Galaxy rotation curves and the tight scaling relations that accompany them are not easily reproduced by collisionless cold dark matter alone, while modified-gravity descriptions that succeed at galactic scales encounter difficulties elsewhere. We examine a minimal alternative that leaves general relativity untouched and instead gives the dark sector a small internal pressure. The dark component is treated as a barotropic fluid in hydrostatic equilibrium with an isothermal equation of state, so that pressure resists gravitational collapse and halos settle into finite-density cores rather than cusps. The resulting rotation law is analytic and carries two free parameters per galaxy, the same number as a standard halo. Fitting 165 galaxies from the SPARC database under a fixed-baryon protocol applied identically to every model, we obtain a median coefficient of determination , against 0.924 for the standard halo profile at the same two-parameter count, the comparison that isolates the dark sector. The model is favoured by the Akaike criterion, which penalises parameters, in 99 of the 165 galaxies. Calibrating two population scaling relations on photometry alone and predicting each rotation curve with no per-galaxy fitting reproduces the radial acceleration relation with a scatter of 0.192 dex, matching the zero-parameter modified-gravity value of 0.202 dex on the same points. The model also generates an acceleration scale of order the observed one, but a structured rather than universal one: it correlates with baryonic surface density (), a correlation forbidden for a universal scale and thus a direct point of separation from modified gravity. It recovers the baryonic Tully-Fisher relation with a slope near four. Pressure support thus accounts for the principal galactic regularities without modifying gravity, as a modest extension of the cold dark matter picture.

A dark matter halo supported by internal pressure makes a two-sided prediction: at fixed mass its core grows with the halo velocity scale (expansion), and at fixed velocity its core shrinks as the central baryonic surface density rises (compression). All previous evidence for this came from rotation-curve fits of the pressure model itself, leaving open the objection that the pattern is a fitting artifact. Here we test both signs in core measurements involving no such fit: 55 galaxies from three published samples, measured by three independent teams using three independent techniques, H i density profiles, stellar kinematics, and Hα velocity fields. The analysis is pre-registered: predictions, sign conventions, statistics, and verdict criteria were fixed before the data were examined, with permutation-based significance and the code deposited publicly. The expansion sign appears at partial r = +0.53 (p = 0.007, N = 26). The compression sign, the load-bearing prediction, is aligned in all three samples and reaches a combined one-sided p = 0.031 (N = 55) under a rule in which a reversed sign would have counted against the model; this value is marginal under sequential correction and does not survive removal of the strongest sample (p = 0.174). Of six tests, five carry the predicted sign, one is null, none is reversed. A parameter-free Jeans ratio holds with 0.197 dex scatter. These results establish the pattern at first-sighting strength, not the mechanism: it remains degenerate with feedback-driven core formation, and the discriminating simulation test is the sequel.

Dark-sector models that fit the same rotation curves can be forced into mutually exclusive coefficients for how halo core size scales with velocity and central density. An isothermal pressure-supported core predicts one specific pair through its Jeans length, constant surface density predicts another, and disk-feedback cores a third, near zero. We measure this law from Burkert fits to the SPARC sample, using outer-curve velocities with the baryons subtracted, so no model's own fitter enters the regression. The measured coefficients, a = 0.86 and b = −0.67 from 131 galaxies, exclude all three predictions, but unequally: surface-density and feedback are strongly excluded, the isothermal point only mildly disfavoured. A closure test on synthetic isothermal populations, passed through the identical pipeline, recovers the injected coefficients unbiased: the velocity exponent matches +1, and the deviation is physical, residing in the density exponent. Calibrating the same pipeline on polytropic halos gives an effective equation-of-state index of 1.39, excluding the isothermal value. The pressure model's invariant combination scatters at 0.19 dex against 0.35 dex for the surface-density invariant, and out-predicts both NFW and Burkert on held-out data, with cosmologically compliant NFW concentrations. These are structural-law coefficients, a first instrument-calibrated equation-of-state constraint, not a detection of pressure.

