Computational Methodology Research Articles

Inheriting the glory of PāṆinian grammar into computer science

the integration of Pāṇinian grammar principles into computer science represents a significant advancement in computational linguistics and natural language processing. Pāṇini, an ancient Indian grammarian, formulated an elaborate system of grammar known as Aṣṭādhyāyī around the 4th century BCE, which has been revered for its systematic and concise rules governing Sanskrit language structure. In recent years, there has been a growing interest in adapting Pāṇinian principles to enhance computational frameworks for understanding human languages. This paper explores the theoretical foundations of Pāṇinian grammar and its applicability in the context of modern computational linguistics. The integration of Paninian grammar into computer science enhances the accuracy and efficiency of natural language processing system and contributes to preservation and dissemination of ancient linguistic knowledge. Through this the researchers aim to bridge gap between traditional linguistic scholarship and modern computational methodologies. By embracing the systematic and rigorous framework laid out by Pāṇini millennia ago, researchers are poised to achieve new breakthroughs in understanding and processing human language through innovative computational techniques. The journey from ancient Sanskrit grammar to innovative computer science exemplifies a harmonious blend of tradition and innovation in the quest for linguistic knowledge and technological advancement. Keywords - Pāṇini, Aṣṭādhyāyī, Computational linguistics, Machine translation, semantic analysis, syntactic rules.

Advanced Computational Methods for News Classification: A Study in Neural Networks and CNN integrated with GPT

In an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision and efficacy of news categorization. The rapid digital dissemination of news necessitates advanced computational methodologies capable of accurate classification and event prediction that include finance and economic events. Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. Over 232 days, our methodology classified 33,979 news articles into Education & Learning, Health & Medicine, and Science & Technology, with further subcategorization into 32 distinct subcategories. For evaluation, a sample of 5,000 articles was assessed using metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Precision, Recall, and F1-Score. In comparison with the existing studies, the proposed method achieving significantly higher with average scores of 0.986 (Precision), 0.987 (Recall), and 0.987 (F1-Score). This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. This integration advances computational news classification and exemplifies how sophisticated mathematical frameworks enhance large-scale text data analysis, marking a pivotal advancement in applying advanced computational methods in real-world scenarios.

Molecular Dynamics Simulation Studies of Beta-Glucogallin and Dihydro Dehydro Coniferyl Alcohol from Syzygium cumini for its Antimicrobial Activity on Staphylococcus aureus.

With the escalating threat of antimicrobial resistance (AMR), discovering novel therapeutic agents against resistant pathogens like Staphylococcus aureus is crucial. This study explores phytochemicals from Syzygium cumini for their potential efficacy against AMR S. aureus infections, elucidating their mechanisms through in silico methods. We investigated 83 compounds from S. cumini, sourced from PubMed, using rigorous docking analysis against the ATP binding domain AgrC of S. aureus with AMdock with Autodock Vina v1.5.2. Drug-likeness predictions were assessed using SwissADME v2023 and Pass online v2.0. Molecular dynamics (MD) simulations identified promising compounds, focusing on stability and interaction dynamics. Beta-Glucogallin (BEG) and Dihydro Dehydro Coniferyl alcohol (DIH) emerged as significant hits. MD simulations with GROMACS v2020.6 revealed stable BEG and DIH complexes with AgrC, forming six hydrogen bonds with six key amino acids (Arg-303, Asp-338, Glu-342, Glu-384, Lys-389, Gly-396), indicating strong and stable bonding. The binding affinities for DIH and BEG are -73.474 ± 11.104 kJ/mol and -6.319 ± 18.823 kJ/mol with 4BXI, respectively. Our findings highlight BEG and DIH as promising candidates against AMR S. aureus infections, showing favourable binding affinities and stable interactions with AgrC. This study underscores the importance of natural products in combating AMR and demonstrates the utility of computational methodologies in drug discovery. Further experimental validation is warranted to fully exploit these phytochemicals' therapeutic potential.

