Control of a Batch Processing Machine Serving Compatible Job Families

We consider the control of a single batch processing machine with random arrivals, random processing times, and compatible job families (jobs from different families may be processed together in the same batch, with the processing time distribution of the entire batch determined by the job family in the batch having the greatest expected processing time). The objective is to minimize the long-run average time that jobs spend in the system. We present properties possessed by the optimal policies and discuss the structure of these policies. We next develop a simple heuristic scheduling policy to control the machine. Simulation results are provided to demonstrate the effectiveness of our heuristic over a wide range of problem instances.     

A study of demographic embodiments of product recommendation agents in electronic commerce

Product Recommendation Agents (PRAs) and other web-based decision aids are deployed extensively to provide online shoppers with virtual advising services. While the design of PRA’s functional features has received a high degree of attention in academic studies, the social aspects of human–PRA interactions are comparatively less explored.This paper investigates the potential of enhancing users’ social experiences of interacting with an anthropomorphic PRA (i.e., an agent with human-like characteristics, such as facial expressions, body gestures, or speech output) by manipulating its demographic embodiments. The two demographic variables assessed are ethnicity and gender. As suggested by similarity-attraction theory and social identity theory, the results of our laboratory experiment reveal that PRAs that match the ethnicity, though not the gender, of their users are perceived as more sociable, more enjoyable, and more useful to interact with than the mismatched ones. More interestingly, the “matching-up” effects of ethnicity are more significant among female users than males. Implications for practitioners on how to use an anthropomorphic agent’s demographic characteristics to enhance users’ interaction experience are also discussed.     

Encapsulation of food grade antioxidant in natural biopolymer by electrospinning technique: A physicochemical study based on zein–gallic acid system

Gallic acid was successfully incorporated into zein ultra-fine fibres at different loading amount (5%, 10% and 20%) in order to develop an encapsulating technology for functional ingredient delivery using electrospinning. The produced fibres exhibit diameters ranging from 327 to 387 nm. The physical and thermal properties of encapsulated gallic acid were determined by X-ray diffraction (XRD) and differential scanning calorimetry (DSC); and the interaction between gallic acid and zein was attested by attenuated total reflection-Fourier transform infrared (ATR-FTIR). Thermogravimetric analysis (TGA) demonstrated a different thermal stability of the fabricated complex before and after the gallic acid incorporation. Lastly, the 1,1′-diphenyl-2-picrylhydrazyl (DPPH) assay showed that the gallic acid had retained its antioxidant activity after incorporation in zein electrospun fibres. Overall, electrospinning technique had shown promising results as an efficient and effective method for the preparation of sub-micron structured encapsulated functional ingredient that may find uses in food industry.     

Analytical wide-band modeling of high frequency resistance in integrated spiral inductors

For integrated spiral inductor synthesis, designers and design automation tools require efficient modeling techniques during the initial design space exploration process. In this paper, we introduce an analytical frequency-dependent resistance model for integrated spiral inductors. Based on our resistance formulation, we have developed a systematic technique for creating wide-band circuit models for accurate time domain simulation. The analytical resistance model provides a fast alternative to field solver-based approaches with typical errors of less than 2.6 percent while surpassing the accuracy of several other analytical modeling techniques by an order of magnitude. Furthermore, the wide-band circuit generation technique captures the frequency-dependent resistance of the inductor with typical errors of less than 3.2 percent.     

Parasitic-aware analytical modeling of integrated CMOS inductively degenerated narrow-band low noise amplifiers

In this paper, we present an analytical modeling methodology for fully integrated inductively-degenerated CMOS narrow-band cascode Low Noise Amplifiers (LNA) that captures short channel transistor effects to enable rapid design space exploration in current and future process technologies. The modeling methodology captures the impact of parasitics on passive components, ESD-protection structures, and devices to accurately predict both impedance matching and noise figure. Our modeling is suitable for numerical optimization and fully automated synthesis for LNAs. The results indicate that the methodology models ESD-protected LNA circuits with 47.9% better accuracy in noise figure when compared with several current analytical modeling techniques with four orders of magnitude improvement in simulation time over circuit-level simulation. Given its speed and accuracy, the analytical modeling methodology is well-suited for design space exploration.     

