Access to distribution grid for distributed photovoltaic （short for PV）,technically, it determines solutions in the economic evaluation method.The method combines with comprehensive scoring method, Access to the dis...Access to distribution grid for distributed photovoltaic （short for PV）,technically, it determines solutions in the economic evaluation method.The method combines with comprehensive scoring method, Access to the distribution grid will be different from each program in economic construction,load flow,voltage,power quality,supply reliability and other aspects .Also the transformation of the distribution grid to rate the degree by the weighted average method that determines each PV access solutions the final performance of grid PV Construction method of the process, mainly AHP through index calculation,the access of PV program is evaluated to determine the economic and technical level access solution.This study will greatly enhance the PV grid security that helps PV in our country to develop PV.展开更多
The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayto...The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in...In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.展开更多
BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth o...BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state b...Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.展开更多
Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the su...Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.展开更多
China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict ...China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict the evaluation efficiency and exploration process of dry hot rocks.This paper is based on the understanding of the geologic features and genesis mechanisms of hot dry rocks in China and abroad.By integrating the main controlling factors of hot dry rock formation,and using index grading and quantification,the fuzzy hierarchical comprehensive method is applied to establish an evaluation system and standards for favorable areas of hot dry rocks.The evaluation system is based on four indicators:heat source,thermal channel,thermal reservoir and cap rock.It includes 11 evaluation parameters,including time of magmatic/volcanic activity,depth of molten mass or magma chamber,distribution of discordogenic faults,burial depth of thermal reservoir,cap rock type and thickness,surface thermal anomaly,heat flow,geothermal gradient,Moho depth,Curie depth,Earthquake magnitude and focal depth.Each parameter is divided into 3 levels.Applying this evaluation system to assess hot dry rock in central Inner Mongolia revealed that Class I favorable zones cover approximately 494 km^(2),while Class II favorable zones span about 5.7×10^(4) km^(2).The Jirgalangtu Sag and Honghaershute Sag in the Erlian Basin,along with Reshuitang Town in Keshiketeng Banner,Reshui Town in Ningcheng County,and Reshuitang Town in Aohan Banner of Chifeng City,are identified as Class I favorable zones for hot dry rock resources.These areas are characterized by high-temperature subsurface molten bodies or magma chambers serving as high-quality heat sources,shallow thermal reservoir depths,and overlying thick sedimentary rock layers acting as caprock.The establishment and application of the evaluation system for favorable areas of hot dry rock are expected to provide new approaches and scientific basis for guiding the practice of selecting hot dry rock areas in China.展开更多
OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHO...OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHODS:A single-center,retrospective analysis was conducted on patients diagnosed with IMN who met predefined inclusion and exclusion criteria.Data were collected from the Department of Nephrology at Longhua Hospital,affiliated with Shanghai University of Traditional Chinese Medicine,between January 2007 and December 2011.Clinical parameters including 24-h urinary protein,serum albumin,serum creatinine,and estimated glomerular filtration rate(e GFR,EPI)were assessed at baseline and at 1,3,5,and 10 years of follow-up.The efficacy of the Strengthening Spleen and Draining Dampness therapy was analyzed using repeated measures analysis of variance(ANOVA).Kaplan-Meier survival curves and multivariate proportional hazards model(Cox regression models)were employed to identify factors associated with treatment outcomes.RESULTS:A total of 265 patients were included,with a median follow-up duration of 96 months(36,122).TCM treatment significantly reduced 24-h urinary protein levels(P<0.001),and increased serum albumin levels(P<0.001),while serum creatinine remained stable(P=0.187).Remission rates at 1,3,5,and 10 years were 52.81%,69.71%,68.39%,and 72.36%,respectively,and the rates of avoiding composite outcome events at the same intervals were 98.27%,94.29%,94.19%,and 93.50%.In the subgroup receiving TCM only,remission rates were 56.67%,84.44%,76.32%,and 82.86%.For patients treated initially with Western Medicine followed by TCM,the rates were 52.83%,65.85%,67.47%and 67.75%.In the cohort of patients who received TCM as their first-line therapy,remission rates were 49.23%,62.50%,61.76%,and 69.23%.Multivariate Cox regression analysis revealed that the duration of TCM treatment[hazard ratio(HR)=0.826,95%confidence interval(CI)(0.779,0.876),P<0.001],presence of hypertension[HR=1.912,95%CI(1.181,3.094),P=0.008],baseline serum albumin level[HR=0.930,95%CI(0.894,0.969),P<0.001],and the rate of serum albumin increase within the first year of treatment[HR=0.930,95%CI(0.909,0.957),P<0.001]were significantly associated with clinical outcomes.CONCLUSION:The Strengthening Spleen and Draining Dampness therapy demonstrated robust short-and longterm efficacy in treating IMN,with high rates of remission and renal survival over 10 years.Key factors influencing clinical remission included the duration of TCM treatment,baseline serum albumin levels,the presence of hypertension,and the rate of increase in serum albumin within the first year.These findings suggest that this TCM approach provides a viable long-term treatment option for IMN.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation wind...With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.展开更多
To scientifically evaluate the restoration performance of ancient city walls,Terahertz time-domain spectroscopy(THz-TDS)and infrared thermal imaging technology were applied to assess the Desheng Fortress(Ming Dynasty)...To scientifically evaluate the restoration performance of ancient city walls,Terahertz time-domain spectroscopy(THz-TDS)and infrared thermal imaging technology were applied to assess the Desheng Fortress(Ming Dynasty).Three representative sections were examined:adobe brick masonry repaired(Area 1),well-preserved original(Area 2),and layer-by-layer ramming repaired(Area 3).THz spectral data revealed significant differences between Area 1(time delay:3.72 ps;refractive index:2.224)and Area 2(time delay:3.02 ps;refractive index:2.107),while Area 3(time delay:3.12 ps;refractive index:2.098)demonstrated nearly identical THz spectral data to Area 2.Infrared thermal imaging also showed that the Area 3 restored by layer-by-layer ramming exhibited greater uniformity with fewer instances of cracks,capillary phenomena,or biological diseases.The proposed point-surface integrated evaluation methodology synergistically combines infrared thermography mapping of heritage surfaces with THz spectral datasets acquired through in-situ micro-sampling,enabling quantitative restoration assessment and providing a novel approach for scientifically validating traditional conservation techniques.展开更多
Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants h...Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.展开更多
Introduction: The WHO’s measles control strategy is based on epidemiological surveillance and vaccination. Little is currently known about the performance of the surveillance system, particularly in outlying areas. I...Introduction: The WHO’s measles control strategy is based on epidemiological surveillance and vaccination. Little is currently known about the performance of the surveillance system, particularly in outlying areas. It is in this context that the present study was carried out to evaluate the measles epidemiological surveillance system in the Kangaba health district. Method: This was a descriptive cross-sectional study with retrospective data collection for the period from 1 January to 31 December 2022 in the Kangaba health district with 18 epidemiological surveillance officers. Data were collected using an observation grid and documentary analysis. Results: We found a simplicity of 97%, a representativeness of 95% and a reactivity of 53%. Not all notified cases were sampled (64%). Of the 17 CSCom, 8 were positive. A total of 34 confirmed cases of measles were recorded, with one death. Conclusion: Overall, the system is functional and well-established in the health centres, but it remains important to ensure that suspected cases are investigated and that the time between sampling and the availability of laboratory results is respected.展开更多
Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controll...Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).展开更多
Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new...Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new engine of economic development,but also a gas pedal of social development,which has had a profound impact on the field of education.In the face of the opportunities and challenges of the AI era,it is particularly urgent to build a scientific and reasonable education evaluation system.This paper combines the context of the times with the new technology of AI to discuss the opportunities,challenges,and implementation strategies of educational evaluation reform in the era of AI,with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.展开更多
In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave pow...In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
文摘Access to distribution grid for distributed photovoltaic （short for PV）,technically, it determines solutions in the economic evaluation method.The method combines with comprehensive scoring method, Access to the distribution grid will be different from each program in economic construction,load flow,voltage,power quality,supply reliability and other aspects .Also the transformation of the distribution grid to rate the degree by the weighted average method that determines each PV access solutions the final performance of grid PV Construction method of the process, mainly AHP through index calculation,the access of PV program is evaluated to determine the economic and technical level access solution.This study will greatly enhance the PV grid security that helps PV in our country to develop PV.
