The Role of Energy Storage in the Transition Toward a Carbon-Neutral Economy

Energy transitions to less-carbon intense technologies, such as wind and solar energy, in the electricity, sector are crucial to realizing international climate goals because the electricity sector has been one of the main global carbon emitter for decades, and environmental plans in most countries involve electrifying heavily polluting industries with clean electricity generation. The intermittency problem of renewable energy has been the main stumbling block on the path, and energy storage is now referred to as the key in these transitions because it can boost renewable energy use while also improving the efficiency of conventional power plants (Hall and Bain, 2008). However, existing large-scale energy storage is still expensive. Innovation has been playing a decisive role in reducing costs and expanding capacity.Referring to a conversion of electrical energy from a power network to a storable form for later use (Price, 2011), energy storage is relatively newly developed, compared with generation technologies. Economists have been seeking to contribute to the understanding of innovation and its links with environmental policies and outputs, such as the key determinants of innovation in energy storage and which policies are successful at promoting it. My dissertation focuses on the innovation in energy storage, especially its role in the transition toward a carbon-neutral economy. Using patent data from 1978 to 2019 across 1881 regions, Chapter 1 studies the innovation trend in energy storage at the global level and estimates the main determinants. Results show an overall positive trend in storage patents, indicating its importance in the electricity sector. In addition, the results highlight the role of energy prices and past innovation in shaping innovation. Specifically, a one-unit increase in electricity prices leads to a 15.54% reduction in the ratio of storage to electricity generation patents. These results imply the need for a combination of energy policies and innovation policies to boost innovation in energy storage. In Chapter 2, I examine the impact of market-based environmental policies on innovation in energy storage. My results highlight the role of environmental taxes, feed-in tariffs for solar energy, and tradable certificates for CO2 emissions in promoting firms’ patenting activity, whereas renewable energy certificates and energy efficiency certificates discourage it. These results imply the need for more stringent market-based environmental policies to incentivize innovation in energy storage. Chapter 3 focuses on the role of energy storage in realizing energy transitions and whether energy storage subsidies successfully accelerate such transitions. While many point to energy storage as the solution to the intermittency problem of renewable resources, the relationship between energy storage and nonrenewable resources receives far less attention. By modifying the theoretical model of directed technical change, a subsidy to energy storage is presented as a mechanism that benefits both clean and dirty sectors and influences the optimal allocation. Finally, it might play a more important role in energy transitions by easing the substitution between clean and dirty inputs than encouraging innovation directly

Improved split fluorescent proteins for endogenous protein labeling.

Self-complementing split fluorescent proteins (FPs) have been widely used for protein labeling, visualization of subcellular protein localization, and detection of cell-cell contact. To expand this toolset, we have developed a screening strategy for the direct engineering of self-complementing split FPs. Via this strategy, we have generated a yellow-green split-mNeonGreen21-10/11 that improves the ratio of complemented signal to the background of FP1-10-expressing cells compared to the commonly used split GFP1-10/11; as well as a 10-fold brighter red-colored split-sfCherry21-10/11. Based on split sfCherry2, we have engineered a photoactivatable variant that enables single-molecule localization-based super-resolution microscopy. We have demonstrated dual-color endogenous protein tagging with sfCherry211 and GFP11, revealing that endoplasmic reticulum translocon complex Sec61B has reduced abundance in certain peripheral tubules. These new split FPs not only offer multiple colors for imaging interaction networks of endogenous proteins, but also hold the potential to provide orthogonal handles for biochemical isolation of native protein complexes.Split fluorescent proteins (FPs) have been widely used to visualise proteins in cells. Here the authors develop a screen for engineering new split FPs, and report a yellow-green split-mNeonGreen2 with reduced background, a red split-sfCherry2 for multicolour labeling, and its photoactivatable variant for super-resolution use

An exact solution of spherical mean-field plus orbit-dependent non-separable pairing model with two non-degenerate j-orbits

