Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of ^131mXe, ¹³³Xe, ^133mXe, and ¹³⁵Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.     

Impacts of Surface Gold Mining on Land Use Systems in Western Ghana

Land use conflicts are becoming increasingly apparent from local to global scales. Surface gold mining is an extreme source of such a conflict, but mining impacts on local livelihoods often remain unclear. Our goal here was to assess land cover change due to gold surface mining in Western Ghana, one of the world’s leading gold mining regions, and to study how these changes affected land use systems. We used Landsat satellite images from 1986–2002 to map land cover change and field interviews with farmers to understand the livelihood implications of mining-related land cover change. Our results showed that surface mining resulted in deforestation (58%), a substantial loss of farmland (45%) within mining concessions, and widespread spill-over effects as relocated farmers expand farmland into forests. This points to rapidly eroding livelihood foundations, suggesting that the environmental and social costs of Ghana’s gold boom may be much higher than previously thought.     

基于FA-BAS-ELM的海洋油气管道外腐蚀速率预测

为提高海洋油气管道外腐蚀速率预测的精度和效率,建立基于因子分析(FA)和天牛须搜索算法(BAS)的极限学习机(ELM)腐蚀速率预测模型。利用FA对影响因素数据集进行降维处理,确定预测模型的输入变量;建立ELM预测模型,并采用BAS对ELM模型的参数进行优化,避免参数取值随机性对模型预测性能的影响;以实海挂片试验为例,通过建模仿真评价模型的预测性能,并与其他模型进行对比分析。结果表明：FA-BAS-ELM预测模型的平均绝对误差(MAPE)仅为1.92%,决定系数R²高达0.994 9,相比于其他模型,该模型具有更优的预测性能。     

Design and evaluation of a local analytic-deliberative process for climate adaptation planning

In the midst of rapidly proliferating engagement efforts around climate adaptation, attention to the design and evaluation of decision support processes and products is warranted. We report on the development and evaluation of a process framework called the Vulnerability, Consequences, and Adaptation Planning Scenarios (VCAPS) process. VCAPS is a systematic approach to integrate local knowledge with scientific understanding by providing opportunities for facilitated, deliberative learning-based activities with local decision makers about climate change vulnerability and adaptation. We introduce the conceptual basis of the process in analytic-deliberation, hazard management, and vulnerability. Our evaluations from eight coastal communities where the approach was applied point to four assets of VCAPS: it promotes synthesis of local and scientific knowledge; it stimulates systems thinking and learning; it facilitates governance by producing action plans with transparent justifications; and it accommodates participant time constraints and preferences.     

Not by nutrients alone: a call to broaden the soil fertility initiative

The article discusses agricultural practices in sub-Saharan Africa, especially in terms of maintaining soil fertility. It disagrees with alarmist tendencies in the current debate, exaggerating the state of soil impoverishment in the African region. Nor does it subscribe to the idea that the remedy lies primarily in a massive influx of chemical fertilisers, especially phosphorus.
Instead, the article advocates a more balanced approach, where soil nutrient replenishment, as an improvement in natural capital, is but one part of the investment African agriculture needs. To achieve sustainable rural livelihoods, other forms of capital are also required, such as physical capital, i.e. infrastructure (roads, means of communication); financial capital in the form of credit and savings schemes; and human- and social capital, especially improved access to information and knowledge for farmers as well as improved local organisation and empowerment.
The article reviews nine case studies, taken from different climate zones in the region and representing different soil conditions. All case studies were projects using the participatory learning and action research (PLAR) method, which is discussed in detail.     

Sustainability indicator system and policy processes in Malaysia: a framework for utilisation and learning

Formulation of effective sustainability indicators for national assessment demands a comprehensive understanding of the utilisation, diffusion and dissemination of information in policy processes. To illustrate the dynamic of sustainability assessment within the context of policy processes, this paper uses a case study of national sustainability indicators development in Malaysia. Subsequently, this paper ascribes the limited achievement of national sustainability assessment in Malaysia to four types of constraints: meta-policy issues; technical capacities; communication concerns; and the inherent knowledge gaps within the indicator developer community vis-a-vis their theoretical limitations. It is proposed that such constraints will be encountered in many countries. Drawing from the literature on public policy, this paper outlines a framework for investigating indicator behaviour within policy processes based on well-established concepts such as knowledge utilisation and policy learning. I conclude this paper by elaborating on the corresponding future challenges that must be addressed before effective integration of sustainability indicators within policy systems can occur.     

