Coupled Information Diffusion–Pest Dynamics Models Predict Delayed Benefits of Farmer Cooperation in Pest Management Programs

Worldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' “diffusion of innovation theory”. In particular, the success of integrated pest management (IPM) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. Here we developed an innovative approach through an agent-based model (ABM) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest. The model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the Ecuadorian Andes. Our results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. However, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in IPM diffusion and applications. We further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. Consistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs. This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations

Complex processing of primary aluminum to remove impurities of non-ferrous metals

The paper presents researches on the complex processing of primary aluminum produced by electrolysis with flux treatment with boron-containing materials and further filtration refining through granular filters. The research methods and applied instruments used for the analysis of the chemical composition and electron microscopy are described. The results are presented before and after the complex treatment of primary aluminum, showing a significant reduction in non-ferrous metal impurities (vanadium, titanium, etc.) and other undesirable impurities

The Filling of Gaps in Geophysical Time Series by Artificial Neural Networks

From the 17th International Radiocarbon Conference held in Jerusalem, Israel, June 18-23, 2000.Nowadays, there is a large number of time series of natural data to study geophysical and astrophysical phenomena and their characteristics. However, short length and data gaps pose a substantial problem for obtaining results on properties of the underlying physical phenomena with existing algorithms. Using only an equidistant subset of the data with coarse steps leads to loss of information. We present a method to recover missing data in time series. The approach is based on modeling the time series with manifolds of small dimension, and it is implemented with the help of neural networks. We applied this approach to real data on cosmogenic isotopes, demonstrating that it could successfully repair gaps where data was purposely left out. Multi-fractal analysis was applied to a true radiocarbon time series after recovering missing data.The Radiocarbon archives are made available by Radiocarbon and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform February 202