Free-stream noise and transition measurements on a cone in a Mach 3.5 pilot low-disturbance tunnel

A small scale Mach 3.5 wind tunnel incorporating certain novel design features and intended for boundary-layer-transition research has been tested. The free stream noise intensities and spectral distributions were determined throughout the test section for several values of unit Reynolds number and for nozzle boundary layer bleed on and off. The boundary layer transition location on a slender cone and the response of this to changes in the noise environment were determined. Root mean square free stream noise levels ranged from less than one tenth up to values approaching those for conventional nozzles, with the lowest values prevailing at upstream locations within the nozzle. For low noise conditions, cone transition Reynolds numbers were in the range of those for free flight; whereas for high noise conditions, they were in the range of those in conventional tunnels

A dynamic truck dispatching problem in marine container terminal

In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vessels by a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations. In order to service more vessels, any unnecessary idle time between quay crane (QC) operations need to be minimised to speed up the container transfer process. Due to the unpredictable port situations that can affect routing plans and the short calculation time allowed to generate one, static solution methods are not suitable for this problem. In this paper, we introduce a new mathematical model that minimises both the QC makespan and the truck travelling time. Three dynamic heuristics are proposed and a genetic algorithm hyperheuristic (GAHH) under development is also described. Experiment results show promising capabilities the GAHH may offer

Ontogeny and cross species comparison of pathways involved in drug absorption, distribution, metabolism and excretion in neonates (review) : KIDNEY

The kidneys play an important role in many processes, including urine formation, water conservation, acid-base equilibrium, and elimination of waste. The anatomic and functional development of the kidney has different maturation time points in humans versus animals, with critical differences between species in maturation before and after birth. Absorption, distribution, metabolism, and excretion (ADME) of drugs vary depending on age and maturation, which will lead to differences in toxicity and efficacy. When neonate/juvenile laboratory animal studies are designed, a thorough knowledge of the differences in kidney development between newborns/children and laboratory animals is essential. The human and laboratory animal data must be combined to obtain a more complete picture of the development in the kidneys around the neonatal period and the complexity of ADME in newborns and children. This review examines the ontogeny and cross-species differences in ADME processes in the developing kidney in preterm and term laboratory animals and children. It provides an overview of insights into ADME functionality in the kidney by identifying what is currently known and which gaps still exist. Currently important renal function properties such as glomerular filtration rate, renal blood flow, and ability to concentrate are generally well known, while detailed knowledge about transporter and metabolism maturation is growing but is still lacking. Preclinical data in those properties is limited to rodents and generally covers only the expression levels of transporter or enzyme-encoding genes. More knowledge on a functional level is needed to predict the kinetics and toxicity in neonate/juvenile toxicity and efficacy studies

Influence of ions and pH on formation of solid and liquid-like melanin

Melanin is a natural pigment with broadband absorption and effective ability to dissipate the energy absorbed. The macromolecular structure of melanin shows a delicate balance between short-range ordered and disordered structures without being a random aggregate. The presence of ions or the variation in pH or ionic strength can alter the self-assembly process which subsequently changes the structure of melanin. To understand these relationships, this study investigates the influence of ions and pH in melanin formation. The types of ions present and pH have a profound influence on the formation and structure of melanin particles, while only minor changes are observed in the absorption and excitation-emission analysis. In some conditions, the formation of discernible particles with significant refractive index contrast is avoided while retaining the spectroscopic characteristics of melanin, leading to liquid-like melanin. These findings identify potential pathways which can be used to manipulate the melanin macromolecular structure while providing the desired spectral properties to enable novel bio-engineering applications.

Vision-Based Autonomous Driving: A Model Learning Approach

We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator. Our approach first builds a model for the environment, then trains a policy exploiting the learned model to identify the action to take at each time-step. To build a model for the environment, we leverage several deep learning algorithms. To that end, first we train a variational autoencoder to encode the input image into an abstract latent representation. We then utilize a recurrent neural network to predict the latent representation of the next frame and handle temporal information. Finally, we utilize an evolutionary-based reinforcement learning algorithm to train a controller based on these latent representations to identify the action to take. We evaluate our approach in CARLA, a high-fidelity urban driving simulator, and conduct an extensive generalization study. Our results demonstrate that our approach outperforms several previously reported approaches in terms of the percentage of successfully completed episodes for a lane keeping task.Comment:

Product Knowledge Graph Embedding for E-commerce

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks