Laser Ablation of Energetic Materials

The initiation of explosives by laser is a new initiation method. Compared with traditional initiation methods, laser initiation has the characteristics of high reliability and high safety. It can be used as one of the alternative technologies for future initiation device. A microscopic understanding of the complex physical and chemical processes involved in the reaction process is essential for laser initiation. Shock initiation technology of laser-driven flyer was studied. Several typical laser-driven flyer targets researches were introduced. Some significant characteristics including velocity and impact stress of flyers were tested via photonic Doppler velocimetry and polyvinylidene fluoride pressure sensor, respectively. Some types of flyers including Al and Cu single-layer flyers and CuO/Cu, CuO/Al, and CuO/Al/Cu multilayer flyers with relatively high velocities were used to initiate PETN explosive. In order to give a better understanding of the mechanism of laser interaction with typical energetic materials (RDX, HMX, TNT, and HNS), a time of flight mass spectrometer (TOFMS) was used to detect the positive ions and the negative ions were produced in the laser-induced dissociation processes. The influences of laser wavelength, the laser fluence, and the delay time of the decomposition process have been studied as well. The results may throw some light on the laser interaction mechanism of energetic materials

Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high accuracy multislice image. We then compare our results to those of regression methods. We find that using the deep learning model Generative Adversarial Network (GAN) provides us with the best results and performs at a similar accuracy level to previous regression models on the same dataset. Codes and data for this project can be found in this GitHub repository, https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice

Imidazole-Based pH-Sensitive Convertible Liposomes for Anticancer Drug Delivery

In efforts to enhance the activity of liposomal drugs against solid tumors, three novel lipids that carry imidazole-based headgroups of incremental basicity were prepared and incorporated into the membrane of PEGylated liposomes containing doxorubicin (DOX) to render pH-sensitive convertible liposomes (ICL). The imidazole lipids were designed to protonate and cluster with negatively charged phosphatidylethanolamine-polyethylene glycol when pH drops from 7.4 to 6.0, thereby triggering ICL in acidic tumor interstitium. Upon the drop of pH, ICL gained more positive surface charges, displayed lipid phase separation in TEM and DSC, and aggregated with cell membrane-mimetic model liposomes. The drop of pH also enhanced DOX release from ICL consisting of one of the imidazole lipids, sn-2-((2,3-dihexadecyloxypropyl)thio)-5-methyl-1H-imidazole. ICL demonstrated superior activities against monolayer cells and several 3D MCS than the analogous PEGylated, pH-insensitive liposomes containing DOX, which serves as a control and clinical benchmark. The presence of cholesterol in ICL enhanced their colloidal stability but diminished their pH-sensitivity. ICL with the most basic imidazole lipid showed the highest activity in monolayer Hela cells; ICL with the imidazole lipid of medium basicity showed the highest anticancer activity in 3D MCS. ICL that balances the needs of tissue penetration, cell-binding, and drug release would yield optimal activity against solid tumors

Dynamics Modeling and Characteristics Analysis of Distributed Drive Electric Vehicles

Due to the short transmission chain, compact structure, and the feature of quick and accurate torque generation, distributed drive electric vehicle (DDEV) has attracted many researchers from academia and industry. The significantly redundant execution characteristic of four independently driven in-wheel motors also provides more potential to guarantee the vehicle dynamics performance. Moreover, the unique torque vector control of DDEV generates the direct yaw moment control mode. It has been proven to be effective to modify the vehicle steering characteristics. Through a reasonable torque vector allocation strategy, the energy-saving can also be realized. This chapter will introduce the distributed drive electric vehicle from the viewpoint of the dynamics modeling, stability performance analysis, and energy-saving strategy

A high energy output and low onset temperature nanothermite based on three-dimensional ordered macroporous nano-NiFe2O4

Three-dimensional ordered macroporous (3DOM) Al/NiFe2O4 nanothermite has been obtained by colloidal crystal templating method combined with magnetron sputtering processing. Owing to the superior material properties and unique 3DOM structural characteristics of composite metal oxides, the heat output of the Al/NiFe2O4 nanothermite is up to 2921.7 J g− 1, which is more than the values of Al/NiO and Al/Fe2O3 nanothermites in literature. More importantly, by comparison to the other two nanothermites, the onset temperature of 298.2 °C from Al/NiFe2O4 is remarkably low, which means it can be ignited more easily. Laser ignition experiment indicate that the synthesized Al/NiFe2O4 nanothermite can be easily ignited by laser. In addition, the preparation process is highly compatible with the MEMS technology. These exciting achievements have great potential to expand the scope of nanothermite applications

3D ordered macroporous NiO/Al nanothermite film with significantly improved higher heat output, lower ignition temperature and less gas production

The performances of nanothermites largely rely on a meticulous design of nanoarchitectures and the close assembly of components. Three-dimensionally ordered macroporous (3DOM) NiO/Al nanothermite film has been successfully fabricated by integrating colloidal crystal template (CCT) method and controllable magnetron sputtering. The as-prepared NiO/Al film shows uniform structure and homogeneous dispersity, with greatly improved interfacial contact between fuel and oxidizer at the nanoscale. The total heat output of 3DOM NiO/Al nanothermite has reached 2461.27 J·g−1 at optimal deposition time of 20 min, which is significantly more than the values of other NiO/Al structural systems that have been reported before. Intrinsic reduced ignition temperature (onset temperature) and less gas production render the wide applications of 3DOM NiO/Al nanothermite. Moreover, this design strategy can also be readily generalized to realize diverse 3DOM structured nanothermites

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This paper instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveal a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly

A predictive model for early death in elderly colorectal cancer patients: a population-based study

PurposeThe purpose of this study is to determine what variables contribute to the early death of elderly colorectal cancer patients (ECRC) and to generate predictive nomograms for this population.MethodsThis retrospective cohort analysis included elderly individuals (≥75 years old) diagnosed with colorectal cancer (CRC) from 2010-2015 in the Surveillance, Epidemiology, and End Result databases (SEER) databases. The external validation was conducted using a sample of the Chinese population obtained from the China-Japan Union Hospital of Jilin University. Logistic regression analyses were used to ascertain variables associated with early death and to develop nomograms. The nomograms were internally and externally validated with the help of the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe SEER cohort consisted of 28,111 individuals, while the Chinese cohort contained 315 cases. Logistic regression analyses shown that race, marital status, tumor size, Grade, T stage, N stage, M stage, brain metastasis, liver metastasis, bone metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for all-cause and cancer-specific early death in ECRC patients; The variable of sex was only related to an increased risk of all-cause early death, whereas the factor of insurance status was solely associated with an increased risk of cancer-specific early death. Subsequently, two nomograms were devised to estimate the likelihood of all-cause and cancer-specific early death among individuals with ECRC. The nomograms exhibited robust predictive accuracy for predicting early death of ECRC patients, as evidenced by both internal and external validation.ConclusionWe developed two easy-to-use nomograms to predicting the likelihood of early death in ECRC patients, which would contribute significantly to the improvement of clinical decision-making and the formulation of personalized treatment approaches for this particular population

Neuroimaging Studies Reveal the Subtle Difference Among Social Network Size Measurements and Shed Light on New Directions

Social network size is a key feature when we explore the constructions of human social networks. Despite the disparate understanding of individuals’ social networks, researchers have reached a consensus that human’s social networks are hierarchically organized with different layers, which represent emotional bonds and interaction frequency. Social brain hypothesis emphasizes the significance of complex and demanding social interaction environments and assumes that the cognitive constraints may have an impact on the social network size. This paper reviews neuroimaging studies on social networks that explored the connection between individuals’ social network size and neural mechanisms and finds that Social Network Index (SNI) and Social Network Questionnaires (SNQs) are the mostly-adopted measurements of one’s social network size. The two assessments have subtle difference in essence as they measure the different sublayers of one’s social network. The former measures the relatively outer sub-layer of one’s stable social relationship, similar to the sympathy group, while the latter assesses the innermost layer—the core of one’s social network, often referred to as support clique. This subtle difference is also corroborated by neuroimaging studies, as SNI-measured social network size is largely correlated with the amygdala, while SNQ-assessed social network size is closely related to both the amygdala and the orbitofrontal cortex. The two brain regions respond to disparate degrees of social closeness, respectively. Finally, it proposes a careful choice among the measurements for specific purposes and some new approaches to assess individuals’ social network size