Advanced Computational Tools Research Articles

Enhancing Energy Efficiency in Greenhouses: Gas-Radiant Heating with Preheated Ventilation

This paper presents an engineering methodology for calculating the heating system of a cultivation facility, employing ceiling-mounted infrared radiators as the primary heat source. The methodology addresses the challenge of maintaining consistent soil surface temperature amidst fluctuating weather conditions. Gas-fired air heaters supplement the system, preheating incoming air to achieve the desired thermal regime within the space. This approach enables designers to swiftly estimate the required heating equipment capacity and water consumption for soil irrigation under specified conditions. However, for more precise calculations encompassing the full spectrum of microclimate parameters and heat fluxes within the facility, advanced computational tools are necessary. The article details the essential input data for the engineering calculations (including approximate values where applicable) and analyzes the key findings. A case study of the “Farmer 7.5” industrial greenhouse in Moscow, Russia, demonstrates the application. The calculated results for the gas-radiant heating system capacity (34.0 kW), preheating energy consumption (38.9 kW), and irrigation water requirement (32.0 kg/h) were validated through computational analysis.

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Machine Learning and Artificial Intelligence for Advanced Materials Processing: A review on opportunities and challenges

This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.

Analysis Computational Fluid Dynamics in a State of Ballast Loading on a Passenger Ship Prototype

Indonesia, as the largest archipelagic country in the world, has a very favourable geographical position because it is located between the Indian Ocean and the Pacific Ocean. The background of this research stems from the critical importance of effectively managing ballast loading on passenger ships to ensure stability, safety, and operational efficiency during voyages. Traditionally, methods for assessing ballast loading have often relied on empirical formulas or simplified models, which may not fully capture the complex fluid dynamics and structural interactions inherent in modern ship designs. This gap highlights the need for advanced computational tools like computational fluid dynamics (CFD), which can provide a more detailed and accurate analysis of how ballast loading affects the ship’s behaviour. CFD simulations offer the capability to model and analyse complex flow patterns, pressure distributions, and structural responses under various ballasting scenarios. By leveraging CFD, this research aims to enhance understanding and optimize the management of ballast loading on passenger ships, thereby addressing the limitations of traditional methods and advancing the state-of-the-art in maritime engineering practices. The simulation was carried out at different speeds, namely 1 knot, 10 knots, and 20 knots. When moving at a speed of 1 knot, the obstacles encountered have a range of 30–40 cm/s and a maximum speed of 83.0971 cm/s. Likewise, when moving at a speed of 10 knots, the obstacle has a range of 200–400 cm/s and a maximum speed of 766.921 cm/s. Finally, at a speed of 20 knots, facing obstacles with speeds ranging from 400 to 800 cm/s and a maximum speed of 1504.56 cm/s, the ship’s hull remained unaffected in terms of damage. However, the fluid speed magnifies the occurrence of friction.

Direct Electromagnetic Wave Scattering Calculation Using Methods of Moments through Layered Rough Surface

This thesis focuses on the direct calculation of electromagnetic wave scattering through layered rough surfaces using the Method of Moments. The study aims to contribute to existing knowledge by addressing the problems associated with accurately predicting scattered electromagnetic fields from rough surfaces with layered structures. By using the Method of Moments, this research seeks to provide insights into the behavior of electromagnetic waves when interacting with multi-layered rough surfaces, offering valuable implications for practical engineering applications and the development of advanced computational tools for electromagnetic wave analysis. In practical terms, the surface is typically divided into small patches, and the electromagnetic field at each patch is determined using MoM. This involves solving integral equations describing the interaction between the induced currents on the surface and the incident field. The layered structure may require additional equations to model the reflection of waves and the transmission at the boundaries between the layers. The direct problem is also examined in a two-dimensional picture, where the rough surface profile and the medium are known parameters, and the electromagnetic waves scattered from the surface to the entire space are the unknowns. The rough surface is considered to have a finite length and can be deterministic or arise from a stationary stochastic random process, with a focus on Gaussian-type rough surfaces. The roughness is considered as two basic types: as a perfect electric conductor (PEC) in free space and as a boundary between two dielectric media. The equations of integration obtained are based on the scalar solution of Green's theory, Helmholtz equations, and using Hankel type green functions as kernels due to the one-dimensional nature. The MoM is extensively used to solve these integral equations and is compared to asymptotic approaches and analytical. Various simulations are conducted to test the feasibility of the algorithms, and the results are thoroughly discussed. The study of electromagnetic wave scattering in one-dimensional rough surfaces involves the direct problem, where the field distribution in the whole space is determined using the equation of integration. These equations take into account the surface roughness and medium parameters they are solved using the Method of Moments (MoM) by expressing the field distribution on the surface using basis functions. The direct calculation of electromagnetic wave scattering through a layered rough surface using the Method of Moments (MoM) is a computationally intensive process and complex and. It contains simulating the interaction of EM waves with a complex surface, which presents challenges due to the multiple layers with different properties, and a roughly surface causing and diffraction of incident waves and scattering. When working with a layered rough surface increases the electromagnetic wave interaction complexity. Additional multiple layers introduce considerations, such as layer interfaces and accounting for different material abilities. This requires careful modeling and analysis to exactly capture the behavior of electromagnetic waves as they traverse through and interact with layers. MoM, a commonly used numerical method for solving electromagnetic scattering problems, discreteness the surface into small segments and solves integral equations to determine the fields that is scattered. This approach allows for a maximum analysis of electromagnetic interactions at a grained level.

MOLECULAR BIOLOGY AND DRUG THERAPIES OF CHRONIC MYELOID LEUKEMIA: A REVIEW OF RECENT LITERATURE

Background: Chronic myeloid leukaemia (CML) is a clonal myeloproliferative disorder of blood stem cells, which is typically characterized by the presence of a Philadelphia chromosome. Even though there have been several studies on the molecular genetics, pharmacogenomics, pharmacological treatments for CML, and its mechanism is still not fully understood. Objectives: This study is designed to provide new updates and better insights into CML molecular biology and drug therapy, along with their benefits and drawbacks. Methodology: For this review, the recent literature was searched from January 2019 to 2023 through various electronic databases. Results: Review findings further suggested that imatinib mesylate was the first tyrosine kinase inhibitor (TKI) licensed as first-line therapy for affected people with CML in the chronic, blast, and rapid phases. Dasatinib is another second-generation multi-target kinase inhibitor, for imatinib-resistant CML treatment in all stages, which is 325 times more effective than imatinib and 16-fold more effective than nilotinib. Nilotinib received approval from the Food and Drug Administration (FDA) for handling imatinib-resistant patients. Bosutinib and ponatinib are other renowned TKIs taken orally. European Medicine Agency (EMA) and FDA-approved asciminib in moderate to severe, rapid, and blast-phase CML patients intolerant to previous therapies. Conclusion: In conclusion, this study indicated the need to include advanced computational tools in addition to the large sample size of cohort studies, which may result in a better understanding of pathophysiology and better clinical outcomes.

Computational insights into NIMA-related kinase 6: unraveling mutational effects on structure and function.

The NEK6 (NIMA-related kinase 6) serine/threonine kinase is a pivotal player in a multitude of cellular processes, including the regulation of the cell cycle and the response to DNA damage. Its significance extends to disease pathogenesis, as changes in NEK6 activity have been linked to the development of cancer. Non-synonymous single nucleotide polymorphisms (nsSNPs) in NEK6 have been linked to cancer as they alter the protein's native structure and function. The association between NEK6 activity and cancer development has prompted researchers to explore the effects of genetic variations within the NEK6 gene. Therefore, we utilized advanced computational tools to analyze 155 high-confidence nsSNPs in the NEK6 gene. From this analysis, 21 nsSNPs were identified as potentially harmful, raising concerns about their impact on NEK6 activity and cancer risk. These 21 mutations were then examined for structural alterations, and eight of nsSNPs (I51M, V76A, I134N, Y152D, R171Q, V186G, L237R, and C285S) were found to destabilize the protein. Among the destabilizing mutations screened, a specific mutation, R171Q, stood out due to its conserved nature. To understand its impact on the protein and conformation, all-atom molecular dynamics simulations (MDS) for 100ns were performed for both Wildtype NEK6 (WT-NEK6) and R171Q. The simulations revealed that the R171Q variant was unstable and led to significant conformational changes in NEK6. This study provides valuable insights into NEK6 dysfunction caused by single amino acid alterations, offering a novel understanding of the molecular mechanisms underlying NEK6-related cancer progression.

The Dynamics of Verbal and Non-Verbal Linguistic Communication in The Saudi Sports Community

The study aimed to identify and examine the paradigms of verbal and non-verbal communicative dynamics, encompassing the nuanced dimensions of body language, symbolic cues, and facial expressions between foreign and indigenous participants situated within the Kingdom of Saudi Arabia. This analysis focuses on the theoretical of the constructs of linguistic competence and intercultural communicative competence. The salience of these theoretical underpinnings lies in their pivotal role in orchestrating the attainment of precise and effective communication within the purview of the concerned stakeholders. To galvanize this endeavor, an empirical data has been garnered through the process of direct surveillance and scrutiny. This repository encompasses a multifaceted, encompassing video recordings, photographic archives, and textual discourse exchanged among the participants. This analytical process is underpinned by the advanced computational tools of artificial intelligence. The results show that, for harmonious discourse, a universal lingua franca, exemplified by the likes of the English language, assumes the role of a linchpin, serving as the quintessential vehicle for fostering interaction and communication with the indigenous populace. Simultaneously, the strategic deployment of somatic expressions, and the emotional gesticulations emerge as potent catalysts, amplifying the efficacy of cross-cultural dialogues and igniting an enhanced rapport with foreign customs and traditions.

Computational study of BisGMA: A component of dental resin material

BisGMA, a crucial component of dental resin widely employed in oral care, undergoes an in-depth computational investigation in this study. Employing advanced computational tools such as Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and General Utility Lattice Program (GULP), we explore a wide range of physical parameters. These encompass the stress–strain variations, Young’s and linear moduli, pair distribution functions, RMSD trajectory analysis, enthalpy, entropy, elastic constants, self-energy, zero-point energy and optical and dielectric constants. This multifaceted analysis enhances our understanding on intricate correlation of this material, offering insights that are frequently challenging to obtain.

Evaluation of earthquake-related damages on masonry structures due to the 6 February 2023 Kahramanmaraş-Türkiye earthquakes: A case study for Hatay Governorship Building

The present study contributes to enriching the sparse literature on the seismic analysis of historical masonry buildings through field observations and advanced computational tools. To do this, the findings from field observations in the area affected by the February 6th, 2023, Kahramanmaraş earthquakes (Mw = 7.7 and Mw = 7.6) are evaluated to point out the possible reasons for damages occurred in masonry buildings. Further, numerical simulations of a historical building in Hatay which has a significant amount of historical masonry building stock in the region. Comparisons of the results obtained from non-linear finite element analyses and field observations are made to explain the crack patterns occurred and damage mechanisms. Nonlinear finite element analyses confirmed that the considered structure did not collapse despite being seriously damaged in the first two earthquakes (Mw = 7.7 and Mw = 7.6), but a part of it completely collapsed because of the third earthquake (Mw = 6.4) on February 20th, 2023, in Defne district. The damage patterns observed in the field are also obtained exactly by the numerical simulations. Some retrofitting proposals are given to assist the authorities in charge of the reconstruction and rehabilitation programs to account for a better interpretation and understanding of the seismic performance of masonry buildings.

Application of computational fluid dynamics simulation in predicting food protein denaturation: numerical studies on selected food products - a review*

Abstract Protein denaturation is a common process in the food industry, which can impact food quality and safety. Computational Fluid Dynamics (CFD) is a powerful tool that can be used to predict protein denaturation in food products. In this review article, we present an overview of the application of CFD simulation in predicting protein denaturation in food products. We discuss the factors that influence protein denaturation, the importance of predicting protein denaturation, and the various numerical methods used in protein denaturation studies. The main focus of the article is the use of CFD simulation in predicting protein denaturation in selected food products, such as milk, meat, and eggs. We provide examples of numerical studies that have been conducted on these products, and we discuss the results and implications of these studies. The use of CFD simulation can help to optimize food processing conditions, improve food quality and safety, and reduce waste and costs in the food industry. Overall, this review article highlights the importance of using advanced computational tools such as CFD simulation in food science, research and development. Highlights The use of CFD simulation can predict protein denaturation in food products. Numerical studies were conducted on selected food products to analyze protein denaturation. CFD simulation provides a powerful tool for optimizing food processing technologies. The results from the numerical studies can be used to improve the quality and safety of food products. The application of CFD simulation can lead to more efficient and sustainable food production practices.

A guide to ERK dynamics, part 1: mechanisms and models.

Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.

Exploring Volatility: Evolution, Advancements, Trends, and Applications

Volatility is a fundamental notion in financial markets, influencing investment decisions, risk management techniques, and market dynamics. This paper provides a thorough overview of the historical evolution and practical implications of volatility, focusing on important works and key advancements in the field. The overview begins with early conceptions of volatility and the necessity for measurement prompted by market collapses, then progresses to advanced quantitative models and computer tools. The study includes key innovations such as the Black-Scholes model, which revolutionized options pricing and pioneered the concept of implied volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models introduced frameworks for modeling time-varying volatility, paving the way for greater forecasting accuracy. Advancements in computing techniques have made it easier to analyze high-frequency data and estimate realized volatility, providing timely insights into market trends. The review also investigates contemporary trends, such as the use of machine learning algorithms and the issues provided by cryptocurrency marketplaces. Furthermore, the article examines the various characteristics and metrics of volatility, emphasizing its multidimensional nature and diverse uses in risk management, portfolio optimization, derivative pricing, and market analysis. Practical examples show how investors, traders, and financial professionals may use volatility to navigate complex market settings and make sound judgments. Finally, the study highlights the enduring significance of volatility in financial markets and highlights the need for continuing research and analysis to improve our understanding of market behavior. Acknowledging the complexities of volatility prepares market participants with valuable understandings to manage risks effectively and capitalize on market opportunities, thus contributing to financial stability and optimal portfolio performance.

