Robust multi-fidelity design of a micro re-entry unmanned space vehicle

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials

Regional Climate Trends and Scenarios for the U.S. National Climate Assessment Part 4. Climate of the U.S. Great Plains

This document is one of series of regional climate descriptions designed to provide input that can be used in the development of the National Climate Assessment (NCA). As part of a sustained assessment approach, it is intended that these documents will be updated as new and well-vetted model results are available and as new climate scenario needs become clear. It is also hoped that these documents (and associated data and resources) are of direct benefit to decision makers and communities seeking to use this information in developing adaptation plans. There are nine reports in this series, one each for eight regions defined by the NCA, and one for the contiguous U.S. The eight NCA regions are the Northeast, Southeast, Midwest, Great Plains, Northwest, Southwest, Alaska, and Hawai‘i/Pacific Islands. These documents include a description of the observed historical climate conditions for each region and a set of climate scenarios as plausible futures – these components are described in more detail below. While the datasets and simulations in these regional climate documents are not, by themselves, new, (they have been previously published in various sources), these documents represent a more complete and targeted synthesis of historical and plausible future climate conditions around the specific regions of the NCA. There are two components of these descriptions. One component is a description of the historical climate conditions in the region. The other component is a description of the climate conditions associated with two future pathways of greenhouse gas emissions

Photoinduced coupled twisted intramolecular charge transfer and excited-state proton transfer via intermolecular hydrogen bonding: a DFT/TD-DFT study

We discuss theoretically the geometric and electronic structure properties of the thiazolidinedione derivative A and its hydrogen-bonded complex in dimethylformamide (DMF) solution in the S0 and S1 states. To gain insight into the photoinduced coupled excited-state proton transfer (ESPT) and twisted intramolecular charge transfer (TICT) associated with intermolecular hydrogen bonding, the potential energy profiles are provided along the Osingle bondH bond and the twisted angle. It is predicted that TICT in S1 can facilitate ESPT initiated by intermolecular hydrogen-bond strengthening in the S1 state. The coupling of ESPT and TICT is energetically preferable

High reduction of ozone and particulate matter during the 2016 G-20 summit in Hangzhou by forced emission controls of industry and traffic

Many regions in China experience air pollution episodes because of the rapid urbanization and industrialization over the past decades. Here we analyzed the effect of emission controls implemented during the G-20 2016 Hangzhou summit on air quality. Emission controls included a forced closure of highly polluting industries, and limiting traffic and construction emissions in the cities and surroundings. Particles with aerodynamic diameter lower than 2.5 μm (PM_(2.5)) and ozone (O_3) were measured. We also simulated air quality using a forecast system consisting of the two-way coupled Weather Research and Forecast and Community Multi-scale Air Quality (WRF-CMAQ) model. Results show PM_(2.5) and ozone levels in Hangzhou during the G-20 Summit were considerably lower than previous to the G-20 Summit. The predicted concentrations of ozone were reduced by 25.4%, whereas the predicted concentrations of PM_(2.5) were reduced by 56%

Nitrogen-Doped Ti3_3C2_2Tx_x Coated with a Molecularly Imprinted Polymer as Efficient Cathode Material for Lithium-Sulfur Batteries

Due to their high energy density (2600 Wh/kg), low cost, and low environmental impact, lithium-sulfur batteries are considered a promising alternative to lithium-ion batteries. However, their commercial viability remains a formidable scientific challenge mainly because of the sluggish reaction kinetics at the cathode and the so-called "shuttling effect" of soluble polysulfides, which results in capacity decay and a shortened lifespan. Herein, molecular imprinting with Li$_2$S$_8$ as a target molecule in combination with a two-dimensional material, MXene, is proposed to overcome these issues. Molecularly imprinted polymer-coated nitrogen-doped Ti-based MXene was successfully synthesized and demonstrated to exhibit an appealing electrochemical performance, namely a high specific capacity of 1095 mAh/g at 0.1 C and an extended cycling stability (300 mAh/g at 1.0 C after 300 cycles). X-ray photoelectron spectroscopy was applied to elucidate the underlying mechanisms and proved that Li$_2$S$_8$-imprinted polymer polyacrylamide serves as a polysulfide trap through strong chemical affinity towards the long-chain lithium polysulfides, while N-doped Ti-based MXene promotes the redox kinetics by accelerating the conversion of lithium polysulfides. This distinct interfacial strategy is expected to result in more effective and stable Li-S batteries

Digital Twin-enabled IoMT System for Surgical Simulation using rAC-GAN

A digital twin-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier generative adversarial network (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the digital twin-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92, and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for digital twin-based surgical implementation

Rectifying interphases for preventing Li dendrite propagation in solid-state electrolytes

Solid-state electrolytes have emerged as the grail for safe and energy-dense Li metal batteries but still face significant challenges of Li dendrite propagation and interfacial incompatibility. In this work, an interface engineering approach is applied to introduce an electronic rectifying interphase between the solid-state electrolyte and Li metal anode. The rectifying behaviour restrains electron infiltration into the electrolyte, resulting in effective dendrite reduction. This interphase consists of a p-Si/n-TiO2 junction and an external Al layer, created using a multi-step sputter deposition technique on the surface of garnet pellets. The electronic rectifying behaviour is investigated via the asymmetric I-V responses of on-chip devices and further confirmed via the one-order of magnitude lower current response by electronic conductivity measurements on the pellets. The Al layer contributes to interface compatibility, which is verified from the lithiophilic surface and reduced interfacial impedance. Electrochemical measurements via Li symmetric cells show a significantly improved lifetime from dozens of hours to over two months. The reduction of the Li dendrite propagation behaviour is observed through 3D reconstructed morphologies of the solid-state electrolyte by X-ray computed tomography

Long-term wetland biomonitoring highlights the differential impact of land use on macroinvertebrate diversity in Dongting Lake in China

Freshwater wetlands have experienced disproportionate biodiversity loss due to environmental change. The lack of long-term biological data limits our understanding of wetland biodiversity dynamics and principal drivers. Here, we combine aquatic biomonitoring with satellite-derived imagery and spatial-explicit analysis to assess the changes in macroinvertebrate diversity and driving factors from 1988 to 2017 in Dongting Lake, China. The results revealed declining macroinvertebrate alpha diversity (species richness and functional redundancy) but increased beta diversity (taxonomic and functional dissimilarity). An increase in nutrients due to changes in land-use intensity in the surrounding terrestrial ecosystem was the primary mechanism for the losses of species and functional groups. Alpha diversity changes were most influenced by land-use changes in adjacent land areas 1.5 kilometers from the sampling sites, while beta diversity was affected within 50 meters. Our results highlight a need for attenuating land-use effects by establishing protected buffer areas to protect biodiversity in the future