Waterlogging and salinity management in the Sindh Province. Volume 1 - The irrigated landscape: resource availability across the hydrological divides

Irrigation management / River basins / Irrigated farming / Climate / Irrigation systems / Irrigation canals / Discharges / Water balance / Waterlogging / Salinity / Groundwater development / Tube wells / Water table / Drainage / Public sector / Land reclamation / Pakistan / Sindh Province / Indus Basin / Rohri / Larkana / Shikarpur / Hairdin / North Dadu / Ghotki / East Khairpur / Sukkur Barrage

Synthesis and characterization of silver nanoarticles from extract of Eucalyptus citriodora

The primary motivation for the study to develop simple eco-friendly green synthesis of silver nanoparticles using leaf extract of Eucalyptus citriodora as reducing and capping agent. The green synthesis process was quite fast and silver nanoparticles were formed within 0.5 h. The synthesis of the particles was observed by UV-visible spectroscopy by noting increase in absorbance. Characterization of the particles was carried out by X-ray diffraction, FTIR and electron microscopy. The developed nanoparticles demonstrated that E. citriodora is good source of reducing agents. UV-visible absorption spectra of the reaction medium containing silver nanoparticles showed maximum absorbance at 460 nm. FTIR analysis confirmed reduction of Ag+ to Ag0 atom in silver nanoparticles. The XRD pattern revealed the crystalline structure of silver nanoparticles. The SEM analysis showed the size and shape of the nanoparticles. The method being green, fast, easy and cost effective can be recommended for large scale production of AgNPs for their use in food, medicine and materials

Deep learning control for digital feedback systems: Improved performance with robustness against parameter change

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control

Deep learning for robust adaptive inverse control of nonlinear dynamic systems: Improved settling time with an autoencoder

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system

PROGESTERONE AND ESTRADIOL PROFILES DURING ESTROUS CYCLE AND GESTATION IN DWARF GOATS (CAPRA HIRCUS)

Serum progesterone and estradiol profiles during estrous cycle, gestation and parturition in four Dwarf goat females (Capra hircus) were monitored. Blood sampling was carried out daily during estrous cycle and on alternate days during gestation till parturition. Observations regarding length of estrous cycle, gestation length, litter size and birth weight of kids were recorded. With the initiation of cyclicity, estradiol attained higher levels (7.7 ± 1.7 pg/ml) at estrus phase and dropped down to the lower levels within 3 to 4 days post-estrus. Concomitantly, progesterone started to increase from the mean basal value of 0.1 ± 0.03 ng/ml on day-0 to 3.0 ± 0.9 ng/ml on day-6 of estrous cycle and reached the peak value of 7.7 ± 0.6 ng/ml on day-12. From day-15, a decline was observed in progesterone values till the end of the cycle. A second estradiol rise of 14.0 ± 1.2pg/ml was observed on day-18 of the cycle. The mean estrous cycle length was 18.2 ± 2.1 days. During gestation, higher progesterone levels were maintained in the range of 4.3–11.0 ng/ml. Estradiol remained at lower concentrations for 30-50 days of gestation, then gradually increased and reached 270 ± 13.0 pg/ml a few days before parturition. It dropped again to basal values within 1-2 days postpartum. The mean gestation length in Dwarf goats was 144.8 ± 3.9 days and the litter size was 1.8 ± 0.5. It was concluded that Dwarf goat is a prolific breed, having a short gestation length with multiple births being common