Detecting multiple cracks in beams using hierarchical genetic algorithms

This study deals with a method to identify multiple cracks in a beam. The novelty of this study is the use of a hierarchical genetic algorithm to detect the number, location, and the extent of multiple cracks. To demonstrate the feasibility of the present method, this algorithm is applied to the identification of double or triple cracks in a beam as well as four cracks. The detected crack locations and sizes are in excellent agreement with the actual ones. The numerical simulation reveal the HGA substantially reduces the total number of FE computation required and they are many orders smaller compared to conventional GA. The results also demonstrate the advantages of HGA from the viewpoints of its ability to avoid premature convergence

Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty

Low-rank tensor completion (LRTC) is an important problem in computer vision and machine learning. The minimax-concave penalty (MCP) function as a non-convex relaxation has achieved good results in the LRTC problem. To makes all the constant parameters of the MCP function as variables so that futherly improving the adaptability to the change of singular values in the LRTC problem, we propose the bivariate equivalent minimax-concave penalty (BEMCP) theorem. Applying the BEMCP theorem to tensor singular values leads to the bivariate equivalent weighted tensor $\Gamma$-norm (BEWTGN) theorem, and we analyze and discuss its corresponding properties. Besides, to facilitate the solution of the LRTC problem, we give the proximal operators of the BEMCP theorem and BEWTGN. Meanwhile, we propose a BEMCP model for the LRTC problem, which is optimally solved based on alternating direction multiplier (ADMM). Finally, the proposed method is applied to the data restorations of multispectral image (MSI), magnetic resonance imaging (MRI) and color video (CV) in real-world, and the experimental results demonstrate that it outperforms the state-of-arts methods.Comment: arXiv admin note: text overlap with arXiv:2109.1225

Negative Magnetoresistance in Dirac Semimetal Cd3As2

A large negative magnetoresistance is anticipated in topological semimetals in the parallel magnetic and electric field configuration as a consequence of the nontrivial topological properties. The negative magnetoresistance is believed to demonstrate the chiral anomaly, a long-sought high-energy physics effect, in solid-state systems. Recent experiments reveal that Cd3As2, a Dirac topological semimetal, has the record-high mobility and exhibits positive linear magnetoresistance in the orthogonal magnetic and electric field configuration. However, the negative magnetoresistance in the parallel magnetic and electric field configuration remains unveiled. Here, we report the observation of the negative magnetoresistance in Cd3As2 microribbons in the parallel magnetic and electric field configuration as large as 66% at 50 K and even visible at room temperatures. The observed negative magnetoresistance is sensitive to the angle between magnetic and electrical field, robust against temperature, and dependent on the carrier density. We have found that carrier densities of our Cd3As2 samples obey an Arrhenius's law, decreasing from 3.0x10^17 cm^-3 at 300 K to 2.2x10^16 cm^-3 below 50 K. The low carrier densities result in the large values of the negative magnetoresistance. We therefore attribute the observed negative magnetoresistance to the chiral anomaly. Furthermore, in the perpendicular magnetic and electric field configuration a positive non-saturating linear magnetoresistance up to 1670% at 14 T and 2 K is also observed. This work demonstrates potential applications of topological semimetals in magnetic devices

Mechanism Design with Predicted Task Revenue for Bike Sharing Systems

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called {\em TruPreTar} to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least $\sqrt{2}$ that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.Comment: Accepted by AAAI 2020; This is the full version that contains all the proof

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods

How Extreme Events in China Would Be Affected by Global Warming-Insights From a Bias-Corrected CMIP6 Ensemble

In recent years, concurrent climate extreme conditions (i.e., hot-dry, cold-dry, hot-wet, and cold-wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra-extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias-corrected CMIP6 ensemble under two continuously-warming scenarios to evaluate the impact of global warming on ultra-extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot-wet events with concurrence in the same month and year would be expected for China under the continuously-warming scenarios. This is particularly obvious for the west where more than 6 hot-wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra-extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot-wet and cold-wet extremes concurring in the same month will be larger than those of dry-related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction

Angle-selective perfect absorption with two-dimensional materials

Two-dimensional (2D) materials have great potential in photonic and optoelectronic devices. However, the relatively weak light absorption in 2D materials hinders their application in practical devices. Here, we propose a general approach to achieve angle-selective perfect light absorption in 2D materials. As a demonstration of the concept, we experimentally show giant light absorption by placing large-area single-layer graphene on a structure consisting of a chalcogenide layer atop a mirror and achieving a total absorption of 77.6% in the mid-infrared wavelength range (~13 μm), where the graphene contributes a record-high 47.2% absorptivity of mid-infrared light. Construction of such an angle-selective thin optical element is important for solar and thermal energy harvesting, photo-detection and sensing applications. Our study points to a new opportunity to combine 2D materials with photonic structures to enable novel device applications

Laser-induced incandescence particle image velocimetry (LII-PIV) for two-phase flow velocity measurement

We demonstrate the use of laser-induced incandescence (LII) of submicron tungsten carbide (WC) particles as a method for particle image velocimetry (PIV). The technique allows a single laser to be used for separate measurements of velocity of two phases in a droplet-laden flow. Submicron WC particles are intentionally seeded into a two-phase flow, and heated by a light sheet generated by a double-pulsed PIV laser operating at sufficiently high pulse energy. The small size and large absorption cross section allows particles to be heated up to several thousand degrees Kelvin to emit strong incandescence signals, whilst the laser-induced temperature increase in liquid droplets/large particles is negligible. The incandescence signal from WC and Mie scattering from droplets/large particles are separately captured by deploying different filters to a PIV camera. The consecutive images of the laser-induced incandescence (LII) are used to determine the velocity field of the gas-phase flow, and those of Mie scatter are used to extract the velocity of droplets/large particles. The proposed technique is demonstrated in an air jet first and compared with the result given by a normal PIV test, which shows that submicron WC particles can accurately follow the gas flow, and that the LII images can be used to perform cross-correlations. We then apply this technique on an ethanol droplet/air jet (non-reacting), demonstrating the resulting slip velocity between two phases. The proposed technique combining PIV and LII with a single laser requires little additional equipment, and is applicable to a much higher droplet/particle density than previously feasible. Finally, the possibility of applying this technique to a flame is demonstrated and discussed

Dexmedetomidine Ameliorates the Neurotoxicity of Sevoflurane on the Immature Brain Through the BMP/SMAD Signaling Pathway

Numerous studies have demonstrated that general anesthetics might damage the nervous system, thus, the effect of general anesthetics on the developing brain has attracted much attention. Dexmedetomidine (Dex) exhibits a certain neuroprotective effect, but the mechanism is obscure. In our study, pregnant rats on gestational day 20 (G20) were exposed to 3% sevoflurane for 2 h or 4 h, and the neuronal apoptosis in hippocampal CA1 region of the offspring rats was detected by quantification of TUNEL positive cells and cleaved-caspase3 (cl-caspase3). Different doses of Dex were intraperitoneally injected before sevoflurane anesthesia; then, the expression of apoptotic-related proteins including BCL-2, BAX and cl-caspase3 as well as amyloid precursor protein (APP, a marker of axonal injury), p-CRMP-2 and CRMP-2 were measured at postnatal days 0, 1and 3 (P0, P1, and P3, respectively). As an antagonist of the bone morphgenetic proteins (BMP) receptor, DMH1 was co-administered with sevoflurane plus Dex to investigate whether BMP/SMAD is associated with the neuroprotective effects of Dex. The results showed that prenatal sevoflurane anesthesia for 4 h activated apoptosis transiently, as manifested by the caspase3 activity peaked on P1 and disappeared on P3. In addition, the expressions of APP and p-CRMP-2/CRMP-2 in postnatal rat hippocampus were significantly increased, which revealed that prenatal sevoflurane anesthesia caused axonal injury of offspring. The long-term learning and memory ability of offspring rats was also impaired after prenatal sevoflurane anesthesia. These damaging effects of sevoflurane could be mitigated by Dex and DMH1 reversed the neuroprotective effect of Dex. Our results indicated that prenatal exposure to 3% sevoflurane for 4 h increased apoptosis and axonal injury, even caused long-term learning and memory dysfunction in the offspring rats. Dex dose-dependently reduced sevoflurane- anesthesia-induced the neurotoxicity by activating the BMP/SMAD signaling pathway