Selenium(Se),an essential micronutrient among the 15 vital elements required for human physiology,exerts its biological functions primarily through its incorporation into selenoproteins.To date,approximately 25 seleno...Selenium(Se),an essential micronutrient among the 15 vital elements required for human physiology,exerts its biological functions primarily through its incorporation into selenoproteins.To date,approximately 25 selenoproteins have been characterized in mammalian systems,including glutathione peroxidase(GPX),thioredoxin reductase(TrxR),and iodothyronine deiodinases(DIOs),all of which exhibit indispensable physiological functions.展开更多
Visible light photocatalytic redox catalysis has become a powerful tool for organic synthesis, and has opened up new avenues for the formation of challenging structural skeletons and chemical bonds. In this respect, d...Visible light photocatalytic redox catalysis has become a powerful tool for organic synthesis, and has opened up new avenues for the formation of challenging structural skeletons and chemical bonds. In this respect, diverse photocatalysts, including ruthenium(II), iridium(Ⅲ), and organic dyes, have been most commonly applied.展开更多
Gold,unlike other transition metals such as Pd,Ni,and Cu,offers unique reactivity profiles and has emerged as an attractive area of research in organic chemistry over the last two decades.Initially,gold catalysts were...Gold,unlike other transition metals such as Pd,Ni,and Cu,offers unique reactivity profiles and has emerged as an attractive area of research in organic chemistry over the last two decades.Initially,gold catalysts were widely used for theπ-activation of unsaturated carbon−carbon bonds,particularly alkynes.Moreover,they exhibit favorable functional-group compatibility,good biocompatibility,and,generally,gold-catalyzed reactions are not sensitive to air or water.展开更多
Skeletal editing has emerged as a powerful tool in organic chemistry,enabling the simplification of synthetic routes to complex molecules[1].Indoles,electron-rich nitrogen-containing building blocks,represent privileg...Skeletal editing has emerged as a powerful tool in organic chemistry,enabling the simplification of synthetic routes to complex molecules[1].Indoles,electron-rich nitrogen-containing building blocks,represent privileged scaffolds prevalent in pharmaceuticals,natural products,and bioactive compounds.The application of skeletal editing strategies to modify such structures is highly valuable and in growing demand.Leveraging the electronrich nature of indoles at C2 and C3,single-carbon atom insertion using cationic carbyne equivalents offers an efficient approach for indole ring expansion to quinoline(Scheme 1a).However,existing methods predominantly rely on halocarbene precursors,which restricts the functional groups of ring-expanded products to halogen[2],alkyl,aryl,heteroaryl and ester moieties[3].This limitation hinders their utility in late-stage skeletal modifications of complex targets.展开更多
Amino acids are the building blocks of proteins and play vital roles in both biological systems and drug development.In recent years,increasing attention has been given to the functionalization of amino acid derivativ...Amino acids are the building blocks of proteins and play vital roles in both biological systems and drug development.In recent years,increasing attention has been given to the functionalization of amino acid derivatives.Since the introduction of therapeutic insulin in the early 20th century,the conjugation of drug molecules with amino acids and peptides has been pivotal in driving advancements in drug discovery and become an integral part of modern medical practice.Currently,over a hundred peptide-drug conjugates have received global approval and are widely used to treat diseases such as diabetes,cancer,chronic pain,and multiple sclerosis.Key technologies for conjugating peptides with bioactive molecules include antibody-drug conjugates(ADCs),peptide-drug conjugates(PDCs),and proteolysis targeting chimeras(PROTACs).Significant efforts have been dedicated to developing strategies for the modification of amino acids and peptides,with particular focus on site-selective C-H alkylation/arylation reactions.These reactions are crucial for synthesizing bioactive molecules,as they enable the precise introduction of functional groups at specific positions,thereby improving the pharmacological properties of the resulting compounds.展开更多
