In-situ interfacial passivation and self-adaptability synergistically stabilizing all-solid-state lithium metal batteries

The function of solid electrolytes and the composition of solid electrolyte interphase(SEI)are highly significant for inhibiting the growth of Li dendrites.Herein,we report an in-situ interfacial passivation combined with self-adaptability strategy to reinforce Li_(0.33)La_(0.557)TiO_(3)(LLTO)-based solid-state batteries.Specifically,a functional SEI enriched with LiF/Li_(3)PO_(4) is formed by in-situ electrochemical conversion,which is greatly beneficial to improving interface compatibility and enhancing ion transport.While the polarized dielectric BaTiO_(3)-polyamic acid(BTO-PAA,BP)film greatly improves the Li-ion transport kinetics and homogenizes the Li deposition.As expected,the resulting electrolyte offers considerable ionic conductivity at room temperature(4.3 x 10~(-4)S cm^(-1))and appreciable electrochemical decomposition voltage(5.23 V)after electrochemical passivation.For Li-LiFePO_(4) batteries,it shows a high specific capacity of 153 mA h g^(-1)at 0.2C after 100 cycles and a long-term durability of 115 mA h g^(-1)at 1.0 C after 800 cycles.Additionally,a stable Li plating/stripping can be achieved for more than 900 h at 0.5 mA cm^(-2).The stabilization mechanisms are elucidated by ex-situ XRD,ex-situ XPS,and ex-situ FTIR techniques,and the corresponding results reveal that the interfacial passivation combined with polarization effect is an effective strategy for improving the electrochemical performance.The present study provides a deeper insight into the dynamic adjustment of electrode-electrolyte interfacial for solid-state lithium batteries.

Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.

Bidirectional rotating direct-current triboelectric nanogenerator with self-adaptive mechanical switching for harvesting reciprocating motion

Amid the growing interest in triboelectric nanogenerators(TENGs)as novel energy-harvesting devices,several studies have focused on direct current(DC)TENGs to generate a stable DC output for operating electronic devices.However,owing to the working mechanisms of conventional DC TENGs,generating a stable DC output from reciprocating motion remains a challenge.Accordingly,we propose a bidirectional rotating DC TENG(BiR-TENG),which can generate DC outputs,regardless of the direction of rotation,from reciprocating motions.The distinct design of the BiR-TENG enables the mechanical rectification of the alternating current output into a rotational-direction-dependent DC output.Furthermore,it allows the conversion of the rotational-direction-dependent DC output into a unidirectional DC output by adapting the configurations depending on the rotational direction.Owing to these tailored design strategies and subsequent optimizations,the BiR-TENG could generate an effective unidirectional DC output.Applications of the BiR-TENG for the reciprocating motions of swinging doors and waves were demonstrated by harnessing this output.This study demonstrates the potential of the BiR-TENG design strategy as an effective and versatile solution for energy harvesting from reciprocating motions,highlighting the suitability of DC outputs as an energy source for electronic devices.

Achieving Ultra-Broad Microwave Absorption Bandwidth Around Millimeter-Wave Atmospheric Window Through an Intentional Manipulation on Multi-Magnetic Resonance Behavior

The utilization of electromagnetic waves is rapidly advancing into the millimeter-wave frequency range,posing increasingly severe challenges in terms of electromagnetic pollution prevention and radar stealth.However,existing millimeter-wave absorbers are still inadequate in addressing these issues due to their monotonous magnetic resonance pattern.In this work,rare-earth La^(3+)and non-magnetic Zr^(4+)ions are simultaneously incorporated into M-type barium ferrite(BaM)to intentionally manipulate the multi-magnetic resonance behavior.By leveraging the contrary impact of La^(3+)and Zr^(4+)ions on magnetocrystalline anisotropy field,the restrictive relationship between intensity and frequency of the multi-magnetic resonance is successfully eliminated.The magnetic resonance peak-differentiating and imitating results confirm that significant multi-magnetic resonance phenomenon emerges around 35 GHz due to the reinforced exchange coupling effect between Fe^(3+)and Fe^(2+)ions.Additionally,Mosbauer spectra analysis,first-principle calculations,and least square fitting collectively identify that additional La^(3+)doping leads to a profound rearrangement of Zr^(4+)occupation and thus makes the portion of polarization/conduction loss increase gradually.As a consequence,the La^(3+)-Zr^(4+)co-doped BaM achieves an ultra-broad bandwidth of 12.5+GHz covering from 27.5 to 40+GHz,which holds remarkable potential for millimeter-wave absorbers around the atmospheric window of 35 GHz.

An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.

Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

Capability Analysis of Chaotic Mutation and Its Self-Adaption

Through studying several kinds of chaotic mappings' distributions of orbital points, we analyze the capabilityof the chaotic mutations based on these mappings. Nunerical experiments support our conclusions very well. Thecapability analysis also led to a self-adaptive mechanism of chaotic mutation. The introducing of the self-adaptivechaotic mutation can improve the performance of genetic algorithm very prominently.

Supply Chain Finance Credit Risk Evaluation Method Based on Self-Adaption Weight

Credit risk is the core issue of supply chain finance. In the supply chain, problems happened in different enterprises can influent the whole to different degrees through transferring, thus statuses of all enterprises and their different influences should be considered when evaluating the supply chain’s credit risk. We examine the characters of supply chain network and complex network, use the local growing complex network to simulate the real supply chain, use cluster analysis to classify the company into several levels;Introducing each level’s self-adaption weight formula according to the company’s quantity and degrees of this level and use the weight to improve the credit evaluation method. The research results indicate that complex network can be used to simulate the supply chain. The credit risk evaluation (CRE) of an enterprise level with bigger note degrees has a greater weight in the supply chain system’s CRE, thus has greater effect on the whole chain. Considering different influences of different enterprise levels can improve credit risk evaluation method’s sensitivity.

Frequency detection of self-adaption control based on chaotic theory

Low-order Duffing and high-order Rossler chaotic oscillator are connected together and new self-adaption frequency detection method is presented. The frequency difference control between unknown signal and the periodic driving force is realized in this paper and the self-adaption is obtained. Thus, the detection precision and speed are promoted. The limitation that there are too many chaotic oscillators in Duffing system is broken. Meanwhile the disadvantage that the detection speed is lower in R ssler chaotic control is overcome. The self-adaption choice of frequency difference control is realized using the Duffing and Rssler different chaotic oscillators to obtain unknown signal frequency. The simulation results show that the presented method is feasible and effective.

Minimizing Age of Information in the Uplink Multi-User Networks via Dynamic Bandwidth Allocation

Improving the information freshness is critical for the monitoring and controlling applications in the cellular Internet of Things(IoT).In this paper,we are interested in optimizing the bandwidth allocation dynamically to improve the information freshness of the short packet based uplink status updates,which is characterized by a recently proposed metric,age of information(Ao I).We first design a status update scheme with channel distribution information(CDI).By relaxing the hard bandwidth constraint and introducing a Lagrangian multiplier,we first decouple the multi-MTCD bandwidth allocation problem into a single MTCD Markov decision process(MDP).Under the MDP framework,after variable substitution,we obtain the single-MTCD status update scheme by solving a linear programming problem.Then,we adjust the Lagrangian multiplier to make the obtained scheme satisfy the relaxed bandwidth constraint.Finally,a greedy policy is built on the proposed scheme to adjust the bandwidth allocation in each slot to satisfy the hard bandwidth constraint.In the unknown environment without CDI,we further design a bandwidth allocation scheme which only maximizes the expected sum Ao I drop within each time slot.Simulation results show that in terms of AoI,the proposed schemes outperform the benchmark schemes.

Initiating Binary Metal Oxides Microcubes Electromagnetic Wave Absorber Toward Ultrabroad Absorption Bandwidth Through Interfacial and Defects Modulation

Cobalt nickel bimetallic oxides(NiCo_(2)O_(4))have received numerous attentions in terms of their controllable morphology,high temperature,corrosion resistance and strong electromagnetic wave(EMW)absorption capability.However,broadening the absorption bandwidth is still a huge challenge for NiCo_(2)O_(4)-based absorbers.Herein,the unique NiCo_(2)O_(4)@C core-shell microcubes with hollow structures were fabricated via a facile sacrificial template strategy.The concentration of oxygen vacancies and morphologies of the three-dimensional(3D)cubic hollow core-shell NiCo_(2)O_(4)@C framework were effectively optimized by adjusting the calcination temperature.The specially designed 3D framework structure facilitated the multiple reflections of incident electromagnetic waves and provided rich interfaces between multiple components,generating significant interfacial polarization losses.Dipole polarizations induced by oxygen vacancies could further enhance the attenuation ability for the incident EM waves.The optimized NiCo_(2)O_(4)@C hollow microcubes exhibit superior EMW absorption capability with minimum RL(RLmin)of-84.45 dB at 8.4 GHz for the thickness of 3.0 mm.Moreover,ultrabroad effective absorption bandwidth(EAB)as large as 12.48 GHz(5.52-18 GHz)is obtained.This work is believed to illuminate the path to synthesis of high-performance cobalt nickel bimetallic oxides for EMW absorbers with excellent EMW absorption capability,especially in broadening effective absorption bandwidth.

Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

A self-adaptive resource provisioning on demand is a critical factor in cloud computing.The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests.Therefore,a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload.In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy(CARSS)Framework that formally addresses these issues and is more expressive than traditional approaches.The decision making in CARSS is based on more than one factors.TheMAPE-K based framework determines the state of the resources based on their current utilization.Timed-Arc Petri Net(TAPN)is used to model system formally and behaviour is expressed in TCTL,while TAPAAL model checker verifies the underline properties of the system.

Self-adaptive bulk/surface engineering of Bi_(x)O_(y)Br_(z) towards enhanced photocatalysis:Current status and future challenges

The bulk/surface states of semiconductor photocatalysts are imperative parameters to maneuver their performance by significantly affecting the key processes of photocatalysis including light absorption,separation of charge carrier,and surface site reaction.Recent years have witnessed the encouraging progress of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z) for photocatalytic applications spanning various fields.However,despite the maturity of current research,the interaction between the bulk/surface state and the performance of Bi_(x)O_(y)Br_(z) has not yet been fully understood and highlighted.In this regard,a timely tutorial overview is quite urgent to summarize the most recent key progress and outline developing obstacles in this exciting area.Herein,the structural characteristics and fundamental principles of Bi_(x)O_(y)Br_(z)for driving photocatalytic reaction as well as related key issues are firstly reviewed.Then,we for the first time summarized different self-adaptive engineering processes over Bi_(x)O_(y)Br_(z)followed by a classification of the generation approaches towards diverse Bi_(x)O_(y)Br_(z)materials.The features of different strategies,the up-to-date characterization techniques to detect bulk/surface states,and the effect of bulk/surface states on improving the photoactivity of Bi_(x)O_(y)Br_(z)in expanded applications are further discussed.Finally,the present research status,challenges,and future research opportunities of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z)are prospected.It is anticipated that this critical review can trigger deeper investigations and attract upcoming innovative ideas on the rational design of Bi_(x)O_(y)Br_(z)-based photocatalysts.

Planar InAlAs/InGaAs avalanche photodiode with 360 GHz gain×bandwidth product

This paper describes a guardring-free planar InAlAs/InGaAs avalanche photodiode(APD)by computational simulations and experimental results.The APD adopts the structure of separate absorption,charge,and multiplication(SACM)with top-illuminated.Computational simulations demonstrate how edge breakdown effect is suppressed in the guardringfree structure.The fabricated APD experiment results show that it can obtain a very low dark current while achieving a high gain×bandwidth(GB)product.The dark current is 3 nA at 0.9Vb r,and the unit responsivity is 0.4 A/W.The maximum3 dB bandwidth of 24 GHz and a GB product of 360 GHz are achieved for the fabricated APD operating at 1.55μm.

