From Government to Market?:A Discrete Choice Analysis of Policy Instruments for Electric Vehicle Adoption (CEIBS Working Paper, No. 039/2020/POM, 2020)

With the calls for policy instruments to shift from “government” to “market”, surging interest leads to a broad debate on the role of market-oriented policy instruments in promoting the adoption of electric vehicles (EVs). As the two prime examples of market-oriented policy instruments, personal carbon trading (PCT) and tradable driving credit (TDC) schemes are theoretically regarded to alter consumers’ EV preferences by both economic and psychological motivations. However, limited studies validate such effects. To fill the gaps, we conduct a discrete choice experimental survey by integrating vehicle, psychological, and policy attributes together. The empirical results from China reveal how consumers make trade-offs between economic and psychological motivations. In particular, although PCT and TDC can stimulate consumers’ EV adoption behaviors through monetary revenues, the positive effect of more revenues from PCT and TDC in promoting EV adoption is not always supported because EV adoption is subject to some psychological attributes, especially perceived norm pressures. It implies that consumers with stricter norms will be driven more by social and moral pressures than by monetary revenues. Even so, PCT and TDC are considered to be more powerful and sustainable than existing financial incentives. These findings not only contribute to the understanding of the interaction between psychological and policy attributes, but also provide insights for policymakers to design novel policy instruments to promote EV adoption

Damage Characteristics of Argillaceous Quartz Sandstone Mesostructure under Different Wetting-drying Conditions

Extensive water–rock interaction in the Three Gorges Reservoir area of the Yangtze River leads to rock mass deterioration along the reservoir banks. However, mineral evolution behavior and its effect on the mesostructure deterioration of rocks under the wetting–drying cycle condition remain unknown. So, the wetting–drying cycle tests were conducted on peculiar argillaceous quartz sandstone in TGRA under neutral (pH = 7) and alkaline (pH = 10) water environments. Here, we provided detailed physical and microscopy images data to determine the control mechanism of mineral behavior on the evolution of sandstone’s mesostructure. Under the neutral condition, repeated “absorption and swelling–dehydration and contraction” of clay minerals leads to the repeated physical action of “squeezing–unloading” in the interior of a rock. This results in the initiation and gradual expansion of cracks in the framework mineral quartz, exhibiting failure mode from the interior to the exterior. In contrast, under the alkaline condition, the dissolution on the surface of quartz particles leads to the expansion and connection of pores, implying that the sandstone exhibits failure mode from the exterior to the interior. Moreover, the internal mechanical analysis indicates the minerals are at high pressure because of the expansion of clay minerals in the neutral solution. However, in an alkaline water environment, the extrusion pressure of framework mineral quartz decreases significantly and is not easily broken due to increased porosity. Thus, the evolution behavior of minerals in different water environments plays an important role in the damage of the rock

EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs.

BackgroundPolycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic subunit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are characterized by high tissue-specificity; however, little is known about the tissue profile of the EZH2- interacting lncRNAs.ObjectiveHere we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno- precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In addition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, majority of the lncRNAs were firstly reported to be associated with EZH2.ConclusionOur findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation

Coordination method for DC fault current suppression and clearance in DC grids

The modular multilevel converter (MMC) based DC grid is considered as a future solution for bulk renewable energy integration and transmission. However, the high probability of DC faults and their rapid propagation speed are the main challenges of the development of DC grids. Existing research mainly focuses on the DC fault clearance methods, while the fault current suppression methods are still under researched. Additionally, the coordination method of fault current suppression and clearance needs to be optimized. In this paper, the technical characteristics of the current suppression methods are studied, based on which the coordinated methods of fault current suppression and clearance are proposed. At last, a cost comparison of these methods is presented. The research results show that the proposed strategies can reduce the cost of the protection equipment

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}

Changes and rebuilding of university education in China

학위논문(박사)--서울대학교 대학원 :교육학과 교육사회학전공,2004.Docto

A bi-objective programming model for carbon emission quota allocation: evidence from the Pearl River Delta region

As a core component of the emission trading scheme (ETS), the initial allocation of carbon quotas is extremely important. Currently, most allocation methods mainly focus on the realization of a single performance goal, which will result in conflicts between different levels of participants. To overcome this deficiency, this paper develops a bi-objective programming model (BPM) with two sub-objective functions of abatement costs and carbon assets. Meanwhile, cost-oriented model (CM) and asset-oriented model (AM) are implemented as comparison approaches that represent the minimization of regional abatement costs and the maximization of individual interests, respectively. The empirical results of the Pearl River Delta (PRD) region reveal that BPM is the most efficient and feasible approach to some extent. More precisely, BPM can motivate the enthusiasm of all participants while optimizing abatement costs. With the increase of regional total quotas, the advantage of BPM becomes more and more prominent. The contribution of this paper is to present a novel method for carbon emission quota allocation, which fills the gap in the existing literature. Furthermore, the proposed method that can be deployed in other similar regions assists policymakers in enacting an effective emission reduction policy and in better understanding the objectives of economy, energy and environment