Small worm effects in the harmonious unifying hybrid preferential model （HUHPM） networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweig...Small worm effects in the harmonious unifying hybrid preferential model （HUHPM） networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE rnodel, so-called HUHPM-BA, HUHPM-BBV and HUHPM- TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-iveighted models but also for the weighted models.展开更多
This paper modifies the Farnes’ unifying theory of dark energy and dark matter which are negative-mass, created continuously from the negative-mass universe in the positive-negative mass universe pair. The first modi...This paper modifies the Farnes’ unifying theory of dark energy and dark matter which are negative-mass, created continuously from the negative-mass universe in the positive-negative mass universe pair. The first modification explains that observed dark energy is 68.6%, greater than 50% for the symmetrical positive-negative mass universe pair. This paper starts with the proposed positive-negative-mass 11D universe pair (without kinetic energy) which is transformed into the positive-negative mass 10D universe pair and the external dual gravities as in the Randall-Sundrum model, resulting in the four equal and separate universes consisting of the positive-mass 10D universe, the positive-mass massive external gravity, the negative-mass 10D universe and the negative-mass massive external gravity. The positive-mass 10D universe is transformed into 4D universe (home universe) with kinetic energy through the inflation and the Big Bang to create positive-mass dark matter which is five times of positive-mass baryonic matter. The other three universes without kinetic energy oscillate between 10D and 10D through 4D, resulting in the hidden universes when D > 4 and dark energy when D = 4, which is created continuously to our 4D home universe with the maximum dark energy = 3/4 = 75%. In the second modification to explain dark matter in the CMB, dark matter initially is not repulsive. The condensed baryonic gas at the critical surface density induces dark matter repulsive force to transform dark matter in the region into repulsive dark matter repulsing one another. The calculated percentages of dark energy, dark matter, and baryonic matter are 68.6 (as an input from the observation), 26 and 5.2, respectively, in agreement with observed 68.6, 26.5 and 4.9, respectively, and dark energy started in 4.33 billion years ago in agreement with the observed 4.71 <span style="white-space:nowrap;">±</span> 0.98 billion years ago. In conclusion, the modified Farnes’ unifying theory reinterprets the Farnes’ equations, and is a unifying theory of dark energy, dark matter, and baryonic matter in the positive-negative mass universe pair. The unifying theory explains protogalaxy and galaxy evolutions in agreement with the observations.展开更多
An agreement signed in Kigali, capital of Rwanda, has caught the world＇s attention. On March 21, a total of 44 African countries agreed to establish the African Continental Free Trade Area （AfCFTA）, aimed at creat ...An agreement signed in Kigali, capital of Rwanda, has caught the world＇s attention. On March 21, a total of 44 African countries agreed to establish the African Continental Free Trade Area （AfCFTA）, aimed at creat ing a single continental market for goods and services with free movement of busi nesses and investments. The agreement, signed at the 10th Extraordinary Session of the Assembly of the African Union （AU）, wlll be submitted for ratification by state parties in accordance with their domestic laws.展开更多
Media has the ability to play an important role in providing readers with information with which to shape opinions, drive initiatives and open discussion on a wide variety of topics.ChinAfrica reader, Christopher S.A....Media has the ability to play an important role in providing readers with information with which to shape opinions, drive initiatives and open discussion on a wide variety of topics.ChinAfrica reader, Christopher S.A.Gonolinje.展开更多
Objective.The objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of computational anatomy for brain mapping.Impact State...Objective.The objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of computational anatomy for brain mapping.Impact Statement.We present a unified representation theory for brain mapping based on geometric varifold measures of the microscale deterministic structure and function with the statistical ensembles of the spatially aggregated tissue scales.Introduction.Mapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale.New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic functional identity.We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions.The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles—using mathematical structures as first introduced by Dirac as the class of generalized functions—to the tissue scales with methods originally introduced by Euler for