Distribution Of Channels Research Articles

Optimization of an anatomically and electrically detailed rodent subthalamic nucleus neuron model.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, but its mechanisms of action remain unclear. Detailed multicompartment computational models of STN neurons are often used to study how DBS electric fields modulate the neurons. However, currently available STN neuron models have some limitations in their biophysical realism. In turn, the goal of this study was to update a detailed rodent STN neuron model originally developed by Gillies and Willshaw in 2006. Our design requirements consisted of explicitly representing an axon connected to the neuron and updating the ion channel distributions based on the experimental literature to match established electrophysiological features of rodent STN neurons. We found that adding an axon to the STN neuron model substantially altered its firing characteristics. We then used a genetic algorithm to optimize biophysical parameters of the model. The updated model exhibited spontaneous firing, action potential shape, hyperpolarization response, and frequency-current curve that aligned well with experimental recordings from STN neurons. Subsequently, we evaluated the general compatibility of the updated biophysics by applying them to 26 different STN neuron morphologies derived from three-dimensional anatomical reconstructions. The different morphologies affected the firing behavior of the model, but the updated biophysics were robustly capable of maintaining the desired electrophysiological features. The new STN neuron model developed in this work offers a valuable tool for studying STN neuron firing properties and may find application in simulating STN local field potentials and analyzing the effects of STN DBS.NEW & NOTEWORTHY This study presents an anatomically and biophysically realistic rodent STN neuron model. The work showcases the use of a genetic algorithm to optimize the model parameters. We noted a substantial influence of the axon on the electrophysiological characteristics of STN neurons. The updated model offers a valuable tool to investigate the firing of STN neurons and their modulation by intrinsic and/or extrinsic factors.

Scour-depth variability controls channel-scale stratigraphy in experimental braided rivers

ABSTRACT Braided rivers distribute sediment across landscapes, often forming wide channel belts that are preserved in stratigraphy as coarse-grained deposits. Theoretical work has established quantitative links between the depth distribution of formative channels in a braided river and the geometry of their preserved strata. However, testing these predictive relationships between geomorphic process and stratigraphic product requires examining how braided rivers and their deposits coevolve, with high resolution in both space and time. Here, using a series of four runs of a physical experiment, we examine the controls of water discharge and slope on the resulting geometry of preserved deposits. Specifically, we focus on how a twofold variation in water discharge and initial riverbed slope affects the spatiotemporal distribution of channel depths and the geometry of preserved deposits of a braided river. We find that the channel depths in the laboratory experiment are described by a two-parameter gamma distribution and the deepest scours correspond to zones of erosion at channel-belt margins and channel-thread confluences in the channel belt. We use a reduced-complexity flow model to reconstruct flow depths, which were shallower compared to channel thalweg depths. Synthetic stratigraphy built from timeseries of topographic surfaces shows that the distribution of cut-and-fill-unit thickness is invariant across the experiments and is determined by the variability in scour depths. We show that the distribution of cut-and-fill-unit thickness can be used to reconstruct formative-channel-depth distributions and that the mean thickness of these units is 0.31 to 0.62 times the mean formative flow depth across all experiments. Our results suggest that variations in discharge and slope do not translate to measurable differences in preserved cut-and-fill-unit thickness, suggesting that changes in external forcings are likely to be preserved in braided river deposits only when they exceed a certain threshold of change.

Dos Hemisferios: putting high-quality Ecuadorian wines on the map

Learning outcomes The learning objectives include understanding the unique environment and challenges that business leaders face when developing new businesses in emerging markets, evaluating the firm’s internal and external environments, analyzing sales data and distribution channels and formulating new strategies. Case overview/synopsis Dos Hemisferios Winery, founded in 1999 as a hobby, grew into a family business. The Ecuadorian winery expanded production after winning an international award for its Paradoja blend in 2009. With a $10m investment in a new plant in 2017, the winery capacity increased to 500,000 bottles. President Robert Wright recognized the need to increase sales, aiming to sell at least 425,000 bottles annually at an average price of $8 per bottle to break even and become profitable in 2024. To tap into Ecuador’s top market in Quito, representing 46% of sales, Dos Hemisferios aimed to boost monthly revenues to $50,000 by addressing challenges such as low awareness and consumer reluctance. Initiatives under consideration included partnerships and events, winery tours, enhanced social media, new products and improved sales channel distribution. Complexity academic level The Dos Hemisferios case is appropriate for upper-division undergraduate and graduate students in global business and strategy courses. The learning objectives for the case study include: understanding the unique environment and challenges business leaders face when developing new businesses in emerging markets; evaluating the firm’s internal and external environments to determine its strengths, weaknesses, opportunities and threats; analyzing sales data and distribution channels for the business; and providing students with the opportunity to formulate strategies to gain more share of the Ecuadorian wine market. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Some aspects of bidomain modeling with volume conductors

The concept of dynamic multiscaling has changed our approach to multi-neuronal cable theory. Previously, computational neuroscientists considered individual neurons as neural masses or compartmental models, but now, a distributed representation of single neurons as ionic cable structures is most likely to lead to a greater understanding of how the distribution of ionic channels and synaptic input along the dendrites of a few neurons can offset the collective behavior of a large ensemble of neurons and, therefore, provide a measure of the dynamical brain. This change in perspective forms the basis of volume conductor-bidomain modeling, a new method that captures multiscalar electrophysiology.

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Abstract Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.

Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback.

In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from reinforcement learning, or explicitly model the channel environment by training a generative channel model. In both cases, over-the-air training of transmitter and receiver requires a feedback channel to sound the channel environment and obtain measurements of the learning objective. The use of continuous feedback not only demands extra system resources but also makes the training process more susceptible to adversarial attacks. Conversely, opting for a feedback-free approach to train the models over the forward link, exclusively on the receiver side, could pose challenges to reliably end the training process without intermittent testing over the actual channel environment. In this article, we propose a novel method for the over-the-air training of wireless communication systems that does not require a feedback channel to train the transmitter and receiver. Random samples are transmitted through the channel environment to train a mixture density network to approximate the channel distribution on the receiver side of the network. The transmitter and receiver models are trained with the resulting channel model, and the transmitter can be deployed after training. We show that the block error rate measurements obtained with the simulated channel are suitable for monitoring as a stopping criterion during the training process. The resulting method is demonstrated to have equivalent performance to the end-to-end autoencoder training on small message sequences.

Sex-dependent polymorphism in urinary output is reversed upon deletion of exchange protein directly activated by cAMP (Epac)

It is well-described that there are notable sex-dependent differences in urinary production with females having a smaller volume and greater urinary concentrating ability. Apart from contribution of the androgenic inputs, the underlying signaling intermediates remain largely unexplored. We previously showed that exchange protein directly activated by cAMP (Epac) is a critical regulator of water-solute transport in the proximal tubule and collecting duct. Deletion of either Epac1 or Epac2 isoforms increases urinary output and Na+ excretion. Here, we used metabolic cage balance studies to test whether Epac signaling mediates sex-dependent differences in urinary production. We found that concomitant deletion of Epac1&2 moderately increases 24h urinary volume by 38% (from 1.82+/-0.13 to 2.52+/-0.21 ml) in males, but drastically augment urinary production in females by 320% (from 1.39+/-0.10 to 4.45+/-0.37 ml). This was associated with only a mild though significant reduction in urinary osmolarity in males by 35%, whereas urine osmolarity was markedly decreased from 2511+/-5 to 977+/-60 mOsm in females. Consistently, plasma osmolarity was not different in EpacWT versus Epac1&2−/− males, but became significantly more hypertonic in Epac1&2−/− females. We further determine the functional consequences of impaired Epac cascade on fluid reabsorption in the proximal tubule and collecting duct in males and females. Deletion of Epac1 and Epac2 decreases NHE-3 expression in the proximal tubule in an additive manner. In addition, Na+/H+-dependent recovery after acidification was drastically reduced in freshly isolated split-opened proximal tubules from Epac1&2−/− mice when compared to EpacWT. Interestingly, the reduction of NHE-3 expression was more pronounced in Epac1&2−/− females (by 89%) than in males (by 59%). Immunofluorescence confocal images revealed much more diffuse intracellular distribution of AQP2 water channel in Epac1&2−/− females when compared to males. Furthermore, osmotically-driven water influx was reduced in principal cells within split-opened collecting ducts of Epac1&2−/− females but not males, when compared to respective EpacWT. In summary, our results reveal a substantially greater role of Epac signaling in renal water handling in females than in males. Interestingly, Epac1&2 deletion leads to a reversed pattern of sexual dimorphism in urinary production with females excreting larger volumes of hypotonic urine than males. This research was supported by NIH-NIDDK DK117865, DK119170, AHA EIA35260097 (to O. Pochynyuk). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Iterative Adaptive Based Multi-Polarimetric SAR Tomography of the Forested Areas

