Essays on mechanisms of technological catch-up and industrial upgrading in economic development

This thesis examines the channels and mechanisms of technological catch-up and industrial upgrading in the context of economic development. Technological progress is critical for a country's sustainable growth and for the successful transition of a country from imitation to innovation. Therefore, to clarify the main channels and mechanisms driving the accumulation of knowledge and technologies in an economy contributes to an understanding of the sources of economic growth. The specific aspects of technological catch-up and industrial upgrading covered in the thesis include inter-sectoral industrial upgrading, the intensification of R&D activities, a country's tapping into foreign sources of knowledge, and a country's changing position in the global value chain. In studying these channels and mechanisms, in-depth theoretical discussion and quantitative methods are applied. In terms of theoretical discussion, the thesis covers many issues relating to the factors contributing to technological progress and draws our attention to the key aspects of such progress. In terms of quantitative methods, advanced econometric methods such as Generalized Method of Moments (GMM), the estimator from Kyriazidou (1997), the Heckman two-step estimator, the Tobit and Probit estimators and various instrumental variable estimators are employed to address different econometric issues and data structures in model estimations. The thesis finds evidence of the critical role of institutional quality in promoting the productive use of scarce tertiary human capital, in stimulating the Research and Development (R&D) investment of firms, and in attracting R&D investment in host countries by multinational enterprises. The thesis also reveals the importance of human capital as an essential input to the process of technological catch-up and industrial upgrading. A case study of Chinese manufacturing firms clarifies the determinants of firm-level R&D investment, which helps us understand and predict the prospects for innovation in the Chinese economy. By linking firm-level production and customs datasets, the thesis probes into the important question of how trade participation affects innovation in the context of the Chinese economy, which is an especially interesting case due to the huge contribution from trade to China's growth miracle to date. The findings draw attention to processing trade and suggest that under some circumstances deep and long-term engagement in processing trade may adversely influence the R&D investment and innovation prospect of firms. This point reflects the difficulty of technological catch-up and industrial upgrading in a world where global production sharing continues to deepen. Based on the results of empirical and quantitative analyses, several policy suggestions are proposed. These include (1) enhancing institutional quality to accompany other growth-promoting policies, (2) encouraging individual and household-level investment in human capital, (3) nurturing domestic R&D stock and research talents at relatively early stages of development and (4) looking beyond the direct targets of industrial and trade policies to take into account the implications for technological catch-up and industrial upgrading when making such policies. The thesis also points out some directions for future research to extract from the dynamics of the world economy those channels and mechanisms of technological catch-up and industrial upgrading yet unclarified by this thesis

Equation of State Dependence of Nonlinear Mode-tide Coupling in Coalescing Binary Neutron Stars

Recently, an instability due to the nonlinear coupling of p-modes to g-modes in tidally deformed neutron stars in coalescing binaries has been studied in some detail. The result is significant because it could influence the inspiral and leave an imprint on the gravitational wave signal that depends on the neutron star equation of state (EOS). Because of its potential importance, the details of the instability should be further elucidated and its sensitivity to the EOS should be investigated. To this end, we carry out a numerical analysis with six representative EOSs for both static and non-static tides. We confirm that the absence of the p-g instability under static tides, as well as its return under non-static tides, is generic across EOSs, and further reveal a new contribution to it that becomes important for moderately high-order p-g pairs (previous studies concentrated on very high order modes), whose associated coupling strength can vary by factors of ~10-100 depending on the EOS. We find that, for stars with stiffer EOSs and smaller buoyancy frequencies, the instability onsets earlier in the inspiral and the unstable modes grow faster. These results suggest that the instability's impact on the gravitational wave signal might be sensitive to the neutron star EOS. To fully assess this prospect, future studies will need to investigate its saturation as a function of the EOS and the binary parameters.Comment: 21 pages, 14 figure

The amplitude of solar p-mode oscillations from three-dimensional convection simulations

The amplitude of solar p-mode oscillations is governed by stochastic excitation and mode damping, both of which take place in the surface convection zone. However, the time-dependent, turbulent nature of convection makes it difficult to self-consistently study excitation and damping processes through the use of traditional one-dimensional hydrostatic models. To this end, we carried out \textit{ab initio} three-dimensional, hydrodynamical numerical simulations of the solar atmosphere to investigate how p-modes are driven and dissipated in the Sun. The description of surface convection in the simulations is free from the tuneable parameters typically adopted in traditional one-dimensional models. Mode excitation and damping rates are computed based on analytical expressions whose ingredients are evaluated directly from the three-dimensional model. With excitation and damping rates both available, we estimate the theoretical oscillation amplitude and frequency of maximum power, $\nu_{\max}$, for the Sun. We compare our numerical results with helioseismic observations, finding encouraging agreement between the two. The numerical method presented here provides a novel way to investigate the physical processes responsible for mode driving and damping, and should be valid for all solar-type oscillating stars.Comment: 11 pages, 8 figures, accepted for publication in Ap

Asteroseismology with 3D magneto-hydrodynamical simulations of stellar convection

The last decade has seen a rapid development in asteroseismology thanks to the CoRoT and Kepler missions. With more detailed asteroseismic observations available, it is becoming possible to infer exactly how oscillations are driven and dissipated in solar-type stars. To study this problem from a theoretical perspective, I carried out three-dimensional (3D) stellar atmosphere simulations together with one-dimensional (1D) stellar structural models of the Sun as well as key benchmark turn-off and subgiant stars. Mode excitation and damping rates are extracted from 3D and 1D stellar models based on analytical expressions. Mode velocity amplitudes are determined by the balance between stochastic excitation and linear damping, which then allows the estimation of the frequency of maximum oscillation power, $\nu_{\max}$, for the first time based on ab initio and parameter-free modelling. I have made detailed comparisons between my numerical results and observational data and achieved very encouraging agreement for all of the target stars. This opens the exciting prospect of using such realistic 3D hydrodynamical stellar models to predict solar-like oscillations across the Hertzsprung-Russell diagram, thereby enabling accurate estimates of stellar properties such as mass, radius, and age. Solar-like oscillations can be observed in photometry and spectroscopy. The photometry method, represented by space-borne missions such as CoRoT, Kepler and TESS, detects stellar oscillations by measuring the variation of stellar luminosity. The spectroscopy method, represented by ground-based telescopes such as BiSON and SONG, use the wavelength shift of spectral lines as a probe to stellar oscillation. The relationship between the two types of measurement is of great importance, as it can not only guide asteroseismic observations but also serves as an additional constraint to stellar atmosphere models. I have carried out ab initio, 3D hydrodynamical numerical simulations of stellar atmosphere as well as realistic spectral line formation calculations to quantify the ratio between luminosity and radial velocity amplitude for the Sun and the red giant $\epsilon$ Tau. Luminosity amplitudes are computed based on the bolometric flux predicted by 3D simulations. Radial velocity amplitudes are determined from the wavelength shift of synthesized spectral lines with methods closely resemble BiSON and SONG observations. The resulting amplitude ratios are directly comparable with corresponding observations, and encouraging agreements between predicted and observed values are achieved for both the Sun and $\epsilon$ Tau. The numerical method presented here provides a novel way of simulating asteroseismic observations from detailed modelling and meanwhile opens an exciting prospect of bridging luminosity and radial velocity amplitude of solar-like oscillations with 3D stellar model atmospheres

Unsupervised CNN-Based DIC for 2D Displacement Measurement

Digital image correlation method is a non contact deformation measurement technique. Despite years of development, it is still difficult to solve the contradiction between calculation efficiency and seed point quantity.With the development of deep learning, the DIC algorithm based on deep learning provides a new solution for the problem of insufficient calculation efficiency in DIC.All supervised learning DIC methods requires a large set of high quality training set. However, obtaining such a dataset can be challenging and time consuming in generating ground truth. To fix the problem,we propose an unsupervised CNN Based DIC for 2D Displacement Measurement.The speckle image warp model is created to transform the target speckle image to the corresponding predicted reference speckle image by predicted 2D displacement map, the predicted reference speckle image is compared with the original reference speckle image to realize the unsupervised training of the CNN.The network's parameters are optimized using a composite loss function that incorporates both the Mean Squared Error and Pearson correlation coefficient.Our proposed method has a significant advantage of eliminating the need for extensive training data annotations. We conducted several experiments to demonstrate the validity and robustness of the proposed method. The experimental results demonstrate that our method can achieve can achieve accuracy comparable to previous supervised methods. The PyTorch code will be available at the following URL: https://github.com/fead1