The functional forms of galactic rotation curves provide a fundamental test for models of gravity and dark matter. In this work, we conduct a rigorous statistical comparison of five distinct models against the complete sample of 175 galaxies from the SPARC database. The models under consideration are: (1) a one-parameter empirical law, physically constrained by each galaxy’s measured flat disc rotation velocity (); (2) a classical two-parameter Freeman disk; (3) a three-parameter Navarro-Frenk-White (NFW) dark matter halo plus baryons model; (4) a one-parameter Modified Newtonian Dynamics (MOND) model; and (5) a two-parameter Bosma/Dark Matter Disk model. We employ a Bayesian framework with Markov Chain Monte Carlo (MCMC) for parameter estimation, and use the Akaike Information Criterion (AICc) to assess the goodness-of-fit and statistical parsimony of the best-fit models. Our results reveal a decisive preference for the constrained one-parameter empirical model. It is selected as the best descriptor for a clear majority of the sample (60.6%) and, critically, also achieves the best median goodness-of-fit, with a reduced chi-squared of . In contrast, the standard NFW framework is preferred in only 12.6% of cases, while the MOND and Freeman disk models are selected as optimal for just 10.3% and 9.1%, respectively. The Bosma model, despite linking dynamics to the gas distribution, is preferred in only 7.4% of cases. The pronounced statistical and descriptive success of this simple, physically-anchored law over more complex, established theories suggests that its functional form represents a fundamental and highly efficient organizing principle in the dynamics of disk galaxies.

An epileptic seizure is a neurological disorder that significantly impacts the lives of many individuals. According to the World Health Organization (WHO), approximately 10 out of every 1000 people worldwide are affected by epilepsy. Developing an accurate prediction mechanism for seizures is crucial for effective management and treatment; however, this task remains challenging due to the complex, noisy, and non-stationary nature of electroencephalography (EEG) signals. This paper presents a hybrid deep learning model designed for the prediction and analysis of epileptic seizures with high accuracy. The model integrates wavelet transform, 1D convolutional layers, and a multi-head attention mechanism, leveraging the strengths of convolutional neural networks (CNNs) in capturing spatial patterns while enabling simultaneous focus on various temporal aspects of the EEG signal. To enhance the model’s robustness and generalization capabilities, we employed multiple regularization techniques, including dropout layers, L2 regularization, and early stopping, to effectively mitigate overfitting. Notably, the inherent robustness of the architecture proved sufficient to manage the dataset’s class imbalance, achieving high performance without the need for explicit over-sampling techniques that are often required for such data. The model achieved an accuracy of 99.71% using the University of California Irvine (UCI) Seizure datasets, outperforming all existing models. These findings contribute significantly to the advancement of automated tools for epileptic seizure detection, paving the way for more accurate diagnoses and improved clinical applications in prediction systems.

Cardiovascular diseases, particularly cardiac arrhythmias, remain a leading cause of global mortality, necessitating efficient and accurate diagnostic tools. Despite advances in deep learning for ECG analysis, current models face challenges in cross-population performance, signal noise robustness, limited training data efficiency, and clinical result interpretability. Additionally, most current approaches struggle to generalize across different ECG databases and require extensive computational resources for real-time analysis. This paper presents a novel hybrid deep learning framework for automated ECG analysis, combining one-dimensional convolutional neural networks (1D-CNN) with a specialized attention mechanism. The proposed architecture implements a four-stage CNN backbone enhanced with a squeeze-and-excitation attention block, enabling adaptive feature selection across multiple scales. The model …

The Quasi-Newton (QN) method is a widely used stationary iterative method for solving unconstrained optimization problems. One particular method within the Quasi-Newton family is the Symmetric Rank-One(SR1) method. In this research, we propose a new variant of the Quasi-Newton SR1 method that utilizes theBarzilai-Borwein step size. Our analysis demonstrates that the updated matrix resulting from the proposed method is both symmetric and positive definite. Additionally, our numerical experiments show that the proposed SR1 method, when combined with the PCG method, is effective in solving unconstrained optimization problems, as evidenced by its low number of iterations and function evaluations. Furthermore, we demonstrate that our proposed SR1 method is more efficient in solving large-scale problems with a varying number of variables compared to the original method. The numerical results of applying the new SR1 method to neural network problems also reveal its effectiveness.

Logistic regression is a type of statistical model that can be used to predict the probability of an out come occurring, given a set of input features. In the case of diabetes, logistic regression could be used to predict the probability that an individual has diabetes, based on a set of risk factors such as age, family history, and body mass index. Logistic regression is a popular method for predicting binary outcomes, like the presence or absence of a disease, and can be a useful tool for identifying individuals at high risk of developing diabetes.However, Logistic Regression is a simple method and its predictions are based on a linear combination of input features, it may not work very well when the relationship between the inputs and the outcome is non-linear or when there is a high degree of interaction between the input features, In this research, we are going to apply logistic regression on a Kaggle dataset to predict the probability of an individual having diabetes, based on a set of risk factors such as age, family history, and body mass index. The model will be trained on the data available in the Kaggle dataset and will be used to make predictions on new, unseen data.

