This paper focuses on the direct and inverse problems for a third-order self-adjoint differential operator with non-local potential and anti-periodic boundary conditions.Firstly,we obtain the expressions for the chara...This paper focuses on the direct and inverse problems for a third-order self-adjoint differential operator with non-local potential and anti-periodic boundary conditions.Firstly,we obtain the expressions for the characteristic function and resolvent of this third-order differential operator.Secondly,by using the expression for the resolvent of the operator,we prove that the spectrum for this operator consists of simple eigenvalues and a finite number of eigenvalues with multiplicity 2.Finally,we solve the inverse problem for this operator,which states that the non-local potential function can be reconstructed from four spectra.Specially,we prove the Ambarzumyan theorem and indicate that odd or even potential functions can be reconstructed by three spectra.展开更多
In this paper,we study a class of Sturm-Liouville problems,where the boundary conditions involve eigenparameters.Firstly,by defining a new inner product which depends on the transmission conditions,we obtain a new Hil...In this paper,we study a class of Sturm-Liouville problems,where the boundary conditions involve eigenparameters.Firstly,by defining a new inner product which depends on the transmission conditions,we obtain a new Hilbert space,on which the concerned operator A is self-adjoint.Then we construct the fundamental solutions to the problem,obtain the necessary and sufficient conditions for eigenvalues,and prove that the eigenvalues are simple.Finally,we investigate Green’s functions of such problem.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a g...Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a global survey of 630 scientists across diverse disciplines,genders,regions,and experience levels,Structural Equation Modelling(SEM)was employed to assess the influence of 29 factors related to researcher characteristics,research attributes,publication strategies,institutional support,and national roles.Findings:The study validated the Quintuple Helix Model,uncovering complex interdependencies.Institutional support significantly affects research impact by covering leadership,resources,recognition,and funding.Researcher attributes,including academic experience and domain knowledge,also play a crucial role.National socioeconomic conditions indirectly influence research impact by supporting institutions,underscoring the importance of conducive national frameworks.Research limitations:While the study offers valuable insights,it has limitations.Although statistically sufficient,the response rate was below 10%,suggesting that the findings may not fully represent the entire global research community.The reliance on self-reported data may also introduce bias,as perceptions of impact can be subjective.Practical implications:The findings have a significant impact on researchers aiming to enhance their work’s societal,economic,and cultural significance,institutions seeking supportive environments,and policymakers interested in creating favourable national conditions for impactful research.The study advocates for a strategic alignment among national policies,institutional practices,and individual researcher efforts to maximise research impact and effectively address global challenges.Originality/value:By empirically validating the Research Impact Quintuple Helix Model,this study offers a holistic framework for understanding the synergy of factors that drive impactful research.展开更多
In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To addr...In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions.展开更多
In addition to the organic matter type,abundance,thermal maturity,and shale reservoir space,the preservation conditions of source rocks play a key factor in affecting the quantity and quality of retained hydrocarbons ...In addition to the organic matter type,abundance,thermal maturity,and shale reservoir space,the preservation conditions of source rocks play a key factor in affecting the quantity and quality of retained hydrocarbons in source rocks of lacustrine shale,yet this aspect has received little attention.This paper,based on the case analysis,explores how preservation conditions influence the enrichment of mobile hydrocarbons in shale oil.Research showns that good preservation conditions play three key roles.(1)Ensure the retention of sufficient light hydrocarbons(C_(1)–C_(13)),medium hydrocarbons(C_(14)–C_(25))and small molecular aromatics(including 1–2 benzene rings)in the formation,which enhances the fluidity and flow of shale oil;(2)Maintain a high energy field(abnormally high pressure),thus facilitating the maximum outflow of shale oil;(3)Ensure that the retained hydrocarbons have the miscible flow condition of multi-component hydrocarbons(light hydrocarbons,medium hydrocarbons,heavy hydrocarbons,and heteroatomic compounds),so that the heavy hydrocarbons(C_(25+))and heavy components(non-hydrocarbons and asphaltenes)have improved fluidity and maximum flow capacity.In conclusion,in addition to the advantages of organic matter type,abundance,thermal maturity,and reservoir space,good preservation conditions of shale layers are essential for the formation of economically viable shale oil reservoirs,which should be incorporated into the evaluation criteria of shale oil-rich areas/segments and considered a necessary factor when selecting favorable exploration targets.展开更多
To realize carbon neutrality,there is an urgent need to develop sustainable,green energy systems(especially solar energy systems)owing to the environmental friendliness of solar energy,given the substantial greenhouse...To realize carbon neutrality,there is an urgent need to develop sustainable,green energy systems(especially solar energy systems)owing to the environmental friendliness of solar energy,given the substantial greenhouse gas emissions from fossil fuel-based power sources.When it comes to the evolution of intelligent green energy systems,Internet of Things(IoT)-based green-smart photovoltaic(PV)systems have been brought into the spotlight owing to their cutting-edge sensing and data-processing technologies.This review is focused on three critical segments of IoT-based green-smart PV systems.First,the climatic parameters and sensing technologies for IoT-based PV systems under extreme weather conditions are presented.Second,the methods for processing data from smart sensors are discussed,in order to realize health monitoring of PV systems under extreme environmental conditions.Third,the smart materials applied to sensors and the insulation materials used in PV backsheets are susceptible to aging,and these materials and their aging phenomena are highlighted in this review.This review also offers new perspectives for optimizing the current international standards for green energy systems using big data from IoT-based smart sensors.展开更多
This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan–Porsezian–Daniel equations with variable coefficients under nonzero boundary conditions.These equations are utilized to model the ...This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan–Porsezian–Daniel equations with variable coefficients under nonzero boundary conditions.These equations are utilized to model the phenomenon of nonlinear waves propagating simultaneously in non-uniform optical fibers.By analyzing the Lax pair and the Riemann–Hilbert problem,we aim to provide a comprehensive understanding of the dynamics and interactions of solitons of this system.Furthermore,we study the impacts of group velocity dispersion or the fourth-order dispersion on soliton behaviors.Through appropriate parameter selections,we observe various nonlinear phenomena,including the disappearance of solitons after interaction and their transformation into breather-like solitons,as well as the propagation of breathers with variable periodicity and interactions between solitons with variable periodicities.展开更多
The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper stud...The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.展开更多
Tectonism is one of the dominant factors affecting the shale pore structure.However,the control of shale pore structure by tectonic movements is still controversial,which limits the research progress of shale gas accu...Tectonism is one of the dominant factors affecting the shale pore structure.However,the control of shale pore structure by tectonic movements is still controversial,which limits the research progress of shale gas accumulation mechanism in the complex tectonic region of southern China.In this study,34 samples were collected from two exploratory wells located in different tectonic locations.Diverse experiments,e.g.,organic geochemistry,XRD analysis,FE-SEM,low-pressure gas adsorption,and high-pressure mercury intrusion,were conducted to fully characterize the shale reservoir.The TOC,Ro,and mineral composition of the shale samples between the two wells are similar,which reflects that the shale samples of the two wells have proximate pores-generating capacity and pores-supporting capacity.However,the pore characteristics of shale samples from two wells are significantly different.Compared with the stabilized zone shale,the porosity,pore volume,and specific surface area of the deformed zone shale were reduced by 60.61%,64.85%,and 27.81%,respectively.Moreover,the macroscopic and fine pores were reduced by 54.01%and 84.95%,respectively.Fault activity and uplift denudation are not conducive to pore preservation,and the rigid basement of Huangling uplift can promote pore preservation.These three factors are important reasons for controlling the difference in pore structure between two wells shales.We established a conceptual model of shale pores evolution under different tectonic preservation conditions.This study is significant to clarify the scale of shale gas formation and enrichment in complex tectonic regions,and helps in the selection of shale sweet spots.展开更多
