification (QCN) has been ratified as the standard layer 2 congestion control mechanism. Since the congestion control algorithm of QCN conducts nonlinear switching between the rate increasing and decreasing stages, it is very difficult to analyze QCN theoretically. Previous works derive the stability condition of QCN using tools from linear control theory. However, the tools cannot reveal the reason why the queue at a congested link oscillates persistently when QCN is enabled. In this paper, we use the describing function method from nonlinear control theory to analyze queue oscillation. We show that unsuitable network configurations like more flows, a higher link capacity and a larger link delay will cause more severe oscillation of the queue. In particular, the impacts of the QCN parameters, such as the sampling probability and the constant in the active increase phase in QCN, on the oscillation magnitude of the queue are also analyzed. The simulation results validate the conclusion from our theoretical analysis well.

The high complexities and coupling among modules of existing Cloud data center (CDC) simulators have greatly hindered their deployment, expansion and redevelopment. In order to resolve the issue, in this paper, we introduce a lightweight CDC simulation framework, CloudMatrix Lite. CloudMatrix Lite is driven by real virtual machine (VM) traces and mainly simulates the server consolidation of the CDC in four phases: Physical machine (PM) underloading detection, PM overloading detection, VM selection for migration and VM placement. With the low coupling in the design of CloudMatrix Lite, users are able to swiftly add and extend customized functions and modules. Also, CloudMatrix Lite supports most current mainstream open-source machine learning (ML) frameworks such as Pytorch. Hence users can conveniently develop ML-based methods to predict the fluctuated workloads in order to conclude resource management decisions more efficiently.