The Rise of Community-Based Natural Resource Management Strategies as Explained by Transaction Costs

Community-based natural resource management (CBNRM) is favored over its predecessor, the fortress approach, for common pool resource (CPR) management. We strive to identify variables associated with successful CBNRM programs, and analyze whether their presence transaction costs can explain the shift in favored conservation strategy. By examining nine case studies of large mammals in Africa, we found that out of eight variables, moderate monitoring and adequate program incentives were the most critical various factors in determining CBNRM program outcomes. Furthermore, these variables, as well as others, contributed to a decrease in transaction costs

Associations of neighborhood characteristics with active park use : an observational study in two cities in the USA and Belgium

Background: Public parks can be an important setting for physical activity promotion, but to increase park use and the activity levels of park users, the crucial attributes related to active park use need to be defined. Not only user characteristics and structural park attributes, but also characteristics of the surrounding neighborhood are important to examine. Furthermore, internationally comparable studies are needed, to find out if similar intervention strategies might be effective worldwide. The main aim of this study was to examine whether the overall number of park visitors and their activity levels depend on study site, neighborhood walkability and neighborhood income. Methods: Data were collected in 20 parks in Ghent, Belgium and San Diego, USA. Two trained observers systematically coded park characteristics using the Environmental Assessment of Public Recreation Spaces (EAPRS) tool, and park user characteristics using the System for Observing Play and recreation in Communities (SOPARC) tool. Multilevel multiple regression models were conducted in MLwiN 2.25. Results: In San Diego parks, activity levels of park visitors and number of vigorously active visitors were higher than in Ghent, while the number of visitors walking and the overall number of park visitors were lower. Neighborhood walkability was positively associated with the overall number of visitors, the number of visitors walking, number of sedentary visitors and mean activity levels of visitors. Neighborhood income was positively associated with the overall number of visitors, but negatively with the number of visitors being vigorously active. Conclusions: Neighborhood characteristics are important to explain park use. Neighborhood walkability-related attributes should be taken into account when promoting the use of existing parks or creating new parks. Because no strong differences were found between parks in high-and low-income neighborhoods, it seems that promoting park use might be a promising strategy to increase physical activity in low-income populations, known to be at higher risk for overweight and obesity

Burstiness and Stochasticity in the Malleability of Physical Activity

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability

Cyclone Boiler Field Testing of Advanced Layered NOx Control Technology in Sioux Unit 1

A four week testing program was completed during this project to assess the ability of the combination of deep staging, Rich Reagent Injection (RRI), and Selective Non-Catalytic Reduction (SNCR) to reduce NOx emissions below 0.15 lb/MBtu in a cyclone fired boiler. The host site for the tests was AmerenUE's Sioux Unit 1, a 500 MW cyclone fired boiler located near St. Louis, MO. Reaction Engineering International (REI) led the project team including AmerenUE, FuelTech Inc., and the Electric Power Research Institute (EPRI). This layered approach to NOx reduction is termed the Advanced Layered Technology Approach (ALTA). Installed RRI and SNCR port locations were guided by computational fluid dynamics (CFD) based modeling conducted by REI. During the parametric testing, NOx emissions of 0.12 lb/MBtu were achieved consistently from overfire air (OFA)-only baseline NOx emissions of 0.25 lb/MBtu or less, when firing the typical 80/20 fuel blend of Powder River Basin (PRB) and Illinois No.6 coals. From OFA-only baseline levels of 0.20 lb/MBtu, NOx emissions of 0.12 lb/MBtu were also achieved, but at significantly reduced urea flow rates. Under the deeply staged conditions that were tested, RRI performance was observed to degrade as higher blends of Illinois No.6 were used. NOx emissions achieved with ALTA while firing a 60/40 blend were approximately 0.15 lb/MBtu. NOx emissions while firing 100% Illinois No.6 were approximately 0.165 lb/MBtu. Based on the performance results of these tests, economics analyses of the application of ALTA to a nominal 500 MW cyclone unit show that the levelized cost to achieve 0.15 lb/MBtu is well below 75% of the cost of a state of the art SCR

A Markov Approach for Increasing Precision in the Assessment of Data-Intensive Behavioral Interventions

Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals’ responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available. The procedure projects a univariate time-series into discrete states and empirically determines the probability of transitioning from one state to another. State transition probabilities are summarized separately in phase-specific transition matrices. Comparing transition matrices illuminates intricate, quantifiable differences in behavior between intervention phases. Statistical significance is estimated via bootstrapping techniques. This paper introduces the methodology via three case studies from a secondhand smoke reduction trial utilizing real-time air particle sensors. Analysis enabled the identification of complex phenomena such as avoidance and escape behavior in response to punitive contingencies for tobacco use. Additionally, the largest changes in behavior dynamics were associated with the introduction of behavioral feedback. The Markov approach‘s ability to elucidate subtle behavioral details has not typically been feasible with standard methodologies, mainly due to historical limitations associated with infrequent repeated measures. These results suggest that the evaluation of intervention effects in data-intensive single-case designs can be enhanced, providing rich information that can ultimately be used to develop interventions uniquely tailored to specific individuals

