Critical Branching Research Articles

Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.

Unraveling the Mechanical Response of Short-Chain Branched Amorphous Polyethylene: Insights from Molecular Dynamics Simulations

The deformation behavior of short-chain branched, amorphous polyethylene (PE) was investigated using united atom molecular dynamics (MD) simulations under uniaxial stretching. Our research reported in this paper examined the internal mechanisms underlying the mechanical response of the PE by analyzing potential energies, dihedral angle distributions, chain orientation and entanglements. The findings indicated that the short-chain branching generally enhanced Young’s modulus, except for sample B60 with a critical ethyl branch content of 60/1000 (60 ethyl branches per 1000 backbone carbons). The Young’s modulus of B60 was the local minimum value in the various samples. Analysis of the evolution of dihedral angles shows that the short-chain branching hinders the transition from gauche to trans configurations, leading to a decrease in the trans population as the branch content increases. Regarding the sample B60 with critical branch content 60/1000, the orientation parameter and entanglement parameter of the molecular chains along the backbone were lowest in the initial structure, and the efficiency of chain disentanglement was at its lowest during deformation. The simulation results elucidated the deformation mechanism related to the conformational evolution of the molecular chains, which can provide atomic-scale insights into the mechanical properties and processability of branched polyethylene, thereby allowing for optimization in its design and application.

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches—scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common ‘crackling noise’ approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Critical Markov branching process with infinite variance allowing Poisson immigration with increasing intensity

The article studies a single-type critical Markov branching process with infinite variance of the offspring distribution. The process admits also an immigration component at the time points of a non-homogeneous Poisson process. If additionally the mean number of immigrants is infinite, then proper limit distributions are obtained, under suitable normalization of the sample paths, depending on the rate of change of the intensity of the Poisson process.

Refinement of the Main Lemmas of the Theory of Critical Branching Processes

Refinement of the Main Lemmas of the Theory of Critical Branching Processes

Remote sensing image dehazing using generative adversarial network with texture and color space enhancement

Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.

Impact of Artificial Intelligence on Indian Banking Sector- A Study of Banks

Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. Intelligence might be defined as the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent but not intelligent. Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955, was define as “the science and engineering of making intelligent machines”. Much research has humans program machines to behave in a clever way, like playing chess, but, today, people emphasize machines that can learn, at least somewhat like human beings do. Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without micro-management. A hospital delivery robot must autonomously navigate busy corridors to succeed in its task. In AI, autonomy doesn’t have the sense of being self-governing common in politics or biology.

Diffusion approximation of critical controlled multi-type branching processes

Branching processes form an important family of stochastic processes that have been successfully applied in many fields. In this paper, we focus our attention on controlled multi-type branching processes (CMBPs). A Feller-type diffusion approximation is derived for some critical CMBPs. Namely, we consider a sequence of appropriately scaled random step functions formed from a critical CMBP with control distributions having expectations that satisfy a kind of linearity assumption. It is proved that such a sequence converges weakly toward a squared Bessel process supported by a ray determined by an eigenvector of a matrix related to the offspring mean matrix and the control distributions of the branching process in question. As applications, among others, we derive Feller-type diffusion approximations of critical, primitive multi-type branching processes with immigration and some two-sex branching processes. We also describe the asymptotic behaviour of the relative frequencies of distinct types of individuals for critical CMBPs.

Market zone configuration under collusive bidding among the conventional generators and renewable energy sources in the day-ahead electricity market

The European cross-zonal day-ahead (DA) electricity market is transitioning to the flow-based market coupling model for market clearing. With the increasing integration of renewable energy sources (RESs), market participants have opportunities for collusive bidding, resulting in decreased social welfare (SW). Thus, this paper is the first to propose an approach to configure the market zone (MZ) considering collusive bidding among conventional generators (CGs) and RESs in the DA market. Specifically, a bi-level model is developed to analyze collusive bidding among the CGs and RESs. Then, multi-dimensional market performance indices are used to determine the critical branches (CBs), on which the configuration of MZs is based. Finally, test 6-bus and simplified European systems are used to demonstrate the validity and merit of the proposed approach. Our simulation on the 6-bus system shows that when compared with the initial zonal market (ZM), the SW of the optimized ZM increased by 18 %, while the re-dispatch surrogate cost decreased to 0. Also, the penetration of RESs improved by 12 %, which guarantees the development of RESs.

A joint image encryption based on a memristive Rulkov neuron with controllable multistability and compressive sensing

Image encryption, as a critical branch, has attracted increasing attention to the demand for information security specifically in the era of artificial intelligence (AI). A chaotic sequence is regarded as an important encryption source, and compressive sensing provides an effective technology for obtaining and reconstructing sparse or compressible signals in applied electronic engineering. In this work, the detouring matching pursuit algorithm and DNA coding are utilized to increase the performance based on a newly developed chaotic firing neuron. A memristor as the electromagnetic component is proven to enhance synaptic plasticity and emulate the synaptic connections in the brain. A unique discrete memristive neuron is derived for exploring the dynamics of neuron firing. By modifying the feedback from the memristor various coexisting neuronal chaotic firing are possessed. Because of the periodic evolution of the resistor from the memristor, the derived memristive Rulkov neuron exhibits coexisting homogeneous and heterogeneous multistability, which enables amplitude controllability and different types of coexisting chaotic firings. Circuit implementation based on CH32 is built to verify the controllability of the coexisting dynamics.

