Transition metal-catalyzed decarboxylative cross-coupling reactions have recently emerged as a new and important category of organic transformations that find versatile applications in the ...construction of carbon-carbon and carbon-heteroatom bonds. The use of relatively cheap and stable carboxylic acids to replace organometallic reagents enables the decarboxylative cross-coupling reactions to proceed with good selectivities and functional group tolerance. In the present review we summarize the various types of decarboxylative cross-coupling reactions catalyzed by different transition metal complexes. The scope and applications of these reactions are described. The challenges and opportunities in the field are discussed.
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•A new framework is proposed for landslide susceptibility analysis.•Bayesian algorithm is used to optimize the proportion of landslide samples.•The framework is validated by a case ...study, and RF and GBDT outperform SVM.
Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The accuracy of machine learning-based LSA often hinges on the ratio of landslide to non-landslide (or positive/negative, P/N) samples. A proper ratio of the P/N samples will significantly improve the performance of machine learning-based LSA, but an improper ratio can cause inadequate training or data pollution. Conventionally, the determination of the P/N sample ratio is based on experience or by trials and errors, which has substantial uncertainties. This paper proposes a Bayesian optimization method to optimize the P/N sample ratio for machine learning models. Firstly, Anhua County in Hunan province of China is selected as the study area because of numerous landslide disasters that occurred in recent years. Secondly, three representative machine learning models of the support vector machine (SVM), the random forest (RF) and the gradient boost decision tree (GBDT) are adopted to assess the landslide susceptibility. Subsequently, a Bayesian optimization algorithm is used to obtain the optimal P/N sample ratio, considering the effects of various ratios of training/test set. Finally, the improved models and the corresponding landslide susceptibility maps are established using the obtained optimal P/N sample ratio. The results show that the performance of SVM, RF and GBDT are all improved with the optimized P/N sample ratio. The highest AUC value is for the RF model (0.840, improved by 1.3%), followed by GBDT (0.831, improved by 1.3%), and SVM (0.775, improved by 0.7%). However, the RF and GBDT are more suitable than SVM to address sample unbalance issues in LSA. It is suggested to use the Bayesian optimization algorithm to optimize the P/N sample ratio in machine learning-based LSA model.
The innate immune system has the capacity to detect 'non-self' molecules derived from pathogens, known as pathogen-associated molecular patterns, via pattern recognition receptors. In addition, an ...increasing number of endogenous host-derived molecules, termed damage-associated molecular patterns (DAMPs), have been found to be sensed by various innate immune receptors. The recognition of DAMPs, which are produced or released by damaged and dying cells, promotes sterile inflammation, which is important for tissue repair and regeneration, but can also lead to the development of numerous inflammatory diseases, such as metabolic disorders, neurodegenerative diseases, autoimmune diseases and cancer. Here we examine recent discoveries concerning the roles of DAMP-sensing receptors in sterile inflammation and in diseases resulting from dysregulated sterile inflammation, and then discuss insights into the cross-regulation of these receptors and their ligands.
Two crystalline large‐sized porous organic cages (POCs) based on conical calix4arene (C4A) were designed and synthesized. The four‐jaw C4A unit tends to follow the face‐directed self‐assembly law ...with the planar triangular building blocks such as tris(4‐aminophenyl)amine (TAPA) or 1,3,5‐tris(4‐aminophenyl)benzene (TAPB) to generate a predictable cage with a stoichiometry of 6+8. The formation of the large cages is confirmed through their relative molecular mass measured using MALDI‐TOF/TOF spectra. The protonated molecular ion peaks of C4A‐TAPA and C4A‐TAPB were observed at m/z 5109.0 (calculated for C336H240O24N32: m/z 5109.7) and m/z 5594.2 (calculated for C384H264O24N24: m/z 5598.4). C4A‐POCs exhibit I‐type N2 adsorption‐desorption isotherms with the BET surface areas of 1444.9 m2 ⋅ g−1 and 1014.6 m2 ⋅ g−1. The CO2 uptakes at 273 K are 62.1 cm3 ⋅ g−1 and 52.4 cm3 ⋅ g−1 at a pressure of 100 KPa. The saturated iodine vapor static uptakes at 348 K are 3.9 g ⋅ g−1 and 3.5 g ⋅ g−1. The adsorption capacity of C4A‐TAPA for SO2 reaches to 124.4 cm3 ⋅ g−1 at 298 K and 1.3 bar. Additionally, the adsorption capacities of C4A‐TAPA for C2H2, C2H4, and C2H6 were evaluated.
Two octahedral porous organic cages with a significantly high specific surface area have been synthesized and their potential application in gas adsorption investigated by evaluating their capacity to adsorb SO2, CO2, C2H2, C2H4, and C2H6. They also proved successful in efficiently adsorbing static iodine (I2) vapor.
Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. ...Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.