fluids.Methods.We introduce two mathematical methods based on notions of generalized functions and statistical mechanics.We use geometric varifolds,a product measure on space and function,to represent functional states at the micro-scales—electrophysiology,molecular histology—integrated with a Boltzmann-like program to pass from deterministic particle descriptions to empirical probabilities on the functional states at the tissue scales.Results.Our space-function varifold representation provides a recipe for traversing from molecular to tissue scales in terms of a cascade of linear space scaling composed with nonlinear functional feature mapping.Following the cascade implies every scale is a geometric measure so that a universal family of measure norms can be introduced which quantifies the geodesic connection between brains in the orbit independent of the probing technology,whether it be RNA identities,Tau or amyloid histology,spike trains,or dense MR imagery.Conclusions.We demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations.We call the consistent aggregation of tissue scales from particle and cellular scales,molecular computational anatomy.展开更多
Event extraction extracts event frames from text, while grounded situation recognition detects events in images. As real-world applications frequently encounter a multitude of unforeseen events, certain researchers ha...Event extraction extracts event frames from text, while grounded situation recognition detects events in images. As real-world applications frequently encounter a multitude of unforeseen events, certain researchers have introduced cross-domain and in-domain event extraction, while grounded situation recognition primarily explores in-domain scenarios. Therefore, in this paper, we propose cross-domain grounded situation recognition and establish a new benchmark SWiG-XD. In this more challenging setting, we deepen the connection between the two tasks based on their underlying unity in two different modalities and explore how to transfer the generalization ability from text to images. Firstly, we utilize ChatGPT to automatically generate textual data, which can be divided into two categories. One category is directly matched with images, establishing a direct connection with the images. The other category encompasses all event types and possesses greater generalization. Then we employ a unified model framework to establish the association between textual concepts and local image features and achieve cross-domain generalization transfer across modalities through modality-shared prompts and self-attention mechanism. Furthermore, we incorporate textual data with higher generalization to further assist in improving generalization on images. The experimental results on the newly constructed benchmark demonstrate the effectiveness of our method.展开更多
To describe the real world which is a harmonious unification world with both de- terminism and randomness, we propose a harmonious unifying hybrid preferential model (HUHPM) of a certain class of complex dynamical net...To describe the real world which is a harmonious unification world with both de- terminism and randomness, we propose a harmonious unifying hybrid preferential model (HUHPM) of a certain class of complex dynamical networks. HUHPM is gov- erned only by the total hybrid ratio d/r according to the practical need. As some typical examples, the concepts and methods of the HUHPM are applied to the un-weighted BA model proposed by Barabási et al., the weighted BBV model pro- posed by Barat et al. and the weighted TDE model proposed by Wang et al. to get the so-called HUHPM-BA network, HUHPM-BBV network and HUHPM-TDE network. These HUHPM networks are investigated both analytically and numerically. It is found that the HUHPM reveals several universal properties, which more approach to the real-world networks for both un-weighted and weighted networks and have potential for applications.展开更多
I.INTRODUCTION Against the backdrop of profound transformations in the global energy landscape,China,as the world's largest energy producer and consumer,faces multiple challenges,including energy security,energy t...I.INTRODUCTION Against the backdrop of profound transformations in the global energy landscape,China,as the world's largest energy producer and consumer,faces multiple challenges,including energy security,energy transition,and sustainable development.To address these challenges,the Chinese government has formulated a strategic plan to build a unified national energy market and create a new framework for the energy sector.Article 42 of the Energy Law of the People's Republic of China(hereinafter referred to as the Energy Law)explicitly requires the building of a unified national energy trading market and the improvement of transaction mechanisms and rules.展开更多
