Mononuclear cells from the cord blood and granulocytecolony stimulating factor-mobilized peripheral blood:is there a potential for treatment of cerebral palsy?

To investigate a possible therapeutic mechanism of cell therapy in the field of cerebral palsy using granulocyte-colony stimulating factor（G-CSF）-mobilized peripheral blood mononuclear cells（m PBMCs）,we compared the expression of inflammatory cytokines and neurotrophic factors in PBMCs and m PBMCs from children with cerebral palsy to those from healthy adult donors and to cord blood mononuclear cells donated from healthy newborns.No significant differences in expression of neurotrophic factors were found between PBMCs and m PBMCs.However,in cerebral palsy children,the expression of interleukin-6 was significantly increased in m PBMCs as compared to PBMCs,and the expression of interleukin-3 was significantly decreased in m PBMCs as compared to PBMCs.In healthy adults,the expression levels of both interleukin-1βand interleukin-6 were significantly increased in m PBMCs as compared to PBMCs.The expression of brain-derived neurotrophic factors in m PBMC from cerebral palsy children was significantly higher than that in the cord blood or m PBMCs from healthy adults.The expression of G-CSF in m PBMCs from cerebral palsy children was comparable to that in the cord blood but significantly higher than that in m PBMCs from healthy adults.Lower expression of pro-inflammatory cytokines（interleukin-1β,interleukin-3,and-6）and higher expression of anti-inflammatory cytokines（interleukin-8 and interleukin-9）were observed from the cord blood and m PBMCs from cerebral palsy children rather than from healthy adults.These findings indicate that m PBMCs from cerebral palsy and cord blood mononuclear cells from healthy newborns have the potential to become seed cells for treatment of cerebral palsy.

Immunophenotyping of hematopoietic progenitor cells: Comparison between cord blood and adult mobilized blood grafts

AIM:To study the immunophenotype of hematopoietic progenitor cells from cord blood (CB) grafts (n = 39) in comparison with adult apheresis grafts (AG, n = 229) and pre-apheresis peripheral blood (PAPB) samples (n = 908) using flow cytometry analysis.METHODS: First, we performed a qualitative analysis of CD34+ cell sub-populations in both CB and PAPB grafts using the standardized ISHAGE protocol and a wide panel of 20 monoclonal antibodies. Next, we stud-ied some parameters, such as the age of mothers and the weight of newborns, which can influence the qual-ity and the quantity of CD34+ cells from CB. RESULTS: We found that the percentage of apoptotic cells was high in CB in comparison to PAPB (PAPB: 4.6% ± 2.6% vs CB: 53.4% ± 5.2%, P < 0.001). In CB, the weight of newborn and the age of the mother have the influence on CD34+ cells. The follow-up of Ag CD133in the ISHAGE double platform protocol in association with CD45, CD34 and the 7’AAD shows an equal rate between the two cell populations CD133+CD45+CD34+ high and CD34+CD45+ high with a higher percentage. So, is the inclusion of Ac CD133 necessary in the pres-ent panel included in the ISHAGE methodflLast part, we showed a signif icant presence of interferon γ in CB in comparison to PAPB, the annexin showing the high number of apoptotic cells in CB. CONCLUSION: This study demonstrates that many different obstetric factors must be taken into account when processing and cryo-banking umbilical CB units for transplantation.

