Constructing nanofibers with specific therapeutic effects against cancer is a challenge.Here,we present the synthesis approach and application prospects of supramolecular nanofibers,which are based on cucurbit[8]uril(...Constructing nanofibers with specific therapeutic effects against cancer is a challenge.Here,we present the synthesis approach and application prospects of supramolecular nanofibers,which are based on cucurbit[8]uril(CB[8])as the host and terpyridine lanthanum ions metal complex as the vip,constructed by layer-by-layer self-assembly through supramolecular interaction.Moreover,nanofibers with lanthanide luminescence properties exhibit surprising pH-responsive deformation properties and antibacterial behavior.In the tumor micro-environment,the dramatic reduction in the size of the nanofibers enables specific and hierarchical release of anticancer drugs in tumor cells to exert an advanced therapeutic effect.In addition,the synergistic therapeutic efficacy was achieved by reducing the excess of Gram-positive and Gram-negative bacteria surrounding tumor cells.The novel supramolecular nanofibers with sequential drug release and combined therapeutic mode provide new guidance for the synthesis of drug carrier materials and direction for the promotion of nanomaterial-mediated cancer therapy.展开更多
The valorization of agricultural waste into high-value nanomaterials is crucial for advancing sustainable biorefineries.This study presents an efficient approach for extracting carboxylated cellulose nanocrystals(CNCs...The valorization of agricultural waste into high-value nanomaterials is crucial for advancing sustainable biorefineries.This study presents an efficient approach for extracting carboxylated cellulose nanocrystals(CNCs)from poplar leaf waste(PL),an abundant and underutilized biomass.The process involved alkaline treatment and hydrogen peroxide bleaching to purify cellulose(PL-CEL),followed by sequential periodate-chlorite oxidation to produce dicarboxylic cellulose nanocrystals(PL-CNCs).The resulting nanocrystals were comprehensively characterized using compositional analysis,XRD,FTIR,TEM,TGA,and zeta potential measurements.XRD analysis confirmed a high crystallinity index of 82%for PL-CEL,which decreased to 72.2%after oxidation due to the introduction of carboxyl groups.FTIR spectra revealed a prominent peak at 1720 cm-1,confirming successful carboxylation.TEM images showed rod-like nanocrystalswith an average length of 271.22 nmand width of 14.68 nm,while conductometric titration indicated a carboxyl content of 1.9 mmol/g.The PL-CNCs exhibited good colloidal stability with a zeta potential of-30.2mV at pH7.0.TGA demonstratedmoderate thermal stability with enhanced char formation.This work highlights a green and scalable route for converting poplar leaf waste into functional nanocellulose,suitable for applications in composites,adsorption,and sustainable materials.The novelty of this study lies in the pioneering use of poplar leaf waste combined with a sequential periodate-chlorite oxidation to sustainably produce carboxylated CNCs with enhanced functionality.展开更多
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact...Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.展开更多
Monolithic perovskite-silicon tandem solar cells(PSTs)represent a promising avenue for surpassing the efficiency limits of single-junction photovoltaics,but their performance is still hampered by significant open-circ...Monolithic perovskite-silicon tandem solar cells(PSTs)represent a promising avenue for surpassing the efficiency limits of single-junction photovoltaics,but their performance is still hampered by significant open-circuit voltage(V_(OC))losses arising from interfacial inefficient charge extraction and non-radiative recombination.To mitigate these losses,we introduce a push–pull bridging molecule,4′-amino-[1,1′-bi phenyl]-4-carboxylic acid(ABBA),which forms a 2 PACz/ABBA assembly through phosphor-amidate.The 2 PACz/ABBA assembly suppresses 2 PACz aggregation caused byπ-πstacking while simultaneously enhancing the interfacial dipole moment,thereby facilitating the built-in electric field to promote more efficient hole extraction.Meanwhile,–COOH groups within ABBA passivate deep-level defects(e.g.,uncoordinated Pb^(2+))at buried perovskite interface and contribute to the growth of perovskite films with large grains,reduced residual stress,and optimized energy level alignment.Consequently,the champion tandem device fabricated via the two-step sequential vapor-solution process achieves a PCE of 30.37% and an open-circuit voltage(V_(OC))of 1.891 V.Furthermore,unencapsulated devices maintain 88%of their initial performance after 1000 h under maximum power point tracking(MPPT),highlighting its superior stability.展开更多
