SMART Reading is a children's literacy non-profit, volunteer-driven tutoring program local to Oregon for at-risk K-3 readers. SMART was developed by Neil Goldschmidt in 1992. It has grown from serving 585 children at 8 schools at its inception to serving 7,244 children at 204 sites in 2009, and 223 sites in 2011. As of December 2011, the organization's annual budget was $2.7 million.The program concept involves each student getting one-on-one attention twice a week for 30 minutes as they read to a volunteer to help boost their confidence in their reading ability. Additionally, the students get to take two books home each month over the seven months the program runs each year (mid-October to mid-May), in order to make more reading material available at home.The program's effectiveness is backed by a two-year study performed at the Eugene Research Institute which indicates SMART students outperform similar students in word identification and word comprehension by nearly a half standard deviation.SMART aims to partner foremost with schools that have a high percentage of children from low-income families. School staff interviews, community support, and information from the Oregon Department of Education also influence school selection.In 2010, SMART participated in Pepsi's Refresh Challenge and running for the $250,000 prize..
Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery and deep learning have achieved remarkable progress, many challenges exist: most existing studies are inherently predictive, failing to reflect the generative nature of urban planning; although generative AI and diffusion models have seen explosive growth in satellite imagery, they lack the urban functional generation (e.g., energy layer); third, aligned high-quality high-resolution building energy data with satellite imagery is limited and scarce. Here we propose SENSE (Satellite-based ENergy Synthesis for Sustainable Environment), a unified generative UBEM framework that jointly synthesizes realistic urban satellite imagery and aligned high-quality building energy consumption and height maps. By conditioning on road networks and urban density metrics, SENSE, based on a controllable diffusion model, leverages the knowledge learned by large vision models to generate urban building energy consumption and height information (annotations) in the latent space. Experiments across four cities (New York City, Boston, Lyon, Busan) demonstrate that SENSE achieves high visual fidelity and strong physical consistency, satisfying the ASHRAE standard metric. Experiments demonstrate that SENSE can generate enough annotated synthetic data using less than 20
Oil palm (Elaeis guineensis Jacq.), the world’s most land-efficient oil crop, underpins global vegetable oil supply yet faces mounting constraints from limited expansion, climate stress, and disease pressure. These challenges highlight the urgent need for genomic resources that capture species-wide diversity to support sustainable improvement. While recent reference assemblies have advanced trait discovery, single linear genomes fail to represent the full spectrum of structural and gene-content variation, limiting resolution of agronomic alleles. Here, we constructed a graph-based pan-genome from 30 diverse oil palm assemblies representing wild, semi-domesticated, and commercial accessions. We characterized structural variants, gene presence–absence variation, and copy-number gains, with focusing on functional stratification and resistance gene dynamics. The graph-based pan-genome revealed extensive structural and gene-content variation, including a large conserved core, complemented by shell and unique fractions enriched or biased toward regulatory, stress-responsive, and defense-related functions. Structural variation and duplication-derived copy-number gains contributed substantially to gene-content diversity, with semi-domesticated accessions exhibiting the greatest variability. Resistance gene repertoires showed contrasting patterns: receptor-like kinases remained comparatively stable, whereas the CNL subclass of NLR genes contributed disproportionately to shell-genome variation and duplication-associated turnover. This graph-based pan-genome provides a curated multi-assembly reference and comparative framework for oil palm genomics. By capturing structural variants, gene-content variations, copy-number gains, and resistance gene dynamics across domestication gradients, it establishes a foundation for future pan-GWAS analysis, functional genomics, and molecular breeding strategies aimed at improving resilience and productivity in this globally important crop.
When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, and traces. Reloading all of it is wasteful, but dropping a piece the change depends on can leave a required property unsupported. Given the properties a change must preserve, its obligations, we ask which least-cost subset of the available evidence re-establishes them, and we call such a subset a task-conditioned assurance envelope. Evidence and the rules that combine it form a typed inference graph; an obligation is met when forward chaining from the selected evidence reaches it, and we validate every selection by that closure rather than by trusting the optimizer. The software-derived graphs in our evaluation come from preserved outcomes of prior AI coding-agent runs; we freeze those artifacts and ask which accumulated evidence should be restored for a later task. Small graphs from Rust, IronBlocks, and Pong outcomes show that the minimum envelope depends on the task, that none may exist when current evidence cannot re-establish a required property, that some properties need several pieces of evidence together, and that expanding the requirements adds evidence rather than replacing it. A prespecified synthetic benchmark of 249 instances characterizes computation: a baseline that discards the 'several pieces together' structure necessarily fails to re-derive them; every completed exact cross-check agreed with the CP-SAT optimizer; and median solve time stayed below 20 ms at 500-evidence graphs, except that graphs with many alternative derivations per target timed out at far smaller sizes, so structure, not raw size, drives difficulty. The contribution is a bounded application of established optimization to selecting assurance context for a software change; discovering the obligations and downstream agent benefit remain open.
Specialized dairy farms have a high workload, with most of their labor resources devoted to livestock-related tasks. The feasibility of reducing pesticide use is influenced by work dimensions. Using a discrete choice experiment, we assessed trade-offs among determinants that can hinder reducing pesticide use on dairy farms, particularly workload. The attributes of the experiment were the reduction in pesticide use, change in gross operating surplus, risk of yield loss, number of labor peaks during the year, skills and knowledge, and mental workload. 94 dairy farmers in Brittany (western France) were interviewed face-to-face. Three classes were identified: (1) 20 farmers expressed negative preferences for reducing pesticide use, and this class exhibited a higher workload, (2) for 21 farmers, reduction in pesticide use had no significant effect on preferences and those farms had the most intensive milk-production systems, and (3) 53 farmers were willing to reduce pesticide use, most of the farmers in this group were already involved in a pesticide-reduction program. An increase in the number of labor peaks without outsourcing and an increase in mental workload had a significant negative effect on all of the three classes. Public policies should address the work dimension of reducing pesticide use on particularly labor-intensive farms such as dairy farms.
Urban leftover spaces tend to be integrated into urban strategy as an opportunity to prefigure the city of the future. Public decision makers are now encouraging inhabitants to take ownership of these spaces. On the model of the traditional agricultural fallow, which consists in working the land to prepare for future crops, we introduce the notion of urban fallow to qualify these spaces made available to the initiatives of local players to develop a transitional project. In any complex system, it is the sub-systems, through their diversity and capacity for creation/innovation, that give the whole (the system) a capacity for adaptation, enabling it to re-produce itself over time. It is thus at the level of its neighborhoods (of its sub-systems) that the city will draw the springs of its resilience. Urban fallows will enable the innovative and experimental capacities present in neighborhoods to express themselves in temporary projects. The aim of urban fallows is to take targeted action on specific points and locations in the area, in order to extend its effects and ultimately respond more broadly to problems diagnosed on the wider scale of the district. Five main issues that will guide the nature of the projects to be prioritized on these urban fallow lands: 1) enhancing and restoring an identity to the site, 2) revitalizing social and cultural life, 3) improving biodiversity and the quality of life, 4) revitalizing the local economic fabric, and 5)strengthening urban cohesion. Planned and thought out as part of an urban acupuncture strategy, these urban fallows will reinforce the dynamism of the city's various territories and, in so doing, contribute to the city's resilience.