Human life is not determined by mechanical fatalism or a single material factor;instead,based on the dualistic ontology and active force mechanism in the Unified Complex Systems Theory(UCST),it can be actively designe...Human life is not determined by mechanical fatalism or a single material factor;instead,based on the dualistic ontology and active force mechanism in the Unified Complex Systems Theory(UCST),it can be actively designed under the guidance of mind,in accordance with causal laws,and through systematic interactions.This study integrates the dualistic ontology of UCST,as well as the cooperative mechanism of active force(Fa)and passive force(Fp).Furthermore,by incorporating Master Jiqun’s philosophy of“life design”and the practical principle of“destiny establishment and transformation”from The Four Lessons of Liaofan Yuan,it constructs a three-dimensional framework for life design encompassing the dimensions of science,philosophy,and practice.The significance of this research lies in breaking through the predicament of materialism in the AI(artificial intelligence)era,explaining the autonomy and initiative of life,providing feasible pathways for life design,and ultimately achieving the in-depth integration of scientific rationality and the wisdom of traditional Eastern culture.展开更多
Despite advances in current anti-cancer therapies,challenges such as drug resistance,toxicity,and tumor heterogeneity persist.The limitations of traditional single-target drugs and simple combination therapies are bec...Despite advances in current anti-cancer therapies,challenges such as drug resistance,toxicity,and tumor heterogeneity persist.The limitations of traditional single-target drugs and simple combination therapies are becoming increasingly apparent1.To address these issues,a novel treatment strategy,the artificially intelligent synergistic engineered drug(AISED)paradigm,merits further exploration.This paradigm is based on the systematic engineered integration of multiple active ingredients into a unified single entity through artificial intelligence(AI).This strategy is aimed at developing new anti-cancer drug designs involving multiple ingredients,multiple molecular targets,and multiple biological effects,for multiple cancer types,thereby providing a novel theoretical paradigm for overcoming existing treatment bottlenecks.展开更多
Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology...Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology remain largely unexplored.Leveraging the advantages of the finite-discrete element method(FDEM)for explicitly simulating fracture propagation and the strengths of the unifiedpipe model(UPM)for efficientlymodeling dual-permeability seepage,we propose a new hydromechanical(HM)coupling approach for modeling hydraulic fracturing.Validated against benchmark examples,the proposed FDEM-UPM model is further augmented by incorporating a Fourier-based methodology for reconstructing non-planar fractures,enabling quantitative analysis of hydraulic fracturing behavior within rough discrete fracture networks(DFNs).The FDEM-UPM model demonstrates computational advantages in accurately capturing transient hydraulic seepage phenomena,while the asynchronous time-stepping schemes between hydraulic and mechanical analyses substantially enhanced computational efficiencywithout compromising computational accuracy.Our results show that fracture morphology can affect both macroscopic fracture networks and microscopic interaction types between hydraulic fractures(HFs)and natural fractures(NFs).In an isotropic stress field,the initiation azimuth,propagation direction and microcracking mechanism are significantly influencedby fracture roughness.In an anisotropic stress field,HFs invariably propagate parallel to the direction of the maximum principal stress,reducing the overall complexity of the stimulated fracture networks.Additionally,stress concentration and perturbation attributed to fracture morphology tend to be compromised as the leak-off increases,while the breakdown and propagation pressures remain insensitive to fracture morphology.These findingsprovide new insights into the hydraulic fracturing mechanisms of fractured reservoirs containing complex rough DFNs.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.展开更多
For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-crit...For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.展开更多
RGB and CYMK are two major coloring schemes currently available for light colors and pigment colors,respectively.Both systems use letter-based color codes that require a large range of values to represent different co...RGB and CYMK are two major coloring schemes currently available for light colors and pigment colors,respectively.Both systems use letter-based color codes that require a large range of values to represent different colors.The problem is that these two systems are hard to use for manipulating any operations involving combinations of colors,and they lack the capacity for inter-changeability or unification.Based on prime number theory and Goldbach's conjecture,this study presents a universal color system(C235)using a number-based structure to encode,compute and unify all colors on a color wheel.The proposed C235 system offers a unified representation for the efficient encoding and effective manipulation of color.It can be applied to designing a high-rate LCD system and colorizing objects with multiple attributes and DNA codons,opening the door to manipulating colors and lights for even broader applications.展开更多
