The Economic Value of Groundwater Irrigation for Food Security Under Climate Change: Implication of Representative Concentration Pathway Climate Scenarios

Sustainable management of groundwater resources to support food security under the potential effects of climate change is an emerging area of research and particularly relevant in the context of Small Island Developing States. Employing three regional downscaled Representative Connection Pathway (RCP 2.6, RCP4.5, and RCP 8.5) emission scenarios that have been linked to an economic evaluation framework, the potential impact of climate change on groundwater scarcity, economic value of groundwater irrigation, food security, and farming livelihoods is investigated. A nonlinear hydro-economic framework, which integrates groundwater hydrology, climate data, land use, economics and institutions, has been applied for the island of Barbados. Results indicate that climate change would intensify the dependency on groundwater irrigation overtime, modulated by climate intensity. The strength of climate change will boost the marginal value of groundwater irrigation, as food price will scale up, presenting negative impacts on food security and reducing farming livelihoods. The climate change would also result in higher cost of producing foods resulting from increased cost of pumping, mainly driven by the effect of meeting abstraction needs for domestic and municipal consumption. Our primary results show that for a small island, sustaining groundwater resources will be a challenging objective to achieve under severe climate change.     

Efficient Rate-Distortion Optimized Media Streaming for Tree-Structured Packet Dependencies

When streaming packetized media data over a lossy packet network, it is desirable to use transmission strategies that minimize the expected distortion subject to a constraint on the expected transmission rate. Because the computation of such optimal strategies is usually an intractable problem, fast heuristic techniques are often used. We first show that when the graph that gives the decoding dependencies between the data packets is reducible to a tree, optimal transmission strategies can be efficiently computed with dynamic programming algorithms. The proposed algorithms are much faster than other exact algorithms developed for arbitrary dependency graphs. They are slower than previous heuristic techniques but can provide much better solutions. We also show how to apply our algorithms to find high-quality approximate solutions when the dependency graph is not tree reducible. To validate our approach, we run simulations for MPEG1 and H.264 video data. We first consider a simulated packet erasure channel. Then we implement a real video streaming system and provide experimental results for an Internet connection.     

Positive sliding mode control for blood glucose regulation

Biological systems involving positive variables as concentrations are some examples of so-called positive systems. This is the case of the glycemia–insulinemia system considered in this paper. To cope with these physical constraints, it is shown that a positive sliding mode control (SMC) can be designed for glycemia regulation. The largest positive invariant set (PIS) is obtained for the insulinemia subsystem in open and closed loop. The existence of a positive SMC for glycemia regulation is shown here for the first time. Necessary conditions to design the sliding surface and the discontinuity gain are derived to guarantee a positive SMC for the insulin dynamics. SMC is designed to be positive everywhere in the largest closed-loop PIS of plasma insulin system. Two-stage SMC is employed; the last stage SMC2 block uses the glycemia error to design the desired insulin trajectory. Then the plasma insulin state is forced to track the reference via SMC1. The resulting desired insulin trajectory is the required virtual control input of the glycemia system to eliminate blood glucose (BG) error. The positive control is tested in silico on type-1 diabetic patients model derived from real-life clinical data.     

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

Abstract—This article presents an efficient multi-objective optimization approach based on the supervised big bang–big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The MathWorks, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.     

A hybrid HNN-QP approach for dynamic economic dispatch problem

This paper introduces a solution of the dynamic economic dispatch (DED) problem using a hybrid approach of Hopfield neural network (HNN) and quadratic programming (QP). The hybrid algorithm is based on using enhanced HNN; to solve the static part of the problem; the QP algorithm for solving the dynamic part of the DED. This technique guarantees the global optimality of the solution due to its look-ahead capability. The new algorithm is applied and tested to an example from the literature and the solution is then compared with that obtained by some other techniques to prove the superiority and effectiveness of the proposed algorithm.     

Full and reduced-order H∞ filtering of Takagi–Sugeno fuzzy time-varying delay systems: Input–output approach

This article addresses the issue of delay-dependent H_∞ filtering design for TakagiŮSugeno fuzzy time-varying delay systems using the input–output approach. A three-term approximation model has been used to transform the original system into two interconnected subsystems. Since the nonquadratic Lyapunov–Krasovskii functional requires to deal with the membership function's (MF) time derivative, upper-bound inequalities have been added to the obtained conditions. Based on the scaled small gain theorem, nonquadratic Lyapunov–Krasovskii functional approach and considering the bounds of the MF time-derivative, the H_∞ full- and reduced-order filters are designed and then formulated in terms of linear matrix inequalities. Finally, illustrative examples are presented to demonstrate the validity of the proposed methods.     

A passivity‐based simple adaptive synergetic control for a class of nonlinear systems

For a class of nonlinear systems with the representation {A(x), B(x), C} and where the system parameters and dynamics are unknown, a simple adaptive synergetic controller ensuring the asymptotic convergence of the system to a desired manifold is proposed based on the technique of simple adaptive control (SAC). It is well known that the design of the synergetic control (SC) law requires a thorough knowledge of the system parameters and dynamics. Such problem obstructs the synthesis of the SC law and the designer is prompted to pass through the estimation methods, which, in turn, poses a problem of increasing the computation time of the control algorithm. To cope with this problem, a solution is proposed by modifying the original SC law to develop an SAC‐like adaptive SC law without the need of prior knowledge of the system. The stability of the proposed adaptive controller is formally proven via the Lyapunov approach. Experimental application to a quadrotor system is given to validate the theoretical results.     

Distribution system reconfiguration using a modified Tabu Search algorithm

This article presents an efficient meta-heuristic method for reconfiguration of distribution systems. A modified Tabu Search (MTS) algorithm is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off sectionalizing switches. TS algorithm is introduced with some modifications such as using a tabu list with variable size according to the system size. Also, a random multiplicative move is used in the search process to diversify the search toward unexplored regions. The Kirchhoff algebraic method is adopted to check the radial topology of the system. A salient feature of the MTS method is that it can quickly provide a global optimal or near-optimal solution to the network reconfiguration problem. To verify the effectiveness of the proposed approach, the effect of load variation is taken into consideration and comparative studies are conducted on three test systems with rather encouraging results. The obtained results, using the proposed MTS approach, are compared with that obtained using other approaches in the previous work.