46 research outputs found
A stepwise cluster analysis approach for downscaled climate projection – A Canadian case study
No full textPreferences and Consensus in the Philosophy of Xunzi
No full textTo understand Confucian ethics as a “hierarchical mode of association” is to think that it is incapable of dealing with a society of strangers or to understand Confucian ethics as “authoritarian” or “collectivist”, and to criticize that it ignores human characteristics and freedoms is to mistake one part of Confucianism for the whole. The Confucian theory of the individual and community actually has a tightly woven structure wherein its recognition of the plurality of diverse individuals and its appeal the unity of a common consensus are combined. From an exploration into the relationship between Xunzi’s concepts of “preference” (teyi 特意) and “consensus” (gongshi 共予) and from the way he uses such concepts as “similarity and difference” (tongyi 同異), “one and ten thousand” (yiwan 一萬), and “unity and division” (tongfen 統分), I show how Xunzi included the diversity of individuals within a pluralist society in a unified community with a collective consensus where this community at the same time guarantees the freedoms and preferences of each individual member that belongs to it
What Is the “Similarity” of Humankind? A Difference between Confucian and Mohist Religious Ethics
No full textBoth pre-Qin Confucianism and Mohism aimed to find a universalist consensus of belief in order to deal with social chaos and disunity, but they diverged at a basic level on what is the fundamental universality of humankind and how to achieve it. In the debate between Mencius and Mohists, Mohists believed that the fundamental “similarity” of humankind lies in the field of “self-interest”, so they emphasized the acquisition of Heaven’s favor through external achievements and sacrificial offerings. On the contrary, Mencius took the fundamental similarity of humans to the level of the inner heart-mind and believed that the fundamental “sameness” of humans lies in their “heart-mind” rather than in being “self-interested”, so he advocated the connection with the heavenly part within us when we follow the guidance of the heart-mind. Their differing interpretations of the fundamental question of “the similarity of humankind” ultimately resulted in the divergences and discrepancies between two religious ethical forms and their outcomes in early China
Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada
No full textA coupled dynamical-copula downscaling approach for temperature projections over the Canadian Prairies
No full textA mixed-level factorial inference approach for ensemble long-term hydrological projections over the Jing River Basin
No full textLong-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow
Potential benefits of limiting global warming for the mitigation of temperature extremes in China
No full textIn this study, we attempt to quantify the potential impacts of two global warming levels (i.e., 1.5 °C and 2.0 °C) on extreme temperature indices across China. The CMIP6 dataset is first evaluated against the CN05.1 observation for the historical period of 1995–2014. Then, future spatiotemporal patterns of changes in extreme temperature at two global warming levels under two shared socio-economic pathway scenarios (SSP245 and SSP585) are further analyzed. Overall, China will experience more frequent and intense high temperature events, such as summer days (SU), tropical nights (TR), warm days (TX90p) and nights (TN90p). On the other hand, under the SSP585, the number of icing days and frost days is projected to decrease at two global warming levels, with the maximal days of decrease (exceeding 20 days) seen in the west of China. Our results suggest that limiting global warming to 1.5 °C rather than 2.0 °C is beneficial to reduce extreme temperature risks. As temperature increases to 1.5 °C and then 2.0 °C above preindustrial levels, the most extreme temperature indices are expected to increase proportionately more during the final 0.5° than during the first 1.5° across most regions of China. For some warm indices, such as the warmest day (TXx), summer days (SU), and warm days (TX90p), the largest incremental changes (from 1.5° to 2.0°) tend to be found in the southwest. Under the SSP585, the incremental changes are similar to the change in the SSP245, but smaller magnitude and spatial extent
Learning from Demonstrations of Critical Driving Behaviours Using Driver’s Risk Field
No full textIn recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous work on IL planners shows sample inefficiency and low generalisation in safety-critical scenarios, on which they are rarely tested. As a result, IL planners can reach a performance plateau where adding more training data ceases to improve the learnt policy. First, our work presents an IL model using spline coefficient parameterisation and offline expert queries to enhance safety and training efficiency. Then, we expose the weakness of the learnt IL policy by synthetically generating critical scenarios through optimisation of parameters of the driver's risk field (DRF), a parametric human driving behaviour model implemented in a multi-agent traffic simulator based on the Lyft Prediction Dataset. To continuously improve the learnt policy, we retrain the IL model with augmented data. Thanks to the expressivity and interpretability of the DRF, the desired driving behaviours can be encoded and aggregated to the original training data. Our work constitutes a full development cycle that can efficiently and continuously improve the learnt IL policies in closed-loop. Finally, we show that our IL planner developed with 30 times less training resource still has superior performance compared to the previous state-of-the-art.Mechanical Engineerin
Prediction of long-term photovoltaic power generation in the context of climate change
No full textAccurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %
