State capacity and naval buildup : the Sino-Japanese divergence in the late nineteenth century

The East Asian modernization divergence in the late nineteenth century has long puzzled historians and social scientists. As Qing China, given its vast territory, large population and dominating influence spreading to neighboring countries, failed to modernize herself as its small island neighbor Japan did after the forced opening up by the West. One important divergence is their military capability, especially that of navy. The relatively higher capacity of the Imperial Japanese Navy has played a decisive role in its victory over the Beiyang Fleet in the 1894/95 First Sino-Japanese War. Following the defeat, Qing China was burdened with huge indemnity, eventually collapsed and entered long decades of chaos, whereas Meiji Japan continued rapid modernization, further demonstrated its military power in war with Russia, and became the only recognized power in Asia. Explaining the Sino-Japanese divergence in naval buildup is the first step to tackle the entire modernization puzzle. Having challenged two conventional explanations of national security decision-making and economic modernization, this thesis offers a new perspective by arguing that the root of divergence lies in their different resource mobilization capacity. Specifically, I demonstrate that the elastic tax revenue, fiscal centralization and enormous borrowing capacity equipped Meiji Japan as a strong state able to quickly mobilize a vast sum of resource for expensive naval buildup and war. In contrast, in Qing China, the growingly decentralized fiscal system, together with the stagnated tax revenue and limited borrowing capacity, made resource mobilization a prolonged struggle for the central government. Consequently, despite the statesmen’s repetitive emphasis of naval security and buildup, the Chinese state’s weak resource mobilization capacity has significantly hindered its pursuit of naval power and gradually widened the gap with the stronger Japanese state

Sex Difference In Identification Of Predictive Tumor Tissue Metabolites Associated With Colorectal Cancer Prognosis

Colorectal cancer (CRC) is the third major cause of cancer-related deaths in the United States in 2020. Sex-related differences in CRC stage, prognosis, and metabolism have become increasingly popular in cancer research. Males have poorer survival for CRC, but females with right-sided colon cancer (RCC) have aberrant metabolism correlated with poor survival. Delay in knowing the condition of CRC in female patients would result in poor prognosis, which could be avoided by predicting prognostic outcomes. Random Survival Forest (RSF) is ideal for exploration and making predictions using metabolomics data with high dimension, strong collinearity, and heterogeneity, which CPH models could not efficiently address. In this retrospective study including 197 patients, we applied an RSF prediction method based on the backward selection algorithm in 5-year overall survival (OS) for 95 female CRC patients and validated its performance. We also investigated Cox proportional hazard models (CPH), lasso penalized Cox regression (Cox-Lasso), and Logistic Regression (LR) and compared their predictive performances. RSF using the backward selection algorithm showed the best performance with the C-index of the training and testing sets reaching 0.81(95% CI: 0.810-0.813) and 0.78 (95% CI: 0.776-0.777) respectively and identified the five most predictive metabolites for female 5-year OS: glutathione, citrulline, phosphoenolpyruvate, lysoPC (16:0), and asparagine. Accordingly, the backward selection algorithm-based Random Survival Forest model using tumor tissue metabolic profile is promising for predicting 5-year OS for female CRC patients. The results could be easily interpreted and applied in preventive medicine and precision medicine, guiding clinicians in choosing targeted treatments by sex for better survival and avoiding unnecessary treatments

Formative and Summative Analyses of Disciplinary Engagement and Learning in Big Open Online Course

Situative theories of knowing and participatory approaches to learning and assessment were used to design and then analyze learning in a “big open online course” (“BOOC”) on educational assessment. The course was delivered using Google’s Course Builder platform which was customized extensively to support both summative and formative analyses of disciplinary social engagement and individual learning. The course featured personalized “wikifolio” public assignments peer commenting, endorsement, & promotion, formal online examinations, open digital badges, and participatory learning analytics. The course was first completed by 60 students in 2013 and impressive levels of engagement and learning were documented. The course was further refined in 2014 with embedded streaming videos, embedded formative assessments, and streamlined learning analytics. Of the sixty students who registered for the course, 22 completed it. This paper illustrates the more formative learning analytics used to advance the shared discourse in the course as well as the other new features and provides detailed evidence of engagement & learning.Googl

Undergraduates’ Self-reported Learning Outcomes of General Education Courses: A Case Study of a Chinese Elite University

Based on a conceptual framework of college impact, this article studies the impact of gender, graduation paths, family cultural capital and disciplines on undergraduates’ self-reported learning outcomes of general education courses. The conclusions are as follows: female students report significantly higher learning outcomes of general education courses than male students; students who will enter the labor market after graduation report significantly higher learning outcomes of general education courses than students who will enter graduate school; students majoring in social sciences report higher learning outcomes of general education courses than students from other disciplines. Familial cultural capital has no significant influence on undergraduates’ self-reported learning outcomes of general education courses. This article makes exploratory explanations of the above results from two perspectives

D2D^2SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm

In recent years, aerial swarm technology has developed rapidly. In order to accomplish a fully autonomous aerial swarm, a key technology is decentralized and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates the relative pose and the consistent global trajectories. In this paper, we propose $D^2$SLAM: a decentralized and distributed ($D^2$) collaborative SLAM algorithm. This algorithm has high local accuracy and global consistency, and the distributed architecture allows it to scale up. $D^2$SLAM covers swarm state estimation in two scenarios: near-field state estimation for high real-time accuracy at close range and far-field state estimation for globally consistent trajectories estimation at the long-range between UAVs. Distributed optimization algorithms are adopted as the backend to achieve the $D^2$ goal. $D^2$SLAM is robust to transient loss of communication, network delays, and other factors. Thanks to the flexible architecture, $D^2$SLAM has the potential of applying in various scenarios

Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine