Monitoring Galvanic Replacement Through Three-Dimensional Morphological and Chemical Mapping

Galvanic replacement reactions on metal nanoparticles are often used for the preparation of hollow nanostructures with tunable porosity and chemical composition, leading to tailored optical and catalytic properties. However, the precise interplay between the three-dimensional (3D) morphology and chemical composition of nanostructures during Galvanic replacement is not always well understood as the 3D chemical imaging of nanoscale materials is still challenging. It is especially far from straightforward to obtain detailed information from the inside of hollow nanostructures using electron microscopy techniques such as SEM or TEM. We demonstrate here that a combination of state-of-the-art EDX mapping with electron tomography results in the unambiguous determination of both morphology transformation and elemental composition of nanostructures in 3D, during Galvanic replacement of Ag nanocubes. This work provides direct and unambiguous experimental evidence leading to new insights in the understanding of the galvanic replacement reaction. In addition, the powerful approach presented here can be applied to a wide range of nanoscale transformation processes, which will undoubtedly guide the development of novel nanostructures

Praktische opdrachten bij Grieks en Latijn

Dit is een Ico-Isor rappor

Preparing Students for the Advanced Manufacturing Environment Through Robotics, Mechatronics, and Automation Training

Automation is one of the key areas for modern manufacturing systems. It requires coordination of different machines to support manufacturing operations in a company. Recent studies show that there is a gap in the STEM workforce preparation in regards to highly automated production environments. Industrial robots have become an essential part of these semi-automated and automated manufacturing systems. Their control and programming requires adequate education and training in robotics theory and applications. Various engineering technology departments offer different courses related to the application of robotics. These courses are a great way to inspire students to learn about science, math, engineering, and technology while providing them with workforce skills. However, some challenges are present in the delivery of such courses. One of these challenges includes the enrollment of students who come from different engineering departments and backgrounds. Such a multidisciplinary group of students can pose a challenge for the instructor to successfully develop the courses and match the content to different learning styles and math levels. To overcome that challenge, and to spark students\u27 interest, the certified education robot training can greatly support the teaching of basic and advanced topics in robotics, kinematics, dynamics, control, modeling, design, CAD/CAM, vision, manufacturing systems, simulation, automation, and mechatronics. This paper will explain how effective this course can be in unifying different engineering disciplines when using problem solving related to various important manufacturing automaton problems. These courses are focused on educational innovations related to the development of student competency in the use of equipment and tools common to the discipline, and associated curriculum development at three public institutions, in three different departments of mechanical engineering technology. Through these courses students make connections between the theory and real industrial applications. This aspect is especially important for tactile or kinesthetic learners who learn through experiencing and doing things. They apply real mathematical models and understand physical implications through labs on industrial grade robotic equipment and mobile robots

Improved Care for Teens in Trouble With Drugs, Alcohol, and Crime

Outlines how drug treatment providers developed RWJF's Reclaiming Futures model for collaborating with others and integrating evidence-based practices to sustain improvement in the juvenile justice system's treatment programs. Includes recommendations

Inhibition of Salmonella Typhimurium by medium chain fatty acids in an in vitro simulation of the porcine caecum

To lower the contamination of pork meat with Salmonella, feed additives such as medium chain fatty acids (MCFA\u27s) can be applied at the primary production level. An in vitro continuous culture system, simulating the porcine caecum, was developed for investigating the effect of MCFAs on the pig intestinal microbial community. The system was monitored by plating on selective media, 16S rDNA PCR denaturing gradient gel electrophoresis (PCR-DGGE) and HPLC analysis of fermentation products. In a simulation of the porcine caecum without MCFA treatment, with Salmonella Typhimurium added after stabilization of the microbial community, the strain could establish itself at a stable population size of about 5 log cfu/ml. The effect of selected MCFAs was observed from all monitored parameters and depended on chain length and concentration applied. At a dose of 15 mM, caproate and caprinate did not show any pronounced effect, while a clear Salmonella inhibiting effect (3 log units reduction) was found for caprylate. Doubling the caprylate dose did not result in enhanced Salmonella inhibition

Predicting daily physical activity in a lifestyle intervention program

The growing number of people adopting a sedentary lifestyle these days creates a serious need for effective physical activity promotion programs. Often, these programs monitor activity, provide feedback about activity and offer coaching to increase activity. Some programs rely on a human coach who creates an activity goal that is tailored to the characteristics of a participant. Throughout the program, the coach motivates the participant to reach his personal goal or adapt the goal, if needed. Both the timing and the content of the coaching are important for the coaching. Insights on the near future state on, for instance, behaviour and motivation of a participant can be helpful to realize an effective proactive coaching style that is personalized in terms of timing and content. As a first step towards providing these insights to a coach, this chapter discusses results of a study on predicting daily physical activity level (PAL) data from past data of participants in a lifestyle intervention program. A mobile body-worn activity monitor with a built-in triaxial accelerometer was used to record PAL data of a participant for a period of 13 weeks. Predicting future PAL data for all days in a given period was done by employing autoregressive integrated moving average (ARIMA) models on the PAL data from days in the period before. By using a newly proposed categorized-ARIMA (CARIMA) prediction method, we achieved a large reduction in computation time without a significant loss in prediction accuracy in comparison with traditional ARIMA models. In CARIMA, PAL data are categorized as stationary, trend or seasonal data by assessing their autocorrelation functions. Then, an ARIMA model that is most appropriate to these three categories is automatically selected based on an objective penalty function criterion. The results show that our CARIMA method performs well in terms of PAL prediction accuracy (~9% mean absolute percentage error), model parsimony and robustness

Characterizing receptive field selectivity in area V2

The computations performed by neurons in area V1 are reasonably well understood, but computation in subsequent areas such as V2 have been more difficult to characterize. When stimulated with visual stimuli traditionally used to investigate V1, such as sinusoidal gratings, V2 neurons exhibit similar selectivity (but with larger receptive fields, and weaker responses) relative to V1 neurons. However, we find that V2 responses to synthetic stimuli designed to produce naturalistic patterns of joint activity in a model V1 population are more vigorous than responses to control stimuli that lacked this naturalistic structure (Freeman, et. al. 2013). Armed with this signature of V2 computation, we have been investigating how it might arise from canonical computational elements commonly used to explain V1 responses. The invariance of V1 complex cell responses to spatial phase has been previously captured by summing over multiple “subunits” (rectified responses of simple cell-like filters with the same orientation and spatial frequency selectivity, but differing in their receptive field locations). We modeled V2 responses using a similar architecture: V2 subunits were formed from the rectified responses of filters computing the derivatives of the V1 response map over frequencies, orientations, and spatial positions. A V2 complex cell” sums the output of such subunits across frequency, orientation, and position. This model can qualitatively account for much of the behavior of our sample of recorded V2 neurons, including their V1-like spectral tuning in response to sinusoidal gratings as well as the pattern of increased sensitivity to naturalistic images