Computation, Volume 11, Issue 11 (November 2023) – 26 articles

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

This article is devoted to methods of processing random processes. This task becomes particularly relevant in cases where the random process is broadband and non-stationary; then, the measurement of a random process can be associated with an assessment of its probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation with a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real-time pace (for example, radio telemetry systems in spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods, to determine probability characteristics in this case is not possible. In addition, the on-board computing systems of spacecraft operate under conditions of restrictions on mass-dimensional characteristics and energy consumption. Therefore, there is a need to apply accelerated methods of processing measured random processes. This article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall’s statistics is considered, as are methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. This article presents the results of statistical modeling using the Mathcad program. Full article

Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, which depend on the change in the physical characteristics of waveforms as they propagate to determine damage states. However, the complexity of geometry and material discontinuity (e.g., the roughness of a weldment with or without defects) could lead to complicated wave reflection and scatters, thus increasing the difficulty in the signal processing. Artificial intelligence and machine learning exhibit their capability for data fusion, including processing signals originally from ultrasonic-guided waves. This study aims to utilize deep learning approaches, including a convolutional neural network (CNN), Long-short term memory network (LSTM), or hybrid CNN-LSTM model, to demonstrate the capability in automation for damage detection for pipes with welded joints embedded in soil. The damage features in terms of welding defect types and severity as well as multiple defects are used to understand the effectiveness of the hybrid CNN-LSTM model, which is further compared to the two commonly used deep learning approaches, CNN and LSTM. The results showed the hybrid CNN-LSTM model has much higher classification accuracy for damage states under all scenarios in comparison with the CNN and LSTM models. Furthermore, the impacts of the pipelines embedded in different types of materials, ranging from loose sand to stiff soil, on signal processing and data classification were further calibrated. The results demonstrated these deep learning approaches can still perform well to detect various pipeline damage under varying embedment conditions. However, the results demonstrate when concrete is used as an embedding material, high attention to absorbing the signal energy of concrete could pose a challenge for the signal processing, particularly under high noise levels. Full article

In this paper, we study a model that enhances our understanding of cytokine-influenced HIV-1 infection. The impact of adaptive immune response (cytotoxic T lymphocytes (CTLs) and antibodies) and time delay on HIV-1 infection is included. The model takes into account two types of distributional delays, (i) the delay in the HIV-1 infection of CD4⁺T cells and (ii) the maturation delay of new virions. We first investigated the fundamental characteristics of the system, then found the system’s equilibria. We derived five threshold parameters, ${\Re }_i$, i = 0, 1,…, 4, which completely determine the existence and stability of the equilibria. The Lyapunov method was used to prove the global asymptotic stability for all equilibria. We illustrate the theoretical results by performing numerical simulations. We also performed a sensitivity analysis on the basic reproduction number ${\Re }_0$ and identified the most-sensitive parameters. We found that pyroptosis contributes to the number ${\Re }_0$, and then, neglecting it will make ${\Re }_0$ underevaluated. Necrosulfonamide and highly active antiretroviral drug therapy (HAART) can be effective in preventing pyroptosis and at reducing viral replication. Further, it was also found that increasing time delays can effectively decrease ${\Re }_0$ and, then, inhibit HIV-1 replication. Furthermore, it is shown that both CTLs and antibody immune responses have no effect on ${\Re }_0$, while this can result in less HIV-1 infection. Full article

Roboat is an autonomous surface vessel (ASV) for urban waterways, developed as a research project by the AMS Institute and MIT. The platform can provide numerous functions to a city, such as transport, dynamic infrastructure, and an autonomous waste management system. This paper presents the development of a learning-based controller for the Roboat platform with the goal of achieving robustness and generalization properties. Specifically, when subject to uncertainty in the model or external disturbances, the proposed controller should be able to track set trajectories with less tracking error than the current nonlinear model predictive controller (NMPC) used on the ASV. To achieve this, a simulation of the system dynamics was developed as part of this work, based on the research presented in the literature and on the previous research performed on the Roboat platform. The simulation process also included the modeling of the necessary uncertainties and disturbances. In this simulation, a trajectory tracking agent was trained using the proximal policy optimization (PPO) algorithm. The trajectory tracking of the trained agent was then validated and compared to the current control strategy both in simulations and in the real world. Full article

An efficient modified leapfrog time-marching scheme for the diblock copolymer model is investigated in this paper. The proposed scheme offers three main advantages. Firstly, it is linear in time, requiring only a linear algebra system to be solved at each time-marching step. This leads to a significant reduction in computational cost compared to other methods. Secondly, the scheme ensures unconditional energy stability, allowing for a large time step to be used without compromising solution stability. Thirdly, the existence and uniqueness of the numerical solution at each time step is rigorously proven, ensuring the reliability and accuracy of the method. A numerical example is also included to demonstrate and validate the proposed algorithm, showing its accuracy and efficiency in practical applications. Full article

Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently allocate DGs within a distribution test system. The study focuses on wind turbines, photovoltaic units, and fuel cells as prominent DG due to their growing usage trends. The optimization goals include minimizing voltage losses, reducing costs, and mitigating greenhouse gas emissions in the distribution system. The proposed algorithm is implemented and evaluated on the IEEE 70-bus test system, with a comparative analysis conducted against other evolutionary methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Honey Bee Mating Optimization (HBMO), and Teaching-Learning-Based Optimization (TLBO) algorithms. Results indicate that the proposed algorithm is effective in allocating the DGs. Statistical testing confirms significant results (probability < 0.1), indicating superior optimization capabilities for this specific problem. Crucially, the proposed algorithm excels in both accuracy and computational speed compared to other methods studied. Full article

This paper analyzes the interaction of wind turbines and losses in wind farms using computational fluid dynamics (CFD). The mathematical model used consisted of three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations, while the presence of wind turbines in the flow was simulated as additional source terms. The novelty of the research is the definition of the source term as a velocity-dependent actuator disc model (ADM). This allowed for modeling the operation of a wind farm consisting of real wind turbines, characterized by power coefficients $C_p$ and thrust force coefficients $C_T$, which are a function of atmospheric wind speed. The calculations presented used a real 5 MW Gamesa turbine. Two different turbine spacings, 5D and 10D, where D is the diameter of the turbine, and two different locations corresponding to the offshore and onshore conditions were examined. The proposed model can be used to analyze wind farm losses not only in terms of the geometric distribution of individual turbines but also in terms of a specific type of wind turbine and in the entire wind speed spectrum. Full article

This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on Clustal$\Omega$, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal choice for large-scale genomic analyses. The comparative performance of MSAs assembled using MergeAlign demonstrates that MAFFT and MUSCLE consistently exhibit higher accuracy than Clustal$\Omega$ in both reference-based and consensus-based approaches. The evaluation of genotyping effectiveness reveals that the addition of a reference sequence, such as the SARS-CoV-2 Wuhan-Hu-1 isolate, does not significantly affect the alignment process, suggesting that using consensus sequences derived from individual MSA alignments may yield comparable genotyping outcomes. Investigating single-nucleotide polymorphisms (SNPs) and mutations highlights distinctive features of MSA programs. Clustal$\Omega$ and MAFFT show similar counts, while MUSCLE displays the highest SNP count. High-frequency SNP analysis identifies MAFFT as the most accurate MSA program, emphasizing its reliability. Comparisons between Saudi and global SARS-CoV-2 populations underscore regional genetic variations. Saudis exhibit consistently higher frequencies of high-frequency SNPs, attributed to genetic similarity within the population. Transmission dynamics analysis reveals a higher frequency of co-mutations in the Saudi dataset, suggesting shared evolutionary patterns. These findings emphasize the importance of considering regional diversity in genetic analyses. Full article

Identifying the relationship between human mobility, air pollution, and communicable disease poses a challenge for impact evaluation and public health planning. Specifically, Coronavirus disease 2019 (COVID-19) and air pollution from fine particulates (PM2.5), by which human mobility is mediated in a public health emergency. To describe the interplay between human mobility and PM2.5 during the spread of COVID-19, we proposed a nonlinear model of the time-dependent transmission rate as a function of these factors. A compartmental epidemic model, together with daily confirmed case data in Bangkok, Thailand during 2020–2021, was used to estimate the intrinsic parameters that can determine the impact on the transmission dynamic of the two earlier outbreaks. The results suggested a positive association between mobility and transmission, but this was strongly dependent on the context and the temporal characteristics of the data. For the ascending phase of an epidemic, the estimated coefficient of mobility variable in the second wave was greater than in the first wave, but the value of the mobility component in the transmission rate was smaller. Due to the influence of the baseline value and PM2.5, the estimated basic reproduction number of the second wave was higher than that of the first wave, even though mobility had a greater influence. For the descending phase, the value of the mobility component in the second wave was greater, due to the negative value of the estimated mobility coefficient. Despite this scaling effect, the results suggest a negative association between PM2.5 and the transmission rates. Although this conclusion agrees with some previous studies, the true effect of PM2.5 remains inconclusive and requires further investigation. Full article

The paper focuses on predicting the next purchase day (NPD) for customers in e-commerce, a task with applications in marketing, inventory management, and customer retention. A novel transformer-based model for NPD prediction is introduced and compared to traditional methods such as ARIMA, XGBoost, and LSTM. Transformers offer advantages in capturing long-term dependencies within time series data through self-attention mechanisms. This adaptability to various time series patterns, including trends, seasonality, and irregularities, makes them a promising choice for NPD prediction. The transformer model demonstrates improvements in prediction accuracy compared to the baselines. Additionally, a clustered transformer model is proposed, which further enhances accuracy, emphasizing the potential of this architecture for NPD prediction. Full article

The authors suggest a methodology that involves conducting a preliminary analysis of inertia in financial time series. Inertia here means the manifestation of some kind of long-term memory. Such effects may take place in complex processes of a stochastic kind. If the decision is negative, they do not recommend using predictive management strategies based on trend analysis. The study uses computational schemes to detect and confirm trends in financial market data. The effectiveness of these schemes is evaluated by analyzing the frequency of trend confirmation over different time intervals and with different levels of trend confirmation. Furthermore, the study highlights the limitations of using smoothed curves for trend analysis due to the lag in the dynamics of the curve, emphasizing the importance of considering real-time data in trend analysis for more accurate predictions. Full article