Total-Factor Energy Efficiency in China’s Agricultural Sector: Trends, Disparities and Potentials

This paper investigates total-factor energy efficiency and analyses the trends of the efficiency changes in China’s agricultural production across 30 provinces and three regions from 2001 to 2011, based on data envelopment analysis (DEA) approach. The potential amount of energy savings and five potential factors for energy efficiency improvement are also empirically studied by Tobit regression model. The findings show that (1) total-factor energy efficiency in China’s agricultural sector is increasing over years but performs heterogeneously across regions; (2) agriculture intensive regions and energy abundant provinces tend to be relatively energy inefficient in agricultural production; and (3) economic structure, agricultural production structure, technological progress and income effect are major potentials for improving energy efficiency, whereas energy price is not a significant factor. This phenomenon results from the divergence of economic development, endowment effects as well as the scale of agricultural production. Policy implications drawn from this research are to upgrade industrial structure and promote agricultural transformation to enhance farmers’ income as well as to establish a land market with entitling land property rights to farmers. This conclusion can assist to form more scientific rural energy policy decision-making in China and also can be extended to other developing economies for sustainable agriculture

Historical Occurrence of Algal Blooms in the Northern Beibu Gulf of China and Implications for Future Trends

Large-scale harmful algal blooms (HABs) occur in the coastal waters of the northern Beibu Gulf, China, and have deleterious effects on the marine ecosystem. The frequency, duration, and extent of HAB events in this region have increased over the last 30 years. However, the underlying causes of HABs and their likely future trends are unclear. To investigate, we evaluated historical data for temporal trends of HABs in the Beibu Gulf, and association with environmental factors as possible drivers. The results confirmed that HAB events had increased in frequency, from 6 reported events during the period 1985–2000, to 13 during 2001–2010, and 20 during 2011–2017. We also found that the geographic scale of algal blooms had increased from tens of km2 to hundreds of km2. There were temporal changes in HAB trigger species: prior to 2000, the cyanobacteria Microcystis aeruginosa was the dominant species, while during the period 2001–2010, blooms of cyanobacteria, dinoflagellates, and diatoms co-occurred, and during 2011–2017, the haptophyte Phaeocystis globosa became the dominant algal bloom species. Principal component analysis and variation partitioning analysis indicated that nutrient discharge, industrial development, and human activities were the key drivers of HAB events, and redundancy analysis showed that variation in the algal community tended to be driven by nutrient structure. Other factors, such as shipping activities and mariculture, also contributed to HAB events and algal succession, especially to P. globosa blooms. We speculated that the increasing severity of algal blooms in the northern Beibu Gulf reflects a more complex aquatic environment and highlights the damaging effects of anthropogenic inputs, urbanization development, and an expanding industrial marine-economy on the marine ecosystem. This research provides more insight into the increase of HABs and will aid their management in the Beibu Gulf

Continual Learning for Abdominal Multi-Organ and Tumor Segmentation

The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of the old classes when learning from new classes because of the catastrophic forgetting problem. In this paper, we first empirically demonstrate that simply using high-quality pseudo labels can fairly mitigate this problem in the setting of organ segmentation. Furthermore, we put forward an innovative architecture designed specifically for continuous organ and tumor segmentation, which incurs minimal computational overhead. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads, thereby offering the flexibility to accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings into the organ-specific heads. These embeddings encapsulate the semantic information of each class, informed by extensive image-text co-training. The proposed method is evaluated on both in-house and public abdominal CT datasets under organ and tumor segmentation tasks. Empirical results suggest that the proposed design improves the segmentation performance of a baseline neural network on newly-introduced and previously-learned classes along the learning trajectory.Comment: MICCAI-202