Computation

This study examines how U.S. senators strategically used hashtags to create political communities on Twitter during the 2022 Midterm Elections. We propose a way to model topic-based implicit interactions among Twitter users and introduce the concept of Building Political Hashtag Communities (BPHC). Using multiplex network analysis, we provide a comprehensive view of elites’ behavior. Through AI-driven topic modeling on real-world data, we observe that, at a general level, Democrats heavily rely on BPHC. Yet, when disaggregating the network across layers, this trend does not uniformly persist. Specifically, while Republicans engage more intensively in BPHC discussions related to immigration, Democrats heavily rely on BPHC in topics related to identity and women. However, only a select group of Democratic actors engage in BPHC for topics on labor and the environment—domains where Republicans scarcely, if at all, participate in BPHC efforts. This research contributes to the understanding of digital political communication, offering new insights into echo chamber dynamics and the role of politicians in polarization. Full article

Stochastic circuits are used in applications that require low area and power consumption. The computing performed using these circuits is referred to as Stochastic computing (SC). The arithmetic operations in this computing can be realized using minimum logic circuits. The SC system allows a tradeoff of computational accuracy and area; thereby, the challenge in SC is improving the accuracy. The accuracy depends on the SC system’s stochastic number generator (SNG) part. SNGs provide the appropriate stochastic input required for stochastic computation. Hence we explore the accuracy in SC for various arithmetic operations performed using stochastic computing with the help of logic circuits. The contributions in this paper are; first, we have performed stochastic computing for arithmetic components using two different SNGs. The SNGs considered are Linear Feed-back Shift Register (LFSR) -based traditional stochastic number generators and S-box-based stochastic number generators. Second, the arithmetic components are implemented in a combinational circuit for algebraic expression in the stochastic domain using two different SNGs. Third, computational analysis for stochastic arithmetic components and the stochastic algebraic equation has been conducted. Finally, accuracy analysis and measurement are performed between LFSR-based computation and S-box-based computation. The novel aspect of this work is the use of S-box-based SNG in the development of stochastic computing in arithmetic components. Also, the implementation of stochastic computing in the combinational circuit using the developed basic arithmetic components, and exploration of accuracy with respect to stochastic number generators used is presented. Full article

The baking process in tunnel ovens can be influenced by many parameters. Among these, the most important can be considered as: the baking time, the volume of dough pieces, the texture and humidity of the dough, the distribution of temperature inside the oven, as well as the flow of air currents applied in the baking chamber. In order to obtain a constant quality of bakery or pastry products, and for the efficient operation of the oven, it is necessary that the solution made by the designers be subjected to modelling, simulation and analysis processes, before their manufacture, and in this sense it can be applied to the Computational Fluid Dynamics (CFD) numerical simulation tool. In this study, we made an analysis of the air flow inside the baking chamber of an oven. The analyzed oven was used very frequently on the pastry lines. After performing the modelling and simulation, the temperature distribution inside the oven was obtained in the longitudinal and transverse planes. For the experimental validation of the temperatures obtained in the computer-assisted simulation, the temperatures inside the analyzed electric oven were measured. The measured temperatures validated the simulation results with a maximum error of 7.6%. Full article

SO₂ (sulfur dioxide) is a toxic substance emitted into the environment due to burning sulfur-containing fossil fuels in cars, factories, power plants, and homes. This issue is of grave concern because of its negative effects on the environment and human health. Therefore, the search for a material capable of interacting to detect SO₂ and the research on developing effective materials for gas detection holds significant importance in the realm of environmental and health applications. It is well known that one of the effective methods for predicting the structure and electronic properties of systems capable of interacting with a molecule is a method based on quantum mechanical approaches. In this work, the DFT (Density Functional Theory) program DMol³ in Materials Studio was used to study the interactions between the SO₂ molecule and four systems. The adsorption energy, bond lengths, bond angle, charge transfer, and density of states of SO₂ molecule on pristine graphene, N-doped graphene, Ga-doped graphene, and -Ga-N- co-doped graphene were investigated using DFT calculations. The obtained data indicate that the bonding between the SO₂ molecule and pristine graphene is relatively weak, with a binding energy of −0.32 eV and a bond length of 3.06 Å, indicating physical adsorption. Next, the adsorption of the molecule on an N-doped graphene system was considered. The adsorption of SO₂ molecules on N-doped graphene is negligible; generally, the interaction of SO₂ molecules with this system does not significantly change the electronic properties. However, the adsorption energy of the gas molecule on Ga-doped graphene relative to pristine graphene increased significantly. The evidence of chemisorption is increased adsorption energy and decreased adsorption distance between SO₂ and Ga-doped graphene. In addition, our results show that introducing -Ga-N- co-dopants of an “ortho” configuration into pristine graphene significantly affects the adsorption between the gas molecule and graphene. Thus, this approach is significantly practical in the adsorption of SO₂ molecules. Full article

