scholarly journals Scale-related governance challenges in the water–energy–food nexus: toward a diagnostic approach

2020 ◽  
Author(s):  
Claudia Pahl-Wostl ◽  
Philipp Gorris ◽  
Nicolas Jager ◽  
Larissa Koch ◽  
Louis Lebel ◽  
...  
Keyword(s):  
Institutional Design ◽  
The Sustainable Development ◽  
Multi Scale ◽  
Space And Time ◽  
Trade Offs ◽  
Starting Point ◽  
Governance Challenges ◽  
Multi Level ◽  
Multiple Levels ◽  
Different Levels

AbstractThe notion of a water–energy–food (WEF) nexus was introduced to encourage a more holistic perspective on the sustainable development of natural resources. Most attention has been directed at identifying potential synergies and trade-offs among sectors that could be addressed with improved technologies and management. The governance of the WEF nexus more broadly has received comparatively little attention, and the importance of scale in space and time has been largely ignored. Inspired by scholarship on multi-level governance in individual sectors, this paper identifies four scale-related governance challenges in the WEF nexus, namely: (1) scalar fit, which arises when planning and operating procedures work at different levels along the scales of space and time in different sectors; (2) scalar strategies, wherever the levels at which actors have influence and in which action takes place are contested and negotiated; (3) institutional interplay, where rules and norms in different sectors influence each other at different levels; (4) scalar uncertainty, arising out of the complexity of multi-level and multi-scale interactions. The relevance of these four challenges is illustrated with case studies from developed and developing countries. These examples show the importance of considering multiple levels and scales when assessing the likely effectiveness of WEF nexus governance mechanisms or proposals. The cases underline the need to pay close attention to issues of power, contestation, and negotiation, in addition to the analysis of institutional design. Thus, this paper recommends that nexus governance efforts and proposals be scrutinized for scale assumptions. The four identified challenges offer a suitable starting point for diagnosis.

A Multi-Level Approach to Concept Selection in Sustainable Design

2014 ◽  
Author(s):  
Daniel Stratton ◽  
Sara Behdad ◽  
Kemper Lewis ◽  
Sundar Krishnamurty
Keyword(s):  
Environmental Sustainability ◽  
Utility Score ◽  
Concept Selection ◽  
Trade Offs ◽  
Design Alternatives ◽  
Multi Level ◽  
Early Phases ◽  
Different Levels ◽  
Selection Of ◽  
Lca Results

The motivation behind this work is to integrate economic and environmental sustainability into decision making at the early phases of design through the development of a hierarchical concept selection method. Life Cycle Assessment (LCA) is a frequently implemented technique used to assess the environmental impacts of products, but it does not provide a simple means for including preference at different levels that can be used for comparison across design alternatives. A method is proposed to accommodate this issue expanding the Hypothetical Equivalents and Inequivalents Method (HEIM) to handle multi-level and multi-attribute trade-offs. The selection of a coffee maker design is used as an example to illustrate the implementation of the method with actual LCA results. The example provides valuable insights into how preferences may be elicited at different hierarchical levels and then combined to create a single utility score that represents to what extent each design alternative is preferred by the decision maker.

Supporting the Management of Ecosystem Services in Protected Areas: Trade-Offs Between Effort and Accuracy in Evaluation

2017 ◽  
Vol 19 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Uta Schirpke ◽  
Rocco Scolozzi ◽  
Benedetta Concetti ◽  
Bruna Comini ◽  
Ulrike Tappeiner
Keyword(s):  
Ecosystem Services ◽  
Protected Areas ◽  
Spatial Scale ◽  
Well Being ◽  
Natura 2000 ◽  
Qualitative And Quantitative ◽  
Monetary Valuation ◽  
Trade Offs ◽  
Multiple Levels ◽  
Different Levels

Integrating ecosystem services (ES) into the management of protected areas, such as European Natura 2000 sites, can improve biodiversity conservation and human well-being; yet, the assessment and application of ES remains challenging. In this study, we propose a roadmap to guide managers in the assessment of ES at multiple levels, including a non-monetary valuation in qualitative and quantitative terms, as well as a monetary valuation, and suggesting the appropriate applications related to ES mapping, communication and planning. The roadmap proceeds through four steps and along a gradient of accuracy and effort required in the assessment methods, with different levels of spatial scale, to effectively support managers. Together with the description of the roadmap, this paper provides insights from its application to terrestrial Natura 2000 sites in Italy.

A Multi-Scale Materials Modeling Method With Seamless Zooming Capability Based on Surfacelets

2014 ◽  
Author(s):  
Wei Huang ◽  
Yan Wang ◽  
David W. Rosen
Keyword(s):  
Multiple Scales ◽  
Modeling Method ◽  
Regions Of Interest ◽  
Multi Scale ◽  
Materials Modeling ◽  
Separation Of Scales ◽  
Feature Based ◽  
Properties Of Materials ◽  
Multiple Levels ◽  
Different Levels

In multi-scale materials modeling, it is desirable that different levels of details can be specified in different regions of interest without the separation of scales so that the geometric and physical properties of materials can be designed and characterized. Existing materials modeling approaches focus on the representation of the distributions of material compositions captured from images. In this paper, a multi-scale materials modeling method is proposed to support interactive specification and visualization of material microstructures at multiple levels of details, where designer’s intent at multiple scales is captured. This method provides a feature-based modeling approach based on a recently developed surfacelet basis. It has the capability to support seamless zoom-in and zoom-out. The modeling, operation, and elucidation of materials are realized in both the surfacelet space and the image space.

scholarly journals Actions to Align Energy Projects With the Sustainable Development Goals

2020 ◽  
Author(s):  
Constança Martins Leite de Almeida ◽  
Elin Bergqvist ◽  
Scott Thacker ◽  
Francesco Fuso Nerini
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Scientific Literature ◽  
The Sustainable Development ◽  
2030 Agenda ◽  
Trade Offs ◽  
Starting Point ◽  
Energy Projects ◽  
Development Goals

Abstract The 2030 Agenda is an aspiring set of goals and targets that aims to prompt humanity towards a sustainable development by 2030. In order to achieve this, actions that mitigate trade-offs and enhance synergies within the Sustainable Development Goals (SDGs) need to be identified. However, for the energy sector these actions are dispersed across the scientific literature, which is a clear barrier to encourage practitioners to have a proactive and pragmatic approach towards the SDGs. For this reason, a set of actions for energy projects was compiled. This compilation addresses the synergies and trade-offs identified in the Sustainable Development Goals Impact Assessment Framework for Energy Projects (SDG-IAE). One case study was used to test the actions, the lighthouse Project VARGA. Subsequently, an analysis was conducted to understand how possible actions can impact different technologies, project stages, actors and SDG targets. In this way, enabling policymakers and project developers to define areas of action when evaluating policies or considering specific intervention. This article aims to be the starting point of stakeholder discussions that consistently frame energy projects within the achievement of the SDGs.

scholarly journals Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 32
Author(s):  
Gang Sun ◽  
Hancheng Yu ◽  
Xiangtao Jiang ◽  
Mingkui Feng
Keyword(s):  
Edge Detection ◽  
Loss Function ◽  
State Of The Art ◽  
Cross Entropy ◽  
Post Processing ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Different Levels ◽  
F Measure

Edge detection is one of the fundamental computer vision tasks. Recent methods for edge detection based on a convolutional neural network (CNN) typically employ the weighted cross-entropy loss. Their predicted results being thick and needing post-processing before calculating the optimal dataset scale (ODS) F-measure for evaluation. To achieve end-to-end training, we propose a non-maximum suppression layer (NMS) to obtain sharp boundaries without the need for post-processing. The ODS F-measure can be calculated based on these sharp boundaries. So, the ODS F-measure loss function is proposed to train the network. Besides, we propose an adaptive multi-level feature pyramid network (AFPN) to better fuse different levels of features. Furthermore, to enrich multi-scale features learned by AFPN, we introduce a pyramid context module (PCM) that includes dilated convolution to extract multi-scale features. Experimental results indicate that the proposed AFPN achieves state-of-the-art performance on the BSDS500 dataset (ODS F-score of 0.837) and the NYUDv2 dataset (ODS F-score of 0.780).

