The rapid development of wearable electronics requires a revolution power accessories regarding flexibility and energy density. Li-CO2 battery was recently proposed as novel promising candidate for next-generation energy-storage systems. However, the current batteries usually suffer from difficulties poor stability, low efficiency, leakage liquid electrolyte, few flexible have been reported so far. Herein, quasi-solid-state fiber-shaped with overpotential high by employing ultrafine Mo2 C...

Abstract Li–CO 2 batteries are regarded as a promising candidate for the next‐generation high‐performance electrochemical energy storage system owing to their ultrahigh theoretical density and environmentally friendly CO fixation ability. Until now, majority of reported catalysts in powder state. Thus, air electrodes produced 2D rigid bulk structure properties negatively influenced by binder. The nondeformable feature unsatisfactory performance cathode have already become main obstacles that...

Novel multi-scaled porous nitrogen-doped carbon is synthesized by enriching the simple R–F method with: addition of melamine and PEO–PPO–PEO micelles for nitrogen-doping duct percolation; integration CO₂ activation process most critical formation abundant 2 nm pores.

In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted totally un-coordinated un-synchronizated illuminator receivers. It is assumed data demodulation performed separately based on direct-path signal, error-prone estimated symbols are made available to receivers, which estimate in two stages. First, formulate problem joint estimation delay-Doppler reflectors errors, exploiting types sparsities...

Universal domain adaptation (UniDA) aims to transfer knowledge from the source target without any prior about label set. The challenge lies in how determine whether samples belong common categories. mainstream methods make judgments based on sample features, which overemphasizes global information while ignoring most crucial local objects image, resulting limited accuracy. To address this issue, we propose a Attention Matching (UniAM) framework by exploiting self-attention mechanism vision...

Although studies have been done on nitrogen doped carbon materials as lithium–oxygen (Li–O2) battery cathodes, few of them focus the binder-free electrode structure, although they proved to bring improved performance. To fill this gap work not only cathode but also determines performance these cathodes with different levels doping. make electrodes, CNTs and N-CNTs were synthesized nickel foam by a floating catalyst chemical vapor deposition method. The study found that electrochemical N-CNT...

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on point scattering model not suitable for characterizing line-segment-scattering surface-scattering features distributed targets. Second, hyperparameters in these are hard tune optimal values. Third, due a large...

In federated learning (FL), clients may have diverse objectives, and merging all clients' knowledge into one global model will cause negative transfer to local performance. Thus, clustered FL is proposed group similar clusters maintain several models. the literature, centralized algorithms require assumption of number hence are not effective enough explore latent relationships among clients. this paper, without assuming clusters, we propose a peer-to-peer (P2P) algorithm named <monospace...

We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and space mismatch-based variants, essentially categorizing them unified problem, guiding comprehensive framework for thoroughly solving variants. The key challenge is developing identifying novel categories while estimating...

Diffusion-based Neural Combinatorial Optimization (NCO) has demonstrated effectiveness in solving NP-complete (NPC) problems by learning discrete diffusion models for solution generation, eliminating hand-crafted domain knowledge. Despite their success, existing NCO methods face significant challenges both cross-scale and cross-problem generalization, high training costs compared to traditional solvers. While recent studies have introduced training-free guidance approaches that leverage...

Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data across clients make locally updated parameters imprecisely aligned, disabling coordinate-based parameter averaging. Traditional neurons do not explicitly consider position information. Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing position-related values (i.e., encodings) into neuron...

Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market challenging. In this paper, we propose novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) portfolio task, which incorporates optimistic or pessimistic deep reinforcement learning that reflected influence from prediction errors. Dow Jones 30 component stocks are selected as our trading their daily prices used training testing data. We...

In this paper, we consider an un-cooperative spectrum sharing scenario, where a radar system is to be overlaid pre-existing wireless communication system. Given the order of magnitude transmitted powers in play, focus on issue interference mitigation at receiver. We explicitly account for reverberation produced by (typically high-power) transmitter whose signal hits scattering centers (whether targets or clutter) producing onto receiver, which assumed operate un-synchronized and...

Stock price prediction is important for value investments in the stock market. In particular, short-term that exploits financial news articles promising recent years. this paper, we propose a novel deep neural network DP-LSTM prediction, which incorporates as hidden information and integrates difference sources through differential privacy mechanism. First, based on autoregressive moving average model (ARMA), sentiment-ARMA formulated by taking into consideration of model. Then, an...

Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in stepped-frequency radar super-resolution angle-range-doppler imaging. We consider an uncooperative spectrum sharing scenario where tasked with imaging sparse scene amidst communication that frequency-sparse due to underutilization, motivating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Federated learning (FL) is a collaborative machine technique to train global model (GM) without obtaining clients' private data. The main challenges in FL are statistical diversity among clients, limited computing capability equipment, and the excessive communication overhead between server clients. To address these challenges, we propose novel sparse personalized scheme via maximizing correlation (FedMac). By incorporating an approximated <inline-formula...

This study introduces an economical and environmentally friendly way of synthesizing LiFePO4/C to be used as cathode material in lithium ion batteries via two processes: (1) the synthesis using a low cost divalent precursor ferrous phosphate, Fe3 (PO4)2·8H2O, iron source polyol process (2) modification morphology this by varying reaction time coprecipitation process. The examines effects different structures morphologies on structure electrochemical performance as-synthesized LiFePO4/C....

Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a novel personalized federated method via Bayesian variational inference named pFedBayes. alleviate overfitting, weight uncertainty is introduced neural networks for clients server. achieve personalization, each client updates its local distribution parameters by balancing construction error over private KL...

The relationship between θ and the spectral characteristic parameters was found to follow spatial distribution model of plasma: a cos 4 + b . R is nearly linear.

Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed neural network (the {\it teacher}) to weaker one student}). A peculiar phenomenon is that more accurate model doesn't necessarily teach better, and temperature adjustment can neither alleviate mismatched capacity. To explain this, we decompose efficacy KD into three parts: correct guidance}, smooth regularization}, class discriminability}. The last term describes distinctness wrong probabilities} teacher...

Inside the smooth triangular cavity, plasma is compressed by shock waves and more emitted light reflected into collecting system.

Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data. Its excellent privacy security potential promotes a wide range of federated applications in Internet Things (IoT), wireless networks, mobile devices, autonomous vehicles, cloud medical treatment. However, the FL method suffers from poor model performance on non-independent identically distributed (non-i.i.d.) data excessive traffic volume. To this end, we propose...