The Overall Analysis and Regional Difference of China’s Urbanization

Development of urbanization is the primary driving force of restructuring China’s economic and stimulating domestic demand. It’s important to study the progress of China’s current urbanization. It has an important practical significance to research on the determinants of China’s contemporary urbanization. In this paper, the authors use panel data of 31 provinces from 2001 to 2011 to analyze the determinants of China’s urbanization. On the whole, it’s found that economic growth, industrial structure, investment of fixed assets, education level, economic gap between urban and rural areas, farmers’ income structure have positive and significant effects. In the background of the difference of natural conditions and economic fundamentals, the determinants of urbanization has obvious regional differences

Time dependence of heart rate variability during treadmill running

To investigate the time dependence of the heart rate variability (HRV) during treadmill running, a feedback control loop was implemented to eliminate the potentially confounding influence of cardiovascular drift. Without cardiovascular drift, observed changes in HRV can be directly attributed to time only and not to drift-related increases in heart rate. To quantify the time-dependence of HRV, standard HRV metrics for two consecutive windows of equal duration (12.5 min) were computed and compared. Eight participants were included. The outcome measures showed an overall tendency to decrease over time. Seven of the 10 HRV metrics were significantly lower (p<0.05); three HRV metrics showed moderate evidence of decrease over time, viz. average control power P∇u (p = 0.053), very-low frequency power (VLF) of the RR-signal (p = 0.072) and low frequency power (LF) of the RR-signal (p = 0.12). Taken together, these results provide evidence of a decrease in HRV over time during treadmill running; the employment of feedback control of heart rate is important as cardiovascular drift was eliminated. Further work is required to optimize the experimental design and to use a larger sample size to improve the statistical power of the results

Heart rate control using first- and second-order models during treadmill exercise

Heart rate control using first- and second-order models was compared using a novel control design strategy which shapes the input sensitivity function. Ten participants performed two feedback control test series on a treadmill with square wave and constant references. Using a repeated measures, counterbalanced study design, each series compared controllers C1 and C2 based on first- and second-order models, respectively. In the first series, tracking accuracy root-mean-square tracking error (RMSE) was not significantly lower for C2: 2.59 bpm vs. 2.69 bpm (mean, C1 vs. C2), p = 0.79. But average control signal power was significantly higher for C2: 11.29 × 10^{−4} m2/s2 vs. 27.91 × 10^{−4} m2/s2, p = 3.1 × 10^{−10}. In the second series, RMSE was also not significantly lower for C2: 1.99 bpm vs. 1.94 bpm, p = 0.39; but average control signal power was again significantly higher for C2: 2.20 × 10^{−4} m2/s2 vs. 2.78 × 10^{−4} m2/s2, p = 0.045. The results provide no evidence that controllers based on second-order models lead to better tracking accuracy, despite the finding that they are significantly more dynamic. Further investigation using a substantially larger sample size is warranted

Identification of heart rate dynamics during treadmill exercise: comparison of first- and second-order models

Background: Characterisation of heart rate (HR) dynamics and their dependence on exercise intensity provides a basis for feedback design of automatic HR control systems. This work aimed to investigate whether the second-order models with separate Phase I and Phase II components of HR response can achieve better fitting performance compared to the first-order models that do not delineate the two phases. Methods: Eleven participants each performed two open-loop identification tests while running at moderate-to-vigorous intensity on a treadmill. Treadmill speed was changed as a pseudo-random binary sequence (PRBS) to excite both the Phase I and Phase II components. A counterbalanced cross-validation approach was implemented for model parameter estimation and validation. Results: Comparison of validation outcomes for 22 pairs of first- and second-order models showed that root-mean-square error (RMSE) was significantly lower and fit (normalised RMSE) significantly higher for the second-order models: RMSE was 2.07 bpm ± 0.36 bpm vs. 2.27 bpm ± 0.36 bpm (bpm = beats per min), second order vs. first order, with p = 2.8 × 10^{−10} ; fit was 54.5% ± 5.2 % vs. 50.2% ± 4.8 %, p = 6.8 × 10^{−10}. Conclusion: Second-order models give significantly better goodness-of-fit than firstorder models, likely due to the inclusion of both Phase I and Phase II components of heart rate response. Future work should investigate alternative parameterisations of the PRBS excitation, and whether feedback controllers calculated using second-order models give better performance than those based on first-order models

An Empirical Analysis on the Relationship Among Fiscal Expansion, Credit, and the New Urbanization

At present, China’s new urbanization is in rapid development stage. Infrastructure construction and the construction of social medical security system need a lot of money. Thus, the financing problem becomes the bottleneck of the development of the new urbanization. Fiscal expenditure and credit is the main source of funds of the new urbanization. So, this paper uses the time series data of 1952-2012 to analyze the relationship among fiscal expansion, credit and the new urbanization. The result shows that there is a long-term and stable relationship among fiscal expansion, credit, and the new urbanization. In addition, fiscal expansion is the granger causality of the new urbanization, and the new urbanization is the granger causality of credit. It indicates that fiscal expansion has played a positive role on the development of the new urbanization, while credit is still in demand following

