A large number of runaway electrons(REs)generated during disruption can cause significant damage to next-generation large-scale tokamaks.The influence of three-dimensional(3D)helical magnetic perturbations on the supp...A large number of runaway electrons(REs)generated during disruption can cause significant damage to next-generation large-scale tokamaks.The influence of three-dimensional(3D)helical magnetic perturbations on the suppression of RE generation was explored using a set of 3D helical coils in J-TEXT tokamak,which can excite m/n=-2/2 helical magnetic perturbations.Experimental evidence shows that the-2/2 magnetic perturbations caused by the opposite coil current direct plasma toward the high-field side,simultaneously enhancing the magnetic fluctuations,which would enhance the radial loss of REs and even prevent RE generation.On the other hand,-2/2 magnetic perturbations can also reduce the cooling time during the disruption phase and generate a population of high-energy REs,which can interact with high-frequency magnetic fluctuations and in turn suppress RE generation.The critical helical coil current was found to correlate with electron density,requiring higher coil currents at higher densities.According to the statistical analysis of RE generation at different electron densities,the applied-2/2 magnetic perturbations can increase the magnetic fluctuations to the same level at lower electron densities,which can decrease the threshold electron density for RE suppression.This will be beneficial for RE mitigation in future large tokamak devices.展开更多
Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-targe...Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the“therapeutic module”of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.展开更多
BACKGROUND Identifying factors that influence non-curative resection(NCR)is critical to optimize treatment strategies and improve patient outcomes in patients with early gastric cancer(EGC).AIM To investigate the fact...BACKGROUND Identifying factors that influence non-curative resection(NCR)is critical to optimize treatment strategies and improve patient outcomes in patients with early gastric cancer(EGC).AIM To investigate the factors influencing the NCR of EGC and to evaluate the predictive value of these factors.METHODS The clinical data of 173 patients with EGC admitted between July 2020 and July 2023 were retrospectively collected.According to radical resection criteria,the patients were further divided into curative resection group(n=143)and NCR group(n=30).Clinical information was collected,including surgical method,tumor diameter,tumor site,ulcer formation,depth of invasion,pathological type,and lymph node metastasis.Logistic regression analysis was used to explore the factors affecting non-curable resection.RESULTS Multivariate logistic regression analysis showed that ulcer formation[odds ratio(OR)=3.53;95%confidence interval(CI):1.55-8.01,P=0.003],pathological type(OR=3.73;95%CI:1.60-8.74,P=0.002),tumor diameter(OR=3.15;95%CI:1.40-7.05,P=0.005),tumor location(OR=3.50;95%CI:1.16-10.58,P=0.027),lymph node metastasis(OR=4.40;95%CI:1.83-10.57,P=0.001),and depth of penetration(OR=3.75;95%CI:1.60-8.74,P=0.002)were all risk factors for NCR in EGC patients.Predictive analysis showed varying area under the curve values for factors such as tumor diameter(0.636),tumor location(0.608),ulcer formation(0.652),infiltration depth(0.658),pathological type(0.656),and lymph node metastasis(0.674).CONCLUSION The results suggest that factors such as tumor diameter,tumor location,ulcer formation,depth of invasion,pathological type,and lymph node metastasis increase the risk of NCR in EGC patients.展开更多
The large-scale exploitation of vanadium(Ⅴ) bearing minerals has led to a massive accumulation of Ⅴ tailings, of which Ⅴ pollution poses severe ecological risks. Although the mechanisms of Ⅴ stress to the microbia...The large-scale exploitation of vanadium(Ⅴ) bearing minerals has led to a massive accumulation of Ⅴ tailings, of which Ⅴ pollution poses severe ecological risks. Although the mechanisms of Ⅴ stress to the microbial community have been reported, the influential pathways in a multi-medium-containing system, for example, the soil-tailings-groundwater system,are unknown. The dynamic redox conditions and substance exchange within the system exhibited complex Ⅴ stress on the local microbial communities. In this study, the influence pathways of Ⅴ stress to the microbial community in the soil-tailings-groundwater system were first investigated. High Ⅴ contents were observed in groundwater(139.2 ± 0.15 μg/L) and soil(98.0–323.8 ± 0.02 mg/kg), respectively. Distinct microbial composition was observed for soil and groundwater, where soil showed the highest level of diversity and richness. Firmicutes, Proteobacteria, Actinobacteria, and Acidobacteria were dominant in soil and groundwater with a sum relative abundance of around 80 %. Based on redundancy analysis and structural equation models, Ⅴ was one of the vital driving factors affecting microbial communities. Groundwater microbial communities were influenced by Ⅴ via Cr, dissolved oxygen, and total nitrogen, while Fe, Mn, and total phosphorus were the key mediators for Ⅴ to affect soil microbial communities. Ⅴ affected the microbial community via metabolic pathways related to carbonaceous matter, which was involved in the establishment of survival strategies for metal stress. This study provides novel insights into the influence pathways of Ⅴ on the microorganisms in tailings reservoir for pollution bioremediation.展开更多
[Objective]The construction of weirs changes the hydraulic characteristics of rivers and affects the structure of phytoplankton communities and the health of aquatic ecosystems in the river.This study aims to explore ...[Objective]The construction of weirs changes the hydraulic characteristics of rivers and affects the structure of phytoplankton communities and the health of aquatic ecosystems in the river.This study aims to explore the nonlinear response relationship between phytoplankton community structure and its driving factors in spring and autumn in Furong Creek under the construction of cascade weirs.[Methods]The structure of phytoplankton communities and related environmental factors were investigated in Furong Creek from 2023 to 2024.This study focused on the analysis of the changes of nutrient concentrations and biomass of phytoplankton in autumn and spring within the same dry season in Furong Creek.Redundancy analysis was used to identify the key factors influencing the structure of phytoplankton communities.The MIKE 11 model was employed to simulate the hydrodynamic changes in the river.Combined with total nitrogen and permanganate index,a GAM model of phytoplankton diversity index and hydrodynamic factors was developed,and the change of phytoplankton diversity after the optimized layout of the cascade weirs was fitted.[Results]The result showed that the annual average value of Shannon-Wiener diversity index of phytoplankton in Furong Creek was 2.79,which was in a state of mild pollution.A total of 239 species from 95 genera in 8 phyla were identified.Among the phytoplankton,Chlorophyta was the dominant group throughout the year in Furong Creek,followed by Bacillariophyta and Cyanophyta.The cell abundance of phytoplankton ranged from 3.11 to 20.64 mg/L and from 0.23 to 6.31 mg/L in spring and autumn,which indicated a clear seasonal succession of phytoplankton community structure.Compared with autumn,the relative abundance of Cyanophyta significantly decreased in spring across the whole river section,while Chrysophyta and Dinophyta showed significant increase at some monitoring sites,leading to water bloom phenomenon and a noticeable decline in the diversity of phytoplankton.The dominant species in the water bodies throughout the year were Cyclotella catenata,Chlorella vulgaris,Scenedesmus bijuga,Scenedesmus quadricauda,Chroomonas acuta,Cryptomonas ovata,and Cryptomonas erosa.Redundancy analysis(RDA)showed that hydrodynamic factors(v,h)and water environmental factors(TN,COD_(Mn))were the main influencing factors of phytoplankton community structure.[Conclusion]The result show that the nutrient concentration,phytoplankton biomass,and density in Furong Creek in spring are significantly higher than in autumn.The GAM model,constructed by combining hydrodynamic and environmental factors,can effectively reflect the nonlinear relationship between phytoplankton diversity index and its driving factors.In spring,with an increase in nutrient concentration,the habitat conditions of low flow speed and high water depths formed by overflow weirs will lead to a decrease in the Shannon-Wiener index of phytoplankton and an intensified risk of eutrophication.However,a reasonable layout scheme of cascade weirs will improve the diversity of phytoplankton and reduce the risk of eutrophication in the river.The findings of this study can help deepen the understanding of the ecological and environmental effects of cascade weir construction in the river.展开更多
The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability ...The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.展开更多
In recent years,universal adversarial per-turbation(UAP)has attracted the attention of many re-searchers due to its good generalization.However,in order to generate an appropriate UAP,current methods usually require e...In recent years,universal adversarial per-turbation(UAP)has attracted the attention of many re-searchers due to its good generalization.However,in order to generate an appropriate UAP,current methods usually require either accessing the original dataset or meticulously constructing optimization functions and proxy datasets.In this paper,we aim to elimi-nate any dependency on proxy datasets and explore a method for generating Universal Adversarial Pertur-bations(UAP)on a single image.After revisiting re-search on UAP,we discovered that the key to gener-ating UAP lies in the accumulation of Individual Ad-versarial Perturbation(IAP)gradient,which prompted us to study the method of accumulating gradients from an IAP.We designed a simple and effective process to generate UAP,which only includes three steps:pre-cessing,generating an IAP and scaling the perturba-tions.Through our proposed process,any IAP gener-ated on an image can be constructed into a UAP with comparable performance,indicating that UAP can be generated free of data.Extensive experiments on var-ious classifiers and attack approaches demonstrate the superiority of our method on efficiency and aggressiveness.展开更多
The spatial-temporal distribution of charging loads for electric vehicles is influenced by multiple factors,Nowadays,the accuracy of the forecasts needs to be improved and the completeness of the modeling is relativel...The spatial-temporal distribution of charging loads for electric vehicles is influenced by multiple factors,Nowadays,the accuracy of the forecasts needs to be improved and the completeness of the modeling is relatively lacking.Therefore,this paper proposes a method for modeling the charging load of electric vehicles based on the influence of multiple factors.First,an in-depth analysis of the factors affecting the charging load of electric vehicles was conducted.Then,a model of electric vehicle electricity consumption per unit kilometer was constructed based on the influencing factors.Next,the electric vehicle,the charging station,the traffic network and the grid are modeled separately.In addition,a unified model of vehicle-station-road-network was constructed through the interaction and coupling of information between the models.Finally,the spatial-temporal distribution of electric vehicle charging loads was simulated using real data from a region.The study shows that the model is able to simulate the charging load of electric vehicles more accurately.Different traffic flows and areas have a significant impact on the charging load distribution.展开更多
1 No one likes acknowledging that their thoughts and behaviors are easily influenced outside of their awareness.However,Daniel M.Wegner,author of The Illusion of Conscious Will,explains that people ignore how easily t...1 No one likes acknowledging that their thoughts and behaviors are easily influenced outside of their awareness.However,Daniel M.Wegner,author of The Illusion of Conscious Will,explains that people ignore how easily they can be manipulated(操纵)because they don't feel they are being manipulated.This is what happens:After our unconscious(无意识)mind motivates a thought or action,this urge or thought is then sent to our conscious mind.Here,we become aware of it and have the false impression of generating it independently.展开更多
Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patient...Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patients,and provide a theoretical basis for clinical practice.Therefore,in the context of the new era,it is necessary to deeply analyze the essence and connotation of personalized nursing,and analyze the existing deficiencies in current emergency pediatric personalized nursing,so as to develop effective improvement plans.Research shows that personalized nursing can significantly improve the satisfaction of emergency pediatric nursing,largely avoid nursing risks,and has strong clinical application value.This article summarizes and explores the research on the influence of personalized nursing on improving the satisfaction of emergency pediatric nursing,and puts forward corresponding views.展开更多
Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulati...Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulations such as the General Data Protection Regulation(GDPR),which explicitly restrict the processing of data that can re-identify individuals.Although existing anonymization approaches such as the AnonymizingAutoEncoder(AAE)can reduce the risk of re-identification,they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task(e.g.,human activity recognition).This study proposes a novel sensor data anonymization method based onAdversarial Perturbations(AP)to address these limitations.By generating minimal yet targeted noise,the proposed method significantly degrades the accuracy of identity classification while retaining essential features for multiple tasks such as activity,gender,or device-position recognition.Moreover,to enhance robustness against frequency-domain analysis,additional models trained on transformed(e.g.,short-time Fourier transform(STFT))representations are incorporated into the perturbation process.A multi-task formulation is introduced that selectively suppresses person-identifying features while reinforcing those relevant to other desired tasks without retraining large autoencoder-based architectures.The proposed framework is,to our knowledge,the first AP-based anonymization technique that(i)defends simultaneously against time-and frequency-domain attacks and(ii)allows per-task trade-off control on a single forward-back-propagation run,enabling real-time,on-device deployment on commodity hardware.On three public datasets,the proposed method reduces person-identification accuracy from 60–90%to near-chance levels(≤5%)while preserving the original activity-recognition F1 both in the time and frequency domains.Compared with the baseline AAE,the proposed method improves downstream task F1 and lowers waveform mean squared error,demonstrating a better privacy-utility trade-off without additional model retraining.These findings underscore the effectiveness and flexibility of AP in privacy-preserving sensor-data processing,offering a practical solution that safeguards user identity while retaining rich,application-critical information.展开更多
With increasingly severe global climate change,a low-carbon economy has become an inevitable trend in the development of the international community.Low-carbon economy is not only related to environmental protection b...With increasingly severe global climate change,a low-carbon economy has become an inevitable trend in the development of the international community.Low-carbon economy is not only related to environmental protection but also has a profound impact on international trade.The purpose of this paper is to explore the impact of a low-carbon economy on the development of international trade and put forward corresponding strategy suggestions.By analyzing the connotation,characteristics,and mechanism of the low-carbon economy on international trade,this paper reveals the important role of the low-carbon economy in promoting the optimization of international trade structure,promoting green technology innovation,and strengthening international cooperation.At the same time,given the challenges brought by a low-carbon economy,this paper puts forward strategies such as strengthening policy guidance,promoting green technology innovation,and improving international trade rules to provide a reference for the sustainable development of international trade[1,2].展开更多
The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a ...The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions.In the past,the“1-phase,1-partition”rule was usually adopted in numerical analysis,where one constituent phase at the mesoscale formed one partition.The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the same constituent,while it also introduces numerical inaccuracy.This study proposes a new partitioning method for fibrous unitcells under a reduced-order homogenization methodology.In this method,the fiber phase remains 1 partition,but the matrix phase is divided into 2 partitions,which refers to the“12”partitioning scheme.Analytical elastic influence+functions are derived by introducing the elastic strain energy equivalence(Hill-Mandel condition).This research also obtains the analytical eigenstrain influence functions by alleviating the so-called“inclusion-locking”phenomenon.In addition,a numerical approach to minimize the error of strain energy density is introduced to determine the partitioning of the matrix phase.Several numerical examples are presented to compare the differences among direct numerical simulation(DNS),“11”,and“12”partitioning schemes.The numerical simulations show improved++numerical accuracy by the“12”partitioning scheme.展开更多
Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-ra...Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.展开更多
Landfalling tropical cyclones(TCs)pose tremendous hazards to East Asian coastal areas,particularly in East China,a densely populated and economically vital center.This underscores the critical need for a more in-depth...Landfalling tropical cyclones(TCs)pose tremendous hazards to East Asian coastal areas,particularly in East China,a densely populated and economically vital center.This underscores the critical need for a more in-depth investigation into the evolving characteristics and influences of these landfalling TCs.In this study,we explored changes in landfalling TC activity during 1965–2022 and estimated their influences in East China.Our findings demonstrate that the annual frequency of landfalling TCs has exhibited a slight increase since the mid-1990s,while their overall influences have significantly intensified.This intensification is closely associated with the prolonged duration of TCs over land after landfall.The results also reveal that longer overland sustainment is attributed to the descending vertical wind shear(VWS)and ascending low-layer moisture supply over the corresponding areas.In addition,the annual mean genesis location of these landfalling TCs has shown a significant westward migration,which may be advantageous to the increase in TC influences.展开更多
By large-scale cold mold experiments,pressure pulsation signals within the jet influence zone of riser reactor are processed by using Hilbert-Huang analysis(HHT)in this study.Effects of different jet forms and operati...By large-scale cold mold experiments,pressure pulsation signals within the jet influence zone of riser reactor are processed by using Hilbert-Huang analysis(HHT)in this study.Effects of different jet forms and operating conditions on the intrinsic mode function(IMF)energy and Hilbert-Huang spectrum are compared.Results show that the IMF energy and Hilbert-Huang spectrum of pressure pulsation signals show significant differences under the influence of upward and downward jets.Moreover,the change of jet velocity will also lead to significant changes in IMF energy and Hilbert-Huang spectrum.Among them,energy values and energy proportions corresponding to high-frequency pressure pulsations show a good correlation with the jet velocity.On this basis,energy value and energy proportion data in the high frequency range of the original pressure signal are clustered and analyzed by using the K-means clustering algorithm.Based on clustering results,the jet influence zone of riser can be defined into three regions.From partitioning results,it is found that the introduction of downward inclined jets could effectively improve the gas-solid mixing in the feed injection zone of riser.展开更多
With the progress of information technology,the digital transformation of enterprises has developed into a key strategy to improve competitiveness.This paper studies the influence of digital transformation of enterpri...With the progress of information technology,the digital transformation of enterprises has developed into a key strategy to improve competitiveness.This paper studies the influence of digital transformation of enterprises on the quality of accounting information and its countermeasures,discusses how digital transformation reshapes the ability of accounting information processing,transparency,sharing,and decision support,and analyzes the challenges in technology,management,and data security during this period.Through in-depth analysis,this paper puts forward a series of targeted countermeasures,including strengthening technology and system construction,optimizing management and processes,strengthening data security and privacy protection,and promoting the improvement of laws and standards,hoping to provide practical guidance for improving the quality of accounting information in the digital transformation of enterprises.展开更多
The J oilfield in the Bohai has a long development history and has undergone comprehensive adjustment measures,including water injection and polymer injection.Following these adjustments,the injection and production w...The J oilfield in the Bohai has a long development history and has undergone comprehensive adjustment measures,including water injection and polymer injection.Following these adjustments,the injection and production well network now features coexistence of both polymer injection wells and water injection wells,which has negatively impacted production dynamics.Firstly,based on the adjusted reservoir well network in the J oilfield,a representative water-polymer co-injection well network was established.Subsequently,a numerical simulation model of this typical reservoir unit was developed using reservoir numerical simulation methods to confirm the interference issues associated with water-polymer co-injection.Multiple reservoir numerical simulation models were designed to investigate various factors influencing water-polymer interference,resulting in graphical representations of each factor’s impact under different conditions.Finally,gray relational analysis was employed to rank the influence of these factors,yielding the following order of significance:polymer concentration,the ratio of drainage distance to well spacing,horizontal permeability variation,interlayer permeability variation,and intralayer permeability variation.This understanding provides robust guidance for future adjustments in the oilfield.展开更多
Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly effi...Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.展开更多
Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),i...Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),in particular the resonant portion,is found to provide the dominant contribution to the total toroidal torque under the slow plasma flow regime in ITER.While the electromagnetic torque always opposes the plasma flow,the toroidal torque associated with the Reynolds stress enhances the plasma flow independent of the flow direction.A peculiar double-peak structure for the net NTV torque is robustly computed for ITER,as the toroidal rotation frequency is scanned near the zero value.This structure is found to be ultimately due to a non-monotonic behavior of the wave-particle resonance integral(over the particle pitch angle)in the superbanana plateau NTV regime in ITER.These findings are qualitatively insensitive to variations of a range of factors including the wall resistivity,the plasma pedestal flow and the assumed frequency of the rotating RMP field.展开更多
基金supported by the National Magnetic Confinement Fusion Energy R&D Program of China (Nos.2018YFE0309103 and 2019YFE03010004)National Natural Science Foundation of China (Nos.12475222,12205122,and 51821005)Hubei International Science and Technology Cooperation Projects (No.2022EHB003)。
文摘A large number of runaway electrons(REs)generated during disruption can cause significant damage to next-generation large-scale tokamaks.The influence of three-dimensional(3D)helical magnetic perturbations on the suppression of RE generation was explored using a set of 3D helical coils in J-TEXT tokamak,which can excite m/n=-2/2 helical magnetic perturbations.Experimental evidence shows that the-2/2 magnetic perturbations caused by the opposite coil current direct plasma toward the high-field side,simultaneously enhancing the magnetic fluctuations,which would enhance the radial loss of REs and even prevent RE generation.On the other hand,-2/2 magnetic perturbations can also reduce the cooling time during the disruption phase and generate a population of high-energy REs,which can interact with high-frequency magnetic fluctuations and in turn suppress RE generation.The critical helical coil current was found to correlate with electron density,requiring higher coil currents at higher densities.According to the statistical analysis of RE generation at different electron densities,the applied-2/2 magnetic perturbations can increase the magnetic fluctuations to the same level at lower electron densities,which can decrease the threshold electron density for RE suppression.This will be beneficial for RE mitigation in future large tokamak devices.
基金supported by the National Natural Science Foundation of China(Grant Nos.:32271292,31872723,32200778,and 22377089)the Jiangsu Students Innovation and Entrepre-neurship Training Program,China(Program No.:202210285081Z)+6 种基金the Project of MOE Key Laboratory of Geriatric Diseases and Immunology,China(Project No.:JYN202404)Proj-ect Funded by the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions,Natural Science Foundation of Jiangsu Province,China(Project No.:BK20220494)Suzhou Medical and Health Technology Innovation Project,China(Grant No.:SKY2022107)the Clinical Research Center of Neuro-logical Disease in The Second Affiliated Hospital of Soochow University,China(Grant No.:ND2022A04)State Key Laboratory of Drug Research(Grant No.:SKLDR-2023-KF-05)Jiangsu Shuang-chuang Program for Doctor,Young Science Talents Promotion Project of Jiangsu Science and Technology Association(Program No.:TJ-2023-019)Young Science Talents Promotion Project of Suzhou Science and Technology Association,Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases,and startup funding(Grant Nos.:NH21500221,NH21500122,and NH21500123)to Qifei Cong.
文摘Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the“therapeutic module”of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.
文摘BACKGROUND Identifying factors that influence non-curative resection(NCR)is critical to optimize treatment strategies and improve patient outcomes in patients with early gastric cancer(EGC).AIM To investigate the factors influencing the NCR of EGC and to evaluate the predictive value of these factors.METHODS The clinical data of 173 patients with EGC admitted between July 2020 and July 2023 were retrospectively collected.According to radical resection criteria,the patients were further divided into curative resection group(n=143)and NCR group(n=30).Clinical information was collected,including surgical method,tumor diameter,tumor site,ulcer formation,depth of invasion,pathological type,and lymph node metastasis.Logistic regression analysis was used to explore the factors affecting non-curable resection.RESULTS Multivariate logistic regression analysis showed that ulcer formation[odds ratio(OR)=3.53;95%confidence interval(CI):1.55-8.01,P=0.003],pathological type(OR=3.73;95%CI:1.60-8.74,P=0.002),tumor diameter(OR=3.15;95%CI:1.40-7.05,P=0.005),tumor location(OR=3.50;95%CI:1.16-10.58,P=0.027),lymph node metastasis(OR=4.40;95%CI:1.83-10.57,P=0.001),and depth of penetration(OR=3.75;95%CI:1.60-8.74,P=0.002)were all risk factors for NCR in EGC patients.Predictive analysis showed varying area under the curve values for factors such as tumor diameter(0.636),tumor location(0.608),ulcer formation(0.652),infiltration depth(0.658),pathological type(0.656),and lymph node metastasis(0.674).CONCLUSION The results suggest that factors such as tumor diameter,tumor location,ulcer formation,depth of invasion,pathological type,and lymph node metastasis increase the risk of NCR in EGC patients.
基金supported by the National Natural Science Foundation of China(No.42377415)the Natural Science Foundation of Sichuan Province(No.2023NSFSC0811),Sichuan Science and Technology Program(Nos.2021JDTD0013 and 2021YFQ0066)+1 种基金the Science and Technology Major Project of Xizhang Autonomous Region of China(No.XZ202201ZD0004G06)the Everest Scientific Research Program(No.80000-2023ZF11405).
文摘The large-scale exploitation of vanadium(Ⅴ) bearing minerals has led to a massive accumulation of Ⅴ tailings, of which Ⅴ pollution poses severe ecological risks. Although the mechanisms of Ⅴ stress to the microbial community have been reported, the influential pathways in a multi-medium-containing system, for example, the soil-tailings-groundwater system,are unknown. The dynamic redox conditions and substance exchange within the system exhibited complex Ⅴ stress on the local microbial communities. In this study, the influence pathways of Ⅴ stress to the microbial community in the soil-tailings-groundwater system were first investigated. High Ⅴ contents were observed in groundwater(139.2 ± 0.15 μg/L) and soil(98.0–323.8 ± 0.02 mg/kg), respectively. Distinct microbial composition was observed for soil and groundwater, where soil showed the highest level of diversity and richness. Firmicutes, Proteobacteria, Actinobacteria, and Acidobacteria were dominant in soil and groundwater with a sum relative abundance of around 80 %. Based on redundancy analysis and structural equation models, Ⅴ was one of the vital driving factors affecting microbial communities. Groundwater microbial communities were influenced by Ⅴ via Cr, dissolved oxygen, and total nitrogen, while Fe, Mn, and total phosphorus were the key mediators for Ⅴ to affect soil microbial communities. Ⅴ affected the microbial community via metabolic pathways related to carbonaceous matter, which was involved in the establishment of survival strategies for metal stress. This study provides novel insights into the influence pathways of Ⅴ on the microorganisms in tailings reservoir for pollution bioremediation.
文摘[Objective]The construction of weirs changes the hydraulic characteristics of rivers and affects the structure of phytoplankton communities and the health of aquatic ecosystems in the river.This study aims to explore the nonlinear response relationship between phytoplankton community structure and its driving factors in spring and autumn in Furong Creek under the construction of cascade weirs.[Methods]The structure of phytoplankton communities and related environmental factors were investigated in Furong Creek from 2023 to 2024.This study focused on the analysis of the changes of nutrient concentrations and biomass of phytoplankton in autumn and spring within the same dry season in Furong Creek.Redundancy analysis was used to identify the key factors influencing the structure of phytoplankton communities.The MIKE 11 model was employed to simulate the hydrodynamic changes in the river.Combined with total nitrogen and permanganate index,a GAM model of phytoplankton diversity index and hydrodynamic factors was developed,and the change of phytoplankton diversity after the optimized layout of the cascade weirs was fitted.[Results]The result showed that the annual average value of Shannon-Wiener diversity index of phytoplankton in Furong Creek was 2.79,which was in a state of mild pollution.A total of 239 species from 95 genera in 8 phyla were identified.Among the phytoplankton,Chlorophyta was the dominant group throughout the year in Furong Creek,followed by Bacillariophyta and Cyanophyta.The cell abundance of phytoplankton ranged from 3.11 to 20.64 mg/L and from 0.23 to 6.31 mg/L in spring and autumn,which indicated a clear seasonal succession of phytoplankton community structure.Compared with autumn,the relative abundance of Cyanophyta significantly decreased in spring across the whole river section,while Chrysophyta and Dinophyta showed significant increase at some monitoring sites,leading to water bloom phenomenon and a noticeable decline in the diversity of phytoplankton.The dominant species in the water bodies throughout the year were Cyclotella catenata,Chlorella vulgaris,Scenedesmus bijuga,Scenedesmus quadricauda,Chroomonas acuta,Cryptomonas ovata,and Cryptomonas erosa.Redundancy analysis(RDA)showed that hydrodynamic factors(v,h)and water environmental factors(TN,COD_(Mn))were the main influencing factors of phytoplankton community structure.[Conclusion]The result show that the nutrient concentration,phytoplankton biomass,and density in Furong Creek in spring are significantly higher than in autumn.The GAM model,constructed by combining hydrodynamic and environmental factors,can effectively reflect the nonlinear relationship between phytoplankton diversity index and its driving factors.In spring,with an increase in nutrient concentration,the habitat conditions of low flow speed and high water depths formed by overflow weirs will lead to a decrease in the Shannon-Wiener index of phytoplankton and an intensified risk of eutrophication.However,a reasonable layout scheme of cascade weirs will improve the diversity of phytoplankton and reduce the risk of eutrophication in the river.The findings of this study can help deepen the understanding of the ecological and environmental effects of cascade weir construction in the river.
基金supported by the National Natural Science Foundation of China(Grant Nos.42005005 and 42030607)the Science and Technology Department of Shaanxi Province(Grant No.2024JC-YBQN-0248)+2 种基金the Education Department of Shaanxi Province(Grant No.23JK0686)a Xi'an Science and Technology Project(Grant No.22GXFW0131)the Young Talent fund of the University Association for Science and Technology in Shaanxi(Grant No.20210706)。
文摘The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.
基金supported in part by the Natural Science Foundation of China under Grant 62372395in part by the Research Foundation of Education Bureau of Hunan Province under Grant No.24A0105in part by the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20230546).
文摘In recent years,universal adversarial per-turbation(UAP)has attracted the attention of many re-searchers due to its good generalization.However,in order to generate an appropriate UAP,current methods usually require either accessing the original dataset or meticulously constructing optimization functions and proxy datasets.In this paper,we aim to elimi-nate any dependency on proxy datasets and explore a method for generating Universal Adversarial Pertur-bations(UAP)on a single image.After revisiting re-search on UAP,we discovered that the key to gener-ating UAP lies in the accumulation of Individual Ad-versarial Perturbation(IAP)gradient,which prompted us to study the method of accumulating gradients from an IAP.We designed a simple and effective process to generate UAP,which only includes three steps:pre-cessing,generating an IAP and scaling the perturba-tions.Through our proposed process,any IAP gener-ated on an image can be constructed into a UAP with comparable performance,indicating that UAP can be generated free of data.Extensive experiments on var-ious classifiers and attack approaches demonstrate the superiority of our method on efficiency and aggressiveness.
基金the National Key Research and Development Program of China(No.2021YFB2501602)the National Natural Science Foundation of China(No.52077208)。
文摘The spatial-temporal distribution of charging loads for electric vehicles is influenced by multiple factors,Nowadays,the accuracy of the forecasts needs to be improved and the completeness of the modeling is relatively lacking.Therefore,this paper proposes a method for modeling the charging load of electric vehicles based on the influence of multiple factors.First,an in-depth analysis of the factors affecting the charging load of electric vehicles was conducted.Then,a model of electric vehicle electricity consumption per unit kilometer was constructed based on the influencing factors.Next,the electric vehicle,the charging station,the traffic network and the grid are modeled separately.In addition,a unified model of vehicle-station-road-network was constructed through the interaction and coupling of information between the models.Finally,the spatial-temporal distribution of electric vehicle charging loads was simulated using real data from a region.The study shows that the model is able to simulate the charging load of electric vehicles more accurately.Different traffic flows and areas have a significant impact on the charging load distribution.
文摘1 No one likes acknowledging that their thoughts and behaviors are easily influenced outside of their awareness.However,Daniel M.Wegner,author of The Illusion of Conscious Will,explains that people ignore how easily they can be manipulated(操纵)because they don't feel they are being manipulated.This is what happens:After our unconscious(无意识)mind motivates a thought or action,this urge or thought is then sent to our conscious mind.Here,we become aware of it and have the false impression of generating it independently.
文摘Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patients,and provide a theoretical basis for clinical practice.Therefore,in the context of the new era,it is necessary to deeply analyze the essence and connotation of personalized nursing,and analyze the existing deficiencies in current emergency pediatric personalized nursing,so as to develop effective improvement plans.Research shows that personalized nursing can significantly improve the satisfaction of emergency pediatric nursing,largely avoid nursing risks,and has strong clinical application value.This article summarizes and explores the research on the influence of personalized nursing on improving the satisfaction of emergency pediatric nursing,and puts forward corresponding views.
基金supported in part by the Japan Society for the Promotion of Science(JSPS)KAKENHI Grant-in-Aid for Scientific Research(C)under Grants 23K11164.
