Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Empirical Analysis of Agricultural Cultural Resources Value Evaluation under DEA Model

Agricultural culture is a productive activity about education and management. It aims at high efficiency and high quality, uses technology as its means, and takes nature as its carrier. Agricultural cultural resources are the product of the rapid development of modern economy. It promotes the development of the national economy and profoundly affects people's production and life. DEA model, also known as data envelope analysis method, is an algorithm that uses multiple data decision units for input and output training to obtain the final model. This article explains the concept and basic characteristics of agricultural culture. Through questionnaire surveys and expert interviews, we collected development data, screened human, material, and financial data, and calculated information on economic and social resources. On this basis, this paper establishes the evaluation index of agricultural culture based on DEA model. Then, through empirical analysis from a specific perspective, it can be concluded that increasing human, material and financial input can achieve economic and social benefits. Generally speaking, cultural investment can promote the development of the industry. The research results of this paper laid a theoretical foundation for the development of agricultural culture, and put forward a development model focusing on technology development, improving investment efficiency, and investing in material resources.     

The Regulatory Functionality of Exosomes Derived from hUMSCs in 3D Culture for Alzheimer's Disease Therapy

Reducing amyloid‐β (Aβ) accumulation could be a potential therapeutic approach for Alzheimer's disease (AD). Particular functional biomolecules in exosomes vested by the microenvironment in which the original cells resided can be transferred to recipient cells to improve pathological conditions. However, there are few reports addressing whether exosomes derived from cells cultured on scaffolds with varying dimension can reduce Aβ deposition or ameliorate cognitive decline for AD therapy. Herein, both 3D graphene scaffold and 2D graphene film are used as the matrix for human umbilical cord mesenchymal stem cell culture, from which the supernatants are obtained to isolate exosomes. The levels of 195 kinds of miRNAs and proteins, including neprilysin, insulin‐degrading enzyme and heat shock protein 70, in 3D‐cultured exosomes (3D‐Exo) are dramatically different from those obtained from 2D culture. Hence, 3D‐Exo could up‐regulate the expression of α‐secretase and down‐regulate the β‐secretase to reduce Aβ production in both AD pathology cells and transgenic mice, through their special cargo. With rescuing Aβ pathology, 3D‐Exo exerts enhanced therapeutic effects on ameliorating the memory and cognitive deficits in AD mice. These findings provide a novel clinical application for scaffold materials and functional exosomes derived from stem cells.     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

Template Engineering of CuBi2O4 Single‐Crystal Thin Film Photocathodes

To develop strategies for efficient photo‐electrochemical water‐splitting, it is important to understand the fundamental properties of oxide photoelectrodes by synthesizing and investigating their single‐crystal thin films. However, it is challenging to synthesize high‐quality single‐crystal thin films from copper‐based oxide photoelectrodes due to the occurrence of significant defects such as copper or oxygen vacancies and grains. Here, the CuBi₂O₄ (CBO) single‐crystal thin film photocathode is achieved using a NiO template layer grown on single‐crystal SrTiO₃ (STO) (001) substrate via pulsed laser deposition. The NiO template layer plays a role as a buffer layer of large lattice mismatch between CBO and STO (001) substrate through domain‐matching epitaxy, and forms a type‐II band alignment with CBO, which prohibits the transfer of photogenerated electrons toward bottom electrode. The photocurrent densities of the CBO single‐crystal thin film photocathode demonstrate ?0.4 and ?0.7 mA cm^?2 at even 0 V_RHE with no severe dark current under illumination in a 0.1 m potassium phosphate buffer solution without and with H₂O₂ as an electron scavenger, respectively. The successful synthesis of high‐quality CBO single‐crystal thin film would be a cornerstone for the in‐depth understanding of the fundamental properties of CBO toward efficient photo‐electrochemical water‐splitting.     

