Optical Bistability in Nonlinear Optical Coupler with Negative Index Channel

We discuss a novel kind of nonlinear coupler with one channel filled with a negative index material (NIM). The opposite directionality of the phase velocity and the energy flow in the NIM channel facilitates an effective feedback mechanism that leads to optical bistability and gap soliton formation

Calorimetric and acoustic study of binary mixtures containing an isomeric chlorobutane and butyl ethyl ether or methyl tert-butyl ether

Densities and speeds of sound in the temperature range 283.15-313.15 K have been measured for the binary mixtures formed by an isomeric chlorobutane (1-chlorobutane, 2-chlorobutane, 1-chloro-2-methylpropane, or 2-chloro-2-methylpropane) and butyl ethyl ether or methyl tert-butyl ether. Excess isentropic compressibilities were calculated from the experimental data. Excess enthalpies at T = 298.15 K are also included for the same binary mixtures. All these properties provide an insight into the nature of interactions operating on the present systems. Finally, the Prigogine-Flory-Patterson theory has been used to analyze the H E results and to estimate the isentropic compressibility values of the mixtures at T = 298.15 K

Vessel Recognition in Induction Heating Appliances - A Deep-Learning Approach

The selection of a vessel by an induction-hob user has a significant impact on the performance of the appliance. Due to the induction heating physical phenomena, there exist many factors that modify the equivalent impedance of induction hobs and, consequently, the operational conditions of the inverter. In particular, the type of vessel, which is a sole decision of the user, strongly affects these parameters. Besides, the ferromagnetic properties of the different materials the vessels are made with, vary differently with the excitation level, and given that most of the domestic induction hobs are based on an ac-bus voltage arrangement, the excitation level continuously varies. The algorithm proposed in this work takes advantage of this fact to identify the equivalent impedance of the load and recognize the pot. This is accomplished through a phase-sensitive detector that was already proposed in the literature and the application of deep learning. Different convolutional neural networks are tested on an augmented experimental-based dataset and the proposed algorithm is implemented in an experimental prototype with a system-on-chip. The proposed implementation is presented as an effective and accurate method to characterize and discriminate between different pots that could enable further functionalities in new generations of induction hobs

Output voltage estimation of a half-bridge inverter for domestic induction heating applications

The power supplied to a vessel by a domestic induction-heating appliance is strongly dependent on several parameters the designer of the system has no control over: the type and the size of the vessel, misalignments between the pot and the inductor, temperatures, etc. A reliable estimation of the power is essential to ensure that the home appliance works under the expected conditions and the user experience is suitable. Furthermore, any reduction of hardware is totally welcome by consumer-electronics manufacturers. In this work, two methods to estimate the output voltage of a half-bridge inverter without digitizing it with an analog-to-digital converter are proposed and the effects that this estimation has on the power calculation are evaluated. Both methods are implemented and experimentally verified in a real prototype with an FPGA (Field-Programmable Gate Array)

Soc-based in-cycle load identification of induction heating appliances

The equivalent load of an induction hob is strongly dependent on many parameters such as the switching frequency, the excitation level and the size, type, and material of the vessel. However, real-time methods with the ability to capture the variation of the load with the excitation level have not been proposed in the literature. This is an essential issue as most of the commercial induction hobs are based on an ac-bus voltage arrangement. This article proposes a method based on a phase-sensitive detector that offers an online tracking of the equivalent impedance for this type of arrangements. This algorithm enables advanced control functionalities such as clustering of vessels, material recognition, and premature detection of ferromagnetic saturation, among others. After simulation and experimental validation, the method is implemented into a prototype with a system-on-chip to verify its real-time behavior. The proposed approach is applied to different real-life situations that prove its great performance and applicability

Reduced-order models of series resonant inverters in induction heating applications

From the controller design framework, a simple analytical model that captures the dominant behavior in the range of interest is the optimal. When modeling resonant circuits, complex mathematical models are obtained. These high-order models are not the most suitable for controller design. Although some assumptions can be made for simplifying these models, variable frequency operation or load uncertainty can make these premises no longer valid. In this work, a systematic modeling order reduction technique, Slowly Varying Amplitude and Phase (SVAP), is considered for obtaining simpler analytical models of resonant inverters. SVAP gives identical results as the classical model-order residualization technique from automatic control theory. A slight modification of SVAP, Slowly Varying Amplitude Derivative and Phase (SVADP) is applied in this paper to obtain a better validity range. SVADP is validated for a half-bridge series resonant inverter (HBSRI) and for a high- order plant, a dual-half bridge series resonant inverter (DHBSRI) giving analytical second-order transfer functions for both topologies. Simulation and experimental results are provided to show the validity range of the reduced-order models

Long-lived quantum coherence in photosynthetic complexes at physiological temperature

Photosynthetic antenna complexes capture and concentrate solar radiation by transferring the excitation to the reaction center which stores energy from the photon in chemical bonds. This process occurs with near-perfect quantum efficiency. Recent experiments at cryogenic temperatures have revealed that coherent energy transfer - a wavelike transfer mechanism - occurs in many photosynthetic pigment-protein complexes (1-4). Using the Fenna-Matthews-Olson antenna complex (FMO) as a model system, theoretical studies incorporating both incoherent and coherent transfer as well as thermal dephasing predict that environmentally assisted quantum transfer efficiency peaks near physiological temperature; these studies further show that this process is equivalent to a quantum random walk algorithm (5-8). This theory requires long-lived quantum coherence at room temperature, which never has been observed in FMO. Here we present the first evidence that quantum coherence survives in FMO at physiological temperature for at least 300 fs, long enough to perform a rudimentary quantum computational operation. This data proves that the wave-like energy transfer process discovered at 77 K is directly relevant to biological function. Microscopically, we attribute this long coherence lifetime to correlated motions within the protein matrix encapsulating the chromophores, and we find that the degree of protection afforded by the protein appears constant between 77 K and 277 K. The protein shapes the energy landscape and mediates an efficient energy transfer despite thermal fluctuations. The persistence of quantum coherence in a dynamic, disordered system under these conditions suggests a new biomimetic strategy for designing dedicated quantum computational devices that can operate at high temperature.Comment: PDF files, 15 pages, 3 figures (included in the PDF file

Secuenciación del ITS-1 del ADN ribosomal de Galba truncatula (Gastropoda, Lymnaeidae) y su impacto potencial en la transmisión de la fascioliasis en Mendoza, Argentina

Sequencing of the rDNA ITS–1 proved that the lymnaeid snail species Galba truncatula is present in Argentina and that it belongs to the haplotype HC, the same as that responsible for the fascioliasis transmission in the human hyperendemic area with the highest human prevalences and intensities known, the Northern Bolivian Altiplano.La secuenciación del ITS–1 del ADNr demostró que la especie de gasterópodo lymnaeido Galba truncatula se encuentra en Argentina y que pertenece al haplotipo HC, el mismo responsable de la transmisión de la fascioliasis en el área de hiperendemia humana con las mayores prevalencias e intensidades de fascioliasis conocidas, el Altiplano Norte Boliviano