Influence of substrate effect on near-field radiative modulator based on biaxial hyperbolic materials

Relative rotation between the emitter and receiver could effectively modulate the near-field radiative heat transfer(NFRHT)in anisotropic media.Due to the strong in-plane anisotropy,natural hyperbolic materials can be used to construct near-field radiative modulators with excellent modulation effects.However,in practical applications,natural hyperbolic materials need to be deposited on the substrate,and the influence of substrate on modulation effect has not been studied yet.In this work,we investigate the influence of substrate effect on near-field radiative modulator based onα-MoO_(3).The results show that compared to the situation without a substrate,the presence of both lossless and lossy substrate will reduce the modulation contrast(MC)for different film thicknesses.When the real or imaginary component of the substrate permittivity increases,the mismatch of hyperbolic phonon polaritons(HPPs)weakens,resulting in a reduction in MC.By reducing the real and imaginary components of substrate permittivity,the MC can be significantly improved,reaching 4.64 forε_(s)=3 at t=10 nm.This work indicates that choosing a substrate with a smaller permittivity helps to achieve a better modulation effect,and provides guidance for the application of natural hyperbolic materials in the near-field radiative modulator.

Calcium-sensitive protein MLC1 as a possible modulator of the astrocyte functional state

Astrocytes,the main population of glial cells in the central nervous system(CNS),exert essential tasks for the control of brain tissue homeostasis,supporting neuron and other glial cell activity from the developmental stage to adult life.To maintain the optimal functionality of the brain,astroglial cells are particularly committed to reacting to every change in tissue homeostatic conditions,from mild modifications of the physiological environment,a process called astrocyte activation,to the more severe alterations occurring in pathological situations causing astrocyte reactivity or reactive astrogliosis(Escartin et al.,2021).During these reactive states,astrocytes mount an active,progressive response encompassing morphological,molecular,and interactional remodeling,leading to the acquisition of new functions and the loss of others,whose intensity,duration,and reversibility are dependent on the nature of the stimulus and regulated in a context-specific manner.

Neuropeptide cholecystokinin:a key neuromodulator for hippocampal functions

Spatial memory is crucial for survival within external surroundings and wild environments.The hippocampus,a critical hub for spatial learning and memory formation,has received extensive investigations on how neuromodulators shape its functions(Teixeira et al.,2018;Zhang et al.,2024).However,the landscape of neuromodulations in the hippocampal system remains poorly understood because most studies focus on classical monoamine neuromodulators,such as acetylcholine,serotonin,dopamine,and noradrenaline.The neuropeptides,comprising the most abundant neuromodulators in the central nervous system,play a pivotal role in neural information processing in the hippocampal system.Cholecystokinin(CCK),one of the most abundant neuropeptides,has been implicated in regulating various physiological and neurobiological statuses(Chen et al.,2019).CCK-A receptor(CCK-AR)and CCK-B receptors(CCK-BR)are two key receptors mediating the biological functions of CCK,both of which belong to class-A sevenfold transmembrane G protein-coupled receptors(Nishimura et al.,2015).CCK-AR preferentially reacts to sulfated CCK,whereas CCK-BR binds both CCK and gastrin with similar affinities(Ding et al.,2022).The expression patterns of CCK-AR and CCK-BR are distinct,implying that CCK has various functions in target regions.For instance,CCK-AR is widely expressed in the GI and brain subregions and is hence implicated in the control of digestive function and satiety regulation.Conversely,CCK-BR is abundantly and widely distributed in the central nervous system,which majorly regulates anxiety,learning,and memory(Ding et al.,2022).However,the roles of endogenous CCK and CCK receptors in regulating hippocampal function at electrophysiological and behavioral levels have received less attention.

