Behavior of a Deactivating HZSM-5 with Varying Al3+ Content in Vapor Phase Aminocyclization

Aminocyclization of aldehydes to synthesize pyridine bases is an interesting reaction with HZSM-5 catalyst undergoing rapid deactivation. The catalyst characterization studies like NH₃-TPD, XRD, TGA/DTA, Pore size distribution, SEM, CHNS, IR for the fresh and deactivated catalyst at various Si/Al ratio content as evidenced by the striking difference in the behavior of HZSM-5 catalyst in terms of its physicochemical characteristics, coke formation tendencies and higher selectivity to pyridine formation.     

Statistical process control for monitoring non-linear profiles using wavelet filtering and B-Spline approximation

A statistical process control framework is proposed to monitor non-linear profiles. The proposed methodology aims at identifying mean shifts or ‘shape changes’ in a profile. Discrete wavelet transformation (DWT) is applied to separate variation or noise from profile contours. B-splines are adopted to generate critical points to define the shape of a profile. The proposed method is innovative in that users can divide a profile into multiple segments to be monitored simultaneously. The high dimensionality problem that hinders the implementation of multivariate control charts can be solved by this framework. The distance difference statistic for each segment provides diagnostic information when the process of interest is out of control. These proposed statistics form a vector to be fed into any multivariate control chart such as the Hotelling T ² control chart. A decomposition method can also be applied on the T ² statistics when an out-of-control profile is detected. A simulation study applied to a forging process is conducted to demonstrate the property of the proposed method. The proposed method is capable of detecting profile shifts and identifying the exact location of problematic segments.     

Calibration of artificial neural network and partial least squares regression models for the prediction of secoisolariciresinol diglucoside contents in microwave-assisted extracts of various flaxseed (Linum usitatissimum L.) samples

Partial least squares (PLS) and artificial neural network (ANN) regression models were calibrated for predicting the content of secoisolariciresinol diglucoside (SDG) in six flaxseed cultivars, defatted flaxseed meal and flax hulls extracts. The SDG was quantified by HPLC after microwave-assisted extraction (MAE) from flaxseed; the data were used in conjunction with the light absorption of the extracts measured after Folin–Ciocalteu’s assay at 289, 298, 343 and 765 nm, in order to calibrate the predictive PLS and ANN models. The accuracy and the predictive ability of the models ranged from good to excellent as indicated by RPD values (the ratio of the standard deviation of the reference values to the standard error of prediction) of 5.03–13.7. The PLS and ANN predictive models are useful to the flaxseed processing industry for rapidly and accurately predicting the SDG contents of various flaxseed samples based on their UV–Vis light absorption.     

DART—a High Level Software-Defined Radio Platform Model for Developing the Run-Time Controller

Novel cognitive radio platforms, such as IMEC’s COgnitive Baseband RAdio (COBRA), should ensure the feasibility of multiple streams and their reconfigurability and scalability during run-time. The control over these tasks should be dedicated to a run-time controller that (re)allocates the resources on the platform. E.g., when the channel conditions change requiring a switching to different modulation and coding scheme or a user starts a new stream. Current transaction level models are too detailed for rapid exploration of all run-time options and the high-level data-flow frameworks (such as Kahn process networks) lack the dynamism and reconfigurability that is essential for the exploration. In this paper we propose the DAtaflow for Run-Time (DART), the high-level dynamic data-flow platform model framework, suited for rapid run-time control development. We sketch how to use this framework to develop such a controller in the reactive and more challenging, proactive way. We derive the component timing based on Instruction Set Simulator (ISS) simulation and the reconfiguration timing based on Transaction Level Modeling (TLM) simulation. Finally, we verify results of our DART approach with full TLM simulation of our platform.     

Protein Denaturation,Rheology, and Gelation Characteristics of Radio-Frequency Heated Egg White Dispersions

Gel properties of radio frequency (RF) heated egg white dispersions at 27.12 MHz were studied as function of concentration (2.5–2.5 kg/100 kg sample), pH (3–11) and heating time (60–180 s). Egg white dispersions demonstrated a gradual liquid-solid transformation as they denatured and gelled during RF treatment. The critical concentration and heating period for egg white protein denaturation and gelation were found to be 7.5% (w/w) and 150 seconds. The elastic modulus (G′) of RF-heated samples increased with concentration and heating period (temperature), whereas complex viscosity (η*) increased exponentially with concentration. In an alkaline condition, the egg white dispersion did not produce a gel; however, in acidic condition it resulted in a strong gel with significantly (P?<?0.05) higher G′. This could be attributed to the high dielectric constant (ε′) and loss factor (ε″) values of acidified samples as compared to the alkaline and control egg white dispersion. Effect of heating rate (1, 5, 10, and 20°C/min) in situ on rheometer plate significantly affected gel rigidity; the RF treated sample rigidity was comparable to samples heated at the rate of 5 and 10°C/min. Differential scanning calorimetry, dielectric measurement, and sodium dodecyl sulfate (SDS) PAGE electrophoresis results were used to confirm gelation behavior during both conventional and RF heating conditions.     

Dielectric Properties of Pork Muscle

Dielectric properties were measured across a frequency range from 200 MHz to 20 GHz and temperature range from 25 to 85°C for pork meat samples (grouped in four different quality classes). Dielectric properties varied widely with frequency and the loss tangents of different meat quality classes followed different trends. Discriminant analysis indicated that 2400 MHz was most significant (P < 0.05) for separation of the different classes. There were significant changes (P < 0.05) in the dielectric properties within the range of the temperatures corresponding to protein denaturation temperatures. There was clear segregation between the firm and non-exudative (red, firm, and non-exudative; pale, firm, and non-exudative) and the soft and exudative (red, soft, and exudative; pale, soft and exudative) samples. The results showed that dielectric properties could be useful in meat quality classifications.