The efficient removal of radioactive uranium from aqueous solution is of great significance for the safe and sustainable development of nuclear power. An ultrathin 2D metal–organic framework (MOF) ...nanosheet with cavity structures was elaborately fabricated based on a calix4arene ligand. Incorporating the permanent cavity structures on MOF nanosheet can fully utilize its structural characteristics of largely exposed surface area and accessible adsorption sites in pollutant removal, achieving ultrafast adsorption kinetics, and the functionalized cavity structure would endow the MOF nanosheets with the ability to achieve preconcentration and extraction of uranium from aqueous solution, affording ultrahigh removal efficiency even in ultra‐low concentrations. Thus, more than 97% uranium can be removed from the concentration range of 50–500 µg L−1 within 5 min. Moreover, the 2D nano‐material exhibits ultra‐high anti‐interference ability, which can efficiently remove uranium from groundwater and seawater. The adsorption mechanism was investigated by X‐ray photoelectron spectroscopy (XPS), Fourier transform infrared (FT‐IR) analysis, and density functional theory (DFT) calculations, which revealed that the cavity structure plays an important role in uranium capture. This study not only realizes highly efficient uranium removal from aqueous solution but also opens the door to achieving ultrathin MOF nanosheets with cavity structures, which will greatly expand the applications of MOF nanosheets.
A carboxylate‐functionalized 2D metal–organic framework (MOF) nanosheet with cage‐like cavities was constructed based on the calix4arene, which not only facilitates MOF exfoliation and pollutant capture but also prevents pollutant desorption. The as‐synthesized MOF nanosheets show an ultrahigh selectivity and anti‐interference performance for uranium.
Food safety is a major concern for the Chinese public. This study collected 465 published papers on heavy metal pollution rates (the ratio of the samples exceeding the Grade II limits for Chinese ...soils, the Soil Environmental Quality Standard-1995) in farmland soil throughout China. The results showed that Cd had the highest pollution rate of 7.75%, followed by Hg, Cu, Ni and Zn, Pb and Cr had the lowest pollution rates at lower than 1%. The total pollution rate in Chinese farmland soil was 10.18%, mainly from Cd, Hg, Cu, and Ni. The human activities of mining and smelting, industry, irrigation by sewage, urban development, and fertilizer application released certain amounts of heavy metals into soil, which resulted in the farmland soil being polluted. Considering the spatial variations of grain production, about 13.86% of grain production was affected due to the heavy metal pollution in farmland soil. These results many provide valuable information for agricultural soil management and protection in China.
Federated learning is a distributed machine learning technology that can protect users' data privacy, so it has attracted more and more attention in the industry and academia. Nonetheless, most of ...the existing works focused on the cost optimization of the entire process, while the cost of individual participants cannot be considered. In this article, we explore a min-max cost-optimal problem to guarantee the convergence rate of federated learning in terms of cost in wireless edge networks. In particular, we minimize the cost of the worst-case participant subject to the delay, local CPU-cycle frequency, power allocation, local accuracy, and subcarrier assignment constraints. Considering that the formulated problem is a mixed-integer nonlinear programming problem, we decompose it into several sub-problems to derive its solutions, in which the subcarrier assignment and power allocation are obtained by utilizing the Lagrangian dual decomposition method, the CPU-cycle frequency is obtained by a heuristic algorithm, and the local accuracy is obtained by an iteration algorithm. Simulation results show the convergence of the proposed algorithm and reveal that the proposed scheme can accomplish a tradeoff between the cost and fairness by comparing the proposed scheme with the existing schemes.
•Three Neoproterozoic tectono-magmatic belts have been identified in South China.•An intra-oceanic arc and two active continental margins formed successively.•Their final collision welded the South ...China and yielded the Jiangnan Orogen.•In the end, the Jiangnan Orogen collapsed and the Nanhua rift basin formed.
According to the temporal-spatial distribution of Neoproterozoic igneous rocks and relative rocks in South China, including ophiolites, arc volcanic and intrusive rocks and subsequent bimodal magmatism, we identified the presence of a Neoproterozoic intra-oceanic arc, continent-arc-continent collision and three tectono-magmatic zones between the Yangtze Block and Cathaysia Block. We have also unraveled the amalgamation and tectono-magmatic histories between the Yangtze and Cathaysia blocks: At ∼1000–860Ma, northwestward ocean-ocean subduction and southeastward ocean-continent subduction resulted in the intra-oceanic arc magmatism and active continental margin magmatism in the Cathaysia Block respectively. At ∼860–825Ma, the steepening subduction caused development of back-arc basin in the intra-oceanic arc zone and the slab rollback induced the arc and back-arc magmatism in the Cathaysia Block. Meanwhile, a shallow dip northwestward ocean-continent subduction formed active continental margin magmatism in the Yangtze Block. At ∼825–805Ma, the continent-arc-continent collision and final amalgamation between the Yangtze and Cathaysia blocks yielded the Jiangnan Orogen. At ∼805–750Ma, the Jiangnan Orogen collapsed, and the Nanhua rift basin formed. Our study also rules out any Grenvillian Orogenic event and mantle plume activity in South China and indicates a marginal position of South China in the Rodinia supercontinent.
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