Unveiling the role of lithiophilic sites denseness in regulating lithium ion deposition

The construction of lithiophilic sites is an effective way to achieve uniform lithium(Li)ion deposition for stably cycling Li metal batteries.However,in-depth investigations involving lithiophilic sites denseness(LSD)in impacting Li ion deposition remain unknown.Herein we propose an insight into this issue by probing the effect of LSD on determining the Li ion deposition.Experimental characterization and theoretical simulation demonstrate that rational LSD plays a vital role in both Li nucleation and the subsequent Li ion plating behaviors.By tailoring the LSD from low to high,the accompanied Li nucleation overpotentials continuously decrease.Additionally,the Li ion mobility increases first and then weakens in the subsequent Li ion plating stage.Consequently,the Li metal with a moderate LSD allows a dendritefree morphology and satisfactory long-term cycling performances.This work affords a deeper fundamental understanding of lithiophilic chemistry that directs the development of efficient strategies to realize dendrite-free Li metal batteries.

A quantum blind signature scheme based on dense coding for non-entangled states

In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non-entangled quantum blind signature scheme based on dense encoding. The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources. After receiving particles, the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature. Then the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification.Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection,and higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature. By simultaneously utilizing the secondary distribution and rearrangement of non-entangled quantum states, a non-entangled quantum state representation of three bits of classical information is achieved, reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency while, at the same time, this scheme meets the requirements of unforgeability, non-repudiation, and prevention of information leakage.

Rotating Massive Strangeon Stars and X-Ray Plateau of Short GRBs

Strangeon stars,which are proposed to describe the nature of pulsar-like compact stars,have passed various observational tests.The maximum mass of a non-rotating strangeon star could be high,which implies that the remnants of binary strangeon star mergers could even be long-lived massive strangeon stars.We study rigidly rotating strangeon stars in the slowly rotating approximation,using the Lennard-Jones model for the equation of state.Rotation can significantly increase the maximum mass of strangeon stars with unchanged baryon numbers,enlarging the mass-range of long-lived strangeon stars.During spin-down after merger,the decrease of radius of the remnant will lead to the release of gravitational energy.Taking into account the efficiency of converting the gravitational energy luminosity to the observed X-ray luminosity,we find that the gravitational energy could provide an alternative energy source for the plateau emission of X-ray afterglow.The fitting results of X-ray plateau emission of some short gamma-ray bursts suggest that the magnetic dipole field strength of the remnants can be much smaller than that of expected when the plateau emission is powered only by spin-down luminosity of magnetars.

量子Loop代数U_(q)(L(sl_(2)))的单权模

用构造的方法解决量子Loop代数U_(q)(L(sl_(2)))具有一个一维权空间的单权模的结构问题,得到了任意一个具有一维权空间的单权模必同构于U_(q)(L(sl_(2)))的四类单权模之一.此外,还构造了一类权空间维数为2的既非最高权也非最低权的量子Loop代数U_(q)(L(sl_(2)))的单权模.

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

Twisted Yangian Y(sp_(2))的单权模

讨论了Twisted Yangian Y(sp_(N))的权模问题,构造了Y(sp_(2))的一类无限维单权模,分类了具有一个一维权空间的Y(sp_(2))单权模.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Shear wave splitting analysis of local earthquakes from dense arrays in Shimian,Sichuan

The Shimian area of Sichuan sits at the junction of the Bayan Har block.Sichuan-Yunnan rhombic block,and Yangtze block,where several faults intersect.This region features intense tectonic activity and frequent earthquakes.In this study,we used local seismic waveform data recorded using dense arrays deployed in the Shimian area to obtain the shear wave splitting parameters at 55 seismic stations and thereby determine the crustal anisotropic characteristics of the region.We then analyzed the crustal stress pattern and tectonic setting and explored their relationship in the study area.Although some stations returned a polarization direction of NNW-SSE.a dominant polarization direction of NW-SE was obtained for the fast shear wave at most seismic stations in the study area.The polarization directions of the fast shear wave were highly consistent throughout the study-area.This orientation was in accordance with the direction of the regional principal compressive stress and parallel to the trend of the Xianshuihe and Daliangshan faults.The distribution of crustal anisotropy in this area was affected by the regional tectonic stress field and the fault structures.The mean delay time between fast and slow shear waves was 3.83 ms/km.slightly greater than the values obtained in other regions of Sichuan.This indicates that the crustal media in our study area had a high anisotropic strength and also reveals the influence of tectonic complexity resulting from the intersection of multiple faults on the strength of seismic anisotropy.

Siamese Dense Pixel-Level Fusion Network for Real-Time UAV Tracking

Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.

