Abstract We propose to employ the framework of information geometry to detect anomalies in Very Low Frequency (VLF) and Low Frequency (LF) signal propagation, measured globally across amplitude and phase channels. Using a sliding‐window approach, the probability distributions of signal data are compared over adjacent intervals, defining a statistical measure of distinguishability, named information velocity. Information velocity enables the identification of anomalies in VLF/LF signals and connect them to various atmospheric, geophysical, and space weather phenomena. We have shown the effectiveness of information geometry in quantifying signal variability, offering a powerful framework for anomaly detection in VLF data and wider in the geophysical and atmospheric sciences context. The results demonstrate its potential for uncovering insights into ionospheric behavior, atmospheric disturbances, and their interplay with Earth's electromagnetic environment.

Dementia, including Alzheimer’s disease and frontotemporal dementia, is a progressive brain disorder that disrupts memory, thinking, and behavior, with early diagnosis being critical for effective intervention. This study examines the alteration of brain activity caused by dementia by analyzing electroencephalogram (EEG) signals using an information geometry method known as information rate, which captures the evolving patterns of brain signals over time rather than relying on static averages. This method is applied across standard EEG frequency bands – delta, theta, alpha, beta, and gamma – in participants with dementia and healthy controls. The characteristics of the distribution of information rate are studied through the statistical moments (such as mean, variance, skewness, and kurtosis) and Shannon entropy. The statistical comparisons are accessed using the Kruskal-Wallis test with Dunn’s post-hoc analysis, and results are compared against a conventional average-base method using Jensen-Shannon distance. The results show that dynamic features of EEG signals – particularly in the theta, alpha, and beta bands – effectively distinguish Alzheimer’s patients from healthy individuals, while the Shannon entropy of signal dynamics in frontal region differentiates frontotemporal dementia patients across the theta to gamma bands. Moreover, changes in the occipital region detected by information rate, but not by traditional method, further highlight the importance of capturing temporal variability. The method also successfully distinguishes individuals with Mild Cognitive Impairment from healthy controls, which conventional analysis failed to achieve. These results suggest that analyzing the dynamics properties of the brain signals provides a more sensitive and informative approach for identifying and distinguishing various forms of dementia.

Generation of zonal flows (ZF) by drift wave turbulence in numerical simulations based on the modified Hasegawa-Wakatani model is investigated using probability density functions (PDFs) and information rate which quantifies the number of statistically distinct states generated per unit time in the non-equilibrium system. The evolution of time-dependent PDFs of the electrostatic potential, density, and vorticity is quantified by the information rate and is directly compared. We examine this evolution for the system dominated by the isotropic turbulence as well as the system dominated by anisotropic ZF. Impact of ZF on turbulence is captured by a narrower PDF of fluctuating velocity perpendicular to ZF. The information rates of the turbulent potential and density, which are coupled via fast electron parallel transport, are similar confirming the strong coupling between these quantities during their evolution. In contrast, zonal parts of these fields exhibit a distinct information rate evolution. This suggests that the zonal density structure may develop independently of ZF, consistent with recent finding in gyrokinetic simulations.

Advancements in cloud computing and distributed computing have fostered research activities in Computer science. As a result, researchers have made significant progress in Neural Networks, Evolutionary Computing Algorithms like Genetic, and Differential evolution algorithms. These algorithms are used to develop clustering, recommendation, and question-and-answering systems using various text representation and similarity measurement techniques. In this research paper, Universal Sentence Encoder (USE) is used to capture the semantic similarity of text; And the transfer learning technique is used to apply Genetic Algorithm (GA) and Differential Evolution (DE) algorithms to search and retrieve relevant top N documents based on user query. The proposed approach is applied to the Stanford Question and Answer (SQuAD) Dataset to identify a user query. Finally, through experiments, we prove that text documents can be efficiently represented as sentence embedding vectors using USE to capture the semantic similarity, and by comparing the results of the Manhattan Distance, GA, and DE algorithms we prove that the evolutionary algorithms are good at finding the top N results than the traditional ranking approach.

Using a tumor-immune growth model, we investigate how immunotherapy affects its dynamical characteristics. Specifically, we extend the prey–predator model of tumor cells and immune cells by including periodic immunotherapy, the nonlinear damping of cancer cells, and the dynamics of a healthy cell population, and investigate the effects of the model parameters. The ideal value of immunotherapy, which promotes the growth of immune (and healthy) cells while contributing to the elimination or control of the cancer cells, is determined by using Fisher information as a measure of variability throughout our study.