对珠子参中皂苷类成分进行定性鉴别分析,建立珠子参皂苷类多成分含量测定方法。采用超高效液相色谱-飞行时间质谱(UPLC-Q-TOF-MS/MS)技术对珠子参进行正、负离子模式扫描,使用UNIFI天然产物信息平台对珠子参所含的化学成分进行定性鉴别...对珠子参中皂苷类成分进行定性鉴别分析,建立珠子参皂苷类多成分含量测定方法。采用超高效液相色谱-飞行时间质谱(UPLC-Q-TOF-MS/MS)技术对珠子参进行正、负离子模式扫描,使用UNIFI天然产物信息平台对珠子参所含的化学成分进行定性鉴别分析;以0.1%磷酸水-乙腈溶液为流动相进行梯度洗脱,柱温30℃,流速0.3 mL/min,进行珠子参皂苷类多成分含量测定。珠子参中共鉴定出39个化学成分,包括37个皂苷类化合物和2个皂苷母核,并总结了皂苷类化合物的裂解规律,建立了UPLC同时测定珠子参中人参皂苷Rb1、人参皂苷Ro、人参皂苷Rb3、竹节参皂苷IV、竹节参皂苷IVa、人参皂苷Rd、姜状三七皂苷R1和金盏花苷E的多指标含量测定方法,该方法中8个待测成分在检测质量浓度范围内线性关系良好,精密度、重复性、稳定性的相对标准偏差(relative standard deviation,RSD)均小于3.0%,样品中人参皂苷Rb1、人参皂苷Ro、人参皂苷Rb3、竹节参皂苷IV、竹节参皂苷IVa、人参皂苷Rd、姜状三七皂苷R1和金盏花苷E的平均加样回收率分别为102.4%、103.1%、97.97%、99.42%、102.7%、102.1%、95.23%、100.5%,RSD分别为1.0%、0.98%、0.81%、2.3%、0.81%、1.9%、0.96%、1.8%。该研究建立的方法可快速、准确地对珠子参中的皂苷类成分进行定性及定量分析。展开更多
If the singularity of the cosmic Big Bang is taken as the origin of the reference coordinate system,the surrounding vacuum in the initial moments of it would exhibit radially-outward right-handed spiral motion at ligh...If the singularity of the cosmic Big Bang is taken as the origin of the reference coordinate system,the surrounding vacuum in the initial moments of it would exhibit radially-outward right-handed spiral motion at light speed.Based on this spatial motion hypothesis,we derive a unified field equation and a set of Maxwell’s equations for vacuum SWs(Scalar Waves)generating a huge spiral force field that drives the energy to spiral inwardly and distort,leading to the formation of mass.Furthermore,they also uncover that mass is fundamentally an ultimate expression of energy,manifesting as the result of spiral motion of space at light speed.And then,we indirectly validate the theory that coherent light waves’collision generate SWs and subsequently mass through the experiment verifying the Breit-Wheeler process.The establishment of our theory offers a new analytical tool for the exploration of mass origin,the cosmic Big Bang,unified field theories.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
基金The project supported by National Natural Science Foundation of China under Grant Nos. 70431002 and 70371068
文摘Small worm effects in the harmonious unifying hybrid preferential model （HUHPM） networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE rnodel, so-called HUHPM-BA, HUHPM-BBV and HUHPM- TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-iveighted models but also for the weighted models.
文摘This paper modifies the Farnes’ unifying theory of dark energy and dark matter which are negative-mass, created continuously from the negative-mass universe in the positive-negative mass universe pair. The first modification explains that observed dark energy is 68.6%, greater than 50% for the symmetrical positive-negative mass universe pair. This paper starts with the proposed positive-negative-mass 11D universe pair (without kinetic energy) which is transformed into the positive-negative mass 10D universe pair and the external dual gravities as in the Randall-Sundrum model, resulting in the four equal and separate universes consisting of the positive-mass 10D universe, the positive-mass massive external gravity, the negative-mass 10D universe and the negative-mass massive external gravity. The positive-mass 10D universe is transformed into 4D universe (home universe) with kinetic energy through the inflation and the Big Bang to create positive-mass dark matter which is five times of positive-mass baryonic matter. The other three universes without kinetic energy oscillate between 10D and 10D through 4D, resulting in the hidden universes when D > 4 and dark energy when D = 4, which is created continuously to our 4D home universe with the maximum dark energy = 3/4 = 75%. In the second modification to explain dark matter in the CMB, dark matter initially is not repulsive. The condensed baryonic gas at the critical surface density induces dark matter repulsive force to transform dark matter in the region into repulsive dark matter repulsing one another. The calculated percentages of dark energy, dark matter, and baryonic matter are 68.6 (as an input from the observation), 26 and 5.2, respectively, in agreement with observed 68.6, 26.5 and 4.9, respectively, and dark energy started in 4.33 billion years ago in agreement with the observed 4.71 <span style="white-space:nowrap;">±</span> 0.98 billion years ago. In conclusion, the modified Farnes’ unifying theory reinterprets the Farnes’ equations, and is a unifying theory of dark energy, dark matter, and baryonic matter in the positive-negative mass universe pair. The unifying theory explains protogalaxy and galaxy evolutions in agreement with the observations.