The Riemann–Hilbert approach is demonstrated to investigate the defocusing Lakshmanan–Porsezian–Daniel equation under fully asymmetric nonzero boundary conditions.In contrast to the symmetry case,this paper focuses...The Riemann–Hilbert approach is demonstrated to investigate the defocusing Lakshmanan–Porsezian–Daniel equation under fully asymmetric nonzero boundary conditions.In contrast to the symmetry case,this paper focuses on the branch points related to the scattering problem rather than using the Riemann surfaces.For the direct problem,we analyze the Jost solution of lax pairs and some properties of scattering matrix,including two kinds of symmetries.The inverse problem at branch points can be presented,corresponding to the associated Riemann–Hilbert.Moreover,we investigate the time evolution problem and estimate the value of solving the solutions by Jost function.For the inverse problem,we construct it as a Riemann–Hilbert problem and formulate the reconstruction formula for the defocusing Lakshmanan–Porsezian–Daniel equation.The solutions of the Riemann–Hilbert problem can be constructed by estimating the solutions.Finally,we work out the solutions under fully asymmetric nonzero boundary conditions precisely via utilizing the Sokhotski–Plemelj formula and the square of the negative column transformation with the assistance of Riemann surfaces.These results are valuable for understanding physical phenomena and developing further applications of optical problems.展开更多
Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits ex...Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.展开更多
Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation a...Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.展开更多
To reveal the enrichment conditions and resource potential of coal-rock gas in the Ordos Basin,this paper presents a systematic research on the sedimentary environment,distribution,physical properties,reservoir charac...To reveal the enrichment conditions and resource potential of coal-rock gas in the Ordos Basin,this paper presents a systematic research on the sedimentary environment,distribution,physical properties,reservoir characteristics,gas-bearing characteristics and gas accumulation play of deep coals.The results show that thick coals are widely distributed in the Carboniferous–Permian of the Ordos Basin.The main coal seams Carboniferous 5~#and Permian 8~#in the Carboniferous–Permian have strong hydrocarbon generation capacity and high thermal evolution degree,which provide abundant materials for the formation of coal-rock gas.Deep coal reservoirs have good physical properties,especially porosity and permeability.Coal seams Carboniferous 5^(#)and Permian 8^(#)exhibit the average porosity of 4.1%and 6.4%,and the average permeability of 8.7×10^(-3)μm^(2)and 15.7×10^(-3)μm^(2),respectively.Cleats and fissures are developed in the coals,and together with the micropores,constitute the main storage space.With the increase of evolution degree,the micropore volume tends to increase.The development degree of cleats and fissures has a great impact on permeability.The coal reservoirs and their industrial compositions exhibit significantly heterogeneous distribution in the vertical direction.The bright coal seam,which is in the middle and upper section,less affected by ash filling compared with the lower section,and contains well-developed pores and fissures,is a high-quality reservoir interval.The deep coals present good gas-bearing characteristics in Ordos Basin,with the gas content of 7.5–20.0 m^(3)/t,and the proportion of free gas(greater than 10%,mostly 11.0%–55.1%)in coal-rock gas significantly higher than that in shallow coals.The enrichment degree of free gas in deep coals is controlled by the number of macropores and microfractures.The coal rock pressure testing shows that the coal-limestone and coal-mudstone combinations for gas accumulation have good sealing capacity,and the mudstone/limestone(roof)-coal-mudstone(floor)combination generally indicates high coal-rock gas values.The coal-rock gas resources in the Ordos Basin were preliminarily estimated by the volume method to be 22.38×10^(12)m^(3),and the main coal-rock gas prospects in the Ordos Basin were defined.In the central-east of the Ordos Basin,Wushenqi,Hengshan-Suide,Yan'an,Zichang,and Yichuan are coal-rock gas prospects for the coal seam#8 of the Benxi Formation,and Linxian West,Mizhi,Yichuan-Huangling,Yulin,and Wushenqi-Hengshan are coal-rock gas prospects for the coal seam#5 of the Shanxi Formation,which are expected to become new areas for increased gas reserves and production.展开更多
Background:Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.Methods:We used a population-based cohort from Danish national reg...Background:Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.Methods:We used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016,for a total of 83.9 million person-years.We assessed 10 broad types of mental disorders and 9 broad categories of medical conditions(which encompassed 31 specific conditions).We used Cox regression models to calculate overall hazard ratios and time-dependent hazard ratios for pairs of mental disorders and medical conditions,after adjustment for age,sex,calendar time,and previous mental disorders.Absolute risks were estimated with the use of competing-risks survival analyses.展开更多