Cobalt-free nickel-manganese binary materials are one of the most promising cathode candidates for lithium-ion batteries due to the low reserves, high price,political and ecological unfriendliness of cobalt. The prepa...Cobalt-free nickel-manganese binary materials are one of the most promising cathode candidates for lithium-ion batteries due to the low reserves, high price,political and ecological unfriendliness of cobalt. The preparation of high-performance Ni-Mn bimetallic materials through controlled synthesis conditions holds significant importance for industrial applications. In this work,through systematic modulation of calcination temperatures and nickel ratios, we have effectively addressed critical challenges in binary layered cathodes, including cationic disordering, detrimental H2-H3 phase transitions, and severe interfacial side reactions. The electrochemical performance and thermal stability tests demonstrate that the medium-nickel cathode calcined at 850℃(NM64) exhibit superior comprehensive performance, including moderate discharge capacity(181.34 mAh g^(-1)at 1C), enhanced thermal stability and cycling stability(90% capacity retention after 100 cycles), excellent rate performance(125 mAh g^(-1)at high rate of 10C). Moreover, a 10 kg sample was prepared further verified its commercial application prospects. The soft-pack battery with commercial graphite anode and NM64-850 cathode achieve a discharge capacity of 171.0 mAh g^(-1)and retains 86.5% capacity after 180 cycles. The optimized integration of nickel content and calcination temperature endows binary cathodes with balanced electrochemical performance,enabling commercial viability.展开更多
Density functional theory(DFT) calculations are performed to investigate the electronic and structural properties of the stoichiometric thorium oxide clusters(ThO2)n-/0(n = 1~5). Generalized Koopmans' theorem is a...Density functional theory(DFT) calculations are performed to investigate the electronic and structural properties of the stoichiometric thorium oxide clusters(ThO2)n-/0(n = 1~5). Generalized Koopmans' theorem is applied to predict the vertical detachment energies(VDEs)which are used to simulate the anionic photoelectron spectra(PES). Molecular orbital analyses are performed as well to analyze the chemical bonding in these thorium oxide clusters. The results show that the ground states of(ThO2)_n-/0(n = 1~5) clusters prefer the low-spin structures. With increasing of the cluster size(n), the structure parameters of(ThO2)n-/0(n = 1~5) gradually evolve toward bulk thorium oxide species. It shows that both the coordination number and the average bond length increase gradually in(ThO2)n-/0(n = 1~5) to approach that of ThO2 bulk. What's more, the vibration frequencies of Th=O double bonds are found to be decreasing along with the increased cluster size.展开更多
Visible light-induced organic reactions have gained much attention in recent years due to their mild conditions and high efficiency[1,2].In this context,many efficient photocatalysts including transition metal complex...Visible light-induced organic reactions have gained much attention in recent years due to their mild conditions and high efficiency[1,2].In this context,many efficient photocatalysts including transition metal complexes and organic dyes have been developed for various organic transformations.展开更多
Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and m...Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.展开更多
Direct dynamics simulations are a useful and general approach for studying the atomistic properties of complex chemical systems because they do not require fitting an analytic potential energy function.Hessian-based p...Direct dynamics simulations are a useful and general approach for studying the atomistic properties of complex chemical systems because they do not require fitting an analytic potential energy function.Hessian-based predictor-corrector integrators are a widely used approach for calculating the trajectories of moving atoms in direct dynamics simulations.We employ a monodromy matrix to propose a tool for evaluating the accuracy of integrators in the trajectory calculation.We choose a general velocity Verlet as a different object.We also simulate molecular with hydrogen(CO_2) and molecular with hydrogen(H_2O) motions.Comparing the eigenvalues of monodromy matrix,many simulations show that Hessian-based predictor-corrector integrators perform well for Hessian updates and non-Hessian updates.Hessian-based predictor-corrector integrator with Hessian update has a strong performance in the H_2O simulations.Hessian-based predictor-corrector integrator with Hessian update has a strong performance when the integrating step of the velocity Verlet approach is tripled for the predicting step.In the CO_2 simulations,a strong performance occurs when the integrating step is a multiple of five.展开更多
