Mixing Channel Research Articles

MBene Nanosheets with DNA Adsorbability for Circulating Tumor DNA Assay via Fluorescence Biosensing and Paper‐Based Microfluidic POCT

AbstractThe detection of circulating tumor DNA (ctDNA) in blood is significant for non‐invasive cancer diagnosis and treatment monitoring. Herein, MBene nanosheets is synthesized and compared with MXene via Density Functional Theory (DFT) calculations and fluorescence kinetic evaluations, for first time, revealing MBene's exceptional DNA adsorbability and discrimination to single‐stranded DNA (ssDNA) and double‐stranded DNA (dsDNA). Then, a sensitive fluorescence biosensor for ctDNA detection is developed, demonstrating impressive performance. To facilitate point‐of‐care testing (POCT) ctDNA, a paper‐based microfluidic chip incorporated a delay area and a mixing channel is designed. The constricted‐expanded structure channel optimization is guided by numerical simulations and experiments. A WeChat mini program named “ctDNA Detection” is designed for readout assay. Furthermore, cell and mice serum samples are analyzed, with Magnetic Bead@Graphene Oxide (MB@GO) and clutch probes for magnetic pre‐enrichment. The results accuracy is confirmed by its consistency with standard qPCR results (AUC = 1). The successful detection of ctDNA in post‐surgery mouse models underscored the biosensor's potential for cancer treatment monitoring. Thus, this research not only advanced the understanding of the MBene‐DNA interaction in biosensing, but also can pave the way for novel applications in bioimaging and nanopore‐based nucleic acid sequencing, leveraging the digital transformation of DNA base‐MBene adsorption differences.

Diffusive micromixing combined with dynamic in situ laser scattering allows shedding light on lipid nanoparticle precipitation

Pharmaceutical formulations are increasingly based on drug nanoparticles or carrier nanoparticles encapsulating drugs or mRNA molecules. Sizes and monodispersity of the nanoparticles regulate bioavailability, pharmacokinetics and pharmacology. Microfluidic mixers promise unique conditions for their continuous preparation. A novel microfluidic antisolvent precipitation device was realized by two-photon-polymerization with a mixing channel in which the organic phase formed a sheet with a homogeneous thickness of down to 7 μm completely wrapped in the aqueous phase. Homogeneous diffusion through the sheet accelerates mixing. Optical access was implemented to allow in-situ dynamic light scattering. By centering the thin sheet in the microchannel cross-section, two important requirements are met. On the one hand, the organic phase never reaches the channel walls, avoiding fouling and unstable flow conditions. On the other hand, in the sheet positioned at the maximum of the parabolic flow profile the nanoparticle velocities are homogenized which enables flow-compensated Dynamic Light Scattering (flowDLS). These unique features allowed in-situ particle size determination for the first time. Monitoring of lipid nanoparticle precipitation was demonstrated for different rates of solvent and antisolvent flows. This breakthrough innovation will not only enable feedback control of nanoparticle production but also will provide new insights into the dynamics of nanoparticle precipitation.

Ergodic and mixing quantum channels: From two-qubit to many-body quantum systems

Ergodic and mixing quantum channels: From two-qubit to many-body quantum systems

An experimental study of transient front velocity for miscible exchange flow in a rectangular channel

ABSTRACT This experimental study considers the exchange flow of two miscible fluids in a rectangular channel. The main purpose is to understand the mixing dynamics and explore how mixing and different channel inclinations can affect variations in front velocity. In contrast to previous studies, no steady state is assumed. The larger channel cross-section used in the current study provides an opportunity to visualize and analyze the inertia dominated regime in finer details. For horizontal channels, after opening the gate valve and allowing the transient phase to pass, it is observed that the front velocity remains almost constant for a period before gradually decreasing. In inclined flows, the mixing and transition to inertia dominated flow begin when the viscous flow rate of the main flow increases. This flow rate may exceed the maximum flow rate that the front can tolerate according to the equations describing the momentum balance at the front. At sufficiently high density contrasts or large inclinations, the front velocity may continue to decrease as the flow progresses due to interfacial instabilities and mixing. Under such conditions, the results may also cast doubt on the presence of a true steady state front velocity in miscible exchange flow.

