Calculation and prediction of divertor detachment via impurity seeding by using one-dimensional model

Achieving the detachment of divertor can help to alleviate excessive heat load and sputtering problems on the target plates,thereby extending the lifetime of divertor components for fusion devices.In order to provide a fast but relatively reliable prediction of plasma parameters along the flux tube for future device design,a one-dimensional(1D)modeling code for the operating point of impurity seeded detached divertor is developed based on Python language,which is a fluid model based on previous work(Plasma Phys.Control.Fusion 58045013(2016)).The experimental observation of the onset of divertor detachment by neon(Ne)and argon(Ar)seeding in EAST is well reproduced by using the 1D modeling code.The comparison between the 1D modeling and two-dimensional(2D)simulation by the SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows that they are in good agreement.We also predict the radiative power loss and corresponding impurity concentration requirement for achieving divertor detachment via different impurity seeding under high heating power conditions in EAST and CFETR phase II by using the 1D model.Based on the predictions,the optimized parameter space for divertor detachment operation on EAST and CFETR is also determined.Such a simple but reliable 1D model can provide a reasonable parameter input for a detailed and accurate analysis by 2D or three-dimensional(3D)modeling tools through rapid parameter scanning.

Numerical Investigations on the Resonance Errors of Multiscale Discontinuous Galerkin Methods for One-Dimensional Stationary Schrödinger Equation

In this paper,numerical experiments are carried out to investigate the impact of penalty parameters in the numerical traces on the resonance errors of high-order multiscale discontinuous Galerkin(DG)methods(Dong et al.in J Sci Comput 66:321–345,2016;Dong and Wang in J Comput Appl Math 380:1–11,2020)for a one-dimensional stationary Schrödinger equation.Previous work showed that penalty parameters were required to be positive in error analysis,but the methods with zero penalty parameters worked fine in numerical simulations on coarse meshes.In this work,by performing extensive numerical experiments,we discover that zero penalty parameters lead to resonance errors in the multiscale DG methods,and taking positive penalty parameters can effectively reduce resonance errors and make the matrix in the global linear system have better condition numbers.

Prediction of one-dimensional CrN nanostructure as a promising ferromagnetic half-metal

Searching for one-dimensional(1D)nanostructure with ferromagnetic(FM)half-metallicity is of significance for the development of miniature spintronic devices.Here,based on the first-principles calculations,we propose that the 1D CrN nanostructure is a FM half-metal,which can generate the fully spin-polarized current.The ab initio molecular dynamic simulation and the phonon spectrum calculation demonstrate that the 1D CrN nanostructure is thermodynamically stable.The partially occupied Cr-d orbitals endow the nanostructure with FM half-metallicity,in which the half-metallic gap(?s)reaches up to 1.58 eV.The ferromagnetism in the nanostructure is attributed to the superexchange interaction between the magnetic Cr atoms,and a sizable magnetocrystalline anisotropy energy(MAE)is obtained.Moreover,the transverse stretching of nanostructure can effectively modulate?s and MAE,accompanied by the preservation of half-metallicity.A nanocable is designed by encapsulating the CrN nanostructure with a BN nanotube,and the intriguing magnetic and electronic properties of the nanostructure are retained.These novel characteristics render the 1D CrN nanostructure as a compelling candidate for exploiting high-performance spintronic devices.

Piezo1 channel exaggerates ferroptosis of nucleus pulposus cells by mediating mechanical stress-induced iron influx

To date,several molecules have been found to facilitate iron influx,while the types of iron influx channels remain to be elucidated.Here,Piezo1 channel was identified as a key iron transporter in response to mechanical stress.Piezo1-mediated iron overload disturbed iron metabolism and exaggerated ferroptosis in nucleus pulposus cells(NPCs).Importantly,Piezo1-induced iron influx was independent of the transferrin receptor(TFRC),a well-recognized iron gatekeeper.Furthermore,pharmacological inactivation of Piezo1 profoundly reduced iron accumulation,alleviated mitochondrial ROS,and suppressed ferroptotic alterations in stimulation of mechanical stress.Moreover,conditional knockout of Piezo1(Col2a1-CreERT Piezo1^(flox/flox))attenuated the mechanical injury-induced intervertebral disc degeneration(IVDD).Notably,the protective effect of Piezo1 deficiency in IVDD was dampened in Piezo1/Gpx4 conditional double knockout(cDKO)mice(Col2a1-CreERT Piezo1^(flox/flox)/Gpx4^(flox/flox)).These findings suggest that Piezo1 is a potential determinant of iron influx,indicating that the Piezo1-iron-ferroptosis axis might shed light on the treatment of mechanical stress-induced diseases.