Enhancing quantum temporal steering via frequency modulation

Various strategies have been proposed to harness and protect space-like quantum correlations in different models under decoherence.However,little attention has been given to temporal-like correlations,such as quantum temporal steering(TS),in this context.In this work,we investigate TS in a frequency-modulated two-level system coupled to a zero-temperature reservoir in both the weak and strong coupling regimes.We analyze the impact of various frequency-modulated parameters on the behavior of TS and non-Markovian.The results demonstrate that appropriate frequency-modulated parameters can enhance the TS of the two-level system,regardless of whether the system is experiencing Markovian or non-Markovian dynamics.Furthermore,a suitable ratio between modulation strength and frequency(i.e.,all zeroes of the 0th Bessel function J_(0)(δ/?))can significantly enhance TS in the strong coupling regime.These findings indicate that efficient and effective manipulation of quantum TS can be achieved through a frequency-modulated approach.

Exploring the modulatory role of bovine lactoferrin on the microbiome and the immune response in healthy and Shiga toxin‑producing E.coli challenged weaned piglets

Background Post-weaned piglets suffer from F18+Escherichia coli(E.coli)infections resulting in post-weaning diar-rhoea or oedema disease.Frequently used management strategies,including colistin and zinc oxide,have contrib-uted to the emergence and spread of antimicrobial resistance.Novel antimicrobials capable of directly interacting with pathogens and modulating the host immune responses are being investigated.Lactoferrin has shown promising results against porcine enterotoxigenic E.coli strains,both in vitro and in vivo.Results We investigated the influence of bovine lactoferrin(bLF)on the microbiome of healthy and infected weaned piglets.Additionally,we assessed whether bLF influenced the immune responses upon Shiga toxin-producing E.coli(STEC)infection.Therefore,2 in vivo trials were conducted:a microbiome trial and a challenge infection trial,using an F18+STEC strain.BLF did not affect theα-andβ-diversity.However,bLF groups showed a higher relative abundance(RA)for the Actinobacteria phylum and the Bifidobacterium genus in the ileal mucosa.When analysing the immune response upon infection,the STEC group exhibited a significant increase in F18-specific IgG serum levels,whereas this response was absent in the bLF group.Conclusion Taken together,the oral administration of bLF did not have a notable impact on theα-andβ-diversity of the gut microbiome in weaned piglets.Nevertheless,it did increase the RA of the Actinobacteria phylum and Bifi-dobacterium genus,which have previously been shown to play an important role in maintaining gut homeostasis.Furthermore,bLF administration during STEC infection resulted in the absence of F18-specific serum IgG responses.

A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.

Light-field modulation and optimization near metal nanostructures utilizing spatial light modulators

Plasmonic modes within metal nanostructures play a pivotal role in various nanophotonic applications.However,a significant challenge arises from the fixed shapes of nanostructures post-fabrication,resulting in limited modes under ordinary illumination.A promising solution lies in far-field control facilitated by spatial light modulators(SLMs),which enable on-site,real-time,and non-destructive manipulation of plasmon excitation.Through the robust modulation of the incident light using SLMs,this approach enables the generation,optimization,and dynamic control of surface plasmon polariton(SPP)and localized surface plasmon(LSP)modes.The versatility of this technique introduces a rich array of tunable degrees of freedom to plasmon-enhanced spectroscopy,offering novel approaches for signal optimization and functional expansion in this field.This paper provides a comprehensive review of the generation and modulation of SPP and LSP modes through far-field control with SLMs and highlights the diverse applications of this optical technology in plasmon-enhanced spectroscopy.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artifi-cial intelligence.However,great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years.Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation,improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.Herein,several fascinating strategies for synap-tic plasticity modulation through chemical techniques,device structure design,and physical signal sensing are reviewed.For chemical techniques,the underly-ing mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted.Based on device structure design,the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions.Besides,integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light,strain,and temperature.Finally,considering that the relevant technology is still in the basic exploration stage,some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Improvement and security analysis of multi-ring discrete modulation continuous variable quantum secret sharing scheme