A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model

Aiming at solving the issues that fire detection is prone to be affected by environmental factors, and the accuracy of flame and smoke detection remains relatively low at the incipient stage of fire, a fire detection algorithm based on GCM-YOLO is put forward. Firstly, GhostNet is introduced to optimize the backbone network, enabling the model to be lightweight without sacrificing model accuracy. Secondly, the upsampling module is reorganized with content-aware features to enhance the detail capture and information fusion effect of the model. Finally, by incorporating the mixed local channel attention mechanism in the neck, the model can enhance the processing capability of complex scenes. The experimental results reveal that, compared with the baseline model YOLOv8n, the GCM-YOLO model in fire detection increases the mAP@0.5 by 1.2%, and the number of parameters and model size decrease by 38.3% and 34.9%, respectively. The GCM-YOLO model can raise the accuracy of fire detection while reducing the computational burden and is suitable for deployment in practical application scenarios such as mobile terminals.

Unsteady flow in a compound channel during flooding: Insights from a combined two-dimensional and three-dimensional numerical simulation

Natural channels often take the form of compound channels during flooding events. The unsteadiness of floods is crucial for flood management and river-floodplain ecosystems. However, the impact of unsteady flow on three-dimensional hydrodynamic processes within compound channels remains poorly understood. This study employed a validated two-dimensional/three-dimensional numerical model to examine how unsteady features influence key hydrodynamic indicators of compound channels, such as velocity distribution, turbulence characteristics, and discharge partitioning between the main channel and floodplain. A definitive positive correlation was found between water level fluctuation amplitudes and integrated discharge during unsteady events, as well as between mean water levels and mean discharges. The proportion of discharge conveyed by the main channel relative to the total was directly linked to the water depth ratio. The exchange at the main channel–floodplain interface exhibits a dual-layer flow pattern: surface waters spill into the floodplain, while deeper waters are siphoned into the main channel. Unsteady conditions principally modulate the intensity of these exchanges, altering hydraulic interactions and water transfer between areas. Pronounced wall shear stresses were detected at the main channel–floodplain interface due to swift channel flows and momentum exchange at the floodplain margin, with maxima adjacent to the floodplain edge correlating with peak flow velocities. Under deep conditions, cross-sectional flow across the compound channel's mixing layer was essentially two-dimensional. These findings elucidate the complex hydrodynamics inherent to unsteady overbank flows while holding implications for enhancing floodplain management and advancing the understanding of unsteady fluvial hydraulics.

Advanced Neural Network-Based Equalization in Intensity-Modulated Direct-Detection Optical Systems: Current Status and Future Trends

Intensity-modulated direct-detection (IM/DD) optical systems are most widely employed in short-reach optical interconnects due to their simple structure and cost-effectiveness. However, IM/DD systems face mixed linear and nonlinear channel impairments, mainly induced by the combination of square-law detection and chromatic dispersion, as well as the utilization of low-cost non-ideal transceivers. To solve this issue, recent years have witnessed a growing trend of introducing machine learning technologies such as neural networks (NNs) into IM/DD systems for channel equalization. NNs usually present better system performance than traditional approaches, and various types of NNs have been investigated. Despite the excellent system performance, the associated high computational complexity is a major drawback that hinders the practical application of NN-based equalizers. This paper focuses on the performance and complexity trade-off of NNs employed in IM/DD systems, presenting a systematic review of the current status of NN-based equalizers as well as a number of effective complexity reduction approaches. The future trends of leveraging advanced NN in IM/DD links are also discussed.

