Supramolecular polymer-based gel fracturing fluid with a double network applied in ultra-deep hydraulic fracturing

A gel based on polyacrylamide,exhibiting delayed crosslinking characteristics,emerges as the preferred solution for mitigating degradation under conditions of high temperature and extended shear in ultralong wellbores.High viscosity/viscoelasticity of the fracturing fluid was required to maintain excellent proppant suspension properties before gelling.Taking into account both the cost and the potential damage to reservoirs,polymers with lower concentrations and molecular weights are generally preferred.In this work,the supramolecular action was integrated into the polymer,resulting in significant increases in the viscosity and viscoelasticity of the synthesized supramolecular polymer system.The double network gel,which is formed by the combination of the supramolecular polymer system and a small quantity of Zr-crosslinker,effectively resists temperature while minimizing permeability damage to the reservoir.The results indicate that the supramolecular polymer system with a molecular weight of(268—380)×10^(4)g/mol can achieve the same viscosity and viscoelasticity at 0.4 wt%due to the supramolecular interaction between polymers,compared to the 0.6 wt%traditional polymer(hydrolyzed polyacrylamide,molecular weight of 1078×10^(4)g/mol).The supramolecular polymer system possessed excellent proppant suspension properties with a 0.55 cm/min sedimentation rate at 0.4 wt%,whereas the0.6 wt%traditional polymer had a rate of 0.57 cm/min.In comparison to the traditional gel with a Zrcrosslinker concentration of 0.6 wt%and an elastic modulus of 7.77 Pa,the double network gel with a higher elastic modulus(9.00 Pa)could be formed only at 0.1 wt%Zr-crosslinker,which greatly reduced the amount of residue of the fluid after gel-breaking.The viscosity of the double network gel was66 m Pa s after 2 h shearing,whereas the traditional gel only reached 27 m Pa s.

Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection

We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.

Unlocking the potentials of gel conformance for water shutoff in fractured reservoirs: Favorable attributes of the double network gel for enhancing oil recovery

The double-network prepared with an in-situ monomer gel and a fast-crosslinked Cr(III) gel is introduced to develop a thixotropic and high-strength gel (THSG), which is found to have many advantages over the traditional gels. The THSG gel demonstrates remarkable thermal stability, and no syneresis is observed after 12 months with high salinity brine (95,500 mg/L). Moreover, the SEM and XRD results indicate that the gel is intercalated into the lamellar structures of Na-MMT, where the gel can form a uniform and compact structure. In addition, the THSG gel has an excellent swelling behavior, even in the high salinity brine. In the slim tube experiments, the THSG gel exhibits high rupture pressure and improves blocking capacity after being ruptured. The core flooding results show that a layer of gel filter cake is formed on the face of the fracture, which may be promoted by a high matrix permeability, a small aperture fracture, and a high injection rate. After the gel treatment, the fracture can be completely blocked by the THSG gel. It is found that a high incremental oil recovery (65.3%) can be achieved when the fracture was completely blocked, compared to 40.2% if the gel is ruptured. Although the swelling of ruptured gel can improve oil recovery, part of the injected brine may be channeled through the gel-filled fractures, resulting in a decrease in the sweep efficiency. Therefore, the improved blocking ability by gel swelling (e.g., in fresh water) may be less efficient to contribute to an enhancement of oil recovery. It is also found that the pressure gradient and residual resistance factor to water (Frrw) are higher if the matrix is less permeable, indicating that the fractured reservoir with lower matrix permeability may require a higher gel strength for treatment. The findings of this study may provide novel insights on designing robust double network gels for water shutoff in fractured reservoirs.

A Double Network Hydrogel with High Mechanical Strength and Shape Memory Properties

Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly.

Polyzwitterionic cross-linked double network hydrogel electrolyte enabling high-stable Zn anode

Zn metal anode suffers from dendrite issues and passive byproducts,which severely plagues the practical application of aqueous Zn metal batteries.Herein,a polyzwitterionic cross-linked double network hydrogel electrolyte composed of physical crosslinking(hyaluronic acid)and chemical crosslinking(synthetic zwitterionic monomer copolymerized with acrylamide)is introduced to overcome these obstacles.On the one hand,highly hydrophilic physical network provides an energy dissipation channel to buffer stress and builds a H_(2)O-poor interface to avoid side reactions.On the other hand,the charged groups(sulfonic and imidazolyl)in chemical crosslinking structure build anion/cation transport channels to boost ions’kinetics migration and regulate the typical solvent structure[Zn(H_(2)O)_(6)]^(2+)to R-SO_(3)^(−)[Zn(H_(2)O)_(4)]^(2+),with uniform electric field distribution and significant resistance to dendrites and parasitic reactions.Based on the above functions,the symmetric zinc cell exhibits superior cycle stability for more than 420 h at a high current density of 5 mA·cm^(−2),and Zn||MnO_(2)full cell has a reversible specific capacity of 150 mAh·g^(−1)after 1000 cycles at 2 C with this hydrogel electrolyte.Furthermore,the pouch cell delivers impressive flexibility and cyclability for energy-storage applications.