Innovative Characterization of Coffee Waste Extracts and Their Derivatives as Efficient Ecological Corrosion Inhibitors for Copper Alloy in 3% NaCl: Phytochemical Investigations, Electrochemical Explorations, Morphological Assessment and New Computational Methodology

Innovative Characterization of Coffee Waste Extracts and Their Derivatives as Efficient Ecological Corrosion Inhibitors for Copper Alloy in 3% NaCl: Phytochemical Investigations, Electrochemical Explorations, Morphological Assessment and New Computational Methodology

Beef cattle mating recommendation based on bioeconomic models

Recognizing the pivotal role of mating in animal breeding, this study strives to establish a robust strategy for recommending optimal matings among bovines. This strategy is built to maximize a single value derived from the economic selection index of full-cycle system in Brangus cattle. The study endeavors to apply computational methodologies to explore economically significant traits comprehensively, thereby leading to amplified financial gains for Brangus cattle breeders. Anchored within this overarching objective, a strategic deployment of a genetic algorithm is employed to formulate mating recommendations that precisely align with the priority traits designated by the genetic evaluation program of the Brazilian Brangus Association (BBA). The data set of the BBA for the simulations in this study encompass a range of selection criteria, including: i) birth weight; ii) mature cow weight; iii) ribeye area; iv) subcutaneous fat thickness; v) subcutaneous fat thickness at the rump; vi) escape speed; vii) nematode egg count per gram of feces; and viii) tick count. The research findings underscore that the recommendations provided by the computational strategy converge to increase the bioeconomic index while controlling the trade-off between this index and progeny inbreeding.

A computational methodology for invariant manifold connections between quasi-periodic libration point orbits in non-autonomous problems

Semi-analytical and numerical techniques to systematically analyze and compute natural connections between quasi-periodic orbits associated to non-autonomous systems are considered. Focusing in the non-autonomous Sun–Earth+Moon coherent QBCP model, the center-unstable and center-stable invariant manifolds of Lyapunov quasi-periodic orbits are parameterized and a methodology to detect heteroclinic connections between manifolds is introduced. The methodology aims to decrease the dimensionality of the problem and to address the issue of searching good initial conditions inside the high dimensional state space. Then, connections are identified by searching for approximate patch points in a state-time Poincaré section. These points are subsequently refined to obtain smooth paths that are further continued inside their families.

The Genetics of Human Sleep and Sleep Disorders.

Healthy sleep is vital for humans to achieve optimal health and longevity. Poor sleep and sleep disorders are strongly associated with increased morbidity and mortality. However, the importance of good sleep continues to be underrecognized. Mechanisms regulating sleep and its functions in humans remain mostly unclear even after decades of dedicated research. Advancements in gene sequencing techniques and computational methodologies have paved the way for various genetic analysis approaches, which have provided some insights into human sleep genetics. This review summarizes our current knowledge of the genetic basis underlying human sleep traits and sleep disorders. We also highlight the use of animal models to validate genetic findings from human sleep studies and discuss potential molecular mechanisms and signaling pathways involved in the regulation of human sleep.

An efficient finite element computation using subparametric transformation up to cubic-order for curved triangular elements

PurposeA finite element computational methodology on a curved boundary using an efficient subparametric point transformation is presented. The proposed collocation method uses one-side curved and two-side straight triangular elements to derive exact subparametric shape functions.Design/methodology/approachOur proposed method builds upon the domain discretization into linear, quadratic and cubic-order elements using subparametric spaces and such a discretization greatly reduces the computational complexity. A unique subparametric transformation for each triangle is derived from the unique parabolic arcs via a one-of-a-kind relationship between the nodal points.FindingsThe novel transformation derived in this paper is shown to increase the accuracy of the finite element approximation of the boundary value problem (BVP). Our overall strategy is shown to perform well for the BVP considered in this work. The accuracy of the finite element approximate solution increases with higher-order parabolic arcs.Originality/valueThe proposed collocation method uses one-side curved and two-side straight triangular elements to derive exact subparametric shape functions.