带检测环节的制造单元布局优化

针对带检测环节的智能制造单元，考虑其具有随机性因素的特点，本文对其机器布局问题进行研究。首先对该智能制造单元的生产过程及特点进行分析，建立其机器布局优化问题的随机非线性整数规划的数学模型；然后建立该智能制造单元对应的仿真模型，对该优化问题进行求解；最后分析抽检率的变化对最优解造成的影响，并对该智能制造单元的性能指标进行方差分析，研究不同的布局方案和抽检率对系统性能的影响程度。研究成果为企业进行智能制造单元的机器布局提供决策依据。     

基于阳极氧化法的TiO2纳米管制备及生成过程分析

运用阳极氧化法制备TiO₂纳米管，探讨了TiO₂纳米管紫外光催化净化头孢噻肟钠抗生素废水的效果，采用扫描电镜（SEM）和X射线衍射仪（XRD）对TiO₂纳米管进行了表征分析。SEM结果显示，制备的TiO₂材料表面呈阵列排布的多孔状，内径大小均匀，管径分布在30~45nm之间，管阵列呈蜂窝状，与钛板表面垂直，对比煅烧前后的TiO₂纳米管XRD图，发现煅烧后样品的XRD图出现了TiO₂锐钛矿型特征衍射峰和金红石型特征衍射峰。讨论了TiO₂纳米管的生成机理，认为纳米管的形成过程主要有最初氧化层的形成、孔核的形成、由孔核演变成微孔、微孔生长并出现空隙、形成纳米阵列管过程。     

耦合膜分离的新型CO2低温捕集系统性能优化

CO₂捕集作为温室气体排放控制的有效手段已成为重要研究课题。作为新兴捕集技术之一，低温CO₂捕集因产品纯度高、无附加污染等优势受到关注。然而，该技术能耗和捕集率对于气体中CO₂浓度十分敏感，对于高CO₂浓度气体可获得较高的CO₂捕集率和较低能耗水平。基于此，本文提出了耦合膜分离的新型CO₂低温捕集系统，通过膜材料选择渗透性实现待捕集气体CO₂浓度主动调控，并在最优浓度下进行CO₂低温捕集。首先基于不同传统低温捕集系统特点，对比分析了不同耦合系统模式，从而确定了最优耦合系统结构。针对最优耦合系统进行了运行参数优化，并分别基于实现系统捕集能耗最低与捕集率最高的目标，获得了膜渗透侧CO₂浓度与进气CO₂浓度间的关系式，为该耦合系统中膜组件选型提供指导。研究表明，本文提出的耦合系统捕集能耗为1.92MJ/kgCO₂，相比于传统单一低温系统捕集能耗可降低16.5%。     

基于特征贡献率的机械故障分类方法

为提高往复压缩机、航空发动机等复杂机械故障分类的准确率，依据特征参数对不同故障的敏感度存在差异的特性，提出一种狄利克雷过程混合模型(Dirichlet process mixture model,简称DPMM)与贝叶斯推断贡献(Bayesian inference contribution,简称BIC)相结合的分析方法。采用DPMM方法自学习机械振动信号高维特征的统计分布模型，并依据BIC理论计算得到各特征参数对模型的贡献率，通过对比观测数据与各类故障数据特征贡献率间的差异实现故障分类。试验结果表明，该方法的平均分类准确率比基于高斯混合模型(Gaussian mixture model,简称GMM)的故障诊断方法的平均分类准确率提高19.29%，比基于Relief算法的故障诊断方法的平均分类准确率提高32.71%，且该方法的时效性高，泛化性能强，能够更有效地进行复杂机械故障分类。     

考虑边信息的多层贝叶斯需求预测模型

随着工业互联网经济的发展,需求的不确定性日益增大,为提高需求预测的准确性,提出一个考虑边信息的多层贝叶斯需求预测模型(DFSI)。DFSI模型通过构造隐层的网络结构以实现对客户需求更加精确的刻画,该隐层结构主要包含两组参数:一组用于描述客户需求在时间上固有的连续性特征,另一组则用于融合相关的边信息特征。进一步,以贝叶斯推断为理论基础,以最大化后验概率为目标,推导出了DFSI的优化目标,并基于梯度下降方法设计了相应的求解算法。使用京东商城及某制造企业的真实销售数据对提出的模型进行了检验。结果显示,与常用的需求预测模型相比,DFSI能获得更好的预测结果。