use natural gas consumption in 201Scenarios to decarbonize residential water heating in California

This paper presents the first detailed long-term stock turnover model to investigate scenarios to decarbonize the residential water heating sector in California, which is currently dominated by natural gas. We model a mix of water heating (WH) technologies including conventional and on-demand (tank-less) natural gas heating, electric resistance, existing electric heat pumps, advanced heat pumps with low global warming refrigerants and solar thermal water heaters. Technically feasible policy scenarios are developed by considering combinations of WH technologies with efficiency gains within each technology, lowering global warming potential of refrigerants and decreasing grid carbon intensity. We then evaluate energy demand, emissions and equipment replacement costs of the pathways. We develop multiple scenarios by which the annual greenhouse gas emissions from residential water heaters in California can be reduced by over 80% from 1990 levels resulting in an annual savings of over 10 Million Metric Tons by 2050. The overall cost of transition will depend on future cost reductions in heat pump and solar thermal water heating equipment, energy costs, and hot water consumption

Improvement of modal scaling factors using mass additive technique

A general investigation into the improvement of modal scaling factors of an experimental modal model using additive technique is discussed. Data base required by the proposed method consists of an experimental modal model (a set of complex eigenvalues and eigenvectors) of the original structure and a corresponding set of complex eigenvalues of the mass-added structure. Three analytical methods,i.e., first order and second order perturbation methods, and local eigenvalue modification technique, are proposed to predict the improved modal scaling factors. Difficulties encountered in scaling closely spaced modes are discussed. Methods to compute the necessary rotational modal vectors at the mass additive points are also proposed to increase the accuracy of the analytical prediction

A new method to real-normalize measured complex modes

A time domain subspace iteration technique is presented to compute a set of normal modes from the measured complex modes. By using the proposed method, a large number of physical coordinates are reduced to a smaller number of model or principal coordinates. Subspace free decay time responses are computed using properly scaled complex modal vectors. Companion matrix for the general case of nonproportional damping is then derived in the selected vector subspace. Subspace normal modes are obtained through eigenvalue solution of the (M sub N) sup -1 (K sub N) matrix and transformed back to the physical coordinates to get a set of normal modes. A numerical example is presented to demonstrate the outlined theory

How Well-Targeted Are Payroll Tax Cuts as a Response to COVID-19? Evidence from China

Numerous countries cut payroll taxes in response to economic downturns caused by COVID-19. This includes China, which completely exempted most firms from making social insurance (SI) contributions, resulting in an average tax cut of 21 percentage points on formal labor costs and approximately 20% of total tax remittances made by firms. We use novel data on 900,000 firms in one Chinese province to document new facts about the structure of SI in China and evaluate payroll tax cuts as a COVID-19 relief measure. We calculate that labor informality causes 54% of tax-registered firms---representing 24% of aggregate economic activity---to receive no benefits. Labor formality also increases with firm size, further skewing the benefit of payroll tax cuts towards large firms. But despite the mistargeting that results from these facts, the benefit of the tax cuts relative to firms\u27 operating costs and liquidity is likely larger both for smaller firms and in industries most affected by the COVID-19 shock because these firms and industries are more labor-intensive

DreamEdit: Subject-driven Image Editing

Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the previous works cannot precisely control the background and position of the target subject. In this work, we aspire to fill the void and propose two novel subject-driven sub-tasks, i.e., Subject Replacement and Subject Addition. The new tasks are challenging in multiple aspects: replacing a subject with a customized one can change its shape, texture, and color, while adding a target subject to a designated position in a provided scene necessitates a context-aware posture. To conquer these two novel tasks, we first manually curate a new dataset DreamEditBench containing 22 different types of subjects, and 440 source images with different difficulty levels. We plan to host DreamEditBench as a platform and hire trained evaluators for standard human evaluation. We also devise an innovative method DreamEditor to resolve these tasks by performing iterative generation, which enables a smooth adaptation to the customized subject. In this project, we conduct automatic and human evaluations to understand the performance of DreamEditor and baselines on DreamEditBench. For Subject Replacement, we found that the existing models are sensitive to the shape and color of the original subject. The model failure rate will dramatically increase when the source and target subjects are highly different. For Subject Addition, we found that the existing models cannot easily blend the customized subjects into the background smoothly, leading to noticeable artifacts in the generated image. We hope DreamEditBench can become a standard platform to enable future investigations toward building more controllable subject-driven image editing. Our project homepage is https://dreameditbenchteam.github.io/