A Comprehensive Review on Redundancy usage of Animal models in Novel Drug testing

Millions of animals are used for laboratory research and development purposes each year; these living creatures endure suffering before being killed. Animal testing has other drawbacks in addition to bioethical concerns, such as high costs, the need for specialized labor, permission, and time commitment. As a result, Researchers have worked to make several substitute techniques that may replace using animals in tests. These techniques potentially save millions of animals' lives every year, in addition to providing precise findings. In- silico pharmacology methods are used in conjunction with computer and robotics research methodologies to develop alternative techniques for animal experimentation. In this context, several approaches are listed. Certain techniques are just as trustworthy as in-vivo animal models when it comes to precisely predicting the actions of drugs. These substitute techniques also have a number of benefits over using animals in experiments. Given that up to 90% of clinical trials fail, there is rising ethical concern over the use of excessive animals in drug research and development. The non-animal models described in this article have the potential to accelerate the medication delivery process at a faster pace. This review provides researchers and readers who are not aware of about predictive in- silico techniques a basic knowledge of the underlying theory. New advancements, software, acceptability hurdles, integrated techniques, and current applications are all covered, with links to more resources provided for each area. Furthermore, these alternative methods offer a variety of advantages over experimental animals.

Multi‐Pop: Enhancing user engagement with content‐based multimodal popularity prediction in social media

AbstractSocial media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing Multi‐Pop, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post‐content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics.

Kinetic model implementation in raceway pond reactors with hydrodynamic and radiation fields

Vertical mixing plays a critical role in solar-driven processes using raceway pond reactors (RPRs). However, incorporating the time history of radiation for each fluid element is still an open issue. This work aims to develop an effective methodology using Computational Fluid Dynamics (CFD) tools that couples hydrodynamics and radiation fields into kinetic models of biomass growth or cell lysis enhanced from radiation. First, three methodologies to assess vertical mixing were investigated. It was found that, under typical RPR flow conditions, the velocity in the direction of solar light incidence can maintain particles in constant motion and a near-homogenous particle distribution. In addition, two RPR applications were studied regarding radiation influence analysis: the production of microalgae and an innovative approach for waste activated sludge (WAS) pre-treatment, fostering biogas production. Regarding microalgae production, coupling the biokinetic models with CFD data enables the development of a cost-effective computational methodology to describe the growth of microalgae cultures accounting for hydrodynamics and radiation fields. This work was successful in introducing hydrodynamics and radiation conditions in models to design and optimise RPRs. Reduced geometries based in 2D and Periodic Boundary Conditions were used for CFD simulations to make it feasible for RPRs design purposes.

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives.

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

Stable cycling of halide solid state electrolyte enabled by a dynamic layered solid electrolyte interphase between Li metal and Li3YCl4Br2

Halides are potential electrolytes for Li metal solid state batteries owing to their combination of high ionic conductivity, ductility and electrochemical stability against oxidation. However, their reactivity with the Li metal electrode may result in the formation of secondary compounds hindering their practical utility in terms of cycling performance as key indicator in battery operation. In this work, we investigate the high performance of symmetric cells with Li3YCl4Br2 halide and bare Li-metal electrode, able to withstand 1000 h of Li electrodeposition-dissolution with an overpotential as low as 46 mV. Through a comprehensive analysis employing physico-chemical and electrochemical characterizations, complemented by computational methodologies, we unravel the dynamics of the complex of the Li/halide interface and its evolution during cycling. The reactivity between Li3YCl4Br2 with metallic Li results in the reduction of the halide into LiCl, LiBr and Y metal. Surprisingly, during cycling, those secondary products from the reduction of the halide build a structured solid electrolyte interphase, containing a Y-rich electronic conductive and LiCl and LiBr ionic conductive layers. The particular chemistry and robustness of this solid electrolyte interphase exhibiting a mixed ionic and electronic conductivity appears to be responsible for the outstanding cycling stability.

Improved Segmentation of Cellular Nuclei Using UNET Architectures for Enhanced Pathology Imaging

Medical imaging is essential for pathology diagnosis and treatment, enhancing decision making and reducing costs, but despite various computational methodologies proposed to improve imaging modalities, further optimization is needed for broader acceptance. This study explores deep learning (DL) methodologies for classifying and segmenting pathological imaging data, optimizing models to accurately predict and generalize from training to new data. Different CNN and U-Net architectures are implemented for segmentation tasks, with their performance evaluated on histological image datasets using enhanced pre-processing techniques such as resizing, normalization, and data augmentation. These are trained, parameterized, and optimized using metrics such as accuracy, the DICE coefficient, and intersection over union (IoU). The experimental results show that the proposed method improves the efficiency of cell segmentation compared to networks, such as U-NET and W-UNET. The results show that the proposed pre-processing has improved the IoU from 0.9077 to 0.9675, about 7% better results; also, the values of the DICE coefficient obtained improved from 0.9215 to 0.9916, about 7% better results, surpassing the results reported in the literature.