Bioisosteres play an important role in pharmaceutical and agricultural compound design that has been developed to enhance the properties of molecules.As a sp3-rich small ring cage hydrocarbons,bicyclo[1.1.1]pentanes(B...Bioisosteres play an important role in pharmaceutical and agricultural compound design that has been developed to enhance the properties of molecules.As a sp3-rich small ring cage hydrocarbons,bicyclo[1.1.1]pentanes(BCPs)were commonly considered as a bioisostere of the para-substituted arenes,internal alkynes,and tert-butyl groups,and its introduction can significantly improve pharmacokinetic properties,including passive permeability,aqueous solubility,metabolic stability[1].Over the past decades,numerous pharmaceutically relevant molecules with biaryl frameworks were improved by the approach of bioisosteric replacement.Conventional strategies for the synthesis of such BCP-aryls derivatives mostly depend on the stepwise C-C formation via the direct addition to bridgehead C-C bond of[1.1.1]propellane to provide active BCP-intermediates and followed by a second transitionmetal cross-coupling sequence.However,these pioneer works were limited by the low step economy,the use of unstable reagents,and harsh conditions in industrial productions.展开更多
Considering the widespread presence of the dithiocarbamate skeleton in pharmaceuticals and bioactive molecules,the development of novel and convenient methods for the synthesis of these useful sulfur-containing compou...Considering the widespread presence of the dithiocarbamate skeleton in pharmaceuticals and bioactive molecules,the development of novel and convenient methods for the synthesis of these useful sulfur-containing compounds is of significant interest.Traditionally,S-aryl dithiocarbamates are constructed through the reaction of amines with thiophenols and thiophosgene[1].Recently,transition-metal-catalyzed Ullmann-type coupling between aryl boronic acids or iodoarenes with tetraalkylthiuram disulfides or sodium dialkyldithiocarbamates has been reported[2].展开更多
At present,the research of blockchain is very popular,but the practical application of blockchain is very few.The main reason is that the concurrency of blockchain is not enough to support application scenarios.After ...At present,the research of blockchain is very popular,but the practical application of blockchain is very few.The main reason is that the concurrency of blockchain is not enough to support application scenarios.After that,applications such as Intervalue increase the concurrency of blockchain transactions.However,due to the problems of network bandwidth and algorithm performance,there is always a broadcast storm,which affects the normal use of nodes in the whole network.However,the emergence of broadcast storms needs to rely on the node itself,which may be very slow.Even if developers debug the corresponding code,they cannot conduct an effective test in the whole network.Broadcast storm problem mainly occurs in scenarios with large transaction volume,such as the financial industry.Due to its characteristics,the concurrency of transactions in the financial industry will increase at a certain time.If there is no effective algorithm to deal with it,the broadcast storm will be triggered and the whole network will be paralyzed.To solve the problem of the broadcast storm,this paper combines blockchain,peer-to-peer network,artificial intelligence,and other technologies,and proposes a broadcast storm detection and processing method based on situation awareness.The purpose is to cut off the further spread of broadcast storms from the node itself and maintain the normal operation of the whole network nodes.展开更多
Background Individual differences have been detected in individuals with opioid use disorders(OUD)in rehabilitation following protracted abstinence.Recent studies suggested that prediction models were effective for in...Background Individual differences have been detected in individuals with opioid use disorders(OUD)in rehabilitation following protracted abstinence.Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders(SUD).Aims This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling(CPM).Methods One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging(fMRI)scans at baseline.The Heroin Craving Questionnaire(HCQ)was used to assess craving levels at baseline and at the 8-month follow-up of abstinence.CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ(HCQtolow V-up-HCQpa baseline).Then,the follow-up aseline predictive ability of identified networks was tested in a separate,heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.Results CPM could predict craving changes induced by long-term abstinence,as shown by a significant correlation between predicted and actual HCQ fllow-up(r=0.417,p<0.001)and changes in HCQ(negative:r=0.334,p=0.002;positive:r=0.233,p=0.038).Identified craving-related prediction networks included the somato-motor network(SMN),salience network(SALN),default mode network(DMN),medial frontal network,visual network and auditory network.In addition,decreased connectivity of frontal-parietal network(FPN)-SMN,FPN-DMN and FPN-SALN and increased connectivity of subcortical network(SCN)-DMN,SCN-SALNandSCN-SMN were positively correlated with craving levels.Conclusions These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence,as well as the generalisation ability;the identified brain networks might be the focus of innovative therapies in the future.展开更多
