Integration Concepts for the Fabrication of LTCC Structures

At the Keck Smart Materials Integration Laboratory at Penn State University, low-temperature co-fired ceramic (LTCC) material systems have been used to fabricate a number of devices for a variety of applications. This article presents an overview of the integration of the concepts and materials that we have used to achieve miniaturization and unique device function. Examples of microwave filters, metamaterial antennas, and a dielectrophoretic cell sorter will be presented, with emphasis on device modeling and design, prototype construction methods, and test results.     

Isosteric heat of sorption of CO2 in poly(ethylene terephthalate)

Sorption isotherms for carbon dioxide in poly(ethylene terephthalate) have been measured at 35–55°C. The isotherms were measured gravimetrically on a Mettler Thermoanalyzer-1 from vacuum to 1 atmosphere. The sorption data were used to generate sorption isotherms from which the isosteric heat of sorption of CO₂ in PET was determined. At 45°C the isosteric heat of sorption increases from −10 kcal/mole at a concentration of 0.5 cm³ (STP)/cm³ (polymer) to −8 kcal mole⁻¹ at a concentration of 1.5 cm³ (STP)/cm³ (polymer). It has been reported in the literature that the isosteric heat of sorption for this system decreased through a minimum before increasing with increasing concentration. Our measurement of the low-pressure sorption isotherms shows that this is not the case.     

Micro-computed tomography scanning approaches to quantify,parameterize and visualize bioturbation activity in clogged streambeds: A proof of concept

Fine particle clogging and faunal bioturbation are two key processes co-occurring in the hyporheic zone that potentially affect hyporheic exchange through modifications in the sediment structure of streambeds. Clogging results from excessive fine sediment infiltration and deposition in rivers, and it is known to decrease matrix porosity and potentially reduce permeability. Faunal bioturbation activity may compensate for the negative effect of clogging by reworking the sediment, increasing porosity, and preventing further infiltration of fines. Although both processes of clogging and bioturbation have received significant attention in the literature separately, their combined effects on streambed sediment structure are not well understood, mostly due to the lack of a standard methodology for their assessment. Here, we illustrate a novel methodology using X-ray computed tomography (CT), as proof of concept, to investigate how, together, clogging and bioturbation affect streambed porosity in a controlled flow-through flume. By visualising gallery formations of an upward conveyor macroinvertebrate; Lumbriculus variegatus as a model species, we quantified bioturbation activity in a clogged streambed, focusing on orientation, depth, and volume at downwelling and upwelling areas of the flume. Gallery creation increased the porosity of the streambed sediment, suggesting a potential improvement in permeability and a possible offset of clogging effects. We illustrate the promising use of X-ray CT as a tool to assess bioturbation in clogged streambeds, and the potential role of bioturbation activity supporting hyporheic exchange processes in streambeds, warranting further studies to understand the extent of bioturbation impacts in natural systems.     

Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets

Algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support.

     

Towards language-to-language transformation

This paper proposes a simplicity-oriented approach and framework for language-to-language transformation of, in particular, graphical languages. Key to simplicity is the decomposition of the transformation specification into sub-rule systems that separately specify purpose-specific aspects. We illustrate this approach by employing a variation of Plotkin’s Structural Operational Semantics (SOS) for pattern-based transformations of typed graphs in order to address the aspect ‘computation’ in a graph rewriting fashion. Key to our approach are two generalizations of Plotkin’s structural rules: the use of graph patterns as the matching concept in the rules, and the introduction of node and edge types. Types do not only allow one to easily distinguish between different kinds of dependencies, like control, data, and priority, but may also be used to define a hierarchical layering structure. The resulting Type-based Structural Operational Semantics (TSOS) supports a well-structured and intuitive specification and realization of semantically involved language-to-language transformations adequate for the generation of purpose-specific views or input formats for certain tools, like, e.g., model checkers. A comparison with the general-purpose transformation frameworks ATL and Groove, illustrates along the educational setting of our graphical WebStory language that TSOS provides quite a flexible format for the definition of a family of purpose-specific transformation languages that are easy to use and come with clear guarantees.

     

What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Enhancement of the hydrogen storage properties of Mg/C nanocomposites prepared by reactive milling with molybdenum

The effect of Mo on the morphology, crystal structure and hydrogen sorption properties of Mg/C composites prepared by reactive milling was studied. Transmission electron microscopic(TEM) observation shows that Mg/C composites prepared with the addition of Mo are of nanoscale with particle size about 20-120 nm after 3 h of milling under 1 MPaH_2. MgH_2 of tetrahedral crystal structure predominates in the materials with the geometric shape of oblique hexagonal prism. From X-ray diffraction(XRD) and hydrogen content studies, Mo and crystallitic carbon have a synergistic effect on promoting the hydrogenation rate in the reactive milling process. From differential scanning calorimetric(DSC) studies, the dehydrogenation peak temperature of the Mg/C materials with Mo is lowered to 299-340 ℃.     

Reaction mechanism and microstructure evolution of reaction sintered <Emphasis Type="Italic">h</Emphasis>-BN

Hexagonal boron nitride ceramic (h-BN) based on the nitridation of B powders was obtained by reaction sintering method. The effects of sintering temperature on the mechanical properties and microstructure of the resultant products were investigated and the reaction mechanism was discussed. Results showed that the reaction between B and N₂ occurred vigorously at temperatures ranging from 1 000 °C to 1 300 °C, which resulted in the generation of t-BN. When the temperature exceeded 1 450 °C, transformation from t-BN to h-BN began to occur. As the sintering temperature increased, the spherical particles of t-BN gradually transformed into fine sheet particles of h-BN. These particles subsequently displayed a compact arrangement to achieve a more uniform microstructure, thereby increasing the strength.     

Numerical simulation of the multicomponent mass transfer during Bridgman growth of CdZnTe crystal using Maxwell-Stefan diffusion model

To reveal the complicated mechanism of the multicomponent mass transfer during the growth of ternary compound semiconductors, a numerical model based on Maxwell-Stefan equations was developed to simulate the Bridgman growth of CdZnTe crystal. The Maxwell-Stefan diffusion coefficients in the melt were estimated. Distributions of Zn, Cd, and Te were calculated with variable ampoule traveling rate and diffusion coefficients. The experimental results show that Zn in melt near the growth interface decreases and diffuses from the bulk melt to the growth interface. For Cd, the situation is just the opposite. The coupling effects of Zn and Cd diffusions result in an uphill diffusion of Te at the beginning of the growth. Throughout the growth, the concentration of Te in the melt keeps low near the growth interface but high far from the growth interface. Increasing the ampoule traveling rate will aggravate the segregation of Zn and Cd, and hence deteriorate the uniformity of Te. We also find that not only the diffusion coefficients but also the ratios between them have significant influence on the species diffusions.