In order to avoid the complexity of Gaussian modulation and the problem that the traditional point-to-point communication DM-CVQKD protocol cannot meet the demand for multi-user key sharing at the same time, we propose a multi-ring discrete modulation continuous variable quantum key sharing scheme(MR-DM-CVQSS). In this paper, we primarily compare single-ring and multi-ring M-symbol amplitude and phase-shift keying modulations. We analyze their asymptotic key rates against collective attacks and consider the security key rates under finite-size effects. Leveraging the characteristics of discrete modulation, we improve the quantum secret sharing scheme. Non-dealer participants only require simple phase shifters to complete quantum secret sharing. We also provide the general design of the MR-DM-CVQSS protocol.We conduct a comprehensive analysis of the improved protocol's performance, confirming that the enhancement through multi-ring M-PSK allows for longer-distance quantum key distribution. Additionally, it reduces the deployment complexity of the system, thereby increasing the practical value.

Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

Identification of FDA-Approved Drugs as Modulators of Multidrug Resistance Protein 2 (MRP2/ABCC2) Expression Levels in MRP2-Overexpressing Cells: Preliminary Data

Multidrug Resistance Protein 2 (MRP2) is an ATP-dependent transmembrane protein that plays a pivotal role in the efflux of a wide variety of physiological substrates across the plasma membrane. Several studies have shown that MRP2 can significantly affect the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of many therapeutic drugs and chemicals found in the environment and diet. This transporter can also efflux newly developed anticancer agents that target specific signaling pathways and are major clinical markers associated with multidrug resistance (MDR) of several types of cancers. MDR remains a major limitation to the advancement of the combinatorial chemotherapy regimen in cancer treatment. In addition to anticancer agents, MRP2 reduces the efficacy of various drug classes such as antivirals, antimalarials, and antibiotics. The unique role of MRP2 and its contribution to MDR makes it essential to profile drug-transporter interactions for all new and promising drugs. Thus, this current research seeks to identify modulators of MRP2 protein expression levels using cell-based assays. A unique recently approved FDA library (372 drugs) was screened using a high-throughput In-Cell ELISA assay to determine the effect of these therapeutic agents on protein expression levels of MRP2. A total of 49 FDA drugs altered MRP2 protein expression levels by more than 50% representing 13.17% of the compounds screened. Among the identified hits, thirty-nine (39) drugs increased protein expression levels whereas 10 drugs lowered protein expression levels of MRP2 after drug treatment. Our findings from this initial drug screening showed that modulators of MRP2 peregrinate multiple drug families and signify the importance of profiling drug interactions with this transporter. Data from this study provides essential information to improve combinatorial drug therapy and precision medicine as well as reduce the drug toxicity of various cancer chemotherapies.

Efficient and Stable Inverted Perovskite Solar Modules Enabled by Solid-Liquid Two-Step Film Formation

A considerable efficiency gap exists between large-area perovskite solar modules and small-area perovskite solar cells.The control of forming uniform and large-area film and perovskite crystallization is still the main obstacle restricting the efficiency of PSMs.In this work,we adopted a solid-liquid two-step film formation technique,which involved the evaporation of a lead iodide film and blade coating of an organic ammonium halide solution to prepare perovskite films.This method possesses the advantages of integrating vapor deposition and solution methods,which could apply to substrates with different roughness and avoid using toxic solvents to achieve a more uniform,large-area perovskite film.Furthermore,modification of the NiO_(x)/perovskite buried interface and introduction of Urea additives were utilized to reduce interface recombination and regulate perovskite crystallization.As a result,a large-area perovskite film possessing larger grains,fewer pinholes,and reduced defects could be achieved.The inverted PSM with an active area of 61.56 cm^(2)(10×10 cm^(2)substrate)achieved a champion power conversion efficiency of 20.56%and significantly improved stability.This method suggests an innovative approach to resolving the uniformity issue associated with large-area film fabrication.