Comparative analysis on gas–solid drag models in MFIX-DEM simulations of bubbling fluidized bed

In this study,the open-source software MFIX-DEM simulations of a bubbling fluidized bed(BFB)are applied to assess nine drag models according to experimental and direct numerical simulation(DNS)results.The influence of superficial gas velocity on gas–solid flow is also examined.The results show that according to the distribution of time-averaged particle axial velocity in y direction,except for Wen–Yu and Tenneti–Garg–Subramaniam(TGS),other drag models are consistent with the experimental and DNS results.For the TGS drag model,the layer-by-layer movement of particles is observed,which indicates the particle velocity is not correctly predicted.The time domain and frequency domain analysis results of pressure drop of each drag model are similar.It is recommended to use the drag model derived from DNS or fine grid computational fluid dynamics–discrete element method(CFD-DEM)data first for CFD-DEM simulations.For the investigated BFB,the superficial gas velocity less than 0.9 m·s^(-1) should be adopted to obtain normal hydrodynamics.

The failure propagation of weakly stable sediment:A reason for the formation of high-velocity turbidity currents in submarine canyons

The long-distance movement of turbidity currents in submarine canyons can transport large amounts of sediment to deep-sea plains.Previous studies show obvious differences in the turbidity current velocities derived from the multiple cables damage events ranging from 5.9 to 28.0 m/s and those of field observations between 0.15 and 7.2 m/s.Therefore,questions remain regarding whether a turbid fluid in an undersea environment can flow through a submarine canyon for a long distance at a high speed.A new model based on weakly stable sediment is proposed(proposed failure propagation model for weakly stable sediments,WS S-PFP model for short)to explain the high-speed and long-range motion of turbidity currents in submarine canyons through the combination of laboratory tests and numerical analogs.The model is based on two mechanisms:1)the original turbidity current triggers the destabilization of the weakly stable sediment bed and promotes the destabilization and transport of the soft sediment in the downstream direction and 2)the excitation wave that forms when the original turbidity current moves into the canyon leads to the destabilization and transport of the weakly stable sediment in the downstream direction.The proposed model will provide dynamic process interpretation for the study of deep-sea deposition,pollutant transport,and optical cable damage.

Discovery of pockmarks in the Zengmu Basin,southern South China Sea and the implication

The Zengmu Basin located in the shallow water area of the southern South China Sea,is rich in oil and gas resources,within which faults and mud-diapir are developed,but it is unknown whether oil and gas migrate to the seafloor surface.The newly collected multibeam data across the Zengmu Basin reveal a large number of depressions,with depths of 2-4 m,widths of several tens of meters,large distribution range of 1.8-8 km along survey line,up to~50 km,and their backscatter intensity(-26 dB)is much greater than that of the surrounding area(-38 dB).Combined with the developed mud-diapir and fracture structures,and abundant oil and gas resources within this basin,these depressions are presumed to be pockmarks.Furthermore,more than 110 mono-sized small circular pockmarks,with a depth of less than 1 m and a width of 5 m,are observed in an area of less than 0.03 km2,which are not obliterated by sediment infilling with high sedimentation rate,implying an existence of unit-pockmarks that are or recently were active.In addition,seismic profiles across the Zengmu Basin show characterization of upward migration of hydrocarbons,expressed as mud-diapir structures,bright spots in the shallow formation with characteristics of“low frequency increase and high frequency attenuation”.The subbottom profiles show the mud-diapir structures,as well as the gas-bearing blank zones beneath the seafloor.These features suggest large gas leaking and occurrence of large amounts of carbonate nodules on the seafloor.This indicates the complex and variable substrate type in the Zengmu Basin,while the area was once thought to be mainly silty sand and find sand.This is the first report on the discovery of pockmarks in the Zengmu Basin;it will provide basic information for submarine stability and marine engineering in China’s maritime boundaries.

Low Complexity Joint Spectrum Resource and Power Allocation for Ultra Dense Networks

In this paper,we propose a low complexity spectrum resource allocation scheme cross the access points(APs)for the ultra dense networks(UDNs),in which all the APs are divided into several AP groups(APGs)and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells.Furthermore,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput.The problem is formulated as a mixed-integer nonconvex optimization(MINCP)problem which is difficult to solve in general.The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively.For the spectrum allocation,we model it as a auction problem and a combinatorial auction approach is proposed to tackle it.In addition,the DC programming method is adopted to optimize the power allocation subproblem.To decrease the signaling and computational overhead,we propose a distributed algorithm based on the Lagrangian dual method.Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.

Spectrum-Efficient User Association in Caching Enabled Dense Small Cell Network

In this paper,we reveal the fundamental limitation of network densification on the performance of caching enabled small cell network(CSCN)under two typical user association rules,namely,contentand distance-based rules.It indicates that immoderately caching content would significantly change the interference distribution in CSCN,which may degrade the network area spectral efficiency(ASE).Meanwhile,it is shown that content-based rule outperforms the distance-based rule in terms of network ASE only when small cell base stations(BSs)are sparsely deployed with low decoding thresholds.Moreover,it is proved that network ASE under distance-based user association serves as the upper bound of that under content-based rule in dense BS regime.To enable more spectrum-efficient user association in dense CSCN,we further optimize network ASE by designing a probabilistic content retrieving strategy based on distance-based rule.With the optimized retrieving probability,network ASE could be substantially enhanced and even increase with the growing BS density in dense BS regime.