出行即服务系统理论与实践:回顾与展望

交通是城市绿色低碳转型中最受关注的领域之一,也是数字化渗透及数字平台最为活跃的领域。出行即服务(Mobility as a Service, MaaS)系统是绿色交通的典型代表,是一种新型交通组织和供给方式,反映了当前出行需求的深刻变化和城市交通组织范式转变的耦合。全球范围内已出现了上百个大小规模不等和模式各异的MaaS实践创新,北京MaaS是中国持续至今、影响最大的MaaS实践。目前MaaS实践提出的理论和方法主要基于欧美发达国家,无法充分描述和分析中国实践。在文献研究的基础上,延伸纳入了中国经验,提出了具有全球普适性的一个MaaS系统分析框架,强调辨析全球范围内的MaaS异同均可以从三个维度展开,即嵌入的社会背景、发展目标和产生的社会经济环境影响;并应用此框架对国内外五个典型MaaS进行了比较研究,重点解码了北京MaaS的激励机制、商业模式和商业生态。本文旨在推动MaaS理论和研究方法的全球发展,重点提出了四个方面的关注:(1)MaaS系统的发展再次考验着城市交通如何回归其公共属性;(2)MaaS实践嵌入在城市社会背景中,具有明显的差异性。模式选择是对城市既有社会背景和交通格局的继承,但也可能就此发生转向。MaaS打开了一次城市交通转型的机会窗口;(3)MaaS系统的可持续运营依然面临挑战;(4)数字技术带来数据产权、数据隐私和安全等亟待解决的新问题。所有研究案例表明,数字技术的快速发展需要匹配治理模式创新,MaaS生态的协同进化至关重要。

2024 Wheelchair Compendium of Physical Activities:An update of activity codes and energy expenditure values

Purpose:This paper presents an update of the 2011 Wheelchair Compendium of Physical Activities designed for wheelchair users and is referred to as the 2024 Wheelchair Compendium.The Wheelchair Compendium aims to curate existing knowledge of the energy expenditure for wheelchair physical activities(PAs).Methods:A systematic review of the published energy expenditure of PA for wheelchair users was completed between 2011 and May 2023.We added these data to the 2011 Wheelchair Compendium data that was compiled previously in a systematic review through 2011.Results:A total of 47 studies were included,and 124 different wheelchair PA reported energy expenditure values ranging from 0.8 metabolic equivalents for wheelchair users(filing papers,light effort)to 11.8 metabolic equivalents for wheelchair users(Nordic sit skiing).Conclusion:In introducing the updated 2024 Wheelchair Compendium,we hope to bridge the resource gap and challenge the prevailing narratives that inadvertently exclude wheelchair users from physical fitness and health PAs.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Modeling the performance of perovskite solar cells with inserting porous insulating alumina nanoplates

Peng et al.[Science 379683(2023)]reported an effective method to improve the performance of perovskite solar cells by using thicker porous insulator contact(PIC)-alumina nanoplates.This method overcomes the trade-off between the open-circuit voltage and the fill factor through two mechanisms:reduced surface recombination velocity and increased bulk recombination lifetime due to better perovskite crystallinity.From arguments of drift-diffusion simulations,we find that an increase in mobility and carrier recombination lifetime in bulk are the key factors for minimizing the resistance-effect from thicker PICs and achieving a maximum power conversion efficiency(PCE)at approximately 25%reduced contact area.Furthermore,the partially replacement of perovskite films with thicker PICs would result in a reduction in short-current density,but the relative low refractive index of the PICs imbedded into the high refractive index perovskite creates light trapping structures that compensate for this loss.

Mechanism of high Li-ion conductivity in poly(vinylene carbonate)-poly(ethylene oxide)cross-linked network based electrolyte revealed by solid-state NMR

Solid polymer electrolytes(SPEs)have become increasingly important in advanced lithium-ion batteries(LIBs)due to their improved safety and mechanical properties compared to organic liquid electrolytes.Cross-linked polymers have the potential to further improve the mechanical property without trading off Li-ion conductivity.In this study,focusing on a recently developed cross-linked SPE,i.e.,the one based on poly(vinylene carbonate)-poly(ethylene oxide)cross-linked network(PVCN),we used solid-state nuclear magnetic resonance(NMR)techniques to investigate the fundamental interaction between the chain segments and Li ions,as well as the lithium-ion motion.By utilizing homonuclear/heteronuclear correlation,CP(cross-polarization)kinetics,and spin-lattice relaxation experiments,etc.,we revealed the structural characteristics and their relations to lithium-ion mobilities.It is found that the network formation prevents poly(ethylene oxide)chains from crystallization,which could create sufficient space for segmental tumbling and Li-ion co nductio n.As such,the mechanical property is greatly improved with even higher Li-ion mobilities compared to the poly(vinylene carbonate)or poly(ethylene oxide)based SPE analogues.

