Publikační činnost IT4Innovations / Publications of IT4Innovations (9600)

Permanent URI for this collectionhttp://hdl.handle.net/10084/96109

Browse

Now showing 1 - 20 out of 905 results

Stochastic dynamics and control in nonlinear waves with Darboux transformations, quasi-periodic behavior, and noise-induced transitions
(MDPI, 2026) Jhangeer, Adil; Imran, Mudassar
Stochastically forced nonlinear wave systems are commonly associated with complex dynamical behavior, although little is known about the general interaction of nonlinear dispersion, irrational forcing frequencies, and multiplicative noise. To fill this gap, we consider a generalized stochastic SIdV equation and examine the effects of deterministic and stochastic influences on the long-term behavior of the equation. The PDE was modeled using a stochastic traveling-wave transformation that simplifies it into a planar system, which was studied using Darboux-seeded constructions, Poincar & eacute; maps, bifurcation patterns, Lyapunov exponents, recurrence plots, and sensitivity diagnostics. We discovered that natural, implicit, and unique seeds produce highly diverse transformed wave fields exhibiting both irrational and golden-ratio forcing, controlling the transition from quasi-periodicity to chaos. Stochastic perturbation is demonstrated to suppress as well as to amplify chaotic states, based on noise levels, altering attractor geometry, predictability, and multistability. Meanwhile, OGY control is demonstrated to be able to stabilize chosen unstable periodic orbits of the double-well regime. A stochastic bifurcation analysis was performed with respect to noise strength sigma, revealing that the attractor structure of the system remains robust under stochastic excitation, with noise inducing only bounded fluctuations rather than qualitative dynamical transitions within the investigated parameter regime. These findings demonstrate that the emergence, deformation, and controllability of complex oscillatory patterns of stochastic nonlinear wave models are jointly controlled by nonlinear structure, external forcing, and noise.
Measuring the energy for the molecular graphs of antiviral agents: Hydroxychloroquine, Chloroquine and Remdesivir
(Elsevier, 2024) Aftab, Muhammad Haroon; Akgül, Ali; Riaz, Muhammad Bilal; Hussain, Muhammad; Jebreen, Kamel; Kanj, Hassan, Hassan
We consider the energy for the molecular graphs of antiviral agents like Hydroxychloroquine, Remdesivir and Chloroquine. These drugs play a vital role in the treatment of COVID-19. Let Gamma(1), Gamma(2) and Gamma(3) be the n-dimensional graphs of the molecular structures of antiviral agents Hydroxychloroquine, Chloroquine and Remdesivir, respectively. We define their energies as E '(Gamma(1)) = Sigma vertical bar lambda(i)'vertical bar, E '(Gamma 2) = Sigma vertical bar lambda(j)'vertical bar and E '(Gamma 3) = Sigma vertical bar lambda(k)'vertical bar, respectively. Where the sets {lambda(1)'(Gamma(1)), lambda(2)'(Gamma(1)), lambda(3)'(Gamma(1)), ..., lambda(n)'(Gamma(1))}, {lambda(1)'(Gamma(2)), lambda(2)'(Gamma(2)), lambda(3)'(Gamma(2)), ..., lambda(n)'(Gamma(2))} and { lambda(1)'(Gamma 3), lambda(2)'(Gamma 3), lambda(3)'(Gamma 3), ..., lambda(n)'(Gamma 3)} depict the eigenvalues for the adjacency matrices of Gamma 1, Gamma 2 and Gamma 3, respectively. We have developed some basic ideas and properties in order to measure the energies for the antiviral agents Hydroxychloroquine, Chloroquine and Remdesivir.