The low-to-high confinement (L-H) transition is critical for understanding plasma bifurcations and self-organization in high-temperature fusion plasmas. This paper reports a probabilistic theory of the L-H transition, in particular, a probability density function of power threshold Qc for the first time. Specifically, by utilizing a stochastic prey-predator model with energy-conserving zonal flow-turbulence interactions and extensive GPU computing, we investigate the effects of stochastic noises, external perturbations, time-dependent input power ramping, and initial conditions on the power threshold uncertainty. The information geometry theory (information rate, causal information rate) is employed to highlight how statistical properties of turbulence, zonal flows, and mean pressure gradient change over the transition, clarifying self-regulation and causal relations among them.

Astrophysical and fusion plasmas share significant similarities, particularly in their ubiquitous turbulence, coherent structures, and self-organization. This paper focuses on magnetic confinement fusion plasmas, emphasizing their inherently non-equilibrium nature and the use of non-perturbative statistical approaches to quantify them. The statistical properties of fusion plasmas often deviate from Gaussian distributions, rendering low-order moments—such as means and standard deviations—inadequate for fully characterizing turbulence and its impact. The low-to-high confinement (L–H) transition, a key plasma bifurcation leading to improved confinement, is examined as a stochastic bifurcation, where the transition occurs probabilistically for a given input power. Probability density function methods help reveal how hidden variables influence the power threshold. Additionally, information theory is employed to uncover nonlinear plasma interactions, including self-regulation and causality.

The low-to-high confinement (L-H) transition signifies one of the important plasma bifurcations occurring in magnetic confinement plasmas, with vital implications for exploring high-performance regimes in future fusion reactors. In particular, the accurate turbulence statistical description of self-regulation and causal relation among turbulence and shear flows is essential for accessing enhanced plasma performance and advanced operation scenarios. To address this, we provide a nonperturbative theory of the L-H transition by stochastic simulations of a reduced L-H transition model and detailed statistical analysis. By calculating time-dependent probability density functions (PDFs) of turbulence, zonal flows, and the mean pressure gradient, we elucidate how statistical properties change over time with the help of the information geometry theory (information rate, causal information rate), highlighting its utility in capturing self-regulation and causal relation among turbulence, zonal flow shears, and the mean flow shears. Furthermore, stochastic noises in turbulence, zonal flows, and/or input power are shown to induce uncertainty in the power threshold Q_{c} above which the L-H transition occurs while leading to a rather gradual L-H transition. A time-dependent PDF of power loss over the L-H transition is presented.

Predicting financial markets remains a critical yet challenging task due to their complex and dynamic nature. This paper introduces a novel approach that combines Elliott Wave Theory (EWT) with Long Short-Term Memory (LSTM) networks to enhance the accuracy and reliability of financial market predictions. Elliott Wave Theory, which hypothesizes that market prices unfold in recognizable patterns driven by investor psychology, is integrated with LSTMs to effectively capture temporal dependencies in price movements. Our methodology comprises four key components: data gathering, preprocessing, model training, and performance simulation. Historical price data for stocks and cryptocurrencies is procured using established financial data APIs and preprocessed to encode wave patterns into a format suitable for LSTM processing. The LSTM model is trained on this data, focusing on recognizing and predicting future price movements based on identified wave patterns. The model's effectiveness is validated through a 15-day trading simulation, which netted a 2.2% gain, demonstrating its potential to outperform traditional predictive models. This paper not only underscores the feasibility of automating wave pattern recognition but also highlights the advantages of hybrid models in financial forecasting.

Natural disasters cause devastation, chaos, destruction, death, displacement, and much more when they occur. With the advancement of recent technologies especially in artificial intelligence and deep learning, the severity and impact of natural disasters could be reduced by predicting their occurrence. In this research, the flood detection (using images) system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). In this paper, the main aim is to propose an automated System for precision disaster detection, in particular flood detection. The intelligent system will monitor vast problematic areas that are dubbed flood risk areas during the season. The intelligent automated system will systematically take images with real-time processing to detect floods. If a flood is detected, the system will automatically communicate to the ground station with the early warning to issue an early warning to all the inhabitants in the path of the flood. Since this paper will prove a concept, the other main contribution of this paper is a new dataset consisting of 9000 images collected from various sources on the Internet, labeled, and preprocessed. The Deep CNN architectures were trained and tested by constructing a new dataset of more than 9000 labeled images with flood and non-flood collected from different sources. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. Experimental results show that 92.50% accuracy was achieved using GoogleNet. The second highest performance was achieved using AlexNet with an accuracy of 92.20%.