An optimal image transform for threshold-based cloud detection using heteroscedastic discriminant analysis

We present a simple image transform that optimally combines four image channels into a greyscale image for threshold-based cloud detection. These image channels, namely blue, green, red and near infrared, are present on many low Earth-orbit resource satellites. Applying a single threshold to a greyscale image is a computationally efficient method suitable for onboard implementation. We used heteroscedastic discriminant analysis (HDA), which is a generalization of the popular dimension-reducing linear discriminant analysis, to transform the image. Comparative tests between HDA, existing transforms from the remote-sensing literature (the haze optimized and D transforms), as well as the single red and blue image channels were conducted. Although thin clouds remain challenging for global threshold-based techniques, the HDA transform consistently gave the best average segmentation errors across the test dataset. This dataset consisted of 32 1 megapixel Quickbird and Landsat images. HDA has not previously been applied to remote-sensing data.     

Designing intelligent systems to handle system failures: Enhancing explanatory power with less restrictive user interfaces and deep explanations

This research empirically investigates the design choices that can be made to facilitate problem solving when intelligent systems fail. One way is to provide deep explanations, which are explanations that justify system actions. Another way is to manipulate system restrictiveness of the user interface. An experiment was conducted to investigate the effectiveness of deep explanation support, as well as manipulations of system restrictiveness. Results suggest that the less restrictive system was more effective for problem‐solving situations where system failure occurred. In addition, deep explanations were found to be somewhat helpful in system understanding, and this, in turn, led to improved problem‐solving performance.     

High optical quality nanocrystalline diamond with reduced non-diamond contamination

It is important for optical applications of nanocrystalline diamond to achieve low optical absorption as well as optical scattering. We discuss the optical and photocurrent spectra measured by the transmittance and reflectance, photothermal deflection spectroscopy, laser calorimetry and dual beam photocurrent spectroscopy of low non-diamond content nanocrystalline diamond films grown by microwave plasma enhanced chemical vapor deposition on fused silica glass substrates.     

Contextualized access to knowledge: theoretical perspectives and a process-tracing study

This paper investigates the theoretical foundations and empirical evidence concerning contextualized access to task domain knowledge enabled by hypertext-style links. It examines several relevant theoretical perspectives, including theories of discourse comprehension, contextualized learning and the production paradox, and reports on an exploratory study in the knowledge-based systems (KBS) domain. Process-tracing data was collected using a 'thinking-aloud' procedure, and data analysis focused on some highly illustrative verbal protocols. Results indicate that contextualized access to domain knowledge can be critical for understanding KBS output, and that lack of it can cause comprehension difficulties. Contextualized access is highly effective for resolving comprehension difficulties arising from the users' lack of task domain knowledge and for reducing the motivational 'cost' of learning. We conclude that it has the potential for substantially increasing the effectiveness of information systems.     

Understanding the Impact of Inductance in Carbon Nanotube Bundles for VLSI Interconnect Using Scalable Modeling Techniques

In this paper, we develop accurate and scalable models for the magnetic inductance in bundles of single-walled carbon nanotubes, which have been proposed as a means to alleviate the increasingly critical resistance problems associated with traditional copper interconnect in very large scale integration (VLSI) applications. The models consider the density and statistical distribution of both metallic and semiconducting nanotubes within the bundle. We evaluate the speed, accuracy, and scalability of our magnetic inductance modeling techniques and previously proposed inductance models. The inductance model with the best performance evaluates the magnetic inductance of nanotube bundles with excellent accuracy when compared to modeling each nanotube individually and provides orders of magnitude improvement in CPU time as the bundle size increases. Leveraging the magnetic inductance modeling techniques, we determine the relative impact of magnetic and kinetic inductance. Based on our results, the relative value of magnetic and kinetic inductance on single-walled carbon nanotube (SWCNT) bundles is highly dependent on the bundle geometry and the per unit length kinetic inductance