Phosphoric acid salts of amino acids as a source of oligopeptides on the early Earth
(Springer Nature, 2024) Šponer, Judit E.; Coulon, Rémi; Otyepka, Michal; Šponer, Jiří; Siegle, Alexander F.; Trapp, Oliver; Ślepokura, Katarzyna; Zdráhal, Zbyněk; Šedo, Ondrej
Because of their unique proton-conductivity, chains of phosphoric acid molecules are excellent proton-transfer catalysts. Here we demonstrate that this property could have been exploited for the prebiotic synthesis of the first oligopeptide sequences on our planet. Our results suggest that drying highly diluted solutions containing amino acids (like glycine, histidine and arginine) and phosphates in comparable concentrations at elevated temperatures (ca. 80 degrees C) in an acidic environment could lead to the accumulation of amino acid:phosphoric acid crystalline salts. Subsequent heating of these materials at 100 degrees C for 1-3 days results in the formation of oligoglycines consisting of up to 24 monomeric units, while arginine and histidine form shorter oligomers (up to trimers) only. Overall, our results suggest that combining the catalytic effect of phosphate chains with the crystalline order present in amino acid:phosphoric acid salts represents a viable solution that could be utilized to generate the first oligopeptide sequences in a mild acidic hydrothermal field scenario. Further, we propose that crystallization could help overcoming cyclic oligomer formation that is a generally known bottleneck of prebiotic polymerization processes preventing further chain growth.
Introducing the new arcsine-generator distribution family: An in-depth exploration with an illustrative example of the inverse weibull distribution for analyzing healthcare industry data
(Elsevier, 2024) Sindhu, Tabassum Naz; Shafiq, Anum; Riaz, Muhammad Bilal; Abushal, Tahani A.; Ahmad, Hijaz; Almetwally, Ehab M.; Askar, Sameh
The study is about a novel Arcsin-function based generator of new families of distributions. We chose the inverse Weibull distribution as the reference distribution to see if the generator could be employed. This generator helps for developing a distribution called the novel Arcsin inverse Weibull. The main features of the suggested distribution have been taken into account. Some of the indicators used in this class include the density function, complete and incomplete moments, average deviation, and aging indicators. The model's parameters are determined using the maximum likelihood method in both simulations and data analysis. The effectiveness of the suggested model in the healthcare sector is demonstrated by analyzing five sets of data, revealing its superior fit compared to the traditional inverse sine model, which is associated with the inverse Weibull model.
Rational design of MXene-based vacancy-confined single-atom catalyst for efficient oxygen evolution reaction
(Elsevier, 2024) Fu, Zhongheng; Hai, Guangtong; Ma, Xia-Xia; Legut, Dominik; Zheng, Yongchao; Chen, Xiang
Two-dimensional transition metal carbides (MXenes) have been demonstrated to be promising supports for single-atom catalysts (SACs) to enable efficient oxygen evolution reaction (OER). However, the rational design of MXene-based SACs depends on an experimental trial-and-error approach. A theoretical guidance principle is highly expected for the efficient evaluation of MXene-based SACs. Herein, highthroughput screening was performed through first-principles calculations and machine learning techniques. Ti3C2(OH)x, V3C2(OH)x, Zr3C2(OH)x, Nb3C2(OH)x, Hf3C2(OH)x, Ta3C2(OH)x, and W3C2(OH)x were screened out based on their excellent stability. Zn, Pd, Ag, Cd, Au, and Hg were proposed to be promising single atoms anchored in MXenes based on cohesive energy analysis. Hf3C2(OH)x with a Pd single atom delivers a theoretical overpotential of 81 mV. Both moderate electron-deficient state and high covalency of metal-carbon bonds were critical features for the high OER reactivity. This principle is expected to be a promising approach to the rational design of OER catalysts for metal-air batteries, fuel cells, and other OER-based energy storage devices. (c) 2024 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
A novel numerical solution of nonlinear stochastic model for the propagation of malicious codes in wireless sensor networks using a high order spectral collocation technique
(Springer Nature, 2025) Zhu, Junjie; Ullah, Misbah; Ullah, Saif; Riaz, Muhammad Bilal; Saqib, Abdul Baseer; Alamri, Atif M.; Alqahtani, Salman A.
The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs. In this paper, we formulate a general stochastic compartmental model for analyzing the dynamics of malicious code distribution in WSNs and suggest its possible control. We incorporate the stochasticity in the classical deterministic model for the inherent unpredictability in code propagation, which results in a more appropriate representation of the dynamics. A basic theoretical analysis including the stability results of the model with randomness is carried out. Moreover, a higher-order spectral collocation technique is applied for the numerical solution of the proposed stochastic model. The accuracy and numerical stability of the model is presented. Finally, a comprehensive simulation is depicted to verify theoretical results and depict the impact of parameters on the model's dynamic behavior. This study incorporates stochasticity in a deterministic model of malicious codes spread in WSNs with the implementation of spectral numerical scheme which helps to capture these networks' inherent uncertainties and complex nature.
Real time tracking of nanoconfined water-assisted ion transfer in functionalized graphene derivatives supercapacitor electrodes
(Wiley, 2024) Padinjareveetil, Akshay Kumar K.; Pykal, Martin; Bakandritsos, Aristides; Zbořil, Radek; Otyepka, Michal; Pumera, Martin
Water molecules confined in nanoscale spaces of 2D graphene layers have fascinated researchers worldwide for the past several years, especially in the context of energy storage applications. The water molecules exchanged along with ions during the electrochemical process can aid in wetting and stabilizing the layered materials resulting in an anomalous enhancement in the performance of supercapacitor electrodes. Engineering of 2D carbon electrode materials with various functionalities (oxygen (& horbar;O), fluorine (& horbar;F), nitrile (& horbar;C equivalent to N), carboxylic (& horbar;COOH), carbonyl (& horbar;C & boxH;O), nitrogen (& horbar;N)) can alter the ion/water organization in graphene derivatives, and eventually their inherent ion storage ability. Thus, in the current study, a comparative set of functionalized graphene derivatives-fluorine-doped cyanographene (G-F-CN), cyanographene (G-CN), graphene acid (G-COOH), oxidized graphene acid (G-COOH (O)) and nitrogen superdoped graphene (G-N) is systematically evaluated toward charge storage in various aqueous-based electrolyte systems. Differences in functionalization on graphene derivatives influence the electrochemical properties, and the real-time mass exchange during the electrochemical process is monitored by electrochemical quartz crystal microbalance (EQCM). Electrogravimetric assessment revealed that oxidized 2D acid derivatives (G-COOH (O)) are shown to exhibit high ion storage performance along with maximum water transfer during the electrochemical process. The complex understanding of the processes gained during supercapacitor electrode charging in aqueous electrolytes paves the way toward the rational utilization of graphene derivatives in forefront energy storage applications.
Band engineering in iron and silver co-doped double perovskite nanocrystals for selective photocatalytic CO2 reduction
(Royal Society of Chemistry, 2024) Ahmad, Razi; Zhang, Yu; Navrátil, Jan; Błoński, Piotr; Zdražil, Lukáš; Kalytchuk, Sergii; Naldoni, Alberto; Rogach, Andrey L.; Otyepka, Michal; Zbořil, Radek; Kment, Štěpán
Double metal cation halide perovskites are promising alternatives to lead halide perovskites due to their exceptional compositional flexibility and stability. However, their utilization in solar-light harvesting applications has been hindered by their large band gap and the complexity of producing doped or alloyed materials with desirable optoelectronic properties. In this study, we report the colloidal synthesis of iron-doped Cs2NaInCl6 double perovskite nanocrystals (NCs), leading to a significant extension of the absorption edge from 330 nm to 505 nm. We also demonstrate that simultaneous doping with Fe3+ and Ag+ ions allows significant reduction of the optical band gap and precise tuning of electronic band structures of the resulting NCs. The enhanced absorption in the visible region is attributed to the substitution of In-5s by the Fe-3d state, while the introduction of the Ag 4d state upshifts the valence band maximum, inducing a transformative change in the band structure, as confirmed by density functional theory (DFT) calculations. Remarkably, by precisely controlling the band positions of the Fe3+-doped Cs2Ag0.5Na0.5InCl6 NCs, we accomplished the selective photocatalytic reduction of CO2 into CH4, making them readily available for solar-energy conversion technologies.