scholarly journals Actions to align energy projects with the Sustainable Development Goals

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Constança Martins Leite de Almeida ◽  
Elin Bergqvist ◽  
Scott Thacker ◽  
Francesco Fuso Nerini
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Scientific Literature ◽  
Pragmatic Approach ◽  
The Sustainable Development ◽  
2030 Agenda ◽  
Trade Offs ◽  
Starting Point ◽  
Energy Projects ◽  
Development Goals

AbstractThe 2030 Agenda is an aspiring set of goals and targets that aims to prompt humanity towards a sustainable development by 2030. In order to achieve this, actions that mitigate trade-offs and enhance synergies within the Sustainable Development Goals (SDGs) need to be identified. However, for the energy sector these actions are dispersed across the scientific literature, which is a clear barrier to encourage practitioners to have a proactive and pragmatic approach towards the SDGs. For this reason, a set of actions for energy projects was compiled. This compilation addresses the synergies and trade-offs identified in the Sustainable Development Goals Impact Assessment Framework for Energy Projects (SDG-IAE). One case of application was used to test the actions, the lighthouse Project VARGA. Subsequently, an analysis was conducted to understand how possible actions can impact different technologies, project stages, actors and SDG targets. In this way, enabling policymakers and project developers to define areas of action when evaluating policies or considering specific interventions. This article aims to be the starting point of stakeholder discussions that consistently frame energy projects within the achievement of the SDGs.

scholarly journals M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network

2019 ◽  
Vol 33 ◽  
pp. 9259-9266 ◽  
Author(s):  
Qijie Zhao ◽  
Tao Sheng ◽  
Yongtao Wang ◽  
Zhi Tang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Feature Fusion ◽  
State Of The Art ◽  
Single Shot ◽  
Multi Scale ◽  
One Stage ◽  
Single Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Multiple Levels ◽  
Inference Strategy

Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale variation across object instances. Although these object detectors with feature pyramids achieve encouraging results, they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale, pyramidal architecture of the backbones which are originally designed for object classification task. Newly, in this work, we present Multi-Level Feature Pyramid Network (MLFPN) to construct more effective feature pyramids for detecting objects of different scales. First, we fuse multi-level features (i.e. multiple layers) extracted by backbone as the base feature. Second, we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects. Finally, we gather up the decoder layers with equivalent scales (sizes) to construct a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels. To evaluate the effectiveness of the proposed MLFPN, we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD, and achieve better detection performance than state-of-the-art one-stage detectors. Specifically, on MSCOCO benchmark, M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are the new stateof-the-art results among one-stage detectors. The code will be made available on https://github.com/qijiezhao/M2Det.

scholarly journals Reinforcement Learning Experience Reuse with Policy Residual Representation

2019 ◽  
Author(s):  
WenJi Zhou ◽  
Yang Yu ◽  
Yingfeng Chen ◽  
Kai Guan ◽  
Tangjie Lv ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Video Game ◽  
State Of The Art ◽  
Learning Experience ◽  
Critical Issues ◽  
Experience Reuse ◽  
Multi Level ◽  
Multiple Levels ◽  
Multiple Granularities ◽  
Different Levels

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may require experience across multiple granularities. In this paper, we propose the policy residual representation (PRR) network, which can extract and store multiple levels of experience. PRR network is trained on a set of tasks with a multi-level architecture, where a module in each level corresponds to a subset of the tasks. Therefore, the PRR network represents the experience in a spectrum-like way. When training on a new task, PRR can provide different levels of experience for accelerating the learning. We experiment with the PRR network on a set of grid world navigation tasks, locomotion tasks, and fighting tasks in a video game. The results show that the PRR network leads to better reuse of experience and thus outperforms some state-of-the-art approaches.

Inventory Classification Using Multi-Level Association Rule Mining

2019 ◽  
Vol 11 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Levels Of Abstraction ◽  
Frequent Items ◽  
Multi Level ◽  
Multiple Levels ◽  
Real Scenario ◽  
The Impact ◽  
Different Levels

Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.

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