Supergene Enrichment of Silver Ores with Special Reference to those from Neihart, Montana.

In the development of a technique it was necessary to learn the fundamentals of ore microscopy as applied to the various minerals of silver, which included the use of reflected polarized light, etch reactions, micro­chemical analysis, and sight recognition of mineral. In addition it was necessary to become familiar with the accepted criteria of sequence, replacement, and other textural phenomena.

A Study and Application of the Process of Supergene Enrichment of Silver Ores.

In many deposits of silver ores the grade of the ore de­creases considerably a few hundred feet below the surface. It is believed that in many cases the better ores owe their richness in part to the process of sulphide enrichment. It is recognized, however, that many rich silver ores are hypogene deposits that have been affected very little, if any, by processes of enrichment.

Secondary Enrichment of Copper at the Madison Gold Skarn Deposit, Silver Star District, Montana

Secondary Enrichment of Copper at the Madison Gold Skarn Deposit, Silver Star District, Montana. This paper focuses on the chemical reactions responsible for secondary enrichment of copper...we argue that most of the secondary Cu enrichment occurred during a late hydrothermal event that replaced the high temperature skarn mineral assemblage with hematitic jasperoid. Evidence favoring this "hypogene" Cu enrichment hypothesis is presented.

An Architecture for End-User TV Content Enrichment

This paper proposes an extension to the televisionwatching paradigm that permits an end-user to enrich broadcast content. Examples of this enriched content are: virtual edits that allow the order of presentation within the content to be changed or that allow the content to be subsetted; conditional text, graphic or video objects that can be placed to appear within content and triggered by viewer interaction; additional navigation links that can be added to structure how other users view the base content object. The enriched content can be viewed directly within the context of the TV viewing experience. It may also be shared with other users within a distributed peer group. Our architecture is based on a model that allows the original content to remain unaltered, and which respects DRM restrictions on content reuse. The fundamental approach we use is to define an intermediate content enhancement layer that is based on the W3C’s SMIL language. Using a pen-based enhancement interface, end-users can manipulate content that is saved in a home PDR setting. This paper describes our architecture and it provides several examples of how our system handles content enhancement. We also describe a reference implementation for creating and viewing enhancements.

Mouse enrichment

The provision of environmental enrichment to mice used in research is a topic that generates often widely divergent opinions as to the value of this husbandry practice. The literature is replete with examples of the positive effects enrichment has on the animals’ welfare. However, many published studies have demonstrated that enrichment can impact research in unforeseen and unpredictable ways, and thus may pose a concern as a confounding variable. Indeed, an analysis of the outcomes of murine enrichment studies suggests that both views are accurate, and thus inclusion of environmental enrichment in the mouse cage should be done with the understanding that while the use of appropriate cage complexities can certainly enhance animal welfare and concomitantly provide a better animal model for scientific purposes, the wrong choice of enrichment may negatively impact the welfare of the animals or the research. This chapter describes our current understanding regarding the effects of several commonly used mouse enrichments.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.