Under the“dual carbon”goals,it is imperative to incorporate carbon emissions-related factors into research of power grid risk assessment to meet the green transformation needs of the power grid.Therefore,this paper ...Under the“dual carbon”goals,it is imperative to incorporate carbon emissions-related factors into research of power grid risk assessment to meet the green transformation needs of the power grid.Therefore,this paper conducts a study on the risk assessment of carbon emissions changes in regional power grids based on dynamic carbon emission factors,aiming to quantitatively analyze the impact of random disturbances such as equipment failures or fluctuations in renewable energy generation on the carbon emission intensity of regional power grids.First,carbon emission change risk indicators are constructed from three dimensions:the probability,frequency,and magnitude of carbon emission changes.Second,a dynamic carbon emission factor calculation model is proposed to reflect the spatiotemporal change of carbon emissions in the regional power grid,considering output of different types of generators and the components of inter-area power transmission.Finally,with the premise of ensuring safe and stable operation of power grid,a quantitative assessment model for carbon emission change risks is proposed under the objective of minimizing the electricity loss.The sampling convergence conditions of the model are also derived.The results from the MRTS79 case study demonstrate the proposed method can effectively quantify and analyze the risk of carbon emissions changes in regional power grids,validating the effectiveness of the proposed model.展开更多
The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi...The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.展开更多
The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of m...The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of mechanical parameters.In this study,a novel time-varying reliability analysis framework based on sequential Bayesian updating of mechanical parameters is proposed.The inverse parameters account for damage time-dependent behavior,incorporating water effect and a strain-driven softening-hardening process that depends on sliding states.The likelihood function is enhanced to simultaneously consider observation error,surrogate model prediction error,and model structural error,with the introduction of physical penalty.Exploration of the high-dimensional parameter space is achieved via the Hamiltonian Monte Carlo(HMC)method and the physics knowledge-based time-dependent deformation surrogate model.The time-varying reliability analysis of the slope is performed using the multi-grid method.Taking a reservoir bank slope as a case study,the sequential updating of 12 mechanical parameters is conducted based on deformation time series from 16 monitoring points,thereby validating the proposed framework.The results indicate that the proposed framework effectively captures the posterior distribution of mechanical parameters,with the case slope remaining in a critically stable state after overall sliding,showing a high failure probability.Introducing model structural error can reduce parameter compensation,and a reasonable sequential updating step size can improve inversion accuracy.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive,hindering accurate assessment on environmental risks and effectiveness of remediation strategies...Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive,hindering accurate assessment on environmental risks and effectiveness of remediation strategies.This study evaluated the feasibility of European Community Bureau of Reference(BCR)sequential extraction,Ca(NO_(3))_(2)extraction,and water extraction on assessing Cd and Pb availability in agricultural soil amended with slaked lime,magnesium hydroxide,corn stover biochar,and calcium dihydrogen phosphate.Moreover,the enriched isotope tracing technique(^(112)Cd and^(206)Pb)was employed to evaluate the aging process of newly introduced Cd and Pbwithin 56 days’incubation.Results demonstrated that extractable pools by BCR and Ca(NO_(3))_(2)extraction were little impacted by amendments and showed little correlation with soil pH.This is notable because soil pH is closely linked to metal availability,indicating these extraction methods may not adequately reflect metal availability.Conversely,water-soluble concentrations of Cd and Pb were markedly influenced by amendments and exhibited strong correlations with pH(Pearson’s r:-0.908 to-0.825,P<0.001),suggesting water extraction as a more sensitive approach.Furthermore,newly introduced metals underwent a more evident aging process as demonstrated by acid-soluble and water-soluble pools.Additionally,water-soluble concentrations of essential metals were impacted by soil amendments,raising caution on their potential effects on plant growth.These findings suggest water extraction as a promising and attractive method to evaluate Cd and Pb availability,which will help provide assessment guidance for environmental risks caused by heavy metals and develop efficient remediation strategies.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube samplin...For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.展开更多
Hollow multi-shelled structure(HoMS)is the novel multifunctional structural system,which are con-structed with nanoparticles as structural units,featuring two or more shells,multiple interfaces,and numerous chan-nels ...Hollow multi-shelled structure(HoMS)is the novel multifunctional structural system,which are con-structed with nanoparticles as structural units,featuring two or more shells,multiple interfaces,and numerous chan-nels and demonstrating outstanding properties in energy conversion and mass transfer.In recent years,owing to the breakthroughs in synthetic methods,the diversity of composition and structure of HoMS has been greatly enriched,showing broad application prospects in energy,catalysis,environment and other fields.This review focuses on the research status of HoMS for catalytic applications.Firstly,the new synthesis method for HoMS,namely the sequen-tial templating approach,is introduced from both practical and theoretical perspectives.Then,it summarizes and discusses the structure-performance relationship between the shell structure and catalytic performance.The unique temporal-spatial ordering property of mass transport in HoMS and the major breakthroughs it brings in catalytic applications are discussed.Finally,it looks forward to the opportunities and challenges in the development of HoMS.展开更多
Achieving artificial simulations of multi-step energy transfer processes and conversions in nature remains a challenge.In this study,we present a three-step sequential energy transfer process,which was constructed thr...Achieving artificial simulations of multi-step energy transfer processes and conversions in nature remains a challenge.In this study,we present a three-step sequential energy transfer process,which was constructed through host-vip interactions between a piperazine derivative(PPE-BPI)with aggregationinduced emission(AIE)and cucurbit[7]uril(CB[7])in water to serve as ideal energy donors.To achieve multi-step sequential energy transfer,we employ three distinct fluorescent dyes Eosin B(EsB),Sulforhodamine 101(SR101),and Cyanine 5(Cy5)as energy acceptors.The PPE-PBI-2CB[7]+EsB+SR101+Cy5 system demonstrates a highly efficient three-step sequential energy transfer mechanism,starting with PPEPBI-2CB[7]and transferring energy successively to EsB,SR101,and finally to Cy5,with remarkable energy transfer efficiencies.More interestingly,with the progressive transfer of energy in the multi-step energy transfer system,the generation efficiency of superoxide anion radical(O_(2)•-)increased gradually,which can be used as photocatalysts for selectively photooxidation of N-phenyltetrahydroisoquinoline in an aqueous medium with a high yield of 86%after irradiation for 18 h.This study offers a valuable investigation into the simulation of multi-step energy transfer processes and transformations in the natural world,paving the way for further research in the field.展开更多
Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent ...Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent autonomy through depression and well-being remain underexplored.This study aimed to test a sequential mediation model among filial piety beliefs,depression,well-being,and autonomy in Taiwan region of China university students.Methods:A total of 566 Taiwan region of China undergraduate and graduate students,comprising 390 females and 176 males,and including 399 undergraduates and 167 graduate students,were recruited through convenience sampling.Data were collected via an online questionnaire.Validated instruments were employed,including the Filial Piety Scale(FPS),the Center for Epidemiological Studies Depression Scale(CES-D),the Chinese Well-being Inventory(CHI),and the Adolescent Autonomy Scale-Short Form(AAS-SF).Statistical analyses included group comparisons,correlation analyses,and structural equation modeling to examine the hypothesized relationships and mediation effects.Results:The results revealed that filial piety beliefs exerted a significant positive impact on adolescent autonomy,with depression and well-being serving as key mediators in this relationship.A sequential mediation effect was confirmed through structural equation modeling(β=0.052,95%CI[0.028,0.091]),with good model fit indices(x^(2)/df=4.25,RMSEA=0.076,CFI=0.968),supporting the hypothesized pathway from filial piety to autonomy via depression and well-being.In terms of demographic differences,male students showed significantly higher autonomy than females(p<0.001);students from single-parent families reported significantly higher depression levels than those from two-parent families(p<0.05);and graduate students exhibited significantly higher autonomy and well-being than undergraduates(p<0.05).Conclusions:These findings underscore not only the importance of filial piety beliefs for developing youth autonomy but also the critical role that mental health factors,such as depression and well-being,play in this process.The study concludes with a discussion of both theoretical implications and practical recommendations.These include strategies to foster reciprocal filial piety,strengthen parent-child relationships,and promote mental health.Additionally,the study outlines its limitations and proposes directions for future research.展开更多
Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the co...Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.展开更多
A gradient coating containing collagen and inorganic strontium/calcium phosphate(Sr/CaP)was fabricated on plasma-electrolytically oxidised magnesium via one-step cathodic electrodeposition.First,Sr-doped dicalcium pho...A gradient coating containing collagen and inorganic strontium/calcium phosphate(Sr/CaP)was fabricated on plasma-electrolytically oxidised magnesium via one-step cathodic electrodeposition.First,Sr-doped dicalcium phosphate dihydrate and hydroxyapatite(DCPD and HA)was deposited,followed by a collagen/CaP layer.The morphological evolution,sequential degradation behaviour,and in vitro bio-properties of the coatings were investigated.The incorporation of collagen remarkably refined the morphology of the CaP,and a more aggregated nano-spherical morphology was observed with increasing collagen concentration.Sr could partially replace Ca in the CaP crystals.Collagen combined with CaP formed a relatively stable skeletal frame,which provided sufficient barrier properties and more sites for the re-precipitation of bone tissue,as well as a more promising proliferation and differentiation ability of osteoblasts.A gradient coating that matches the requirements of bone growth at various periods is suggested for implantation.展开更多
Sequential processing(SqP)of the active layer offers independent optimization of the donor and acceptor with more targeted solvent design,which is considered the most promising strategy for achieving efficient organic...Sequential processing(SqP)of the active layer offers independent optimization of the donor and acceptor with more targeted solvent design,which is considered the most promising strategy for achieving efficient organic solar cells(OSCs).In the SqP method,the favorable interpenetrating network seriously depends on the fine control of the bottom layer swelling.However,the choice of solvent(s)for both the donor and acceptor have been mostly based on a trial-and-error manner.A single solvent often cannot achieve sufficient yet not excessive swelling,which has long been a difficulty in the high efficient SqP OSCs.Herein,two new isomeric molecules are introduced to fine-tune the nucleation and crystallization dynamics that allows judicious control over the swelling of the bottom layer.The strong non-covalent interaction between the isomeric molecule and active materials provides an excellent driving force for optimize the swelling-process.Among them,the molecule with high dipole moment promotes earlier nucleation of the PM6 and provides extended time for crystallization during SqP,improving bulk morphology and vertical phase segregation.As a result,champion efficiencies of 17.38%and 20.00%(certified 19.70%)are achieved based on PM6/PYF-T-o(all-polymer)and PM6/BTP-eC9 devices casted by toluene solvent.展开更多
Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency....Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency.We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population.Methods:This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region,China.We first performed questionnaire assessment involving 23,381 individuals(7,042 outpatients and 16,339 individuals from the community),and those assessed as“high-risk”were then invited to participate in serological assays and endoscopic examinations.The serological biomarker model was derived based on logistic regression,with predictors selected via the Akaike information criterion.Model performance was evaluated by the area under the receiver operating characteristic curve(AUC).Results:A total of 2,011 participants were ultimately included for analysis.The final serological biomarker model had three predictors,comprising pepsinogenⅠ(PGI),pepsinogenⅠ/Ⅱratio(PGR),and anti-Helicobacter pylori immunoglobulin G(anti-H.pylori IgG)antibodies.This model generated an AUC of 0.733(95%confidence interval:0.655-0.812)and demonstrated the best discriminative ability compared with previously developed serological biomarker models.As the risk cut-off value of our model rose,the detection rate increased and the number of endoscopies needed to detect one case decreased.Conclusions:PGI,PGR,and anti-H.pylori Ig G could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment,providing insight for the development of a multi-stage riskbased sequential strategy for GC screening.展开更多
Polycyclic compounds are widely found in natural products and drug molecules with important biological activities,which attracted the attention of many chemists.Phosphine-catalyzed nucleophilic addition is one of the ...Polycyclic compounds are widely found in natural products and drug molecules with important biological activities,which attracted the attention of many chemists.Phosphine-catalyzed nucleophilic addition is one of the most powerful tools for the construction of various cyclic compounds with the advantages of atom economy,mild reaction conditions and simplicity of operation.Allenolates,Morita−Baylis−Hillman(MBH)alcohols and their derivatives(MBHADs),electron-deficient olefins and alkynes are very efficient substrates in phosphine mediated annulations,which formed many phosphonium species such asβ-phosphonium enolates,β-phosphonium dienolates and vinyl phosphonium ylides as intermediates.This review describes the reactivities of these phosphonium zwitterions and summarizes the synthesis of polycycle compounds through phosphine-mediated intramolecular and intermolecular sequential annulations.Thus,a systematic summary of the research process based on the phosphine-mediated sequential annulations of allenolates,MBH alcohols and MBHADs,electron-deficient olefins and alkynes are presented in Chapters 2-6,respectively.展开更多
基金supported by the National Natural Science Foundation of China(No.82273919)Natural Science Foundation of Heilongjiang Province(No.LH2024H013)China Postdoctoral Science Foundation(No.2022MD723781).
文摘Constructing nanofibers with specific therapeutic effects against cancer is a challenge.Here,we present the synthesis approach and application prospects of supramolecular nanofibers,which are based on cucurbit[8]uril(CB[8])as the host and terpyridine lanthanum ions metal complex as the vip,constructed by layer-by-layer self-assembly through supramolecular interaction.Moreover,nanofibers with lanthanide luminescence properties exhibit surprising pH-responsive deformation properties and antibacterial behavior.In the tumor micro-environment,the dramatic reduction in the size of the nanofibers enables specific and hierarchical release of anticancer drugs in tumor cells to exert an advanced therapeutic effect.In addition,the synergistic therapeutic efficacy was achieved by reducing the excess of Gram-positive and Gram-negative bacteria surrounding tumor cells.The novel supramolecular nanofibers with sequential drug release and combined therapeutic mode provide new guidance for the synthesis of drug carrier materials and direction for the promotion of nanomaterial-mediated cancer therapy.
基金funded by the basic scientific research Funds project of Heilongjiang Universities,grant number 2023-KYYWF-0570.
文摘The valorization of agricultural waste into high-value nanomaterials is crucial for advancing sustainable biorefineries.This study presents an efficient approach for extracting carboxylated cellulose nanocrystals(CNCs)from poplar leaf waste(PL),an abundant and underutilized biomass.The process involved alkaline treatment and hydrogen peroxide bleaching to purify cellulose(PL-CEL),followed by sequential periodate-chlorite oxidation to produce dicarboxylic cellulose nanocrystals(PL-CNCs).The resulting nanocrystals were comprehensively characterized using compositional analysis,XRD,FTIR,TEM,TGA,and zeta potential measurements.XRD analysis confirmed a high crystallinity index of 82%for PL-CEL,which decreased to 72.2%after oxidation due to the introduction of carboxyl groups.FTIR spectra revealed a prominent peak at 1720 cm-1,confirming successful carboxylation.TEM images showed rod-like nanocrystalswith an average length of 271.22 nmand width of 14.68 nm,while conductometric titration indicated a carboxyl content of 1.9 mmol/g.The PL-CNCs exhibited good colloidal stability with a zeta potential of-30.2mV at pH7.0.TGA demonstratedmoderate thermal stability with enhanced char formation.This work highlights a green and scalable route for converting poplar leaf waste into functional nanocellulose,suitable for applications in composites,adsorption,and sustainable materials.The novelty of this study lies in the pioneering use of poplar leaf waste combined with a sequential periodate-chlorite oxidation to sustainably produce carboxylated CNCs with enhanced functionality.
基金supported by the National Key R&D Program of China[2022YFF0902703]the State Administration for Market Regulation Science and Technology Plan Project(2024MK033).
文摘Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.
基金supported by the National Key Research and Development Program of China(No.2023YFB4202501)the National Natural Science Foundation of China(No.62274026)+1 种基金the Sichuan Science and Technology Program(No.2024NSFSC0216)the Fundamental Research Funds for the Central Universities of China(ZYGX2025XT010 and ZYGX2025TS005)。
文摘Monolithic perovskite-silicon tandem solar cells(PSTs)represent a promising avenue for surpassing the efficiency limits of single-junction photovoltaics,but their performance is still hampered by significant open-circuit voltage(V_(OC))losses arising from interfacial inefficient charge extraction and non-radiative recombination.To mitigate these losses,we introduce a push–pull bridging molecule,4′-amino-[1,1′-bi phenyl]-4-carboxylic acid(ABBA),which forms a 2 PACz/ABBA assembly through phosphor-amidate.The 2 PACz/ABBA assembly suppresses 2 PACz aggregation caused byπ-πstacking while simultaneously enhancing the interfacial dipole moment,thereby facilitating the built-in electric field to promote more efficient hole extraction.Meanwhile,–COOH groups within ABBA passivate deep-level defects(e.g.,uncoordinated Pb^(2+))at buried perovskite interface and contribute to the growth of perovskite films with large grains,reduced residual stress,and optimized energy level alignment.Consequently,the champion tandem device fabricated via the two-step sequential vapor-solution process achieves a PCE of 30.37% and an open-circuit voltage(V_(OC))of 1.891 V.Furthermore,unencapsulated devices maintain 88%of their initial performance after 1000 h under maximum power point tracking(MPPT),highlighting its superior stability.
基金supported by the Key R&D Program of Hubei Province(2023BAB002)the Science and technology project of State Grid Hubei Electric Power Co.,Ltd.(52153223000A)。
文摘Under the“dual carbon”goals,it is imperative to incorporate carbon emissions-related factors into research of power grid risk assessment to meet the green transformation needs of the power grid.Therefore,this paper conducts a study on the risk assessment of carbon emissions changes in regional power grids based on dynamic carbon emission factors,aiming to quantitatively analyze the impact of random disturbances such as equipment failures or fluctuations in renewable energy generation on the carbon emission intensity of regional power grids.First,carbon emission change risk indicators are constructed from three dimensions:the probability,frequency,and magnitude of carbon emission changes.Second,a dynamic carbon emission factor calculation model is proposed to reflect the spatiotemporal change of carbon emissions in the regional power grid,considering output of different types of generators and the components of inter-area power transmission.Finally,with the premise of ensuring safe and stable operation of power grid,a quantitative assessment model for carbon emission change risks is proposed under the objective of minimizing the electricity loss.The sampling convergence conditions of the model are also derived.The results from the MRTS79 case study demonstrate the proposed method can effectively quantify and analyze the risk of carbon emissions changes in regional power grids,validating the effectiveness of the proposed model.
基金supported by the Program for NIM-Basic Research Business Expenses Key Field Program,China(No.AKYCX2315).
文摘The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.
基金supported by the National Natural Science Foundation of China(Grant No.41961134032).
文摘The probabilistic stability evolution analysis of reservoir bank slopes is a crucial aspect of risk assessment,with core challenges including the consideration of deformation mechanisms and accurate determination of mechanical parameters.In this study,a novel time-varying reliability analysis framework based on sequential Bayesian updating of mechanical parameters is proposed.The inverse parameters account for damage time-dependent behavior,incorporating water effect and a strain-driven softening-hardening process that depends on sliding states.The likelihood function is enhanced to simultaneously consider observation error,surrogate model prediction error,and model structural error,with the introduction of physical penalty.Exploration of the high-dimensional parameter space is achieved via the Hamiltonian Monte Carlo(HMC)method and the physics knowledge-based time-dependent deformation surrogate model.The time-varying reliability analysis of the slope is performed using the multi-grid method.Taking a reservoir bank slope as a case study,the sequential updating of 12 mechanical parameters is conducted based on deformation time series from 16 monitoring points,thereby validating the proposed framework.The results indicate that the proposed framework effectively captures the posterior distribution of mechanical parameters,with the case slope remaining in a critically stable state after overall sliding,showing a high failure probability.Introducing model structural error can reduce parameter compensation,and a reasonable sequential updating step size can improve inversion accuracy.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金supported by the National Natural Science Foundation of Shandong(No.ZR2020ZD20)the National Natural Science Foundation of China(No.22193051)+1 种基金the National Young Top-Notch Talents(No.W03070030)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.Y202011).
文摘Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive,hindering accurate assessment on environmental risks and effectiveness of remediation strategies.This study evaluated the feasibility of European Community Bureau of Reference(BCR)sequential extraction,Ca(NO_(3))_(2)extraction,and water extraction on assessing Cd and Pb availability in agricultural soil amended with slaked lime,magnesium hydroxide,corn stover biochar,and calcium dihydrogen phosphate.Moreover,the enriched isotope tracing technique(^(112)Cd and^(206)Pb)was employed to evaluate the aging process of newly introduced Cd and Pbwithin 56 days’incubation.Results demonstrated that extractable pools by BCR and Ca(NO_(3))_(2)extraction were little impacted by amendments and showed little correlation with soil pH.This is notable because soil pH is closely linked to metal availability,indicating these extraction methods may not adequately reflect metal availability.Conversely,water-soluble concentrations of Cd and Pb were markedly influenced by amendments and exhibited strong correlations with pH(Pearson’s r:-0.908 to-0.825,P<0.001),suggesting water extraction as a more sensitive approach.Furthermore,newly introduced metals underwent a more evident aging process as demonstrated by acid-soluble and water-soluble pools.Additionally,water-soluble concentrations of essential metals were impacted by soil amendments,raising caution on their potential effects on plant growth.These findings suggest water extraction as a promising and attractive method to evaluate Cd and Pb availability,which will help provide assessment guidance for environmental risks caused by heavy metals and develop efficient remediation strategies.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015)the Natural Science Foundation of Beijing Municipality,China(No.L221008)+1 种基金Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005)the Tianmushan Laboratory Project,China(No.TK2023-B-001)。
文摘For uncertainty quantification of complex models with high-dimensional,nonlinear,multi-component coupling like digital twins,traditional statistical sampling methods,such as random sampling and Latin hypercube sampling,require a large number of samples,which entails huge computational costs.Therefore,how to construct a small-size sample space has been a hot issue of interest for researchers.To this end,this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification.First,the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis.Then,the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the"space filling"criterion.Finally,the sample selection function is established,and the next most informative sample is optimally selected to obtain the sequential test sample.Compared with the classical sampling method,the generated samples can retain more information on the basis of sparsity.A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme,which is a way to provide reliable uncertainty quantification results with small sample sizes.
文摘Hollow multi-shelled structure(HoMS)is the novel multifunctional structural system,which are con-structed with nanoparticles as structural units,featuring two or more shells,multiple interfaces,and numerous chan-nels and demonstrating outstanding properties in energy conversion and mass transfer.In recent years,owing to the breakthroughs in synthetic methods,the diversity of composition and structure of HoMS has been greatly enriched,showing broad application prospects in energy,catalysis,environment and other fields.This review focuses on the research status of HoMS for catalytic applications.Firstly,the new synthesis method for HoMS,namely the sequen-tial templating approach,is introduced from both practical and theoretical perspectives.Then,it summarizes and discusses the structure-performance relationship between the shell structure and catalytic performance.The unique temporal-spatial ordering property of mass transport in HoMS and the major breakthroughs it brings in catalytic applications are discussed.Finally,it looks forward to the opportunities and challenges in the development of HoMS.
