Economic analysis of water temperature reduction practices in a large river floodplain: an exploratory study of the Willamette River,Oregon

This paper examines ecosystem restoration practices that focus on water temperature reductions in the upper mainstem Willamette River, Oregon, for the benefit of endangered salmonids and other native cold‐water species. The analysis integrates hydrologic, natural science and economic models to determine the cost‐effectiveness of alternative water temperature reduction strategies. A temperature model is used to simulate the effects of combinations of upstream riparian shading and flow augmentations on downstream water temperatures. Costs associated with these strategies are estimated and consist of the opportunity costs of lost agricultural production and recreation opportunities due to flow releases from an up‐stream reservoir. Temperature reductions from another strategy, hyporheic flow enhancement, are also examined. Restoration strategies associated with enhanced hyporheic cooling consist of removal/reconnection of current obstacles to the creation of dynamic river channel complexity. The observed reduction of summer water temperatures associated with enhanced channel complexity indicates that restoring hyporheic flow processes is more likely to achieve cost‐effective temperature reductions and meet the total maximum daily load (TMDL) target than conventional approaches that rely on increased riparian shading or/and combinations of flow augmentation. Although the costs associated with the hyporheic flow enhancement approach are substantial, the effects of such a long‐term ecological improvement of the floodplain are expected to assist the recovery of salmonid populations and provide ancillary benefits to society. Copyright © 2008 John Wiley & Sons, Ltd.     

Addressing the information gaps associated with valuing green infrastructure in west Michigan: INtegrated Valuation of Ecosystem Services Tool (INVEST)

Land use change is profoundly influencing the environmental resources of the Great Lakes region. In West Michigan, population growth and rapid land use change have resulted in urbanization and a loss in the diversity of green infrastructure that provides a variety of benefits–ecosystem services–to regional residents and visitors. To address these changes in land use and cover in ways that are objective, transparent, and defensible, local policy makers need tools to help inform local and regional land use decisions. An interdisciplinary team, engaged by a regional policy organization, developed one such online tool, the INtegrated Valuation of Ecosystem Services Tool (INVEST). INVEST utilizes static GIS maps, graphs, and tables, to help educate local and regional decision-makers about the underlying values of ecosystem services associated with green infrastructure, particularly those services that do not pass through traditional commercial markets. In this article we provide a general overview of the development of INVEST and examine its current application as an educational tool, including the specific challenges associated with addressing the information gaps.     

A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting

A hybrid machine learning is a combination of multiple types of machine learning algorithms for improving the performance of single classifiers. Currently, cyber intrusion detection systems require high-performance methods for classifications because attackers can develop invasive methods and evade the detection tools. In this paper, the cyber intrusion detection architecture based on new hybrid machine learning is proposed for multiple cyber intrusion detection. In addition, the correlation-based feature selection is adopted for reducing the irrelevant features and the weight vote of adaptive boosting that is adopted to combine multiple classifiers is concentrated. In the experiments, UNB-CICT or network traffic dataset is used for evaluating the performance of the proposed method. The results show that the proposed method can achieve higher efficiency in every attack type detection. Furthermore, the experiments with Phishing website dataset UNSW-NB 15 dataset NSL-KDD dataset and KDD Cup’99 dataset are also conducted, and the results show that the proposed method can produce higher efficiency as well.     

Exploring market-based environmental policy for groundwater management and ecosystem protection for the Great Lakes region: Lessons learned

The Great Lakes–St. Lawrence River Basin Water Resources Compact (the Compact) was created to protect future water supplies and aquatic ecosystems in the Great Lakes. The Compact requires the eight Great Lakes state to regulate, among other things, large withdrawals of groundwater and surface water so that they do not negatively affect stream flows and ecosystems within the Great Lakes Basin. Thus, the Compact raises the possibility of increased restrictions on groundwater withdrawals in many locations throughout the Great Lakes region. However, restricting withdrawals is likely to encounter opposition from water users when such restrictions are viewed as an infringement on existing water use rights and/or as negatively impacting local economic development. Such conflicts could hinder effective implementation of state and regional water policy. This paper explores the application of a market-based environmental management tool called “Conservation Credit Offsets Trading (CCOT)” that could facilitate allocation of groundwater withdrawals, and develops a framework for guiding the implementation of CCOT within the context of a groundwater permitting system. Using a watershed in southwestern Michigan, this study demonstrates how bio-physical information and input from various local stakeholders were combined to aid groundwater policy designed to achieve the objective of no net (adverse) impact on stream ecosystems. By allowing flexibility through trading of conservation credit offsets, this groundwater policy tool appears to be more politically acceptable than traditional, less flexible, regulations. The results and discussion provide useful lessons learned with relevance to other areas in the Great Lakes Basin.     

Highly Flexible Tribovoltaic Nanogenerator Based-on P-N Junction Interface: Comparative Study on Output Dependency Dominated by Photovoltaic Effect in Freestanding-Mode

The emergence of tribovoltaic nanogenerators (TVNGs) paves the way for developing a new kind of semiconductor-based energy harvester that overcomes the restriction of low output current in a conventional approach. The traditional TVNG generally depends on the frictional pair between two rigid semiconductors (or metal-semiconductor), limiting the practicability of flexible and portable electronics. Recent developments require the fundamental understanding of charge generation in diverse operating modes and structures. Here, a flexible TVNG based on the p-Cu₂O/n-g-C₃N₄ interface is presented. Operating in a freestanding mode, the proposed TVNG can generate a stable signal in any optical conditions including UV illumination, dark, and ambient. Under UV illumination, the electrical outputs of the TVNG reach 0.43 V and 2.1 µA cm⁻², which are significantly larger than those obtained from dark and ambient conditions. The results demonstrate the coupling effect of three phenomena: tribovoltaic, photovoltaic, and triboelectric effects, and the unique mechanism to the observed signal is proposed. Additionally, the TVNG shows the practical feasibility of energy harvesting with capacitor charging and charge-boosting circuits. This study showcases the unique concept with potential for developing a novel flexible nanogenerator in many aspects, including material, structure, and fundamental mechanism.