文摘Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare,sports,and other domains but this has also raised critical privacy concerns,especially under tightening regulations such as the General Data Protection Regulation(GDPR),which explicitly restrict the processing of data that can re-identify individuals.Although existing anonymization approaches such as the AnonymizingAutoEncoder(AAE)can reduce the risk of re-identification,they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task(e.g.,human activity recognition).This study proposes a novel sensor data anonymization method based onAdversarial Perturbations(AP)to address these limitations.By generating minimal yet targeted noise,the proposed method significantly degrades the accuracy of identity classification while retaining essential features for multiple tasks such as activity,gender,or device-position recognition.Moreover,to enhance robustness against frequency-domain analysis,additional models trained on transformed(e.g.,short-time Fourier transform(STFT))representations are incorporated into the perturbation process.A multi-task formulation is introduced that selectively suppresses person-identifying features while reinforcing those relevant to other desired tasks without retraining large autoencoder-based architectures.The proposed framework is,to our knowledge,the first AP-based anonymization technique that(i)defends simultaneously against time-and frequency-domain attacks and(ii)allows per-task trade-off control on a single forward-back-propagation run,enabling real-time,on-device deployment on commodity hardware.On three public datasets,the proposed method reduces person-identification accuracy from 60–90%to near-chance levels(≤5%)while preserving the original activity-recognition F1 both in the time and frequency domains.Compared with the baseline AAE,the proposed method improves downstream task F1 and lowers waveform mean squared error,demonstrating a better privacy-utility trade-off without additional model retraining.These findings underscore the effectiveness and flexibility of AP in privacy-preserving sensor-data processing,offering a practical solution that safeguards user identity while retaining rich,application-critical information.
文摘With increasingly severe global climate change,a low-carbon economy has become an inevitable trend in the development of the international community.Low-carbon economy is not only related to environmental protection but also has a profound impact on international trade.The purpose of this paper is to explore the impact of a low-carbon economy on the development of international trade and put forward corresponding strategy suggestions.By analyzing the connotation,characteristics,and mechanism of the low-carbon economy on international trade,this paper reveals the important role of the low-carbon economy in promoting the optimization of international trade structure,promoting green technology innovation,and strengthening international cooperation.At the same time,given the challenges brought by a low-carbon economy,this paper puts forward strategies such as strengthening policy guidance,promoting green technology innovation,and improving international trade rules to provide a reference for the sustainable development of international trade[1,2].
基金funded by the National Key R&D Program of China(Grant No.2023YFA1008901)the National Natural Science Foundation of China(Grant Nos.11988102,12172009)“The Fundamental Research Funds for the Central Universities,Peking University”.
文摘The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions.In the past,the“1-phase,1-partition”rule was usually adopted in numerical analysis,where one constituent phase at the mesoscale formed one partition.The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the same constituent,while it also introduces numerical inaccuracy.This study proposes a new partitioning method for fibrous unitcells under a reduced-order homogenization methodology.In this method,the fiber phase remains 1 partition,but the matrix phase is divided into 2 partitions,which refers to the“12”partitioning scheme.Analytical elastic influence+functions are derived by introducing the elastic strain energy equivalence(Hill-Mandel condition).This research also obtains the analytical eigenstrain influence functions by alleviating the so-called“inclusion-locking”phenomenon.In addition,a numerical approach to minimize the error of strain energy density is introduced to determine the partitioning of the matrix phase.Several numerical examples are presented to compare the differences among direct numerical simulation(DNS),“11”,and“12”partitioning schemes.The numerical simulations show improved++numerical accuracy by the“12”partitioning scheme.
基金supported by the joint funds of the Chinese National Natural Science Foundation(NSFC)(Grant No.U2242213)the funds of the NSFC(Grant No.42341209)+2 种基金the National Key Research and Development(R&D)Program of the Ministry of Science and Technology of China(Grant No.2021YFC3000902)the National Science Foundation for Young Scholars(Grant No.42205166)the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSQ008)。
文摘Pangu-Weather(PGW),trained with deep learning–based methods(DL-based model),shows significant potential for global medium-range weather forecasting.However,the interpretability and trustworthiness of global medium-range DLbased models raise many concerns.This study uses the singular vector(SV)initial condition(IC)perturbations of the China Meteorological Administration's Global Ensemble Prediction System(CMA-GEPS)as inputs of PGW for global ensemble prediction(PGW-GEPS)to investigate the ensemble forecast sensitivity of DL-based models to the IC errors.Meanwhile,the CMA-GEPS forecasts serve as benchmarks for comparison and verification.The spatial structures and prediction performance of PGW-GEPS are discussed and compared to CMA-GEPS based on seasonal ensemble experiments.The results show that the ensemble mean and dispersion of PGW-GEPS are similar to those of CMA-GEPS in the medium range but with smoother forecasts.Meanwhile,PGW-GEPS is sensitive to the SV IC perturbations.Specifically,PGWGEPS can generate realistic ensemble spread beyond the sub-synoptic scale(wavenumbers≤64)with SV IC perturbations.However,PGW's kinetic energy is significantly reduced at the sub-synoptic scale,leading to error growth behavior inconsistent with CMA-GEPS at that scale.Thus,this behavior indicates that the effective resolution of PGW-GEPS is beyond the sub-synoptic scale and is limited to predicting mesoscale atmospheric motions.In terms of the global mediumrange ensemble prediction performance,the probability prediction skill of PGW-GEPS is comparable to CMA-GEPS in the extratropic when they use the same IC perturbations.That means that PGW has a general ability to provide skillful global medium-range forecasts with different ICs from numerical weather prediction.
