Removal of impurities from liquid solutions by vacuum desorption with solvent evaporation

Mass transfer with solvent evaporation in the vapor-liquid two-phase film evaporators used for the removal of undesirable impurities from liquid solutions at low pressure is studied. The average concentrations of solution components in the falling liquid film are determined. The most efficient operating conditions for impurity removal, in which the resistance to mass transfer is concentrated in the liquid phase, are found. Original Russian Text ? V.N. Babak, T.B. Babak, L.P. Kholpanov, 2008, published in Teoreticheskie Osnovy Khimicheskoi Tekhnologii, 2008, Vol. 42, No. 6, pp. 654–670.     

Optimization of electromagnetic phased-arrays for hyperthermia via magnetic resonance temperature estimation

A technique for the optimization of electromagnetic annular phased arrays (APAs) for therapeutic hyperthermia has been developed and implemented. The controllable inputs are the amplitudes and phases of the driving signals of each element of the array. Magnetic resonance imaging (MRI) is used to estimate noninvasively the temperature distribution based on the temperature dependence of the proton resonance frequency (PRF). A parametric model of the dynamics that couple the control inputs to the resultant temperature elevations is developed based on physical considerations. The unknown parameters of this model are estimated during a pretreatment identification phase and can be continuously updated as new measurement data become available. Based on the parametric model, a controller automatically chooses optimal phases and amplitudes of the driving signals of the APA. An advantage of this approach to optimizing the APA is that no a priori information is required, eliminating the need for patient-specific computational modeling and optimization. Additionally, this approach represents a first step toward employing temperature feedback to make the optimization of the APA robust with respect to modeling errors and physiological changes. The ability of the controller to choose therapeutically beneficial driving amplitudes and phases is demonstrated via simulation. Experimental results are presented which demonstrate the ability of the controller to choose optimal phases for the APA using only information from magnetic resonance thermometry (MRT).     

Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.     

Biodegradable microfabricated plug-filters for glaucoma drainage devices

We report on the development of a batch fabricated biodegradable truncated-cone-shaped plug filter to overcome the postoperative hypotony in nonvalved glaucoma drainage devices. Plug filters are composed of biodegradable polymers that disappear once wound healing and bleb formation has progressed past the stage where hypotony from overfiltration may cause complications in the human eye. The biodegradable nature of device eliminates the risks associated with permanent valves that may become blocked or influence the aqueous fluid flow rate in the long term. The plug-filter geometry simplifies its integration with commercial shunts. Aqueous humor outflow regulation is achieved by controlling the diameter of a laser-drilled through-hole. The batch compatible fabrication involves a modified SU-8 molding to achieve truncated-cone-shaped pillars, polydimethylsiloxane micromolding, and hot embossing of biodegradable polymers. The developed plug filter is 500?μm long with base and apex plane diameters of 500 and 300?μm, respectively, and incorporates a laser-drilled through-hole with 44-μm effective diameter in the center.     

MXene Composite and Coaxial Fibers with High Stretchability and Conductivity for Wearable Strain Sensing Textiles

The integration of nanomaterials with high conductivity into stretchable polymer fibers can achieve novel functionalities such as sensing physical deformations. With a metallic conductivity that exceeds other solution‐processed nanomaterials, 2D titanium carbide MXene is an attractive material to produce conducting and stretchable fibers. Here, a scalable wet‐spinning technique is used to produce Ti₃C₂T_x MXene/polyurethane (PU) composite fibers that show both conductivity and high stretchability. The conductivity at a very low percolation threshold of ≈1 wt% is demonstrated, which is lower than the previously reported values for MXene‐based polymer composites. When used as a strain sensor, the MXene/PU composite fibers show a high gauge factor of ≈12900 (≈238 at 50% strain) and a large sensing strain of ≈152%. The cyclic strain sensing performance is further improved by producing fibers with MXene/PU sheath and pure PU core using a coaxial wet‐spinning process. Using a commercial‐scale knitting machine, MXene/PU fibers are knitted into a one‐piece elbow sleeve, which can track various movements of the wearer's elbow. This study establishes fundamental insights into the behavior of MXene in elastomeric composites and presents strategies to achieve MXene‐based fibers and textiles with strain sensing properties suitable for applications in health, sports, and entertainment.     

Inkjet Printing of Self‐Assembled 2D Titanium Carbide and Protein Electrodes for Stimuli‐Responsive Electromagnetic Shielding

2D titanium carbides (MXene) possess significant characteristics including high conductivity and electromagnetic interference shielding efficiency (EMI SE) that are important for applications in printed and flexible electronics. However, MXene‐based ink formulations are yet to be demonstrated for proper inkjet printing of MXene patterns. Here, tandem repeat synthetic proteins based on squid ring teeth (SRT) are employed as templates of molecular self‐assembly to engineer MXene inks that can be printed as stimuli‐responsive electrodes on various substrates including cellulose paper, glass, and flexible polyethylene terephthalate (PET). MXene electrodes printed on PET substrates are able to display electrical conductivity values as high as 1080 ± 175 S cm^?1, which significantly exceeds electrical conductivity values of state‐of‐the‐art inkjet‐printed electrodes composed of other 2D materials including graphene (250 S cm^?1) and reduced graphene oxide (340 S cm^?1). Furthermore, this high electrical conductivity is sustained under excessive bending deformation. These flexible electrodes also exhibit effective EMI SE values reaching 50 dB at films with thicknesses of 1.35 µm, which mainly originate from their high electrical conductivity and layered structure.     

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

     

The effect of nanoparticles on wettability alteration for enhanced oil recovery: micromodel experimental studies and CFD simulation

The applications of nanotechnology in oilfields have attracted the attention of researchers to nanofluid injection as a novel approach for enhanced oil recovery. To better understand the prevailing mechanisms in such new displacement scenarios, micromodel experiments provide powerful tools to visually observe the way that nanoparticles may mobilize the trapped oil. In this work, the effect of silicon oxide nanoparticles on the alteration of wettability of glass micromodels was investigated in both experimental and numerical simulation approaches. The displacement experiments were performed on the original water-wet and imposed oil-wet (after aging in stearic acid/n-heptane solution) glass micromodels. The results of injection of nanofluids into the oil-saturated micromodels were then compared with those of the water injection scenarios. The flooding scenarios in the micromodels were also simulated numerically with the computational fluid dynamics (CFD) method. A good agreement between the experimental and simulation results was observed. An increase of 9% and 13% in the oil recovery was obtained by nanofluid flooding in experimental tests and CFD calculations, respectively.     

Agent-oriented techniques for network supervision

This paper presents the results of the Réseau futé (smart net) project, the goal of which is to use distributed AI and multi-agent techniques for network management and supervision. More precisely, these techniques have been applied to the partial automation of the dynamic processing (what is known about a network is always incomplete and can change at any time) of alarms and of various event notifications received by network management platforms. The system that we propose is able for example to automatically handle some alarms or to filter events of no-interest for a given operator. To achieve this goal, an assistant, or interface agent according to the model proposed by Patti Maes [MK93], has been realized. The goal of the assistant is first to learn, by observation, the behavior of the network supervision operator and second to reproduce such a behavior when the conditions in which the behavior has been learned are detected again. The learned information are stored using chronicles [Gha94]. A chronicle is a data-structure allowing programmers to represent sequences of events while taking temporal knowledge into account. Our assistant has been implemented and tested within Magenta which is a program, written in Smalltalk, that simulates (in a simplified way) a network management platform. This program respects roughly the gdmo and cmis standards.     

Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data.