During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on 3 October 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant. Full article

A full potential all-electron density functional method within generalized gradient approximation is used herein to investigate correlations of the electronic, elastic and thermo-electric transport properties of cubic copper sulphide and copper selenide. The electronic band structure and density of states suggest a metallic behaviour with a zero-energy band gap for both materials. Elastic property calculations suggest stiff materials, with bulk to shear modulus ratios of 0.35 and 0.44 for Cu₂S and Cu₂Se, respectively. Thermo-electric transport properties were estimated using the Boltzmann transport approach. The Seebeck coefficient, electrical conductivity, thermal conductivity and power factor all suggest a potential p-type conductivity for α-Cu₂S and n-type conductivity for α-Cu₂Se. Full article

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination, $R^2$, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while $gC{O}_{2}eq$ represents the amount of CO${}_2$ emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science. Full article

Analyzing stochastic algorithms for comprehensive performance and comparison across diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a wide spectrum of test functions, including both classical benchmarks and CEC-C06 2019 conference functions, distinct patterns of performance emerge. In specific situations, underscoring the importance of choosing algorithms contextually. Additionally, researchers have encountered a critical issue by employing a statistical model randomly to determine significance values without conducting other studies to select a specific model for evaluating performance outcomes. To address this concern, this study employs rigorous statistical testing to underscore substantial performance variations between pairs of algorithms, thereby emphasizing the pivotal role of statistical significance in comparative analysis. It also yields valuable insights into the suitability of algorithms for various optimization challenges, providing professionals with information to make informed decisions. This is achieved by pinpointing algorithm pairs with favorable statistical distributions, facilitating practical algorithm selection. The study encompasses multiple nonparametric statistical hypothesis models, such as the Wilcoxon rank-sum test, single-factor analysis, and two-factor ANOVA tests. This thorough evaluation enhances our grasp of algorithm performance across various evaluation criteria. Notably, the research addresses discrepancies in previous statistical test findings in algorithm comparisons, enhancing result reliability in the later research. The results proved that there are differences in significance results, as seen in examples like Leo versus the FDO, the DA versus the WOA, and so on. It highlights the need to tailor test models to specific scenarios, as p-value outcomes differ among various tests within the same algorithm pair. Full article

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE) to include constrained optimization, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a differential amplifier, a two-stage operational amplifier (op-amp), and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works. The Ts-CPD implementation was performed in Matlab using Ngspice and can be found on GitHub (see Data Availability Statement). Full article

In this work, anomalous solute transport using adsorption effects and the decomposition of solute was studied. During the filtration of inhomogeneous liquids, a number of new phenomena arise, and this is very important for understanding the mechanisms of the filtration process. Recently, issues of mathematical modeling of substance transfer processes have been intensively discussed. Modeling approaches are based on the law of matter balance in a certain control volume using additional phenomenological relationships. The process of anomalous solute transport in a porous medium was modeled by differential equations with a fractional derivative. A new mobile—immobile model is proposed to describe anomalous solute transport with a scale-dependent dispersion in inhomogeneous porous media. The profiles of changes in the concentrations of suspended particles in the macropore and micropore were determined. The influence of the order of the derivative with respect to the coordinate and time, i.e., the fractal dimension of the medium, was estimated based on the characteristics of the solute transport in both zones. The hydrodynamic dispersion was set through various relations: constant, linear, and exponential. Based on the numerical results, the concentration fields were determined for different values of the initial data and different relations of hydrodynamic dispersion. Full article

