浅谈建筑幕墙设计中的成本控制

建筑幕墙成本控制的重点在设计阶段 ,设计方案对成本的控制是决定性的。设计对项目成本的控制是战略性、全局性的 ,而施工过程的管理对项目成本的控制是战术性、局部性的。幕墙设计中如何达到控制成本的目的至关重要。把技术与经济融为一体 ,在设计中既保证幕墙安全、实用、美观 ,又做到控制和降低成本 ,是施工企业适应市场的关键     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

On the Critical Value Behaviour of Multiple Decision Procedures

In this paper we investigate the asymptotic critical value behaviour of certain multiple decision procedures as e.g. simultaneous confidence intervals and simultaneous as well as stepwise multiple test procedures. Supposing that n hypotheses or parameters of interest are under consideration we investigate the critical value behaviour when n increases. More specifically, we answer e.g. the question by which amount the lengths of confidence intervals increase when an additional parameter is added to the statistical analysis. Furthermore, critical values of different multiple decision procedures as for instance step-down and step-up procedures will be compared. Some general theoretic results are derived and applied for various distributions.     

Decision settings analysis – a tool for analysis and design of human activity systems

The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic analysis of the information requirements of an organization is proposed. This methodology also indicates organizational discrepancies and information imbalances. The methodology focuses the settings of the decisions on all levels of organizations. The methodology can be regarded as a dynamic, learning system. The author proposes further research on the individuals decision making abilities.     

Making space for ‘the social’: connecting sociology and professional practices in urban lighting design

Lighting is increasingly recognized as a significant social intervention by both lighting professionals and academic social scientists. However, what counts as ‘the social’ is diverse and contested, with consequences for what kind of ‘social’ is performed or invented. Based on a long‐term research programme, we argue that collaboration between sociologists and lighting professionals requires negotiating discourses and practices of ‘the social’. This paper explores the quality and kinds of spaces made for ‘the social’ in professional practices and academic collaborations, focusing on two case studies of urban lighting that demonstrate how the space of ‘the social’ is constrained and impoverished by an institutionalized division between technical and aesthetic lighting. We consider the potential role of sociologists in making more productive spaces for ‘the social’ in urban design, as part of the central sociological task of ‘inventing the social’ (Marres, Guggenheim and Wilkie 2018) in the process of studying it.     

A comparison of recent nonparametric methods for testing effects in two-by-two factorial designs

The two-way two-levels crossed factorial design is a commonly used design by practitioners at the exploratory phase of industrial experiments. The F-test in the usual linear model for analysis of variance (ANOVA) is a key instrument to assess the impact of each factor and of their interactions on the response variable. However, if assumptions such as normal distribution and homoscedasticity of errors are violated, the conventional wisdom is to resort to nonparametric tests. Nonparametric methods, rank-based as well as permutation, have been a subject of recent investigations to make them effective in testing the hypotheses of interest and to improve their performance in small sample situations. In this study, we assess the performances of some nonparametric methods and, more importantly, we compare their powers. Specifically, we examine three permutation methods (Constrained Synchronized Permutations, Unconstrained Synchronized Permutations and Wald-Type Permutation Test), a rank-based method (Aligned Rank Transform) and a parametric method (ANOVA-Type Test). In the simulations, we generate datasets with different configurations of distribution of errors, variance, factor's effect and number of replicates. The objective is to elicit practical advice and guides to practitioners regarding the sensitivity of the tests in the various configurations, the conditions under which some tests cannot be used, the tradeoff between power and type I error, and the bias of the power on one main factor analysis due to the presence of effect of the other factor. A dataset from an industrial engineering experiment for thermoformed packaging production is used to illustrate the application of the various methods of analysis, taking into account the power of the test suggested by the objective of the experiment.     

Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.     

Recent developments in D-optimal designs

In the paper, some issues concerned with the determining spring balance weighing designs satisfying the criterion of D-optimality under the assumption measurement errors are uncorrelated and they have the same variances are discussed. In addition, highly D-efficient spring balance weighing designs are also considered. Some conditions under which any spring balance weighing design is regular D-optimal or highly D-efficient are proved. What is more, new construction methods of regular D-optimal and highly D-efficient spring balance weighing designs are presented.     

论中国历史上的三次制度设计

自三代始,迄于近代,上下四千年。其间,制度设计之事,史不绝书。然而,择其具有原创意义、影响中国历史进程的制度设计,主要有三次,分别出现在周代、秦朝和近代。在中国文化语境中,古代制度设计权属于王和圣,虽为价值有涉,但也使“天下为家”体现出“天下为公”的理想。前两次制度设计以家天下政治为核心,先后形成了王制和帝制的模式;近代以孙中山为首的民族资产阶级的制度设计,效仿法、美模式,开启中国民主制度的先河,奠定了中国政治的基本方向。从历史背景、设计者及思想来源、系统性、创新性、本质性和对外影响来比较,三次制度设计既有相同性也存在着本质差别。三次制度设计对中国历史的进程有重要影响,可以作为中国历史上时代划分的标志。三次制度设计皆发生在中国历史转折时期,其影响之深远,意义之重大,留下了宝贵的历史经验、启示和借鉴。     

A dynamic business modelling approach to design and experiment new business venture strategies

Business Modelling has evolved as a key activity to reflect new business venture strategy by framing the way a firm will operate and how it will function in achieving its goals (e.g., profitability, growth, innovation, social impact). However, scholars and practitioners have criticized the adoption of a too static perspective in the design and use of conventional Business Model representations. Such a static perspective prevents nascent entrepreneurs experimenting with their Business Models and, as a result, identifying the most effective strategies, especially in terms of business sustainability and profitability. In this paper, we argue that combining conventional Business Model schemas with System Dynamics modelling results in a strategy design tool that may overcome several limitations related to a static view of Business Model representation. Mapping the different key elements underlying value creation processes into a system of causal interdependencies – through the use of simulation – allows strategy analysts and entrepreneurs to experiment and learn how the business reacts to strategic and organizational changes in terms of performance, innovation and value creation. As such, Dynamic Business Models provide useful insights to strategy formulation and business venturing by capturing how critical Business Model elements interact to produce enduring competitive advantages over time.