Design of a photonic crystal fiber polarization beam splitter with simple structure and ultra-wide bandwidth

A novel polarization beam splitter(PBS)based on dual-core photonic crystal fiber(DC-PCF)is proposed in this work.The proposed DC-PCF PBS contains two kinds of lattices and three kinds of air holes to form the asymmetrical elliptic dual-core structure.By using the full-vector finite element method,the propagation characteristics of the proposed DC-PCF PBS are investigated.The simulation results show that the bandwidth of the proposed DC-PCF PBS can reach to 340 nm,which covers the S+C+L+U communication bands,the shortest splitting length is 1.97 mm,and the maximum extinction ratio appears near wavelength 1550 nm.Moreover,the insertion loss of the proposed DC-PCF PBS is very low.It is believed that the proposed DC-PCF PBS has important applications in the field of all-optical communication and network.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

Temporal Convolutional Network for Speech Bandwidth Extension

In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.

Microwave absorption and bandwidth study of Y_(2)Co_(17)rare earth soft magnetic alloy with easy-plane anisotropy

The easy-plane anisotropy of the Y_(2)Co_(17)rare earth soft magnetic alloy has high saturation magnetization and operating frequency,and good impedance matching.Therefore,it is expected to become a kind of high-performance microwave absorbing material.In this paper,Y_(2)Co_(17)alloy was prepared by a reduction-diffusion method,and its micropowder was prepared as polyurethane(PU)based composite absorbing materials(Y_(2)Co_(17)/PU composites).The microwave properties of composites with different volume fractions were calculated.The composites showed outstanding absorption characteristics in the range of 20-30 vol%,and the minimum reflection loss(RL)was less than-50 d B.When the volume fraction was25%,the effective absorption bandwidth could cover the X-band at a thickness of 1.5 mm,and the Ku-band at a thickness of1.08 mm.The absorption mechanism was analyzed by the interface reflection model.The RL absorption peak bandwidth mechanism was discussed by using the amplitude relation and calculating the effective absorption bandwidth at different thicknesses.The effective absorption bandwidth values were in good agreement with the theoretical expectation.

Enhancing Bandwidth Utilization of IP Telephony Over IPv6 Networks

The demand for the telecommunication services,such as IP telephony,has increased dramatically during the COVID-19 pandemic lockdown.IP tele-phony should be enhanced to provide the expected quality.One of the issues that should be investigated in IP telephony is bandwidth utilization.IP telephony pro-duces very small speech samples attached to a large packet header.The header of the IP telephony consumes a considerable share of the bandwidth allotted to the IP telephony.This wastes the network's bandwidth and influences the IP telephony quality.This paper proposes a mechanism(called Smallerize)that reduces the bandwidth consumed by both the speech sample and the header.This is achieved by assembling numerous IP telephony packets in one header and use the header'sfields to carry the speech sample.Several metrics have been used to measure the achievement Smallerize mechanism.The number of calls has been increased by 245.1%compared to the typical mechanism.The bandwidth saving has also reached 68%with the G.28 codec.Therefore,Smallerize is a possible mechanism to enhance bandwidth utilization of the IP telephony.

Efficient Bandwidth Allocation and Computation Configuration in Industrial IoT

With the advancement of the Industrial Internet of Things(IoT),the rapidly growing demand for data collection and processing poses a huge challenge to the design of data transmission and computation resources in the industrial scenario.Taking advantage of improved model accuracy by machine learning algorithms,we investigate the inner relationship of system performance and data transmission and computation resources,and then analyze the impacts of bandwidth allocation and computation resources on the accuracy of the system model in this paper.A joint bandwidth allocation and computation resource configuration scheme is proposed and the Karush-Kuhn-Tucker(KKT)conditions are used to get an optimal bandwidth allocation and computation configuration decision,which can minimize the total computation resource requirement and ensure the system accuracy meets the industrial requirements.Simulation results show that the proposed bandwidth allocation and computation resource configuration scheme can reduce the computing resource usage by 10%when compared to the average allocation strategy.