Since the conceptual germina- tion of ＂human rights＂ in the modern age, the controversy over it and its connotationshave been constant. Scholars at home and abroad either attempt to interpret the connotation of huma...Since the conceptual germina- tion of ＂human rights＂ in the modern age, the controversy over it and its connotationshave been constant. Scholars at home and abroad either attempt to interpret the connotation of human rights by em- phasizing the real social foundation of human rights from a political and eco- nomic perspective, or elaborate on the essence of human rights by focusing on traditional values and dignity from the angle of cultural and moral traditions. However, because of differences in sys- tem and culture in different countries,展开更多
Electrocatalytic conversion of biomass-derived compounds and nitrate pollutants offers a promising route toward sustainable chemical synthesis and environmental remediation.In this work,a bifunctional NiO-NiCoP cataly...Electrocatalytic conversion of biomass-derived compounds and nitrate pollutants offers a promising route toward sustainable chemical synthesis and environmental remediation.In this work,a bifunctional NiO-NiCoP catalyst with a well-defined heterogeneous interface is synthesized via a low-temperature co-precipitation,annealing and phosphidation process to enable the coupled electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR) and nitrate reduction reaction(NO_(3)^(-)RR).X-ray photoelectron spectroscopy(XPS),high-resolution transmission electron microscopy(HRTEM),open-circuit potential(OCP),and in-situ electrochemical impedance spectroscopy(in-situ EIS) confirm the formation of the heterogeneous interface,which facilitates electron redistribution,enhances charge transfer,and optimizes reactant adsorption.The catalyst exhibits excellent HMFOR activity,achieving 99.46% HMF conversion,97.23% 2,5-furandicarboxylic acid(FDCA) yield,and 97.62% Faradaic efficiency(FE) at 1.40 V vs.RHE.For NO_(3)^(-)RR,nearly 100% FE and an NH_(3) yield of 8.82 mg h^(-1)cm^(-2)are obtained at-0.40 V vs.RHE.In a paired HMFOR//NO_(3)^(-)RR electrolyzer,the NiO-NiCoP catalyst demonstrates superior current density,product selectivity,and long-term stability compared to conventional oxygen evolution reaction//hydrogen evolution reaction(OER//HER) systems.At 1.60 V,the HMFOR//NO_(3)^(-)RR system achieved a maximum HMF conversion of 95.84%,an FDCA yield of94.83%,and a FE of 89.53%,while at 1.90 V,it reached a maximum NH_(3) yield of 32.50 mg h^(-1)cm^(-2)with an FE of 94.63%.This study underscores the catalytic advantages of heterogeneous interface engineering and provides a viable strategy for integrated biomass valorization and nitrogen-cycle remediation.展开更多
It is difficult to extract targets under strong environmental disturbance in practice.Ghost imaging(GI)is an innovative antiinterference imaging technology.In this paper,we propose a scheme for target extraction based...It is difficult to extract targets under strong environmental disturbance in practice.Ghost imaging(GI)is an innovative antiinterference imaging technology.In this paper,we propose a scheme for target extraction based on characteristicenhanced pseudo-thermal GI.Unlike traditional GI which relies on training the detected signals or imaging results,our scheme trains the illuminating light fields using a deep learning network to enhance the target’s characteristic response.The simulation and experimental results prove that our imaging scheme is sufficient to perform single-and multiple-target extraction at low measurements.In addition,the effect of a strong scattering environment is discussed,and the results show that the scattering disturbance hardly affects the target extraction effect.The proposed scheme presents the potential application in target extraction through scattering media.展开更多
基金Financial support from the Science and Technology Innovation Program of Hunan Province(No.2022RC4044)。
文摘Selenium(Se),an essential micronutrient among the 15 vital elements required for human physiology,exerts its biological functions primarily through its incorporation into selenoproteins.To date,approximately 25 selenoproteins have been characterized in mammalian systems,including glutathione peroxidase(GPX),thioredoxin reductase(TrxR),and iodothyronine deiodinases(DIOs),all of which exhibit indispensable physiological functions.
文摘Visible light photocatalytic redox catalysis has become a powerful tool for organic synthesis, and has opened up new avenues for the formation of challenging structural skeletons and chemical bonds. In this respect, diverse photocatalysts, including ruthenium(II), iridium(Ⅲ), and organic dyes, have been most commonly applied.
文摘Gold,unlike other transition metals such as Pd,Ni,and Cu,offers unique reactivity profiles and has emerged as an attractive area of research in organic chemistry over the last two decades.Initially,gold catalysts were widely used for theπ-activation of unsaturated carbon−carbon bonds,particularly alkynes.Moreover,they exhibit favorable functional-group compatibility,good biocompatibility,and,generally,gold-catalyzed reactions are not sensitive to air or water.
文摘Skeletal editing has emerged as a powerful tool in organic chemistry,enabling the simplification of synthetic routes to complex molecules[1].Indoles,electron-rich nitrogen-containing building blocks,represent privileged scaffolds prevalent in pharmaceuticals,natural products,and bioactive compounds.The application of skeletal editing strategies to modify such structures is highly valuable and in growing demand.Leveraging the electronrich nature of indoles at C2 and C3,single-carbon atom insertion using cationic carbyne equivalents offers an efficient approach for indole ring expansion to quinoline(Scheme 1a).However,existing methods predominantly rely on halocarbene precursors,which restricts the functional groups of ring-expanded products to halogen[2],alkyl,aryl,heteroaryl and ester moieties[3].This limitation hinders their utility in late-stage skeletal modifications of complex targets.
文摘Amino acids are the building blocks of proteins and play vital roles in both biological systems and drug development.In recent years,increasing attention has been given to the functionalization of amino acid derivatives.Since the introduction of therapeutic insulin in the early 20th century,the conjugation of drug molecules with amino acids and peptides has been pivotal in driving advancements in drug discovery and become an integral part of modern medical practice.Currently,over a hundred peptide-drug conjugates have received global approval and are widely used to treat diseases such as diabetes,cancer,chronic pain,and multiple sclerosis.Key technologies for conjugating peptides with bioactive molecules include antibody-drug conjugates(ADCs),peptide-drug conjugates(PDCs),and proteolysis targeting chimeras(PROTACs).Significant efforts have been dedicated to developing strategies for the modification of amino acids and peptides,with particular focus on site-selective C-H alkylation/arylation reactions.These reactions are crucial for synthesizing bioactive molecules,as they enable the precise introduction of functional groups at specific positions,thereby improving the pharmacological properties of the resulting compounds.