Synthetic aperture radar tomography (TomoSAR) is an extension of synthetic aperture radar (SAR) imaging. It introduces the synthetic aperture principle into the elevation direction to achieve three-dimensional (3-D) reconstruction of the observed target. Compressive sensing (CS) is a favorable technology for sparse elevation recovery. However, for the non-sparse elevation distribution of the forested areas, if CS is selected to reconstruct it, it is necessary to utilize some orthogonal bases to first represent the elevation reflectivity sparsely. The iterative adaptive approach (IAA) is a non-parametric algorithm that enables super-resolution reconstruction with minimal snapshots, eliminates the need for hyperparameter optimization, and requires fewer iterations. This paper introduces IAA to tomographicinversion of the forested areas and proposes a novel multi-polarimetric-channel joint 3-D imaging method. The proposed method relies on the characteristics of the consistent support of the elevation distribution of different polarimetric channels and uses the L2-norm to constrain the IAA-based 3-D reconstruction of each polarimetric channel. Compared with typical spectral estimation (SE)-based algorithms, the proposed method suppresses the elevation sidelobes and ambiguity and, hence, improves the quality of the recovered 3-D image. Compared with the wavelet-based CS algorithm, it reduces computational cost and avoids the influence of orthogonal basis selection. In addition, in comparison to the IAA, it demonstrates greater accuracy in identifying the support of the elevation distribution in forested areas. Experimental results based on BioSAR 2008 data are used to validate the proposed method.

CAVIN1-Mediated hERG Dynamics: A Novel Mechanism Underlying the Interindividual Variability in Drug-Induced Long QT.

Drug-induced QT prolongation (diLQT) is a feared side effect that could expose susceptible individuals to fatal arrhythmias. The occurrence of diLQT is primarily attributed to unintended drug interactions with cardiac ion channels, notably the hERG (human ether-a-go-go-related gene) channels that generate the delayed-rectifier potassium current (IKr) and thereby regulate the late repolarization phase. There is an important interindividual susceptibility to develop diLQT, which is of unknown origin but can be reproduced in patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs). We aimed to investigate the dynamics of hERG channels in response to sotalol and to identify regulators of the susceptibility to developing diLQT. We measured electrophysiological activity and cellular distribution of hERG channels after hERG blocker treatment in iPS-CMs derived from patients with highest sensitivity (HS) or lowest sensitivity (LS) to sotalol administration in vivo (ie, on the basis of the measure of the maximal change in QT interval 3 hours after administration). Specific small interfering RNAs and CAVIN1-T2A-GFP adenovirus were used to manipulate CAVIN1 expression. Whereas HS and LS iPS-CMs showed similar electrophysiological characteristics at baseline, the late repolarization phase was prolonged and IKr significantly decreased after exposure of HS iPS-CMs to low sotalol concentrations. IKr reduction was caused by a rapid translocation of hERG channel from the membrane to the cytoskeleton-associated fractions upon sotalol application. CAVIN1, essential for caveolae biogenesis, was 2× more highly expressed in HS iPS-CMs, and its knockdown by small interfering RNA reduced their sensitivity to sotalol. CAVIN1 overexpression in LS iPS-CMs using adenovirus showed reciprocal effects. We found that treatment with sotalol promoted translocation of the hERG channel from the plasma membrane to the cytoskeleton fractions in a process dependent on CAVIN1 (caveolae associated protein 1) expression. CAVIN1 silencing reduced the number of caveolae at the membrane and abrogated the translocation of hERG channel in sotalol-treated HS iPS-CMs. CAVIN1 also controlled cardiomyocyte responses to other hERG blockers, such as E4031, vandetanib, and clarithromycin. Our study identifies unbridled turnover of the potassium channel hERG as a mechanism supporting the interindividual susceptibility underlying diLQT development and demonstrates how this phenomenon is finely tuned by CAVIN1.