A Comprehensive Method for Computing Non-Linear Elastokinematic Properties of Passenger Car Suspension Systems: Double Wishbone Case Study

Abstract Suspension and steering design play a major role in ensuring the correct dynamic behavior of road vehicles. Passenger cars are especially demanding from this point of view: NVH and ride comfort requirements often collide with active safety-related requirements such as road holding in steady-state conditions and stability in transients. Driving pleasure is also important for market success, therefore accurate steering feedback and predictable handling properties are additional priorities. Since flexible bushings are used as interface between the suspension arms and the chassis, extra degrees-of-freedom make the design process a complex task. While the use of a multibody software is common practice in the industry, a dedicated computational tool can be more practical and straightforward, especially when undertaking the design of a new suspension concept ground-up. The paper presents a computational methodology for the design of an independent suspension with the associated kinematic and compliance attributes. Typical elastokinematic properties like toe, camber, wheelbase, and track variations versus tyre forces and moments can be computed by means of a dedicated software tool. A sort of validation was performed either by means of a comparison with a MathWorks Simscape® Multibody based model. Finally, a sensitivity analysis is given as an example. Computationally, the method proposed is intuitively based on the equilibrium equations. The nonlinear equations are then solved with Newton–Raphson algorithm. The method can be also optimized for computational efficiency and is thoroughly described so that the reader can easily replicate it in the desired programing environment.

A volumetric approach to Monge's optimal transport on surfaces

We propose a volumetric formulation for computing the Optimal Transport problem defined on surfaces in R3, found in disciplines like optics, computer graphics, and computational methodologies. Instead of directly tackling the original problem on the surface, we define a new Optimal Transport problem on a thin tubular region, Tϵ, adjacent to the surface. This extension offers enhanced flexibility and simplicity for numerical discretization on Cartesian grids. The Optimal Transport mapping and potential function computed on Tϵ are consistent with the original problem on surfaces. We demonstrate that, with the proposed volumetric approach, it is possible to use simple and straightforward numerical methods to solve Optimal Transport for Γ=S2 and the 2-torus.

Computational Methodologies in Synthesis, Preparation and Application of Antimicrobial Polymers, Biomolecules, and Nanocomposites.

The design and optimization of antimicrobial materials (polymers, biomolecules, or nanocomposites) can be significantly advanced by computational methodologies like molecular dynamics (MD), which provide insights into the interactions and stability of the antimicrobial agents within the polymer matrix, and machine learning (ML) or design of experiment (DOE), which predicts and optimizes antimicrobial efficacy and material properties. These innovations not only enhance the efficiency of developing antimicrobial polymers but also enable the creation of materials with tailored properties to meet specific application needs, ensuring safety and longevity in their usage. Therefore, this paper will present the computational methodologies employed in the synthesis and application of antimicrobial polymers, biomolecules, and nanocomposites. By leveraging advanced computational techniques such as MD, ML, or DOE, significant advancements in the design and optimization of antimicrobial materials are achieved. A comprehensive review on recent progress, together with highlights of the most relevant methodologies' contributions to state-of-the-art materials science will be discussed, as well as future directions in the field will be foreseen. Finally, future possibilities and opportunities will be derived from the current state-of-the-art methodologies, providing perspectives on the potential evolution of polymer science and engineering of novel materials.

