Differential Evolution Algorithm with Diversified Vicinity Operator for Optimal Routing and Clustering of Energy Efficient Wireless Sensor Networks

Due to large dimension of clusters and increasing size of sensor nodes, finding the optimal route and cluster for large wireless sensor networks (WSN) seems to be highly complex and cumbersome. This paper proposes a new method to determine a reasonably better solution of the clustering and routing problem with the highest concern of efficient energy consumption of the sensor nodes for extending network life time. The proposed method is based on the Differential Evolution (DE) algorithm with an improvised search operator called Diversified Vicinity Procedure (DVP), which models a trade-off between energy consumption of the cluster heads and delay in forwarding the data packets. The obtained route using the proposed method from all the gateways to the base station is comparatively lesser in overall distance with less number of data forwards. Extensive numerical experiments demonstrate the superiority of the proposed method in managing energy consumption of the WSN and the results are compared with the other algorithms reported in the literature

Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition

Electricity price forecasting is one of the more complex processes, due to its non-linearity and highly varying nature. However, in today\u27s deregulated market and smart grid environment, the forecasted price is one of the important data sources used by producers in the bidding process. It also helps the consumer know the hourly price in order to manage the monthly electricity price. In this paper, a novel electricity price forecasting method is presented, based on the Artificial Bee Colony optimized Extreme Learning Machine (ABC-ELM) with wavelet decomposition technique. This has been attempted with two different input data formats. Each data format is decomposed using wavelet decomposition, Daubechies Db4 at level 6; all the decomposed data are forecasted using the proposed method and aggregate is formed for the final prediction. This prediction has been attempted in three different electricity markets, in Finland, Switzerland and India. The forecasted values of the three different countries, using the proposed method are compared with various other methods, using graph plots and error metrics and the proposed method is found to provide better accuracy

Isotopic and spin selectivity of H_2 adsorbed in bundles of carbon nanotubes

Due to its large surface area and strongly attractive potential, a bundle of carbon nanotubes is an ideal substrate material for gas storage. In addition, adsorption in nanotubes can be exploited in order to separate the components of a mixture. In this paper, we investigate the preferential adsorption of D_2 versus H_2(isotope selectivity) and of ortho versus para(spin selectivity) molecules confined in the one-dimensional grooves and interstitial channels of carbon nanotube bundles. We perform selectivity calculations in the low coverage regime, neglecting interactions between adsorbate molecules. We find substantial spin selectivity for a range of temperatures up to 100 K, and even greater isotope selectivity for an extended range of temperatures,up to 300 K. This isotope selectivity is consistent with recent experimental data, which exhibit a large difference between the isosteric heats of D_2 and H_2 adsorbed in these bundles.Comment: Paper submitted to Phys.Rev. B; 17 pages, 2 tables, 6 figure

Determinants of Adherence to Recommendations of the Dietary Approach to Stop Hypertension in Adults with Hypertension Treated in a Hospital in Benin

Abstract The dietary approach to stop hypertension (DASH) is an effective nutritional strategy to prevent and treat cardiovascular disease. Optimal benefit from dietary recommendations in management of hypertension depends on the compliance. This analytic cross sectional study aimed at establishing determinants of DASH among adults with hypertension treated at hospital in Benin. The study included 150 hypertensive adults selected during medical visit for blood pressure monitoring at hospital Saint-Luc in Cotonou from June 3 rd to July 1 st , 2014. Data on consumption of sodium, fruits and vegetables, alcohol, saturated and trans fat rich products were collected by questionnaire. A score of adherence to DASH was built. Determinants of adherence to DASH were identified using logistic regression model. Only 20% of subjects showed adherence to DASH. Better knowledge on hypertension OR=5.18 (95%IC 1.98-13.22) and healthy dietary habits and lifestyle prior to diagnosis of hypertension OR=4.26 (95%IC 1.67-13.18) increased the likelihood of adherence to dietary recommendations for hypertension management. Nutrition education and information of patients on hypertension and its complications during medical consultations may increase their adherence to dietary recommendations for management of the disease

Perlecan (HSPG2) promotes structural, contractile, and metabolic development of human cardiomyocytes

Perlecan (HSPG2), a heparan sulfate proteoglycan similar to agrin, is key for extracellular matrix (ECM) maturation and stabilization. Although crucial for cardiac development, its role remains elusive. We show that perlecan expression increases as cardiomyocytes mature in vivo and during human pluripotent stem cell differentiation to cardiomyocytes (hPSC-CMs). Perlecan-haploinsuffient hPSCs (HSPG2+/−) differentiate efficiently, but late-stage CMs have structural, contractile, metabolic, and ECM gene dysregulation. In keeping with this, late-stage HSPG2+/− hPSC-CMs have immature features, including reduced ⍺-actinin expression and increased glycolytic metabolism and proliferation. Moreover, perlecan-haploinsuffient engineered heart tissues have reduced tissue thickness and force generation. Conversely, hPSC-CMs grown on a perlecan-peptide substrate are enlarged and display increased nucleation, typical of hypertrophic growth. Together, perlecan appears to play the opposite role of agrin, promoting cellular maturation rather than hyperplasia and proliferation. Perlecan signaling is likely mediated via its binding to the dystroglycan complex. Targeting perlecan-dependent signaling may help reverse the phenotypic switch common to heart failure

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Dispatching a 19-Unit Indian Utility System Using a Refined Differential Evolution Algorithm

This paper presents a differential evolution with neighborhood based mutation (DE-NM) technique to solve Dynamic Economic Dispatch (DED) problem with valve point effects and multiple fuel options. A new mutation scheme based on neighborhood topology is presented with an aim to achieve the cost reduction together satisfying the dynamic behavior of the generating units over the considered time period. The neighborhood based mutation (NM) balances the exploration and exploitation of the search effort of differential evolution (DE) technique. The NM method enhances the convergence speed and the performance of the DE technique. The performance of the DE-NM is tested on a 10-unit and a real public Indian utility system with 19 generating units. Both the test systems are illustrated under different load patterns. The dispatch results obtained using the proposed method for the Indian system have considerably reduced the operating cost and optimized its operation

Deterministically guided differential evolution for constrained power dispatch with prohibited operating zones

Department of Electrical Engineering, Anna University Regional Centre, Coimbatore, India This paper presents a new approach to solve economic load dispatch (ELD) problem in thermal units with non-convex cost functions using differential evolution technique (DE). In practical ELD problem, the fuel cost function is highly non linear due to inclusion of real time constraints such as valve point loading, prohibited operating zones and network transmission losses. This makes the traditional methods fail in finding the optimum solution. The DE algorithm is an evolutionary algorithm with less stochastic approach to problem solving than classical evolutionary algorithms.DE have the potential of simple in structure, fast convergence property and quality of solution. This paper presents a combination of DE and variable neighborhood search (VNS) to improve the quality of solution and convergence speed. Differential evolution (DE) is first introduced to find the locality of the solution, and then VNS is applied to tune the solution. To validate the DE-VNS method, it is applied to four test systems with non-smooth cost functions. The effectiveness of the DE-VNS over other techniques is shown in general