Analysis and Optimization of Validation Procedure in Blockchain-Enhanced Wireless Resource Sharing and Transactions

To ensure the security of resource and intelligence sharing in 6G,blockchain has been widely adopted in wireless communications and applications.Although blockchain can ensure the traceability and non-tamperability of data in the concatenated blocks,it cannot guarantee the honest behaviors of users in the application before the generation of transactions.Thus,additional technologies are required to ensure that the source of blockchain data is reliable.In this paper,the detailed procedure is designed for the application-oriented task validation in the blockchainenhanced computing resource sharing and transactions in ultra dense networks(UDN).The corresponding queuing model is built and analyzed with the consideration of the wireless re-transmission and the probability of malicious deception by users.Based on the analysis results,the UDN deployment is optimized to save network cost while ensuring latency performance.Numerical results verify our analysis,and the optimized system deployment including the number and service capacities of both base stations and mobile edge computing(MEC)servers are also given with various system settings.

Ethical Principles and Governance Technology Development of AI in China

Ethics and governance are vital to the healthy and sustainable development of artificial intelligence(AI).With the long-term goal of keeping AI beneficial to human society,governments,research organizations,and companies in China have published ethical guidelines and principles for AI,and have launched projects to develop AI governance technologies.This paper presents a survey of these efforts and highlights the preliminary outcomes in China.It also describes the major research challenges in AI governance research and discusses future research directions.

Proximal Policy Optimization-Based Committee Selection Algorithm in Blockchain-Enabled Mobile Edge Computing Systems

To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles(Io V)and Industrial Internet of Things(IIo T),the blockchain-enabled Mobile Edge Computing(MEC)system has received extensive attention.However,blockchain is a computing and communication intensive technology due to the complex consensus mechanisms.To facilitate the implementation of blockchain in the MEC system,this paper adopts the committee-based Practical Byzantine Fault Tolerance(PBFT)consensus algorithm and focuses on the committee selection problem.Vehicles and IIo T devices generate the transactions which are records of the application tasks.Base Stations(BSs)with MEC servers,which serve the transactions according to the wireless channel quality and the available computing resources,are blockchain nodes and candidates for committee members.The income of transaction service fees,the penalty of service delay,the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index.The committee selection problem is modeled as a Markov decision process,and the Proximal Policy Optimization(PPO)algorithm is adopted in the solution.Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods.

Cosensitization process effect of D-A-π-A featured dyes on photovoltaic performances

Cosensitization based on two or multiple dyes as "dye cocktails" can hit the target on compensating and broadening light-harvesting region.Two indoline D-A-π-A motif sensitizers(WS-2 and WS-39) that possess similar light response area but distinctly reversed feature in photovoltaic performance are selected as the specific cosensitization couple. That is, WS-2 shows quite high photocurrent but low photovoltage, and WS-39 gives relatively low photocurrent but quite high photo voltage. Due to the obvious "barrel effect",both dyes show medium PCE around8.50%. In contrast with the previous cosensitization strategy mostly focused on the compensation of light response region, herein we perform different cosensitization sequence, for taking insight into the balance of photocurrent and photo voltage, and achieving the synergistic improvement in power conversion efficiency(PCE). Electronic impedance spectra(EIS) indicate that exploiting dye WS-39 with high V_(OC) value as the primary sensitizer can repress the charge recombination more effectively, resulting in superior V_(OC) rather than using dye WS-2 with high J_(SC)as the primary sensitizer. As a consequence, a high PCE value of 9.48% is obtained with the delicate cosensitization using WS-39 as primary dye and WS-2 as accessory dye, which is higher than the corresponding devices sensitized by each individual dye(around 8.48-8.67%). It provides an effective optimizing strategy of cosensitization how to combine the individual dye advantages for developing highly efficient solar cells.

A fast approach to optimize dye loading of photoanode via ultrasonic technique for highly efficient dye-sensitized solar cells

A distinctive method is proposed by simply utilizing ultrasonic technique in Ti02 electrode fabrication in order to improve the optoelectronic performance of dye-sensitized solar cells （DSSCs）. Dye molecules are at random and single molecular state in the ultrasonic field and the ultrasonic wave favors the diffusion and adsorption processes of dye molecules. As a result, the introduction of ultrasonic technique at room temperature leads to faster and more well-distributed dye adsorption on TiO2 as well as higher cell efficiency than regular deposition, thus the fabrication time is markedly reduced. It is found that the device based on 40 kHz ultrasonic （within 1 h） with N719 exhibits a Voc of 789 mV, Jsc of 14.94 mA]cm2 and fill factor （FF） of 69.3, yielding power conversion efficiency （PCE） of 8.16%, which is higher than device regularly dyed for 12 h （PCE = 8.06%）. In addition, the DSSC devices obtain the best efficiency （PCE = 8.68%） when the ultrasonic deposition time increases to 2.5 h. The DSSCs fabricated via ultrasonic technique presents more dye loading, larger photocurrent, less charge recombination and higher photovoltage. The charge extraction and electron impedance spectroscopy （EIS） were performed to understand the influence of ultrasonic technique on the electron recombination and performance of DSSCs.

High-Efficiency(Over 10%) Parallel Tandem Dye-Sensitized Solar Cells Based on Tri-Carbon Electrodes

Ensuring high power conversion efficiency,partially or completely replacing Pt electrodes with inexpensive materials is one of the important development directions of dye-sensitized solar cells(DSSCs).In this work,we have developed a threecomponent(MWCNTs,carbon black and graphite) carbon(tri-carbon) electrode material for DSSC devices combined with the advantages of high electron transfer kinetics of MWCNTs,plentiful catalytic sites in crystal edges of carbon black and superior electrical conductivity and catalytic activity of graphite.Using a tri-carbon electrode,a Pt electrode,and two N719-sensitized photoanodes,a parallel tandem dye-sensitized solar cells are assembled obtaining a high PCE of 10.26%(V_(OC)=0.70 V,J_(SC)=19.99 mA/cm~2,FF=73.33%).It opens up a new avenue for the development of low-cost and highperformance DSSCs.

Preliminary Study on Interpreting Stock Price Forecasting Based on Tree Regularization of GRU

The gated recurrent unit (GRU) deep model is interpreted to predict price’s falling or rising. By using a technique called Tree Regularization of Deep Models for Interpretability, a GRU network is converted to a decision tree (called GRU-Tree) to interpret its prediction rules. This approach was tested by experimenting on a few sample stocks (e.g., the Gree company) and a main stock market index (SSE Composite Index) in China. The discovered prediction rules actually reflect a general rule called Mean Reversion in stock market. Results show that the GRU-Tree is more effective (higher AUC) than the decision tree directly trained from the data for small and moderate average path length (APL) of trees. And the fidelity between GRU and its generated GRU-Tree is high (about 0.8).

App Store Analysis: Using Regression Model for App Downloads Prediction

App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.

A Peer Grading Tool for MOOCs on Programming

In massive open online courses (MOOCs), peer grading will play an important role to promote MOOCs development. In this paper, we develop a peer grading tool for programming courses on MOOCs. It is capable of dealing with large and diverse student population, and providing them with targeted subjective assessment. This tool firstly partition the submissions into small chunks to reduce the task of reviewers and give us flexibility to scale the code review process. Next we use code normalization and chunks clustering to assign similar chunks to the same student for increasing reviewer efficiency. Besides, the tool use a random allocation strategy and workload classification to assure reviewers workload balance while every student can get diverse feedback. Finally our evaluation experiments on a number of students in school indicate that the tool has achieved a significant improvement over the peer grading on MOOCs.

