Function Of State Research Articles

Research on Graph Multi-Attention Neural Network for Power System Transient Stability Assessment

Abstract Diagnosing the stability of power grids based on artificial intelligence technology is a research trend. The existing artificial intelligence stability analysis methods rely on a large number of fault case data, while the graph neural network method ignores the correlation characteristics of nodes themselves and the correlation of long-distance nodes. In order to solve the problems, the graph multi-attention neural network (GMANN) was proposed. The self-attention, which characterizes the correlation between different state quantities of a single node, is proposed, firstly. Then, long-distance association attention, which represents the correlation of distant nodes in the power grid, was proposed. The long-distance correlation attention and the adjacent correlation attention in graph attention network are fused to form a global attention that reflects the global importance of the power grid. The channel attention that characterizes the importance of different pooling methods of graph convolutional network is extracted and used to obtain the importance of different convolution operations. Finally, based on the multi-source attention fusion strategy, global attention and channel attention are embedded in the graph attention neural network to form a multi-level evaluation model to achieve power grid stability evaluation efficiently guided by multiple attentions. The proposed GMANN is verified based on simulated fault cases obtained from the 10-machine 39-node system in New England. The results show that the accuracy of the GMANN can reach 97.34%, which is better than other methods. And the missed judgment rate of important indicators is significantly better than other methods.

A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis

Reliability analysis of highly sensitive structures is crucial to prevent catastrophic failures and ensure safety. Therefore, these safety-critical systems are to be designed for extremely rare failure events. Accurate statistical quantification of these events associated with a very low probability of occurrence requires millions of evaluations of the limit state function (LSF) involving computationally expensive numerical simulations. Variance reduction techniques like importance sampling (IS) reduce such repetitions to a few thousand. The use of a data-centric metamodel can further cut it down to a few hundred. In data-centric metamodeling approaches, the actual complex numerical analysis is performed at a few points to train the metamodel for approximating the structural response. On the other hand, a physics-informed neural network (PINN) can predict the structural response based on the governing differential equation describing the physics of the problem, without a single evaluation of the complex numerical solver, i.e., data-free. However, the existing PINN models for reliability analysis have been effective only in estimating a large range of failure probabilities (10-1∼10-3). To address this issue, the present study develops a PINN-based data-free reliability analysis for low failure probabilities (<10-5). In doing so, a two-stage PINN integrated with IS (PINN-IS) is proposed. The first stage is employed to approximate the most probable failure point (MPP) appropriately while the second stage enhances the accuracy of LSF predictions at the IS population centred on the approximated MPP. The effectiveness of the proposed approach is numerically illustrated by three structural reliability analysis examples.

Distribution reconstruction and reliability assessment of complex LSFs via an adaptive Non-parametric Density Estimation Method

Complex limit state functions (LSFs), often characterized by strong nonlinearity, non-smoothness, or discontinuity, pose challenges for structural reliability analysis in engineering practices. Conventional methods for uncertainty propagation and reliability assessment may struggle to handle these issues effectively. This paper introduces a novel approach to adaptively reconstruct the unknown distributions of complex LSFs. The Non-parametric Density Estimation Method based on Harmonic Transform (NDEM-HT) is employed as the tool for this purpose. An adaptive strategy is then proposed to determine the number of harmonic moments required in NDEM-HT for achieving high accuracy. Specifically, the Adaptive Kernel Density Estimation (AKDE) method is also adopted to provide an initial estimation of the rough distribution. Subsequently, the optimal number of harmonic moments is determined by minimizing the relative entropy between the distributions obtained by AKDE and NDEM-HT. The efficacy of the proposed method is demonstrated through five numerical examples, considering various types of complex LSFs. Comparative results are also provided employing MCS along with both conventional and state-of-the-art methods.