基金supported by the National Natural Science Foundation of China(Nos.52074113,22005091 and 22005092)Shanxi Province Transformation Program of Scientific and Technological Achievements(No.202304021301032)+8 种基金the Fundamental Research Program of Shanxi Province(No.202403021211075)Hunan University Outstanding Youth Science Foundation(No.531118040319)The science and technology innovation Program of Hunan Province(No.2021RC3055)Changsha Municipal Natural Science Foundation(No.43184)the CITIC Metals Ningbo Energy Co.Ltd.(No.H202191380246)Chongqing Talents:Exceptional Young Talents Project(No.CQYC202105015)Shenzhen Virtual University Park Basic Research Project of Free exploration(No.2021Szvup036)the National Key Research and Development Program of China(No.2022YFB2402400)Shenzhen Virtual University Park Basic Research Project of Free exploration(No.2021Szvup036)
文摘Cobalt-free nickel-manganese binary materials are one of the most promising cathode candidates for lithium-ion batteries due to the low reserves, high price,political and ecological unfriendliness of cobalt. The preparation of high-performance Ni-Mn bimetallic materials through controlled synthesis conditions holds significant importance for industrial applications. In this work,through systematic modulation of calcination temperatures and nickel ratios, we have effectively addressed critical challenges in binary layered cathodes, including cationic disordering, detrimental H2-H3 phase transitions, and severe interfacial side reactions. The electrochemical performance and thermal stability tests demonstrate that the medium-nickel cathode calcined at 850℃(NM64) exhibit superior comprehensive performance, including moderate discharge capacity(181.34 mAh g^(-1)at 1C), enhanced thermal stability and cycling stability(90% capacity retention after 100 cycles), excellent rate performance(125 mAh g^(-1)at high rate of 10C). Moreover, a 10 kg sample was prepared further verified its commercial application prospects. The soft-pack battery with commercial graphite anode and NM64-850 cathode achieve a discharge capacity of 171.0 mAh g^(-1)and retains 86.5% capacity after 180 cycles. The optimized integration of nickel content and calcination temperature endows binary cathodes with balanced electrochemical performance,enabling commercial viability.
基金supported by Hunan Police Academy Research Innovation Team-Key Technologies of Road Traffic Safety Law Enforcementthe Key Laboratory of Impression Evidence Examination and Identification Technology,Ministry of Public Security,People’s Republic of China
文摘Density functional theory(DFT) calculations are performed to investigate the electronic and structural properties of the stoichiometric thorium oxide clusters(ThO2)n-/0(n = 1~5). Generalized Koopmans' theorem is applied to predict the vertical detachment energies(VDEs)which are used to simulate the anionic photoelectron spectra(PES). Molecular orbital analyses are performed as well to analyze the chemical bonding in these thorium oxide clusters. The results show that the ground states of(ThO2)_n-/0(n = 1~5) clusters prefer the low-spin structures. With increasing of the cluster size(n), the structure parameters of(ThO2)n-/0(n = 1~5) gradually evolve toward bulk thorium oxide species. It shows that both the coordination number and the average bond length increase gradually in(ThO2)n-/0(n = 1~5) to approach that of ThO2 bulk. What's more, the vibration frequencies of Th=O double bonds are found to be decreasing along with the increased cluster size.
文摘Visible light-induced organic reactions have gained much attention in recent years due to their mild conditions and high efficiency[1,2].In this context,many efficient photocatalysts including transition metal complexes and organic dyes have been developed for various organic transformations.
基金supported in part by the National Natural Science Foundation of China under Grant 62272062the Researchers Supporting Project number.(RSP2023R102)King Saud University+5 种基金Riyadh,Saudi Arabia,the Open Research Fund of the Hunan Provincial Key Laboratory of Network Investigational Technology under Grant 2018WLZC003the National Science Foundation of Hunan Province under Grant 2020JJ2029the Hunan Provincial Key Research and Development Program under Grant 2022GK2019the Science Fund for Creative Research Groups of Hunan Province under Grant 2020JJ1006the Scientific Research Fund of Hunan Provincial Transportation Department under Grant 202143the Open Fund of Key Laboratory of Safety Control of Bridge Engineering,Ministry of Education(Changsha University of Science Technology)under Grant 21KB07.
文摘Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.