Data management for distributed computational workflows: An iRODS-based setup and its performance
(PLOS, 2026) Hayek, Mohamad; Golasowski, Martin; Hachinger, Stephan; García-Hernández, Ruben J.; Munke, Johannes; Lindner, Gabriel; Slaninová, Kateřina; Tunka, Philipp; Vondrák, Vít; Kranzlmüller, Dieter; Martinovič, Jan
Modern data-management frameworks promise a flexible and efficient management of data and metadata across storage backends. However, such claims need to be put to a meaningful test in daily practice. We conjecture that such frameworks should be fit to construct a data backend for workflows which use geographically distributed high-performance and cloud computing systems. Cross-site data transfers within such a backend should largely saturate network bandwidth, in particular when parameters such as buffer sizes are optimized. To explore this further, we evaluate the "integrated Rule-Oriented Data System" iRODS with EUDAT's B2SAFE module as data backend for the "Distributed Data Infrastructure" within the LEXIS Platform for complex computing workflow orchestration and distributed data management. The focus of our study is on testing our conjectures-i.e., on construction and assessment of the data infrastructure and on measurements of data-transfer performance over the wide-area network between two selected supercomputing sites connected to LEXIS. We analyze limitations and identify optimization opportunities. Efficient utilization of the available network bandwidth is possible and depends on suitable client configuration and file size. Our work shows that systems such as iRODS nowadays fit the requirements for integration in federated computing infrastructures involving web-based authentication flows with OpenID Connect and rich on-line services. We are continuing to exploit these properties in the EXA4MIND project, where we aim at optimizing data-heavy workflows, integrating various systems for managing structured and unstructured data.
Innovative thermal management in the presence of ferromagnetic hybrid nanoparticles
(Springer Nature, 2024) Khan, Saraj; Asjad, Muhammad Imran; Riaz, Muhammad Bilal; Muhammad, Taseer; Aslam, Muhammad Naeem
In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter beta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document} to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703e-10, 7.1313ee-10, 3.1462e-10, and 4.8747e-10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems.
Toward transitioning to green and sustainable supercapacitors
(Elsevier, 2026) Dědek, Ivan; Kupka, Vojtěch; Šedajová, Veronika; Jakubec, Petr; Navrátil, Matěj; Otyepka, Michal
The rapid advancement of supercapacitor technologies has intensified the demand for sustainable electrode fabrication methods that minimize environmental impact. A major challenge arises from the extensive use of the toxic solvent N-methyl-2-pyrrolidone (NMP), which is subject to increasingly stringent regulatory restrictions. We demonstrate that NMP can be effectively replaced with two green solvents, dihydrolevoglucosenone (Cyrene) and N-butyl-2-pyrrolidone (Tamisolve), without compromising electrochemical performance. Electrode formulations were prepared using nitrogen-doped graphene as the active material, in combination with either polyvinylidene fluoride (PVDF), a widely employed fluorinated binder, or polyvinylpyrrolidone (PVP), a nonfluorinated alternative with improved environmental profile. Optimized coatings exhibited high mass loadings exceeding 6 mg cm- 2 while maintaining strong adhesion to current collectors, an often overlooked yet critical parameter for scalable manufacturing. Electrochemical testing revealed that PVDF in Cyrene delivered an energy density of 66.6 Wh kg- 1 at a power density of 1.85 kW kg- 1, with excellent cycling stability, retaining 91 % of capacitance after 100,000 cycles. PVP in Cyrene provided a more environmentally benign alternative, achieving an energy density of 64.2 Wh kg- 1 at 1.80 kW kg- 1, with 78 % capacitance retention after 100,000 cycles, thereby highlighting the trade-off between performance and sustainability.
Rational doping regulation of Cu(OH)2 and Cu3P obtained by three-step continuous transformation for overall water splitting
(Elsevier, 2025) Zhang, Xinzheng; Wu, Jiwen; Jin, Suyu; Gu, Zewei; Zhang, Tianchen; Pei, Jiamin; Fu, Zhongheng; Legut, Dominik; Zheng, Jinlong; Zhang, Dawei
Non-precious metal-based electrocatalysts are earth-abundant and cost-effective, which are expected to be alternative to traditional precious metal-based electrocatalysts. However, Cu-based electrocatalysts are plagued by low intrinsically catalytic activity for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) and deactivation at high potentials. Herein, the doping effect of 3d transition metal elements including Co, Cr, Fe, Mn, and Ni on the HER activity of Cu3P and the OER activity of CuOOH were theoretically determined by first principles calculations. Co–Cu3P and Co–CuOOH exhibit improved HER and OER activities, respectively, which are attributed to the decreasing electron transfer between the substrate and OH/H induced by the doping Co atom. Experiments confirmed that Co–Cu(OH)2 and Co–Cu3P yield overpotentials of 242.6 mV and 78.8 mV at a current density of 10 mA cm−2 for OER and HER, respectively. A working voltage of 1.54 V at a current density of 10 mA cm−2 was achieved for a Co–Cu(OH)2 || Co–Cu3P electrolyzer, comparable with that of the commercial RuO2/CF || Pt/C/CF. These findings show the enormous potential of theory-guided rational design of electrocatalysts, providing a new pathway to develop high-performance electrocatalysts.
Thermoelectric power factors of defective scandium nitride nanostructures from first principles
(Elsevier, 2026) Cigarini, Luigi; Wdowik, Urszula D.; Legut, Dominik
The thermoelectric properties of scandium nitride are strongly influenced by structural and electronic factors arising from defects and impurities. Nevertheless, the mechanisms by which these microscopic features affect transport are not yet fully understood. Experiments show a large variability in the electronic transport properties, with a strong dependence on the experimental conditions, and attempts to improve thermoelectric efficiency often lead to conflicting effects. In this work, we employ the Landauer approach to analyze the effects of different kinds of structural defects and impurities on electronic transport in scandium nitride. This approach allows us to relate the transport mechanisms to the structural and electronic modifications introduced in the lattice, with atomistic resolution. In light of these new insights, we propose a rationale relating part of the experimental variability to its microscopic origin.
Rule-based profit taxation in dynamic Cournot oligopoly: Transmission, stability and welfare
(Elsevier, 2026) Nálepová, Veronika; Lampart, Marek
This study develops a dynamic Cournot model to examine whether profit taxation can stabilise oligopolistic markets hit by demand shocks. The tax rate is updated each period by a simple welfare rule, allowing fiscal policy to respond automatically to changing market conditions. The analysis connects the effectiveness with which taxes influence firms' decision-making (the transmission strength) to market stability. Simulations and chaos analysis show that when the tax signal is strong, firms adjust smoothly, volatility falls and competition is preserved. In contrast, when transmission is weak, feedback effects magnify shocks, increasing exit risk and market concentration. Moderate shocks are absorbed through temporary tax changes, while stronger demand shocks in the model mainly threaten the high-cost firm. Overall, transparent and predictable profit taxation serves as a practical stabiliser in concentrated industries, limiting volatility without ad hoc measures and providing a scalable framework for future fiscal design.
Classification enhanced machine learning model for energetic stability of binary compounds
(Elsevier, 2024) Liu, Y. K.; Liu, Z. R.; Xu, T. F.; Legut, Dominik; Yin, X.; Zhang, R. F.
As contemporary computational technologies and machine learning methodologies rapidly evolve, machine learning (ML) models for predicting formation enthalpies of materials exhibited convincible numerical precision and remarkable predictive efficiency, thus establishing a solid foundation for materials thermodynamic design. Despite achieving numerically high global probability accuracy, current ML models for formation enthalpy nonetheless exhibit suboptimal local accuracy within specific physical domain, which can be attributed to the misalignment between the physical constraints of chemical bonds and the critical descriptors capturing classspecific traits. Herein, we propose a novel approach to improve the local precision of the ML model for predicting formation enthalpy by utilizing Miedema theory-based classification, which segments data into distinct categories according to the electronegativity difference, electron density discontinuity and atomic size difference. Utilizing ML algorithms to build surrogate models guided by the classification strategy significantly improves the local predictive accuracy of formation enthalpy for specific binary compounds, significantly raising the R2 value from 0.4-0.9 to 0.8-0.9 compared to an unclassified method. Furthermore, feature importance analysis demonstrates that the pivotal factors for each category vary in some manner, highlighting the insufficiency of a sole ML model in classifying large-dimensional data, which can be addressed by adopting a physicsinformed classification strategy. Our results suggest that employing physical-informed classification scheme into ML equips the models with broad applicability and local accuracy, which also shed light for other material properties predication.
