Chronic disease prediction presents ongoing challenges in healthcare, primarily due to the complexity of medical data and the need for models that are both accurate and interpretable. This study introduces a quantum-enhanced machine learning model specifically designed for the prediction of hypertension, combining quantum feature transformation with classical algorithms to...
Obesity is a critical global health challenge, characterized by its complex etiology and association with numerous chronic diseases. Leveraging machine learning (ML) techniques offers promising avenues for improving obesity classification and risk prediction. This study aims to evaluate the efficacy of various ML algorithms, including Decision Trees (DT), Extra Trees Classifier...
Classification of biomedical sounds using Artificial Intelligence (AI), especially the examination of heart sounds, is of great importance. However, existing feature extraction methods often fall short in performance due to their limited capacity for frequency analysis and potential information loss. This study proposes a novel feature extraction model called Dual Frequency...
We proposed and solved a thought-provoking Olympiad-level problem with the aim to introduce high-school students to real-life physics and engineering principles encompassing energy production-wise and environmentally relevant technologies. In the first part, we discussed the physics behind hydraulic jumps and embedded it in several heat engine applications comprising of a Pelton...
The effect of embedded plasmonic gold nanoparticles on the crater morphology was studied in 160 µm-thick UDMA–TEGDMA copolymer films irradiated by femtosecond single pulses of a Ti:Sa laser. The plasmonic absorption of the embedded gold nanorods had a resonance at the wavelength of the laser. It was observed that by increasing the laser intensity the diameter of the craters...
The study aimed to assess the discriminative capacity of a machine learning algorithm in distinguishing between individuals with Major Depressive Disorder and healthy controls based on a dataset collected during the performance of a Stroop Color and Word Test combined with an n-back component in functional magnetic resonance imaging. A total of 50 participants were recruited...
This work proposes a new mathematical model incorporating the interest rate, investment demand, price index, profit margin, and time-delay feedback factor. The linear stability of the system at each of the existing equilibria is examined, and it is found that the system undergoes a Hopf bifurcation through changing stability to instability around the equilibria due to the time...
This paper presents a novel approach to sustain transient chaos in the Lorenz system through the estimation of safety functions using a transformer-based model. Unlike classical methods that rely on iterative computations, the proposed model directly predicts safety functions without requiring fine-tuning or extensive system knowledge. The results demonstrate that this approach...
This mini-review examines functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) among opioid users. The goal is to summarize existing research data and clarify the implications of altered brain connectivity in this population. The first part of the review addresses the critical question of how opioid addiction influences the functional...
We use nanotechnology-improved targets for femtosecond laser pulse shots in order to take advantage of plasmonic effects when accelerating electrons and ions. We seek to reach proton energies sufficient for igniting nuclear fusion processes with the surrounding material. In particular, the pB reaction is aimed at, not producing primary neutrons, just alpha particles. This paper...
In this paper, we examine the wide-ranging impact of artificial intelligence on society, focusing on its potential to both help and harm global equity, cognitive abilities, and economic stability. We argue that while artificial intelligence offers significant opportunities for progress in areas like healthcare, education, and scientific research, its rapid growth—mainly driven by...
This special issue compiles 20 contributions, covering a wide range of latest achievements on dynamical modeling, data-driven algorithms, response predictions, multiple practical applications, and inverse problems. Data science plays a crucial role, helping us constructing more accurate dynamical models that capture and reflect the true dynamical changes of a system. At the same...
This editorial focuses on the special issue titled “Ultra-Wide-Bandgap Semiconductors”. It elaborates on the motivation for this special issue, introduces the application potentials and challenges of semiconductors such as gallium oxide, aluminum nitride, diamond, and cubic boron nitride in various fields, summarizes the research content of relevant papers, presents the current...
In this study, the resonant behaviour of gold nanoparticles in the dense medium under intense $$\sim$$ 1015–1017 W/cm2 laser irradiation by infrared pulses is explored. In particular, the enhancement of the energy absorption by the dopes in media is addressed. The particle-in-cell numeral method is used. Using numerical modelling with the EPOCH software, we investigate how...
Surface plasmon polaritons are the light of the nanoworld, with a broad spectrum of special properties. These properties open the field for a high number of applications, both in the fields of low and high intensities. In polymer samples, localized surface plasmon polaritons (LSPPs) have been resonantly excited by ultrashort (n. 10 fs), high intensity (up to n. 1017 W/cm2) pulses...
Radio antennas have become a standard tool for the detection of cosmic-ray air showers in the energy range above $$10^{16}\,$$ eV. The radio signal of these air showers is generated mostly due to the deflection of electrons and positrons in the geomagnetic field, and contains information about the energy and the depth of the maximum of the air showers. Unlike the traditional air...
We present the results of a novel type of numerical simulation that realizes a rotating Universe with a shear-free, rigid body rotation inspired by a Gödel-like metric. We run cosmological simulations of unperturbed glasses with various degrees of rotation in the Einstein–de Sitter and the $$\Lambda$$ CDM cosmologies. To achieve this, we use the StePS N-body code capable of...
High-intensity femtosecond laser irradiation experiments have been carried out on polymer targets doped with gold nanorods in order to study the occurrence of resonant plasmon field enhancement in the course of ion acceleration. The motivation is the exploitation of the resonant plasmonic effect for increasing the nuclear fusion rate in suitable targets, pursued by the...
In recent years, the study of neuronal models has provided significant insights into brain dynamics and neurological disorders. Map-based neuronal models, such as the Rulkov map, have gained considerable popularity due to their computational efficiency and ability to replicate complex neuronal dynamics. We thus here study the collective dynamics of an unidirectional ring network...
The origin of cosmic rays has remained an enduring mystery in astrophysics since their discovery by Victor Hess in 1912. However, extensive studies on the energy spectrum, mass composition, and angular distributions of cosmic rays have been conducted through both space-based and ground-based experiments. Although space-based experiments are limited to measuring cosmic rays with...
This special issue is dedicated to showcasing pioneering research that bridges multiple disciplines, fostering a holistic understanding of biocomplexity. This presents four important contributions that provide characterization, modeling, and design of methods focused on cardiovascular biocomplexity and dynamics; cognitive and neurological insights; computational and analytical...