基金Project(2016JJ2029)supported by Hunan Provincial Natural Science Foundation of ChinaProject(2016WLZC014)supported by the Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational TechnologyProject(2015HNWLFZ059)supported by the Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges,China
文摘Direct dynamics simulations are a useful and general approach for studying the atomistic properties of complex chemical systems because they do not require fitting an analytic potential energy function.Hessian-based predictor-corrector integrators are a widely used approach for calculating the trajectories of moving atoms in direct dynamics simulations.We employ a monodromy matrix to propose a tool for evaluating the accuracy of integrators in the trajectory calculation.We choose a general velocity Verlet as a different object.We also simulate molecular with hydrogen(CO_2) and molecular with hydrogen(H_2O) motions.Comparing the eigenvalues of monodromy matrix,many simulations show that Hessian-based predictor-corrector integrators perform well for Hessian updates and non-Hessian updates.Hessian-based predictor-corrector integrator with Hessian update has a strong performance in the H_2O simulations.Hessian-based predictor-corrector integrator with Hessian update has a strong performance when the integrating step of the velocity Verlet approach is tripled for the predicting step.In the CO_2 simulations,a strong performance occurs when the integrating step is a multiple of five.
文摘Bioisosteres play an important role in pharmaceutical and agricultural compound design that has been developed to enhance the properties of molecules.As a sp3-rich small ring cage hydrocarbons,bicyclo[1.1.1]pentanes(BCPs)were commonly considered as a bioisostere of the para-substituted arenes,internal alkynes,and tert-butyl groups,and its introduction can significantly improve pharmacokinetic properties,including passive permeability,aqueous solubility,metabolic stability[1].Over the past decades,numerous pharmaceutically relevant molecules with biaryl frameworks were improved by the approach of bioisosteric replacement.Conventional strategies for the synthesis of such BCP-aryls derivatives mostly depend on the stepwise C-C formation via the direct addition to bridgehead C-C bond of[1.1.1]propellane to provide active BCP-intermediates and followed by a second transitionmetal cross-coupling sequence.However,these pioneer works were limited by the low step economy,the use of unstable reagents,and harsh conditions in industrial productions.
文摘Considering the widespread presence of the dithiocarbamate skeleton in pharmaceuticals and bioactive molecules,the development of novel and convenient methods for the synthesis of these useful sulfur-containing compounds is of significant interest.Traditionally,S-aryl dithiocarbamates are constructed through the reaction of amines with thiophenols and thiophosgene[1].Recently,transition-metal-catalyzed Ullmann-type coupling between aryl boronic acids or iodoarenes with tetraalkylthiuram disulfides or sodium dialkyldithiocarbamates has been reported[2].
基金Supported by the Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges,Grant No.2018WLFZZC003.
文摘At present,the research of blockchain is very popular,but the practical application of blockchain is very few.The main reason is that the concurrency of blockchain is not enough to support application scenarios.After that,applications such as Intervalue increase the concurrency of blockchain transactions.However,due to the problems of network bandwidth and algorithm performance,there is always a broadcast storm,which affects the normal use of nodes in the whole network.However,the emergence of broadcast storms needs to rely on the node itself,which may be very slow.Even if developers debug the corresponding code,they cannot conduct an effective test in the whole network.Broadcast storm problem mainly occurs in scenarios with large transaction volume,such as the financial industry.Due to its characteristics,the concurrency of transactions in the financial industry will increase at a certain time.If there is no effective algorithm to deal with it,the broadcast storm will be triggered and the whole network will be paralyzed.To solve the problem of the broadcast storm,this paper combines blockchain,peer-to-peer network,artificial intelligence,and other technologies,and proposes a broadcast storm detection and processing method based on situation awareness.The purpose is to cut off the further spread of broadcast storms from the node itself and maintain the normal operation of the whole network nodes.
文摘Background Individual differences have been detected in individuals with opioid use disorders(OUD)in rehabilitation following protracted abstinence.Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders(SUD).Aims This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling(CPM).Methods One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging(fMRI)scans at baseline.The Heroin Craving Questionnaire(HCQ)was used to assess craving levels at baseline and at the 8-month follow-up of abstinence.CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ(HCQtolow V-up-HCQpa baseline).Then,the follow-up aseline predictive ability of identified networks was tested in a separate,heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.Results CPM could predict craving changes induced by long-term abstinence,as shown by a significant correlation between predicted and actual HCQ fllow-up(r=0.417,p<0.001)and changes in HCQ(negative:r=0.334,p=0.002;positive:r=0.233,p=0.038).Identified craving-related prediction networks included the somato-motor network(SMN),salience network(SALN),default mode network(DMN),medial frontal network,visual network and auditory network.In addition,decreased connectivity of frontal-parietal network(FPN)-SMN,FPN-DMN and FPN-SALN and increased connectivity of subcortical network(SCN)-DMN,SCN-SALNandSCN-SMN were positively correlated with craving levels.Conclusions These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence,as well as the generalisation ability;the identified brain networks might be the focus of innovative therapies in the future.