Novel numerical approach toward hybrid nanofluid flow subject to Lorentz force and homogenous/heterogeneous chemical reaction across coaxial cylinders
(AIP Publishing, 2024) Janjua, Khuram Hina; Bilal, Muhammad; Riaz, Muhammad Bilal; Saqib, Abdul Baseer; Ismail, Emad A. A.; Awwad, Fuad A.
The combination of AA7075 and Ti6Al4V aluminum alloys provides an effective balance of endurance, corrosion resistance, and lightness. Some potential applications include aviation components, marine structures with anti-corrosion characteristics, surgical instruments, and athletic apparel. Therefore, the hybrid nanofluid (Hnf) consists of aluminum alloys (AA7075-Ti6Al4V), water (50%), and ethylene glycol (EG-50%) in the current analysis. The Hnf flow subject to heat radiation and Lorentz force is studied through coaxial cylinders. In addition, the flow has been observed under the impacts of homogeneous-heterogeneous (HH) chemical reaction and exponential heat source/sink. The modeled equations (continuity, momentum, HH, and heat equations) are renovated into the non-dimensional form through the similarity approach, which are further numerically computed by employing the ND-solve technique coupling with the shooting method. It can be noticed from the graphical results that the flow rate of Hnf drops with the rising effect of porosity and magnetic field parameters. The addition of AA7075-Ti6Al4V nanoparticles (NPs) also reduces the fluid temperature and velocity profile. Furthermore, the concentration distribution diminishes with the flourishing effect of HH parameters.
Graphene acid: A potent carbocatalyst for the friedel-crafts arylation of aldehydes with indoles
(Wiley, 2025) Galathri, Eirini M.; Hrubý, Vítězslav; Mountanea, Olga G.; Mantzourani, Christiana; Chronopoulos, Demetrios D.; Otyepka, Michal; Kokotos, Christoforos G.
Carbocatalysis represents a highly attractive and effective field within the realm of metal-free nanocatalysis, significantly advancing sustainability in synthetic chemistry. Graphene acid (GA) emerges as a well-defined graphene derivative, characterized by a high density of homogeneously distributed carboxylic groups over graphene lattice. This unique and uniform structure positions GA as an elegant alternative to other 2D carbocatalysts, namely graphene oxide. GA was successfully employed as the catalyst in a Friedel-Crafts-type reaction between indoles and aldehydes, facilitating the synthesis of bis(indolyl)methanes, organic compounds exhibiting interesting biological properties and significant pharmaceutical potential. The metal-free nature of GA, combined with the performance of the reaction "on water" under mild conditions, highlight the green credentials of the developed protocol. Comprehensive substrate screening, including a plethora of aliphatic or aromatic aldehydes and various 1- or 2-substituted indoles, resulted in moderate to high yields of variously functionalized bis(indolyl)methanes. Mechanistic and recovering studies showed that GA acts catalytically as a Br & oslash;nsted acid, maintaining its catalytic activity at a high rate for six subsequent cycles.
Analytical solutions and dynamical behaviors of the extended Bogoyavlensky-Konopelchenko equation in deep water dynamics
(IOP Publishing, 2025) Jhangeer, Adil; Beenish, Abdallah M.; Talafha, Abdallah M.; Ansari, Ali R.
In this study, we delve into the mathematical intricacies of the novel Bogoyavlensky-Konopelchenko equation, which finds practical applications in understanding the dynamics of internal waves in deep water. This equation holds significance across scientific fields such as plasma physics, nonlinear optics, and fluid dynamics. The equation extends the (2+1)-dimensional Bogoyavlensky-Konopelchenko equation by adding the second-order derivative terms B mu x mu x and B mu y mu y due to second-order dissipative elements. The generalized exponential rational function method, crucial in mechanical engineering, analyzes analytical solutions featuring symmetric waveform representations. The planar dynamical system, derived via Galilean transformation with mathematical models and parameter values, enhances problem comprehension. Sensitivity analysis and phase portraits of equilibrium points highlight symmetrical properties. The global analysis identifies periodic, quasi-periodic, and chaotic behaviors, corroborated by Poincar & eacute; maps, attractor, power spectrum, return map, and a symmetric basin of the largest Lyapunov exponent.