An unexpected limitation of blood supply to the brain and heart causes the majority of strokes. Stroke severity can be reduced by being aware of the many stroke warning signs in advance. A stroke may result if the flow of blood to a portion of the brain stops suddenly. In this research, we present a strategy for predicting the early start of stroke disease by using Logistic Regression (LR) algorithms. To improve the performance of the model, preprocessing techniques including SMOTE, feature selection and outlier handling were applied to the dataset. This method helped in achieving a balance of class distribution, identifying and removing unimportant features andhandling outliers. with the existence of increased blood pressure, body mass, heart conditions, average blood glucose levels, smoking status, prior stroke, and age. Impairment occurs as the brain's neurons gradually die,depending on which area of the brain is affected by the reduced blood supply. Early diagnosis of symptoms can be extremely helpful in predicting stroke and supporting a healthy lifestyle. Furthermore, we performed an experiment using logistic regression (LR) and compared it to a number of other studies that used the same machine learning model, which is logistic regression (LR), and the same dataset. The results showed that our method successfully achieved the highest F1 score and area under curve (AUC) score, which can be a successful tool for stroke disease prediction with an accuracy of 86% compared to the other five studies in the same field. The predictive model for stroke has prospective applications, and as a result, it is still significant for academics and practitioners in the fields of medicine and health sciences.

Human muscles can be read by using electromyography (EMG) sensors, which are electrical signals generated by the muscles of human and animal bodies. This means it is possible to use electricity generated by muscles to control actuators/servo motors for any specific task. This could support a wide range of applications, especially for people with disabilities. One such application would be making bionic limbs based on servo motors. According to a study held by the K4D helpdesk report based on estimations that 15.3% of the world’s population has a moderate or severe disability, this proportion is likely to increase to 18-20% in conflict-affected areas(Thompson, 2017). The ultimate goal of this study is to make bionic limbs affordable by minimizing the cost while maintaining accuracy at an acceptable rate. To achieve this goal, the study suggests a new idea for using electromyography (EMG) sensors in bionic limbs, which suggests a decrease in the number of EMG sensors to decrease the cost and then power consumption. Decreasing the number of EMG sensors will result in loss of accuracy on controlling actuators (servo motors) because usually each sensor is responsible for activating one servo motor. In normal projects, one will need at least six EMG sensors to control six servo motors. The study will use only three EMG sensors to control/activate six servo motors by depending on the binary trick idea suggested by this study, which is manipulating all three input signals from EMG sensors at once and then decide which servo motor to activate by using a supervised machine learning technique such as K-nearest neighbors (kNN).

Conjugate gradient methods (CG) constitute excellent neural network training methods that are simplicity, flexibility, numerical efficiency, and low memory requirements. In this paper, we introduce a new three-term conjugate gradient method, for solving optimization problems and it has been tested on artificial neural networks (ANN) for training a feed-forward neural network. The new method satisfied the descent condition and sufficient descent condition. Global convergence of the new (NTTCG)method has been tested. The results of numerical experiences on some well-known test function shown that our new modified method is very effective,by relying on the number of functions evaluation and number of iterations,also included the numerical results for training feed-forward neural networks with other well-known method in this field.

Abstract—Extracting and tracking face in image sequences is a required first step in many applications such as face recognition facial expression classification and face tracking, it is a challenging problem in computer vision field because of many factors that effects on the image, some of these factors are luminosity, different face colors, background patterns, face orientation and variability in size, shape, and expression. The objective of this paper is to Experiment wide range of parameters for HOG face detector and setting up the most suitable kernel for Support Vector Machine (SVM) and then, comparing this method with some well-known methods for face detection and identifying the most reliable one. The aim of this study is not providing the best face detector method rather than a try to find out the performance of HOG feature for detecting a face, experimenting different kernels and eventually finding the tuned parameters for HOG descriptors for detecting a face, in this study based on experimental results as shown in Table IV. The HOG + SVM scores the highest value of precision, accuracy, and sensitivity. As 0.8824, 0.9986 and 0.75 respectively compared to Viola-Jones method which scores 0.6512, 0.9973 and 0.7 finally skin color method which scores 0.3968, 0.9947 and 0.625.