Research on Value Evaluation Method of Investment Project Based on Fuzzy Composite Real Options

Venture capital investments are characterized by high input,high yield,and high risk.Due to the complexity of the market environment,stage-by-stage investment is becoming increasingly important.Traditional evaluation methods like comparison,proportion,maturity,internal rate of return,scenario analysis,decision trees,and net present value cannot fully consider the uncertainty and stage characteristics of the project.The fuzzy real options method addresses this by combining real option theory,fuzzy number theory,and composite option theory to provide a more accurate and objective evaluation of Public-Private Partnership(PPP)projects.It effectively considers the interaction of options and the ambiguity of project parameters,making it a valuable tool for project evaluation in the context of venture capital investment.

弹性退休制度下谁更愿意延迟退休?——基于Option Value模型的微观模拟

人口老龄化背景下延迟退休年龄、建立弹性退休制度是大势所趋。养老金激励是弹性退休制度的重要内容。建立期权价值模型和养老金给付及奖惩因子模型,基于中国家庭收入调查项目(CHIP2018)的数据,对不同特征人群的养老金峰值、期权价值、内部报酬率进行模拟。研究发现:养老金总财富随退休年龄“先增后减”,男性的峰值年龄早于女性;引入养老金“奖惩”机制有助于提高最优退休年龄,激励劳动者延迟退休;考虑闲暇偏好的异质性,男性参保者更倾向于早退休,而女性参保者特别是女性较高收入群体更愿意延迟退休;厌恶风险的参保者更有可能选择早退休。建议尽早建立弹性退休年龄政策体系,增加劳动者的选择权和制度灵活性;引入精算调节因子构建养老金奖惩机制,完善养老保险待遇计发办法。

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

基于IUV_5G Option3X架构网络规划与部署设计

本文基于IUV_5G Option3X架构,详细阐述了5G网络的规划与部署设计过程。首先介绍了5G NSA组网架构的背景和优势,然后深入探讨了IUV_5G全网部署与优化虚拟仿真平台的功能和应用。重点描述了核心网、无线网和承载网的部署过程,包括设备部署、数据配置和线路连接等具体步骤。通过业务验证确认了网络部署的成功,并对5G技术的未来发展进行了展望。

血-迷路屏障通透性与突聋发病机制及预后的关系:在3D real IR的发现

目的分析突聋患者的内耳钆造影MRI三维真实重建反转恢复(three dimensional real inversion recovery,3D real IR)成像上的表现,探讨血-迷路屏障的通透性与突聋发病机制及其预后的关系。方法对41例单侧突聋患者行内耳钆造影MRI,测量患耳和健耳的耳蜗信号强度,并测量延髓信号强度,分别计算出耳蜗/延髓比值(cochlear/medulla ratio,CM ratio),以CM比值作为血-迷路屏障通透性的标志物,分析突聋患者患耳、健耳CM比值的不对称程度与疗效之间的关系。结果41例患者中,33例(80.48%)患耳的CM比值高于健耳,差异有统计学意义(P<0.05);患耳CM比值为健耳的1.5倍以下者18例,治疗有效率为77.78%(14/18);患侧CM比值不高于健侧者8例,治疗有效率为100%;达到健耳的1.5倍至1.75倍之间者7例,治疗有效率为100%(7/7);达到健耳的1.75倍至2.0倍之间者2例,治疗有效率为50%(1/2);达到健耳的2.0倍以上者14例,治疗有效率为14.28%(12/14);差异有统计学意义(P<0.05)。结论内耳3D Real IR可显示突聋患者血-迷路屏障通透性的改变,80.48%的突聋患者患侧耳蜗出现高信号,患耳CM比值达健耳的1.75倍以上者多数预后不良。

基于互信息优化的Option-Critic算法

时序抽象作为分层强化学习的重要研究内容,允许分层强化学习智能体在不同的时间尺度上学习策略,可以有效解决深度强化学习难以处理的稀疏奖励问题。如何端到端地学习到优秀的时序抽象策略一直是分层强化学习研究面临的挑战。Option-Critic(OC)框架在Option框架的基础上,通过策略梯度理论,可以有效解决此问题。然而,在策略学习过程中,OC框架会出现Option内部策略动作分布变得十分相似的退化问题。该退化问题影响了OC框架的实验性能,导致Option的可解释性变差。为了解决上述问题,引入互信息知识作为内部奖励,并提出基于互信息优化的Option-Critic算法(Option-Critic Algorithm with Mutual Information Optimization,MIOOC)。MIOOC算法结合了近端策略Option-Critic(Proximal Policy Option-Critic,PPOC)算法,可以保证下层策略的多样性。为了验证算法的有效性,把MIOOC算法和几种常见的强化学习方法在连续实验环境中进行对比实验。实验结果表明,MIOOC算法可以加快模型学习速度,实验性能更优,Option内部策略更有区分度。

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

Real eigenvalues determined by recursion of eigenstates

Quantum physics is primarily concerned with real eigenvalues,stemming from the unitarity of time evolutions.With the introduction of PT symmetry,a widely accepted consensus is that,even if the Hamiltonian of the system is not Hermitian,the eigenvalues can still be purely real under specific symmetry.Hence,great enthusiasm has been devoted to exploring the eigenvalue problem of non-Hermitian systems.In this work,from a distinct perspective,we demonstrate that real eigenvalues can also emerge under the appropriate recursive condition of eigenstates.Consequently,our findings provide another path to extract the real energy spectrum of non-Hermitian systems,which guarantees the conservation of probability and stimulates future experimental observations.

Modeling the Spatio-Temporal Dynamics of Local Context for a Contextualized Diffusion of Agroecological Intensification Options in Niger

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

基于Real ESRGAN的视频修复系统研究

图像修复和视频修复是计算机视觉的一项重要任务,其中图像修复又是视频修复的基础。为此,如何有效提升图像质量是实现视频质量提升的关键。传统的图像修复算法主要以样本信息为基础,通过对样本内容的扩撒来实现对破损区域的修复;由于这种方式对于图像样本有一定要求,从而制约传统图像修复技术的发展。为此,以生成新图像内容为基础的神经网络如GAN的出现,为图像修复技术转向深度学习提供方向。该课题主要以Real ESRGAN网络的图像修复技术为基础,通过对音频视频数据的隔离处理以及相同帧数据的优化和标记,构建视频修复处理流程。通过对随机视频样本的测试,并通过对单帧图片质量和视频数据流畅性与协调性的评估,该视频处理方法表现出较好的系统性能。

A Stochastic Model to Assess the Epidemiological Impact of Vaccine Booster Doses on COVID-19 and Viral Hepatitis B Co-Dynamics with Real Data

A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19.The model is fitted to real COVID-19 data from Pakistan.The proposed model incorporates logistic growth and saturated incidence functions.Rigorous analyses using the tools of stochastic calculus,are performed to study appropriate conditions for the existence of unique global solutions,stationary distribution in the sense of ergodicity and disease extinction.The stochastic threshold estimated from the data fitting is given by:R_(0)^(S)=3.0651.Numerical assessments are implemented to illustrate the impact of double-dose vaccination and saturated incidence functions on the dynamics of both diseases.The effects of stochastic white noise intensities are also highlighted.

A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.

A Comparative Study between Landmark Based and Real Time Ultrasound Guided Sub Arachnoid Block

Background: Sub arachnoid block (SAB) performed by traditional landmark palpation technique can be inaccurate. This problem is exacerbated by altered patient anatomy due to obesity and age-related changes. A pre-procedural ultrasound scan of the lumbar spine has been shown to be of benefit in guiding lumbar epidural insertion in obstetric patients. Information on the use of real-time ultrasound (RUS) guided SAB, to date, been limited. This study compared RUS guided SAB to traditional landmark guided technique in patients undergoing spinal anesthesia for different surgical procedures. Methods: This was a prospective, single center, comparative observational study conducted in the department of anesthesiology at our center. 560 patients who underwent spinal anesthesia either by landmark based technique or real-time ultrasound-guided methods. The primary outcome was the first attempt success rate of dural puncture when employing the two methods. Results: Baseline characteristics were similar in the two study groups. The first attempt success rate of dural puncture in landmark guided group was 64.3% compared to 72.6% in the ultrasound guided group. This difference was not statistically significant. The procedure performance time was significantly shorter with landmark palpation compared to use of real-time ultrasound guided method. Conclusion: Use of RUS-guided technique does not significantly improve the first attempt success rate of SAB dural puncture during spinal anesthesia compared to the traditional landmark-guided technique.