文摘An agreement signed in Kigali, capital of Rwanda, has caught the world＇s attention. On March 21, a total of 44 African countries agreed to establish the African Continental Free Trade Area （AfCFTA）, aimed at creat ing a single continental market for goods and services with free movement of busi nesses and investments. The agreement, signed at the 10th Extraordinary Session of the Assembly of the African Union （AU）, wlll be submitted for ratification by state parties in accordance with their domestic laws.
文摘Media has the ability to play an important role in providing readers with information with which to shape opinions, drive initiatives and open discussion on a wide variety of topics.ChinAfrica reader, Christopher S.A.Gonolinje.
基金supported by the National Institutes of Health (NIH) (http://www.nih.gov)grants R01EB020062 (MM),R01NS102670 (MM),U19AG033655 (MM),P41-EB031771 (MM and Tward),and R01MH105660 (MM)the National Science Foundation (NSF) (http://www.nsf.gov)16-569 NeuroNex contract 1707298 (MM)+3 种基金supported by the NSF grant ACI1548562Johns Hopkins University Alzheimer’s Disease Research Center with NIH grant P50AG05146the Dana Foundation’s (http://www.dana.org)clinical neuroscience research programthe Kavli Neuroscience Discovery Institute (http://kavlijhu.org)supported by the Kavli Foundation (http://www.kavlifoundation.org) (MM and DT).
文摘Objective.The objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of computational anatomy for brain mapping.Impact Statement.We present a unified representation theory for brain mapping based on geometric varifold measures of the microscale deterministic structure and function with the statistical ensembles of the spatially aggregated tissue scales.Introduction.Mapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale.New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic functional identity.We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions.The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles—using mathematical structures as first introduced by Dirac as the class of generalized functions—to the tissue scales with methods originally introduced by Euler for fluids.Methods.We introduce two mathematical methods based on notions of generalized functions and statistical mechanics.We use geometric varifolds,a product measure on space and function,to represent functional states at the micro-scales—electrophysiology,molecular histology—integrated with a Boltzmann-like program to pass from deterministic particle descriptions to empirical probabilities on the functional states at the tissue scales.Results.Our space-function varifold representation provides a recipe for traversing from molecular to tissue scales in terms of a cascade of linear space scaling composed with nonlinear functional feature mapping.Following the cascade implies every scale is a geometric measure so that a universal family of measure norms can be introduced which quantifies the geodesic connection between brains in the orbit independent of the probing technology,whether it be RNA identities,Tau or amyloid histology,spike trains,or dense MR imagery.Conclusions.We demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations.We call the consistent aggregation of tissue scales from particle and cellular scales,molecular computational anatomy.
基金supported by National Natural Science Foundation of China(No.62176058)National Key RD Program of China(2023YFF1204800).
文摘Event extraction extracts event frames from text, while grounded situation recognition detects events in images. As real-world applications frequently encounter a multitude of unforeseen events, certain researchers have introduced cross-domain and in-domain event extraction, while grounded situation recognition primarily explores in-domain scenarios. Therefore, in this paper, we propose cross-domain grounded situation recognition and establish a new benchmark SWiG-XD. In this more challenging setting, we deepen the connection between the two tasks based on their underlying unity in two different modalities and explore how to transfer the generalization ability from text to images. Firstly, we utilize ChatGPT to automatically generate textual data, which can be divided into two categories. One category is directly matched with images, establishing a direct connection with the images. The other category encompasses all event types and possesses greater generalization. Then we employ a unified model framework to establish the association between textual concepts and local image features and achieve cross-domain generalization transfer across modalities through modality-shared prompts and self-attention mechanism. Furthermore, we incorporate textual data with higher generalization to further assist in improving generalization on images. The experimental results on the newly constructed benchmark demonstrate the effectiveness of our method.
