The collapse of the former Soviet Union signaled failure of large-scale experiment in communitarian property. Privatization reform consequently was taken as the start point to transfer the planned economy to a market economy by the post socialist countries. This also occurred in economic transition countries such as China. However, in overcoming the tragedy of the commons privatization might create anticommons problems. Here we develop a nested common-private interface framework from the perspective of resource system and resource units and apply this framework to explain reforms of rangeland property in China and Kyrgyzstan. We confirmed that the root of the dilemma, either caused by commons or anticommons, can be attributed to the interface mismatch between individual elements and common elements. Trying to overcome the dilemma by changing property arrangements alone cannot eliminate the incentive mismatch caused by the common-private interface. Institutions aimed at alleviating the mismatch are accordingly required. Theoretically, this framework converts Ostrom's concept of commons into liberal commons that the members have options to exit, which is becoming increasingly common in the current global context of marketization. In the real world, this framework can serve to understand the property reform progress of transition countries, and may enlighten future property reforms.
With the big popularity and success of Judea Pearl's original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a forecast scenario. It further discusses some potential benefits and challenges for causal inference with time series forecasting when modeling the counterfactuals, estimating the uncertainty and incorporating prior knowledge to estimate causal effects in different forecasting scenarios.
Horizontally aligned carbon nanotube (HACNT) arrays hold significant potential for various applications in nanoelectronics and material science. However, their high-throughput characterization remains challenging due to the lack of methods with both high efficiency and high accuracy. Here, we present a novel technique, Calibrated Absolute Optical Contrast (CAOC), achieved through the implementation of differential principles to filter out stray signals and high-resolution calibration to endow optical contrast with physical significance. CAOC offers major advantages over previous characterization techniques, providing consistent and reliable measurements of HACNT array density with high throughput and non-destructive assessment. To validate its utility, we demonstrate wafer-scale uniformity assessment by rapid density mapping. This technique not only facilitates the practical evaluation of HACNT arrays but also provides insights into balancing high throughput and high resolution in nanomaterial characterization.
Textual information from online news is more timely than insurance claim data during catastrophes, and there is value in using this information to achieve earlier damage estimates. In this paper, we use text-based information to predict the duration and severity of catastrophes. We construct text vectors through Word2Vec and BERT models, using Random Forest, LightGBM, and XGBoost as different learners, all of which show more satisfactory prediction results. This new approach is informative in providing timely warnings of the severity of a catastrophe, which can aid decision-making and support appropriate responses.