Report of the Section of Agencies and Institutions Division of Child Welfare State Department of Social Welfare, 1943

History of child and social welfare in the State of Iowa including legal responsibilities, rules and regulations, inspections and licensing and supervision. it also covers cooperation with state agencies. It also describes in detail the histories, functions and problems of individual welfare homes and schools.

Iowa Autism Council By-Laws and Operating Procedures, Revised 2012

These By-laws and Operating Procedures are designed to guide the membership and work of the Iowa Autism Council. The Iowa Autism Council is a collaborative resource that envisions its role as an advocate for children and adults living with Autism Spectrum Disorder (autism and Asperger's and other conditions represented on the autism spectrum) and their families. As such, it is committed to representing individuals with diverse and changing educational needs. Iowa Autism Council (IAC) shall have, and will perform, functions and duties as specified by law. Responsibilities include offering advice, consultation, and recommendations to Governor Culver and the Iowa legislature regarding matters concerning the ASD population. The role of the Council members is to advise, not advocate, for an individual position. Advise means to inform, counsel, recommend, suggest or guide. To advocate means to plead for your case or position, to favor an individual case or argument. The advisory Council is to provide advice, based on facts and good judgment.

Integration of Bridge Damage Detection Concepts and Components (3 volumes), TR-636, 2013

This work is divided into three volumes: Volume I: Strain-Based Damage Detection; Volume II: Acceleration-Based Damage Detection; Volume III: Wireless Bridge Monitoring Hardware. Volume I: In this work, a previously-developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. The statistical damage-detection tool, control-chart-based damage-detection methodologies, were further investigated and advanced. For the validation of the damage-detection approaches, strain data were obtained from a sacrificial specimen attached to the previously-utilized US 30 Bridge over the South Skunk River (in Ames, Iowa), which had simulated damage,. To provide for an enhanced ability to detect changes in the behavior of the structural system, various control chart rules were evaluated. False indications and true indications were studied to compare the damage detection ability in regard to each methodology and each control chart rule. An autonomous software program called Bridge Engineering Center Assessment Software (BECAS) was developed to control all aspects of the damage detection processes. BECAS requires no user intervention after initial configuration and training. Volume II: In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. The objective of this part of the project was to validate/integrate a vibration-based damage-detection algorithm with the strain-based methodology formulated by the Iowa State University Bridge Engineering Center. This report volume (Volume II) presents the use of vibration-based damage-detection approaches as local methods to quantify damage at critical areas in structures. Acceleration data were collected and analyzed to evaluate the relationships between sensors and with changes in environmental conditions. A sacrificial specimen was investigated to verify the damage-detection capabilities and this volume presents a transmissibility concept and damage-detection algorithm that show potential to sense local changes in the dynamic stiffness between points across a joint of a real structure. The validation and integration of the vibration-based and strain-based damage-detection methodologies will add significant value to Iowa’s current and future bridge maintenance, planning, and management Volume III: In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. This report volume (Volume III) summarizes the energy harvesting techniques and prototype development for a bridge monitoring system that uses wireless sensors. The wireless sensor nodes are used to collect strain measurements at critical locations on a bridge. The bridge monitoring hardware system consists of a base station and multiple self-powered wireless sensor nodes. The base station is responsible for the synchronization of data sampling on all nodes and data aggregation. Each wireless sensor node include a sensing element, a processing and wireless communication module, and an energy harvesting module. The hardware prototype for a wireless bridge monitoring system was developed and tested on the US 30 Bridge over the South Skunk River in Ames, Iowa. The functions and performance of the developed system, including strain data, energy harvesting capacity, and wireless transmission quality, were studied and are covered in this volume.

Report of the Governmental Reorganization Commission to Governor William S. Beardsley for Submission to the Fifty-fourth General Assembly,1950

This report deals with the reorganization of the government of Iowa. The Fifty-third General Assembly established and authorized this commission to examine and recommend changes in all state agencies in order to accomplish the following purposes : a reduction of expenditures and the promotion of economies; an increase in efficiency of governmental operations; a coordination and consolidation of judicial districts; a reduction of agencies by a consolidation of those having similar functions; and an elimination of overlappoing and duplicating of efforts on the part of existing agencies.

Executive Order Number Seventeen, September 25, 2009

This Executive Order expects the State of Iowa executive agencies and Regent Institutions own, purchase, build and lease property, including office space, worth billions of dollars for educational, correctional, recreational, conservation, transportation, communication, public health, workforce and related public functions and the placement of state offices in historic central business districts "downtown" can strengthen and revitalize Iowa's cities and towns.

Iowa Department of Transportation Performance Results Achieved for Fiscal Year 2009

The DOT monitors performance of five core functions, under which are eight services, products and activities (SPAs). In all, 56 measures are used to monitor the core functions and SPAs in the DOT’s performance plan. (See Iowa DOT Performance Report – FY2009, pages 1A-9A.) Overall, DOT’s performance was good in fiscal year 2009. Of the 56 measures in the DOT’s performance plan, 43 measures (77%) met or exceeded their targets. Of the 13 measures falling short, ten were within four percent of their target. This means 95 percent of DOT measures met or exceeded 96 percent of their preset target. Performance measures monitoring the core functions of Physical Asset Management and Resource Management showed the DOT did a good job managing resources. A total of 11 of the 14 (79%) core function and SPA measures met or exceeded their predetermined targets. Two of the three measures falling short were within four percent of its target. Core function and SPA measures within the Transportation Systems core function indicated good performance. A total of 22 of the 30 (73%) core function and SPA measures met or exceeded their predetermined targets. Overall, six of the eight measures falling short were within four percent of their target. Performance measures monitoring the core functions of Enforcement and Investigation and Regulation and Compliance showed the DOT performed well. A total of 10 of the 12 (83%) core function and SPA measures met or exceeded their predetermined targets. Both of the measures falling short were within four percent of their target.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.