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By implementing various forms of preference policies, countries around the world intervene in their economies for their own political and economic purposes. Likewise, twenty-five states in the U.S. have implemented in-state preference policies (NASPO, 2012) to protect and support their own vendors from out-of-state competition to achieve similar purposes. The purpose of this paper is to show the connection between protectionist public policy instruments noted in the international trade literature and the in-state preference policies within the United States. This paper argues that the reasons and the rationales for adopting these preference policies in international trade and the states' contexts are similar. Given the similarity in policy outcomes, the paper further argues that the international trade literature provides an overarching explanation to help understand what states could expect in applying in-state preference policies.

The purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots.

In this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics.

First, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results.

Based on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

This paper examines how voters' initiatives used in 24 states affect state budget balance. The author first uses a qualitative approach to investigate the extent to which ballot initiatives are used and identifies the initiatives that have had significant impacts on state revenues and expenditures. The review shows that the impact of initiatives differs from a state to state. The heavy initiative user states have experienced substantial impact on their budgets. Second, as their impact on state budget is particularly significant during economic downturn, a linear regression analysis is performed to examine the relationship between the use of initiatives and state budget balance measured in terms of the state budget gap as percentage of FY 2010 general fund. The regression analysis shows that the number of expenditure-induced initiatives have a statistically significant effect on state budget gap, while revenue-restrained measures (e.g. number of tax-limiting measures and the use of super majority or popular vote to approve tax increase) only marginally affect the FY 2010 budget gap.

The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.

A control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (D_O) concentration and nitrate nitrogen (S_No) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.

By application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.

The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.

The use of public procurement as a vehicle for implementing various socioeconomic preference policies has a long history. This article reviews the current state of affairs of procurement preference programs with regard to U.S. state and local governments and analyzes their impact on both the recipients and on the public procurement process. Opportunities for further research are noted, and the authors conclude that the ability to navigate the difficult waters of socioeconomic preferences should be a core competency of state and local public procurement officials.

Public agencies have started to shift away from the traditional lowest responsive and responsible bid to other approaches in purchasing certain items and services. These alternative approaches emphasize the quality of the products and the qualifications of the vendors. The purpose of this article is to explore the use qualifications-based selection (QBS) and other non-traditional source selection methods in public procurement processes. An online survey was sent out to 1665 members of the National Institute of Governmental Purchasing, and a mail survey sent a random sample to 300 American Public Works Association members. The survey results show that while the traditional lowest responsive and responsible bidding is still the dominant selection method when all procurement is considered, QBS and other non-traditional methods have gained wide acceptance and use in public agencies, especially for the purchase of professional services and information technology.

Project bundling is an innovative practice that groups or bundles several infrastructure projects into a single contract. While project bundling has various benefits, agencies are facing some challenges when bundling their projects, including properly assessing the feasibility (or infeasibility) of project delivery methods (PDMs) of interest. More specifically, project owners face the challenge of properly selecting between traditional and alternative PDMs for their bundled projects. Although some research efforts were devoted to providing guidelines in relation to different aspects related to project bundling, no previous study was conducted to help project owners performing PDMs-related feasibility analysis for bundled projects, which differ from normal, singly delivered projects. To fill this knowledge gap, this paper develops a decision-support tool that assists agencies in deciding whether they should select a traditional or alternative PDM (i.e. whether to go with the Design-Bid-Build (DBB) PDM or not) for their bundled projects.

An analytical methodology comprised of four main steps was followed in this paper. First, an expert survey was developed and distributed to industry experts to quantify the importance of 25 project bundling objectives. Second, principal component analysis was used to determine the weights for the different project bundling objectives. Third, a series of statistical tests was implemented to identify different feasibility tiers. Fourth, a user-friendly decision-support tool was developed, and its capabilities were demonstrated.

The results showed that six tiers exist to classify the feasibility (or infeasibility) of traditional PDMs (i.e. the DBB method) for bundled projects. The research outcomes have also reflected that the following five project bundling objectives contribute the most to making traditional PDMs (i.e. the DBB method) more feasible for bundled projects: (1) Having well-defined design features; (2) Requiring prior knowledge or experience with similar project size and scope; (3) Completing the overall project on schedule; (4) Keeping rate of expenditures within cash flow plan; and (5) Acquiring specific legislative, regulatory and jurisdictional requirements early on.

This research adds to the body of knowledge by equipping agencies and project owners with a decision-support system that helps them identify whether traditional or alternative PDMs are more appropriate for the specific objectives of their bundling program(s). By making the right PDM decision, project owners can enhance their bundling practices (especially in relation to the PDM proper selection) and ultimately the performance of their bundled projects.

Public risk management is a relatively new but important element of public management and public budgeting. As research in this area is limited, this study attempts to advance knowledge on two specific elements of public risk management based on a survey sent to the Public Risk Management Association (PRIMA) members in 2002. 1) How do public entities use various risk funding techniques (e.g., purchasing insurance, self-insurance, and intergovernmental risk pools)? 2) Have public entities implemented integrated risk management in their risk management practices? The survey found evidence that integrated risk management is emerging in public risk management practice. As this is an exploratory study, the author also identifies a series of questions for future research.

This paper uses the Missouri Fiscal Year (FY) 2004 budget as a case study to illustrate two aspects of the recent state budgetary problems: its structural budget deficits and the politics involved in balancing a budget. The paper also highlights the dilemma that government faces in meeting constant public demand for services while revenue sources are restrained.

This article discusses interstate fiscal disparities in the context of welfare reform. It presents the trend of interstate fiscal disparities during 1970-1996 and highlights the need to study how the welfare reform of 1996 affects interstate variances in welfare support.