Characteristics of rural domestic sewage discharge and their driving mechanisms: evidence from the Northern Region, China

The traits of rural domestic sewage emission are unclear,negatively affecting rural domestic sewage treatment and sewage management.This study used data from the Second National Pollution Source Census Bulletin to establish a data set.The spatial distribution characteristics and main factors influencing rural sewage discharge in the Northern Region were studied using spatial autocorrelation analysis and structural equations.The findings demonstrated that(l)a significant Spearman correlation between drainage water volume(DwV),chemical oxygen demand(COD),ammonia nitrogen(NH_(3)-N),total nitrogen(TN),and total phosphorus(TP)and that the correlation coefficients between DWV and COD,NH,-N,TNand TP were 0.87**,1.0**,0.99**,0.99**,respectively;(2)rural sewage discharge showed spatial autocorrelation,and rural domestic sewage discharge in the districts and counties with an administration was significantly higher than in the surrounding areas;and(3)social development was the main driver rural domestic sewage changes(path coefficient was 0.407**),and the main factors influencing rural domestic sewage discharge were the urbanization rate,years of education,and population age structure.This study obtained the spatial variation law and clarified the main influencing factors of rural domestic sewage to provide data support and a theoretical basis for subsequent rural sewage collection and treatment.Use of the Inner Mongolia Autonomous Region in northern China as a typical case,provides a theoretical foundation for scientific decision-making on rural domestic sewage treatment at the national and regional levels and offers new perspectives for managing pollutants.

Exploring the complex trade-offs and synergies of global ecosystem services

The trade-off and synergy relationship of ecosystem services is an important topic in the current assessment.The value of each service provided by the ecosystem is substantially affected by human activities,and conversely,its changes will also affect the relevant human decisions.Due to varying tradeoffs among ecosystem services and synergies between them that can either increase or decrease,it is difficult to optimize multiple ecosystem services simultaneously,making it a huge challenge for ecosystem management.This study firstly develops a global Gross Ecosystem Product(GEP)accounting framework.It uses remote sensing data with a spatial resolution of 1 km to estimate the ecosystem services of forests,wetlands,grasslands,deserts,and farmlands in 179 major countries in 2018.The results show that the range of global GEP values is USD 112e197 trillion,with an average value of USD 155 trillion(the constant price),and the ratio of GEP to gross domestic product(GDP)is 1.85.The tradeoffs and the synergies among different ecosystem services in each continent and income group have been further explored.We found a correspondence between the income levels and the synergy among ecosystem services within each nation.Among specific ecosystem services,there are strong synergies between oxygen release,climate regulation,and carbon sequestration services.A trade-off relationship has been observed between flood regulation and other services,such as water conservation and soil retention services in low-income countries.The results will help clarify the roles and the feedback mechanisms between different stakeholders and provide a scientific basis for optimizing ecosystem management and implementing ecological compensation schemes to enhance human well-being.

High-sensitivity intelligent packaging films harnessing rose anthocyanins and hydrophilic silica aerogel for visual food freshness monitoring

The enhancement of the sensitivity for anthocyanin-based indicator films in food freshness monitoring in real time is important for application.In this study,hydrophilic silica aerogel(SiO2 NA)was incorporated into corn starch(CS)/chitosan(CH)/rose anthocyanins(RACNs)-encapsulated potato amylopectin nanoparticles(APNPs)composite film to increase the sensitivity for shrimp freshness detection.The microstructure of films revealed that the gas absorption capacity was improved by amorphous SiO2 NA via hydrogen interactions.The pore size(1.74–5.60 times),pore volume(3.92–5.60 times),and specific surface area(2.21–2.34 times)of films increased with the addition of SiO2 NA.The sensing of NH3 and pH and the reversibility of films were also reinforced.Meanwhile,the pH-responsive films containing SiO2 NA changed visibly in color from purple–red to orange–gray and finally to gray,enabling effective monitoring of shrimp freshness during storage at 4°C.Thus,anthocyanin-based indicator films with improved sensitivity by adding SiO2 NA were successfully designed for monitoring shrimp freshness.

Dual-bionic regenerative microenvironment for peripheral nerve repair

Autologous nerve grafting serves is considered the gold standard treatment for peripheral nerve defects;however,limited availability and donor area destruction restrict its widespread clinical application.Although the performance of allogeneic decellularized nerve implants has been explored,challenges such as insufficient human donors have been a major drawback to its clinical use.Tissue-engineered neural regeneration materials have been developed over the years,and researchers have explored strategies to mimic the peripheral neural microenvironment during the design of nerve catheter grafts,namely the extracellular matrix(ECM),which includes mechanical,physical,and biochemical signals that support nerve regeneration.In this study,polycaprolactone/silk fibroin(PCL/SF)-aligned electrospun material was modified with ECM derived from human umbilical cord mesenchymal stem cells(hUMSCs),and a dual-bionic nerve regeneration material was successfully fabricated.The results indicated that the developed biomimetic material had excellent biological properties,providing sufficient anchorage for Schwann cells and subsequent axon regeneration and angiogenesis processes.Moreover,the dual-bionic material exerted a similar effect to that of autologous nerve transplantation in bridging peripheral nerve defects in rats.In conclusion,this study provides a new concept for designing neural regeneration materials,and the prepared dual-bionic repair materials have excellent auxiliary regenerative ability and further preclinical testing is warranted to evaluate its clinical application potential.

Selective coordination of polyazin sapanisertib with halide vacancy for printable mesoscopicperovskite solar cells

The mitigation of under-coordinated Pb^(2+)(halide vacancy)defect remains an imperative challenge in the perovskite solar cells,especially printable mesoscopic perovskite solar cells(FP-PsCs).Here we re port a commercial-available polyazin anticancer drug Sapanisertib as coordination passivator of halide vacancies in FP-PSCs,thereby achieving the photoelectric conversion efficiency(PCE)to 18.46%,along with a record certified PCE of 18.27%.In polazin Sapanisertib(Sap),there exists two kinds of nitrogen atoms:in-aromatic ring(in purine and oxazole rings,IAR-Ns)and out-aromatic ring(substituted amino groups,OAR-Ns).Through multiple characterizations,and DFT calculations show that substituted amino groups OAR-Ns hardly get interaction with the halide vacancy due to the distribution of charge density in Sapanisertib.Our work suggests that the selective coordination is of great significance for the design of high-performance passivators for printable mesoscopic perovskite solar cells.

医院信息系统外联的安全管理探索与实践

在国家互联网医疗等政策引导下, 医院信息系统对外提供的医疗信息服务范围越来越广, 与外部的信息交互越来越频繁, 面临的网络安全风险也越来越高。作者分析了医院对外信息服务的业务种类、与外部网络连接的方式以及存在的网络安全问题, 遵循信息系统安全等级保护2.0的思想, 顶层设计, 建立了医院统一的外联网络安全通道, 用严格的管理措施和技术手段, 形成立体的网络安全防护屏障, 保障医院信息系统稳定运行与数据安全。

群体智能系统的动力学模型与群体熵度量

群体智能是自然界普遍存在的现象,受到了国内外研究者的广泛关注.网络和人工智能技术的高速发展,使得大规模群体在网络上通过个体智慧激发和群体智慧汇聚形成超越个体能力的群体智能成为可能.随之而来,如何认知和形成这样的群体智能系统成为了研究的热点.本文将在广义群体智能的概念体系之下,探索群体智能系统的度量理论和方法,以此来促进对群体智能形成机理的更深层认知.本文提出了群体智能系统的基本特性以及其形成的激发汇聚建模,指出群体智能系统实质上是一类复杂非线性动力学系统,包含微观个体激发、宏观群体协作、全局群智汇聚3个核心动力学属性.并以此为基础,讨论了群体智能系统群体熵度量所应满足的基本性质.最后以图的群体化搜索为例,说明了群体的激发和汇聚过程,以及在此过程中如何通过群体熵来度量群体行为的变化,验证了本文提出的群体熵度量思想的有效性.

An evaluation framework for software crowdsourcing

Recently software crowdsourcing has become an emerging area of software engineering. Few papers have pre- sented a systematic analysis on the practices of software crowdsourcing. This paper first presents an evaluation frame- work to evaluate software crowdsourcing projects with re- spect to software quality, costs, diversity of solutions, and competition nature in crowdsourcing. Specifically, competi- tions are evaluated by the min-max relationship from game theory among participants where one party tries to minimize an objective function while the other party tries to maximize the same objective function. The paper then defines a game theory model to analyze the primary factors in these min- max competition rules that affect the nature of participation as well as the software quality. Finally, using the proposed eval- uation framework, this paper illustrates two crowdsourcing processes, Harvard-TopCoder and AppStori. The framework demonstrates the sharp contrasts between both crowdsourc- ing processes as participants will have drastic behaviors in engaging these two projects.

Pirh2 mediates the sensitivity of myeloma cells to bortezomib via canonical NF-κB signaling pathway

Clinical success of the proteasome inhibitor established bortezomib as one of the most effective drugs in treatment of multiple myeloma （MM）. While survival benefit of bortezomib generated new treatment strategies, the primary and secondary resistance of MM cells to bortezomib remains a clinical concern. This study aimed to highlight the role of p53-induced RING-H2 （Pirh2） in the acquisition of bortezomib resistance in MM and to clarify the function and mechanism of action of Pirh2 in MM cell growth and resistance, thereby providing the basis for new therapeutic targets for MM. The proteasome inhibitor bortezomib has been established as one of the most effective drugs for treating MM. We demonstrated that bortezomib resistance in MM cells resulted from a reduction in Pirh2 protein levels. Pirh2 overexpression overcame bortezomib resistance and restored the sensitivity of myeloma cells to bortezomib, while a reduction in Pirh2 levels was correlated with bortezomib resistance. The levels of nuclear factor- kappaB （NF-κB） p65, pp65, plKBa, and IKKa were higher in bortezomib-resistant cells than those in parental cells. Pirh2 overexpression reduced the levels of plKBa and IKKa, while the knockdown of Pirh2 via short hairpin RNAs increased the expression of NF-κB p65, plKBa, and IKKa. Therefore, Pirh2 suppressed the canonical NF- κB signaling pathway by inhibiting the phosphorylation and subsequent degradation of IKBa to overcome acquired bortezomib resistance in MM cells.

Tunable room-temperature phosphorescence and circularly polarized luminescence encoding helical supramolecular polymer

Supramolecular polymers with different functionalities have been continuously developed in the past decade because of their indispensable role in soft materials.However,pure organic supramolecular polymers with stable room temperature phosphorescence(RTP)emission were very rarely reported for the difficulties of synthesis and achieving RTP in solution.Herein,soluble helical supramolecular polymers with circularly polarized room-temperature phosphorescence were developed via a facile hostguest strategy.Through assembly,a transition from pure fluorescence to almost pure RTP emission was achieved.Adjusting the asymmetry of guest could easily control the chiroptical property of supramolecular polymers.This work provides new opportunities for the design and development of intelligent soft functional soft materials.

Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles

Nowadays,Edge Information System(EIS)has received a lot of attentions.In EIS,Distributed Machine Learning(DML),which requires fewer computing resources,can implement many artificial intelligent applications efficiently.However,due to the dynamical network topology and the fluctuating transmission quality at the edge,work node selection affects the performance of DML a lot.In this paper,we focus on the Internet of Vehicles(IoV),one of the typical scenarios of EIS,and consider the DML-based High Definition(HD)mapping and intelligent driving decision model as the example.The worker selection problem is modeled as a Markov Decision Process(MDP),maximizing the DML model aggregate performance related to the timeliness of the local model,the transmission quality of model parameters uploading,and the effective sensing area of the worker.A Deep Reinforcement Learning(DRL)based solution is proposed,called the Worker Selection based on Policy Gradient(PG-WS)algorithm.The policy mapping from the system state to the worker selection action is represented by a deep neural network.The episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy network.Results show that the proposed PG-WS algorithm outperforms other comparation methods.