A review of liquid crystal spatial light modulators:devices and applications

Spatial light modulators,as dynamic flat-panel optical devices,have witnessed rapid development over the past two decades,concomitant with the advancements in micro-and opto-electronic integration technology.In particular,liquid-crystal spatial light modulator(LC-SLM)technologies have been regarded as versatile tools for generating arbitrary optical fields and tailoring all degrees of freedom beyond just phase and amplitude.These devices have gained significant interest in the nascent field of structured light in space and time,facilitated by their ease of use and real-time light manipulation,fueling both fundamental research and practical applications.Here we provide an overview of the key working principles of LC-SLMs and review the significant progress made to date in their deployment for various applications,covering topics as diverse as beam shaping and steering,holography,optical trapping and tweezers,measurement,wavefront coding,optical vortex,and quantum optics.Finally,we conclude with an outlook on the potential opportunities and technical challenges in this rapidly developing field.

A high-contrast imaging coronagraph for segmented-mirror large aperture telescopes using a spatial light modulator

The primary mirrors of current and future large telescopes always employ a segmented mirror configuration.The small but non-negligible gaps between neighboring segments cause additional diffraction,which restricts the performance of high-contrast coronagraph.To solve this problem,we propose a coronagraph system based on a single liquid crystal spatial light modulator(SLM).This spatial light modulator is used for amplitude apodization,and its feasibility and potential performance are demonstrated using a laboratory setup using the stochastic parallel gradient descent(SPGD)algorithm to control the spatial light modulator,which is based on point spread function(PSF)sensing and evaluation and optimized for maximum contrast in the discovery working area as a merit function.The system delivers a contrast in the order of 10−6,and shows excellent potential to be used in current and future large aperture telescopes,both on the ground and in space.

Desired Dynamic Equation for Primary Frequency Modulation Control of Gas Turbines

Gas turbines play core roles in clean energy supply and the construction of comprehensive energy systems.The control performance of primary frequency modulation of gas turbines has a great impact on the frequency control of the power grid.However,there are some control difficulties in the primary frequency modulation control of gas turbines,such as the coupling effect of the fuel control loop and speed control loop,slow tracking speed,and so on.To relieve the abovementioned difficulties,a control strategy based on the desired dynamic equation proportional integral(DDE-PI)is proposed in this paper.Based on the parameter stability region,a parameter tuning procedure is summarized.Simulation is carried out to address the ease of use and simplicity of the proposed tuning method.Finally,DDE-PI is applied to the primary frequency modulation system of an MS6001B heavy-duty gas turbine.The simulation results indicate that the gas turbine with the proposed strategy can obtain the best control performance with a strong ability to deal with system uncertainties.The proposed method shows good engineering application potential.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

Harnessing immunity:Immunomodulatory therapies in COVID-19

An overly exuberant immune response,characterized by a cytokine storm and uncontrolled inflammation,has been identified as a significant driver of severe coronavirus disease 2019(COVID-19)cases.Consequently,deciphering the intricacies of immune dysregulation in COVID-19 is imperative to identify specific targets for intervention and modulation.With these delicate dynamics in mind,immunomodulatory therapies have emerged as a promising avenue for miti-gating the challenges posed by COVID-19.Precision in manipulating immune pathways presents an opportunity to alter the host response,optimizing antiviral defenses while curbing deleterious inflammation.This review article compre-hensively analyzes immunomodulatory interventions in managing COVID-19.We explore diverse approaches to mitigating the hyperactive immune response and its impact,from corticosteroids and non-steroidal drugs to targeted biologics,including anti-viral drugs,cytokine inhibitors,JAK inhibitors,convalescent plasma,monoclonal antibodies(mAbs)to severe acute respiratory syndrome coronavirus 2,cell-based therapies(i.e.,CAR T,etc.).By summarizing the current evidence,we aim to provide a clear roadmap for clinicians and researchers navigating the complex landscape of immunomodulation in COVID-19 treatment.CS Glucocorticoids are among the most widely prescribed drugs with their immune-suppressive and anti-inflammatory effect[84].The current guidelines for the treatment of COVID-19 recommend against the use of dexamethasone or other systemic CS in non-hospitalized patients in the absence of another indication[70].The RECOVERY trial demonstrates the reduced 28-d mortality among hospitalized patients with COVID-19 using dexamethasone compared to the usual standard of care,along with other investigators,such as Ahmed and Hassan[85].The benefit of dexamethasone was seen only among participants receiving either oxygen alone or invasive mechanical ventilation at randomization but not among those receiving no respiratory support at enrollment[85].In a systematic review and meta-analysis,Albuquerque et al[86]showed that in comparison to tocilizumab,baricitinib,and sarilumab are associated with high probabilities of similar mortality reductions among hospitalized COVID-19 concurrently treated with CS.As a result of the absence of SARS-CoV-2-specific antiviral medications,the effectiveness of COVID-19 treatments is reduced.Several COVID-19 therapies are now under investigation.However,the majority of them lack specificity,efficacy,and safety[87].Immunotherapy is a ground-breaking medical treatment that manipulates the immune system to fight diseases.Translational research is rapidly progressing,recognized as a significant breakthrough in 2013[88].Among the immunotherapeutic options for treating COVID-19 are Immunoglobulin,CP,antibodies,mAbs(mAbs),NK cells,T cells,TLR,cytokine therapies and immune modulators.

