PPO-Exp: Keeping Fixed-Wing UAV Formation with Deep Reinforcement Learning

Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV’s control problem and the system’s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attention. However, current methods also require that each UAV makes the decision decentralized, which increases the cost and computation of the whole UAV system. This paper researches a low-cost UAV formation system consisting of one leader (equipped with the intelligence chip) with five followers (without the intelligence chip), and proposes a centralized collision-free formation-keeping method. The communication in the whole process is considered and the protocol is designed by minimizing the communication cost. In addition, an analysis of the Proximal Policy Optimization (PPO) algorithm is provided; the paper derives the estimation error bound, and reveals the relationship between the bound and exploration. To encourage the agent to balance their exploration and estimation error bound, a version of PPO named PPO-Exploration (PPO-Exp) is proposed. It can adjust the clip constraint parameter and make the exploration mechanism more flexible. The results of the experiments show that PPO-Exp performs better than the current algorithms in these tasks

Metal–Organic Framework‐Derived MnO Nanocrystals Embedded in a Spindle Carbon for Rechargeable Aqueous Zinc Battery with a Molten Hydrate Electrolyte

Rechargeable aqueous zinc batteries (RAZBs) are emerging candidates for large‐scale energy storage. However, the lack of high‐capacity cathodes because of the electrostatic interactions between Zn2+ and cathode and the inferior electronic conductivity restricts their performance. The operating voltage limitation imposed by water is another barrier for RAZBs. Herein, manganese oxide (MnO) nanocrystals embedded in a spindle carbon matrix (MnO@C) synthesized from a metal–organic framework are used as a cathode. The uniform distribution of fine‐sized MnO (≈100 nm) in the carbonized matrix (≈5 μm) and the intimate connection between them not only increase the utilization of electroactive material but also eliminate the use of conductive additive. By utilizing the molten hydrate electrolyte, ZnCl2·2.33H2O, a discharge voltage plateau approaching 1.60 V and a high reversible capacity of 106 mAh g−1 after 200 cycles are achieved. This research proposes an approach for affordable RAZBs to fulfill large‐scale energy storage

Manipulation of π-aromatic conjugation in two-dimensional Sn-organic materials for efficient lithium storage

Sn-based materials are promising candidates for lithium storage but suffer generally from huge volume change during the (de)lithiation processes. Sn-organic materials with monodispersed Sn centers surrounded by lithium active ligands can alleviate the volume change of anode materials based on reversible (de)lithiation processes. However, the structural factors governing the kinetics of lithium storage and utilization efficiency of active sites are not well understood to date. Herein, we report three two-dimensional Sn-organic materials with enhanced lithium storage performance by manipulation of π-aromatic conjugation of the ligands. The increasing π-aromatic conjugation plays a key role in promoting efficient lithium storage, and the volume expansion during the (de)lithiation reaction is suppressed in these Sn-organic materials. This work reveals that the π-aromatic conjugation of the ligand is crucial for improving the kinetics of lithium storage and the utilization of active sites in metal-organic materials with minimised volume expansion

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

Abstract Background For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme. Methods TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set. Results Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%. Conclusions Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC

High-Efficiency Lithium-Ion Transport in a Porous Coordination Chain-Based Hydrogen-Bonded Framework

Fast and selective Li+ transport in solid plays a key role for the development of high-performance solid-state electrolytes (SSEs) of lithium metal batteries. Porous compounds with tunable Li+ transport pathways are promising SSEs, but the comprehensive performances in terms of Li+ transport kinetics, electrochemical stability window, and interfacial compatibility are difficult to be achieved simultaneously. Herein, we report a porous coordination chain-based hydrogen-bonded framework (NKU-1000) containing arrayed electronegative sites for Li+ transport, exhibiting a superior Li+ conductivity of 1.13 × 10–3 S cm–1, a high Li+ transfer number of 0.87, and a wide electrochemical window of 5.0 V. The assembled solid-state battery with NKU-1000-based SSE shows a high discharge capacity with 94.4% retention after 500 cycles and can work over a wide temperature range without formation of lithium dendrites, which derives from the linear hopping sites that promote a uniformly high-rate Li+ flux and the flexible structure that can buffer the structural variation during Li+ transport

Additional file 1 of Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

Additional file 1: Supplementary Figure 1. GO Enrichment pathway associated with cellular component, and biological process. Supplementary Figure 2. The differentially expression of 10 plasma protein candidates among three groups. Supplementary Figure 3. The differentially expression of 14 serum amino acids among three groups. Supplementary Figure 4. The differentially expression of 15 bile acids among three groups. Supplementary Figure 5. The differentially expression of six classic tumor markers among three groups. Supplementary Figure 6. Proteins and amnio acids related to NSCLC stage. Supplementary Figure 7. Single index with AUC>0.7 for NSCLC screening. Supplementary Figure 8. Single index with AUC>0.7 in differentiating NSCLC and BPD. Supplementary Figure 9.The process and the result of binary logistic regression with backward elimination methods. Supplementary Table 1. Screened differentially expressed proteins and corresponding validation proteins. Supplementary Table 2. Performance of single predictor in NSCLC screening. Supplementary Table 3. Performance of single predictor in NSCLC diagnosis. Supplementary Table 4. Screening model by stepwise binary logistic regression analysis in training samples. Supplementary Table 5. Performance analysis of 3 models in screening NSCLC. Supplementary Table 6. Testing of 3 models in screening NSCLC. Supplementary Table 7. Diagnosis model by stepwise binary logistic regression analysis in training samples. Supplementary Table 8. Performance analysis of 3 models in differentiating NSCLC and BPD. Supplementary Table 9. Testing of 3 models in differentiating NSCLC and BPD. Supplementary Table 10. The concentration units of these candidates