Heart Rate Dynamics Identification and Control in Cycle Ergometer Exercise: Comparison of First- and Second-Order Performance

Background: Accurate and robust feedback control of human heart rate is important for exercise testing and prescription. Feedback controllers can be designed using first-order, linear, time-invariant models of heart rate dynamics, but it remains to investigate whether second-order models lead to better identification and control performance. The distinguishing contribution of this research is the direct employment of established physiological principles to determine model structure, and to focus the feedbackdesign goals: cardiac physiology proposes a two-phase second-order response, delineated into fast and slow components; the natural phenomenon of broadspectrum heart-rate variability motivates a novel feedback design approach that appropriately shapes the input-sensitivity function. Aim: The aim of this work was to compare the fidelity of first- and second-order models of heart rate response during cycle-ergometer exercise, and to compare the accuracy and dynamics of feedback controllers that were designed using the two model structures. Methods: Twenty-seven participants each took part in two identification tests to generate separate estimation and validation data sets, where ergometer work rate was a pseudorandombinary sequence and in two feedback tests where controllers were designed using the first- or second-order models. Results: Second-order models gave substantially and significantly higher model fit (51.9 % vs. 47.9 %, p < 0.0001; second order vs. first order) and lower root-mean-square model error (2.93 bpm vs. 3.21 bpm, p < 0.0001). There was modest improvement in tracking accuracy with controllers based on second-order models, where mean root-mean-square tracking errors were 2.62 bpm (second order) and 2.77 bpm (first order), with p = 0.052. Controllers based on second-order models were found to be substantially and significantly more dynamic: mean values of average control signal power were 9.61 W^2 and 7.56 W^2, p < 0.0001. Conclusion: The results of this study confirm the hypotheses that second-order models of heart-rate dynamics give better fidelity than first-order models, and that feedback compensator designs that use the additional dynamic mode give more accurate and more dynamic closed-loop control performance

Reducing Neuroinflammation in Psychiatric Disorders: Novel Target of Phosphodiesterase 4 (PDE4) and Developing of the PDE4 Inhibitors

Multiple lines of evidence support the pathogenic role of neuroinflammation in psychiatric illness. Cyclic adenosine monophosphate (cAMP) is a critical regulator of microglia homeostasis; as the predominant negative modulator of cyclic AMP signaling within microglia, and phosphodiesterase 4 (PDE4) represents a promising target for modulating immune function. The approach for pharmacological manipulation of cAMP levels using specifc PDE4 inhibitors provokes an ant-iinflammatory response. Specifcally, PDE4 inhibitors have recently emerged as a potential therapeutic strategy for neuroinflammatory, neurodegenerative, and psychiatric diseases. Mechanistically, PDE4 inhibitors produce an anti-inflammatory and neuroprotection effect by increasing the accumulation of cAMP and activating protein kinase A (PKA), the signaling pathway of which is thought to play an important role in the development of psychiatric disorders. This chapter reviews present knowledge of the relationship between neuroinflammation and classical psychiatric disorders (major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia) and demonstrates the signaling pathways that underlie the use of PDE4 inhibitors in neuroinflammation. In addition, among the four subtypes (A-D) of PDE4, it remains unclear which one exerts suppressive effects on neuroinflammation. Understanding how PDE4 and neuroinflammation interact can reveal pathogenic clues and help target new preventive and symptomatic therapies for psychiatric illness

Robust Knowledge Adaptation for Dynamic Graph Neural Networks

Graph structured data often possess dynamic characters in nature, e.g., the addition of links and nodes, in many real-world applications. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling such graph data, where almost all the existing approaches assume that when a new link is built, the embeddings of the neighbor nodes should be updated by learning the temporal dynamics to propagate new information. However, such approaches suffer from the limitation that if the node introduced by a new connection contains noisy information, propagating its knowledge to other nodes is not reliable and even leads to the collapse of the model. In this paper, we propose AdaNet: a robust knowledge Adaptation framework via reinforcement learning for dynamic graph neural Networks. In contrast to previous approaches immediately updating the embeddings of the neighbor nodes once adding a new link, AdaNet attempts to adaptively determine which nodes should be updated because of the new link involved. Considering that the decision whether to update the embedding of one neighbor node will have great impact on other neighbor nodes, we thus formulate the selection of node update as a sequence decision problem, and address this problem via reinforcement learning. By this means, we can adaptively propagate knowledge to other nodes for learning robust node embedding representations. To the best of our knowledge, our approach constitutes the first attempt to explore robust knowledge adaptation via reinforcement learning for dynamic graph neural networks. Extensive experiments on three benchmark datasets demonstrate that AdaNet achieves the state-of-the-art performance. In addition, we perform the experiments by adding different degrees of noise into the dataset, quantitatively and qualitatively illustrating the robustness of AdaNet.Comment: 14 pages, 6 figure

Explainability for Large Language Models: A Survey

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this paper, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations, and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional machine learning models