Application of interpolated double network model for carbon nanotube composites in electrothermal shape memory behaviors

Multi-wall carbon nanotube filled shape memory polymer composite(MWCNT/SMC)possessed enhanced modulus,strength,and electric conductivity,as well as excellent electrothermal shape memory properties,showing wide design scenarios and engineering application prospects.The thermoelectrically triggered shape memory process contains complex multi-physical mechanisms,especially when coupled with finite deformation rooted on micro-mechanisms.A multi-physical finite deformation model is necessary to get a deep understanding on the coupled electro-thermomechanical properties of electrothermal shape memory composites(ESMCs),beneficial to its design and wide application.Taking into consideration of micro-physical mechanisms of the MWCNTs interacting with double-chain networks,a finite deformation theoretical model is developed in this work based on two superimposed network chains of physically crosslinked network formed among MWCNTs and the chemically crosslinked network.An intact crosslinked chemical network is considered featuring with entropic-hyperelastic properties,superimposed with a physically crosslinked network where percolation theory is based on electric conductivity and electric-heating mechanisms.The model is calibrated by experiments and used for shape recoveries triggered by heating and electric fields.It captures the coupled electro-thermomechanical behavior of ESMCs and provides design guidelines for MWCNTs filled shape memory polymers.

Mechanism of Thermally Radiative Prandtl Nanofluids and Double-Diffusive Convection in Tapered Channel on Peristaltic Flow with Viscous Dissipation and Induced Magnetic Field

The application of mathematical modeling to biological fluids is of utmost importance, as it has diverse applicationsin medicine. The peristaltic mechanism plays a crucial role in understanding numerous biological flows. In thispaper, we present a theoretical investigation of the double diffusion convection in the peristaltic transport of aPrandtl nanofluid through an asymmetric tapered channel under the combined action of thermal radiation andan induced magnetic field. The equations for the current flow scenario are developed, incorporating relevantassumptions, and considering the effect of viscous dissipation. The impact of thermal radiation and doublediffusion on public health is of particular interest. For instance, infrared radiation techniques have been used totreat various skin-related diseases and can also be employed as a measure of thermotherapy for some bones toenhance blood circulation, with radiation increasing blood flow by approximately 80%. To solve the governingequations, we employ a numerical method with the aid of symbolic software such as Mathematica and MATLAB.The velocity, magnetic force function, pressure rise, temperature, solute (species) concentration, and nanoparticlevolume fraction profiles are analytically derived and graphically displayed. The results outcomes are compared withthe findings of limiting situations for verification.

Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

Analysis of rotor eddy current loss and thermal deformation of magnetic liquid double suspension bearing

Magnetic-liquid double suspension bearing(MLDSB)is a new type of suspension bearing based on electromagnetic suspension and supplemented by hydrostatic supporting.Without affecting the electromagnetic suspension force,the hydrostatic supporting effect is increased,and the real-time coupling of magnetic and liquid supporting can be realized.However,due to the high rotation speed,the rotor part produces eddy current loss,resulting in a large temperature rise and large ther-mal deformation,which makes the oil film thickness deviate from the initial design.The support and bearing characteristics are seriously affected.Therefore,this paper intends to explore the internal effects of eddy current loss of the rotor on the temperature rise and thermal deformation of MLDSB.Firstly,the 2D magnetic flow coupling mathematical model of MLDSB is established,and the eddy current loss distribution characteristics of the rotor are numerically simulated by Maxwell software.Secondly,the internal influence of mapping relationship of structural operating parameters such as input current,coil turns and rotor speed on rotor eddy current loss is revealed,and the changing trend of rotor eddy current loss under different design parameters is explored.Thirdly,the eddy cur-rent loss is loaded into the heat transfer finite element calculation model as a heat source,and the temperature rise of the rotor and its thermal deformation are simulated and analyzed,and the influ-ence of eddy current loss on rotor temperature rise and thermal deformation is revealed.Finally,the pressure-flow curve and the distribution law of the internal flow field are tested by the particle image velocimetry(PIV)system.The results show that eddy current loss increases linearly with the in-crease of coil current,coil turns and rotor speed.The effect of rotational speed on eddy current loss is much higher than that of coil current and coil turns.The maximum temperature rise,minimum temperature rise and maximum thermal deformation of the rotor increase with the increase of eddy current loss.The test results of flow-pressure and internal trace curves are basically consistent with the theoretical simulation,which effectively verifies the correctness of the theoretical simulation.The research results can provide theoretical basis for the design and safe and stable operation of magnetic fluid double suspension bearings.

Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis

BACKGROUND Multiple linear stapler firings during double stapling technique(DST)after laparoscopic low anterior resection(LAR)are associated with an increased risk of anastomotic leakage(AL).However,it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.AIM To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging(MRI).METHODS We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis,which were randomly divided into a training set(n=260)and testing set(n=68).Binary logistic regression was adopted to create a clinical model using six factors.The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed.Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks.Sensitivity,specificity,accuracy,positive predictive value(PPV),and area under the receiver operating characteristic curve(AUC)was calculated for each model.RESULTS The prevalence of≥3 linear stapler cartridges was 17.7%(58/328).The prevalence of AL was statistically significantly higher in patients with≥3 cartridges compared to those with≤2 cartridges(25.0%vs 11.8%,P=0.018).Preoperative carcinoembryonic antigen level>5 ng/mL(OR=2.11,95%CI 1.08-4.12,P=0.028)and tumor size≥5 cm(OR=3.57,95%CI 1.61-7.89,P=0.002)were recognized as independent risk factors for use of≥3 linear stapler cartridges.Diagnostic performance was better with the integrated model(accuracy=94.1%,PPV=87.5%,and AUC=0.88)compared with the clinical model(accuracy=86.7%,PPV=38.9%,and AUC=0.72)and the image model(accuracy=91.2%,PPV=83.3%,and AUC=0.81).CONCLUSION MRI-based deep learning model can predict the use of≥3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery.This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for≥3 linear stapler cartridges.

Exceptionally high genetic variance of the doubled haploid(DH)population of poplar

Doubled haploid(DH)plants have been widely used for breeding and biological research in crops.Pop ulus spp.have been used as model woody plant species for biological research.However,the induction of DH poplar plants is onerous,and limited biological or breeding work has been carried out on DH individuals or populations.In this study,we provide an effective protocol for poplar haploid induction based on an anther culture method.A total of 96 whole DH plant lines were obtained using an F1hybrid of Populus simonii×P.nigra as a donor tree.The phenotypes of the DH population showed exceptionally high variance when compared to those of half-sib progeny of the donor tree.Each DH line displayed distinct features compared to those of the other DH lines or the donor tree.Additionally,some excellent homozygous lines have the potential to be model plants in genetic and breeding studies.

基于改进Double U-Net的秀丽隐杆线虫显微图像端泡分割

针对秀丽隐杆线虫显微图像噪声较多,端泡区域像素与虫体及周围环境相似,标准Double U-Net分割效果较差的问题。该研究提出一种改进的Double U-Net网络的秀丽隐杆线虫端泡分割方法,在网络中引入密集连接,并采用改进的损失函数,解决了传统网络无法进行精确分割的问题。实验表明:改进后算法对线虫端泡的分割,Dice Coefficien、准确率和召回率达到了90.12%、87.45%和91.53%。

基于Dueling Double DQN的交通信号控制方法

为了提高交叉口通行效率缓解交通拥堵,深入挖掘交通状态信息中所包含的深层次隐含特征信息,提出了一种基于Dueling Double DQN(D3QN)的单交叉口交通信号控制方法;构建了一个基于深度强化学习Double DQN(DDQN)的交通信号控制模型,对动作-价值函数的估计值和目标值迭代运算过程进行了优化,克服基于深度强化学习DQN的交通信号控制模型存在收敛速度慢的问题;设计了一个新的Dueling Network解耦交通状态和相位动作的价值,增强Double DQN(DDQN)提取深层次特征信息的能力;基于微观仿真平台SUMO搭建了一个单交叉口模拟仿真框架和环境,开展仿真测试;仿真测试结果表明,与传统交通信号控制方法和基于深度强化学习DQN的交通信号控制方法相比,所提方法能够有效减少车辆平均等待时间、车辆平均排队长度和车辆平均停车次数,明显提升交叉口通行效率。

Influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel

The influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel is investigated.The DarcyBrinkman model is used to characterize the fluid flow dynamics in porous materials.The analytical solutions are obtained for the unidirectional and completely developed flow.Based on a normal mode analysis,the generalized eigenvalue problem under a perturbed state is solved.The eigenvalue problem is then solved by the spectral method.Finally,the critical Rayleigh number with the corresponding wavenumber is evaluated at the assigned values of the other flow-governing parameters.The results show that increasing the Darcy number,the Lewis number,the Dufour parameter,or the Soret parameter increases the stability of the system,whereas increasing the inclination angle of the channel destabilizes the flow.Besides,the flow is the most unstable when the channel is vertically oriented.

