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Project Sunroof estimates the solar potential of residential buildings using high quality aerial data. That is, it estimates the potential solar energy (and associated financial savings) that can be captured by buildings if solar panels were to be installed on their roofs. Unfortunately its coverage is limited by the lack of high resolution digital surface map (DSM) data. We present a deep learning approach that bridges this gap by enhancing widely available low-resolution data, thereby dramatically increasing the coverage of Sunroof. We also present some ongoing efforts to potentially improve accuracy even further by replacing certain algorithmic components of the Sunroof processing pipeline with deep learning.

Additional file 5: Figure S5. ApNEAAT1 localization to anterior neural structures. Panels a and a’ show a magnified confocal image of a stage 18 embryo head (see Fig. 5b & b’ for full embryo image) stained with ApNEAAT1 antibody (green), Phalloidin marking F-actin (red), and DAPI marking nuclei (blue) (see color key below panel a’). Panel a shows ApNEAAT1 signal only and panel a’ shows the merged image. The yellow dashed rectangle encloses localization to the corpora cardiaca. Scale bars = 10 µm

Additional file 7: Figure S7. Negative and Positive controls for immunolocalization procedure. Panels a and a’ show confocal images of embryos incubated in only the secondary antibody (negative control). ApVas antibody [46] was used as a positive control to test the effectiveness of the immunostaining protocol used. Panels b and b’ show confocal images of embryos stained with ApVas antibody (positive control). ApNEAAT1 and ApVas antibodies are shown in green, F-actin (Phalloidin) in red, and nuclei (DAPI) in blue (see color key below figure). gm germarium, st stage. Scale bars = 10 µm.

Additional file 6: Figure S6. ApNEAAT1 localization to hemocytes. Panels a and a’ show a confocal image of an isolated hemocyte from the hemolymph of a late-stage embryo. Panel a shows the ApNEAAT1 antibody channel only and panel a’ shows the merged image. The ApNEAAT1 antibody is shown in green, F-actin (Phalloidin) in red, and nuclei (DAPI) in blue (see color key below figure). Panels b and b’ show a magnified view of a prospective hemocyte (“ApNEAAT1-positive cell”) from within a stage 19 embryo (see Fig. 5c & c’ for full embryo image). A preadsorbed (c & c’) and negative (d & d’) control for the hemolymph staining are also shown. Scale bars = 10 µm.

Malikai TLP is a Deepwater manned platform operated by Shell. The Malikai TLP began construction in 2013. The Malikai TLP brought a huge challenge and complexity in terms of materials, quality control and assurance, interfaces between different contractors and subcontractors, construction and installation requirements and competent and experienced personnel. During the constructions phase, there were 3000-4000 workers based at site working together to build the TLP. There were significant amount of hazards that could be realized due to the types of construction and installation related activities such as working at height, working over water, confined space, working with electrical equipment, blasting, painting, grinding activities, scaffolding, lifting and handling of heavy equipment to name a few. The hazards at the site could have led to severe consequences including fatalities, physical injuries, burns and electrocution. With this realization, the Company implemented numerous measures to address the health, safety and environmental concerns including the welfare, competency and safe working environment for the workers. This paper presents an overview of the measures taken with the hope of achieving GOAL ZERO that emphasizes no harm to any personnel working at the site.

Additional file 2: Table S2. Sample numbers for the APC-8904 in situ hybridization experiments by developmental stage.

Additional file 1: Table S1. Sample numbers for the AAAP-536 in situ hybridization experiments by developmental stage.

Research on the CHREC Space Processor (CSP) takes a multifaceted hybrid approach to embedded space computing. Working closely with the NASA Goddard SpaceCube team, researchers at the National Science Foundation (NSF) Center for High-Performance Reconfigurable Computing (CHREC) at the University of Florida and Brigham Young University are developing hybrid space computers that feature an innovative combination of three technologies: commercial-off-the-shelf (COTS) devices, radiation-hardened (RadHard) devices, and fault-tolerant computing. Modern COTS processors provide the utmost in performance and energy-efficiency but are susceptible to ionizing radiation in space, whereas RadHard processors are virtually immune to this radiation but are more expensive, larger, less energy-efficient, and generations behind in speed and functionality. By featuring COTS devices to perform the critical data processing, supported by simpler RadHard devices that monitor and manage the COTS devices, and augmented with novel uses of fault-tolerant hardware, software, information, and networking within and between COTS devices, the resulting system can maximize performance and reliability while minimizing energy consumption and cost. NASA Goddard has adopted the CSP concept and technology with plans underway to feature flight-ready CSP boards on two upcoming space missions.

Exploration activities in general are carried out in remote areas of the planet like the middle of the jungle, in this sense the operation companies have to keep logistic bases where they store large quantities of flammable products (diesel, turbo and gasoline) in flexible containers (bladders) usually about 25,000 gallons capacity. These bladders store flammable and combustible liquids in temporary operations and are located in areas exposed to heavy rainfall, high solar radiation, environmentally sensitive, close to communities or archaeological zones. In case of fuel loss by a fire in the bladders, the replacement time is very high. The available area to place the bladders is also a limitation. In order to avoid the domino effect the bladders should be separated between each other. Our case material of this paper is a logistic base which is located in the jungle of Peru, it is one hour forty five minutes by helicopter or two days by boat from the closer city, that base has the characteristic of temporary and its works like facility to the activities carried out in the exploration block; it needs to store 200,000 gallons of flammable fuel (diesel and turbo) to support exploration activities at an Exploration Block in the jungle of Peru twenty kilometers from Ecuador – Peru border. The products are stored in bladders of 25,000 gallons, in total there are 100,000 gallons of diesel and 100,000gls of turbo. The logistic base is located in an environmental sensitive area because it involves important waterways, primary and secondary forests, nearby communities, as well as a military base, which could be adversely affected in the event of an uncontrolled fire. The available area to place the bladders is limited which does not allow to distance them properly to avoid the domino effect. Also for environmental reasons and permits the 8 bladders must be located in the same pit. In case of a "pool fire", it will be necessary to use large amounts of water and foam to smother it which, among other things, increases the risk of contamination of water resources near the area. It assed different alternatives to protect the pit, based on risk scenarios, the main issue to evaluate is the "pool fire", which can trigger a fire in all the bladders by the domino effect. To reduce risk, reduce investment in firefighting system and use the limited space available, it decided to investigate and define a passive protection system and the purchase of mobile firefighting systems, which can additionally be transferred to future projects. The design of passive protection system must be able to protect the bladders in a case of fire in one bladder the other bladders must not get fire. On the other hand the firefighting system must extinguish the fire in less than 20 minutes and do not need a system of storage water.