In this paper, we present an innovative approach to solve a system of boundary value problems (BVPs), using the newly developed discontinuous Galerkin (DG) method, which eliminates the need for auxiliary variables. This work is the first in a series of papers on DG methods applied to partial differential equations (PDEs). By consecutively applying the DG method to each space variable of the PDE using the method of lines, we transform the problem into a system of ordinary differential equations (ODEs). We investigate the convergence criteria of the DG method on systems of ODEs and generalize the error analysis to PDEs. Our analysis demonstrates that the DG error’s leading term is determined by a combination of specific Jacobi polynomials in each element. Thus, we prove that DG solutions are superconvergent at the roots of these polynomials, with an order of convergence of $O\left(h^{p+2}\right)$. Full article

This study introduces an approach for modeling an arm of a Stewart platform to analyze the location of sections with a high deflection among the arms. Given the dynamic nature of the Stewart platform, its arms experience static and dynamic loads. The static loads originate from the platform’s own weight components, while the dynamic loads arise from the movement or holding of equipment in a specific position using the end-effector. These loads are distributed among the platform arms. The arm encompasses various design categories, including spring-mass, spring-mass-damper, mass-actuator, and spring-mass-actuator. In accordance with these designs, joint points should be strategically placed away from critical sections where maximum buckling or deformation is prominent. The current study presents a novel model employing Euler’s formula, a fundamental concept in buckling analysis, to propose this approach. The results align with experimental and numerical reports in the literature that prove the internal force of the platform arm is affecting the arm stiffness. The equal stiffness of an arm is related to its internal force and its deflection. The study demonstrates how higher levels of dynamic loading influence the dynamic platform, causing variations in the maximum arm’s buckling deflection, its precise location, and the associated deflection slope. Notably, in platform arms capable of adjusting their tilt angles relative to the vertical axis, the angle of inclination directly correlates with deflection and its gradient. The assumption of linearity in Euler’s formula seems to reveal distinctive behavior in deflection gradients concerning dynamic mechanisms. Full article

We propose a within-host mathematical model for the dynamics of Usutu virus infection, incorporating Crowley–Martin functional response. The basic reproduction number ${\mathfrak{R}}_0$ is found by applying the next-generation matrix approach. Depending on this threshold, parameter, global asymptotic stability of one of the two possible equilibria is also established via constructing appropriate Lyapunov functions and using LaSalle’s invariance principle. We present numerical simulations to illustrate the results and a sensitivity analysis of ${\mathfrak{R}}_0$ was also completed. Finally, we fit the model to actual data on Usutu virus titers. Our study provides new insights into the dynamics of Usutu virus infection. Full article

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects the nerve cells in the brain and spinal cord. This condition leads to the loss of motor skills and, in many cases, the inability to speak. Decoding spoken words from electroencephalography (EEG) signals emerges as an essential tool to enhance the quality of life for these patients. This study compares two classification techniques: (1) the extraction of spectral power features across various frequency bands combined with support vector machines (PSD + SVM) and (2) EEGNet, a convolutional neural network specifically designed for EEG-based brain–computer interfaces. An EEG dataset was acquired from 32 electrodes in 28 healthy participants pronouncing five words in Spanish. Average accuracy rates of 91.04 ± 5.82% for Attention vs. Pronunciation, 73.91 ± 10.04% for Short words vs. Long words, 81.23 ± 10.47% for Word vs. Word, and 54.87 ± 14.51% in the multiclass scenario (All words) were achieved. EEGNet outperformed the PSD + SVM method in three of the four classification scenarios. These findings demonstrate the potential of EEGNet for decoding words from EEG signals, laying the groundwork for future research in ALS patients using non-invasive methods. Full article

This article offers experimental studies and a new methodology for analyzing the influence of micro-stresses on human operator activity in man–machine information and search interfaces. Human-centered design is a problem-solving technique that puts real people at the center of the design process. Therefore, mindfulness is one of the most important aspects in various fields such as medicine, industry, and decision-making. The human-operator activity model can be used to create a database of specialized test images and a computer for its implementation. The peculiarity of the tests is that they represent images of real work situations obtained as a result of texture stylization and allow the use of an appropriate search difficulty scale. A mathematical model of a person who makes a decision is built. The requirements for creating a switch to solve the given problem are discussed. This work summarizes the accumulated experience of such studies. Full article

