Separate Channels Research Articles

Формирование навыков командной деятельности и их объективная количественная оценка на основе квантовых представлений

<p>Presented are mathematical and software tools providing the formation and objective quantitative assessment of teamwork skills based on quantum representations. These tools utilize the formal representations of subjects' activities through probability matrices of transitions between the performed elementary operations. Analysis of the subjects' activities is based on comparisons of these matrices for a pattern of actions under study with relevant matrices for patterns of empirical data representing various variants of the subjects' work. When evaluating the teamwork skills, these comparisons are performed according to the "Triangle Scheme", which involves comparing the coordinated actions of the subjects performed in pairs and individually. For this purpose, the quantum spectral analysis is used, which provides effective quantitative comparisons of the hidden periodicities of performing typical elementary operations and their quantities in the spectral metric. The tools for the formation and assessment of teamwork skills are implemented in the form of both a flight simulator with the separation of control channels between pilots and a modified computer game "Sea Battle", which allows to evaluate the dynamics of partners’ communication and cooperation by analyzing their moves as well as tips transmitted to each other. The presented approach to the formation and assessment of teamwork skills is quite universal and can be used in various applications areas, including the assessment and training of flight crews, operators of complex technical systems, students of various levels and other target groups.</p>

Dual-Site Chemosensor for Visualizing •OH-GSH Redox and Tracking Ferroptosis-Inducing Pathways In Vivo.

Oxidative stress, characterized by an imbalance between oxidative and antioxidant processes, results in excessive accumulation of intracellular reactive oxygen species. Among these responses, the regulation of intracellular hydroxyl radicals (•OH) and glutathione (GSH) is vital for physiological processes. Real-time in situ monitoring these two opposing bioactive species and their redox interactions is essential for understanding physiological balance and imbalance. In this study, we developed a dual-site fluorescence chemosensor OG-3, which can independently image both exogenous and endogenous •OH and GSH in separate channels both within cells and in vivo, eliminating issues of spatiotemporal inhomogeneous distribution and cross-interference. With its imaging capabilities of monitoring •OH-GSH redox, OG-3 elucidated two different pathways for ferroptosis induction: (i) inhibition of system xc- to block cystine uptake (extrinsic pathway) and (ii) GPX4 inactivation, leading to the loss of antioxidant defense (intrinsic pathway). Moreover, we assessed the antiferroptotic function and effects of ferroptosis inhibitors by monitoring •OH and GSH fluctuations during ferroptosis. This method provides a reliable platform for identifying potential ferroptosis inhibitors, contributing to our understanding of relevant metabolic and physiological mechanisms. It shows potential for elucidating the regulation of ferroptosis mechanisms and investigating further strategies for therapeutic applications.

Simultaneous Multifocal Plane Fourier Ptychographic Microscopy Utilizing a Standard RGB Camera.

Fourier ptychographic microscopy (FPM) is a computational imaging technology that can acquire high-resolution large-area images for applications ranging from biology to microelectronics. In this study, we utilize multifocal plane imaging to enhance the existing FPM technology. Using an RGB light emitting diode (LED) array to illuminate the sample, raw images are captured using a color camera. Then, exploiting the basic optical principle of wavelength-dependent focal length variation, three focal plane images are extracted from the raw image through simple R, G, and B channel separation. Herein, a single aspherical lens with a numerical aperture (NA) of 0.15 was used as the objective lens, and the illumination NA used for FPM image reconstruction was 0.08. Therefore, simultaneous multifocal plane FPM with a synthetic NA of 0.23 was achieved. The multifocal imaging performance of the enhanced FPM system was then evaluated by inspecting a transparent organic light-emitting diode (OLED) sample. The FPM system was able to simultaneously inspect the individual OLED pixels as well as the surface of the encapsulating glass substrate by separating R, G, and B channel images from the raw image, which was taken in one shot.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography

Objective. With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset. Approach. The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml−1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data. Main Results. Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms. Signficance. In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

Single-Frame Lagrangian Tracking Of 3-D Acoustic Streaming Flows Using Digital Defocusing Micro Particle Streak Velocimetry

In this study, a single-frame particle streak velocimetry technique is applied to the classic DDPIV(Digital Defocusing Particle Image Velocimetry) concept to achieve a single-frame 3-D Lagrangian tracking of particles in microscopic flows. The shortcoming of images suffering low signal to noise ratio due to pinholes can be avoided. With the streak resolving algorithm, it allows extended exposure time when streak images are taken, and the single-frame approach also eliminates the need for high-cost and complicated synchronization hardware for double-pulse frame-straddling light sources. For each long-exposure, color-coded images received from each pinhole equipped with red, green or blue color filters, the streak-resolving algorithm is applied first to find each streak on the separate color channel, followed by an one-time triplet-matching process to group the red, green and blue streak projections. From the streak triplets, 3-D reconstruction can be done on all the resolved points on the streak, and trajectory fitting can then be applied to resolve the tracer particle trajectories in the field of view with temporal history. This procedure was applied for visualizing a microscale 3-D acoustic streaming flow pattern induced by a longitudinal spine-shaped fin oscillating at 12kHz. The height of the channel is 1mm with the spine of 0.3mm height and a 30 。 tip angle. Resolved flow fields show that the particle streak images can be resolved with the same level of accuracy to the particle tracking method, while the throughput of velocity vectors can be significantly higher due to multiple velocity vectors can be resolved from each streak triplet. The methodology has the potential to be applied to various applications that can capture long-exposure images and possible to resolve higher-order information such as acceleration and forces applied on the particles or the flow.

Synergistic Nanoarchitectonics: Precision Membrane Engineering for Rare Earth Selective Separation

AbstractRare earth elements (REEs) are vital in high‐tech industries and defense due to their strategic significance. Crafting an efficient membranes channel for REE separation poses a significant challenge. Employing 2‐methylimidazole‐hydrolyzed OH−, dopamine polymerization is initiated and then Zn2+ coordinates with 2‐methylimidazole on PDA surfaces. The confined symbiotic reaction yields 2D vertical heterojunctions of GO/ZIF‐8/PDA (G/Z/P). During separation, partially dehydrated smaller hydrated lanthanide ions preferentially access interlayers, expanding and stabilizing the interlayer space by coordinating with PDA, thus excluding larger hydrated scandium ions from the membranes. Some scandium ions entering interlayer channels are sequestered by N in size‐matched ZIF‐8 pores. This distinctive mechanism facilitates selective scandium separation from other REEs (other REEs/Sc selectivity ≈68.73), achieving nearly complete Sc3+ rejection in a single step. The methodology offers unprecedented insights into precise nano‐space material synthesis, indicating promising strides in advancing scandium production.

Di-piperidinium side chain functionalized poly(anthracene-co-p-terphenyl piperidinium) anion exchange membranes with microphase separation structure

It is an important research topic to construct microphase separation in anion exchange membranes (AEMs). The integration of rigid anthracene units into the main chains to form poly(anthracene-co-p-terphenyl piperidinium) AEMs is a new method by using π-π stacking to regulate microphase separation and construct ion transport channels in membranes. In this study, anthracene units were introduced for the first time to prepare AEMs with different anthracene contents. It is found that the introduction of rigid anthracene units into the backbone leads to an increase in the fractional free volume (FFV) of the membranes and a decrease in the ion conductive resistance. Moreover, the existence of long flexible di-piperidinium side chain helps to adjust the rigidity of the AEMs and enhances the mechanical properties of the membrane. The ionic conductivity of PA10TP-DMP is 153.8 mS cm−1 (80 °C). In the meanwhile, the conductivity retention of 91.7% was observed after the alkaline resistance test (80 °C, 2 M NaOH) for 1200 h. In addition, the PA10TP-DMP based H2-O2 single cell has a peak power density of 840.3 mW cm−2 and a high durability after 100 h test.

Separation of Channels Subserving Approach and Avoidance/Escape at the Level of the Basal Ganglia and Related Brainstem Structures.

The basal ganglia have the key function of directing our behavior in the context of events from our environment and/or our internal state. This function relies on afferents targeting the main input structures of the basal ganglia, entering bids for action selection at the level of the striatum or signals for behavioral interruption at the level of the subthalamic nucleus, with behavioral reselection facilitated by dopamine signaling. Numerous experiments have studied action selection in relation to inputs from the cerebral cortex. However, less is known about the anatomical and functional link between the basal ganglia and the brainstem. In this review, we describe how brainstem structures also project to the main input structures of the basal ganglia, namely the striatum, the subthalamic nucleus and midbrain dopaminergic neurons, in the context of approach and avoidance (including escape from threat), two fundamental, mutually exclusive behavioral choices in an animal's repertoire in which the brainstem is strongly involved. We focus on three particularly well-described loci involved in approach and avoidance, namely the superior colliculus, the parabrachial nucleus and the periaqueductal grey nucleus. We consider what is known about how these structures are related to the basal ganglia, focusing on their projections toward the striatum, dopaminergic neurons and subthalamic nucleus, and explore the functional consequences of those interactions.

Measuring Systems with Power Supply to the Transmitting Part from the Receiving Part

Abstract: In the article, the author presents the results of an analysis of the metrological parameters of optoelectronic measuring systems with power supply to the transmitting parts from the receiving parts through separate and combined channels for transmitting information and energy. It is shown that circuits with separate channels for transmitting information and energy do not deteriorate the metrological characteristics of the information-measuring measuring system and do not impose any restrictions on the structure of its transmitting part associated with its energy consumption. For circuits with combined channels, with phase or spectral channel multiplexing methods, due to the mutual influence of optical radiation transmitting energy and information on each other, a deterioration in the signal-to-noise ratio of the information-measuring part of the sensor occurs due to the effects of continuum generation and stimulated Raman scattering only in in case where the optical radiation power is significant. If the compaction of the channels is temporary, the mutual influence of the channels can be neglected.

LSDN Empowers the Metaverse Communication to Achieve High-Resolution Visuals with Lower Bandwidth

Deploying super-resolution models on metaverse terminal devices can enhance visual effects without increasing network bandwidth. However, deploying most current super-resolution networks on metaverse terminal devices with limited hardware resources poses a challenge due to their large volumes and high computing power consumption. In this paper, we present a lightweight separation and distillation network (LSDN) aimed at reducing the model complexity by prioritizing network structure. Specifically, we initially present the blueprint separable convolution (BSConv) to decrease model complexity and utilize the BSConv and information distillation mechanism building the channel separation distillation block (CSDB). Subsequently, we develop the enhanced spatial attention block (ESA) and Fused-MBConv (FMBConv) to explore latent information. In addition, we employ three CSDBs, an ESA, and an FMBConv to construct the residual attention unit (RAU). Finally, we concatenate several RAUs and amalgamate their hierarchical results, and transmit them to the upsampler for reconstructing the high-resolution images. We carried out comprehensive experiments on a range of datasets and found conclusive evidence that the LSDN outperforms state-of-the-art approaches, exhibiting notable enhancements in quantitative and qualitative terms.

Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images

The task of multiple-tiny-object detection from diverse perspectives in unmanned aerial vehicles (UAVs) using onboard edge devices is a significant and complex challenge within computer vision. In order to address this challenge, we propose a lightweight and efficient tiny-object-detection algorithm named LE-YOLO, based on the YOLOv8n architecture. To improve the detection performance and optimize the model efficiency, we present the LHGNet backbone, a more extensive feature extraction network, integrating depth-wise separable convolution and channel shuffle modules. This integration facilitates a thorough exploration of the inherent features within the network at deeper layers, promoting the fusion of local detail information and channel characteristics. Furthermore, we introduce the LGS bottleneck and LGSCSP fusion module incorporated into the neck, aiming to decrease the computational complexity while preserving the detector’s accuracy. Additionally, we enhance the detection accuracy by modifying its structure and the size of the feature maps. These improvements significantly enhance the model’s capability to capture tiny objects. The proposed LE-YOLO detector is examined in ablation and comparative experiments on the VisDrone2019 dataset. In contrast to YOLOv8n, the proposed LE-YOLO model achieved a 30.0% reduction in the parameter count, accompanied by a 15.9% increase in the mAP(0.5). These comprehensive experiments indicate that our approach can significantly enhance the detection accuracy and optimize the model efficiency through the organic combination of our suggested enhancements.

Ionic Porous Aromatic Frameworks Embedding Polyoxometalates for Heterogeneous Catalysis.