Study Of Mixing Enhancement Of The 3-D Acoustofluidic Mixer Using Digital In-Line Holographic Micro Particle Tracking Velocimetry

In this study, the 3-D flow patterns and mixing performances are investigated for an acoustofluidic Y-junction micromixer using Digitial In-line Holographic Micro-Particle Tracking Velocimetry (DIHμPTV) and epi-fluorescence flow visualization. Two parallel longitudinal spines were placed in the channel to induce sustaining solenoidal 3-D acoustic streaming vortices, which work as the secondary flow in the mixing channel to reduce the diffusion mixing length and in turn enhance the mixing performances. The two spines have a triangular cross-section, with a base width of 0.2mm and a height of 0.3mm, placed 1.2mm apart from each other in a 2.8mm width and 1mm height channel. The Y-junction has two configurations, one has the spines directly connected to the side wall at the Y-junction, and the other microchannel has a gap between the two spines and a flat junction shape. The driving mechanism is provided by two piezoelectric disks, operating at 12 kHz oscillation frequency and 20V peak-to-peak amplitude. The 3-D flow patterns were recorded by the DIHμPTV in two measurement volumes to fully cover the test section’s height. The holographic reconstruction scheme is based on the 1st Rayleigh-Sommerfeld solution with an extra deconvolution step to improve the accuracy of the reconstruction of the depth position. The deconvolution step utilizes a simulated hologram of the Point-Spread Function (PSF) and reduces the depth uncertainty during the deconvolution of the raw data. The mixing performances of the acoustofluidic Y-junction micro-mixers were evaluated by the epi-fluorescence experiments, and the mixing indices were calculated and compared to verify the improvements compared to the Yjunction micromixers without the acoustic streaming vortices. The results confirmed the improvement of the mixing and the sustaining 3-D flow patterns even with a high flow rate, which could not be achieved with 2-D sharp-edge obstructions tested previously. The novel configuration may have the potential to inspire designs of high-throughput and low-pressure loss active micro-mixers.

A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

ABSTRACT The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed.

Supporting VoIP communication in IEEE 802.11ax networks: A new admission control and scheduling resource allocation scheme

The current IEEE 802.11ax standard enhances Wi-Fi networks with a series of new features, such as multi-user (MU) transmission, an Orthogonal Frequency Division Multiple Access (OFDMA) scheme and the ability to multiplex traffic from different access categories (ACs). These features can be utilized to enhance the QoS support for VoIP traffic and optimize the usage of IEEE 802.11ax network resources. This work proposes a new scheme to multiplex VoIP calls in IEEE 802.11ax MU frames and a new scheduler and resource allocation algorithm specifically designed for VoIP data traffic. Our scheduler allocates VoIP packets requiring longer transmission times into the same frame(s), minimizing the channel air-time assigned to VoIP transmission. In addition, unused radio resources in the MU frame are leveraged to transmit best-effort packets along with VoIP packets. For completeness, we also define a call admission control (CAC) algorithm that anticipates channel saturation conditions to ensure VoIP users can maintain a guaranteed level of QoS. Based on simulation results, our scheme is more efficient in reducing channel utilization than other schedulers such as multi-user round-robin (RR) (implemented by ns-3) or single-user FIFO. For example, for 30 VoIP stations using the G.711 codec under mixed channel conditions, our scheme reduces by 30% the air-time required to transmit VoIP packets. When coupled with the ability to also send best-effort packets along with VoIP packets, this translates into a higher throughput (i.e. 10 Mbit/s vs 4 Mbit/s) and more simultaneous VoIP users with guaranteed QoS (up to 46 VoIP users vs 26 and 28 users for the multi-user RR and single-user FIFO scheduling algorithms, respectively).

Pillar Array Mixer for Postcolumn Derivatization Integrated into Liquid Chromatography-Based Microfluidic Device.

The chemical derivatization of target analytes can enhance the sensitivity and selectivity of separation-based methods for metabolite analysis using microfluidic devices. However, the development of chromatography-based microfluidic devices with integrated derivatization units is challenging. In this study, a novel derivatization unit with a pillar array (PA)-based mixing channel was developed for postcolumn derivatization during on-chip liquid chromatography (LC). The PA mixer enhanced mixing between the derivatization reagents and analytes in the transverse direction, while preventing analyte dispersion in the flow direction. After the concept was confirmed using computational fluid dynamics analysis, microfluidic devices with a LC column and PA mixer were fabricated on a 20 × 20 mm silicon plate. Fluid experiments were performed using a PA mixer with a pillar size of 5 or 10 μm or a hollow-channel mixer, which revealed that the PA mixer enhanced transverse mixing without increasing the width of the analyte peak. Moreover, the developed device enabled the analysis of three amino acids within 40 s by separation via hydrophilic interaction chromatography followed by postcolumn fluorogenic derivatization with naphthalene-2,3-dicarboxaldehyde and fluorescence detection. Our results demonstrate the potential of integrated derivatization units for the development of micrototal analysis systems for use in bioanalysis.

Optimized J to T peak and T peak to T end measurements in nonclinical species administered moxifloxacin and amiodarone

IntroductionCardiovascular safety and the risk of developing the potentially fatal ventricular tachyarrhythmia, Torsades de Pointes (TdP), have long been major concerns of drug development. TdP is associated with a delayed ventricular repolarization represented by QT interval prolongation in the electrocardiogram (ECG), typically due to block of the potassium channel encoded by the human ether-a-go-go related gene (hERG). Importantly however, not all drugs that prolong the QT interval are torsadagenic and not all hERG blockers prolong the QT interval. Recent clinical reports suggest that partitioning the QT interval into early (J to T peak; JTp) and late repolarization (T peak to T end; TpTe) components may be valuable for distinguishing low-risk mixed ion channel blockers (hERG plus calcium and/or late sodium currents) from high-risk pure hERG channel blockers. This strategy, if true for nonclinical animal models, could be used to de-risk QT prolonging compounds earlier in the drug development process. MethodsTo explore this, we investigated JTp and TpTe in ECG data collected from telemetered dogs and/or monkeys administered moxifloxacin or amiodarone at doses targeting relevant clinical exposures. An optimized placement of the Tpeak fiducial mark was utilized, and all intervals were corrected for heart rate (QTc, JTpc, TpTec). ResultsIncreases in QTc and JTpc intervals with administration of the pure hERG blocker moxifloxacin and an initial QTc and JTpc shortening followed by prolongation with the mixed ion channel blocker amiodarone were detected as expected, aligning with clinical data. However, anticipated increases in TpTec by both standard agents were not detected. DiscussionThe inability to detect changes in TpTec reduces the utility of these subintervals for prediction of arrhythmias using continuous single‑lead ECGs collected from freely moving dogs and monkeys.

Modeling Excitable Cells with Memristors

This paper presents an in-depth analysis of an excitable membrane of a biological system by proposing a novel approach that the cells of the excitable membrane can be modeled as the networks of memristors. We provide compelling evidence from the Chay neuron model that the state-independent mixed ion channel is a nonlinear resistor, while the state-dependent voltage-sensitive potassium ion channel and calcium-sensitive potassium ion channel function as generic memristors from the perspective of electrical circuit theory. The mechanisms that give rise to periodic oscillation, aperiodic (chaotic) oscillation, spikes, and bursting in an excitable cell are also analyzed via a small-signal model, a pole-zero diagram of admittance functions, local activity, the edge of chaos, and the Hopf bifurcation theorem. It is also proved that the zeros of the admittance functions are equivalent to the eigen values of the Jacobian matrix, and the presence of the positive real parts of the eigen values between the two bifurcation points lead to the generation of complicated electrical signals in an excitable membrane. The innovative concepts outlined in this paper pave the way for a deeper understanding of the dynamic behavior of excitable cells, offering potent tools for simulating and exploring the fundamental characteristics of biological neurons.

Symbols of mixed unitary quantum channels generated by finite unitary groups

We study the symbols of linear operators generated by a finite unitary group. Under some assumptions, it is shown that the operator can be restored from its symbol. We also introduce symbols of mixed unitary quantum channels generated by finite unitary groups. An example illustrating the technique is given.

Study on flow regimes and mixing of vortex-inducing T-jet reactors with staggered inlet channel

Improving the jet reactor's mixing performance is critical and challenging. In this paper, a new type of vortex-inducing T-jet reactor proposed by researchers is studied. By designing the staggered inlet structure of the T-jet reactor, the vortex is induced in the flow field in the mixing channel to improve the mixing effect. Flow regimes and mixing characteristics in the vortex-inducing T-jet reactor were investigated through numerical simulations (CFD) at 30 ≤ Re ≤ 600. Results show that the staggered inlet channel induces vortices successfully, and the engulfment flow regime occurs at lower Reynolds numbers, thus promoting the mixing. The generation and evolution of vortex structures in the reactor were focused on. It was found that in the unsteady engulfment flow, the two fluids cross each other, which makes the vortex structure constantly twist and merge, which greatly improves the mixing effect. At larger Reynolds numbers, many attached small vortices are generated around the central vortex structure, and these vortices are wound together with the high-speed rotation of the central vortex, avoiding the unsteady symmetric flow pattern with a poor mixing effect. Finally, it is found that the mixing performance of the vortex-inducing T-jet reactor is greatly improved under all regimes.

Maritime distress target detection algorithm based on YOLOv5s‐EFOE network

AbstractTraditional maritime search and rescue methods mainly rely on manual operation, which takes a long time to identify and results in poor search and rescue results. This paper applies computer vision technology to the field of maritime distress object detection and proposes an improved object detection algorithm YOLOv5s‐EFOE based on the YOLOv5s algorithm. Firstly, the authors change the detection head of the YOLOv5s algorithm to use a mixed channel strategy to build a more efficient decoupling head, reducing the number of 3 × 3 convolutional layers in the middle of the decoupling head to only one. The width of the head is scaled by the width multipliers of Backbone and Neck. Secondly, in conjunction with the SimOTA matching algorithm, the positive samples of the target to be tested are dynamically allocated to improve the recognition ability of different targets. Finally, considering the low pixel value of the maritime distress target from the perspective of unmanned aerial vehicles (UAV), the loss function CIoU in the original YOLOv5s is changed to EIoU. EIoU not only considers the distance and aspect ratio of the centre point, but also considers the true difference between the width and height of the prediction box and the real box, which improves the prediction accuracy of the anchor box. Experiments are conducted on a subset of the public dataset SeaDronesSee. The of the YOLOv5s‐EFOE model proposed in this paper reached 75.9%, reached 79.9%, and reached 44.5%. These indicators are superior to the original YOLOv5s model, YOLOv7 series models, and YOLOv8 series models. Compared with the original model, the YOLOv5s‐EFOE model has increased the by 5.4%, by 5.6%, and by 4.6%, improving the difficult to detect and missed detection situations. This model can be deployed on UAVs and can effectively identify maritime distress target, achieving the purpose of search and rescue.

Cross-modality feature fusion for night pedestrian detection

Night pedestrian detection with visible image only suffers from the dilemma of high miss rate due to poor illumination conditions. Cross-modality fusion can ameliorate this dilemma by providing complementary information to each other through infrared and visible images. In this paper, we propose a cross-modal fusion framework based on YOLOv5, which is aimed at addressing the challenges of night pedestrian detection under low-light conditions. The framework employs a dual-stream architecture that processes visible images and infrared images separately. Through the Cross-Modal Feature Rectification Module (CMFRM), visible and infrared features are finely tuned on a granular level, leveraging their spatial correlations to focus on complementary information and substantially reduce uncertainty and noise from different modalities. Additionally, we have introduced a two-stage Feature Fusion Module (FFM), with the first stage introducing a cross-attention mechanism for cross-modal global reasoning, and the second stage using a mixed channel embedding to produce enhanced feature outputs. Moreover, our method involves multi-dimensional interaction, not only correcting feature maps in terms of channel and spatial dimensions but also applying cross-attention at the sequence processing level, which is critical for the effective generalization of cross-modal feature combinations. In summary, our research significantly enhances the accuracy and robustness of nighttime pedestrian detection, offering new perspectives and technical pathways for visual information processing in low-light environments.

Mqdtfit: A collection of Python functions for empirical multichannel quantum defect calculations

The Python functions distributed with this article can be used for calculating the parameters of multichannel quantum defect theory models describing excited bound states of complex atoms. These parameters are obtained by fitting a model to experimental data provided by the user. The two main formulations of the theory are supported, namely the one in which the parameters of the model are a set of eigen channel quantum defects and a transformation matrix, and the one where these parameters are the elements of a reactance matrix. The distribution includes programs for calculating theoretical energy levels, calculating mixing coefficients and channel fractions and producing Lu-Fano plots. Program summaryProgram Title: mqdtfitCPC Library link to program files:https://doi.org/10.17632/nzgygzd96c.1Developer's repository link:https://github.com/durham-qlm/mqdtfitLicensing provisions: BSD 3-clause.Programming language: Python 3.Nature of problem: Multichannel quantum defect theory aims at giving a unified description of the excited states of multielectron systems whereby the properties of entire series can be predicted from models involving a relatively small number of parameters. These parameters are obtained by fitting theory to experimental data, in the empirical version of the theory. The present programs specifically concern the application of this theory to the bound states of complex atomic systems, such as divalent atoms, for which spectroscopic series are often perturbed by isolated states of a different symmetry, making a multichannel description necessary.Solution method: The functions forming this library are grouped and linked to each other into a single Python module. They can be used to calculate the parameters of a given multichannel quantum defect theory model by fitting the model to experimental data provided by the user. These parameters are either a set of eigen channel quantum defects and a transformation matrix, in the eigenchannel parametrization of the theory, or the elements of an orthogonal matrix, in the reactance matrix parametrization of the theory. Given these parameters, this software can also be used to calculate theoretical energy levels, calculate coefficients describing the mixing between the channels considered and produce Lu-Fano plots.

Precisely Patterned Channels in a Vertical Organic Electrochemical Transistor with a Diazirine Photo-Crosslinker.

Organic electrochemical transistors (OECTs) rely on both efficient ionic doping/de-doping process and carrier transport in the mixed ionic-electronic channel under the modulation of gate bias. Moreover, channels that hold photopatterning capability are highly desired to minimize parasitic capacitance and simplify the fabrication process/cost. However, yielding photo-patternable channels with both precise/robust patterning capability and controllable ionic-electronic coupling is still challenging. Herein, double-end trifluoromethyl diazirines (DtFDA) with different chain lengths are introduced in the OECT channel to act as both photo-crosslinker and medium to regulate ionic-electronic transport. Specifically, high-resolution patterns with a minimum line width/gap of 2 μm are realized in p(g2T-T) or Homo-gDPP based channels by introducing DtFDA. Maximum transconductances of 68.6 mS and 81.6 mS, current on/off ratio of 106 and 107 (under a drain voltage of only ±0.1 V), are achieved in p- and n-type vertical OECTs (vOECTs), respectively, along with current densities exceeding 1 kA cm-2 and good cycling stability of more than 100,000 cycles (2000 seconds). This work provides a new and facile strategy for the fabrication of vOECT channels with high resolution and high performance via the introduction of a simple and efficient crosslinker.

Securing cooperative vehicular networks amid obstructing vehicles and mixed fading channels

Smart cities (SCs) were founded on the basis of the Internet of Vehicles paradigm, aiming to enhance the quality of life. Despite not having a unique composition, the intelligent transportation services (ITS) is a key component in most of the SCs. The ITS integrates the new communication systems along with the traditional transportation systems, forming a universal network of static and vehicular entities communicating over wireless broadcast channels. In addition to its vulnerability to co-channel interference (CCI), eavesdropping, and fading, the vehicle-to-vehicle communication channels could be subjected to shadowed fading. That is due to the probabilistic obstruction by big vehicles. Hence, degrading the signal-to-noise ratio at the receiving vehicle severely. This work evaluates the secrecy performance of a practical cooperative vehicular relaying network in terms of its secrecy outage probability (SOP). Due to the probabilistic existence of a vehicular obstacle, three different communication scenarios are considered and a novel closed-form analytical expression for the SOP is obtained over mixed Nakagami-m fading and Nakagami-N-Gamma shadowed fading channels. The system comprises legitimate source and destination operating in full-duplex mode, thus imposing CCI on both the relay and the passive eavesdropper. The result demonstrate the impacts of the secrecy data rate threshold, the strength of shadowing, the density of the obstructing vehicles, and several parameters on the SOP. High levels of shadowing strength and density of obstructing vehicles significantly degrades the secrecy performance by increasing the SOP. Accordingly, adopting the mixed fading channel model is of great importance for practical network modeling.