This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis.

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial leastsquares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions.

Many agent based models require agents to have an awareness of their local peers. The handling of these fixed radius near neighbours (FRNNs) is often a limiting factor of performance. However without a standardised metric to assess the handling of FRNNs, contributions to the field lack the rigorous appraisal necessary to expose their relative benefits. This paper presents a standardised specification of a multi agent based benchmark model. The benchmark model provides a means for the objective assessment of FRNNs performance, through the comparison of implementations. Results collected from implementations of the benchmark model under three agent based modelling frameworks show the 64-bit floating point performance of each framework to scale linearly with agent population, in contrast the GPU accelerated framework's 32-bit floating point performance only became linear after maximal device utilisation around 100,000 agents.

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Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i.e. small size, low weight, and low energy consumption). Research in this area aims to enable robots with similarly efficient flight and cognitive abilities. Visual processing is important to flying insects' impressive flight capabilities, but currently, embodiment of insect-like visual systems is limited by the hardware systems available. Suitable hardware is either prohibitively expensive, difficult to reproduce, cannot accurately simulate insect vision characteristics, and/or is too heavy for small robotic platforms. These limitations hamper the development of platforms for embodiment which in turn hampers the progress on understanding of how biological systems fundamentally work. To address this gap, this paper proposes an inexpensive, lightweight robotic system for modelling insect vision. The system is mounted and tested on a robotic platform for mobile applications, and then the camera and insect vision models are evaluated. We analyse the potential of the system for use in embodiment of higher-level visual processes (i.e. motion detection) and also for development of navigation based on vision for robotics in general. Optic flow from sample camera data is calculated and compared to a perfect, simulated bee world showing an excellent resemblance.