基金Supported by the Key Project of the National Natural Science Foundation of China (Grand No. 70431002)the National Natural Science Foundation of China (Grand No. 70371068)
文摘To describe the real world which is a harmonious unification world with both de- terminism and randomness, we propose a harmonious unifying hybrid preferential model (HUHPM) of a certain class of complex dynamical networks. HUHPM is gov- erned only by the total hybrid ratio d/r according to the practical need. As some typical examples, the concepts and methods of the HUHPM are applied to the un-weighted BA model proposed by Barabási et al., the weighted BBV model pro- posed by Barat et al. and the weighted TDE model proposed by Wang et al. to get the so-called HUHPM-BA network, HUHPM-BBV network and HUHPM-TDE network. These HUHPM networks are investigated both analytically and numerically. It is found that the HUHPM reveals several universal properties, which more approach to the real-world networks for both un-weighted and weighted networks and have potential for applications.
基金research result of the Chongqing Social Science Fund Project,entitled Legal Pathways for the Governance Optimization of Financial Holding Companies(No.21SKJD036).
文摘I.INTRODUCTION Against the backdrop of profound transformations in the global energy landscape,China,as the world's largest energy producer and consumer,faces multiple challenges,including energy security,energy transition,and sustainable development.To address these challenges,the Chinese government has formulated a strategic plan to build a unified national energy market and create a new framework for the energy sector.Article 42 of the Energy Law of the People's Republic of China(hereinafter referred to as the Energy Law)explicitly requires the building of a unified national energy trading market and the improvement of transaction mechanisms and rules.
基金supported by the start-up funding from Westlake University under Grant Number 041030150118 and the scientific research project of Westlake University“Theoretical Research and Demonstration Application of Complex Systems and Deep-Sea Technology(Phase I)”under Grant Number WU2025A006.
文摘Human life is not determined by mechanical fatalism or a single material factor;instead,based on the dualistic ontology and active force mechanism in the Unified Complex Systems Theory(UCST),it can be actively designed under the guidance of mind,in accordance with causal laws,and through systematic interactions.This study integrates the dualistic ontology of UCST,as well as the cooperative mechanism of active force(Fa)and passive force(Fp).Furthermore,by incorporating Master Jiqun’s philosophy of“life design”and the practical principle of“destiny establishment and transformation”from The Four Lessons of Liaofan Yuan,it constructs a three-dimensional framework for life design encompassing the dimensions of science,philosophy,and practice.The significance of this research lies in breaking through the predicament of materialism in the AI(artificial intelligence)era,explaining the autonomy and initiative of life,providing feasible pathways for life design,and ultimately achieving the in-depth integration of scientific rationality and the wisdom of traditional Eastern culture.
文摘Despite advances in current anti-cancer therapies,challenges such as drug resistance,toxicity,and tumor heterogeneity persist.The limitations of traditional single-target drugs and simple combination therapies are becoming increasingly apparent1.To address these issues,a novel treatment strategy,the artificially intelligent synergistic engineered drug(AISED)paradigm,merits further exploration.This paradigm is based on the systematic engineered integration of multiple active ingredients into a unified single entity through artificial intelligence(AI).This strategy is aimed at developing new anti-cancer drug designs involving multiple ingredients,multiple molecular targets,and multiple biological effects,for multiple cancer types,thereby providing a novel theoretical paradigm for overcoming existing treatment bottlenecks.
基金supported by the National Natural Science Foundation of China(Grant Nos.52574103 and 42277150).
