Is more investment needed in Solar & Biogas Energy Sources in Rwanda?

The Rwanda Third National Communication Under the United Nations Framework Convention on Climate Change mentions that Green House Gas (GHG) emissions per capita increased from 532.39kg (2006) to 676.23kg (2015) with an annual increase of 2.46%. As of 2015, the dominant emissions are from agriculture (70.4%) followed by energy (20.11%). Urea application in agriculture have increased from 1,246,400 to 2,559,000 tons of CO2 eq. Charcoal or wood being the primary source for cooking; emissions from domestic energy use has increased from 626,800 to 741,400 tons of CO2 eq. If such trends continue severe health issues, deforestation, soil erosion and droughts will be the heavy price Rwanda will have to pay. However, an investment in solar and biogas energy sources can be a solution in mitigating the afore-mentioned problems. In the developing world, Texas, USA is ranked by the Solar Energy Industries Association (SEIA) as the seventh in USA for cumulative solar capacity as of 2017. SEIA reports that Texas has 532 solar companies including 100 manufacturers. Additionally, around 210,000 Texas homes use solar power due to improved business models, distribution channels, service provider networks and low costs. Moreover, customers of solar systems can claim a 30% tax credit due to a federal government investment tax. The cost of solar is estimated at an average of $0.12/Kwh or 110RWF/Kwh while costs range from 89 – 189 RWF/Kwh (for residential customers) and from 189 – 192 RWF/Kwh (for non-residential customers) in Rwanda. According to Clean Energy Wire, Germany has 8,900 biogas power stations and 183 biomethane plants as of 2015. The biomethane plants feed pure methane into natural gas network to be used as a cooking fuel. The biogas technology also provides a liquid fertilizer which can directly be applied to agricultural fields or home gardens. Biogas power stations and biomethane plants have helped Germany avoid 156.1 million tons of CO2 eq. in 2015. However, Germany deploys heavy investments (e.g.: 15 billion euros in 2015) in renewable energy systems including biogas power stations and biomethane plants. In Rwanda, the National Domestic Biogas Programme (NDBP) has made tremendous efforts in disseminating biogas plants to interested citizens since 2009. To this day, some biogas plants are still operational and others are not due to construction flaws, lack of feedstocks and skilled labor to operate the biogas plants. The failure of some biogas systems doesn’t imply that the biogas technology is not feasible in Rwanda but better policies and better technologies are needed to improve this sector. According to Amy Yee, New York Times Journalist, the cost of biogas plants ($450 to $900) are significantly high for poor families in Rwanda with an average income of$70/month. However, the government of Rwanda is providing great incentives for all parties investing in renewable energies. As of 2019, the government has exempted import taxes of 23% and 18% value added tax paid by customers for all renewable energies. Therefore, it is safe to say that investments in renewable energies such as biogas and solar is worth it. Solar and biogas energy resources have the potential of creating jobs and providing a means of fighting deforestation, soil erosion, droughts and severe health issues. As Rwanda strives to be one of the most pristine places in the worldwide, an investment in solar and biogas is worthwhile

Linear viscoelasticity of entangled wormlike micelles bridged by telechelic polymers : an experimental model for a double transient network

We survey the linear viscoelasticity of a new type of transient network: bridged wormlike micelles, whose structure has been characterized recently [Ramos and Ligoure, (2007)]. This composite material is obtained by adding telechelic copolymers (water-soluble chains with hydrophobic stickers at each extremity) to a solution of entangled wormlike micelles (WM). For comparison, naked WM and WM decorated by amphiphilic copolymers are also investigated. While these latter systems exhibit almost a same single ideal Maxwell behavior, solutions of bridged WM can be described as two Maxwell fluids components blends, characterized by two markedly different characteristic times, t_fast and t_slow, and two elastic moduli, G_fast and G_slow, with G_fast >> G_slow. We show that the slow mode is related to the viscoelasticity of the transient network of entangled WM, and the fast mode to the network of telechelic active chains (i.e. chains that do not form loops but bridge two micelles). The dependence of the viscoelasticity with the surfactant concentration, phi, and the sticker-to-surfactant molar ratio, beta, is discussed. In particular, we show that G_fast is proportional to the number of active chains in the material, phi beta. Simple theoretical expectations allow then to evaluate the bridges/loops ratio for the telechelic polymers

Reviewing Research Trends:A Scientometric Approach Using Gunshot Residue (GSR) Literature as an Example

The ability to manage, distil and disseminate the significant amount of information that is available from published literature is fast becoming a core and critical skill across all research domains, including that of forensic science. In this study, a simplified scientometric approach has been applied to available literature on gunshot residue (GSR) as a test evidence type aiming to evaluate publication trends and explore the interconnectivity between authors. A total of 731 publications were retrieved using the search engine ‘Scopus’ and come from 1589 known authors, of whom 401 contributed to more than one research output on this subject. Out of the total number of publications, only 35 (4.8%) were found to be Open Access (OA). The Compound Annual Growth Rate (CAGR) for years 2006 and 2016 reveals a much higher growth in publications relating to GSR (8.0%) than the benchmark annual growth rate of 3.9%. The distribution of a broad spectrum of keywords generated from the publications confirms a historical trend, in particular regarding the use of analytical techniques, in the study of gunshot residue. The results inform how relevant information extracted from a bibliometric search can be used to explore, analyse and define new research areas

UTILITY SCALE AGRIVOLTAICS DEVELOPMENT PROXIMATE TO MICHIGAN COMMUNITIES WITH 100% RENEWABLE ENERGY GOALS

This report aims to assess the potential of agrivoltaics (combined solar and agricultural systems) for development geographically proximate to the six Michigan (MI) communities that have set 100% renewable energy (RE) goals. I focus on one major research question: What is the total acreage of low-impact sites available for utility-scale (USS) agrivoltaics development proximate (within county boundaries) to MI communities with 100% RE goals? SAM is used to estimate land acreage required for a 10 MW agrivoltaic system development. ArcGIS Pro is used to determine the total acreage of low-impact sites proximate to MI communities with 100% RE goals. Proximate low-impact sites are defined as agricultural land with minimal environmental and land use impacts, having access to transmission and distribution infrastructure, and are located within the same county as the community with the RE goal. This study finds that USS agrivoltaics development is possible in all six counties. On the premise that the benefits and ills of an energy technology should be distributed equitably within society regardless of social and economic factors, USS agrivoltaic systems could provide a source of revenue for farmers and promote local employment within the county. In addition, such systems can help support the state of MI to achieve its current RPS of 15% and carbon neutrality by 2050. This report provides a first step in assessing the potential of agrivoltaic development in Michigan, which can inform future work that integrates other considerations relevant to solar development

HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet, which connects each layer to every other layer in a feed-forward fashion, has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path, but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on 6-month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available at https://www.github.com/josedolz/HyperDenseNet.Comment: Paper accepted at IEEE TMI in October 2018. Last version of this paper updates the reference to the IEEE TMI paper which compares the submissions to the iSEG 2017 MICCAI Challeng

Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis

Unpaired image-to-image translation has been applied successfully to natural images but has received very little attention for manifold-valued data such as in diffusion tensor imaging (DTI). The non-Euclidean nature of DTI prevents current generative adversarial networks (GANs) from generating plausible images and has mainly limited their application to diffusion MRI scalar maps, such as fractional anisotropy (FA) or mean diffusivity (MD). Even if these scalar maps are clinically useful, they mostly ignore fiber orientations and therefore have limited applications for analyzing brain fibers. Here, we propose a manifold-aware CycleGAN that learns the generation of high-resolution DTI from unpaired T1w images. We formulate the objective as a Wasserstein distance minimization problem of data distributions on a Riemannian manifold of symmetric positive definite 3x3 matrices SPD(3), using adversarial and cycle-consistency losses. To ensure that the generated diffusion tensors lie on the SPD(3) manifold, we exploit the theoretical properties of the exponential and logarithm maps of the Log-Euclidean metric. We demonstrate that, unlike standard GANs, our method is able to generate realistic high-resolution DTI that can be used to compute diffusion-based metrics and potentially run fiber tractography algorithms. To evaluate our model's performance, we compute the cosine similarity between the generated tensors principal orientation and their ground-truth orientation, the mean squared error (MSE) of their derived FA values and the Log-Euclidean distance between the tensors. We demonstrate that our method produces 2.5 times better FA MSE than a standard CycleGAN and up to 30% better cosine similarity than a manifold-aware Wasserstein GAN while synthesizing sharp high-resolution DTI.Comment: Accepted at MICCAI 2020 International Workshop on Computational Diffusion MR

Spectral Graph Transformer Networks for Brain Surface Parcellation

The analysis of the brain surface modeled as a graph mesh is a challenging task. Conventional deep learning approaches often rely on data lying in the Euclidean space. As an extension to irregular graphs, convolution operations are defined in the Fourier or spectral domain. This spectral domain is obtained by decomposing the graph Laplacian, which captures relevant shape information. However, the spectral decomposition across different brain graphs causes inconsistencies between the eigenvectors of individual spectral domains, causing the graph learning algorithm to fail. Current spectral graph convolution methods handle this variance by separately aligning the eigenvectors to a reference brain in a slow iterative step. This paper presents a novel approach for learning the transformation matrix required for aligning brain meshes using a direct data-driven approach. Our alignment and graph processing method provides a fast analysis of brain surfaces. The novel Spectral Graph Transformer (SGT) network proposed in this paper uses very few randomly sub-sampled nodes in the spectral domain to learn the alignment matrix for multiple brain surfaces. We validate the use of this SGT network along with a graph convolution network to perform cortical parcellation. Our method on 101 manually-labeled brain surfaces shows improved parcellation performance over a no-alignment strategy, gaining a significant speed (1400 fold) over traditional iterative alignment approaches.Comment: Equal contribution of R. He and K. Gopinat