Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.     

Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring

Structural failures (bridge or building collapses) and geohazards (landslides, ground subsidence or earthquakes) are worldwide problems that often lead to significant economic and loss of life. Monitoring the deformation of both natural phenomena and man-made structures is a major key to assessing structural dynamic responses. Actually, this monitoring process is under real-time demand for developing warning and alert systems.One of the most used techniques for real-time deformation monitoring is the Global Navigation Satellite System (GNSS) real-time procedure, where the relative positioning approach, using a well-known reference station, has been applied.This study was conducted to evaluate the actual quality of the real-time kinematic Precise Point Positioning (PPP) GNSS solution for deformation monitoring, where it can be concluded that a promise tool is under development and should be taken into account on actual and near future real-time deformation monitoring studies and applications.     

Long-run urban dynamics: understanding local housing market change in London

Abstract

Recently, a literature has emerged using empirical techniques to study the evolution of international cities over many centuries; however, few studies examine long-run change within cities. Conventional models and concepts are not always appropriate and data issues make long-run neighbourhood analysis particularly problematic. This paper addresses some of these points. First, it discusses why the analysis of long-run urban change is important for modern urban policy and considers the most important concepts. Second, it constructs a novel data set at the micro level, which allows consistent comparisons of London neighbourhoods in 1881 and 2001. Third, the paper models some of the key factors that affected long-run change, including the role of housing. There is evidence that the relative social positions of local urban areas persist over time but, nevertheless, at fine spatial scales, local areas still exhibit change, arising from aggregate population dynamics, from advances in technology, and also from the effects of shocks, such as wars. In general, where small areas are considered, long-run changes are likely to be greater, because individuals are more mobile over short than long distances. Finally, the paper considers the implications for policy.     

The concept of smartness in cyber–physical systems and connection to urban environment

The next-generation systems are expected to be largely cyber–physical systems (CPSs) that autonomously control physical processes, through sensors and actuators typically in real-time feedback and cooperative control loops distributed among physical and cyber environments. The rapid technological advancements enhance the smartness of these CPSs, pushing their boundaries of performance and efficiency by embedding new information and communication technologies. However, to what extent CPSs should be smarter so that they do not compromise safety and security of safety critical systems? is an open research question. Towards this goal, the purpose of this study is to establish a grounded theory to analyse what makes these systems smart? and eventually, how to find a balance between smartness and safety risks? In this precinct, this article aims to develop a conceptual framework, define the dimensions and derive the characteristics that make CPSs smart. The proposed approach combines an automated informetric and systematic analysis of literature pertinent to the topic of smartness across anthropology, science, engineering and technology. The analysis of a case study building and the discussions presented herein support the connection between the existing understanding of CPSs and smartness offered by the building design approach in urban environment.     

门架式堆取料机在铜冶炼中的生产实践

针对铜冶炼工艺的特点,从门架式堆取料机对前后工序衔接、结构、原理出发,介绍了其在熔炼炉系统和吹炼炉系统中的应用情况。经过试生产期的生产实践证明,门架式堆取料机具有场地利用率高、取料量大,自动化程度高和安全环保等优点。     

摇摆运动下多环路主冷却剂系统自然循环流量的稳定性分析

海洋核动力平台因其突出的安全性已成为当今核能领域热点研究问题之一，但在海洋等非惯性条件下会使一回路系统的热工特性发生变化。针对此问题，本文对几种典型的一回路系统在摇摆条件下的自然循环流量波动特性进行计算分析。分别建立典型的双环路、三环路、四环路的一回路系统模型布置方案，并同时考虑摇摆中心的位置，根据流体动量守恒方程，得到不同状态下一回路系统内的流量变化规律。对于单堆双环路系统，摇摆中心在船上/中/下部位置时，环路流量波动幅度分别为13.2%/11.2%/9.5%，堆芯流量波动幅度分别为0.9%/0.8%/0.6%；对于单堆三环路系统，摇摆中心在船下部时，环路流量和堆芯流量波动幅度分别为9.2%和0.8%；对于单堆四环路系统，摇摆中心在船下部时，布置方案1和方案2的环路流量波动幅度分别为9.5%和9.2%，堆芯流量波动幅度分别为0.9%和0.7%。计算结果表明：采用单堆双环路的设计布置方案是最有利于系统稳定性的。     

嵌入式实时操作系统内核混合代码的自动化验证框架

"如何构造高可信的软件系统"已成为学术界和工业界的研究热点.操作系统内核作为软件系统的基础组件，它的安全可靠是构造高可信软件系统的重要环节.为了确保操作系统内核的安全可靠，将形式化方法引入到操作系统内核验证中，提出了一个自动化验证操作系统内核的框架.该验证框架包括：（1）分别对C语言程序和混合语言程序（C语言和汇编语言）进行验证；（2）在混合语言程序验证中，为汇编程序建立抽象模型，并将C语言程序和抽象模型粘合形成基于C语言验证工具可接收的验证模型；（3）从规范中提取性质，基于该自动验证工具，对性质完成自动验证；（4）该框架不限于特定的硬件架构.成功地运用该验证框架对两种不同硬件平台的嵌入式实时操作系统内核μC/OS-II进行了验证.结果显示：利用该框架在对两个不同的硬件平台上内核验证时，框架的可重复利用率很高，高达到88%，虽然其抽象模型需要根据不同的硬件平台进行重构.在对基于这两种平台的操作系统内核验证中，分别发现了10~12处缺陷.其中，在ARM平台上两处与硬件相关的问题被发现.实验表明，该方法对不同硬件平台的同一个操作系统分析验证具有一定的通用性.     

Calorimetric study and thermodynamic description of Ga-Ge-Li liquid alloys

This publication contains the thermodynamic results received by the drop calorimetry method. The experiments were conducted for four different cross sections, at the temperature of 1080 K. The investigated alloys were as follows: (Ga_0.75Li_0.25)_1-xGe_x, (Ge_0.50Li_0.50)_1-xGa_x, (Ga_0.50Li_0.50)_1-xGe_x, (Ga_0.25Li_0.75)_1-xGe_x. The mixing enthalpy changes measured for all four cross sections of the Ga-Ge-Li system are characterized by negative deviations from the ideal solutions. The Muggianu model with the ternary interaction parameters was applied to elaborate the experimental data of the mixing enthalpy change with the use of the optimized thermodynamic parameters of the binary systems available in the literature.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

陈四楼煤矿“123456双重预防体系”的建设与应用

针对煤矿“双重预防体系”如何落地生根问题，结合陈四楼煤矿在风险分级管控和隐患排查治理方面的实用方法和“双重预防体系”的建设经验，深入研究了“双重预防体系”的相关标准、风险隐患事故之间的关系及其各自的产生和发展机理、安全风险的有关辨识评估方法、事故隐患的排查和治理方法，分析了传统安全管理模式与“双重预防体系”的新型安全管理模式的差别，总结出了“123456双重预防体系”这个囊括了事前安全风险辨识、事中隐患排查治理、事后安全现状评估的创新成果，实现了“事前、事中、事后”的全过程控制，对夯实煤矿安全管理根基、促进矿井平稳有序发展发挥着越来越重要的作用，为煤炭行业“双重预防体系”的落地生根提供了借鉴和参考。