The continuous-time pre-commitment KMM problem in incomplete markets

This paper studies the continuous-time pre-commitment KMM problem proposed by Klibanoff, Marinacci and Mukerji (2005) in incomplete financial markets, which concerns with the portfolio selection under smooth ambiguity. The decision maker (DM) is uncertain about the dominated priors of the financial market, which are characterized by a second-order distribution (SOD). The KMM model separates risk attitudes and ambiguity attitudes apart and the aim of the DM is to maximize the two-fold utility of terminal wealth, which does not belong to the classical subjective utility maximization problem. By constructing the efficient frontier, the original KMM problem is first simplified as an one-fold expected utility problem on the second-order space. In order to solve the equivalent simplified problem, this paper imposes an assumption and introduces a new distorted Legendre transformation to establish the bipolar relation and the distorted duality theorem. Then, under a further assumption that the asymptotic elasticity of the ambiguous attitude is less than 1, the uniqueness and existence of the solution to the KMM problem are shown and we obtain the semi-explicit forms of the optimal terminal wealth and the optimal strategy. Explicit forms of optimal strategies are presented for CRRA, CARA and HARA utilities in the case of Gaussian SOD in a Black-Scholes financial market, which show that DM with higher ambiguity aversion tends to be more concerned about extreme market conditions with larger bias. In the end of this work, numerical comparisons with the DMs ignoring ambiguity are revealed to illustrate the effects of ambiguity on the optimal strategies and value functions.Comment: 53 pages, 7 figure

Intelligent optical performance monitor using multi-task learning based artificial neural network

An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals' amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and OSNR monitoring root-mean-square error of 0.63 dB for the three modulation formats under consideration. Furthermore, the number of neuron needed for the single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity optical performance monitoring devices for real-time performance monitoring

Building Transportation Foundation Model via Generative Graph Transformer

Efficient traffic management is crucial for maintaining urban mobility, especially in densely populated areas where congestion, accidents, and delays can lead to frustrating and expensive commutes. However, existing prediction methods face challenges in terms of optimizing a single objective and understanding the complex composition of the transportation system. Moreover, they lack the ability to understand the macroscopic system and cannot efficiently utilize big data. In this paper, we propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy and provides a foundation for solving complex transportation problems with real data. The proposed approach shows promising results in accurately predicting traffic outcomes in an urban transportation setting

Dai-Huang-Fu-Zi-Tang Alleviates Intestinal Injury Associated with Severe Acute Pancreatitis by Regulating Mitochondrial Permeability Transition Pore of Intestinal Mucosa Epithelial Cells

Objective. The aim of the present study was to examine whether Dai-Huang-Fu-Zi-Tang (DHFZT) could regulate mitochondrial permeability transition pore (MPTP) of intestinal mucosa epithelial cells for alleviating intestinal injury associated with severe acute pancreatitis (SAP). Methods. A total of 72 Sprague-Dawley rats were randomly divided into 3 groups (sham group, SAP group, and DHFZT group, n=24 per group). The rats in each group were divided into 4 subgroups (n=6 per subgroup) accordingly at 1, 3, 6, and 12 h after the operation. The contents of serum amylase, D-lactic acid, diamine oxidase activity, and degree of MPTP were measured by dry chemical method and enzyme-linked immunosorbent assay. The change of mitochondria of intestinal epithelial cells was observed by transmission electron microscopy. Results. The present study showed that DHFZT inhibited the openness of MPTP at 3, 6, and 12 h after the operation. Meanwhile, it reduced the contents of serum D-lactic acid and activity of diamine oxidase activity and also drastically relieved histopathological manifestations and epithelial cells injury of intestine. Conclusion. DHFZT alleviates intestinal injury associated SAP via reducing the openness of MPTP. In addition, DHFZT could also decrease the content of serum diamine oxidase activity and D-lactic acid after SAP

Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis

ObjectivesThe most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate-related biomarkers for LN and analyze the underlying mechanism.MethodsTo identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA.ResultsA sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways ‘antigen processing and presentation’ and ‘NOD-like receptor signaling pathway’. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well.ConclusionOverall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN

Comparative study on morphological differences between weedy rice and cultivated rice in Shanghai area

In recent years,with the promotion of direct seeding paddy technology,weedy rice is becoming more and more harmful to the cultivated rice.Comparing the morphological differences between the weedy rice and the cultivated rice is valuable for us to identify weedy rice from cultivated rice.A total of 98 samples including weedy rice and cultivated rice was collected from the paddy fields of the suburb of Shanghai.The data including individual height,grain length and width,and their ratio,spikelet length,awn length,and grain colors were determined.Based on the above data,clustering analysis,Principal Component Analysis,and Non-matric Multidimentional Scaling (Non-matric MDS) were applied to quantitatively analyze the phonetic similarity among the 98 samples.The results showed that these samples could be divided into three groups,including 57 and 33 and 8 samples,respectively.Based on seed shattering trait,spike shape,seed coat colors and the data of their maturity,Group 1 and Group 2 were identified as weedy rice and cultivated rice,respectively,while Group 3 as the samples with transitional characteristics between the weedy rice and cultivated rice.Analysis of variance on the phonetic data between Group 1 and Group 2 showed that the grain length,grain width,grain length/width,and the spikelet length of the weedy rice are significantly greater than those of the cultivated rice,while individual height and awn length had no significant difference between them

Nis2/Fes Holey Film As Freestanding Electrode For High-Performance Lithium Battery

In this work, a freestanding NiS2/FeS holey film (HF) is prepared after electrochemical anodic and chemical vapor deposition treatments. With the combination of good electrical conductivity and holey structure, the NiS2/FeS HF presents superior electrochemical performance, due to the following reasons: (i) Porous structure of HF provides a large surface area and more active sites/channels/pathways to enhance the ion/mass diffusion. Moreover, the porous structure can reduce the damage from the volumetric expansion. (ii) The as-prepared electrode combines the current collector (residual NiFe alloy) and active materials (sulfides) together, thus reducing the resistance of the electrode. Additionally, the good conductivity of HF can improve electron transport. (iii) Sulfides are more stable as active materials than sulfur, showing only a small capacity decay while retaining high cyclability performance. This work provides a promising way to develop high energy and stable electrode for Li-S battery