Lithium-Ion Charged Polymer Channels Flattening Lithium Metal Anode

The concentration difference in the near-surface region of lithium metal is the main cause of lithium dendrite growth.Resolving this issue will be key to achieving high-performance lithium metal batteries(LMBs).Herein,we construct a lithium nitrate(LiNO_(3))-implanted electroactiveβphase polyvinylidene fluoride-co-hexafluoropropylene(PVDF-HFP)crystalline polymorph layer(PHL).The electronegatively charged polymer chains attain lithium ions on the surface to form lithium-ion charged channels.These channels act as reservoirs to sustainably release Li ions to recompense the ionic flux of electrolytes,decreasing the growth of lithium dendrites.The stretched molecular channels can also accelerate the transport of Li ions.The combined effects enable a high Coulombic efficiency of 97.0%for 250 cycles in lithium(Li)||copper(Cu)cell and a stable symmetric plating/stripping behavior over 2000 h at 3 mA cm^(-2)with ultrahigh Li utilization of 50%.Furthermore,the full cell coupled with PHL-Cu@Li anode and Li Fe PO_(4) cathode exhibits long-term cycle stability with high-capacity retention of 95.9%after 900 cycles.Impressively,the full cell paired with LiNi_(0.87)Co_(0.1)Mn_(0.03)O_(2)maintains a discharge capacity of 170.0 mAh g^(-1)with a capacity retention of 84.3%after 100 cycles even under harsh condition of ultralow N/P ratio of 0.83.This facile strategy will widen the potential application of LiNO_(3)in ester-based electrolyte for practical high-voltage LMBs.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Size effect on light propagation modulation near band edges in one-dimensional periodic structures

Periodic photonic structures can provide rich modulation in propagation of light due to well-defined band structures.Especially near band edges,light localization and the effect of near-zero refractive index have attracted wide attention.However,the practically fabricated structures can only have finite size,i.e.,limited numbers of periods,leading to changes of the light propagation modulation compared with infinite structures.Here,we study the size effect on light localization and near-zero refractive-index propagation near band edges in one-dimensional periodic structures.Near edges of the band gap,as the structure's size shrinks,the broadening of the band gap and the weakening of the light localization are discovered.When the size is small,an added layer on the surface will perform large modulation in the group velocity.Near the degenerate point with Dirac-like dispersion,the zero-refractive-index effects like the zero-phase difference and near-unity transmittance retain as the size changes,while absolute group velocity fluctuates when the size shrinks.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

A Dugdale-Barenblatt model for elliptical orifice problem with asymmetric cracks in one-dimensional orthorhombic quasicrystals

By means of Muskhelishvili’s method and the technique of generalized conformal mapping,the physical plane problems are transformed into regular mathematical problems in quasicrystals(QCs).The analytical solution to an elliptical orifice problem with asymmetric cracks in one-dimensional(1D)orthorhombic QCs is obtained.By using the Dugdale-Barenblatt model,the plastic simulation at the crack tip of the elliptical orifice with asymmetric cracks in 1D orthorhombic QCs is performed.Finally,the size of the atomic cohesive force zone is determined precisely,and the size of the atomic cohesive force zone around the crack tip of an elliptical orifice with a single crack or two symmetric cracks is obtained.

Transient receptor potential channels as predictive marker and potential indicator of chemoresistance in colon cancer

Transient receptor potential(TRP)channels are strongly associated with colon cancer development and progression.This study leveraged a multivariate Cox regression model on publicly available datasets to construct a TRP channels-associated gene signature,with further validation of signature in real world samples from our hospital treated patient samples.Kaplan-Meier(K-M)survival analysis and receiver operating characteristic(ROC)curves were employed to evaluate this gene signature’s predictive accuracy and robustness in both training and testing cohorts,respectively.Additionally,the study utilized the CIBERSORT algorithm and single-sample gene set enrichment analysis to explore the signature’s immune infiltration landscape and underlying functional implications.The support vector machine algorithm was applied to evaluate the signature’s potential in predicting chemotherapy outcomes.The findings unveiled a novel three TRP channels-related gene signature(MCOLN1,TRPM5,and TRPV4)in colon adenocarcinoma(COAD).The ROC and K-M survival curves in the training dataset(AUC=0.761;p=1.58e-05)and testing dataset(AUC=0.699;p=0.004)showed the signature’s robust predictive capability for the overall survival of COAD patients.Analysis of the immune infiltration landscape associated with the signature revealed higher immune infiltration,especially an increased presence of M2 macrophages,in high-risk group patients compared to their low-risk counterparts.High-risk score patients also exhibited potential responsiveness to immune checkpoint inhibitor therapy,evident through increased CD86 and PD-1 expression profiles.Moreover,the TRPM5 gene within the signature was highly expressed in the chemoresistance group(p=0.00095)and associated with poor prognosis(p=0.036)in COAD patients,highlighting its role as a hub gene of chemoresistance.Ultimately,this signature emerged as an independent prognosis factor for COAD patients(p=6.48e-06)and expression of model gene are validated by public data and real-world patients.Overall,this bioinformatics study provides valuable insights into the prognostic implications and potential chemotherapy resistance mechanisms associated with TRPs-related genes in colon cancer.

Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

Analysis of Dynamical Behavior of One-Dimensional Real Maps: An Executable Dynamical Programming Software Approach

The dynamical behavior of real-world phenomena is implausible graphically due to the complexity of mathematical coding. The present article has mainly focused on some one-dimensional real maps’ dynamical behavior irrespective of using coding. In continuation, linear, quadratic, cubic, higher-order, exponential, logarithmic, and absolute value maps have been used to scrutinize their dynamical behavior, including the characteristics of the orbit of points. Dynamical programming software (DPS.exe) will be proposed as a new technique to ascertain the dynamical behavior of said maps. Thus, a mathematician can automatically determine one-dimensional real maps’ dynamical behavior apart from complicated programming code and analytical solutions.

Thermal-induced interfacial behavior of a thin one-dimensional hexagonal quasicrystal film

In this paper,we investigate the interfacial behavior of a thin one-dimensional(1D)hexagonal quasicrystal(QC)film bonded on an elastic substrate subjected to a mismatch strain due to thermal variation.The contact interface is assumed to be nonslipping,with both perfectly bonded and debonded boundary conditions.The Fourier transform technique is adopted to establish the integral equations in terms of interfacial shear stress,which are solved as a linear algebraic system by approximating the unknown phonon interfacial shear stress via the series expansion of the Chebyshev polynomials.The expressions are explicitly obtained for the phonon interfacial shear stress,internal normal stress,and stress intensity factors(SIFs).Finally,based on numerical calculations,we briefly discuss the effects of the material mismatch,the geometry of the QC film,and the debonded length and location on stresses and SIFs.

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

Statistical Channel Modeling for Indoor VLC Communications Based on Channel Measurements

Visible light communication(VLC)has attracted much attention in the research of sixthgeneration(6G)systems.Furthermore,channel modeling is the foundation for designing efficient and robust VLC systems.In this paper,we present extensive VLC channel measurement campaigns in indoor environments,i.e.,an office and a corridor.Based on the measured data,the large-scale fading characteristics and multipath-related characteristics,including omnidirectional optical path loss(OPL),K-factor,power angular spectrum(PAS),angle spread(AS),and clustering characteristics,are analyzed and modeled through a statistical method.Based on the extracted statistics of the above-mentioned channel characteristics,we propose a statistical spatial channel model(SSCM)capable of modeling multipath in the spatial domain.Furthermore,the simulated statistics of the proposed model are compared with the measured statistics.For instance,in the office,the simulated path loss exponent(PLE)and the measured PLE are 1.96and 1.97,respectively.And,the simulated medians of AS and measured medians of AS are 25.94°and 24.84°,respectively.Generally,the fact that the simulated results fit well with measured results has demonstrated the accuracy of our SSCM.