Modeling and simulations for the mitigation of atmospheric carbon dioxide through forest management programs
(AIMS Press, 2024) Riaz, Muhammad Bilal; Raza, Nauman; Martinovič, Jan; Bakar, Abu; Tunç, Osman
The growing global population causes more anthropogenic carbon dioxide (CO2) 2 ) emissions and raises the need for forest products, which in turn causes deforestation and elevated CO2 2 levels. A rise in the concentration of carbon dioxide in the atmosphere is the major reason for global warming. Carbon dioxide concentrations must be reduced soon to achieve the mitigation of climate change. Forest management programs accommodate a way to manage atmospheric CO2 2 levels. For this purpose, we considered a nonlinear fractional model to analyze the impact of forest management policies on mitigating atmospheric CO2 2 concentration. In this investigation, fractional differential equations were solved by utilizing the Atangana Baleanu Caputo derivative operator. It captures memory effects and shows resilience and efficiency in collecting system dynamics with less processing power. This model consists of four compartments, the concentration of carbon dioxide C (t), human population N (t), forest biomass B (t), and forest management programs P (t) at any time t. The existence and uniqueness of the solution for the fractional model are shown. Physical properties of the solution, non-negativity, and boundedness are also proven. The equilibrium points of the model were computed and further analyzed for local and global asymptotic stability. For the numerical solution of the suggested model, the Atangana-Toufik numerical scheme was employed. The acquired results validate analytical results and show the significance of arbitrary order delta . The effect of deforestation activities and forest management strategies were also analyzed on the dynamics of atmospheric carbon dioxide and forest biomass under the suggested technique. The illustrated results describe that the concentration of CO2 2 can be minimized if deforestation activities are controlled and proper forest management policies are developed and implemented. Furthermore, it is determined that switching to low-carbon energy sources, and developing and implementing more effective mitigation measures will result in a decrease in the mitigation of CO 2 .
Applying fractional calculus to malware spread: A fractal-based approach to threat analysis
(PLOS, 2025) Razi, Nausheen; Riaz, Muhammad Bilal; Kamran, Tayyab; Ishtiaq, Umar; Shafiq, Anum
Malware is a common word in modern era. Everyone using computer is aware of it. Some users have to face the problem known as Cyber crimes. Nobody can survive without use of modern technologies based on computer networking. To avoid threat of malware, different companies provide antivirus strategies on a high cost. To prevent the data and keep privacy, companies using computers have to buy these antivirus programs (software). Software varies due to types of malware and is developed on structure of malware with a deep insight on behavior of nodes. We selected a mathematical malware propagation model having variable infection rate. We were interested in examining the impact of memory effects in this dynamical system in the sense of fractal fractional (FF) derivatives. In this paper, theoretical analysis is performed by concepts of fixed point theory. Existence, uniqueness and stability conditions are investigated for FF model. Numerical algorithm based on Lagrange two points interpolation polynomial is formed and simulation is done using Matlab R2016a on the deterministic model. We see the impact of different FF orders using power law kernel. Sensitivity analysis of different parameters such as initial infection rate, variable adjustment to sensitivity of infected nodes, immune rate of antivirus strategies and loss rate of immunity of removed nodes is investigated under FF model and is compared with classical. On investigation, we find that FF model describes the effects of memory on nodes in detail. Antivirus software can be developed considering the effect of FF orders and parameters to reduce persistence and eradication of infection. Small changes cause significant perturbation in infected nodes and malware can be driven into passive mode by understanding its propagation by FF derivatives and may take necessary actions to prevent the disaster caused by cyber crimes.

Browse

Recent Submissions