Business Organization and Coordination in Marketing Specialty Hogs: A Comparative Analysis of Two Firms from Iowa, November 2005

We study business organization and coordination of specialty-market hog production using a comparative analysis of two Iowa pork niche-marketing firms. We describe and analyze each firms management of five key organizational challenges: planning and logistics, quality assurance, process verication and management of �credence attributes,� business structure, and profit sharing. Although each firm is engaged in essentially the same activity, there are substantial differences across the two firms in the way production and marketing are coordinated. These differences are partly explained by the relative size and age of each firm, thus highlighting the importance of organizational evolution in agricultural markets, but are also partly the result of a formal organizational separation between marketing and production activities in one of the firm.

Business Service Training Annual Report, FY 2005

Annual Report

2005 Annual Report of the Iowa Communications Network

This is the Annual Report for Fiscal Year 2005 (July 1, 2004-June 30, 2005) for the Iowa Communications Network.

Iowa Communications Network Performance Report, FY 2004

Agency Performance Report

Iowa Communications Network Performance Report, FY 2005

Agency Performance Report

Iowa Department of Transportation Report of Savings through use of Iowa Communications Network (ICN), FY 2005

Iowa DOT savings through use of Iowa Communications Network (ICN) videoconferencing

Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Final Report for the Iowa Livestock Industry Waste Characterization and Methane Recovery Information Dissemination Project, 2003

The Iowa livestock industry generates large quantities of manure and other organic residues; composed of feces, urine, bedding material, waste feed, dilution water, and mortalities. Often viewed as a waste material, little has been done to characterize and determine the usefulness of this resource. The Iowa Department of Natural Resources initiated the process to assess in detail the manure resource and the potential utilization of this resource through anaerobic digestion coupled with energy recovery. Many of the pieces required to assess the manure resource already exist, albeit in disparate forms and locations. This study began by interpreting and integrating existing Federal, State, ISU studies, and other sources of livestock numbers, housing, and management information. With these data, models were analyzed to determine energy production and economic feasibility of energy recovery using anaerobic digestion facilities on livestock faxms. Having these data individual facilities and clusters that appear economically feasible can be identified specifically through the use of a GIs system for further investigation. Also livestock facilities and clusters of facilities with high methane recovery potential can be the focus of targeted educational programs through Cooperative Extension network and other outreach networks, providing a more intensive counterpoint to broadly based educational efforts.

The HVEM network: new directions in targeting novel costimulatory/co-inhibitory molecules for cancer therapy.

The regulation of the immune system is controlled by many cell surface receptors. A prominent representative is the 'molecular switch' HVEM (herpes virus entry mediator) that can activate either proinflammatory or inhibitory signaling pathways. HVEM ligands belong to two distinct families: the TNF-related cytokines LIGHT and lymphotoxin-α, and the Ig-related membrane proteins BTLA and CD160. HVEM and its ligands have been involved in the pathogenesis of various autoimmune and inflammatory diseases, but recent reports indicate that this network may also be involved in tumor progression and resistance to immune response. Here we summarize the recent advances made regarding the knowledge on HVEM and its ligands in cancer cells, and their potential roles in tumor progression and escape to immune responses. Blockade or enhancement of these pathways may help improving cancer therapy.

Measurement with Some Theory: a New Approach to Evaluate Business Cycle Models

We propose a method to evaluate cyclical models which does not require knowledge of the DGP and the exact empirical specification of the aggregate decision rules. We derive robust restrictions in a class of models; use some to identify structural shocks and others to evaluate the model or contrast sub-models. The approach has good size and excellent power properties, even in small samples. We show how to examine the validity of a class of models, sort out the relevance of certain frictions, evaluate the importance of an added feature, and indirectly estimate structural parameters.

L'adolescent consommateur de substances face au réseau de soins [Substance abuse adolescents and the health care network]

Substance user adolescents were asked to report on each contact they had had with any type of care providers since they had begun to use alcohol or illegal drugs regularly. Primary care doctors and social workers represent the main access to the care network. In one out of two contacts substance use was not discussed.