Fractional Electromagnetic Field Theory and Its Applications

Fractional electromagnetic field theory describes electromagnetic wave propagation through the complex, nonlocal, dissipative, fractal and also recent artificially engineered materials know as fractional metamaterials. In this theory using the fractional Maxwell equations we are also able to consider the well-known effect of electromagnetic memory. In this review we present some applications of the powerful theory of fractional electrodynamics.Comment: First version of the short lecture on "Fractional Electromagnetic Field Theory and Its Applications

Interactional effects of climate change factors on the water status, photosynthetic rate, and metabolic regulation in peach

Environmental stress factors caused by climate change affect plant growth and crop production, and pose a growing threat to sustainable agriculture, especially for tree crops. In this context, we sought to investigate the responses to climate change of two Prunus rootstocks (GF677 and Adesoto) budded with Catherina peach cultivar. Plants were grown in 15 L pots in temperature gradient greenhouses for an 18 days acclimation period after which six treatments were applied: [CO2 levels (400 versus 700 mol mol-1), temperature (ambient versus ambient + 4°C), and water availability (well irrigated versus drought)]. After 23 days, the effects of stress were evaluated as changes in physiological and biochemical traits, including expression of relevant genes. Stem water potential decreased under drought stress in plants grafted on GF677 and Adesoto rootstocks; however, elevated CO2 and temperature affected plant water content differently in both combinations. The photosynthetic rate of plants grafted on GF677 increased under high CO2, but decreased under high temperature and drought conditions. The photosynthetic rates of plants grafted onto Adesoto were only affected by drought treatment. Furthermore, in GF677-Catherina plants, elevated CO2 alleviated the effect of drought, whereas in those grafted onto Adesoto, the same condition produced acclimation in the rate. Stomatal conductance decreased under high CO2and drought stress in both graftedrootstocks, and the combination of these conditions improved water-use efficiency.Changes in the sugar content in scion leaves and roots were significantly differentunder the stress conditions in both combinations. Meanwhile, the expression of mostof the assessed genes was significantly affected by treatment. Regarding genotypes,GF677 rootstock showed more changes at the molecular and transcriptomic level thandid Adesoto rootstock. A coordinated shift was found between the physiological statusand the transcriptomic responses. This study revealed adaptive responses to climatechange at the physiological, metabolic, and transcriptomic levels in twoPrunusrootstocks budded with 'Catherina'. Overall, these results demonstrate the resilient capacity andplasticity of these contrasting genotypes, which can be further used to combat ongoingclimate changes and support sustainable peach production

Renewing the Budget: Recommendations for Louisiana’s Renewable Energy Tax Credit

Long-term operation of energy systems is a complex optimization task. Often, such long-term operational optimizations are solved by direct decomposing the problem into smaller subproblems. However, direct decomposition is not possible for problems with time-coupling constraints and variables. Such time-coupling is common in energy systems, e.g., due to peak power prices and (seasonal) energy storage. To efficiently solve coupled long-term operational optimization problems, we propose a time-series decomposition method. The proposed method calculates lower and upper bounds to obtain a feasible solution of the original problem with known quality. We compute lower bounds by the Branch-and-Cut algorithm. For the upper bound, we decompose complicating constraints and variables into smaller subproblems. The solution of these subproblems are recombined to obtain a feasible solution for the long-term operational optimization. To tighten the upper bound, we iteratively decrease the number of subproblems. In a case study for an industrial energy system, we show that the proposed time-series decomposition method converges fast, outperforming a commercial state-of-the-art solver

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