文摘Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology remain largely unexplored.Leveraging the advantages of the finite-discrete element method(FDEM)for explicitly simulating fracture propagation and the strengths of the unifiedpipe model(UPM)for efficientlymodeling dual-permeability seepage,we propose a new hydromechanical(HM)coupling approach for modeling hydraulic fracturing.Validated against benchmark examples,the proposed FDEM-UPM model is further augmented by incorporating a Fourier-based methodology for reconstructing non-planar fractures,enabling quantitative analysis of hydraulic fracturing behavior within rough discrete fracture networks(DFNs).The FDEM-UPM model demonstrates computational advantages in accurately capturing transient hydraulic seepage phenomena,while the asynchronous time-stepping schemes between hydraulic and mechanical analyses substantially enhanced computational efficiencywithout compromising computational accuracy.Our results show that fracture morphology can affect both macroscopic fracture networks and microscopic interaction types between hydraulic fractures(HFs)and natural fractures(NFs).In an isotropic stress field,the initiation azimuth,propagation direction and microcracking mechanism are significantly influencedby fracture roughness.In an anisotropic stress field,HFs invariably propagate parallel to the direction of the maximum principal stress,reducing the overall complexity of the stimulated fracture networks.Additionally,stress concentration and perturbation attributed to fracture morphology tend to be compromised as the leak-off increases,while the breakdown and propagation pressures remain insensitive to fracture morphology.These findingsprovide new insights into the hydraulic fracturing mechanisms of fractured reservoirs containing complex rough DFNs.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214)。
文摘For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.
文摘RGB and CYMK are two major coloring schemes currently available for light colors and pigment colors,respectively.Both systems use letter-based color codes that require a large range of values to represent different colors.The problem is that these two systems are hard to use for manipulating any operations involving combinations of colors,and they lack the capacity for inter-changeability or unification.Based on prime number theory and Goldbach's conjecture,this study presents a universal color system(C235)using a number-based structure to encode,compute and unify all colors on a color wheel.The proposed C235 system offers a unified representation for the efficient encoding and effective manipulation of color.It can be applied to designing a high-rate LCD system and colorizing objects with multiple attributes and DNA codons,opening the door to manipulating colors and lights for even broader applications.
文摘对珠子参中皂苷类成分进行定性鉴别分析,建立珠子参皂苷类多成分含量测定方法。采用超高效液相色谱-飞行时间质谱(UPLC-Q-TOF-MS/MS)技术对珠子参进行正、负离子模式扫描,使用UNIFI天然产物信息平台对珠子参所含的化学成分进行定性鉴别分析;以0.1%磷酸水-乙腈溶液为流动相进行梯度洗脱,柱温30℃,流速0.3 mL/min,进行珠子参皂苷类多成分含量测定。珠子参中共鉴定出39个化学成分,包括37个皂苷类化合物和2个皂苷母核,并总结了皂苷类化合物的裂解规律,建立了UPLC同时测定珠子参中人参皂苷Rb1、人参皂苷Ro、人参皂苷Rb3、竹节参皂苷IV、竹节参皂苷IVa、人参皂苷Rd、姜状三七皂苷R1和金盏花苷E的多指标含量测定方法,该方法中8个待测成分在检测质量浓度范围内线性关系良好,精密度、重复性、稳定性的相对标准偏差(relative standard deviation,RSD)均小于3.0%,样品中人参皂苷Rb1、人参皂苷Ro、人参皂苷Rb3、竹节参皂苷IV、竹节参皂苷IVa、人参皂苷Rd、姜状三七皂苷R1和金盏花苷E的平均加样回收率分别为102.4%、103.1%、97.97%、99.42%、102.7%、102.1%、95.23%、100.5%,RSD分别为1.0%、0.98%、0.81%、2.3%、0.81%、1.9%、0.96%、1.8%。该研究建立的方法可快速、准确地对珠子参中的皂苷类成分进行定性及定量分析。
文摘If the singularity of the cosmic Big Bang is taken as the origin of the reference coordinate system,the surrounding vacuum in the initial moments of it would exhibit radially-outward right-handed spiral motion at light speed.Based on this spatial motion hypothesis,we derive a unified field equation and a set of Maxwell’s equations for vacuum SWs(Scalar Waves)generating a huge spiral force field that drives the energy to spiral inwardly and distort,leading to the formation of mass.Furthermore,they also uncover that mass is fundamentally an ultimate expression of energy,manifesting as the result of spiral motion of space at light speed.And then,we indirectly validate the theory that coherent light waves’collision generate SWs and subsequently mass through the experiment verifying the Breit-Wheeler process.The establishment of our theory offers a new analytical tool for the exploration of mass origin,the cosmic Big Bang,unified field theories.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