Background:Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.Methods:We used a population-based cohort from Danish national reg...Background:Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.Methods:We used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016,for a total of 83.9 million person-years.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
Hanson and Mond have grven sets of necessary and sufficient conditions for optimality in constrained optimization by introducing classes of generalized functions, called type Ⅰ functions. Recently, Bector definded un...Hanson and Mond have grven sets of necessary and sufficient conditions for optimality in constrained optimization by introducing classes of generalized functions, called type Ⅰ functions. Recently, Bector definded univex functions, a new class of functions that unifies several concepts of generalized convexity. In this paper, additional conditions are attached to the Kuhn Tucker conditions giving a set of conditions which are both necessary and sufficient for optimality in constrained optimization, under appropriate constraint qualifications.展开更多
Some theorems are obtained for the existence of nontrivial solutions of Hamiltonian systems with Lagrangian boundary conditions by the minimax methods.
Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification a...Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.展开更多
基金supported by the Tianjin Municipal Science and Technology Program of China(No.23JCZDJC00070)。
文摘This paper focuses on the direct and inverse problems for a third-order self-adjoint differential operator with non-local potential and anti-periodic boundary conditions.Firstly,we obtain the expressions for the characteristic function and resolvent of this third-order differential operator.Secondly,by using the expression for the resolvent of the operator,we prove that the spectrum for this operator consists of simple eigenvalues and a finite number of eigenvalues with multiplicity 2.Finally,we solve the inverse problem for this operator,which states that the non-local potential function can be reconstructed from four spectra.Specially,we prove the Ambarzumyan theorem and indicate that odd or even potential functions can be reconstructed by three spectra.
基金supported by the National Natural Science Foundation of China(No.12461086)the Natural Science Foundation of Hubei Province(No.2022CFC016)。
文摘In this paper,we study a class of Sturm-Liouville problems,where the boundary conditions involve eigenparameters.Firstly,by defining a new inner product which depends on the transmission conditions,we obtain a new Hilbert space,on which the concerned operator A is self-adjoint.Then we construct the fundamental solutions to the problem,obtain the necessary and sufficient conditions for eigenvalues,and prove that the eigenvalues are simple.Finally,we investigate Green’s functions of such problem.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
基金approved by our institutional Research Ethics Committee(HREC Approval Number H13554).
文摘Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a global survey of 630 scientists across diverse disciplines,genders,regions,and experience levels,Structural Equation Modelling(SEM)was employed to assess the influence of 29 factors related to researcher characteristics,research attributes,publication strategies,institutional support,and national roles.Findings:The study validated the Quintuple Helix Model,uncovering complex interdependencies.Institutional support significantly affects research impact by covering leadership,resources,recognition,and funding.Researcher attributes,including academic experience and domain knowledge,also play a crucial role.National socioeconomic conditions indirectly influence research impact by supporting institutions,underscoring the importance of conducive national frameworks.Research limitations:While the study offers valuable insights,it has limitations.Although statistically sufficient,the response rate was below 10%,suggesting that the findings may not fully represent the entire global research community.The reliance on self-reported data may also introduce bias,as perceptions of impact can be subjective.Practical implications:The findings have a significant impact on researchers aiming to enhance their work’s societal,economic,and cultural significance,institutions seeking supportive environments,and policymakers interested in creating favourable national conditions for impactful research.The study advocates for a strategic alignment among national policies,institutional practices,and individual researcher efforts to maximise research impact and effectively address global challenges.Originality/value:By empirically validating the Research Impact Quintuple Helix Model,this study offers a holistic framework for understanding the synergy of factors that drive impactful research.