Multi-Objective Optimization Analysis of Auxiliary Flux Modulator Magnetic Gear with Unequal Magnetic Poles

To enhance the output torque and minimize the torque ripple of coaxial magnetic gear(CMG),a novel auxiliary flux modulator CMG with unequal magnetic poles is proposed.This design incorporates an inner rotor with an asymmetric sector and a trapezoidal combined N-S pole structure,featuring Halbach arrays for the arrangement of permanent magnets(PMs).The outer rotor PMs adopt a Spoke-type configuration.To optimize the CMG for high output torque and low torque ripple,a sensitivity analysis is conducted to identify key size parameters that significantly influence the optimization objectives.Based on the sensitivity hierarchy of these parameters,a multi-objective optimization analysis is performed using a genetic algorithm(GA)to determine the optimal structural parameter values of the CMG.In addition,a coaxial magnetic gear(CMG)topology with 4 inner and 17 outer pole pairs is adopted,and the parametric model is established.Finally,the electromagnetic properties of the CMG are evaluated using the finite element method.The results indicate a remarkable reduction in torque ripple,specifically by 46.15%.

认知诊断评估中Q矩阵理论及应用

Q矩阵是认知心理学与心理计量学结合的重要载体,Q矩阵在认知诊断中发挥着十分重要的作用。Q矩阵理论和应用研究近年来取得了重要进展。众多研究者从结构化到非结构化、属性二值到多值、简单到复杂模型、独立到一般结构、0-1到多级评分方面不断深入和拓展Q矩阵理论。Q矩阵理论也广泛应用于测验构念效度评价、计算机化自适应测验选题策略设计、Q矩阵学习和标定、认知诊断测验组卷等。与模型无关的Q矩阵理论和适合特定认知诊断模型下Q矩阵理论,以及最新Q矩阵理论的应用都值得深入研究。

一种基于DQN的去中心化优先级卸载策略

边缘计算(EC)可在网络边缘为用户提供低延迟、高响应的服务。因此,资源利用率高、时延低的任务卸载策略成为研究的热门方向。但大部分现有的任务卸载研究是基于中心化的架构,通过中心化设施制定卸载策略并进行资源调度,容易受到单点故障的影响,且会产生较多的能耗和较高的时延。针对以上问题,提出一种基于深度Q网络(DQN)的去中心化优先级(DP-DQN)卸载策略。首先,设置通信矩阵模拟现实中边缘服务器有限的通信状态;其次,通过对任务设定优先级,使任务可以在不同边缘服务器之间跳转,保证各边缘服务器均可以自主制定卸载策略,完成任务卸载的去中心化;最后,根据任务的跳转次数为任务分配更多的计算资源,提高资源利用效率和优化效果。为了验证所提策略的有效性,针对不同DQN下参数的收敛性能进行了研究对比,实验结果表明,在不同测试情景下,DP-DQN的性能均优于本地算法、完全贪婪算法和多目标任务卸载算法,性能可提升约11%~19%。