Intracellular life of protozoan Toxoplasma gondii:Parasitophorous vacuole establishment and survival strategies

Toxoplasma gondii is a protozoan of worldwide distribution and the agent of toxoplasmosis.It is estimated that 30%–50%of the world population could be infected with this parasite.Although the infection in immunocompetent individuals is mostly asymptomatic,the disease in immunosuppressed and pregnant is a risk condition.As a member of the phylum Apicomplexa,T.gondii has an obligatory intracellular lifestyle;therefore,invading a host cell and establishing it inside a parasitophorous vacuole(PV)are mandatories for the survival of this parasite.The construction of a perfect intracellular niche for T.gondii requires the secretion of an arsenal of proteins from unique secretory organelles.These proteins will remodel the vacuolar environment and the host cell organization and functions,allowing the parasite to access essential nutrients and stay“invisible”inside a host cell.In the present review,we will discuss the main steps involved in the PV formation and its differentiation to tissue cyst,focusing mainly on the strategies employed in the acquisition of nutrients and proteins involved in host cell modification.

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

Speech Enhancement via Mask-Mapping Based Residual Dense Network

Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the upper bound of speech enhancement performance.Maskingbased methods need to accurately estimate the masking which is still the key problem.Combining the advantages of above two types of methods,this paper proposes the speech enhancement algorithm MM-RDN(maskingmapping residual dense network)based on masking-mapping(MM)and residual dense network(RDN).Using the logarithmic power spectrogram(LPS)of consecutive frames,MM estimates the ideal ratio masking(IRM)matrix of consecutive frames.RDN can make full use of feature maps of all layers.Meanwhile,using the global residual learning to combine the shallow features and deep features,RDN obtains the global dense features from the LPS,thereby improves estimated accuracy of the IRM matrix.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,MM-RDN can still outperform the existing convolutional recurrent network(CRN)method in themeasures of perceptual evaluation of speech quality(PESQ)and other evaluation indexes.It indicates that the proposed algorithm is more generalized in untrained conditions.

Minimizing FWM Impact in DWDM ROF DP-DQPSK System for Optical

The demonstration of a higher data rate transmission system was amajor aspect to be considered by researchers in recent years. The most relevantaspect to be studied and analyzed is the need for a reliable system to handlenonlinear impairments and reduce them. Therefore, this paper examines theinfluence of Four-Wave Mixing (FWM) impairment on the proposed highdata rate Dual polarization–Differential Quadrature phase shift keying (DPDQPSK)system using the Optisystem software. In the beginning, the impactof varied input power on the proposed system’s performance was evaluated interms of QF and BER metrics. More power is used to improve system performance.However, increasing power would raise theFWMeffects. Accordingly,a−10dBminput power and the proposed system are used to reduce the impactof FWM. Additionally, a hybrid amplification method is proposed to enhancesystem performance by utilizing the major amplification methods of erbiumdopedfiber amplifier (EDFA): semiconductor optical amplifier (SOA) andRadio optical amplifier (ROA). The evaluation demonstrates that the OAEDFAoutperformed the other two key amplification techniques of (EDFASOA)and (EDFA-ROA) in improving Quality factor (QF) and Bit error rate(BER) system results for all distances up to 720 km. Consequently, the methodcontributes to minimizing the impact of FWM. In the future, other forms ofnonlinearity will be investigated and studied to quantify their impact on theproposed system.

Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

Next-generation networks,including the Internet of Things(IoT),fifth-generation cellular systems(5G),and sixth-generation cellular systems(6G),suf-fer from the dramatic increase of the number of deployed devices.This puts high constraints and challenges on the design of such networks.Structural changing of the network is one of such challenges that affect the network performance,includ-ing the required quality of service(QoS).The fractal dimension(FD)is consid-ered one of the main indicators used to represent the structure of the communication network.To this end,this work analyzes the FD of the network and its use for telecommunication networks investigation and planning.The clus-ter growing method for assessing the FD is introduced and analyzed.The article proposes a novel method for estimating the FD of a communication network,based on assessing the network’s connectivity,by searching for the shortest routes.Unlike the cluster growing method,the proposed method does not require multiple iterations,which reduces the number of calculations,and increases the stability of the results obtained.Thus,the proposed method requires less compu-tational cost than the cluster growing method and achieves higher stability.The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks.The developed method is evaluated for two different network structures and compared with the cluster growing method.Results validate the developed method.