Measurement of Ion Mobilities of Selected Amines by Ion Mobility Spectrometry

The ion mobilities of selected amines were measured by IMS. The minimum detection limits and linear ranges of selected amines were detected.

Mobilities, Identities and Diversities: Contemporary Interpretations of a Canonical Chinese Play in Singapore

Globalization increases mobilities and multiplies identities in Asia.Singapore,a city state with diverse Asian communities,amplifies this trend.In the 1990s,Singapore recruited large numbers of Western educated Chinese scholars,who formed new diasporas across Asia and became catalyst for further cultural diversities.This multifaceted Asian phenomenon can be best exemplified in Chinese scholar-director Grant Shen’s contemporary productions of the traditional Chinese opera—The West Wing(2008,2016).This study discusses issues of mobilities,identities,and diversities through The West Wing,in which the performers are predominantly from Asian diasporas.The mobility of Grant Shen from China to Singapore via the US diversifies his cultural identity,at once rooted deeply in Chinese culture while openly embracing Western liberalism and Singaporean plurality.His theatre productions,boldly adopting pop music,employing trendy language,and recreating pro-feminist scenes,vividly attest to cultural diversities.Identity issues come to the fore when this well-received play in Singapore toured Shanghai,where it was welcomed by the young but frowned upon by others as subversive to Chinese tradition.This study views the multiplicity of Singapore as a fertile land for productions foretelling the plural future Asia.

Two-Dimensional Transition Metal Dichalcogenides and Their Charge Carrier Mobilities in Field-Effect Transistors

Two-dimensional(2D) materials have attracted extensive interest due to their excellent electrical, thermal,mechanical, and optical properties. Graphene has been one of the most explored 2D materials. However, its zero band gap has limited its applications in electronic devices. Transition metal dichalcogenide(TMDC), another kind of 2D material,has a nonzero direct band gap(same charge carrier momentum in valence and conduction band) at monolayer state,promising for the efficient switching devices(e.g., field-effect transistors). This review mainly focuses on the recent advances in charge carrier mobility and the challenges to achieve high mobility in the electronic devices based on 2DTMDC materials and also includes an introduction of 2D materials along with the synthesis techniques. Finally, this review describes the possible methodology and future prospective to enhance the charge carrier mobility for electronic devices.

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.

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.

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.

Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total consumption.An increase of 3.3%in consumption is predicted from 2024 to 2032.Tomatoes are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose levels.Thus,tomatoes are globally important due to their widespread usage and nutritional value.To face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize them.Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit.This leads to financial losses and affects the livelihood of farmers.Therefore,automatic disease detection at any stage of the tomato plant is a critical issue.Deep learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters.Also,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region.Moreover,the existing techniques lack in recognizing multiple diseases on the same leaf.To address these concerns,we introduce a customized deep learning-based convolution vision transformer model.Themodel achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories.It requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,respectively.Its training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned models.Thus,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato crops.The periodicmonitoring promotes timely action to prevent the spread of diseases and reduce crop loss.

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.

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.

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.

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.

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.

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

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.

Growth mechanism and characteristics of electron drift instability in Hall thruster with different propellant types

The existence of a significant electron drift instability(EDI) in the Hall thruster is considered as one of the possible causes of the abnormal increase in axial electron mobility near the outlet of the channel. In recent years, extensive simulation research on the characteristics of EDI has been conducted, but the excitation mechanism and growth mechanism of EDI in linear stage and nonlinear stage remain unclear. In this work, a one-dimensional PIC model in the azimuthal direction of the thruster near-exit region is established to gain further insights into the mechanism of the EDI in detail, and the effects of different types of propellants on EDI characteristics are discussed. The changes in axial electron transport caused by EDI under different types of propellants and electromagnetic field strengths are also examined. The results indicate that EDI undergoes a short linear growth phase before transitioning to the nonlinear phase and finally reaching saturation through the ion Landau damping. The EDI drives a significant ion heating in the azimuthal direction through electron–ion friction before entering the quasi-steady state, which increases the axial mobility of the electrons. Using lighter atomic weight propellant can effectively suppress the oscillation amplitude of EDI, but it will increase the linear growth rate, frequency, and phase velocity of EDI. Compared with the classical mobility, the axial electron mobility under the EDI increases by three orders of magnitude, which is consistent with experimental phenomena. The change of propellant type is insufficient to significantly change the axial electron mobility. It is also found that the collisions between electrons and neutral gasescan significantly affect the axial electron mobility under the influence of EDI, and lead the strength of the electric field to increase and the strength of the magnetic field to decrease, thereby both effectively suppressing the axial transport of electrons.

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.

Privacy-Preserving Federated Mobility Prediction with Compound Data and Model Perturbation Mechanism

Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.

Robot-Oriented 6G Satellite-UAV Networks: Requirements, Paradigm Shifts, and Case Studies

Networked robots can perceive their surroundings, interact with each other or humans,and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle(UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links,via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener’s Cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage,enabling new orchestration between network serving and positive mobility control of robots. Moreover,the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing(MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.

Large-scale interplant exchange of macromolecules between soybean and dodder under nutrient stresses

Parasitic plants and their hosts communicate through haustorial connections.Nutrient deficiency is a common stress for plants,yet little is known about whether and how host plants and parasites communicate during adaptation to such nutrient stresses.In this study,we used transcriptomics and proteomics to analyze how soybean(Glycine max)and its parasitizing dodder(Cuscuta australis)respond to nitrate and phosphate deficiency(-N and-P).After-N and-P treatment,the soybean and dodder plants exhibited substantial changes of transcriptome and proteome,although soybean plants showed very few transcriptional responses to-P and dodder did not show any transcriptional changes to either-N or-P.Importantly,large-scale interplant transport of mRNAs and proteins was detected.Although the mobile mRNAs only comprised at most 0.2%of the transcriptomes,the foreign mobile proteins could reach 6.8%of the total proteins,suggesting that proteins may be the major forms of interplant communications.Furthermore,the interplant mobility of macromolecules was specifically affected by the nutrient regimes and the transport of these macromolecules was very likely independently regulated.This study provides new insight into the communication between host plants and parasites under stress conditions.