Channel reflection: Knowledge-driven data augmentation for EEG-based brain–computer interfaces

A brain–computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: (1) CR is effective, i.e., it can noticeably improve the classification accuracy; (2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, (3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further improve the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.

Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure.

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.

Personalized voltage maps guided by cardiac magnetic resonance in the era of high-density mapping

BackgroundVoltage mapping could identify the conducting channels potentially responsible for ventricular tachycardia (VT). Standard thresholds (0.5–1.5 mV) were established using bipolar catheters. No thresholds have been analyzed with high-density mapping catheters. In addition, channels identified by cardiac magnetic resonance (CMR) has been proven to be related with VT. ObjectiveThe purpose of this study was to analyze the diagnostic yield of a personalized voltage map using CMR to guide the adjustment of voltage thresholds. MethodsAll consecutive patients with scar-related VT undergoing ablation after CMR (from October 2018 to December 2020) were included. First, personalized CMR-guided voltage thresholds were defined systematically according to the distribution of the scar and channels. Second, to validate these new thresholds, a comparison with standard thresholds (0.5–1.5 mV) was performed. Tissue characteristics of areas identified as deceleration zones (DZs) were recorded for each pair of thresholds. In addition, the relation of VT circuits with voltage channels was analyzed for both maps. ResultsThirty-two patients were included [mean age 66.6 ± 11.2 years; 25 (78.1%) ischemic cardiomyopathy]. Overall, 52 DZs were observed: 44.2% were identified as border zone tissue with standard cutoffs vs 75.0% using personalized voltage thresholds (P = .003). Of the 31 VT isthmuses detected, only 35.5% correlated with a voltage channel with standard thresholds vs 74.2% using adjusted thresholds (P = .005). Adjusted cutoff bipolar voltages that better matched CMR images were 0.51 ± 0.32 and 1.79 ± 0.71 mV with high interindividual variability (from 0.14–1.68 to 0.7–3.21 mV). ConclusionPersonalized voltage CMR-guided personalized voltage maps enable a better identification of the substrate with a higher correlation with both DZs and VT isthmuses than do conventional voltage maps using fixed thresholds.

Investigation of Characteristics of Ultraviolet Light Pulse Weak Signal Communication System Based on Fourth-Order Frequency-Shift Keying Modulation

In ultraviolet (UV) communication, On–Off Keying (OOK) is the primary modulation technique. Compared to OOK, frequency modulation offers stronger resistance to path attenuation. Currently, research on frequency modulation demodulation schemes for UV communication is limited, mainly employing waveform detection and laser pulse response methods, which require high detection sensitivity to light. This study presents a novel frequency modulation communication scheme using discrete Poisson channel distribution and optical pulse signal processing algorithms, enhancing the signal processing sensitivity of the existing frequency modulation scheme to the level of photons. The proposed system model is rigorously evaluated through theoretical derivations and simulations. Additionally, a hardware system integrating optical pulse counting, frequency detection, and clock data recovery algorithms is developed. Experimental results show the system achieving a 5 kbps transmission rate under frequency modulation. In non-line-of-sight (NLOS) scenarios, communication reaches up to 65 m, with the receiver elevation angle ranging from 10° to 25° and the bit error rate (BER) stabilized at 10−4, while in line-of-sight (LOS) situations, the BER remains at 10−5 up to 400 m and 10−4 up to 700 m, achieving the farthest distance and fastest communication rate achievable in the current FSK modulation scheme of ultraviolet communication systems. The integrated components enhance its applicability in communication systems. This study offers a valuable addition to UV communication technology.

Localised multiple channel distribution strategies of luxury fashion retailers – evidence from China

PurposeThis empirical study aims to examine luxury fashion retailers' localised multiple channel distribution strategies in China.Design/methodology/approachThrough case studies of 15 participating retailers, qualitative data were collected from 33 semi-structured interviews.FindingsStrong impacts of internationalisation strategies, distribution strategies and channel length towards multiple channel retailing are revealed. Multi-channel retailing is widely employed by firms who have entered China and further developed their businesses through local partnerships and adopted a selective distribution strategy via relatively longer channels. Omni-channel retailing is only suitable for the few retailers using an exclusive distribution strategy through direct marketing and wholly owned customer relationship management. As a dynamic transformation from multi- to omni-channel retailing, cross-channel retailing is adopted by those who are withdrawing from local partnerships and shifting to wholly owned expansions and operations in host markets.Research limitations/implicationsThe results are potentially challenged by relatively small sample size.Practical implicationsPractitioners are suggested to adapt multiple channel retailing to their international expansion strategies, distribution strategies and channel length in the host markets.Originality/valueThis paper contributes to the literature in both multiple channel retailing and international retailing by offering insights into the motives, development patterns and suitability of multiple channel retailing in the international retail marketing context.