An exact solution of nuclear spherical mean-field plus orbit-dependent non-separable pairing model with two non-degenerate j-orbits is presented. The extended one-variable Heine-Stieltjes polynomials associated to the Bethe ansatz equations of the solution are determined, of which the sets of the zeros give the solution of the model, and can be determined relatively easily. A comparison of the solution to that of the standard pairing interaction with constant interaction strength among pairs in any orbit is made. It is shown that the overlaps of eigenstates of the model with those of the standard pairing model are always large, especially for the ground and the first excited state. However, the quantum phase crossover in the non-separable pairing model cannot be accounted for by the standard pairing interaction.Comment: 5 pages, 1 figure, LaTe

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment

Design-based causal inference is one of the most widely used frameworks for testing causal null hypotheses or inferring about causal parameters from experimental or observational data. The most significant merit of design-based causal inference is that its statistical validity only comes from the study design (e.g., randomization design) and does not require assuming any outcome-generating distributions or models. Although immune to model misspecification, design-based causal inference can still suffer from other data challenges, among which missingness in outcomes is a significant one. However, compared with model-based causal inference, outcome missingness in design-based causal inference is much less studied, largely due to the challenge that design-based causal inference does not assume any outcome distributions/models and, therefore, cannot directly adopt any existing model-based approaches for missing data. To fill this gap, we systematically study the missing outcomes problem in design-based causal inference. First, we use the potential outcomes framework to clarify the minimal assumption (concerning the outcome missingness mechanism) needed for conducting finite-population-exact randomization tests for the null effect (i.e., Fisher's sharp null) and that needed for constructing finite-population-exact confidence sets with missing outcomes. Second, we propose a general framework called ``imputation and re-imputation" for conducting finite-population-exact randomization tests in design-based causal studies with missing outcomes. Our framework can incorporate any existing outcome imputation algorithms and meanwhile guarantee finite-population-exact type-I error rate control. Third, we extend our framework to conduct covariate adjustment in an exact randomization test with missing outcomes and to construct finite-population-exact confidence sets with missing outcomes

Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl

Early career women in construction: Are their career expectations being met?

The recruitment, retention and development of early career women have always been a challenge in the construction industry. With the focus on early career women or new female construction management degree graduate hires in construction, this study explores: (i) factors influencing their choice of career in construction; (ii) the extent of which their career expectations were met in their first few years of job experience; and (iii) how their met or unmet career expectations are related their overall job satisfaction. Data was collected using an online survey questionnaire. The results show that the top significant factors influencing the respondents’ career choice are career opportunities and belief of getting better pay. Their career expectations, on the other hand, were met or exceeded to a great extent for almost all the measurement items. The results also show that the respondents have a relatively high overall job satisfaction level. Although there is lack of evidence that their overall job satisfaction increased as met career expectations increased, there are statistically significant positive correlations among the career expectation measurement items. These findings have implications for human resource practices of construction employers that aimed to attract early career women into the industry, and to reinforce their met career expectations and job satisfaction

AFPN: Asymptotic Feature Pyramid Network for Object Detection

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}

Exact solution of spherical mean-field plus special orbit-dependent non-separable pairing model with multi non-degenerate j-orbits

Exact solution of spherical mean-field plus a special orbit-dependent non-separable pairing Hamiltonian with multi non-degenerate j-orbits, which is related to two previously known hyperbolic Gaudin models, is explored. It is shown that the Hamiltonian with suitable constraints on the pairing interaction parameters turns to be exactly solvable. The extended one-variable Heine-Stieltjes polynomials associated to the Bethe-Gaudin-Richardson ansatz equations of the solution for any number of pairs k are determined. It is shown that the pair excitation energies can be calculated more easily than those of the separable pairing model studied previously. As examples of the solution, pairing excitation energies with the number of pairs up to the half-filling in the ds-shell with 3 j-orbits and in the pf-shell with 4 j-orbits are presented and compared with those of the mean-field plus the general separable, the special separable, and the standard pairing models. It is shown that the pairing excitation energies of the model are close to those of the mean-field plus special separable pairing or general separable pairing model