文摘In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions.
基金Supported by the National Natural Science Foundation of China(U22B6004)Project of PetroChina Research Institute of Petroleum Exploration and Development(2022yjcq03)Core Technology Key Project of China Petroleum Changqing Oilfield Company(KJZX2023-01).
文摘In addition to the organic matter type,abundance,thermal maturity,and shale reservoir space,the preservation conditions of source rocks play a key factor in affecting the quantity and quality of retained hydrocarbons in source rocks of lacustrine shale,yet this aspect has received little attention.This paper,based on the case analysis,explores how preservation conditions influence the enrichment of mobile hydrocarbons in shale oil.Research showns that good preservation conditions play three key roles.(1)Ensure the retention of sufficient light hydrocarbons(C_(1)–C_(13)),medium hydrocarbons(C_(14)–C_(25))and small molecular aromatics(including 1–2 benzene rings)in the formation,which enhances the fluidity and flow of shale oil;(2)Maintain a high energy field(abnormally high pressure),thus facilitating the maximum outflow of shale oil;(3)Ensure that the retained hydrocarbons have the miscible flow condition of multi-component hydrocarbons(light hydrocarbons,medium hydrocarbons,heavy hydrocarbons,and heteroatomic compounds),so that the heavy hydrocarbons(C_(25+))and heavy components(non-hydrocarbons and asphaltenes)have improved fluidity and maximum flow capacity.In conclusion,in addition to the advantages of organic matter type,abundance,thermal maturity,and reservoir space,good preservation conditions of shale layers are essential for the formation of economically viable shale oil reservoirs,which should be incorporated into the evaluation criteria of shale oil-rich areas/segments and considered a necessary factor when selecting favorable exploration targets.
基金National Key R&D Program of China(Grant No.2023YFE0114600)The National Natural Science Foundation of China(NSFC)-(Grant No.52477029)+1 种基金Joint Laboratory of China-Morocco Green Energy and Advanced Materials,The Youth Innovation Team of Shaanxi Universities,The Xi’an City Science and Technology Project(No.23GXFW0070)Xi’an International Science and Technology Cooperation Base.
文摘To realize carbon neutrality,there is an urgent need to develop sustainable,green energy systems(especially solar energy systems)owing to the environmental friendliness of solar energy,given the substantial greenhouse gas emissions from fossil fuel-based power sources.When it comes to the evolution of intelligent green energy systems,Internet of Things(IoT)-based green-smart photovoltaic(PV)systems have been brought into the spotlight owing to their cutting-edge sensing and data-processing technologies.This review is focused on three critical segments of IoT-based green-smart PV systems.First,the climatic parameters and sensing technologies for IoT-based PV systems under extreme weather conditions are presented.Second,the methods for processing data from smart sensors are discussed,in order to realize health monitoring of PV systems under extreme environmental conditions.Third,the smart materials applied to sensors and the insulation materials used in PV backsheets are susceptible to aging,and these materials and their aging phenomena are highlighted in this review.This review also offers new perspectives for optimizing the current international standards for green energy systems using big data from IoT-based smart sensors.
基金supported by the Natural Science Foundation of Hebei Province,China (Grant No.A2021502004)the Fundamental Research Funds for the Central Universities (Grant No.2024MS126).
文摘This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan–Porsezian–Daniel equations with variable coefficients under nonzero boundary conditions.These equations are utilized to model the phenomenon of nonlinear waves propagating simultaneously in non-uniform optical fibers.By analyzing the Lax pair and the Riemann–Hilbert problem,we aim to provide a comprehensive understanding of the dynamics and interactions of solitons of this system.Furthermore,we study the impacts of group velocity dispersion or the fourth-order dispersion on soliton behaviors.Through appropriate parameter selections,we observe various nonlinear phenomena,including the disappearance of solitons after interaction and their transformation into breather-like solitons,as well as the propagation of breathers with variable periodicity and interactions between solitons with variable periodicities.
