Optical Signal Processing With Discrete-Space Metamaterials

As digital circuits are approaching the limits of Moore’s law, a great deal of efforthas been directed to alternative computing approaches. Among them, the old concept of optical signal processing (OSP) has attracted attention, revisited in the light of metamaterials and nano-photonics. This approach has been successful in realizing basic mathematical operations, such as derivatives and integrals, but it is difficult to be applied to more complex ones. Inspired by digital filters, we propose a radically new OSP approach, able to realize arbitrary mathematical operations over a nano-photonic platform. We demonstrate this concept for the case of spatial differentiation, image compression and color encoding through a heuristic design based on a waveguide with periodic arrays of input/output channels at its opposite walls

Application efficacy of newly released pre-mixed herbicide in winter wheat: Joystick®

Saabunud / Received 29.01.2022 ; Aktsepteeritud / Accepted 28.04.2022 ; Avaldatud veebis / Published online 28.04.2022 ; Vastutav autor / Corresponding authors: Ebrahim Mamnoie, Akbar Aliverdi ; [email protected], [email protected] a field experiment, the efficacy of the newly released pre-mixed herbicide, Joystick®, in comparison with other pre-mixed herbicides was evaluated in winter wheat, Iran. The treatments included: weedy check, weed-free check (hand-weeded), Bromicide®MA at 600 g a.i. ha–1 + Axial® at 60 g a.i. ha–1 , Othello® at 96 g a.i. ha–1 , Axial One® at 55, 65, 75, and 85 g a.i. ha–1 , Joystick® at 80, 94, and 108 g a.i. ha–1 . The latter three treatments mentioned were applied with and without non-ionic surfactant Citogate® at 0.1% v v–1 . The results revealed that all treatments significantly decreased the density and dry biomass of each weed species and increased the grain yield and biological yield of wheat. The highest performing treatment was Bromicide®MA + Axial®, followed by Joystick® at 108 g a.i. ha–1 plus Citogate®. The application of Joystick® at 108 g a.i. ha–1 plus Citogate® decreased the biomass of Malva neglecta, Lolium rigidum, Hirschfeldia incana, Centaurea pallescens, Veronica persica, and Carthamus oxyacantha up to 96.2, 78.1, 100, 91.0, 91.0, and 96.1%; respectively; with an 88% reduction in total weed dry biomass. Because of Joystick® at 108 g a.i. ha–1 plus Citogate® activity against weed species, the grain and biological yields of wheat improved up to 28% as compared to weedy check treatment

Multi‐objective optimal planning of a residential energy hub based on multi‐objective particle swarm optimization algorithm

Abstract With the increasing rate of population in big cities around the world, the tendency to build new buildings in the suburb of main cities or to build large apartments in the main cities has been highlighted. In this regard, building residential complexes has seen a dramatic increase in these areas as it makes it possible to build a large number of residential units within a reasonable space. Although these complexes have brought numerous benefits, they are some challenges regarding their construction processes. One main concern associated with these complexes is how to optimally install energy components such as transformers, combined heat and power (CHP) units, boilers etc., in the shared area of apartments in the residential complex. To address this issue, this paper models the energy system of a residential complex as an energy hub and proposes a novel framework to obtain the optimal planning of such an energy hub. In order to address the conflicting desires of the residential complex's builders and the future residents of the residential units, a multi‐objective (MO) optimization problem has been considered in the proposed method that simultaneously optimizes the investment costs, operation costs, and the reliability of energy supply. In this regard, a Multi‐objective Particle Swarm Optimization (MOPSO) algorithm combined with classical linear programming (LP) optimization method has been proposed to solve the MO optimization problem. In order to demonstrate the effectiveness of the proposed method, a case study including a residential complex with 300 residential units is considered, and the proposed method is implemented in this case study. The numerical results show that the proposed framework can appropriately optimize investment costs, operation costs, and the reliability index simultaneously, and the obtained Pareto frontier gives the investors the freedom to opt for any point from this surface

Parallel Optical Spatial Signal Processing Based on 2x2 MIMO Computational Metasurface

We introduce a novel concept of Multi-Input Multi-Output (MIMO) metasurface processor with asymmetric Optical Transfer Function (OTF) which can perform spatial first-order derivation on two orthogonal distinct input signals for both TM and TE polarizations. Two distinct input signals, regardless of their polarization, simultaneously illuminate the metasurface computer and the resulting differentiated signals are separated from each other via appropriate Spatial Low Pass Filters (SLPF). Our simulations confirm the claim of a 2x2 MIMO optical differentiator processor. The proposed metasurface computer is away from the restrictions such as lack of computing speed, large size of bulky or two additional sub-blocks, supporting only a single input, and constant polarization mode. Our proposed scheme pave the way for real-time massive parallel signal processing by single metasurface

MILP Model of Electricity Distribution System Expansion Planning Considering Incentive Reliability Regulations

This paper aims at proposing a mixed-integer linear formulation to incorporate reliability-oriented costs into the expansion planning model of electricity distribution networks. In this respect, revenue lost associated with the undelivered energy caused by network interruptions as well as costs incurred by the widely used reward-penalty regulations is considered as the major reliability-related costs from distribution companies point of view. A set of mixed-integer linear equations is proposed to calculate the most common distribution system reliability indices, i.e., expected energy not served, system average interruption frequency index, and system average interruption duration index. It is found that these equations can also facilitate the formulation of radiality constraint in the presence of distributed generation units. Moreover, application of the proposed method is investigated through various case studies performed on two test distribution networks with 24 and 54 nodes.Peer reviewe

Multistage Expansion Co-Planning of Integrated Natural Gas and Electricity Distribution Systems

This paper focuses on expansion co-planning studies of natural gas and electricity distribution systems. The aim is to develop a mixed-integer linear programming (MILP) model for such problems to guarantee the finite convergence to optimality. To this end, at first the interconnection of electricity and natural gas networks at demand nodes is modelled by the concept of energy hub (EH). Then, mathematical model of expansion studies associated with the natural gas, electricity and EHs are extracted. The optimization models of these three expansion studies incorporate investment and operation costs. Based on these separate planning problems, which are all in the form of mixed-integer nonlinear programming (MINLP), joint expansion model of multi-carrier energy distribution system is attained and linearized to form a MILP optimization formulation. The presented optimization framework is illustratively applied to an energy distribution network and the results are discussed

Investigating the Sustainable Development of Charging Stations for Plug-in Electric Vehicles: A System Dynamics Approach

International audienc