基金the National Natural Science Foundation of China(No.52205210)the Natural Science Foundation of Shandong Province(Nos.ZR2020MB018,ZR2022QE033 and ZR2021QB049).
文摘Achieving artificial simulations of multi-step energy transfer processes and conversions in nature remains a challenge.In this study,we present a three-step sequential energy transfer process,which was constructed through host-vip interactions between a piperazine derivative(PPE-BPI)with aggregationinduced emission(AIE)and cucurbit[7]uril(CB[7])in water to serve as ideal energy donors.To achieve multi-step sequential energy transfer,we employ three distinct fluorescent dyes Eosin B(EsB),Sulforhodamine 101(SR101),and Cyanine 5(Cy5)as energy acceptors.The PPE-PBI-2CB[7]+EsB+SR101+Cy5 system demonstrates a highly efficient three-step sequential energy transfer mechanism,starting with PPEPBI-2CB[7]and transferring energy successively to EsB,SR101,and finally to Cy5,with remarkable energy transfer efficiencies.More interestingly,with the progressive transfer of energy in the multi-step energy transfer system,the generation efficiency of superoxide anion radical(O_(2)•-)increased gradually,which can be used as photocatalysts for selectively photooxidation of N-phenyltetrahydroisoquinoline in an aqueous medium with a high yield of 86%after irradiation for 18 h.This study offers a valuable investigation into the simulation of multi-step energy transfer processes and transformations in the natural world,paving the way for further research in the field.
文摘Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent autonomy through depression and well-being remain underexplored.This study aimed to test a sequential mediation model among filial piety beliefs,depression,well-being,and autonomy in Taiwan region of China university students.Methods:A total of 566 Taiwan region of China undergraduate and graduate students,comprising 390 females and 176 males,and including 399 undergraduates and 167 graduate students,were recruited through convenience sampling.Data were collected via an online questionnaire.Validated instruments were employed,including the Filial Piety Scale(FPS),the Center for Epidemiological Studies Depression Scale(CES-D),the Chinese Well-being Inventory(CHI),and the Adolescent Autonomy Scale-Short Form(AAS-SF).Statistical analyses included group comparisons,correlation analyses,and structural equation modeling to examine the hypothesized relationships and mediation effects.Results:The results revealed that filial piety beliefs exerted a significant positive impact on adolescent autonomy,with depression and well-being serving as key mediators in this relationship.A sequential mediation effect was confirmed through structural equation modeling(β=0.052,95%CI[0.028,0.091]),with good model fit indices(x^(2)/df=4.25,RMSEA=0.076,CFI=0.968),supporting the hypothesized pathway from filial piety to autonomy via depression and well-being.In terms of demographic differences,male students showed significantly higher autonomy than females(p<0.001);students from single-parent families reported significantly higher depression levels than those from two-parent families(p<0.05);and graduate students exhibited significantly higher autonomy and well-being than undergraduates(p<0.05).Conclusions:These findings underscore not only the importance of filial piety beliefs for developing youth autonomy but also the critical role that mental health factors,such as depression and well-being,play in this process.The study concludes with a discussion of both theoretical implications and practical recommendations.These include strategies to foster reciprocal filial piety,strengthen parent-child relationships,and promote mental health.Additionally,the study outlines its limitations and proposes directions for future research.
基金financially supported by the Natural Science Foundation of Hunan Province,China(No.2024JJ2074)the National Natural Science Foundation of China(No.22376221)the Young Elite Scientists Sponsorship Program by CAST,China(No.2023QNRC001).
文摘Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.
基金support from Mobility Programme of the Sino-German Center(M-0056)National Natural Science Foundation of China(52101286)+2 种基金Natural Science Foundation of Liaoning Province(2022-YGJC-16)Fundamental Research Funds for the Central Universities(N2302017)Supported by Sichuan Science and Technology Program 2023ZYD0115Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program(RC231178).
文摘A gradient coating containing collagen and inorganic strontium/calcium phosphate(Sr/CaP)was fabricated on plasma-electrolytically oxidised magnesium via one-step cathodic electrodeposition.First,Sr-doped dicalcium phosphate dihydrate and hydroxyapatite(DCPD and HA)was deposited,followed by a collagen/CaP layer.The morphological evolution,sequential degradation behaviour,and in vitro bio-properties of the coatings were investigated.The incorporation of collagen remarkably refined the morphology of the CaP,and a more aggregated nano-spherical morphology was observed with increasing collagen concentration.Sr could partially replace Ca in the CaP crystals.Collagen combined with CaP formed a relatively stable skeletal frame,which provided sufficient barrier properties and more sites for the re-precipitation of bone tissue,as well as a more promising proliferation and differentiation ability of osteoblasts.A gradient coating that matches the requirements of bone growth at various periods is suggested for implantation.
