RF cavity design for a low cost 1 MeV proton source

In this paper we present the design for a low-cost RF cavity capable of accelerating protons from 100 keV to 1 MeV. The system is designed to meet the specifications from the proposed Alceli LTD medical proton therapy linac, to deliver a 1 nA proton beam current with a 1 kHz repetition rate. We present a design of an RF normal conducting (NC) re-entrant Cu cavity operating at 40MHz consisting of a coupled two cavity system, both driven by a single Marx generator. The choice of such a low operating frequency for the cavity system enables us to use a relatively low-cost cost Marx Generator as the RF source. Marx generators work in a similar fashion to a Cockcroft-Walton accelerator (without the expensive components), creating a high-voltage pulse by charging a number of capacitors relatively slowly in parallel, then rapidly discharging in series, via spark gaps. Marx generators can deliver 2.5GW, 1 ns pulses, with rise times of 200 ps, and (relatively) low jitter

Nucleoli and Promyelocytic Leukemia Protein (PML) bodies are phase separated nuclear protein quality control compartments for misfolded proteins

We uncovered a role for nucleoli and PML-bodies as phase-separated protein quality control organelles that compartmentalize protein quality control factors and misfolded proteins for their efficient clearance. Failure to dispose misfolded proteins converts nucleoli and PML-bodies into a solid state that immobilizes ubiquitin, limiting its recycling for genome integrity maintenance

Changes in aroma and sensory profile of food ingredients smoked in the presence of a zeolite filter

During smoking, formation of desirable smoky compounds and carcinogenic polycyclic aromatic hydrocarbons (PAH) are inextricably linked. We have previously developed a zeolite filter technology (PureSmoke Technology or PST) that reduces the PAH content of a smoke stream, particularly reducing the concentration of benzo[a]pyrene, a known carcinogen, by up to 93%. The aim of this work was to determine whether there were changes in the volatile and sensory profiles of ingredients smoked using PST compared to the traditional smoking process (Trad). Smoked tomato flakes (either PST or Trad) were added to either low-fat or full-fat cream cheese for sensory profiling and consumer preference tests, and volatile analysis was carried out using solid phase microextraction (SPME) followed by gas chromatography-mass spectrometry (GC-MS). The sensory analysis showed a significant decrease (p < 0.01) in bitterness when the PST was employed and a significant decrease in overall smoky aroma and flavor (p < 0.001), which resulted in an increase in the perception of cheesy aroma and flavor. This was consistent with a decrease in many of the smoky aroma compounds, particularly the guaiacols. However, consumer preference tests showed that there was no adverse effect on the flavor of the products, and there was even a tendency for the PST product to be preferred to the Trad product (p = 0.096). The smoke compounds were quantitated and compared in smoked tomato paste. Odor activity values (OAVs) calculated from the literature thresholds suggested that guaiacol and 4-alk(en)yl-substituted guaiacols are likely to be among the most highly odor-active compounds in these smoked ingredients

New modelling technique for aperiodic-sampling linear systems

A general input-output modelling technique for aperiodic-sampling linear systems has been developed. The procedure describes the dynamics of the system and includes the sequence of sampling periods among the variables to be handled. Some restrictive conditions on the sampling sequence are imposed in order to guarantee the validity of the model. The particularization to the periodic case represents an alternative to the classic methods of discretization of continuous systems without using the Z-transform. This kind of representation can be used largely for identification and control purposes.Comment: 19 pages, 0 figure

Supersymmetric K field theories and defect structures

We construct supersymmetric K field theories (i.e., theories with a non-standard kinetic term) in 1+1 and 2+1 dimensions such that the bosonic sector just consists of a nonstandard kinetic term plus a potential. Further, we study the possibility of topological defect formation in these supersymmetric models. Finally, we consider more general supersymmetric K field theories where, again, topological defects exist in some cases.Comment: Latex, 6 figures, 27 page

Deep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances

Induction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-Time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances

Deep Learning Implementation of Model Predictive Control for Multi-Output Resonant Converters

Flexible-surface induction cooktops rely on multi-coil structures which are powered by means of advanced resonant power converters that achieve high versatility while maintaining high efficiency and power density. The study of multi-output converters has led to cost-effective and reliable implementations even if they present complex control challenges to provide high performance. For this scenario, model predictive control arises as a modern control technique that is capable of handling multivariable problems while dealing with nonlinearities and constraints. However, these controllers are based on the computationally-demanding solution of an optimization problem, which is a challenge for high-frequency real-time implementations. In this context, deep learning presents a potent solution to approximate the optimal control policy while achieving a time-efficient evaluation, which permits an online implementation. This paper proposes and evaluates a multi-output-resonant-inverter model predictive controller and its implementation on an embedded system by means of a deep neural network. The proposal is experimentally validated by a resonant converter applied to domestic induction heating featuring a two-coil 3.6 kW architecture controlled by means of a FPGA. Autho