Variable Magnitude and Frequency Financial Reinforcement is Effective at Increasing Adults’ Free-Living Physical Activity

Financial rewards can increase health behaviors, but little research has quantified the effects of different reinforcement schedules on this process. This analysis compares the average moderate-to-vigorous physical activity (MVPA) associated with six distinct positive reinforcement schedules implemented within a physical activity promotion clinical trial. In this trial, participants (N = 512) wore an accelerometer for 1 year and were prescribed one of two types of MVPA goals: a static 30-min goal or an adaptive goal based on the MVPA produced over the previous 9 days. As participants met goals, they transitioned through a sequence of reinforcement stages, beginning with a continuous-fixed magnitude (CRF-FM), then CRF-variable magnitude (CRF-VM), followed by a series of variable ratio-VM (VR-VM) schedules. The average accumulation of MVPA bouts over the last 24 days of each stage was compared to each other. Average MVPA during stage transitions was also examined. The results indicated that immediate reinforcement resulted in more MVPA relative to a comparison group and that the relative effectiveness of adaptive versus static goals was dependent on the magnitude of daily MVPA goals. Schedule effects were qualitatively different for individuals who frequently met their daily goals (Large Intervention Effect subgroup) versus those who did not (Small Intervention Effect subgroup). For the Large Intervention Effect group, the CRF-VM schedule produced the most MVPA, in particular within the adaptive goal condition, with increases observed immediately upon encountering this schedule. In contrast, the CRF-FM schedule produced small amounts of MVPA. This pattern was reversed for the Small Intervention Effect subgroup, where the most MVPA was associated with the CRF-FM stage. Future interventions should focus on discriminating small versus large intervention effects as quickly as possible so that the optimal reinforcement schedule can be used

Rationale, Design, and Baseline Characteristics of WalkIT Arizona: A Factorial Randomized Trial Testing Adaptive Goals and Financial Reinforcement to Increase Walking Across Higher and Lower Walkable Neighborhoods

Little change over the decades has been seen in adults meeting moderate-to-vigorous physical activity (MVPA) guidelines. Numerous individual-level interventions to increase MVPA have been designed, mostly static interventions without consideration for neighborhood context. Recent technologies make adaptive interventions for MVPA feasible. Unlike static interventions, adaptive intervention components (e.g., goal setting) adjust frequently to an individual\u27s performance. Such technologies also allow for more precise delivery of “smaller, sooner incentives” that may result in greater MVPA than “larger, later incentives”. Combined, these factors could enhance MVPA adoption. Additionally, a central tenet of ecological models is that MVPA is sensitive to neighborhood environment design; lower-walkable neighborhoods constrain MVPA adoption and maintenance, limiting the effects of individual-level interventions. Higher-walkable neighborhoods are hypothesized to enhance MVPA interventions. Few prospective studies have addressed this premise. This report describes the rationale, design, intervention components, and baseline sample of a study testing individual-level adaptive goal-setting and incentive interventions for MVPA adoption and maintenance over 2 years among adults from neighborhoods known to vary in neighborhood walkability. We scaled these evidenced-based interventions and tested them against static-goal-setting and delayed-incentive comparisons in a 2 × 2 factorial randomized trial to increase MVPA among 512 healthy insufficiently-active adults. Participants (64.3% female, M age = 45.5 ± 9.1 years, M BMI = 33.9 ± 7.3 kg/m2, 18.8% Hispanic, 84.0% White) were recruited from May 2016 to May 2018 from block groups ranked on GIS-measured neighborhood walkability and socioeconomic status (SES) and classified into four neighborhood types: “high walkable/high SES,” “high walkable/low SES,” “low walkable/high SES,” and “low walkable/low SES.” Results from this ongoing study will provide evidence for some of the central research questions of ecological models

Lattice Glass Models

Motivated by the concept of geometrical frustration, we introduce a class of statistical mechanics lattice models for the glass transition. Monte Carlo simulations in three dimensions show that they display a dynamical glass transition which is very similar to that observed in other off-lattice systems and which does not depend on a specific dynamical rule. Whereas their analytic solution within the Bethe approximation shows that they do have a discontinuous glass transition compatible with the numerical observations.Comment: 4 pages, 2 figures; minor change