基金General Scientific Research Projects of Jiangsu Provincial Meteorological Service(KM202401)Young Scientists Found of the National Natural Science Foundation of China(42205197)+2 种基金Beijige Fund of Nanjing Joint Institute for Atmospheric Sciences(BJG202501)Joint Research Project for Meteorological Capacity Improvement(22NLTSY009)Key Scientific Research Projects of Jiangsu Provincial Meteorological Bureau(KZ202203)。
文摘Landfalling tropical cyclones(TCs)pose tremendous hazards to East Asian coastal areas,particularly in East China,a densely populated and economically vital center.This underscores the critical need for a more in-depth investigation into the evolving characteristics and influences of these landfalling TCs.In this study,we explored changes in landfalling TC activity during 1965–2022 and estimated their influences in East China.Our findings demonstrate that the annual frequency of landfalling TCs has exhibited a slight increase since the mid-1990s,while their overall influences have significantly intensified.This intensification is closely associated with the prolonged duration of TCs over land after landfall.The results also reveal that longer overland sustainment is attributed to the descending vertical wind shear(VWS)and ascending low-layer moisture supply over the corresponding areas.In addition,the annual mean genesis location of these landfalling TCs has shown a significant westward migration,which may be advantageous to the increase in TC influences.
基金sponsored by the National Key Research and Development Program of China(No.2022YFA1506200)the CNPC Innovation Found(No.2024DQ02-0203)the open foundation of State Key Laboratory of Chemical Engineering(No.SKL-ChE-23B02).
文摘By large-scale cold mold experiments,pressure pulsation signals within the jet influence zone of riser reactor are processed by using Hilbert-Huang analysis(HHT)in this study.Effects of different jet forms and operating conditions on the intrinsic mode function(IMF)energy and Hilbert-Huang spectrum are compared.Results show that the IMF energy and Hilbert-Huang spectrum of pressure pulsation signals show significant differences under the influence of upward and downward jets.Moreover,the change of jet velocity will also lead to significant changes in IMF energy and Hilbert-Huang spectrum.Among them,energy values and energy proportions corresponding to high-frequency pressure pulsations show a good correlation with the jet velocity.On this basis,energy value and energy proportion data in the high frequency range of the original pressure signal are clustered and analyzed by using the K-means clustering algorithm.Based on clustering results,the jet influence zone of riser can be defined into three regions.From partitioning results,it is found that the introduction of downward inclined jets could effectively improve the gas-solid mixing in the feed injection zone of riser.
文摘With the progress of information technology,the digital transformation of enterprises has developed into a key strategy to improve competitiveness.This paper studies the influence of digital transformation of enterprises on the quality of accounting information and its countermeasures,discusses how digital transformation reshapes the ability of accounting information processing,transparency,sharing,and decision support,and analyzes the challenges in technology,management,and data security during this period.Through in-depth analysis,this paper puts forward a series of targeted countermeasures,including strengthening technology and system construction,optimizing management and processes,strengthening data security and privacy protection,and promoting the improvement of laws and standards,hoping to provide practical guidance for improving the quality of accounting information in the digital transformation of enterprises.
基金supported by National Science and Technology Major Project of China(2016ZX05025-001)the Major Science and Technology Project of CNOOC(KJGG2021-0501).
文摘The J oilfield in the Bohai has a long development history and has undergone comprehensive adjustment measures,including water injection and polymer injection.Following these adjustments,the injection and production well network now features coexistence of both polymer injection wells and water injection wells,which has negatively impacted production dynamics.Firstly,based on the adjusted reservoir well network in the J oilfield,a representative water-polymer co-injection well network was established.Subsequently,a numerical simulation model of this typical reservoir unit was developed using reservoir numerical simulation methods to confirm the interference issues associated with water-polymer co-injection.Multiple reservoir numerical simulation models were designed to investigate various factors influencing water-polymer interference,resulting in graphical representations of each factor’s impact under different conditions.Finally,gray relational analysis was employed to rank the influence of these factors,yielding the following order of significance:polymer concentration,the ratio of drainage distance to well spacing,horizontal permeability variation,interlayer permeability variation,and intralayer permeability variation.This understanding provides robust guidance for future adjustments in the oilfield.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42330111,and 42405061)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(Earth Lab).
文摘Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
基金funded by National Natural Science Foundation of China(NSFC)(Nos.12075053,11505021 and 11975068)by National Key R&D Program of China(No.2022YFE 03060002)+1 种基金by Fundamental Research Funds for the Central Universities(No.2232024G-10)supported by the U.S.DoE Office of Science(No.DE-FG02–95ER54309)。
文摘Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),in particular the resonant portion,is found to provide the dominant contribution to the total toroidal torque under the slow plasma flow regime in ITER.While the electromagnetic torque always opposes the plasma flow,the toroidal torque associated with the Reynolds stress enhances the plasma flow independent of the flow direction.A peculiar double-peak structure for the net NTV torque is robustly computed for ITER,as the toroidal rotation frequency is scanned near the zero value.This structure is found to be ultimately due to a non-monotonic behavior of the wave-particle resonance integral(over the particle pitch angle)in the superbanana plateau NTV regime in ITER.These findings are qualitatively insensitive to variations of a range of factors including the wall resistivity,the plasma pedestal flow and the assumed frequency of the rotating RMP field.