A Flow Graph–Based Scalable Critical Branch Identification Approach for AC State Estimation Under Load Redistribution Attacks

A Flow Graph–Based Scalable Critical Branch Identification Approach for AC State Estimation Under Load Redistribution Attacks

Transient angle stability analysis of maximum Lyapunov exponent based on the key branch response information

Based on real-time access to response information on wide-area tributaries, in order to assess the system stability more quickly, this paper combines the simplified branch transient transmission capability (sBTTC) index and the largest Lyapunov exponent (MLE). An online identification method for transient power angle stability is proposed. Firstly, the method is based on a two-machine system to study the consistency of the power angle difference between the two ends of the critical branch and the change of the phase difference of the fleet. Secondly, the transient power angle stability problem is transformed into the MLE analysis problem according to the change rule of sBTTC index of the key branch. Then, by combining the characteristics of MLE curve and sBTTC index curve, the transient power angle stability criterion is given. Finally, the proposed method is verified and analysed by simulation examples. The method has strong industrial applicability because it does not need system model, the information source is easy to measure, it has strong anti-noise ability, and it overcomes the defect of the traditional method that needs to set a fixed symbolic observation window.

Critical branching dynamics in excitable network without refractory periods

Critical state plays an important role in emerged behavior of neural networks. Excitable networks are widely used in simulation studies. However, the simulation of critical state is sophisticated because the state sensitively depends on dynamical and topological features. We study the effect of refractory period of dynamical units and connection symmetry on the excitable network with critical branching ratio. In bidirectional random networks with refractory period, the critical branching ratio is well known. Here, we show that the collective behavior changes from critical state into ceaseless activity, when refractory period vanishes. Instead of mean degree of all nodes, we show that the control parameter is determined by the mean degree of active nodes which is larger than the average degree and induces the transition. When refractory period is present, the number of effective links connected to resting nodes is equal to average degree. The mechanism ensures the critical branching ratio is unity. In directed networks, collective behavior keeps the critical state when refractory period vanished. However, a few reciprocal links make the network without refractory period becoming ceaseless.

On deterministic approximation for nearly critical branching processes with dependent immigration

In this paper, we investigate the asymptotic behavior of a triangular array of branching processes with non-stationary immigration. In the nearly critical case, we prove weak convergence of properly normalized and scaled branching processes with immigration to a deterministic function when the immigration process is generated by dependent random variables.

Локальная предельная теорема для ветвящихся случайных систем Гальтона–Ватсона с иммиграцией и бесконечной дисперсией

The paper considers a stochastic population growth model, which is the critical Galton-Watson stochastic branching system allowing immigration. We consider the case when the mathematical expectation of the law of influx of immigrant particles and the variance of the law of reproduction of aboriginal particles have infinite values. The asymptotic properties of transition probabilities are studied in the case when the states of the system are irreversible. For this case, a local limit theorem has been established.

Occupation time fluctuations of an age-dependent critical binary branching particle system

Occupation time fluctuations of an age-dependent critical binary branching particle system

GCN for Identifying Critical Branches Based on Unlabeled Power Outage Data

GCN for Identifying Critical Branches Based on Unlabeled Power Outage Data

Conditional central limit theorem for critical branching random walk

Conditional central limit theorem for critical branching random walk

Asymptotic behaviour of the survival probability of almost critical branching processes in a random environment

A generalization of the well-known result concerning the survival probability of a critical branching process in random environment $Z_k$ is considered. The triangular array scheme of branching processes in random environment $Z_{k,n}$ that are close to $Z_k$ for large $n$ is studied. The equivalence of the survival probabilities for the processes $Z_{n,n}$ and $Z_n$ is obtained under rather natural assumptions on the closeness of $Z_{k,n}$ and $Z_k$. Bibliography: 7 titles.

Thermal Mixing and Flow Characteristics Study in a T-Junction

The objective of the current paper is to investigate the turbulent flow characteristics, thermal mixing behavior, and backflow features of coolant fluid in a T-junction using three-dimensional numerical simulations. The impact of critical flow parameters and different branch pipe axial and transverse incident angles on thermal mixing performance are explored as a function of momentum ratio. The investigation identifies the impact of jet flow, deflection flow, and wall jet flow patterns. The results reveal that the upside backflow of T-junction is more significant for a lower momentum ratio (<0.32) and decreases with higher momentum ratio (0.32 < momentum ratio < 2.7). The efficiency of thermal mixing is considerably improved by changing the incident angle and momentum ratio. Thermal mixing length decreases by ∼39%, 28%, and 21% as compared to the reference case. The current findings will help researchers to understand flow behavior and thermal mixing in a T-junction. It is also useful in high-temperature engineering applications for the evaluation of thermal cracking and safety management.