Channel Direction Research Articles

Enhanced diffusion in soft-walled channels with a periodically varying curvature.

The motion of particles along channels of finite width is known to be hindered by either the presence of energy barriers along the channel direction or by variations in the width of the channel in the transverse direction (rugged channel). Remarkably, when both features are present, they can interact to produce a counterintuitive result: adding energy barriers to a rugged channel can enhance the rate of diffusion along it. This is the result of competing energetic and entropic effects. Under the approximation of particles instantaneously in equilibrium in the transverse direction, one can tailor the energy barriers to the ruggedness to recover free diffusion. However, such fine-tuning and potentially restrictive approximations are not necessary to observe an enhanced rate of diffusion as we demonstrate by adding a range of (non-fine-tuned) energy barriers to a channel of sinusoidally varying curvature. Furthermore, this was observed to hold for systems with a finite characteristic timescale for motion in the transverse direction, thus, suggesting that the phenomenon lends itself to be exploited for practical applications.

A theoretical insight into diffusion mechanism of benzene-methanol alkylation reaction in ZSM-5 zeolite

Alkylation reaction of benzene and methanol produces desirable product p-xylene, thus understanding the structure-performance relationship in alkylation reaction is significant. Herein, the diffusion behavior of representative components ( i.e. benzene, methanol, olefin and alkylbenzene) involved in the alkylation reaction catalyzed by Al-substituting ZSM-5 (H-ZSM-5) zeolite at 673 K was studied based on molecular dynamics simulations. As a result, the self-diffusion coefficients (D s ) of methanol and benzene at 673 K in H-ZSM-5 zeolite are 2.97 × 10 −8 and 6.50 × 10 −12 m 2 s −1 , respectively; while in CH 3 -ZSM-5 zeolite, the presence of CH 3 O- intermediates does favor to the diffusion of benzene and alkylbenzene, especially p-xylene. Furthermore, due to the distinguished dynamic diameters compared with pore sizes, the reaction species exhibit anisotropic diffusion along x-, y- and z- direction of zeolite channel. This work may not only provide an insight into the diffusion mechanism for alkylation reaction of benzene and methanol, but also help guide the design of zeolite catalysts. A theoretical insight into diffusion dependence of benzene-methanol alkylation reaction in ZSM-5 zeolite. • Diffusion-reaction relationship in alkylation reaction has been investigated. • Molecular dynamics associated with density functional theory is employed. • Both size and electrostatic effects induce the selectivity towards desirable p-xylene.

Multilevel memory and artificial synaptic plasticity in P(VDF-TrFE)-based ferroelectric field effect transistors

Multilevel data memory and artificial synaptic plasticity in poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] ferroelectric field effect transistors were demonstrated. The dynamics of ferroelectric polarization transformation modulate potential of transistor channel, and adjust the electric field of the P(VDF-TrFE) ferroelectric film by potential along channel direction, thus realizing the multilevel nonvolatile memory state. The artificial synaptic plasticity of transistor is simulated, and the long-term plasticity of excited/inhibited postsynaptic current, paired impulse facilitation/inhibition, and spike-rate-dependent plasticity is realized. In long-term potentiation/depression simulation of devices, the conductivity symmetry and linearity are improved by setting the amplitude of the pulse increments. In addition, the vector-matrix multiplication operation was performed in a crossbar array. This work demonstrates the scheme to develop the P(VDF-TrFE) transistors-based multilevel memory and neural morphological computing.

Post-Ionization Dynamics of the Polar Molecule OCS in Asymmetric Laser Fields

We have investigated the dissociation mechanisms of the prototypical heavy polar molecule OCS into the two break-up channels of the dication, OCS2+ → O+ + CS+ and OC+ + S+, in phase-locked two-color intense laser fields. The branching ratio of the breaking of the C–O and C–S bonds followed a pronounced 2π-oscillation with a modulation depth of 11%, depending on the relative phase of the two-color laser fields. The fragment ejection direction of both break-up channels reflects the anisotropy of the tunneling ionization rate, following a 2π-periodicity, as well. The two dissociation pathways in the C–S bond breaking channel show different phase dependencies of the fragment ejection direction, which are assigned to post-ionization dynamics. These observations, resulting from the excitation with asymmetric two-color intense laser fields, supported by state-of-the-art theoretical simulations, reveal the importance of post-ionization population dynamics in addition to tunneling ionization in the molecular fragmentation processes, even for heavy polar molecules.

Federation based joint client and server side Machine Learning for 5G and beyond Wireless Channel Estimation

In this paper, we propose a Machine Learning (ML) based approach to address Wireless Channel Estimation (WCE) problem for 5G and beyond wireless networks. Accurate wireless channel estimation plays a crucial role for provision of high quality of service to billions of wireless devices in current and future wireless networks. Our proposed approach uses contemporary ML technique, Federated Learning (FL), in conjunction with the Stochastic Gradient Descent (SGD) algorithm to optimise the WCE problem. Our proposed approach leverages the local user wireless channel information to locally optimise the objective at users and then uses this locally optimised information at the server to optimise the global objective function. The proposed approach is referred to as joint Federated Server Learning and Federated Client Learning (j-FSL-FCL) in the paper. We formulate the WCE problem with a novel loss function to be used for the optimisation problem. To evaluate the performance of our proposed j-FSL-FCL approach (with and without SGD), we consider a Down Link (DL) wireless channel model with Multiple-Input Multiple-Output (MIMO) setting that mimic closely to the wireless channel for 5G and beyond wireless channel models. The performance measuring parameters for j-FSL-FCL are to minimise the difference between actual and estimated wireless channel parameters (channel strength and direction). We compare the results of our proposed approach with the other techniques in the literature based on Least Squares (LS), Linear Regression (LR) and Mean Square Error (MSE). It is shown that the proposed algorithm converges to the optimal solution quickly when used with SGD compared to other existing techniques. It is also shown that the efficiency of the proposed approach for WCE problem is much higher compared to other LS and MSE based techniques. Finally, we present some interesting futuristic applications of FL in the context of 5G and beyond wireless networks.

Optimization of precoders and rate allocation for Rate Splitting Multiple access over power line communication channel

We consider a transmission scheme of Rate Splitting Multiple access systems over a power line communication channel in this paper. The System model consists of a transmitter and K users, and superimposing signals for K users at the transmitter. Our aim is to maximize the worst transmission rate between users in the system by jointly optimizing the precoder vector and rate allocation with the method of successive convex approximation(SCA). The simulation results manifest that compared with PLC-NOMA, PLC-RSMA adapts to various channel directions (regardless of whether the channel directions are aligned or orthogonal) and network load (underload or overload), PLC-RSMA can reach better transmission rate performance, and as the SNR augment, the rate performance gain of PLC-RSMA becomes more and more obvious. Under the set parameters, when the SNR is 30 dB, the relative rate performance of PLC-RSMA is improved by at least 27% compared to PLC-NOMA.

A comparison study of hyaluronic acid hydrogel exquisite micropatterns with photolithography and light-cured inkjet printing methods

Abstract The microstructure design of hydrogel materials offers a broad range of practical applications and is extensively used in flexible sensors, polymer microneedles, microfluidic chips, and other biomedical engineering fields. Among the bio-sourced hydrogels, oligomeric hyaluronic acid (HA) possesses wound healing, anti-tumor, and angiogenesis properties. However, micropatterning soft hydrogels, such as HA-relative hydrogels containing 90% water by weight, continue to pose difficulties for both high precision and micro-scale lithography. The purpose of this study was to compare the photolithography and light-cured inkjet printing methods of methacryloyl HA hydrogel (HAMA-gel) to those for synthetic light-curable polymer resins. Photolithography and light-cured inkjet printing methods with designed scale, high resolution, and little processing times were used to effectively prepare micropatterns of HAMA-gel. The well-shaped micropatterns consisted of parallel channels in tens of micrometers and strip/grid lines in the hundreds of micrometers. Human vein endothelial cells cultured on the material’s surface demonstrated that HAMA-gel had good biocompatibility. The width of the flow channel (10 and 20 µm) was regulated on the surface of the microstructure to allow for simultaneous control of cell growth along the flow channel and groove directions.

Generating TON zeolites with reduced [0 0 1] length through combined mechanochemical bead-milling and porogen-directed recrystallization with enhanced catalytic property in hydroisomerization

Mechanochemical bead-milling of micron-meter sized zeolites to nanometers provides an effective route to enhance their diffusion and catalytic properties. This post-synthetic method is particularly important to reduce crystals of unidimensional zeolites preferentially along their channel direction, as direct synthesis is difficult to achieve since they habitually grow into rod-like crystals. The milling process is often escorted by surface amorphorization and structure degradation, hence, a subsequent recrystallization is often entailed to restore crystallinity. Herein, taken ZSM-22 as an example, it is demonstrated that secondary crystal growth and coalescence during recrystallization, a process that often offset the effect of milling, could be suppressed by the introduction of porogens in the post-milling recrystallization process. The combined bead-milling and porogen-directed recrystallization method allows the preparation of highly crystalline TON crystals with an axis length of largely 100–300 nm, which is record short in axis [001] channel length. The Si/Al ratio could be manipulated between 40 and 300, permitting the influence of acidity and diffusion to be evaluated. The catalytic assessments in n-heptane hydroisomerization demonstrate that isomer yields can be enhanced from 56% for parent material to up to 74% for an optimized catalyst with Si/Al of 200. Correlating of zeolite structure, kinetic studies and catalytic behavior discloses that low acid site density and improved diffusion property are critical factors to decrease cracking probability and improve isomerization selectivity. This study opens new ways to tailor diffusion properties of unidimensional zeolites, and are potentially extendable to other zeolites.

Intelligent Optimization of Base Station Array Orientations via Scenario-Specific Modeling

Fifth-generation (5G) wireless communications confront explosive growth in mobile data demand and massive intensive user equipment (UE) connections. The optimization of base station (BS) array orientations in large-scale networks can provide high potential performance gain to meet these requirements. However, traditional schemes are highly dependent on experience and difficult in implementation due to their demand on repeated drive tests and large amounts of data samples. In this paper, via exploiting UE locations and channel direction information, we propose an intelligent network optimization framework to maximize the long-term network rate performance. For the augmentation of limited drive test data, a deep Gaussian process regression (DGPR) model is designed to construct a scenario-specific channel modeling. In addition, via a domain-knowledge driven fusion of convolutional neural network (CNN) and multi-layer perceptron (MLP), we propose a multi-branch deep neural network (DNN) to accurately map BS array orientations and concise channel measurements to the network performance. Finally, based on the scenario-specific modeling, an efficient gradient search approach is proposed to optimize BS array orientations via neural network (NN) backpropagation. Both simulations and field tests in 5G experimental networks validate the effectiveness and efficiency of our proposed framework, especially with limited drive test data.

Heat and mass transfer in a proton exchange membrane fuel cell with gradient distribution of platinum loading along flow channel direction

Heat and mass transfer in a proton exchange membrane fuel cell with gradient distribution of platinum loading along flow channel direction

Capillary Flow-Driven and Magnetically Actuated Multi-Use Wax Valves for Controlled Sealing and Releasing of Fluids on Centrifugal Microfluidic Platforms.

Compact disc (CD)-based centrifugal microfluidics is an increasingly popular choice for academic and commercial applications as it enables a portable platform for biological and chemical assays. By rationally designing microfluidic conduits and programming the disc’s rotational speeds and accelerations, one can reliably control propulsion, metering, and valving operations. Valves that either stop fluid flow or allow it to proceed are critical components of a CD platform. Among the valves on a CD, wax valves that liquify at elevated temperatures to open channels and that solidify at room temperature to close them have been previously implemented on CD platforms. However, typical wax valves on the CD fluidic platforms can be actuated only once (to open or to close) and require complex fabrication steps. Here, we present two new multiple-use wax valve designs, driven by capillary or magnetic forces. One wax valve design utilizes a combination of capillary-driven flow of molten wax and centrifugal force to toggle between open and closed configurations. The phase change of the wax is enabled by heat application (e.g., a 500-mW laser). The second wax valve design employs a magnet to move a molten ferroparticle-laden wax in and out of a channel to enable reversible operation. A multi-phase numerical simulation study of the capillary-driven wax valve was carried out and compared with experimental results. The capillary wax valve parameters including response time, angle made by the sidewall of the wax reservoir with the direction of a valve channel, wax solidification time, minimum spin rate of the CD for opening a valve, and the time for melting a wax plug are measured and analyzed theoretically. Additionally, the motion of the molten wax in a valve channel is compared to its theoretical capillary advance with respect to time and are found to be within 18.75% of the error margin.

Multi-level 2-bit/cell operation utilizing Hf-based metal/oxide/nitride/oxide/silicon nonvolatile memory with HfO2 and HfON tunneling layer

This paper investigated the multi-level 2-bit/cell operation utilizing a Hf-based metal/oxide/nitride/oxide/silicon (MONOS) nonvolatile memory (NVM) device with a HfO2 and HfON tunneling layer (TL). The 2-bit/cell operation is realized by utilizing the localized charge injection method. It was found that drain-current–gate-voltage (I D–V G) characteristics of the programmed states were affected by asymmetry localized in a trapped location along the channel direction. Moreover, the amount of localized trapped charge is strongly affected by drain-source voltage (V DS) in the case of HfON TL. HfON TL shows distinguishable separated all programmed states compared to HfO2 TL. Finally, it was found that all programmed states of HfO2 and HfON TL show similar characteristics according to the channel length and width (L/W) of 2–10/15–90 μm.

Research of flow instabilities in parallel channels with opposite flow directions for WCCB blankets

In the current water-cooled ceramic breeder (WCCB) blankets, a special cooling scheme, the flow direction of adjacent channels is opposite, has been designed. In this paper, flow instabilities in parallel channels with opposite flow directions (OFD) for WCCB blankets were studied using RELAP5/MOD3.4. Flow instabilities were investigated under the operating condition matrix composed of different pressure, inlet subcooling, and mass flux. Flow excursion and density-wave oscillations (DWOs) were identified. Their boundaries were analyzed with the variation of operating conditions and synthesized using empirical correlations respectively. The cooling scheme of identified flow directions (IFD) was designed and flow instabilities were calculated as well. By comparing the OFD and IFD schemes, it is concluded that the IFD scheme is more stable than the OFD scheme for flow excursion; for out-phase DWO, the relative stability depends on the operating conditions. This research results can provide reference for the design of WCCB blankets.

A BUBBLE-LAYER-BASED MECHANISTIC MODEL FOR THE SLIGHTLY SUBCOOLED FLOW BOILING IN VERTICAL TUBES

Bubble behaviors and interactions are quite complex in the slightly subcooled flow boiling including sliding, lift-off and condensation. Currently, the predicting of this process mainly relies on numerical simulations combined with bubble-behavior-related sub-models, or a combination of one-dimensional empirical correlations without considering bubble behaviors. In contrast, the theoretical analysis is still lacking. Therefore, the present paper aims to develop a mechanistic model for this process which divides the flow field into the bubble layer region and the core region based on a bubble layer adjacent to the wall. Bubble behaviors in each region, mass and energy exchanges at the interfaces of different regions, and variations of parameters along the channel direction are considered and analyzed through a new set of two-dimensional steady-state conservation equations of mass and energy combined with bubble-behavior-related sub-models. Based on this model, void fraction and liquid temperature distributions are obtained which are verified with experimental results with fairly good accuracy. In addition, distributions of velocity and mass flow rate in each region in the axial direction, and mass flow rate between different regions in the radial direction are also obtained and analyzed. In comparison, the present model can reveal more detailed information than one-dimensional correlations and provides a simple, fast and stable method for the predictions of subcooled flow boiling especially for industrial applications.

An Attention Approach for Dimensionality Reduction That Can Correct Feature Collection Skewness

It has been demonstrated that adding an attention mechanism to a convolutional neural network enhances network performance. However, in practice, the skewness error during the extraction process affects the feature information owing to the implementation of the global average pooling operation, which eventually lowers the performance of the network design. We think that when the weight is activated, the spatial information is effectively captured while the extraction range of the channel information is narrowed. We also think that because the weight is activated using multiple local extraction substitutions, the influence of skewness error can be effectively reduced. As a result, we suggest dimensionality reduction attention(RA) in this study as a new type of attention mechanism. Dimensionality reduction attention is a method of combining spatial information and channel information into a new feature distribution by aggregating dimensionality reduction of feature information, in contrast to other attentions that act on distinct feature tensors in space and channel. Under the premise of capturing long-distance context information and guaranteeing feature coordinates, it may realize the locality of spatial information through this operation and identify the local information of each channel. To completely display the important feature information, the produced feature maps are then encoded into two complimentary attention maps along the spatial and channel directions, respectively. Our dimension reduction focus is a straightforward and all-encompassing module. We run tests on various datasets using various deep architectures and various class designs. The outcomes of the trial demonstrate that our attention-based approach has clear benefits.

A Method for Identifying Channeling Paths in Low-Permeability Fractured Reservoirs

Often oilfield fractured horizontal wells produce water flowing in multiple directions. In this study, a method to identify such channeling paths is developed. The dual-medium model is based on the principle of inter-well connectivity and considers the flow characteristics and related channeling terms. The Lorentz curve is drawn to qualitatively discern the geological type of the low-permeability fractured reservoir and determine the channeling direction and size. The practical application of such an approach to a sample oilfield shows that it can accurately identify the channeling paths of the considered low-permeability fractured reservoir and predict production performances according to the inter-well connectivity model. As a result, early detection of water channeling becomes possible, paving the way to real-time production system optimization in low-permeability fractured reservoirs.

