Effect of curing process on simulated antisoiling properties of sol–gel coating on pine sapwood

Recently, inorganic–organic hybrid coatings produced by the sol–gel route have proven to be a potential protective treatment for wooden surfaces. In this study, the effect of curing process on simulated antisoiling properties of sol–gel coating was investigated. The sol–gel coating was spray-coated on pine sapwood and cured with different temperature and time combinations. The antisoiling properties were studied by measuring the water contact angle of the coating after different curing processes. In addition, abrasion resistance and contact angle of oleic acid were measured. An optical profilometer was used to investigate surface roughness of the coating. Sufficient hydro- and oleophobicity were obtained with all of the curing parameters. The comparison between curing parameters showed that lower temperature and 1–3 h curing time leads to significant improvement in the durability of the water and oil repellent properties of the coating.     

Reasoning with examples: propositional formulae and database dependencies

For humans, looking at how concrete examples behave is an intuitive way of deriving conclusions. The drawback with this method is that it does not necessarily give the correct results. However, under certain conditions example-based deduction can be used to obtain a correct and complete inference procedure. This is the case for Boolean formulae (reasoning with models) and for certain types of database integrity constraints (the use of Armstrong relations). We show that these approaches are closely related, and use the relationship to prove new results about the existence and size of Armstrong relations for Boolean dependencies. Furthermore, we exhibit close relations between the questions of finding keys in relational databases and that of finding abductive explanations. Further applications of the correspondence between these two approaches are also discussed. Received: 19 June 1995 / 31 August 1998     

Multi-objective optimization of skeletal structures under static and seismic loading conditions

Almost every real world problem involves simultaneous optimization of several incommensurable and often competing objectives which constitutes a multi-objective optimization problem. In multi-objective optimization problems the optimal solution is not unique as in single-objective optimization problems. This paper is concerned with large-scale structural optimization of skeletal structures such as space frames and trusses, under static and/or seismic loading conditions with multiple objectives. Combinatorial optimization methods and in particular algorithms based on evolution strategies are implemented for the solution of this type of problems. In treating seismic loading conditions a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. This approach for treating seismic loading is compared with an approximate design approach, based on simplifications adopted by the seismic codes, in the framework of multi-objective optimization.     

On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm

Neural Computing and Applications - Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to...     

The Discrete Basis Problem

Matrix decomposition methods represent a data matrix as a product of two factor matrices: one containing basis vectors that represent meaningful concepts in the data, and another describing how the observed data can be expressed as combinations of the basis vectors. Decomposition methods have been studied extensively, but many methods return real-valued matrices. Interpreting real-valued factor matrices is hard if the original data is Boolean. In this paper, we describe a matrix decomposition formulation for Boolean data, the Discrete Basis Problem. The problem seeks for a Boolean decomposition of a binary matrix, thus allowing the user to easily interpret the basis vectors. We also describe a variation of the problem, the Discrete Basis Partitioning Problem. We show that both problems are NP-hard. For the Discrete Basis Problem, we give a simple greedy algorithm for solving it; for the Discrete Basis Partitioning Problem we show how it can be solved using existing methods. We present experimental results for the greedy algorithm and compare it against other, well known methods. Our algorithm gives intuitive basis vectors, but its reconstruction error is usually larger than with the real-valued methods. We discuss about the reasons for this behavior.     

Information discovery across multiple streams

In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let’s assume that a research or financial analyst is searching for information on a topic, continuously polling data from multiple (and possibly heterogeneous) text streams, such as RSS feeds, blogs, etc. The topic of interest can be described with the aid of several keywords. Current filtering approaches would just identify single text streams containing some of the keywords. However, it would be more flexible and powerful to search across multiple streams, which may collectively answer the analyst’s question. We present such model that takes in consideration the continuous flow of text in streams and uses efficient pipelined algorithms such that results are output as soon as they are available. The proposed model is evaluated analytically and experimentally, where the Enron dataset and a variety of blog datasets are used for our experiments.     

Comparing top-k XML lists

Systems that produce ranked lists of results are abundant. For instance, Web search engines return ranked lists of Web pages. There has been work on distance measure for list permutations, like Kendall tau and Spearman's footrule, as well as extensions to handle top-k lists, which are more common in practice. In addition to ranking whole objects (e.g., Web pages), there is an increasing number of systems that provide keyword search on XML or other semistructured data, and produce ranked lists of XML sub-trees. Unfortunately, previous distance measures are not suitable for ranked lists of sub-trees since they do not account for the possible overlap between the returned sub-trees. That is, two sub-trees differing by a single node would be considered separate objects. In this paper, we present the first distance measures for ranked lists of sub-trees, and show under what conditions these measures are metrics. Furthermore, we present algorithms to efficiently compute these distance measures. Finally, we evaluate and compare the proposed measures on real data using three popular XML keyword proximity search systems.     

Determining Attributes to Maximize Visibility of Objects

In recent years, there has been significant interest in the development of ranking functions and efficient top-k retrieval algorithms to help users in ad hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). We introduce a complementary problem: How to guide a seller in selecting the best attributes of a new tuple (e.g., a new product) to highlight so that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We develop several formulations of this problem. Although the problems are NP-complete, we give several exact and approximation algorithms that work well in practice. One type of exact algorithms is based on Integer Programming (IP) formulations of the problems. Another class of exact methods is based on maximal frequent item set mining algorithms. The approximation algorithms are based on greedy heuristics. A detailed performance study illustrates the benefits of our methods on real and synthetic data.