Potassium and calcium channels in different nerve cells act as therapeutic targets in neurological disorders

The central nervous system, information integration center of the body, is mainly composed of neurons and glial cells. The neuron is one of the most basic and important structural and functional units of the central nervous system, with sensory stimulation and excitation conduction functions. Astrocytes and microglia belong to the glial cell family, which is the main source of cytokines and represents the main defense system of the central nervous system. Nerve cells undergo neurotransmission or gliotransmission, which regulates neuronal activity via the ion channels, receptors, or transporters expressed on nerve cell membranes. Ion channels, composed of large transmembrane proteins, play crucial roles in maintaining nerve cell homeostasis. These channels are also important for control of the membrane potential and in the secretion of neurotransmitters. A variety of cellular functions and life activities, including functional regulation of the central nervous system, the generation and conduction of nerve excitation, the occurrence of receptor potential, heart pulsation, smooth muscle peristalsis, skeletal muscle contraction, and hormone secretion, are closely related to ion channels associated with passive transmembrane transport. Two types of ion channels in the central nervous system, potassium channels and calcium channels, are closely related to various neurological disorders, including Alzheimer's disease, Parkinson's disease, and epilepsy. Accordingly, various drugs that can affect these ion channels have been explored deeply to provide new directions for the treatment of these neurological disorders. In this review, we focus on the functions of potassium and calcium ion channels in different nerve cells and their involvement in neurological disorders such as Parkinson's disease, Alzheimer's disease, depression, epilepsy, autism, and rare disorders. We also describe several clinical drugs that target potassium or calcium channels in nerve cells and could be used to treat these disorders. We concluded that there are few clinical drugs that can improve the pathology these diseases by acting on potassium or calcium ions. Although a few novel ion-channelspecific modulators have been discovered, meaningful therapies have largely not yet been realized. The lack of target-specific drugs, their requirement to cross the blood–brain barrier, and their exact underlying mechanisms all need further attention. This review aims to explain the urgent problems that need research progress and provide comprehensive information aiming to arouse the research community's interest in the development of ion channel-targeting drugs and the identification of new therapeutic targets for that can increase the cure rate of nervous system diseases and reduce the occurrence of adverse reactions in other systems.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

Cav3.2 channel regulates cerebral ischemia/reperfusion injury:a promising target for intervention

Calcium influx into neurons triggers neuronal death during cerebral ischemia/reperfusion injury.Various calcium channels are involved in cerebral ischemia/reperfusion injury.Cav3.2 channel is a main subtype of T-type calcium channels.T-type calcium channel blockers,such as pimozide and mibefradil,have been shown to prevent cerebral ischemia/reperfusion injury-induced brain injury.However,the role of Cav3.2 channels in cerebral ischemia/reperfusion injury remains unclear.Here,in vitro and in vivo models of cerebral ischemia/reperfusion injury were established using middle cerebral artery occlusion in mice and high glucose hypoxia/reoxygenation exposure in primary hippocampal neurons.The results showed that Cav3.2 expression was significantly upregulated in injured hippocampal tissue and primary hippocampal neurons.We further established a Cav3.2 gene-knockout mouse model of cerebral ischemia/reperfusion injury.Cav3.2 knockout markedly reduced infarct volume and brain water content,and alleviated neurological dysfunction after cerebral ischemia/reperfusion injury.Additionally,Cav3.2 knockout attenuated cerebral ischemia/reperfusion injury-induced oxidative stress,inflammatory response,and neuronal apoptosis.In the hippocampus of Cav3.2-knockout mice,calcineurin overexpression offset the beneficial effect of Cav3.2 knockout after cerebral ischemia/reperfusion injury.These findings suggest that the neuroprotective function of Cav3.2 knockout is mediated by calcineurin/nuclear factor of activated T cells 3 signaling.Findings from this study suggest that Cav3.2 could be a promising target for treatment of cerebral ischemia/reperfusion injury.