Anisotropic electromagnetic wave absorption performance of Polyimide/multi-walled carbon nanotubes composite aerogels with aligned slit-like channels structure

Polyimide/carboxyl functionalized multi-walled carbon nanotubes (PI/MWCNTs-COOH) composite aerogels with aligned slit-like channels structure were synthesized through unidirectional freezing, freeze-drying and thermal imidization process in sequence. The composite aerogels with high porosity and low density exhibited highly aligned slit-like channels structure, which further imparted anisotropic electrical conductivity, electromagnetic wave absorption, and compression properties to the aerogels. PI/MWCNTs-COOH-16phr composite aerogel showed the minimum reflection loss (RLmin) of −52 dB at 12.8 GHz and effective absorption bandwidth of 6.7 GHz (10.6 ∼ 17.3 GHz) with a thickness of only 2.5 mm in the perpendicular direction to the aligned slit-like channels, while its RLmin was only −11 dB at 15.9 GHz in the orientation direction of slit-like channels. The composite aerogel had prominent thermal stability, with T5% in nitrogen up to 564 °C. The high temperature resistant and lightweight polyimide-based highly efficient EMW absorbing structural material manufactured by this research had great potential for aerospace applications.

Diagnosis of Alzheimer’s disease via an attention-based multi-scale convolutional neural network

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. Accurate diagnosis of mild cognitive impairment (MCI) in the prodromal stage of AD can delay onset. Therefore, the early diagnosis of AD is particularly essential. The convolutional neural network (CNN) extracts feature of image layer-by-layer, and the observed features are obtained by setting different receptive fields. However, the brain structure is very complicated, and the etiology of AD is unknown, in addition, most of the existing methods do not consider the details and overall structure of the image. To address this issue, we propose a novel multi-scale convolutional neural network (MSCNet) to enhance the model’s feature representation ability. A channel attention mechanism is introduced to improve the interdependence between channels and adaptively recalibrate the channel direction’s characteristic response. To verify the effectiveness of our method, we segment the original MRI data to obtain white matter (WM) and gray matter (GM) datasets and train the model. Extensive experiments show that our method obtains the state-of-the-art performance with fewer parameters and lower computational complexity.

A microscopic ancient river channel identification method based on maximum entropy principle and Wigner-Ville Distribution and its application

In view of the problem of fine characterization of narrow and thin channels, the maximum entropy criterion is used to enhance the focusing characteristics of Wigner-Ville Distribution. On the basis of effectively improving the time-frequency resolution of seismic signal, a new method of microscopic ancient river channel identification is established. Based on the principle of the equivalence between the maximum entropy power spectrum and the AR model power spectrum, the prediction error and the autoregression coefficient of AR model are obtained using the Burg algorithm and Levinson-Durbin recurrence rule. Under the condition of the first derivative of autocorrelation function being 0, the Wigner-Ville Distribution of seismic signal is calculated, and the Wigner-Ville Distribution time-frequency power spectrum (MEWVD) is obtained under the maximum entropy criterion of the microscopic ancient river channel. Through analysis of emulational seismic signal and forward numerical simulation signal of narrow thin model, it is found that MEWVD can effectively avoid the interference of cross term of Wigner-Ville Distribution, and obtain more accurate spectral characteristics than STFT and CWT signal analysis methods. It is also proved that the narrow and thin river channels of different scales can be identified effectively by MEWVD of different frequencies. The method is applied to the third member of Jurassic Shaximiao Formation (J2s33-2) gas reservoir of the Zhongjiang gas field in Sichuan Basin. The spatial information of width and direction of narrow and thin river channels with width less than 500 m and sandstone thickness less than 35 m is accurately identified, providing bases for well deployment and horizontal well fracturing section selection.

An insulator self-blast detection method based on YOLOv4 with aerial images

Due to long-term exposure to the natural environment, insulators are prone to self-blast, threatening the safety and reliability of transmission lines. Because of the different sizes of the insulator self-blast area and the complicated background, it is inevitable that the missed and false detections occur. To solve this problem, this paper proposes a deep neural network called Mina-Net (Multi-Layer INformation Fusion and Attention Mechanism Network). Mina-Net is based on YOLOv4. First, the shallow feature map with more detailed texture information is fused into the feature pyramid. Then, an improved SENet is applied to recalibrate the features of different levels in the channel direction. The experimental results on the actual dataset show that Mina-Net has increased the average precision by 4.78% compared with the YOLOv4.