An experimental investigation of void fraction characteristics of flow boiling in a 5×5 rod bundle channel

Flow boiling in the rod bundle channel is an important phenomenon in nuclear reactors. The local inhomogeneous void fraction distribution in the flow channel will affect the flow and heat transfer in two phase flow. A 5 × 5 rod bundle test section is designed and utilized in the experiments and a conductivity probe is utilized to measure the local void fraction of two phase flow. A remote-control stepping driving assembly is designed to drive the void probe. The local void fraction characteristics at the outlet of a 5 × 5 rod bundle channel during subcooled boiling and saturated boiling are experimentally studied when the system pressure is 2∼3 MPa. The outlet subcooling ranges from 0 °C to 18 °C. The mass flux is 2000 kg/m2s and the heat flux ranges from 186.3 kW/m2 to 320.8 kW/m2.The effects of thermal parameters on local phase interfacial parameters in subcooled boiling and saturated boiling are obtained. The research results are important for revealing the two phase flow mechanisms in rod bundle channels.

PURF: Improving teacher representations by imposing smoothness constraints for knowledge distillation

Knowledge distillation is one of the most persuasive approaches to model compression that transfers the representational expertise from large deep-learning teacher models to a small student network. Although numerous techniques have been proposed to improve teacher representations at the logits level, no study has examined the weaknesses in the representations of the teacher at the feature level during distillation. On the other hand, in a trained deep-learning model, all the kernels are not uniformly activated to make specific predictions. Transferring this knowledge may result in a student learning a suboptimal intrinsic distribution and restrict the existing distillation methods from exploiting their highest potential. Motivated by these issues, this study analyses the generalization capability of teachers with or without a uniformly activated channel distribution. Preliminary investigations and theoretical analyses show that partly uniforming or smoothing feature maps offer improved representation that enriches the generalization capability. Based on these observations, it is hypothesized that distillation-based explicit supervision using smoothed feature maps and cross-entropy loss plays a significant role in improving generalization. Hence, this paper proposes a novel technique called Partly Unified Recalibrated Feature (PURF) map distillation. The proposed method recalibrates the feature maps by intercommunicating the representational cues among nearest-neighbor channels. PURF increases the performance of state-of-the-art knowledge distillation (KD) methods across architectures by improving generalization, model compression, few-shot training, transferability, and robustness transfer on standard benchmark datasets. PURF achieves 1.51% average accuracy improvements on seven diverse architectures in image classification tasks. PURF increases the performance of the state-of-the-art knowledge distillation methods by an average accuracy of 1.91% across architectures. Moreover, PURF achieves an average of 2.02%, and 0.96% higher accuracy in transferability and robustness tasks, respectively, on standard benchmark datasets.

Study on characteristics of gas–liquid two-phase flow in pump as turbine using multiple-size group model

The multi-size group (MUSIG) model is employed in this paper to simulate the gas–liquid two-phase flow in pump as turbine (PAT) since the traditional Eulerian–Eulerian two-fluid model is unable to take into account the phenomena of breakup and coalescence of bubbles. First, the simulation of gas–liquid two-phase flow in a square column is compared with the experiment to verify the accuracy of the MUSIG model. Then, the results of gas–liquid two-phase flow in PAT simulated by the MUSIG model are compared with those by the conventional uniform bubble (UB) model and find that the MUSIG model is more favorable to capture the flow pattern at high gas content compared to the UB model. Based on the MUSIG model, the internal flow characteristics, pressure fluctuation, and bubble size distribution of the PAT are analyzed. The rotation of the blades breaks a part of big bubbles into small bubbles in the volute, resulting in a smaller diameter of the bubbles entering the impeller. As the gas content increases, the number and size of vortices in the impeller flow channel increase. The vortex is formed at locations where the gas phase distribution in the impeller flow channel is concentrated. The outlet of the impeller is more prone to bubble consolidation under high gas content conditions. In conclusion, the MUSIG model can well predict the complex flow characteristics of gas–liquid two-phase inside the PAT and identify the key influencing factors of energy acquisition, which can provide support for improving the performance of the PAT design.

