文化与河流

文化与河流的关系十分特殊，文化既源于河流，又反过来改造河流。文章重点阐述了文化如何由源于河流到改造河流，如何由被动地改造河流到主动地改造河流的过程。     

Identifying regular blocks in valued networks: A heuristic applied to the St. Marks carbon flow data,and international trade in cereal products

While the concept of regular equivalence is equally applicable to dichotomous as well as valued networks, the identification of regular blocks in regular blockmodels is somewhat problematic when dealing with valued networks. Applying the standard procedure for identifying ties in such blockmodels, a procedure perhaps most suited for dichotomous networks, does tend to generate block images and reduced graphs that differ from intuitive notions of such structures.     

A non-profit approach to addressing the effects of urbanization in the Lake Pontchartrain Basin, Louisiana

The Lake Pontchartrain Basin in southeast Louisiana is an estuarine watershed encompassing New Orleans, Baton Rouge, and wetlands that are an integral part of Louisiana's unique culture. In recent decades rapid growth and poorly planned development have resulted in a decrease in water quality and habitat loss, especially in St. Tammany Parish on the north shore of Lake Pontchartrain. In 1989, the Lake Pontchartrain Basin Foundation (LPBF), an environmental, non-profit organization, was formed to address and rectify environmental issues in the Basin. LPBF accomplishes its mission through programs addressing water quality, habitat protection, geographic information science, public access, and education.LPBF used the recent, rapid development occurring in St. Tammany Parish as a case study to examine the relationship between land use, water quality, and development in the Basin by investigating habitat change/urbanization, development in flood plains, and water quality. Through the integration of LPBF programs, it was found that urbanization is having a devastating impact on the parish's natural habitats and severely degrading water quality. Using these results, LPBF is educating Basin citizens on the protection of their habitats and water quality to insure that these resources may be available for future generations.     

Risk,trust and knowledge networks in farmers’ learning

This study reports on New Zealand dairy farmers’ access to and use of information as mediated through conditions of risk and trust within the context of their interpersonal social networks. We located participants’ reports of their information use within their perceived environments of trust and risk, following Giddens's [1990. The consequences of modernity. Polity Press, Stanford, CA] typology of trust and risk in pre-modernity and modernity. The research participants were constant users of interpersonal and print information from numerous sources, and monitored their incoming data in the light of strategic needs, reflecting their roles as both farming practitioners and business owners. Socio-spatial knowledge networks (SSKNs) combine individuals’ explanatory cognitive models of information acquisition and use with a micro-geographical analysis of their interpersonal networks. The participants showed characteristics of pre-modern, modern and even post-modern society in respect of their use of complex interactional forms, as well as a blending of individualistic and communitarian practices and concerns in their professional and personal lives.     

Origins and destinies: Immigration to the United States since World War II

Contemporary immigration to the United States and the formation of new ethnic groups are the complex and unintended social consequences of the expansion of the nation to its post-World War II position of global hegemony. Immigrant communities in the United States today are related to a history of American military, political, economic, and cultural involvement and intervention in the sending countries, especially in Asia and the Caribbean Basin, and to the linkages that are formed in the process that open a variety of legal and illegal migration pathways. The 19.8 million foreign-born persons counted in the 1990 U.S. census formed the largest immigrant population in the world, though in relative terms, only 7.9% of the U.S. population was foreign-born, a lower proportion than earlier in this century. Today's immigrants are extraordinarily diverse, a reflection of polar-opposite types of migrations embedded in very different historical and structural contexts. Also, unlike the expanding economy that absorbed earlier flows from Europe, since the 1970s new immigrants have entered an hourglass economy with reduced opportunities for social mobility, particularly among the less educated, and new waves of refugees have entered a welfare state with expanded opportunities for public assistance. This paper seeks to make sense of the new diversity. A typology of contemporary immigrants is presented, and their patterns of settlement, their distinctive social and economic characteristics compared to major native-born racial-ethnic groups, and their different modes of incorporation in—and consequences for—American society are considered.     

Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

Flexible regression modeling

The author proposes a new method for flexible regression modeling of multi‐dimensional data, where the regression function is approximated by a linear combination of logistic basis functions. The method is adaptive, selecting simple or more complex models as appropriate. The number, location, and (to some extent) shape of the basis functions are automatically determined from the data. The method is also affine invariant, so accuracy of the fit is not affected by rotation or scaling of the covariates. Squared error and absolute error criteria are both available for estimation. The latter provides a robust estimator of the conditional median function. Computation is relatively fast, particularly for large data sets, so the method is well suited for data mining applications.     

Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers

Monthly average sunspot numbers follow irregular cycles with complex nonlinear dynamics. Statistical linear models constructed to forecast them are therefore inappropriate, while nonlinear models produce solutions sensitive to initial conditions. Two computational techniques - neural networks and genetic programming - that have their advantages are applied instead to the monthly numbers and their wavelet-transformed and wavelet-denoised series. The objective is to determine if modeling wavelet-conversions produces better forecasts than those from modeling series' observed values. Because sunspot numbers are indicators of geomagnetic activity their forecast is important. Geomagnetic storms endanger satellites and disrupt communications and power systems on Earth.     

Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.