Enhanced learning from multi-stakeholder partnerships: Lessons from the Enabling Rural Innovation in Africa programme

Despite increasing interest and support for multi‐stakeholder partnerships, empirical applications of participatory evaluation approaches to enhance learning from partnerships are either uncommon or undocumented. This paper draws lessons on the use of participatory self‐reflective approaches that facilitate structured learning on processes and outcomes of partnerships. Such practice is important to building partnerships, because it helps partners understand how they can develop more collaborative and responsive ways of managing partnerships. The paper is based on experience with the Enabling Rural Innovation (ERI) in Africa programme. Results highlight the dynamic process of partnership formation and the key elements that contribute to success. These include: (i) shared vision and complementarity, (ii) consistent support from senior leadership; (iii) evidence of institutional and individual benefits; (iv) investments in human and social capital; (v) joint resources mobilization. However, key challenges require coping with high staff turnover and over‐commitment, conflicting personalities and institutional differences, high transaction costs, and sustaining partnerships with the private business sector. The paper suggests that institutionalizing multi‐stakeholder partnerships requires participatory reflective practices that help structure and enhance learning, and incrementally help in building the capacity of research and development organisations to partner better and ultimately to innovate.     

高校固定资产管理新探

目前，高校资产管理工作在管理意识、管理方法和管理手段等方面都明显滞后，难以适应高校建设与发展的要求。切实加强高校固定资产管理，就要树立全新的固定资产管理理念，建立科学、合理的保障制度，充分发挥财会、审计部门的重要作用，实现资源共建和共享，同时也要建立可行的资产管理绩效考核制度。     

Organizational learning during policy implementation: lessons from U.S. forest planning

ABSTRACT

Learning is critical for land management agencies implementing new policies in the face of rapid social and ecological change. We investigated learning in the U.S. Forest Service as it implemented new planning regulations. Our research objectives were to: (1) identify collective learning processes and outcomes during this time, and (2) understand factors within the organization supporting or impeding learning. Based on participant observation and 25 interviews with planning personnel, we found evidence of collective learning on individual national forests and across the organization. Several factors helped the agency act as a ‘learning organization,’ including internal networks and tools for information sharing, and meetings for staff to exchange lessons learned. Learning was compromised by limited time and capacity, and lack of internal clarity about balancing the desire for innovation with the need to ensure legal compliance and meet deadlines. This work contributes to the empirical foundations of collective learning theory, allowing us to identify learning processes and outcomes at multiple levels in a public organization, and identifying topics for future research. Based on our exploration of organizational learning, we offer suggestions for how to effectively support learning during times of new policy implementation.     

The role of science-policy interface in sustainable urban water transitions: Lessons from Rotterdam

This paper investigates the role of the science-policy interface in leveraging transitions to sustainable urban water management. The paper presents a case study of the Dutch city of Rotterdam, which is increasingly regarded as a global leader in adaptive and resilient urban water management. The analysis reveals that Rotterdam’s transition has occurred incrementally over the past 15 years, driven by policy entrepreneurs: largely municipal policy makers and policy practitioners. Strategic use of the science-policy interface (SPI) has facilitated the development of innovative solutions to achieve policy goals and created the enabling conditions necessary for transformative change. The Rotterdam case suggests that an effective SPI requires: (1) compelling water narrative; (2) cross-sectoral collaboration; (3) co-production of knowledge; (4) experiential evidence-based learning; (5) strategic use of trusted scientists; (6) fostering networks; and (7) generating business from science-based innovation. Rotterdam’s strategic approach to knowledge and innovation coupled with a new narrative around water sets it apart from many other cities and adds a new dimension to debates regarding enabling factors for advancing sustainable practices. These findings will be of interest to those engaged in urban water management policy and practice, environmental governance, and debates over transitions more broadly.