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We examine the effect of varying the temperature points on MEMS inertial sensors’ noise models using Allan variance and least-squares spectral analysis (LSSA). Allan variance is a method of representing root-mean-square random drift error as a function of averaging times. LSSA is an alternative to the classical Fourier methods and has been applied successfully by a number of researchers in the study of the noise characteristics of experimental series. Static data sets are collected at different temperature points using two MEMS-based IMUs, namely MotionPakII and Crossbow AHRS300CC. The performance of the two MEMS inertial sensors is predicted from the Allan variance estimation results at different temperature points and the LSSA is used to study the noise characteristics and define the sensors’ stochastic model parameters. It is shown that the stochastic characteristics of MEMS-based inertial sensors can be identified using Allan variance estimation and LSSA and the sensors’ stochastic model parameters are temperature dependent. Also, the Kaiser window FIR low-pass filter is used to investigate the effect of de-noising stage on the stochastic model. It is shown that the stochastic model is also dependent on the chosen cut-off frequency.

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This study was carried out using 22 promising restorer lines of rice and their parental lines to study genetic variability and genetic advance for yield and yield-associated grain quality traits and floral traits. These genotypes are evaluated in a replicated trial using Randomized Complete Block Design (RCBD) with three replications at the Experimental Farm of Sakha Agricultural Research Station, Sakha, Kafr El-Sheikh, Egypt, during the seasons from 2012 to 2020. Analysis of variance revealed that highly significant variations were observed among the genotypes for all the studied characters. Both GCV% and PCV% were high for the number of spikelets per panicle, the number of filled grains per panicle, and panicle weight. The genetic advance in the percentage of mean was high for days to plant height, panicle length, number of spikelets per panicle, number of filled grains per panicle, panicle weight, grain yield per plant, anther length, anther breadth, duration of floret opening, and head rice percentage. Mean performance of the rice genotypes indicated that the genotypes NRL 59, NRL 55, NRL 62, NRL 63, NRL 66, and NRL 54-2 were promising for grain yield and associated desirable traits. Thus, some of these promising lines can be promoted as a new rice variety and could be used as a source for developing new hybrid combinations in hybrid rice breeding programs. The percentage of advantage over better parent and Giza 178 as the commercial variety was significant and there were highly significant desirable values among the genotypes for all the studied traits in the two years, indicating that the selection is effective in the genetic improvements for these traits.

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal. K E Y W O R D S 1. Neural network. 2. MEMS. 3. INS.

In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at −40 °C, −20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

This study was carried out to investigate the ameliorating effect of dietary curcumin Curcuma longa (CUR) against the subacute toxicity of fipronil (FIP) on Oreochromis niloticus. One hundred and eighty fish were divided into six groups and kept for 30 days; the first group was kept as a control group fed on commercial diet, while the second and third groups were fed on commercial diets supplemented with 1% (CUR1) and 3% (CUR3) curcumin powder/kg diet respectively. The fourth (FIP), fifth (FIP1) and sixth (FIP3) experimental groups were intoxicated with FIP (1/10 96 h LC50), where (FIP1) and (FIP3) groups were fed on a commercial diet supplemented with 1% and 3% of curcumin powder respectively. Hepatorenal damage markers, immunological, tissue antioxidant parameters and anterior kidney expression of IL‐8, IL‐1β and TGF‐β1 genes were determined. Curcumin alleviated the deteriorative effects of FIP intoxication through decreasing hepatic and renal damage markers, improving serum respiratory burst and lysozyme activities. Curcumin provoked a marked decrease in hepatic and renal malondialdehyde and nitric oxide concentration with a significant improvement in tissues' antioxidant status in FIP‐intoxicated fish. Gene expression analysis revealed a significant down‐expression of pro‐inflammatory markers genes after supplementation of curcumin in FIP‐intoxicated fish. In conclusion, the use of 3% curcumin as a feed additive could be implemented to protect fish against the toxic effects of agrochemical wastes via restoring antioxidant and immunological parameters of intoxicated fish.