Porous aromatic frameworks (PAFs) are highly promising functional porous solids known for their feasible amenability and extraordinary stability. When the framework was modified by ionic functional groups, these ionic PAFs (iPAFs) exhibited charged channels for adsorption, separation, and catalysis. However, the surface areas of ionic porous frameworks are usually lower than that of neutral frameworks, and their synthesis is limited by specific strategies and complex modification processes. To address these challenges, an intuitive route to construct ionic porous framework with high specific surface area was proposed. Herein, a multivariate ionic porous aromatic framework (MTV-iPAFs, named PAF-270) was synthesized using readily available building units with ionic functional groups through a multivariable synthesis strategy. PAF-270 exhibited hierarchical structure with the highest specific surface area among reported imidazolium-functionalized PAFs. Utilizing its physical and chemical properties, the availability for polyoxometalate loading and heterogeneous catalysis of PAF-270 were explored. PAF-270 exhibited a high adsorption capacity up to 50 % for both H3O40PW12 (HPW) and (NH4)5H6PV8Mo4O40 (V8). HPW@PAF-270 and V8@PAF-270 exhibited excellent catalytic abilities for oleic acid esterification and extractive oxidative desulfurization, respectively. Due to the stability of PAFs, these materials also showed remarkable resistance to temperature and pH changes. Overall, these results underscore the potential application of MTV-iPAFs as versatile functional porous materials.

Dual-input robust diagnostics for railway point machines via audio signals

ABSTRACT Railway Point Machine (RPM) is a fundamental component of railway infrastructure and plays a crucial role in ensuring the safe operation of trains. Its primary function is to divert trains from one track to another, enabling connections between different lines and facilitating route selection. By judiciously deploying turnouts, railway systems can provide efficient transportation services while ensuring the safety of passengers and cargo. As signal processing technologies develop rapidly, taking the easy acquisition advantages of audio signals, a fault diagnosis method for RPMs is proposed by considering noise and multi-channel signals. The proposed method consists of several stages. Initially, the signal is subjected to pre-processing steps, including cropping and channel separation. Subsequently, the signal undergoes noise addition using the Random Length and Dynamic Position Noises Superposition (RDS) module, followed by conversion to a greyscale image. To enhance the data, Synthetic Minority Oversampling Technique (SMOTE) module is applied. Finally, the training data is fed into a Dual-input Attention Convolutional Neural Network (DIACNN). By employing various experimental techniques and designing diverse datasets, our proposed method demonstrates excellent robustness and achieves an outstanding classification accuracy of 99.73%.

Channel multiplexing of FSO systems based on singular optics approaches

The article proposes two new methods for multiplexing-demultiplexing communication channels in the FSO system, based on singular optics approaches. The first makes use of computer generated vortex holograms installed in the transmitting and receiving modules of the systems, which make it possible to generate and separate communication channels formed by vortex beams with different topological charges. The second method, spatial singular multiplexing, is based on the property of singular beams to restore angular momentum when vortex beams propagate in a medium with physical disturbances. The method uses opaque screens installed in the transmitting block of the system, which diaphragm the vortex beams. As a result of this operation, the centers of the vortices in the receiving part of the system are displaced, which makes it possible to separate the beams that are differently blocked in the transmitting unit. Computer simulation data and experimental results are presented.

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0, were analyzed, requiring the reconstruction of diphoton pairs. In this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

Safety Monitoring and Auto Alert System in Underground Collieries

In the current scenario, significant mine disasters often occur due to explosions and fires. The occurrence of unexpected underground mine landslides poses a significant threat to miner safety, leading to fatalities. Consequently, the proposed system actively monitors key parameters and issues alerts to miners, aiming to mitigate this risk. The system primarily focuses on monitoring gas levels, air quality, temperature, and vibrations in underground mines, with the data being continuously recorded and stored in the ThingSpeak cloud. Visualization of the data is achieved through field charts created within the designated channel. The cloud-stored data is linked with MathWorks to predict missing values, providing a basis for proactive measures. The predicted missing values are then stored in a separate channel on ThingSpeak for continuous chart visualization. This data, complete with timestamps, can be exported to excel for further analysis and application. The scenario of the coal mine is monitored and regular reports are sent to the personnel’s mail which is automated in Thingspeak. In the event of an explosion, the rescue team is promptly notified through SMS alerts using a GSM modem.

