Analytic models for the latency and steady-state throughput of TCP Tahoe, Reno, and SACK

Continuing the process of improvements made to TCP through the addition of new algorithms in Tahoe and Reno, TCP SACK aims to provide robustness to TCP in the presence of multiple losses from the same window. In this paper we present analytic models to estimate the latency and steady-state throughput of TCP Tahoe, Reno, and SACK and validate our models using both simulations and TCP traces collected from the Internet. In addition to being the first models for the latency of finite Tahoe and SACK flows, our model for the latency of TCP Reno gives a more accurate estimation of the transfer times than existing models. The improved accuracy is partly due to a more accurate modeling of the timeouts, evolution of cwnd during slow start and the delayed ACK timer. Our models also show that, under the losses introduced by the droptail queues which dominate most routers in the Internet, current implementations of SACK can fail to provide adequate protection against timeouts and a loss of roughly more than half the packets in a round will lead to timeouts. We also show that with independent losses SACK performs better than Tahoe and Reno and, as losses become correlated, Tahoe can outperform both Reno and SACK.     

An Overview of Next-Generation Androgen Receptor-Targeted Therapeutics in Development for the Treatment of Prostate Cancer

Traditional endocrine therapy for prostate cancer (PCa) has been directed at suppression of the androgen receptor (AR) signaling axis since Huggins et al. discovered that diethylstilbestrol (DES; an estrogen) produced chemical castration and PCa tumor regression. Androgen deprivation therapy (ADT) still remains the first-line PCa therapy. Insufficiency of ADT over time leads to castration-resistant PCa (CRPC) in which the AR axis is still active, despite castrate levels of circulating androgens. Despite the approval and use of multiple generations of competitive AR antagonists (antiandrogens), antiandrogen resistance emerges rapidly in CRPC due to several mechanisms, mostly converging in the AR axis. Recent evidence from multiple groups have defined noncompetitive or noncanonical direct binding sites on AR that can be targeted to inhibit the AR axis. This review discusses new developments in the PCa treatment paradigm that includes the next-generation molecules to noncanonical sites, proteolysis targeting chimera (PROTAC), or noncanonical N-terminal domain (NTD)-binding of selective AR degraders (SARDs). A few lead compounds targeting each of these novel noncanonical sites or with SARD activity are discussed. Many of these ligands are still in preclinical development, and a few early clinical leads have emerged, but successful late-stage clinical data are still lacking. The breadth and diversity of targets provide hope that optimized noncanonical inhibitors and/or SARDs will be able to overcome antiandrogen-resistant CRPC.     

Effect of organic modifiers on dynamic and static nanomechanical properties and crystallinity of intercalated clay–polycaprolactam nanocomposites

Polymer clay nanocomposites (PCN) of Polyamide6 and sodium montmorillonite are prepared using different organic modifiers (12‐aminolauric acid, n‐dodecylamine, and 1,12‐diaminododecane) to study effect of organic modifiers on structure and nanomechanical properties of PCN. Using X‐ray diffraction and differential scanning calorimetry, crystalline nature of PCNs are evaluated. Nanoscale viscoelastic properties of PCNs are evaluated using nanodynamic mechanical analyzer (NanoDMA). Nanoscale elastic modulus and hardness of PCNs are evaluated using nanoindenter. PCNs show enhancement in elastic modulus, storage modulus, loss modulus, and loss factor by maximum amount of 62.88%, 56.38%, 145.74%, and 71.43%, respectively, and decrease in percentage crystallinity by 16.52% compared to pure polymer. This result indicates that organic modifiers have effect on crystallinity and nanomechanical properties of PCN. To evaluate effect of clay loading on nanomechanical properties of PCN, PCN containing 12‐aminolauric acid is synthesized with different weight percent (3, 6, and 9% of weight of polymer) of organically modified montmorillonite (OMMT), which shows that nanomechanical properties of PCN improves with increase in clay loading. Our study reveals that change in crystallinity of polymer in PCN may have role in the enhancement of nanomechanical properties of PCNs in comparison to pristine polymer. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Topography of the flank wear surface

In many applications, topography represents the main external features of a surface. This paper describes the topography of the flank wear surface and also presents the relationship between the maximum flank wear and the topography parameters (roughness parameters) of the flank wear surface during the turning operation. A modern CNC lathe machine (Okuma LH35-N) was used for the machine turning operation. Three-dimensional surface roughness parameters of the flank wear surface were measured by a surface texture instrument (from Talysurf series) using surface topography software (Talymap). Based on the resulting experimental data, it is found that as the flank wear increases, the roughness parameters (sR_a, sR_q, and sR_t) on the flank surface increase significantly. The greater the roughness value of the flank wear surface, the higher the friction of the tool on the workpiece and the greater the heat generation that will occur, thus ultimately causing tool failure. On the other hand, positive skewness (sR_sk) indicates the presence of a small number of spikes on the flank surface of the cutting tool, which could quickly wear off during the machining process.     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.