Robust Stackelberg Equilibria

This paper provides a systematic study of the robust Stackelberg equilibrium (RSE), which naturally generalizes the widely adopted solution concept of the strong Stackelberg equilibrium (SSE). The RSE accounts for any possible up-to-$\delta$ suboptimal follower responses in Stackelberg games and is adopted to improve the robustness of the leader's strategy. While a few variants of robust Stackelberg equilibrium have been considered in previous literature, the RSE solution concept we consider is importantly different -- in some sense, it relaxes previously studied robust Stackelberg strategies and is applicable to much broader sources of uncertainties. We provide a thorough investigation of several fundamental properties of RSE, including its utility guarantees, algorithmics, and learnability. We first show that the RSE we defined always exists and thus is well-defined. Then we characterize how the leader's utility in RSE changes with the robustness level considered. On the algorithmic side, we show that, in sharp contrast to the tractability of computing an SSE, it is NP-hard to obtain a fully polynomial approximation scheme (FPTAS) for any constant robustness level. Nevertheless, we develop a quasi-polynomial approximation scheme (QPTAS) for RSE. Finally, we examine the learnability of the RSE in a natural learning scenario, where both players' utilities are not known in advance, and provide almost tight sample complexity results on learning the RSE. As a corollary of this result, we also obtain an algorithm for learning SSE, which strictly improves a key result of Bai et al. in terms of both utility guarantee and computational efficiency

Calculation and experimental verification of force-magnetic coupling model of magnetised rail based on density functional theory

Metal magnetic memory (MMM) is a widely used non-destructive electromagnetic detection technology. However, the analysis of its underlying principle is still insufficient. The mechanical and magnetic coupling model is a reasonable standpoint from which to study the principle of MMM. In this paper, a mechanical and magnetic coupling model of steel material is established based on density functional theory (DFT) using the CASTEP first-principles analysis software. In order to simulate the practical working environment, the residual magnetism in the rail is assumed to change with the stress on the rail. By applying different stresses to the model, the relationship between the atomic magnetic moment, the lattice constant and stress is explored, as well as the causes of magnetic signals in the stress concentration zone. It is revealed that the atomic magnetic moment and the crystal volume decrease with the increase in compressive stress. The magnetic signal on the surface of the magnetised metal component decreases with the increase in compressive stress, while the tensile stress shows the opposite tendency. Generally speaking, the change in atomic magnetic moment and crystal volume caused by lattice distortion under stress can be seen as the fundamental reason for the change in magnetic signal on the surface of the magnetised metal. The bending experiment of the rail shows that the normal magnetic field decreases with the increase in compressive stress in the stress concentration zone. The conclusion is verified by experiments

Loss of work productivity in a warming world: differences between developed and developing countries

Comparable estimates of the heat-related work productivity loss (WPL) in different countries over the world are difficult partly due to the lack of exact measures and comparable data for different counties. In this study, we analysed 4363 responses to a global online survey on the WPL during heat waves in 2016. The participants were from both developed and developing countries, facilitating estimates of the heat-related WPL across the world for the year. The heat-related WPL for each country involved was then deduced for increases of 1.5, 2, 3 and 4 °C in the global mean surface temperature under the representative concentration pathway scenarios in climate models. The average heat-related WPL in 2016 was 6.6 days for developing countries and 3.5 days for developed countries. The estimated heat-related WPL was negatively correlated with the gross domestic product per capita. When global surface temperatures increased by 1.5, 2, 3 and 4 °C, the corresponding WPL was 9 (19), 12 (31), 22 (61) and 33 (94) days for developed (developing) countries, quantifying how developing countries are more vulnerable to climate change from a particular point of view. Moreover, the heat-related WPL was unevenly distributed among developing countries. In a 2°C-warmer world, the heat-related WPL would be more than two months in Southeast Asia, the most influenced region. The results are considerable for developing strategy of adaptation especially for developing countries

Alcohol consumption, DNA methylation and colorectal cancer risk:Results from pooled cohort studies and Mendelian randomization analysis

Alcohol consumption is thought to be one of the modifiable risk factors for colorectal cancer (CRC). However, the causality and mechanisms by which alcohol exerts its carcinogenic effect are unclear. We evaluated the association between alcohol consumption and CRC risk by analyzing data from 32 cohort studies and conducted two-sample Mendelian randomization (MR) analysis to examine for casual relationship. To explore the effect of alcohol related DNA methylation on CRC risk, we performed an epigenetic MR analysis with data from an epigenome-wide association study (EWAS). We additionally performed gene-alcohol interaction analysis nested in the UK Biobank to assess effect modification between alcohol consumption and susceptibility genes. We discovered distinct effects of alcohol on CRC incidence and mortality from the meta-analyses, and genetic predisposition to alcohol drinking was causally associated with an increased CRC risk (OR = 1.79, 95% CI: 1.23-2.61) using two-sample MR approaches. In epigenetic MR analysis, two alcohol-related CpG sites (cg05593667 and cg10045354 mapped to COLCA1/COLCA2 gene) were identified causally associated with an increased CRC risk (P < 8.20 × 10-4 ). Gene-alcohol interaction analysis revealed that carriage of the risk allele of the eQTL (rs3087967) and mQTL (rs11213823) polymorphism of COLCA1/COLCA2 would interact with alcohol consumption to increase CRC risk (PInteraction  = .027 and PInteraction  = .016). Our study provides comprehensive evidence to elucidate the role of alcohol in CRC and highlights that the pathogenic effect of alcohol on CRC could be partly attributed to DNA methylation by regulating the expression of COLCA1/COLCA2 gene

Initial demonstration of AlGaAs-GaAsP-beta-Ga2O3 n-p-n double heterojunctions

Beta phase gallium oxides, an ultrawide-bandgap semiconductor, has great potential for future power and RF electronics applications but faces challenges in bipolar device applications due to the lack of p-type dopants. In this work, we demonstrate monocrystalline AlGaAs_GaAsP_beta phase gallium oxides n-p-n double-heterojunctions, synthesized using semiconductor grafting technology. By transfer printing an n-AlGaAs_p-GaAsP nanomembrane to the n-beta phase-Ga$_2$O$_3$ epitaxial substrate, we simultaneously achieved AlGaAs_GaAsP epitaxial n-p junction diode with an ideality factor of 1.29 and a rectification ratio of 2.57E3 at +/- 2 V, and grafted GaAsP_beta_phase_gallium oxides p-n junction diode exhibiting an ideality factor of 1.36 and a rectification ratio of 4.85E2 at +/- 2 V.Comment: 12 pages, 4 figure

Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis

The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC