Various single and blended amines (namely, MEA­(monoethanolamine), MEA-DEEA (2-diethylamino ethanol), MEA-MDEA (methyldiethanolamine), MEA-1DMA2P (1-dimethylamino-2-propanol)) with three types of catalysts (H-ZSM-5, MCM-41 and SO4 2–/ZrO2) were studied to determine the respective roles of catalyst and solvent in heat duty and CO2 desorption rate with an initial CO2 loading of 0.5 mol CO2/mol amine at 371 K. The results show that performance of the three catalysts in all the four investigated aqueous solution systems followed the trend: H-ZSM-5 > MCM-41 > SO4 2–/ZrO2. These results highlight the fact that even though HZSM-5 has moderate acidic sites as compared to MCM-41 and SO4 2–/ZrO2, its large B/L acid sites ratio coupled with mesopore surface area had the best performance. Furthermore, based on this study, the blended system of aqueous 5 M MEA+1 M MDEA with H-ZSM-5 provided the best approach for solution regeneration because the strong electron withdrawing chemical structure of MDEA.

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In this paper, a universal charge-controlled mem-elements (including memristor, memcapacitor, and meminductor) emulator consisting of off-the-shelf devices is proposed. With the unchanged topology of the circuit, the emulator can realize memristor, memcapacitor, and meminductor, respectively. The proposed emulation circuit has a simple mathematical relationship and is constructed with few active devices and passive components, which not only reduces the cost but also facilitates reproduction and facilitates future application research. The grounding and floating forms of the circuit are demonstrated, and Multisim circuit simulation and breadboard experiments validate the emulator's effectiveness. Furthermore, a universal mem-elements chaotic circuit is designed by using the proposed mem-elements emulator and other circuit elements, which is a deformation circuit of Chua's dual circuit. In this circuit, no matter whether the mem-element is memristor, memcapacitor, or meminductor, the chaotic circuit structure does not change, and all can generate hyper-chaos.

Introduction Olfactory deficits are prevalent in early Alzheimer's disease (AD) and are predictive of progressive memory loss and dementia. However, direct neural evidence to relate AD neurodegeneration to deficits in olfaction and memory is limited. Methods We combined the University of Pennsylvania Smell Identification Test (UPSIT) with olfactory functional magnetic resonance imaging (fMRI) to investigate links between neurodegeneration, the olfactory network (ON) and the default mode network (DMN) in AD. Results Behaviorally, olfactory and memory scores showed a strong positive correlation in the study cohorts. During olfactory fMRI, the ON showed reduced task‐related activation and the DMN showed reduced task‐related suppression in mild cognitive impairment (MCI) and AD subjects compared to age‐matched cognitively normal subjects. Conclusions The results provide in vivo evidence for selective vulnerability of ON and DMN in AD and significantly improves the viable clinical applications of olfactory testing. A network‐based approach, focusing on network integrity rather than focal pathology, seems beneficial to olfactory prediction of dementia in AD.

Neural networks are favored by academia and industry because of their diversity of dynamics. However, it is difficult for ring neural networks to generate complex dynamical behaviors due to their special structure. In this paper, we present a memristive ring neural network (MRNN) with four neurons and one non-ideal flux-controlled memristor. The memristor is used to describe the effect of external electromagnetic radiation on neurons. The chaotic dynamics of the M-RNN is investigated in detail by employing phase portraits, bifurcation diagrams, Lyapunov exponents and attraction basins. Research results show that the MRN-N not only can generate abundant chaotic and hyperchaotic attractors but also exhibits complex multistability dynamics. Meanwhile, an analog MRNN circuit is experimentally implemented to verify the numerical simulation results. Moreover, a medical image encryption scheme is constructed based on the MRNN from a H. Lin.C. Wang ( ) .

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