A new characterization of second-order stochastic dominance

We provide a new characterization of second-order stochastic dominance, also known as increasing concave order. The result has an intuitive interpretation that adding a risk with negative expected value in adverse scenarios makes the resulting position generally less desirable for risk-averse agents. A similar characterization is also found for convex order and increasing convex order. The proofs techniques for the main result are based on properties of Expected Shortfall, a family of risk measures that is popular in financial regulation

College English Writing on Scaffolding Theory

Based on theories of zones of proximal development and scaffolding instruction, the author manages to combine the two theories and attempts to put it to the practice of college English writing in class on peer composition tutoring. Divided into different groups, students are required to give instructions and suggestion after reviewing their peer’s composition. Key words: Scaffolding; Zone of Proximal Development; Peer revie

Computationally-Efficient Thermal Modeling Techniques for Electric Machines

Electric machines are widely used in industry, ranging from as large as 700Mw generators used in Three Gorges in Yichang, China, to as small as brushless DC motors used in your computer hard drives. For some areas, such as automotive powertrain design, accurate and computationally-efficient models for electric machines are in great demand since they can play important roles as either real-time observers or in vehicle simulations. In this dissertation, computationally-efficient thermal and electromagnetic models for electric machines are developed. In particular, a thermal convection model to capture air region heat convection considering air density variation and slotting effects on stator surface is developed and analyzed; and an electromagnetic model to calculate AC winding resistance of different winding configurations is proposed and integrated. With the developed techniques, thermal and electromagnetic performance can be accurately and efficiently estimated. Furthermore, this dissertation has also conducted a comparative study, which shows the advantages of using thermal models for online loss estimation for electric machines over the conventionally-used electrical model. The conclusions and results of this study provide useful tools for online loss estimators with model uncertainty.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168030/1/yuanying_1.pd

The Fungal CYP51s: Their Functions, Structures, Related Drug Resistance, and Inhibitors

CYP51 (Erg11) belongs to the cytochrome P450 monooxygenase (CYP) superfamily and mediates a crucial step of the synthesis of ergosterol, which is a fungal-specific sterol. It is also the target of azole drugs in clinical practice. In recent years, researches on fungal CYP51 have stepped into a new stage attributing to the discovery of crystal structures of the homologs in Candida albicans, Cryptococcus neoformans and Aspergillus fumigatus. This review summarizes the functions, structures of fungal CYP51 proteins, and the inhibitors targeting these homologs. In particular, several drug-resistant mechanisms associated with the fungal CYP51s are introduced. The sequences and crystal structures of CYP51 proteins in different fungal species are also compared. These will provide new insights for the advancement of research on antifungal agents

MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed

Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy data is collected from the current online model, it suffers from an imbalanced distribution of state-action pairs, resulting in severe overestimation problems during training. On the other hand, random data offers a more uniform distribution of state-action pairs, but is challenging to obtain in industrial scenarios as it could negatively impact platform revenue and user experience due to random exploration. As the two types of data have different distributions, designing an effective strategy to leverage both types of data to enhance the efficacy of the RL model training has become a highly challenging problem. In this study, we propose a framework named Multi-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. Specifically, MDDL incorporates a novel imitation learning signal to mitigate overestimation problems in strategy data and maximizes the RL signal for random data to facilitate effective learning. In our experiments, we evaluated the proposed MDDL framework in a real-world position allocation system and demonstrated its superior performance compared to the previous baseline. MDDL has been fully deployed on the Meituan food delivery platform and currently serves over 300 million users.Comment: 4 pages, 2 figures, accepted by SIGIR 202

Fabrication of nanoscale NiO/Ni heterostructures as electrocatalysts for efficient methanol oxidation

Nanoscale NiO/Ni heterostructures on oxygen-functionalized carbon nanotubes with low Ni loading (3–4 wt%) are fabricated by delicate thermal-annealing treatments, which are designed according to the temperature-programmed thermal analysis. Activity and stability tests demonstrate that NiO/Ni heterostructures with a stable Ni core inside an oxyhydroxide shell (in solution) exhibit enhanced stability and catalytic activity for methanol oxidation

The causal relationship between sarcoidosis and autoimmune diseases: a bidirectional Mendelian randomization study in FinnGen

BackgroundSarcoidosis has been considered to be associated with many autoimmune diseases (ADs), but the cause-and-effect relationship between these two diseases has not been fully explored. Therefore, the objective of this study is to explore the possible genetic association between sarcoidosis and ADs.MethodsWe conducted a bidirectional Mendelian randomization (MR) study using genetic variants associated with ADs and sarcoidosis (4,041 cases and 371,255 controls) from the FinnGen study. The ADs dataset comprised 96,150 cases and 281,127 controls, encompassing 44 distinct types of autoimmune-related diseases. Subsequently, we identified seven diseases within the ADs dataset with a case size exceeding 3,500 and performed subgroup analyses on these specific diseases.ResultsThe MR evidence supported the causal association of genetic predictors of ADs with an increased risk of sarcoidosis (OR = 1.79, 95% CI = 1.59 to 2.02, P IVW-FE = 1.01 × 10-21), and no reverse causation (OR = 1.05, 95% CI 0.99 to 1.12, PIVW-MRE = 9.88 × 10-2). Furthermore, subgroup analyses indicated that genetic predictors of type 1 diabetes mellitus (T1DM), celiac disease, and inflammatory bowel disease (IBD) were causally linked to an elevated risk of sarcoidosis (All P < 6.25 × 10-3). Conversely, genetic predictors of sarcoidosis showed causal associations with a higher risk of type 1 diabetes mellitus (P < 6.25 × 10-3).ConclusionThe present study established a positive causal relationship between genetic predictors of ADs (e.g. T1DM, celiac disease, and IBD) and the risk of sarcoidosis, with no evidence of reverse causation

Comprehensive landscape and future perspectives of non-coding RNAs in esophageal squamous cell carcinoma, a bibliometric analysis from 2008 to 2023

Objectives: Summarize the progress and hot topic evolution of non-coding RNAs (ncRNAs) research in esophageal squamous cell carcinoma (ESCC) in recent years and predict future research directions.Methods: Relevant articles from the Web of Science until 31 October 2023 were obtained. Bibliometric analysis of included articles was performed using software (VOSviewer, CiteSpace, and Bibliometrix). The volume and citation of publications, as well as the country, institution, author, journal, keywords of the articles were used as variables to analyze the research trends and hot spot evolution.Results: 1,118 literature from 2008 to 2023 were retrieved from database, with 25 countries/regions, 793 institutions, 5,426 authors, 261 journals involved. Global cooperation was centered on China, Japan, and the United States. Zhengzhou University, an institution from China, had the highest publication. The most prolific author was Guo Wei, and the most prolific journal was Oncology Letters. Analysis of keywords revealed that the research in this field revolved around the role of ncRNAs in the occurrence, development, diagnosis, treatment, and prognosis of ESCC, mainly including micro RNAs, long non-coding RNAs, and then circular RNAs.Conclusion: Overall, research on ncRNAs in ESCC remains strong. Previous research has mainly focused on the basic research, with a focus on the mechanism of ncRNAs in the occurrence, development, diagnosis, treatment, and prognosis of ESCC. Combining current research with emerging disciplines to further explore its mechanisms of action or shifting the focus of research from preclinical research to clinical research based on diagnosis, treatment, and prognosis, will be the main breakthrough in this field in the future