马乳酒样乳杆菌马乳酒样亚种real-time PCR检测方法的建立与应用

本研究建立了一种特异性实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)检测方法,根据模式菌株马乳酒样乳杆菌马乳酒样亚种ZW3的16S rDNA序列和全基因组序列设计筛选特异性引物,采用SYBR Green I荧光染料建立real-time PCR方法,并对方法的特异性、灵敏度、重复性和混合体系等进行检测。结果表明,本研究所建立的方法特异性强、灵敏度高、重复性好,建立real-time PCR的标准曲线,其决定系数R2为0.965,具有良好的线性关系,且在马乳酒样乳杆菌马乳酒样亚种及混合体系中可以特异性检出。综上,本研究建立的real-time PCR法可以快速、准确地检测马乳酒样乳杆菌马乳酒样亚种,为马乳酒样乳杆菌的特异性定性定量检测提供了一种新的方法。

An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.

基于病菌孢子捕捉和real-time PCR技术的田间空气中小麦白粉病菌孢子动态监测及病情估计模型研究

利用Burkard定容式孢子捕捉器结合real-time PCR定量技术,分别对种植高抗、中感和高感白粉病小麦品种的田间空气中白粉病菌分生孢子浓度进行监测,结果表明,real-time PCR定量与传统的显微观察计数两种方法测得的孢子浓度呈显著正相关(P≤0.01),且两种病菌孢子计数方法在同一抗性品种上监测到的孢子浓度动态相近。此外,两种方法测得的孢子浓度与各气象因子的相关性分析结果一致,空气中的白粉病菌孢子浓度主要与空气相对湿度显著正相关。在此基础上,利用两种方法测定的田间空气中白粉病菌孢子浓度分别建立了基于累积孢子浓度的田间病情估计模型。分析发现,基于两种孢子浓度测定方法建立的病情估计模型间无显著性差异,表明real-time PCR定量技术测定的孢子浓度在构建白粉病病情估计模型上具有一定可行性。该结果为real-time PCR定量技术与病菌孢子捕捉技术相结合用于小麦白粉病的监测和预测提供理论依据。

基于Real-time PCR法检测乳粉中牛源性成分定量研究

基于Real-timePCR建立了乳粉中牛源性成分相对定量检测方法,并对牛的特异性引物与探针进行了特异性、灵敏度和稳定性测试。通过模拟不同浓度牛乳粉与马乳粉混合样本,根据其△Ct值的函数关系进行线性拟合进而绘制标准曲线,建立乳粉中牛源性成分的相对定量检测。结果显示,该方法的最低检测限为0.00001 mg/mL,回收率为91.11%~119.2%,组间变异系数≤0.58%、组内变异系数≤1.44%。说明该方法在特异性与稳定性上适用于乳粉中牛源性成分及含量的掺假检测。

一种基于real-time PCR技术的TTV检测方法的建立及应用

目的:本研究旨在开发一种具有更高灵敏度和特异性的TTV检测技术,为揭示TTV在多种疾病过程中的作用提供重要的技术支持。方法:为了更精确、灵敏的检测TTV,本研究分析了目前公布的所有亚型的TTV基因序列,在此基础上建立了一种基于UTR区域的real-time PCR检测方法,并与文献报道应用较为广泛的PCR检测方法进行了对比。结果:本研究建立的方法在1×10^(7)~1×10^(1) copies/μL标准品浓度范围内具有良好的线性关系,相关系数为1.000,斜率为-3.446,检测下限为1×10^(1) copies/μL。重复性试验结果显示,组内变异系数为7.22%,表明本方法重复性、稳定性较强。针对30份临床样本,使用本研究建立的real-time PCR检测方法及目前被多个研究所使用的4套引物进行对比。结果表明,本研究所建立的方法灵敏度显著高于文献中报道的4种方法(P<0.01);Sanger测序结果表明,本方法检测出的30份阳性样本均为TTV,检测特异性为100%。结论:本研究采用基于TaqMan探针的real-time PCR检测方法,检测灵敏性高、覆盖基因型范围广,尤其对于TTV病毒载量较低的情况下能够进行定量检测,对于TTV病毒的致病性及作为免疫标志物的应用提供重要的技术支持。

Research on the Impact and Opportunities of the Digital Economy Promoting the Development of Real Economy

The digital economy has infused vitality into the transformation and development of the real economy,urging enterprises to break through core technological barriers,address bottleneck issues,and improve their core competitiveness.It fosters the comprehensive digital transformation of agriculture,manufacturing,and service sectors,improving the dynamism of the real economy and fostering more consumption hotspots.Efforts are underway to enhance industrial supply chains and innovation chains,optimize regional resource allocation,and promote a virtuous cycle within the real economy.Initiatives are being undertaken to standardize the development of digital economic platforms,promote the high-quality development of regional economies,and leverage the advantages of the socialist market economy with Chinese characteristics.