基金funded by the National Natural Science Foundation of China(No.51806236,No.51806239)the Fundamental Research Funds for the Central Universities(No.2015XKMS059)+1 种基金Shaanxi Postdoctoral Fund Project(No.2018BSHEDZZ56)Foundation of Key Laboratory of Thermo-Fluid Science and Engineering(Xi'an Jiaotong University),Ministry of Education(No.KLTFSE2017KF01)。
文摘The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.
基金supported by the National Natural Science Foundation of China (42122017,41821002)the Hubei Provincial Natural Science Foundation of China (2020CFB501)+1 种基金the Shandong Provincial Key Research and Development Program (2020ZLYS08)the Independent innovation research program of China University of Petroleum (East China) (21CX06001A)。
文摘Tectonism is one of the dominant factors affecting the shale pore structure.However,the control of shale pore structure by tectonic movements is still controversial,which limits the research progress of shale gas accumulation mechanism in the complex tectonic region of southern China.In this study,34 samples were collected from two exploratory wells located in different tectonic locations.Diverse experiments,e.g.,organic geochemistry,XRD analysis,FE-SEM,low-pressure gas adsorption,and high-pressure mercury intrusion,were conducted to fully characterize the shale reservoir.The TOC,Ro,and mineral composition of the shale samples between the two wells are similar,which reflects that the shale samples of the two wells have proximate pores-generating capacity and pores-supporting capacity.However,the pore characteristics of shale samples from two wells are significantly different.Compared with the stabilized zone shale,the porosity,pore volume,and specific surface area of the deformed zone shale were reduced by 60.61%,64.85%,and 27.81%,respectively.Moreover,the macroscopic and fine pores were reduced by 54.01%and 84.95%,respectively.Fault activity and uplift denudation are not conducive to pore preservation,and the rigid basement of Huangling uplift can promote pore preservation.These three factors are important reasons for controlling the difference in pore structure between two wells shales.We established a conceptual model of shale pores evolution under different tectonic preservation conditions.This study is significant to clarify the scale of shale gas formation and enrichment in complex tectonic regions,and helps in the selection of shale sweet spots.
基金the Fundamental Research Funds for the Central Universities(Grant No.2024MS126).
文摘The Riemann–Hilbert approach is demonstrated to investigate the defocusing Lakshmanan–Porsezian–Daniel equation under fully asymmetric nonzero boundary conditions.In contrast to the symmetry case,this paper focuses on the branch points related to the scattering problem rather than using the Riemann surfaces.For the direct problem,we analyze the Jost solution of lax pairs and some properties of scattering matrix,including two kinds of symmetries.The inverse problem at branch points can be presented,corresponding to the associated Riemann–Hilbert.Moreover,we investigate the time evolution problem and estimate the value of solving the solutions by Jost function.For the inverse problem,we construct it as a Riemann–Hilbert problem and formulate the reconstruction formula for the defocusing Lakshmanan–Porsezian–Daniel equation.The solutions of the Riemann–Hilbert problem can be constructed by estimating the solutions.Finally,we work out the solutions under fully asymmetric nonzero boundary conditions precisely via utilizing the Sokhotski–Plemelj formula and the square of the negative column transformation with the assistance of Riemann surfaces.These results are valuable for understanding physical phenomena and developing further applications of optical problems.
文摘Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.
基金supported by the National Key Research and Development Program of China(Program Number 2021YFB4000100)the Beijing Postdoctoral Research Foundation(Grant Number 2023-ZZ-63).
文摘Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.
