A smart predictive framework for system-level stormwater management optimization.

This study presents a global real-time control (RTC) approach for sustainable and adaptive management of stormwater. A network of inter-connected devices are assumed to dynamically generate the required set-points for the system actuators at the remote control center where global optimization algorithms calculate real-time operational decision-making target values. These target values activate the local controllers to manipulate the spatially distributed detention basin's outlets enabling a smart catchment scale optimal control. A real world watershed with four outlets to a nearby watercourse is chosen to test the applicability and efficiency of the proposed dynamic control approach, based on model simulation results. Results show that the proposed autonomous control approach has the ability to enhance the global performance of the stormwater management system in terms of quality and quantity to balance the network flow dynamics and environmental demands, while reducing the potential for erosion of receiving water bodies. Climate change is specifically discussed as a challenge for the designed control framework. Although, the performance criteria are shown to be affected by the increased rainfall intensities compared to actual rainfall scenarios, the proposed methodology still improves the peak flow reduction and detention time of water, at global scale, up to 54% and 14 h respectively under climate change conditions.

Effects of irrigation intervals and organic manure on morphological traits, essential oil content and yield of oregano ( Origanum vulgare L.).

In order to evaluate the effect of irrigation intervals and cattle manure levels on morphological traits, essential oil content and yield of oregano, an experiment was conducted at the experimental Farm of the Faculty of Agriculture, Urmia University, Iran. The experimental design was split-plots, arranged in randomized complete blocks with three replications. Main plots including irrigation intervals (1, 2 and 3 weeks) and four levels of cattle manure at 0, 10, 20 and 30 t ha-1 were allocated to sub plots. Our results showed that increasing irrigation intervals reduced values of all morphological traits except for proportion of stems. Also, values for stems number, plant spread, stem diameter, leaf area, fresh and dry herb yield increased by increasing cattle manure levels. On the other hand, morphological traits not influenced by interaction of treatments except for plant spread and leaf area. The highest essential oil content (2.07%) and yield (66.62 kg ha-1) obtained in highest irrigation intervals and cattle manure levels. Whereas, 1 week irrigation interval without use of cattle manure produce lowest essential oil content (1.55%). For essential oil yield, the lowest value (46.37 kg ha-1) was found in 2 weeks irrigation interval with application of 20 t ha-1 cattle manure.

Metacognitive Octonion-Valued Neural Networks as They Relate to Time Series Analysis.

In this paper, a metacognitive octonion-valued neural network (Mc-OVNN) learning algorithm and its application to diverse time series prediction are presented. The Mc-OVNN is comprised of two components: the octonion-valued neural network that represents the cognitive component and the metacognitive component that serves to self-regulate the learning algorithm. At each epoch, the metacognitive component decides if, how, and when learning occurs. The algorithm deletes unneeded samples and only stores those that will be used. This decision is determined by the octonion magnitude and the seven phases. To evaluate the Mc-OVNN algorithm's performance, it is applied to five real-world forecasting problems: the power consumption of a home in Honolulu, HI, USA, Box and Jenkins J series, Euro to Algerian Dinar (DZ) real-time conversion rates, the Mackey-Glass equation, and Europe Brent oil price prediction in a time series. When comparing the Mc-OVNN to other relevant techniques, Mc-OVNN displays its capability for efficient time series prediction. The real-time evaluation of the proposed algorithm is presented using the power consumption of a home in Boumerdès, Algeria, as a case study.

Constructing a Mouse Oct4 Promoter/EGFP Vector, as a Whole-Cellular Reporter to Monitor the Pluripotent State of Cells.

BACKGROUND: The transcription factor Oct-4, is an important marker of undifferentiating level and a key regulating factor for maintenance of pluripotency in cells. Establishment of an Oct-4 promoter-based reporter system is an appropriate tool for monitoring the differentiation of embryonic stem cells both in vivo and in vitro. METHODS: In the present study, we report construction of a recombinant vector, pDB2 Oct4 promoter/EGFP, in which expression of Enhanced Green Fluorescent Protein (EGFP) was controlled by the mouse Oct-4 promoter. RESULTS: In transfected mouse embryonic stem cells with this vector, EGFP was predicted to be specifically expressed in pluripotency state. After transfection, high-level expression of EGFP under the control of Oct-4 promoter was observed in manipulated embryonic stem cells. CONCLUSION: Thus, our new cellular reporter showed that both the properties of embryonic cells and expression the EGFP could be of great help in studying the differentiating and reprogramming mechanisms of mESCs.