This article explores the accessibility of symbolic computations, such as using the Wolfram Mathematica environment, in promoting the shift from informal experimentation to formal mathematical justifications. We investigate the accuracy of computational results from mathematical software in the context of a certain summation in trigonometry. In particular, the key issue addressed here is the calculated sum ${\sum }_{n=0}^{44}{\tan \left(1+4n\right)}^{°}.$ This paper utilizes Wolfram Mathematica to handle the irrational numbers in the sum more accurately, which it achieves by representing them symbolically rather than using numerical approximations. Can we rely on the calculated result from Wolfram, especially if almost all the addends are irrational, or must the students eventually prove it mathematically? It is clear that the problem can be solved using software; however, the nature of the result raises questions about its correctness, and this inherent informality can encourage a few students to seek viable mathematical proofs. In this way, a balance is reached between formal and informal mathematics. Full article

The section of the Mantaro River that flows through the department of Huancavelica, Peru, has been affected by toxic wastes and mineral residues from industrial and mining activities, which have directly impacted the water quality. In this work, a grey system model, based on the grey clustering method, was used to assess water quality. The grey clustering method was applied using the central point of triangular whitening weight functions (CTWF). In addition, the Prati index and the Environmental Quality Standards for water from the Peru government were revised and used for this study. In the case study, six physicochemical parameters, pH, DO, BOD, Cd, As, and Pb, at nine monitoring points were assessed along the Mantaro River. The results showed that the sixth monitoring point (P6), which is influenced by mining activity, was highly contaminated, while the other points were classified as noncontaminated. Finally, the results obtained by applying the grey clustering method can be useful to competent authorities, for decision making on water management in this watershed. Full article

Background: The COVID-19 pandemic has profoundly transformed the global scenario, marked by overwhelming infections, fatalities, overburdened healthcare infrastructures, economic upheavals, and significant lifestyle modifications. Concurrently, the Russian full-scale invasion of Ukraine on 24 February 2022, triggered a severe humanitarian and public health crisis, leading to healthcare disruptions, medical resource shortages, and heightened emergency care needs. Italy emerged as a significant refuge for displaced Ukrainians during this period. Aim: This research aims to discern the impact of the Russian full-scale invasion of Ukraine on the COVID-19 transmission dynamics in Italy. Materials and Methods: The study employed advanced simulation methodologies, particularly those integrating machine learning, to model the pandemic’s trajectory. The XGBoost algorithm was adopted to construct a predictive model for the COVID-19 epidemic trajectory in Italy. Results: The model demonstrated a commendable accuracy of 86.03% in forecasting new COVID-19 cases in Italy over 30 days and an impressive 96.29% accuracy in estimating fatalities. When applied to the initial 30 days following the escalation of the conflict (24 February 2022, to 25 March 2022), the model’s projections suggested that the influx of Ukrainian refugees into Italy did not significantly alter the country’s COVID-19 epidemic course. Discussion: While simulation methodologies have been pivotal in the pandemic response, their accuracy is intrinsically linked to data quality, assumptions, and modeling techniques. Enhancing these methodologies can further their applicability in future public health emergencies. The findings from the model underscore that external geopolitical events, such as the mass migration from Ukraine, did not play a determinative role in Italy’s COVID-19 epidemic dynamics during the study period. Conclusion: The research provides empirical evidence negating a substantial influence of the Ukrainian refugee influx due to the Russian full-scale invasion on the COVID-19 epidemic trajectory in Italy. The robust performance of the developed model affirms its potential value in public health analyses. Full article

Antioxidants are molecules that neutralize free radicals. In general, the reaction mechanisms of antioxidants are well known. The main reaction mechanisms of antioxidants are electron transfer (ET), hydrogen transfer (HT), and radical adduction formation (RAF). The study of these mechanisms is helpful in understanding how antioxidants control high free radical levels in the cell. There are many studies focused on determining the main mechanism of an antioxidant to neutralize a wide spectrum of radicals, mainly reactive oxygen species (ROS)-type radicals. Most of these antioxidants are polyphenol-type compounds. Some esters, amides, and metal antioxidants have shown antioxidant activity, but there are few experimental and theoretical studies about the antioxidant reaction mechanism of these compounds. In this work, we show the reaction mechanism proposed for two esters, 11, tri-butyl p-coumarate and its tri-butyl-tin p-coumarate counterpart, using Sn(IV). We show how Sn(IV) increases the electron transfer in polar media and the H transfer in non-polar media. Even though the nature of esters could be polar or non-polar compounds, the antioxidant activity is good for the Sn(IV)-p-coumarate complex in non-polar media. Full article