基金Supported by the China National Petroleum Corporation Science and Technology Project(2023ZZ18)CNPC Changqing Oilfield Company Project(2022D-JB01)。
文摘To reveal the enrichment conditions and resource potential of coal-rock gas in the Ordos Basin,this paper presents a systematic research on the sedimentary environment,distribution,physical properties,reservoir characteristics,gas-bearing characteristics and gas accumulation play of deep coals.The results show that thick coals are widely distributed in the Carboniferous–Permian of the Ordos Basin.The main coal seams Carboniferous 5~#and Permian 8~#in the Carboniferous–Permian have strong hydrocarbon generation capacity and high thermal evolution degree,which provide abundant materials for the formation of coal-rock gas.Deep coal reservoirs have good physical properties,especially porosity and permeability.Coal seams Carboniferous 5^(#)and Permian 8^(#)exhibit the average porosity of 4.1%and 6.4%,and the average permeability of 8.7×10^(-3)μm^(2)and 15.7×10^(-3)μm^(2),respectively.Cleats and fissures are developed in the coals,and together with the micropores,constitute the main storage space.With the increase of evolution degree,the micropore volume tends to increase.The development degree of cleats and fissures has a great impact on permeability.The coal reservoirs and their industrial compositions exhibit significantly heterogeneous distribution in the vertical direction.The bright coal seam,which is in the middle and upper section,less affected by ash filling compared with the lower section,and contains well-developed pores and fissures,is a high-quality reservoir interval.The deep coals present good gas-bearing characteristics in Ordos Basin,with the gas content of 7.5–20.0 m^(3)/t,and the proportion of free gas(greater than 10%,mostly 11.0%–55.1%)in coal-rock gas significantly higher than that in shallow coals.The enrichment degree of free gas in deep coals is controlled by the number of macropores and microfractures.The coal rock pressure testing shows that the coal-limestone and coal-mudstone combinations for gas accumulation have good sealing capacity,and the mudstone/limestone(roof)-coal-mudstone(floor)combination generally indicates high coal-rock gas values.The coal-rock gas resources in the Ordos Basin were preliminarily estimated by the volume method to be 22.38×10^(12)m^(3),and the main coal-rock gas prospects in the Ordos Basin were defined.In the central-east of the Ordos Basin,Wushenqi,Hengshan-Suide,Yan'an,Zichang,and Yichuan are coal-rock gas prospects for the coal seam#8 of the Benxi Formation,and Linxian West,Mizhi,Yichuan-Huangling,Yulin,and Wushenqi-Hengshan are coal-rock gas prospects for the coal seam#5 of the Shanxi Formation,which are expected to become new areas for increased gas reserves and production.
文摘Background:Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.Methods:We used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016,for a total of 83.9 million person-years.We assessed 10 broad types of mental disorders and 9 broad categories of medical conditions(which encompassed 31 specific conditions).We used Cox regression models to calculate overall hazard ratios and time-dependent hazard ratios for pairs of mental disorders and medical conditions,after adjustment for age,sex,calendar time,and previous mental disorders.Absolute risks were estimated with the use of competing-risks survival analyses.
文摘Background:Persons with mental disorders are at a higher risk than the general population for the subsequent development of certain medical conditions.Methods:We used a population-based cohort from Danish national registries that included data on more than 5.9 million persons born in Denmark from 1900 through 2015 and followed them from 2000 through 2016,for a total of 83.9 million person-years.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
文摘Hanson and Mond have grven sets of necessary and sufficient conditions for optimality in constrained optimization by introducing classes of generalized functions, called type Ⅰ functions. Recently, Bector definded univex functions, a new class of functions that unifies several concepts of generalized convexity. In this paper, additional conditions are attached to the Kuhn Tucker conditions giving a set of conditions which are both necessary and sufficient for optimality in constrained optimization, under appropriate constraint qualifications.
基金supported by the National Natural Science Foundation of China and 973 Program of STM.
文摘Some theorems are obtained for the existence of nontrivial solutions of Hamiltonian systems with Lagrangian boundary conditions by the minimax methods.
基金support from the Key Project of the National Natural Science Foundation of China (61034005)Postgraduate Scientific Research and Innovation Projects of Basic Scientific Research Operating Expenses of Ministry of Education (N100604001)
文摘Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.