Assessing team performances in association football (commonly known as football or soccer) is challenging due to its low-scoring and unpredictable nature. While space control and generating open spaces on the field have been used to evaluate strategies for team performance, the temporal nature of space available for the ball carrier has not been explored. This work proposes the temporal nature of space available for the ball carrier in the attacking third as a novel time-series performance evaluation metric to assess team performance. Furthermore, it suggests a novel approach to quantify the relatively open space available for the ball carrier in the attacking third using player information retrieved from television video footage. The machine learning model trained with the features extracted with the proposed time-series metric achieved 80

In association football, predicting the likelihood and outcome of a shot at a goal is useful but challenging. Expected goal (xG) models can be used in a variety of ways including evaluating performance and designing offensive strategies. This study proposed a novel framework that uses the events preceding a shot, to improve the accuracy of the expected goals (xG) metric. A combination of previously explored and unexplored temporal features is utilized in the proposed framework. The new features include; “advancement factor”, and “player position column”. A random forest model was used, which performed better than published single-event-based models in the literature. Results further demonstrated a significant improvement in model performance with the inclusion of preceding event information. The proposed framework and model enable the discovery of event sequences that improve xG, which include; opportunities built up from the sides of the 18-yard box, shots attempted from in front of the goal within the opposition’s 18-yard box, and shots from successful passes to the far post.

A stochastic, prey–predator model of the L–H transition in fusion plasma is investigated. The model concerns the regulation of turbulence by zonal and mean flow shear. Independent delta-correlated Gaussian stochastic noises are used to construct Langevin equations for the amplitudes of turbulence and zonal flow shear. We then find numerical solutions of the equivalent Fokker–Planck equation for the time-dependent joint probability distribution of these quantities. We extend the earlier studies [Kim and Hollerbach, Phys. Rev. Res. 2, 023077 (2020) and Hollerbach et al., Phys. Plasmas 27, 102301 (2020)] by applying different functional forms of the time-dependent external heating (input power), which is increased and then decreased in a symmetric fashion to study hysteresis. The hysteresis is examined through the probability distribution and statistical measures, which include information geometry and entropy. We find strongly non-Gaussian probability distributions with bi-modality being a persistent feature across the input powers; the information length to be a better indicator of distance to equilibrium than the total entropy. Both dithering transitions and direct L-–H transitions are (also) seen when the input power is stepped in time. By increasing the number of steps, we see less hysteresis (in the statistical measures) and a reduced probability of H-mode access; intermittent zonal flow shear is seen to have a role in the initial suppression of turbulence by zonal flow shear and stronger excitation of intermittent zonal flow shear for a faster changing input power.

Purpose: The purpose of this study is to explore the applicability of mindful practice program MindON, which was designed to help mindful practice in daily life, how it functions and what qualitative changes it can make. Methods: To this end, a focus group interview was conducted with 8 participants among the participants who attended the MindON program, and a qualitative analysis was conducted. The transcribed interview data was analyzed according to the 6 steps of thematic analysis. Psychological Results: As a result of the study, the interview contents were classified into a total of 5 categories, 10 sub-themes, and 26 semantic concepts. The main study results are as follows. First, it was possible to confirm the aspect of participating in this mindful practice program by ‘intrinsic motivation’ and ‘extrinsic motivation’. Second, ‘systematic structure of the program’ and ‘development of in-depth mindful practice’ were cited as overall impressions of the MindON program. Third, it was reported that through this program, benefits were obtained in the context of 'mindful practice knowledge acquisition' and 'self-awareness activities'. Fourth, furthermore, the specific effects of this program could be explored by reporting cognitive, emotional, and behavioral changes through this program. Finally, requests for more mindful practice experiences could also be identified. Conclusion: Based on the above study results, the significance and suggestions of this study were discussed.

In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer’s disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal’s instantaneous influence on another signal’s information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer’s disease as presented in this work.

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum "geometric information variability". We validate our methodology through numerical experimentation on the Ornstein-Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein-Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control.

DIII-D plasmas are compared for two upper divertor configurations: with the outer strike point on the small angle slot (SAS) divertor target and with the outer strike point on the horizontal divertor target (HT). Scanning the vertical distance between the magnetic null point and the divertor target over a range 0.10-0.16 m is shown to increase the threshold power, P th , and edge plasma power, P Loss , for the low-to-high confinement (L-H) and H-L transitions respectively, by up to a factor of 1.4. The X-point height scans were performed at three L-mode core plasma line average electron densities, n over bar e = 1.2, 2.2 and 3.6 x 10 19 m - 3 , to investigate the density dependence of divertor magnetic configuration influence on P th . The X-point height, Z x-pt , was further extended across the range 0.16-0.22 m with the more open HT divertor configuration, for which a clear decrease in P th with increasing Z x-pt is observed. The dependence of P th on divertor magnetic geometry is further investigated using a time-dependent probability density function (PDF) model and information geometry to elucidate the roles played by pedestal plasma turbulence and perpendicular velocity flows. The degree of stochasticity of the plasma turbulence is observed to be sensitive to the plasma heating rate. The calculated square of the information rate shows changes in the relative density fluctuations and perpendicular velocity PDFs begin 2-5 ms prior to the L-H transition for three plasmas; providing a crucial measurement of the dynamic timescale of external transport barrier formation. Additionally, both information length and rate provide potential predictors of the L-H transition for these plasmas.

We propose a novel method for fast and accurate training of physics-informed neural networks (PINNs) to find solutions to boundary value problems (BVPs) and initial boundary value problems (IBVPs). By combining the methods of training deep neural networks (DNNs) and Extreme Learning Machines (ELMs), we develop a model which has the expressivity of DNNs with the finetuning ability of ELMs. We showcase the superiority of our proposed method by solving several BVPs and IBVPs which include linear and non-linear ordinary differential equations (ODEs), partial differential equations (PDEs) and coupled PDEs. The examples we consider include a stiff coupled ODE system where traditional numerical methods fail, a 3+1D non-linear PDE, Kovasznay flow and Taylor-Green vortex solutions to incompressible Navier-Stokes equations and pure advection solution of 1+1 D compressible Euler equation.The Theory of Functional Connections (TFC) is used to exactly impose initial and boundary conditions (IBCs) of (I)BVPs on PINNs. We propose a modification to the TFC framework named Reduced TFC and show a significant improvement in the training and inference time of PINNs compared to IBCs imposed using TFC. Furthermore, Reduced TFC is shown to be able to generalize to more complex boundary geometries which is not possible with TFC. We also introduce a method of applying boundary conditions at infinity for BVPs and numerically solve the pure advection in 1+1 D Euler equations using these boundary conditions.

Association football (commonly known as football or soccer) in the modern era places a greater emphasis on collaborating and working together as a team instead of relying solely on individual skills to strategize winning performances. The low-scoring and unpredictable nature of association football makes evaluating team performances challenging. Space creation and space utilization have been discussed in the football world lately. Existing literature evaluates this with on and off-ball runs by players for deceiving defenders to create open spaces. However, the contribution of these team ball movements’ enhanced randomness or chaotic nature to winning performances has yet to be explored. This work proposes a novel entropy-based time-series performance evaluation metric, EDRan, for quantifying this enhanced random nature by analyzing the spatial distribution of game events at regular intervals. Additionally, an unexplored cumulative ball possession matrix is used to quantify randomness. The correlation between the match winner and spatial event distribution randomness at regular intervals was analyzed. The significance of the proposed metric was demonstrated using a generalized linear model (GLM), which achieved an average accuracy of 80% for match-winning performance classification. The GLM p-values and coefficients revealed statistically significant relationships between the extracted temporal features and match-winning performances. Findings further revealed dispersed, highly random event distribution by winning teams during the early phases of the game, implying attacking behavior, followed by a compact, cautious playing style toward the end, suggesting that the game’s first-half performances are more pivotal. Despite the unpredictability of actual scores in association football, the proposed approach effectively captured the differences in performances between stronger and weaker teams with temporal relationships, highlighting its significance as a time-series metric for performance evaluation.

We investigate the stochastic dynamics of the prey-predator model of the Low-to-High confinement mode (L-H) transition in magnetically confined fusion plasmas. By considering stochastic noise in the turbulence and zonal flows as well as constant and time-varying input power Q, we perform multiple stochastic simulations of over a million trajectories using GPU computing. Due to stochastic noise, some trajectories undergo the L-H transition while others do not, leading to a mixture of H-mode and dithering at a given time and/or input power. One of the consequences of this is that H-mode characteristics appear at a smaller input power QQc as a second peak. The coexisting H-mode and dithering near Q=Qc leads to a prominent bimodal PDF with a gradual L-H transition rather than a sudden transition at Q=Qc and uncertainty in the input power. Also, a time-dependent input power leads to increased variability (dispersion) in stochastic trajectories and a more prominent bimodal PDF. We provide an interpretation of the results using information geometry to elucidate self-regulation between zonal flows, turbulence, and information causality rate to unravel causal relations involved in the L-H transition.