Biomimetic designing MOFs nanosheet based membranes with self-recovery two-dimensional selective channels for specific molecules long-lasting precise separation

Two-dimensional (2D) materials-based membranes with artificial transfer channels have shown significant potential for selective separation. However, the challenges such as uncontrollable interlayer spacing and undesirable molecular sieving capabilities of 2D channels have impeded their further application in separation. Inspired by biological selectivity transport channels with proper steric and affinity sites, herein we have designed biomimetic 2D selective transport channels based on a ZIF-L nanosheet membrane. In this design, the UiO-66-NH2 nanoparticles tune the appropriate interlayer confinement and compensate for laminate framework defects of 2D selectivity transport channels, the artificial imprinting recognition sites establishes the essential chemical environment for specific separation. As a result, the obtained MOFs nanosheet based membranes with imprinted recognition sites (MN-IMs) exhibited enhanced permeation flux (J = 1.0847 × 10−3 and 1.0423 × 10−3 mg min−1 cm−2) and permselectivity (α = 3.77 and 4.10), outperforming state-of-the-art similar technologies. Besides, the composite MOFs demonstrated good photoinduced self-recovery ability, which also enables MN-IMs to have long-lasting selective separation performance (the separation efficiency is 90.76 %) in the continuous separation process. This study introduces a novel design strategy for developing sophisticated 2D materials-based membranes and offers new insights into the precise separation of specific molecules.

Cascaded and Separate Channel Estimation based on CNN for RIS-MIMO Systems

Cascaded and Separate Channel Estimation based on CNN for RIS-MIMO Systems

Asymmetric frequency multiplexing topological devices based on a floating edge band

Topological photonics provides a platform for robust energy transport regardless of sharp corners and defects. Recently, the frequency multiplexing topological devices have attracted much attention due to the ability to separate optical signals by wavelength and hence the potential application in optical communication systems. Existing frequency multiplexing topological devices are generally based on the slow light effect. However, the resulting static local spatial mode or finely tuned flat band has zero-group velocity, making it difficult for both experimental excitation and channel out-coupling. Here, we propose and experimentally demonstrate an alternative prototype of asymmetric frequency multiplexing devices including a topological rainbow and frequency router based on floating topological edge mode (instead of localized ones); hence the multiple wavelength channels can be collectively excited with a point source and efficiently routed to separate output ports. The channel separation in our design is achieved by gradually tuning the band gap truncation on a topological edge band over a wide range of frequencies. A crucial feature lies in that the topological edge band is detached from bulk states and floating within the upper and lower photonic band gaps. More interestingly, due to the sandwiched morphology of the edge band, the top and bottom band gaps will each truncate into transport channels that support topological propagation towards opposite directions, and the asymmetrical transportation is realized for the frequency multiplexing topological devices.

An accurate lightweight algorithm for bearings fault diagnosis based on DPW ATTCNN model

Rapid and high-precision fault diagnosis of bearings is an essential safeguard for monitoring the safe operation of machinery. Numerous scholars have proposed bearing fault diagnosis models that achieve excellent identification accuracy. However, owing to the elevated cost, some of fault diagnosis models are too complicated to be deployed in practice. This paper proposes a lightweight model based on Depthwise separable convolution (DPW) and Efficient Channel Attention (ECA) called DPW ATTCNN model. The DPW ATTCNN model first adopts DPW to reduce the parameters of the model. Meanwhile, the ECA attention mechanism is utilized to learn crucial information between neural network channels, which improves the accuracy of bearing fault diagnosis. The DPW ATTCNN model is improved based on the Adaptive Batch Normalization (AdaBN) algorithm, which has excellent domain adaptive capability. DPW ATTCNN model achieves excellent performance using a small number of parameters. The experimental results show that the DPW ATTCNN model can achieve 99.58 % accuracy in bearing fault diagnosis, which is verified on the public dataset Case Western Reserve University (CWRU) dataset. In addition, the anti-interference evaluation experiment shows that the accuracy of DPW ATTCNN model for bearing fault diagnosis is still higher than 95 % even under strong noise interference. In the domain adaptive performance test, the DPW ATTCNN (AdaBN) model achieves an average fault recognition rate of 97.35 %, which further verifies the robustness and effectiveness of the proposed DPW ATTCNN algorithm.