This article examines the relationship between political parties and regional presidents in Italy and Spain, adopting a comparative case study approach based on extensive archival analysis and in-depth interviews with regional politicians. The findings confirm a strong pattern of growing presidentialism at regional level, regardless of whether there are formal mechanisms for direct election, and regardless of the partisan composition of regional government. Regional presidents tend to exert their growing power through a personalised control of regional party organisations, rather than governing past parties in a direct appeal to the electorate. Nevertheless, parties can still present a significant constraint on regional presidents, so successful regional presidents tend to maintain a mediating form of leadership and fully exploit the opportunities for party patronage to build up their support and smooth governing tensions. An autonomist drive helps presidents hold together disparate coalitions or loose parties at regional level, but their lack of internal coherence presents major problems when it comes to political succession.

Additional file 3: Table S3. Real-time PCR primers for AAAP-536, APC-8904, and control genes.

Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree of RR-1 cuticular proteins.Figure S11. Phylogenetic tree of RR-2 cuticular proteins.Figure S12. Comparison of miRNAs in D. vitifoliae and other insect genomes. Figure S13. Phylogenetic tree of aquaporin protein sequences. Figure S14. Comparison of the phylloxera PER protein with other insects. Figure S15. Amino acid alignment of PTTH amino acid sequences. Figure S16. Phylogeny of hemipteran ORs. Figure S17. Phylogeny of hemipteran GRs. Figure S18. Phylogenetic analysis of OBPs. Figure S19. Phylogenetic analysis of CSPs. Figure S20. Phylogenetic analysis of NPC2s. Figure S21. Distribution of cluster sizes of putative effectors. Figure S22. Physical distribution of the three largest clusters of effectors. Table S1. Genes of bacterial and fungal origin. Table S2. Statistics on TEs. Table S3. GO enrichment of genes duplicated at different ancestral nodes. Table S4. Metabolic gaps in the D. vitifoliae reaction network. Table S5. Functional annotation of metabolic genes. Table S6. Genes of the TOLL pathway. Table S7. Genes of the IMD pathway. Table S8. Statistics on cuticular proteins. Table S9. Developmental genes in D. vitifoliae and A. pisum. Table S10. miRNAs. Table S11. Clock-related genes. Table S12. List of ORs and GRs. Table S13. Number of OBPs, CSPs and NPC2s. Table S14. List of Cytochromes P450. Table S15. List of genes involved in detoxification. Table S16. Effector genes with predicted domains and their corresponding functions. Table S17. Statistics on sequence reads and SRA accessions used for the reference genome. Table S18. List of species used to study gene expansions. Table S19. Sampling sites and SRA used for population genomics analyses. Table S20. Prior distribution of parameters used for ABC modeling of invasion routes.

Additional file 3: Table S3. Real-time PCR primers for AAAP-536, APC-8904, and control genes.

Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree of RR-1 cuticular proteins.Figure S11. Phylogenetic tree of RR-2 cuticular proteins.Figure S12. Comparison of miRNAs in D. vitifoliae and other insect genomes. Figure S13. Phylogenetic tree of aquaporin protein sequences. Figure S14. Comparison of the phylloxera PER protein with other insects. Figure S15. Amino acid alignment of PTTH amino acid sequences. Figure S16. Phylogeny of hemipteran ORs. Figure S17. Phylogeny of hemipteran GRs. Figure S18. Phylogenetic analysis of OBPs. Figure S19. Phylogenetic analysis of CSPs. Figure S20. Phylogenetic analysis of NPC2s. Figure S21. Distribution of cluster sizes of putative effectors. Figure S22. Physical distribution of the three largest clusters of effectors. Table S1. Genes of bacterial and fungal origin. Table S2. Statistics on TEs. Table S3. GO enrichment of genes duplicated at different ancestral nodes. Table S4. Metabolic gaps in the D. vitifoliae reaction network. Table S5. Functional annotation of metabolic genes. Table S6. Genes of the TOLL pathway. Table S7. Genes of the IMD pathway. Table S8. Statistics on cuticular proteins. Table S9. Developmental genes in D. vitifoliae and A. pisum. Table S10. miRNAs. Table S11. Clock-related genes. Table S12. List of ORs and GRs. Table S13. Number of OBPs, CSPs and NPC2s. Table S14. List of Cytochromes P450. Table S15. List of genes involved in detoxification. Table S16. Effector genes with predicted domains and their corresponding functions. Table S17. Statistics on sequence reads and SRA accessions used for the reference genome. Table S18. List of species used to study gene expansions. Table S19. Sampling sites and SRA used for population genomics analyses. Table S20. Prior distribution of parameters used for ABC modeling of invasion routes.