Про стійкість обертання вільної системи двох пружно зв’язаних гіроскопів Лагранжа, один з яких має ідеальну рідину

On the basis of the known equations of motion of the system of coupled gyrostats by P. V. Kharlamov and the state function by S. L. Sobolev, the equations of rotation of a free system of two elastically coupled Lagrange gyroscopes were derived, one of which has an arbitrary axisymmetric cavity completely filled with an ideal incompressible fluid. The rigid bodies are connected by an elastic restoring spherical hinge. A transcendental characteristic equation of the perturbed uniform rotation of the mechanical system under consideration is derived. Taking into account the fundamental tone of the fluid oscillation, a characteristic equation of the fifth order is obtained. On the basis of the Liénard – Chipart criterion written in the inor form, the necessary conditions for the stability of the uniform rotation of the Lagrange gyroscopes and the fluid are written out as a system of four inequalities. With respect to the elasticity coefficient, these inequalities have degrees 1, 3, 6 and 8, respectively. Analytical studies of the leading coefficients of these stability conditions are carried out. It is shown that when the center of mass of the first solid body with liquid or the second does not coincide with the common point of these bodies, then at sufficiently large values of the elasticity coefficient the necessary stability conditions will always be satisfied. In the absence of elasticity in the hinge, the characteristic equation has a double zero root and in this case the stability conditions require additional studies. The obtained stability conditions are exact for an ellipsoidal cavity and approximate for other axisymmetric cavities. To clarify the obtained glass conditions for these voids, it is necessary to take into account additional tones of oscillation of an ideal liquid.

ЗАДАЧА ОПТИМАЛЬНОГО СТОХАСТИЧЕСКОГО УПРАВЛЕНИЯ И ОЦЕНКИ СТОИМОСТИ КОРПОРАТИВНЫХ ОБЛИГАЦИЙ, ЗАВИСЯЩИХ ОТ ВРЕМЕНИ И СЛУЧАЙНЫХ СОСТОЯНИЙ ПАРАМЕТРОВ

In the optimal stochastic control problem, one estimates, using the utility indifference method, the bond price and the premium of a default swap contract (Credit default swap) when the model parameters (market interest rate, drift coef-ficient, and volatility of risky underlying assets) are random functions of time and state. Namely, the interest rate of the risk-free asset depends on time, and the prices of risky assets are described by linear homogeneous stochastic dif-ferential equations (SDEs) with multiplicative noise. To do this, the authors consider a portfolio with a risk-free asset and a risky asset with no default risk. For each such portfolio, the authors determine the amount of risky assets that maximizes the expected utility of its final wealth. This quantity allows one to solve the parabolic partial differential equations arising from the Hamilton-Jacobi-Bellman (HJB) equation by transforming them into ordinary differential equations using the method of variable separation to obtain the instantaneous value function of each portfolio. The authors derive the bond price and the default swap-contract premium (CDS) rate, which are the amounts that provide the same level of the expected utility by investing all of one’s wealth in a portfolio that does not contain these credit instruments, or by investing these amounts in credit instruments and the remainder of one’s wealth in the portfolio.

A suboptimal OCT4-SOX2 binding site facilitates the naïve-state specific function of a Klf4 enhancer.

Enhancers have critical functions in the precise, spatiotemporal control of transcription during development. It is thought that enhancer grammar, or the characteristics and arrangements of transcription factor binding sites, underlie the specific functions of developmental enhancers. In this study, we sought to identify grammatical constraints that direct enhancer activity in the naïve state of pluripotency, focusing on the enhancers for the naïve-state specific gene, Klf4. Using a combination of biochemical tests, reporter assays, and endogenous mutations in mouse embryonic stem cells, we have studied the binding sites for the transcription factors OCT4 and SOX2. We have found that the three Klf4 enhancers contain suboptimal OCT4-SOX2 composite binding sites. Substitution with a high-affinity OCT4-SOX2 binding site in Klf4 enhancer E2 rescued enhancer function and Klf4 expression upon loss of the ESRRB and STAT3 binding sites. We also observed that the low-affinity of the OCT4-SOX2 binding site is crucial to drive the naïve-state specific activities of Klf4 enhancer E2. Altogether, our work suggests that the affinity of OCT4-SOX2 binding sites could facilitate enhancer functions in specific states of pluripotency.

Microstate D as a Biomarker in Schizophrenia: Insights from Brain State Transitions.

Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease.

