A new approach for solving nonlinear Thomas-Fermi equation based on fractional order of rational Bessel functions

In this paper, the fractional order of rational Bessel functions collocation method (FRBC) to solve Thomas-Fermi equation which is defined in the semi-infinite domain and has singularity at $x = 0$ and its boundary condition occurs at infinity, have been introduced. We solve the problem on semi-infinite domain without any domain truncation or transformation of the domain of the problem to a finite domain. This approach at first, obtains a sequence of linear differential equations by using the quasilinearization method (QLM), then at each iteration solves it by FRBC method. To illustrate the reliability of this work, we compare the numerical results of the present method with some well-known results in other to show that the new method is accurate, efficient and applicable

Real particle geodesics and thermodynamics of a black hole in Regular Schwarzschild-Anti de Sitter space-time

In this work, we illustrate the geodesics of real particles obtained numerically in a Regular Schwarzschild Anti-de Sitter (RSch-AdS) space-time. The behavior of these geodesics are considered depending on variation of effective parameters such as mass distribution, angular momentum and cosmological constant. Also, using the laws of thermodynamics of black holes, we will study and discuss some aspect of black hole (BH) described by this spacetime such as temperature, entropy, heat capacity and Gibbs free energy.Comment: 22 pages, 26 figure

Velocity Curve Analysis of the Spectroscopic Binary Stars V373 Cas, V2388 Oph, V401 Cyg, GM Dra, V523 Cas, AB And, and HD 141929 by Artificial Neural Networks

We used an Artificial Neural Network (ANN) to derive the orbital parameters of spectroscopic binary stars. Using measured radial velocity data of seven double-lined spectroscopic binary systems V373 Cas, V2388 Oph, V401 Cyg, GM Dra, V523 Cas, AB And, and HD 141929, we found corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by others using more traditional methods.Comment: 13 pages, 8 figures, 14 Table

Comparison of health-related quality of life after percutaneous coronary intervention and coronary artery bypass surgery

BACKGROUND: Health-related quality of life (HRQOL) evaluation is an important measure of the impact of the disease. As more people with coronary heart disease (CHD) live longer, doctors and researchers want to know how they manage in day to day life. It looked like adults with CHD had a decrease QOL. The aim of this study was to comparison of HRQOL of patients who underwent percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG) and to assess its main determinants in the whole sample of coronary artery disease (CAD) patients. METHODS: The study was carried out to estimate HRQOL of 109 patients who underwent invasive coronary revascularization PCI (n = 75) and CABG (n = 34). We applied HRQOL after 6 months and 2 years in both groups and scores were compared. The HRQOL data were obtained using MacNew Heart Disease questionnaire with dimensions emotional, physical and social that estimated. Data entry and analysis were performed by SPSS 16.0. RESULTS: A total MacNew scale in CABG and PCI group in 6 months after treatment were 45.32 ± 13.75 and 53.52 ± 15.63, respectively (P = 0.0100). After 2 years HRQOL mean changed to 51.176 ± 14.80 and 49.55 ± 16.22, respectively, in CABG and PCI group (P = 0.4280). Our results in within-group analysis showed total MacNew scale and its subscales were changed significantly after 2 years in CABG and PCI group�s scores were detected. We found in the whole sample of CAD patients those who had a higher level of income and education and were not either overweight or obese experienced better HRQOL. CONCLUSION: Our results showed that patients who underwent PCI experienced significantly higher HRQOL in 6 months after revascularization but over 24 months follow-up no difference was observed between the two groups. © 2016, Isfahan University of Medical Sciences(IUMS). All rights reserved

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.Comment: 10 pages, 7 figures, 2 table

A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.Comment: Submitted to the Journal of Biomedical Informatic

Insights into the primary radiation damage of silicon by a machine learning interatomic potential

We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials. Impact statement Quantum-mechanical level of accuracy in simulation of primary damage was achieved by a silicon machine learning potential. The results show quantitative and qualitative differences from the damage predicted by any previous models.Peer reviewe

Radiobiological evaluation of combined gamma knife radiosurgery and hyperthermia for pediatric neuro-oncology

Combining radiotherapy (RT) with hyperthermia (HT) has been proven effective in the treatment of a wide range of tumours, but the combination of externally delivered, focused heat and stereotactic radiosurgery has never been investigated. We explore the potential of such treatment enhancement via radiobiological modelling, specifically via the linear-quadratic (LQ) model adapted to thermoradiotherapy through modulating the radiosensitivity of temperature-dependent parame-ters. We extend this well-established model by incorporating oxygenation effects. To illustrate the methodology, we present a clinically relevant application in pediatric oncology, which is novel in two ways. First, it deals with medulloblastoma, the most common malignant brain tumour in children, a type of brain tumour not previously reported in the literature of thermoradiotherapy studies. Second, it makes use of the Gamma Knife for the radiotherapy part, thereby being the first of its kind in this context. Quantitative metrics like the biologically effective dose (BED) and the tumour control probability (TCP) are used to assess the efficacy of the combined plan

Time to first recurrence, pattern of recurrence, and survival after recurrence in endometrial cancer according to the molecular classification.

OBJECTIVE Despite its generally favorable prognosis at primary diagnosis, recurrence of endometrial cancer remains an important clinical challenge. The aim of this study was to analyze the value of molecular classification in recurrent endometrial cancer. METHODS This study included patients with recurrent endometrial cancer who underwent primary surgical treatment between 2004 and 2015 at the Karolinska University Hospital, Sweden and the Bern University Hospital, Switzerland (KImBer cohort) with molecular classification of the primary tumor. RESULTS Out of 594 molecularly classified endometrial cancer patients, 101 patients experienced recurrence, consisting of 2 POLEmut, 33 MMRd, 30 p53abn, and 36 NSMP tumors. Mean age at recurrence was 71 years and mean follow-up was 54 months. Overall, median time to first recurrence was 16 months (95% CI 12-20); with the shortest median time in MMRd patients, with 13 months (95% CI 5-21). The pattern of recurrence was distinct among molecular subgroups: MMRd tumors experienced more locoregional, while p53abn cases showed more abdominal recurrences (P = .042). Median survival after recurrence was best for MMRd cases (43 months, 95% CI 11-76), compared to 39 months (95% CI 21-57) and 10 months (95% CI 7-13) for the NSMP and p53abn cases respectively (log-rank, P = .001). CONCLUSION Molecular classification is a significant indicator of survival after recurrence in endometrial cancer patients, and patterns of recurrence differ by molecular subgroups. While MMRd endometrial cancer show more locoregional recurrence and the best survival rates after recurrence, p53abn patients experience abdominal recurrence more often and had the worst prognosis of all recurrent patients