基金supported by the Guangdong Basic and Applied Basic Research Foundation (2022A1515010875)National Natural Science Foundation of China (12404480)+4 种基金Shenzhen Science and Technology Program (JCYJ20240813113238050, JCYJ20240813113306008)Education Department of Guangdong Province (2021KCXTD045)National Natural Science Foundation of China (12274303)the Shenzhen Key Laboratory of Applied Technologies of Super-Diamond and Functional Crystals (ZDSYS20230626091303007)Characteristic Innovation Foundation of Higher Education Institutions of Guangdong Province (2022KTSCX116)
文摘Sequential processing(SqP)of the active layer offers independent optimization of the donor and acceptor with more targeted solvent design,which is considered the most promising strategy for achieving efficient organic solar cells(OSCs).In the SqP method,the favorable interpenetrating network seriously depends on the fine control of the bottom layer swelling.However,the choice of solvent(s)for both the donor and acceptor have been mostly based on a trial-and-error manner.A single solvent often cannot achieve sufficient yet not excessive swelling,which has long been a difficulty in the high efficient SqP OSCs.Herein,two new isomeric molecules are introduced to fine-tune the nucleation and crystallization dynamics that allows judicious control over the swelling of the bottom layer.The strong non-covalent interaction between the isomeric molecule and active materials provides an excellent driving force for optimize the swelling-process.Among them,the molecule with high dipole moment promotes earlier nucleation of the PM6 and provides extended time for crystallization during SqP,improving bulk morphology and vertical phase segregation.As a result,champion efficiencies of 17.38%and 20.00%(certified 19.70%)are achieved based on PM6/PYF-T-o(all-polymer)and PM6/BTP-eC9 devices casted by toluene solvent.
基金supported by the Tencent Charity Foundationthe Ningxia Hui Autonomous Region Key Research and Development Program(No.2021BEG 02025)+1 种基金the Flexible Introduction of Technological Innovation Teams of Ningxia Hui Autonomous Region(No.2021RXTDLX15)the Natural Science Foundation of China(No.82160644)。
文摘Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency.We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population.Methods:This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region,China.We first performed questionnaire assessment involving 23,381 individuals(7,042 outpatients and 16,339 individuals from the community),and those assessed as“high-risk”were then invited to participate in serological assays and endoscopic examinations.The serological biomarker model was derived based on logistic regression,with predictors selected via the Akaike information criterion.Model performance was evaluated by the area under the receiver operating characteristic curve(AUC).Results:A total of 2,011 participants were ultimately included for analysis.The final serological biomarker model had three predictors,comprising pepsinogenⅠ(PGI),pepsinogenⅠ/Ⅱratio(PGR),and anti-Helicobacter pylori immunoglobulin G(anti-H.pylori IgG)antibodies.This model generated an AUC of 0.733(95%confidence interval:0.655-0.812)and demonstrated the best discriminative ability compared with previously developed serological biomarker models.As the risk cut-off value of our model rose,the detection rate increased and the number of endoscopies needed to detect one case decreased.Conclusions:PGI,PGR,and anti-H.pylori Ig G could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment,providing insight for the development of a multi-stage riskbased sequential strategy for GC screening.
基金the National Natural Science Foundation of China(Nos.22171147 and 21871148)for the financial support。
文摘Polycyclic compounds are widely found in natural products and drug molecules with important biological activities,which attracted the attention of many chemists.Phosphine-catalyzed nucleophilic addition is one of the most powerful tools for the construction of various cyclic compounds with the advantages of atom economy,mild reaction conditions and simplicity of operation.Allenolates,Morita−Baylis−Hillman(MBH)alcohols and their derivatives(MBHADs),electron-deficient olefins and alkynes are very efficient substrates in phosphine mediated annulations,which formed many phosphonium species such asβ-phosphonium enolates,β-phosphonium dienolates and vinyl phosphonium ylides as intermediates.This review describes the reactivities of these phosphonium zwitterions and summarizes the synthesis of polycycle compounds through phosphine-mediated intramolecular and intermolecular sequential annulations.Thus,a systematic summary of the research process based on the phosphine-mediated sequential annulations of allenolates,MBH alcohols and MBHADs,electron-deficient olefins and alkynes are presented in Chapters 2-6,respectively.