Quantitative method for the probability of structural damage based on moment theory

In the context of uncertainty in the inverse problem of damage identification, this study proposes a moment theory based probabilistic damage identification (MbPDI) method. Noise errors in the identification process are treated as uncertain parameters, while structural stiffness parameters are treated as functions of noise errors. A limit state function for damage identification is defined, and its probability is computed. Utilizing the concept of moment theory, the expansion series is performed for the limit state function around the most probable point of failure, with gradient information derived using sensitivity matrices. Based on the gradient information and uncertainty inputs, damage probabilities for various elements can be obtained. Additionally, this paper addresses the calculation of structural damage probabilities under the non-normal distribution of random variables by transforming non-normal variables into equivalent normal variables. The proposed probability index and expected index of structural damage in the probabilistic damage identification method has a clear physical meaning, and the calculation has high accuracy. The proposed method is validated through a mathematical example and a numerical simulation. Meanwhile, the influence of damage extent, noise level, and modal order on identification results are analyzed. Experimental validation of the method is also presented.

A dynamic migration route planning optimization strategy based on real-time energy state observation considering flexibility and energy efficiency of thermal power unit

New energy resources compromising intermittent and fluctuating natures have been integrated into the power grid on a large scale, and thermal power units are obliged to promote the depth of deep peak shaving and flexible response-ability to cope with this new scenario. The safety, flexibility, and high efficiency of coal-fired units are the ultimate goals of its transformation, and there is a mutual influence and mutual restriction relationship among these indicators. To exploit the flexibility of thermal power units, take into account efficiency and safety, and play a supporting role in the new power system, a control strategy that weighs various indicators needs to be developed. Based on heat flow model and dynamic state space model, and updated matrix in real time, the key process parameters affecting the flexibility of heat transfer can be fully characterized. By using the observed state and calculation, the load response index and the energy efficiency index are optimized under the boundary constraint. The results show that the multi-objective optimization methods can avoid over regulation, reduce the flue gas flow by 3.23%–3.59%, and reduce the fluctuation of state quantity under the premise of satisfying the load response rate, about 2.32%–2.52% away from the safety boundary of the design temperature parameter to achieve the flexibility of frequency modulation, high efficiency of energy transmission and operation safety.

M6A mRNA methylation by METTL14 regulates early pancreatic cell differentiation

N6-methyladenosine (m6A) is the most abundant chemical modification in mRNA and plays important roles in human and mouse embryonic stem cell pluripotency, maintenance, and differentiation. We have recently reported that m6A is involved in the postnatal control of β-cell function in physiological states and in type 1 and 2 diabetes. However, the precise mechanisms by which m6A acts to regulate the development of human and mouse pancreas are unexplored. Here, we show that the m6A landscape is dynamic during human pancreas development, and that METTL14, one of the m6A writer complex proteins, is essential for the early differentiation of both human and mouse pancreatic cells.

Understanding the heat generation mechanisms and the interplay between joule heat and entropy effects as a function of state of charge in lithium-ion batteries

The thermal performance of lithium-ion battery cells is critical for ensuring their safe and reliable operation across various applications. In this study, we employed an isothermal calorimetry method to investigate the heat generation of commercial 18650 lithium-ion battery fresh cells during charge and discharge at different current rates, ranging from 0.05C to 0.5C, and across various temperatures: 20 °C, 30 °C, 40 °C, and 50 °C. Our findings revealed a direct correlation between heat generation and current rates, indicating that higher current rates lead to increased heat generation within the cells. Conversely, we observed that heat generation remained relatively stable as the temperature rose, suggesting that temperature changes within this range may not significantly impact the heat generation of fresh cells during typical operations. Furthermore, our study explored irreversible heat generation, which depends on the applied current and overpotential, using the galvanostatic intermittent titration technique at 0.05C–0.5C and 30 °C. Additionally, electrochemical impedance spectroscopy was performed on the same cells during charge and discharge at 20 °C, 30 °C, and 40 °C to analyze cell impedance. Our results indicated a consistent dependence of impedance on the state of charge and depth of discharge, with a significant increase in impedance observed at the end of the discharge process.

