Non-Einstein Viscosity Phenomenon of Acrylonitrile–Butadiene–Styrene Composites Containing Lignin–Polycaprolactone Particulates Highly Dispersed by High-Shear Stress

Lignin powder was modified via ring-opening polymerization of caprolactone to form a lignin–polycaprolactone (LPCL) particulate. The LPCL particulates were mixed with an acrylonitrile–butadiene–styrene (ABS) matrix at an extremely high rotational speed of up to 3000 rpm, which was achieved by a closed-loop screw mixer and in-line melt extruder. Using this high-shear extruding mixer, the LPCL particulate size was controlled in the range of 3395 nm (conventional twin-screw extrusion) down to 638 nm (high-shear mixer of 3000 rpm) by altering the mixing speed and time. The resulting LPCL/ABS composites clearly showed non-Einstein viscosity phenomena, exhibiting reduced viscosity (2130 Pa·s) compared to the general extruded composite one (4270 Pa·s) at 1 s–1 and 210 °C. This is due to the conformational rearrangement and the increased free volume of ABS molecular chains in the vicinity of LPCL particulates. This was supported by the decreased glass transition temperature (Tg, 83.7 °C) of the LPCL/ABS composite specimens, for example, giving a 21.8% decrement compared to that (107 °C) of the neat ABS by the incorporation of 10 wt % LPCL particulates in ABS. The LPCL particulate morphology, damping characteristics, and light transmittance of the developed composites were thoroughly investigated at various levels of applied shear rates and mixing conditions. The non-Einstein rheological phenomena stemming from the incorporation of LPCL particulates suggest an interesting plasticization methodology: to improve the processability of high-loading filler/polymer composites and ultra-high molecular weight polymers that are difficult to process because of their high viscosity

Characteristics and treatments of large cystic brain metastasis: radiosurgery and stereotactic aspiration.

Brain metastasis represents one of the most common causes of intracranial tumors in adults, and the incidence of brain metastasis continues to rise due to the increasing survival of cancer patients. Yet, the development of cystic brain metastasis remains a relatively rare occurrence. In this review, we describe the characteristics of cystic brain metastasis and evaluate the combined use of stereotactic aspiration and radiosurgery in treating large cystic brain metastasis. The results of several studies show that stereotactic radiosurgery produces comparable local tumor control and survival rates as other surgery protocols. When the size of the tumor interferes with radiosurgery, stereotactic aspiration of the metastasis should be considered to reduce the target volume as well as decreasing the chance of radiation induced necrosis and providing symptomatic relief from mass effect. The combined use of stereotactic aspiration and radiosurgery has strong implications in improving patient outcomes

Real-Time Action Recognition Using Multi-level Action Descriptor and DNN

This work presents a novel approach to the problem of real-time human action recognition in intelligent video surveillance. For more efficient and precise labeling of an action, this work proposes a multilevel action descriptor, which delivers complete information of human actions. The action descriptor consists of three levels: posture, locomotion, and gesture level; each of which corresponds to a different group of subactions describing a single human action, for example, smoking while walking. The proposed action recognition method is able to localize and recognize simultaneously the actions of multiple individuals using appearance-based temporal features with multiple convolutional neural networks (CNN). Although appearance cues have been successfully exploited for visual recognition problems, appearance, motion history, and their combined cues with multi-CNNs have not yet been explored. Additionally, the first systematic estimation of several hyperparameters for shape and motion history cues is investigated. The proposed approach achieves a mean average precision (mAP) of 73.2% in the frame-based evaluation over the newly collected large-scale ICVL video dataset. The action recognition model can run at around 25 frames per second, which is suitable for real-time surveillance applications