Enhancing the stability of Ni Fe-layered double hydroxide nanosheet array for alkaline seawater oxidation by Ce doping

Electrocatalytic hydrogen production from seawater holds enormous promise for clean energy generation.Nevertheless,the direct electrolysis of seawater encounters significant challenges due to poor anodic stability caused by detrimental chlorine chemistry.Herein,we present our recent discovery that the incorporation of Ce into Ni Fe layered double hydroxide nanosheet array on Ni foam(Ce-Ni Fe LDH/NF)emerges as a robust electrocatalyst for seawater oxidation.During the seawater oxidation process,CeO_(2)is generated,effectively repelling Cl^(-)and inhibiting the formation of Cl O-,resulting in a notable enhancement in the oxidation activity and stability of alkaline seawater.The prepared Ce-Ni Fe LDH/NF requires only overpotential of 390 m V to achieve the current density of 1 A cm^(-2),while maintaining long-term stability for 500 h,outperforming the performance of Ni Fe LDH/NF(430 m V,150 h)by a significant margin.This study highlights the effectiveness of a Ce-doping strategy in augmenting the activity and stability of materials based on Ni Fe LDH in seawater electrolysis for oxygen evolution.

基于softmax的加权Double Q-Learning算法

强化学习作为机器学习的一个分支,用于描述和解决智能体在与环境的交互过程中,通过学习策略以达成回报最大化的问题。Q-Learning作为无模型强化学习的经典方法,存在过估计引起的最大化偏差问题,并且在环境中奖励存在噪声时表现不佳。Double Q-Learning(DQL)的出现解决了过估计问题,但同时造成了低估问题。为解决以上算法的高低估问题,提出了基于softmax的加权Q-Learning算法,并将其与DQL相结合,提出了一种新的基于softmax的加权Double Q-Learning算法(WDQL-Softmax)。该算法基于加权双估计器的构造,对样本期望值进行softmax操作得到权重,使用权重估计动作价值,有效平衡对动作价值的高估和低估问题,使估计值更加接近理论值。实验结果表明,在离散动作空间中,相比于Q-Learning算法、DQL算法和WDQL算法,WDQL-Softmax算法的收敛速度更快且估计值与理论值的误差更小。

Double contrast-enhanced ultrasonography improves diagnostic accuracy of T staging compared with multi-detector computed tomography in gastric cancer patients

BACKGROUND Gastric cancer(GC)is the most common malignant tumor and ranks third for cancer-related deaths among the worldwide.The disease poses a serious public health problem in China,ranking fifth for incidence and third for mortality.Knowledge of the invasive depth of the tumor is vital to treatment decisions.AIM To evaluate the diagnostic performance of double contrast-enhanced ultrasonography(DCEUS)for preoperative T staging in patients with GC by comparing with multi-detector computed tomography(MDCT).METHODS This single prospective study enrolled patients with GC confirmed by preoperative gastroscopy from July 2021 to March 2023.Patients underwent DCEUS,including ultrasonography(US)and intravenous contrast-enhanced ultrasonography(CEUS),and MDCT examinations for the assessment of preoperative T staging.Features of GC were identified on DCEUS and criteria developed to evaluate T staging according to the 8th edition of AJCC cancer staging manual.The diagnostic performance of DCEUS was evaluated by comparing it with that of MDCT and surgical-pathological findings were considered as the gold standard.RESULTS A total of 229 patients with GC(80 T1,33 T2,59 T3 and 57 T4)were included.Overall accuracies were 86.9%for DCEUS and 61.1%for MDCT(P<0.001).DCEUS was superior to MDCT for T1(92.5%vs 70.0%,P<0.001),T2(72.7%vs 51.5%,P=0.041),T3(86.4%vs 45.8%,P<0.001)and T4(87.7%vs 70.2%,P=0.022)staging of GC.CONCLUSION DCEUS improved the diagnostic accuracy of preoperative T staging in patients with GC compared with MDCT,and constitutes a promising imaging modality for preoperative evaluation of GC to aid individualized treatment decision-making.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.