基金This work was supported of National Natural Science Foundation of China Fund(No.52306033)State Key Laboratory of Engines Fund(No.SKLE-K2022-07)the Jiangxi Provincial Postgraduate Innovation Special Fund(No.YC2022-s513).
文摘The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
文摘In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.
文摘BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
基金the National Key Research Program of China under granted No.92164201National Natural Science Foundation of China for Distinguished Young Scholars No.62325403+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(62304147).
文摘Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(Grant No.42225206)National Natural Science Foundation of China(42207180,42477209,42302320).
文摘Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.
基金Supported by PetroChina Prospective and Basic Technological Project(2022DJ5503).
文摘China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict the evaluation efficiency and exploration process of dry hot rocks.This paper is based on the understanding of the geologic features and genesis mechanisms of hot dry rocks in China and abroad.By integrating the main controlling factors of hot dry rock formation,and using index grading and quantification,the fuzzy hierarchical comprehensive method is applied to establish an evaluation system and standards for favorable areas of hot dry rocks.The evaluation system is based on four indicators:heat source,thermal channel,thermal reservoir and cap rock.It includes 11 evaluation parameters,including time of magmatic/volcanic activity,depth of molten mass or magma chamber,distribution of discordogenic faults,burial depth of thermal reservoir,cap rock type and thickness,surface thermal anomaly,heat flow,geothermal gradient,Moho depth,Curie depth,Earthquake magnitude and focal depth.Each parameter is divided into 3 levels.Applying this evaluation system to assess hot dry rock in central Inner Mongolia revealed that Class I favorable zones cover approximately 494 km^(2),while Class II favorable zones span about 5.7×10^(4) km^(2).The Jirgalangtu Sag and Honghaershute Sag in the Erlian Basin,along with Reshuitang Town in Keshiketeng Banner,Reshui Town in Ningcheng County,and Reshuitang Town in Aohan Banner of Chifeng City,are identified as Class I favorable zones for hot dry rock resources.These areas are characterized by high-temperature subsurface molten bodies or magma chambers serving as high-quality heat sources,shallow thermal reservoir depths,and overlying thick sedimentary rock layers acting as caprock.The establishment and application of the evaluation system for favorable areas of hot dry rock are expected to provide new approaches and scientific basis for guiding the practice of selecting hot dry rock areas in China.
基金Supported by the National Key Research and Development Project,Clinical Study on the Treatment of Refractory Membranous Nephropathy with the Treatment of Strengthening Spleen and Draining Dampness in Method using Single Group Target Value Method(No.2019YFC1709403)Systematic Study on the Diagnosis and Treatment Rules of Membranous Nephropathy in Traditional Chinese Medicine(No.2023YFC35033501,No.2023YFC35033503)。
文摘OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHODS:A single-center,retrospective analysis was conducted on patients diagnosed with IMN who met predefined inclusion and exclusion criteria.Data were collected from the Department of Nephrology at Longhua Hospital,affiliated with Shanghai University of Traditional Chinese Medicine,between January 2007 and December 2011.Clinical parameters including 24-h urinary protein,serum albumin,serum creatinine,and estimated glomerular filtration rate(e GFR,EPI)were assessed at baseline and at 1,3,5,and 10 years of follow-up.The efficacy of the Strengthening Spleen and Draining Dampness therapy was analyzed using repeated measures analysis of variance(ANOVA).Kaplan-Meier survival curves and multivariate proportional hazards model(Cox regression models)were employed to identify factors associated with treatment outcomes.RESULTS:A total of 265 patients were included,with a median follow-up duration of 96 months(36,122).TCM treatment significantly reduced 24-h urinary protein levels(P<0.001),and increased serum albumin levels(P<0.001),while serum creatinine remained stable(P=0.187).Remission rates at 1,3,5,and 10 years were 52.81%,69.71%,68.39%,and 72.36%,respectively,and the rates of avoiding composite outcome events at the same intervals were 98.27%,94.29%,94.19%,and 93.50%.In the subgroup receiving TCM only,remission rates were 56.67%,84.44%,76.32%,and 82.86%.For patients treated initially with Western Medicine followed by TCM,the rates were 52.83%,65.85%,67.47%and 67.75%.In the cohort