Interval observers for hybrid continuous-time stationary systems

The problem of interval observer design for hybrid continuous-time stationary systems under external disturbances and measurement noise is studied. It is assumed that continuous-time dynamic of such systems is described by linear or nonlinear differential state equations with linear function of output. System parameters depend on system states and are switched based on the control system with finite set of states. The relations allowing designing hybrid interval observer of minimal dimension estimating the set of admissible values of the prescribed linear vector function of the system states are derived. To solve the problem, algebra of partitions and linear algebra are used. Theoretical results are illustrated by example.

Classifying Learning Speed Using Brain Networks and Psychological States: Unraveling the Interdependence Between Learning Performance, Psychological States, and Brain Functions.

Introduction The progression of performance learning (PL) may have complex relationships beyond mere concurrent occurrences and may influence each other. This study aimed to classify the speed of PL using a random forest based on brain network and stress state information and to identify the factors necessary for PL. In addition, this study also aimed to clarify the complex interdependent relationships between PL, psychological state, and brain function through these factors, using covariance structure analysis. Methods A total of 20 healthy individuals participated in a choice reaction time task, and brain function was measured using near-infrared spectroscopy (NIRS). Participants were divided into high-PL and low-PL groups based on the median difference in correct responses. Results Random forest analysis identified the left orbitofrontal area, right premotor cortex, right frontal pole, left frontal pole, left dorsolateral prefrontal cortex, and depression and anxiety as key factors. Covariance structure analysis revealed that depression and anxiety affected PL through the frontal pole and prefrontal cortex, suggesting a complex interplay between psychological state, brain function, and learning. Conclusions These findings suggest that psychological states influence brain networks, thereby affecting learning performance. Tailoring rehabilitation programs to address psychological states and providing targeted feedback may improve learning outcomes. The study provides insights into the theoretical and practical applications of understanding the brain's role in PL, as well as the importance of addressing psychological factors to optimize learning and rehabilitation strategies.

Dual color carbon dots for simultaneous dynamic fluorescence tracking of mitochondria and lysosomes

The development of fluorescent probes to monitor mitochondria and lysosomes will be helpful not only to realize the status monitoring of the organelles, but also to gain insights into their functions in both healthy and diseased states. Carbon Dots (CDs), as a nanomaterial with excellent properties, have provided new tools and methods for research in organelle imaging in recent years. In this study, we synthesized green fluorescence-emitting carbon dots (G-CDs) with mitochondria-targeting ability and red fluorescence-emitting carbon dots (R-CDs) with lysosome-targeting ability by using the same precursors. G-CDs are brightly with a quantum yield of up to 51.8 % and R-CDs have a long-wavelength emission of 611 nm. Based on the unique properties of G-CDs and R-CDs, we observed the changes of mitochondria during apoptosis and mitochondrial autophagy in HeLa cells by using G-CDs. And the dynamics of lysosomes in the inhibition of cellular autophagy and cellular necrosis were observed by utilizing the R-CDs. Furthermore, we observed human intestinal organoids samples stained by G-CDs and R-CDs, extending the relevant applications of carbon dots imaging to the organoids field, which brings forth new possibilities for research and drug development of related diseases.

Probabilistic assessments of running ductile fractures in dense-phase and supercritical CO2 pipelines

Running ductile fracture (RDF) is a severe failure mode of high-pressure pipelines. Dense-phase and supercritical CO2 pipelines are particularly susceptible to RDF due to the unique characteristics of the depressurization process resulting from a fracture of the pipeline. The present study carries out probabilistic analyses of RDF in dense-phase and supercritical CO2 pipelines. The well-known Battelle two-curve method is employed to establish the limit state function for RDF by comparing the arrest pressure with the saturation pressure. The arrest pressure is computed using the Battelle through-wall crack model with the adjustment recently proposed in the literature. The saturation pressure is computed based on the one-dimensional isentropic decompression assumption and a rigorous equation of state. The first-order reliability method is employed to evaluate the probabilities of RDF in hypothetical CO2 pipelines designed per DVN-RP-F104 with representative pipe attributes and initial operating conditions by considering uncertainties in the relevant pipe geometric and material properties. The analysis results indicate that the probability of RDF can vary by several orders of magnitude depending on the pipe attributes and initial operating conditions. Furthermore, the results shed light on the key uncertainties associated with the pipe geometric and material properties that influence the probability of RDF.

The Application of Criminal Law in Addressing Corruption Crimes: Strategies and Challenges

Corruption poses a substantial challenge for Indonesia, affecting community life, national development, and state functioning. It leads to significant financial losses and economic repercussions for both perpetrators and the nation. To combat this issue, it's essential to harmonize legislation, enhance international cooperation, and develop an integrated approach to law enforcement while encouraging active community participation. Addressing corruption requires improvements in education, training, and the instillation of moral values. This research aims to understand the characteristics of normative legal knowledge and to explore legal regulations and their principles using a normative juridical approach, relying on secondary data analysis. Active prevention of corruption and a proactive role for corruption courts are crucial. Key findings suggest the need for rigorous law enforcement, the abolition of gratuities, increased transparency, and enhanced public ethics. Collaboration among the government, law enforcement, and the community is vital to effectively tackle corruption. Fair law enforcement and legal certainty must be ensured to build a collective conviction among stakeholders. A multifaceted approach is necessary for effective corruption eradication, including system improvements, educational campaigns, and repressive actions. Sustained efforts can minimize corruption's negative impacts, foster public trust, attract investors, and uphold national dignity while deterring corrupt activities. Furthermore, maximizing the recovery of state funds lost to corruption is essential for the nation’s integrity and development.

The legal status and activities of the authorities of the Ukrainian People’s Republic in exile (end of 1920 – August 22, 1992): historical and legal aspects

A historical and legal study of the legal status and activities of the authorities of the Ukrainian People’s Republic (hereinafter - the UPR) in exile (late 1920 – August 22, 1992). It is concluded that, since at the end of 1920 the authorities of the UPR lost influence on the population as a result of the establishment of Soviet power in most of the Ukrainian territories and the UPR actually ceased to exist, which was formally fixed by a peace treaty between the Polish-Lithuanian Commonwealth and Soviet Russia on March 18, 1921. in Riga, the UNR authorities lost their state status and acquired public status. The content adopted by the Council of People’s Ministers and approved by the Chairman of the Directory on January 9, 1921 was analyzed. a normative act entitled “Law on the Council of the Republic,” in which the Council of the Republic was defined as “a temporary supreme authority of the people, which has full power.” The authorities of the UPR imitated state activity: the Council of the Republic – as legislative, the Council of People’s Ministers - as executive authority, the Head of the Directory – as the head of the UPR. After the termination of the existence of the Council of the Republic in August 1921 the functions of the UNR authorities in exile were carried out by the Head of the Directory and the Council of People’s Ministers. The content of the “Temporary Law on the Reorganization of the State Center of the UNR in Exile” of June 10, 1948, which established the competence of the constituent parts of the State Center of the UNR: President, Vice-President, Government, Ukrainian National Council, was studied. Based on the analysis of the content of this normative act, it was concluded that the State Center of the UPR in exile formally had the status of a public organization, but pretended to be a state authority and imitated state functions. According to this document, the Ukrainian National Council performs “the functions of a temporary legislative representation.” The relationship between the constituent parts of the State Center of the UPR in exile had to be revealed in the rule-making process and in the formation of the Government. The political responsibility of the Government to the Ukrainian National Council and the President of the UPR was envisaged. The Government’s accountability to the Ukrainian National Council and control over the UNR President were established. The UNR President was vested with the right to dissolve the Ukrainian National Council at the suggestion of the Government. The authorities formed on the basis of this normative act had the status of public and performed functions in international, normative, political, scientific and other spheres.

ACTIVITIES OF NON-STATE MILITARY INSTITUTIONS: ECONOMIC ASPECT

The subject of the study is the economic aspect of the activities of non-state military institutions (private military companies), in particular, the increased demand for the services provided by them, and the state regulation of their activities by national legislation of leading foreign countries and international acts. The methodological basis of the study is modern general scientific methods (systemic and structural, formal and logical, analysis and synthesis, induction and deduction, generalisation, comparative); special methods (historical and legal). The purpose of the study is to analyse the economic aspect of non-state military institutions, based on the analysis of scientific works, experience of leading foreign countries and international practice, as well as the analysis of domestic draft laws on legalisation of military consulting activities in Ukraine. In the course of the study, the authors analysed the works of scholars who have studied various aspects of private military campaigns (legal, economic, security) and their regulation in foreign countries, as well as international legal norms on the regulation of the activities under study. In addition, the authors critically analysed domestic draft laws on legalisation, state regulation and supervision (control) over the activities of non-state military institutions in Ukraine. As a result of the study, it was established that the emergence of draft laws on the legalisation of the activities of non-state military institutions, their state regulation and supervision in Ukraine, on the one hand, is due to the aggression of the Russian Federation against Ukraine, which began in 2014. On the other hand, the economic consequences of privatisation in many industries and the need to compare the costs of the state's functions, including security, with the benefits received. Under such conditions, the business environment has demanded the introduction of a promising business activity and business sector – military consulting. Recently, this demand has been reinforced by the availability of a significant number of highly professional military specialists with experience and willingness to participate in relevant operations, including abroad. It is also understood that the demand for the services of non-state military institutions and the possibility of starting such activities in foreign countries where they are legalised will lead to an outflow of qualified specialists for employment abroad, and a loss of human resources and state budget revenues in the form of taxes paid by military consulting entities. At the same time, the existence of such entities in the country will help to improve the level of training of military personnel in the context of Russia's large-scale armed aggression against Ukraine by attracting specialists with relevant experience and willingness to engage in this type of business activity. In the post-war period, the existence of this type of legal activity will solve the problem of employment of former military personnel who wish to continue to work officially in the military and receive decent wages, and thus reduce social tensions in the economic sector.

The state of charge predication of lithium-ion battery energy storage system using contrastive learning

The state of charge (SOC) is a critical state quantity that must be determined in real-time for a battery energy storage system (BESS). It is a prerequisite for the operation of a BESS. However, obtaining the precise value of SOC is challenging due to it being a hidden state quantity. Existing neural network models commonly employ an end-to-end prediction paradigm for SOC estimation, which fails to fully exploit the rich information present in the time-series battery data. Unlike most studies available in the literature, we propose a novel SOC prediction method named CLDMM. This method is the first to apply contrastive learning techniques from the image field to the SOC prediction of lithium batteries. The method utilizes data augmentation, a multi-scale encoder, and multi-layer perceptrons to learn latent representations and mix these with raw data proportionally for downstream predictive tasks. The performance of the proposed method is evaluated using the Panasonic NCR18650PF dataset, and ablation study were conducted. Experimental results show that CLDMM outperforms baseline methods, achieving an average mean absolute error (MAE) of 0.64% and an average maximum error (MAX) of 2.66%.

Capillary-scale Microvessel Imaging with High-frequency Ultrasound Localization Microscopy in Mouse Brain.

Ultrasound localization microscopy is a super-resolution vascular imaging technique which has garnered substantial interest as a tool for small animal neuroimaging, neuroscience research, and the characterization of vascular pathologies. In the pursuit of increasingly high-fidelity reconstructions of microvasculature, there remains several outstanding questions concerning this sub-diffraction imaging technology, including the accurate reconstruction of microvessels approaching the capillary scale and the pragmatic challenges associated with long data acquisition times. In the context of small animal neurovascular imaging, we posit that increasing the ultrasound imaging frequency is a straightforward approach to enable higher concentrations of microbubble contrast agents, thus increasing the likelihood of microvascular/capillary mapping and decreasing the imaging duration. We demonstrate that higher frequency imaging results in improved ULM fidelity and more efficient microbubble localization due to a smaller microbubble point-spread function that is easier to localize, and which can achieve a higher localizable concentration within the same unit volume of tissue. A select example of in vivo capillary-level vascular reconstruction is demonstrated for the highest frequency imaging probe, which has substantial implications for neuroscientists investigating microvascular function in disease states, regulation, and brain development. High frequency ULM yielding a spatial resolution of 7.1μm, as measured by Fourier ring correlation, throughout the entire depth of the brain, highlighting this technology as a highly relevant tool for neuroimaging research.