Grasping the behavior of magnetorheological fluids in gradient pinch mode via microscopic imaging

Magnetorheological (MR) fluids are suspensions of micrometer-sized ferromagnetic particles in a carrier fluid, which react to magnetic fields. The fluids can be operated in several fundamental modes. Contrary to the other modes, the rheology and microstructure formation of the MR fluid in the gradient pinch mode have been studied to a far lesser extent. The magnetic field distribution in the flow channel is intentionally made non-uniform. It is hypothesized that the Venturi-like contraction is achieved via fluid property changes, leading to a unique behavior and the presence of a pseudo-orifice. The main goal is to investigate the presence of the Venturi-like contraction effect in the fluid by means of optical imaging and hydraulic measurements. To accomplish the goal, a unique test rig has been developed including a fluorescence microscope and MR valve prototype. The Venturi-like contraction hypothesis was confirmed. The results indicate that the effective flow channel size decreases by 92% at the maximum magnetic flux applied. This has a direct impact on the flow characteristics of the MR valve. The variation of the pressure–flow rate curve slope with magnetic field was demonstrated. The results provide valuable information for understanding the rheology and microstructure formation mechanism in MR fluids in the pinch mode.

From Finite Element Simulations to Equivalent Circuit Models of Extracellular Neuronal Recording Systems Based on Planar and Mushroom Electrodes.

define a new methodology to build multi-compartment lumped-elements equivalent circuit models for neuron/electrode systems. the equivalent circuit topology is derived by careful scrutiny of accurate and validated multiphysics finite-elements method (FEM) simulations that couple ion transport in the intra- and extracellular fluids, activation of the neuron membrane ion channels, and signal acquisition by the electronic readout. robust and accurate circuit models are systematically derived, suited to represent the dynamics of the sensed extracellular signals over a wide range of geometrical/physical parameters (neuron and electrode sizes, electrolytic cleft thicknesses, readout input impedance, non-uniform ion channel distributions). FEM simulations point out phenomena that escape an accurate description by equivalent circuits; notably: steric effects in the thin electrolytic cleft and the impact of extracellular ion transport on the reversal potentials of the Hodgkin-Huxley neuron model. our multi-compartment equivalent circuits match accurately the FEM simulations. They unveil the existence of an optimum number of compartments for accurate circuit simulation. FEM simulations suggest that while steric effects are in most instances negligible, the extracellular ion transport affects the reversal potentials and consequently the recorded signal if the electrolytic cleft becomes thinner than approximately 100 nm. the proposed methodology and circuit models improve upon the existing area and point contact models. The coupling between the extracellular concentrations and reversal potential highlighted by FEM simulations emerges as a challenge for future developments in lumped-element modeling of the neuron/sensor interface.

In Operando Study of Charge Modulation in MoS2 Transistors by Excitonic Reflection Microscopy.

Monolayers of transition metal dichalcogenides (2D TMDs) experience strong modulation of their optical properties when the charge density is varied. Indeed, the transition from carriers composed mostly of excitons at low electron density to a situation in which trions dominate at high density is accompanied by a significant evolution of both the refractive index and the extinction coefficient. Using optical interference reflection microscopy at the excitonic wavelength, this (n, κ)-q relationship can be exploited to directly image the electron density in operating TMD devices. In this work, we show how this technique, which we call XRM (excitonic reflection microscopy), can be used to study charge distribution in MoS2 field-effect transistors with subsecond throughput, in wide-field mode. Complete maps of the charge distribution in the transistor channel at any drain and gate bias polarization point (VDS, VGS) are obtained, at ∼3 orders of magnitude faster than with scanning probe techniques such as KPFM. We notably show how the advantages of XRM enable real-time mapping of bias-dependent charge inhomogeneities, the study of resistive delays in 2D polycrystalline networks, and the evaluation of the VDS vs VGS competition to control the charge distribution in active devices.