Air layer-engineered Cf@void@SiCnf composites for enhanced electromagnetic wave absorption

Conventional carbon-based absorbers often suffer from poor impedance matching and limited loss mechanisms, which hinder their practical effectiveness. In this work, we propose a novel strategy that combines structural engineering with interfacial modulation to construct a hollow-structured composite, denoted as Cf@void@SiCnf. This architecture consists of a Cf core, a tunable air interlayer, and a shell of silicon carbide nanofibers (SiCnf), fabricated through chemical vapor deposition (CVD) followed by controlled oxidation. The introduction of an interfacial air layer between the carbon fiber core and SiC nanofibers significantly improved impedance matching and interfacial polarization. As a result, the composite achieves a minimum reflection loss (RLmin) of −59.21 dB at 6.96 GHz (2.30 mm thickness) and a maximum effective absorption bandwidth (EABmax) of 2.48 GHz at 1.0 mm. Additionally, the air-layer architecture imparts improved thermal insulation, when placed on a 357.3 °C hot surface, the composite's outer surface remains as low as 128.8 °C (after 5 min), indicating its promise for multifunctional thermal management applications. This study highlights the critical role of structural tuning-especially air layer design-in developing impedance-matched, high-performance EMW absorbers

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging

ATSC DTV channel estimation

À l'heure actuelle, pour le développement à long terme des réseaux sans fil la modélisation et l'estimation du canal s'avèrent très importantes. L'argument qui appuie cette affirmation, c'est que nous pouvons caractériser certains canaux dune manière utile au concepteur de système. Ce document présente une étude statistique du canal sans fil pour le système de transmission à trajets multipes ATSC (Advanced Systems Committee) DTTV (Digital Terrestrial Television). Les banques de données ATSC ont été divisées en plusieurs groupes selon les secteurs de réception et autres conditions de propagation. Nous présentons ici la procédure d'évaluation et les résultats d'expériences effectuées sur des canaux fixes et mobiles comprenant les distributions du nombre de trajets multiples et l'écart-type de l'étalement du délai. Le modèle modifié de Poisson a été appliqué pour comparaison avec le modèle statistique des trajets multiples pour chaque canal. Nous décrivons également les différentes catégories d'égaliseurs du système et comparons ainsi leurs fonctions

FACULTÉ DES SCIENCES ET DE GÉNIE

M~rnoirc pr{; sc n t(~ à la. Faculté des études supérieures de l'Université Lava.l (L-1IlS le cadre du progranlIIlc de rnaîtrisc cn g ( ~ nie électrique ' pour l'obtention du gr ade de m aîtrise ès sciences (lVI.Sc.

Gut Microbiota Modulation on Intestinal Mucosal Adaptive Immunity

The mammalian intestine harbors a remarkable number of microbes and their components and metabolites, which are fundamental for the instigation and development of the host immune system. The intestinal innate and adaptive immunity coordinate and interact with the symbionts contributing to the intestinal homeostasis through establishment of a mutually beneficial relationship by tolerating to symbiotic microbiota and retaining the ability to exert proinflammatory response towards invasive pathogens. Imbalance between the intestinal immune system and commensal organisms disrupts the intestinal microbiological homeostasis, leading to microbiota dysbiosis, compromised integrity of the intestinal barrier, and proinflammatory immune responses towards symbionts. This, in turn, exacerbates the degree of the imbalance. Intestinal adaptive immunity plays a critical role in maintaining immune tolerance towards symbionts and the integrity of intestinal barrier, while the innate immune system regulates the adaptive immune responses to intestinal commensal bacteria. In this review, we will summarize recent findings on the effects and mechanisms of gut microbiota on intestinal adaptive immunity and the plasticity of several immune cells under diverse microenvironmental settings.</jats:p

An improved dynamic programming tracking-before-detection algorithm based on LSTM network value function

The detection and tracking of small and weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, this paper proposes a dynamic programming tracking-before-detection method based on long short-term memory (LSTM) network value function(VL-DP-TBD). With the help of the estimated posterior probability provided by the designed LSTM network, the calculation of the posterior value function of the traditional DP-TBD algorithm can be more accurate, and the detection and tracking effect achieved for maneuvering small and weak targets is improved. Utilizing the LSTM network to model the posterior probability estimation of the target motion state, the posterior probability moving features of the maneuvering target can be learned from the noisy input data. By incorporating these posterior probability estimation values into the traditional DP-TBD algorithm, the accuracy and robustness of the calculation of the posterior value function can be enhanced, so that the improved architecture is capable of effectively recursively accumulating the movement trend of the target. Simulation results show that the improved architecture is able to effectively reduce the aggregation effect of a posterior value function and improve the detection and tracking ability for non-cooperative nonlinear maneuvering dim small target.AbbreviationsLSTM: Long short-term memory; DP-TBD: Dynamic programming-based tracking before detection; DBT: Detection before tracking; TBD: Tracking before detection; HT-TBD: Tracking-before-detection algorithm based on the Hough transform; PF-TBD: Tracking-before-detection algorithm based on particle filtering; RFS-TBD: Tracking-before-detection algorithm based on random finite sets; SNR: Signal-to-noise ratio; DP: Dynamic programming; EVT: Extreme value theory; EVT: Generalized extreme value theory; GLRT: Generalized likelihood ratio detection; KT: Keystone transformation; PGA: Phase gradient autofocusing; CFAR: Constant false-alarm rate; J-CA-CFAR: Joint intensity-spatial CFAR; MF: Merit function; CP-DP-TBD: Candidate plot-based DP-TBD; CIT: Coherent integration time; RNN: Recurrent neural network; CS: Current statistical; Pd: Detection probability; Pt: Tracking probability

Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks

Radar multitarget tracking in a dense clutter environment remains a complex problem to be solved. Most existing solutions still rely on complex motion models and prior distribution knowledge. In this paper, a new online tracking method based on a long short-term memory (LSTM) network is proposed. It combines state prediction, measurement association, and trajectory management functions in an end-to-end manner. We employ LSTM networks to model target motion and trajectory associations, relying on their strong learning ability to learn target motion properties and long-term dependence of trajectory associations from noisy data. Moreover, to address the problem of missing appearance information of radar targets, we propose an architecture based on the LSTM network to calculate similarity function by extracting long-term motion features. And the similarity is applied to trajectory associations to improve their robustness. Our proposed method is validated in simulation scenarios and achieves good results.</jats:p