of patients who received TCM as their first-line therapy,remission rates were 49.23%,62.50%,61.76%,and 69.23%.Multivariate Cox regression analysis revealed that the duration of TCM treatment[hazard ratio(HR)=0.826,95%confidence interval(CI)(0.779,0.876),P<0.001],presence of hypertension[HR=1.912,95%CI(1.181,3.094),P=0.008],baseline serum albumin level[HR=0.930,95%CI(0.894,0.969),P<0.001],and the rate of serum albumin increase within the first year of treatment[HR=0.930,95%CI(0.909,0.957),P<0.001]were significantly associated with clinical outcomes.CONCLUSION:The Strengthening Spleen and Draining Dampness therapy demonstrated robust short-and longterm efficacy in treating IMN,with high rates of remission and renal survival over 10 years.Key factors influencing clinical remission included the duration of TCM treatment,baseline serum albumin levels,the presence of hypertension,and the rate of increase in serum albumin within the first year.These findings suggest that this TCM approach provides a viable long-term treatment option for IMN.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.
文摘To scientifically evaluate the restoration performance of ancient city walls,Terahertz time-domain spectroscopy(THz-TDS)and infrared thermal imaging technology were applied to assess the Desheng Fortress(Ming Dynasty).Three representative sections were examined:adobe brick masonry repaired(Area 1),well-preserved original(Area 2),and layer-by-layer ramming repaired(Area 3).THz spectral data revealed significant differences between Area 1(time delay:3.72 ps;refractive index:2.224)and Area 2(time delay:3.02 ps;refractive index:2.107),while Area 3(time delay:3.12 ps;refractive index:2.098)demonstrated nearly identical THz spectral data to Area 2.Infrared thermal imaging also showed that the Area 3 restored by layer-by-layer ramming exhibited greater uniformity with fewer instances of cracks,capillary phenomena,or biological diseases.The proposed point-surface integrated evaluation methodology synergistically combines infrared thermography mapping of heritage surfaces with THz spectral datasets acquired through in-situ micro-sampling,enabling quantitative restoration assessment and providing a novel approach for scientifically validating traditional conservation techniques.
基金supported by the key project at the central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(Grant number 2060302)the National Natural Science Foundation of China(Grant number 82373982,82173929).
文摘Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.
文摘Introduction: The WHO’s measles control strategy is based on epidemiological surveillance and vaccination. Little is currently known about the performance of the surveillance system, particularly in outlying areas. It is in this context that the present study was carried out to evaluate the measles epidemiological surveillance system in the Kangaba health district. Method: This was a descriptive cross-sectional study with retrospective data collection for the period from 1 January to 31 December 2022 in the Kangaba health district with 18 epidemiological surveillance officers. Data were collected using an observation grid and documentary analysis. Results: We found a simplicity of 97%, a representativeness of 95% and a reactivity of 53%. Not all notified cases were sampled (64%). Of the 17 CSCom, 8 were positive. A total of 34 confirmed cases of measles were recorded, with one death. Conclusion: Overall, the system is functional and well-established in the health centres, but it remains important to ensure that suspected cases are investigated and that the time between sampling and the availability of laboratory results is respected.
基金supported in part by the Scientific Research Fund of National Natural Science Foundation of China(Grant No.62372168)the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ30266)+2 种基金the Research Project on teaching reform in Hunan province(No.HNJG-2022-0791)the Hunan University of Science and Technology(No.2022-44-8)the National Social Science Funds of China(19BZX044).
文摘Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).
文摘Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new engine of economic development,but also a gas pedal of social development,which has had a profound impact on the field of education.In the face of the opportunities and challenges of the AI era,it is particularly urgent to build a scientific and reasonable education evaluation system.This paper combines the context of the times with the new technology of AI to discuss the opportunities,challenges,and implementation strategies of educational evaluation reform in the era of AI,with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.
文摘In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