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Stable photocurrent-voltage characteristics of perovskite single crystal detectors obtained by pulsed bias

Photocurrent-voltage characterization is a crucial method for assessing key parameters in x-ray or y-ray semiconductor detectors,especially the carrier mobility lifetime product.However,the high biases during photocurrent measurements tend to cause severe ion migration,which can lead to the instability and inaccuracy of the test results.Given the mixed electronic-ionic charac teristics,it is imperative to devise novel methods capable of precisely measuring photocurrentvoltage characteristics under high bias conditions,free from interference caused by ion migration.In this paper,pulsed bias is employed to explore the photocurrent-voltage characteristics of MAPbBr_(3) single crystals.The method yields stable photocurrent-voltage characteristics at a pulsed bias of up to 30 V,proving to be effective in mitigating ion migration.Through fitting the modified Hecht equation,we determined the mobility lifetime products of 1.0×10^(2) cm^(2)·V^(-1)for hole and 2.78×10~(-3)cm^(2)·V^(-1)for electron.This approach offers a promising solution for accurately measuring the transport properties of carriers in perovskite.

SAC-Based Computation Offloading for Reconfigurable Intelligent Surface-Aided Mobile Edge Networks

In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

Blockchain-Based MCS Detection Framework of Abnormal Spectrum Usage for Satellite Spectrum Sharing Scenario

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

Mobility edges and localization characteristics in one-dimensional quasiperiodic quantum walk

We construct a one-dimensional quasiperiodic quantum walk to investigate the localization–delocalization transition.The inverse participation ratio and Lyapunov exponent are employed as two indexes to determine the mobility edge, a critical energy to distinguish the energy regions of extended and localized states. The analytical solution of mobility edge is obtained by the Lyapunov exponents in global theory, and the consistency of the two indexes is confirmed. We further study the dynamic characteristics of the quantum walk and show that the probabilities are localized to some specific lattice sites with time evolution. This phenomenon is explained by the effective potential of the Hamiltonian which corresponds to the phase in the coin operator of the quantum walk.

Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks

A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

Enhanced magnetic anisotropy and high hole mobility in magnetic semiconductor Ga_(1-x-y)Fe_(x)Ni_(y)Sb

(Ga,Fe)Sb is a promising magnetic semiconductor(MS)for spintronic applications because its Curie temperature(T_(C))is above 300 K when the Fe concentration is higher than 20%.However,the anisotropy constant Ku of(Ga,Fe)Sb is below 7.6×10^(3)erg/cm^(3)when Fe concentration is lower than 30%,which is one order of magnitude lower than that of(Ga,Mn)As.To address this issue,we grew Ga_(1-x-y)Fe_(x)Ni_(y)Sb films with almost the same x(≈24%)and different y to characterize their magnetic and electrical transport properties.We found that the magnetic anisotropy of Ga_(0.76-y)Fe_(0.24)Ni_(y)Sb can be enhanced by increasing y,in which Ku is negligible at y=1.7%but increases to 3.8×10^(5)erg/cm^(3)at y=6.1%(T_(C)=354 K).In addition,the hole mobility(μ)of Ga_(1-x-y)Fe_(x)Ni_(y)Sb reaches 31.3 cm^(2)/(V∙s)at x=23.7%,y=1.7%(T_(C)=319 K),which is much higher than the mobility of Ga_(1-x)Fe_(x)Sb at x=25.2%(μ=6.2 cm^(2)/(V∙s)).Our results provide useful information for enhancing the magnetic anisotropy and hole mobility of(Ga,Fe)Sb by using Ni co-doping.

Construction of apricot variety search engine based on deep learning

Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.