文摘Since the conceptual germina- tion of ＂human rights＂ in the modern age, the controversy over it and its connotationshave been constant. Scholars at home and abroad either attempt to interpret the connotation of human rights by em- phasizing the real social foundation of human rights from a political and eco- nomic perspective, or elaborate on the essence of human rights by focusing on traditional values and dignity from the angle of cultural and moral traditions. However, because of differences in sys- tem and culture in different countries,
基金the support received from the National Natural Science Foundation of China (22372012,22261160640,22002009)the Natural Science Foundation of Hunan Province (2023JJ20037)the Science and Technology Innovation Program of Hunan Province (2024RC3177)。
文摘Electrocatalytic conversion of biomass-derived compounds and nitrate pollutants offers a promising route toward sustainable chemical synthesis and environmental remediation.In this work,a bifunctional NiO-NiCoP catalyst with a well-defined heterogeneous interface is synthesized via a low-temperature co-precipitation,annealing and phosphidation process to enable the coupled electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR) and nitrate reduction reaction(NO_(3)^(-)RR).X-ray photoelectron spectroscopy(XPS),high-resolution transmission electron microscopy(HRTEM),open-circuit potential(OCP),and in-situ electrochemical impedance spectroscopy(in-situ EIS) confirm the formation of the heterogeneous interface,which facilitates electron redistribution,enhances charge transfer,and optimizes reactant adsorption.The catalyst exhibits excellent HMFOR activity,achieving 99.46% HMF conversion,97.23% 2,5-furandicarboxylic acid(FDCA) yield,and 97.62% Faradaic efficiency(FE) at 1.40 V vs.RHE.For NO_(3)^(-)RR,nearly 100% FE and an NH_(3) yield of 8.82 mg h^(-1)cm^(-2)are obtained at-0.40 V vs.RHE.In a paired HMFOR//NO_(3)^(-)RR electrolyzer,the NiO-NiCoP catalyst demonstrates superior current density,product selectivity,and long-term stability compared to conventional oxygen evolution reaction//hydrogen evolution reaction(OER//HER) systems.At 1.60 V,the HMFOR//NO_(3)^(-)RR system achieved a maximum HMF conversion of 95.84%,an FDCA yield of94.83%,and a FE of 89.53%,while at 1.90 V,it reached a maximum NH_(3) yield of 32.50 mg h^(-1)cm^(-2)with an FE of 94.63%.This study underscores the catalytic advantages of heterogeneous interface engineering and provides a viable strategy for integrated biomass valorization and nitrogen-cycle remediation.
基金supported by the National Natural Science Foundation of China(Nos.61971184,62001162,62101187)the Hunan Provincial Natural Science Foundation(No.2022JJ40091)the Fundamental Research Funds for the Central Universities(No.531118010757)。
文摘It is difficult to extract targets under strong environmental disturbance in practice.Ghost imaging(GI)is an innovative antiinterference imaging technology.In this paper,we propose a scheme for target extraction based on characteristicenhanced pseudo-thermal GI.Unlike traditional GI which relies on training the detected signals or imaging results,our scheme trains the illuminating light fields using a deep learning network to enhance the target’s characteristic response.The simulation and experimental results prove that our imaging scheme is sufficient to perform single-and multiple-target extraction at low measurements.In addition,the effect of a strong scattering environment is discussed,and the results show that the scattering disturbance hardly affects the target extraction effect.The proposed scheme presents the potential application in target extraction through scattering media.