Insights into hydro thermal gasification process of microplastic polyethylene via reactive molecular dynamics simulations

Microplastics have become a pressing environmental issue due to their widespread presence in our ecosystems. Among various plastic components, polyethylene (PE) is a prevalent and persistent contaminant. Hydrothermal gasification (HTG), a promising technology for converting PE into syngas, holds great promise for mitigating the microplastic problem. In this study, we employ ReaxFF molecular dynamics simulations to investigate the HTG process of PE, shedding light on the intricate relationships between temperature, water content, carbon conversion efficiency, and product distributions. The results reveal that hydrothermal gasification of PE is a complex process involving multiple reaction pathways. Consistently with experimental findings, the calculations indicate that the gas phase exhibits a substantial hydrogen fraction, reaching up to 70%. Interestingly, our simulations reveal a dual role of water content in the HTG process. On one hand, water enhances hydrogen production by promoting the gas formation. On the other hand, it elevates the activation energy required for PE decomposition. Depending on the water content, the calculated activation energies range from 176 to 268 kJ/mol, which are significantly lower than those reported for thermal gasification (TG). This suggests that HTG may be a more efficient route for PE conversion. Furthermore, this study highlights the importance of optimizing both temperature and water content in HTG systems to achieve high yields of hydrogen-rich syngas. The results obtained from our ReaxFF MD simulations demonstrate the robustness of this computational methodology in elucidating complex chemical reactions under extreme conditions. Our findings offer critical insights into the design of advanced waste management strategies for microplastics and contribute to the development of sustainable practices for resource recovery. This work underscores the potential of HTG as a key technology for addressing the global challenge of plastic pollution.

A momentum-resolved view of polaron formation in materials

An ab-initio computational methodology for interrogating the phonon contribution to polaron formation in real materials is developed that can be directly compared to experiment. Using LiF as an example, we show that the recent ab-initio theory of Sio et al.1 makes predictions of the momentum- and branch dependent phonon amplitudes in polaron quasiparticles that are testable using ultrafast electron diffuse scattering (UEDS) and related techniques. The large electron polaron in LiF has UEDS signatures that are qualitatively similar to those expected from a simple isotropic strain field model, but the small hole polaron exhibits a profoundly anisotropic UEDS pattern that is in poor agreement with an isotropic strain field. We also show that these polaron diffuse scattering signatures are directly emblematic of the underlying polaron wavefunction. The combination of new time and momentum resolved experimental probes of nonequilibrium phonons with novel computational methods promises to complement the qualitative results obtained via model Hamiltonians with a first principles, material-specific quantitative understanding of polarons and their properties.

Investigating the cardiotoxicity of N-n-butyl haloperidol iodide: Inhibition mechanisms on hERG channels

The human Ether-à-go-go-Related Gene (hERG) encodes a protein responsible for forming the alpha subunit of the IKr channel, which plays a crucial role in cardiac repolarization. The proper functioning of hERG channels is paramount in maintaining a normal cardiac rhythm. Inhibition of these channels can result in the prolongation of the QT interval and potentially life-threatening arrhythmias. Cardiotoxicity is a primary concern in the field of drug development. N-n-Butyl haloperidol iodide (F2), a derivative of haloperidol, has been investigated for its therapeutic potential. However, the impact of this compound on cardiac toxicity, specifically on hERG channels, remains uncertain. This study employs computational and experimental methodologies to examine the inhibitory mechanisms of F2 on hERG channels. Molecular docking and molecular dynamics simulations commonly used techniques in computational biology to predict protein-ligand complexes' binding interactions and stability. In the context of the F2-hERG complex, these methods can provide valuable insights into the potential binding modes and strength of interaction between F2 and the hERG protein. On the other hand, electrophysiological assays are experimental techniques used to characterize the extent and nature of hERG channel inhibition caused by various compounds. By measuring the electrical activity of the hERG channel in response to different stimuli, these assays can provide important information about the functional effects of ligand binding to the channel. The study's key findings indicate that F2 interacts with the hERG channel by forming hydrogen bonding, π-cation interactions, and hydrophobic forces. This interaction leads to the inhibition of hERG currents in a concentration-dependent manner, with an IC50 of 3.75 μM. The results presented in this study demonstrate the potential cardiotoxicity of F2 and underscore the significance of considering hERG channel interactions during its clinical development. This study aims to provide comprehensive insights into the interaction between F2 and hERG, which will may guid us in the safe use of F2 and in the development of new derivatives with high efficiency while low toxicity.