The impact of aging and atrial fibrillation on thrombus formation in-silico

Abstract Background Atrial fibrillation (AF) is an age-related cardiac arrythmia that underlies one third of all strokes, with thromboemboli commonly originating from the left atrial appendage (LAA). Personalised in-silico modelling of Virchow’s triad (blood stasis, hypercoagulability and endothelial damage) can improve stroke risk assessment metrics, such as the CHA2DS2-VASc score, by providing quantitative mechanistic information [1]. The ability to quantify the impact of aging may play a critical role in patient stratification and therapy selection to reduce the risk of AF-related stroke. Aim We aim to perform in-silico modelling to quantify the effect of aging and AF on thrombus formation in the LAA by using advanced computational tools to capture blood flow, clotting mechanisms and endothelial damage [2]. Methods A virtual physiological LA model was derived from cardiac Cine MRI from an adult AF patient imaged during sinus rhythm (SR). Biomarkers for a geriatric patient were obtained from literature and applied as: i) elevated plasma fibrinogen concentration and ii) longer thrombin time-to-peak (ttP) [3]. Blood flow was simulated in SR and AF conditions for 5 cardiac cycles in the LA for both ages (totalling 4 test cases). The catalytic conversion of fibrinogen to a fibrin clot by thrombin was modelled to mimic hypercoagulability [2]. The site of endothelial injury was prescribed in the middle of the LAA. The area under curve for the fibrin concentration (Fn AUC) was measured to quantify thrombus formation. Results In the two SR cases, LA blood flow velocity increased to a maximum of 1.8 m/s after 0.6 s with a mean of 0.63 m/s during the cardiac cycle, while the two AF cases showed 1.3 m/s and 0.39 m/s, respectively, corresponding to a 38% decrease in AF. Peak velocity in the LAA was 0.35 m/s during SR and 0.18 m/s in AF. The fibrin concentration in the adult-SR case rose to 1.1 mmol/m³ over the first cardiac cycle, but both thrombin and fibrin were washed out of the LA after 1.5 s, with a Fn AUC of 0.045mmol s/m³ (Fig. 1a). The geriatric-SR case sustained fibrin generation throughout the 5 cardiac cycles reaching 3.1 mmol/m³, an Fn AUC of 2.4 mmol s/m³, and the formation of an LAA thrombus (Fig. 1b). Similarly, the adult-AF and geriatric-AF cases also saw continuous growth to 5.0 and 7.4 mmol/m³, with Fn AUCs of 6.4 and 12.6 mmol s/m³, respectively, with formation of a thrombus in the LAA in both (Fig. 1c and d). The thrombus formed in the adult-AF case was 25% larger than the geriatric-SR case, while thrombus in the geriatric-AF case was 80% larger. Conclusions This investigation quantifies the increased risk of LAA thrombus formation in older patients, with an Fn AUC increase of 55% between adult and geriatric in SR and a 100% increase when combining geriatric biomarkers with AF conditions. With further validation on larger patient cohorts, this novel in-silico tool may improve our understanding of the risks in AF-related stroke.

Molecular dynamics calculations of the enthalpy of vaporization for different water models

Phase-change heat transfer processes involving water play a key role in numerous societal applications, such as electrical power generation, microelectronics cooling, or water desalination. Advanced computational tools, such as molecular dynamics (MD), are increasingly being used to simulate these processes, especially on nanostructured surfaces and in nanoparticle suspensions, to gain insight into the unique nanoscale physics at play. In these studies, it is critical to use models of the water molecule that yield accurate predictions of water’s thermophysical properties, particularly its enthalpy of vaporization. Here, we use MD to determine the enthalpy of vaporization (hfg) for six common water models and four different values of the cutoff radius (i.e., the intermolecular distance beyond which interactions are simplified or ignored). The most accurate prediction of hfg (within 2 % of the value tabulated by ASHRAE) comes from the TIP3P water model with a cutoff radius of 14 Å. We also conducted a preliminary study of the variation of hfg with pressure and found reasonable agreement between simulations and tabulated ASHRAE data (<7% error). This work provides physical insights into the sensitivity of the predicted macroscopic thermophysical properties of water to minute changes in the atomic-level properties and is expected to inform future MD-based studies of phase change processes of water, especially as they occur at elevated pressures and on nanostructured surfaces and in nanoconfined geometries.

Computational evaluation of flight performance of flapping-wing nano air vehicles using hierarchical coupling of nonlinear dynamic and fluid-structure interaction analyses

This study endeavours to introduce an inventive hierarchical coupling methodology for evaluating the flight capabilities of polymer micromachined flapping-wing nano air vehicles (FWNAVs) using advanced computational tools. Furthermore, our objective is to provide a practical demonstration of FWNAVs in both tethered and airborne scenarios. These dual objectives represent the distinctive and pioneering aspects of this research. Such FWNAVs, which are insect-inspired robots, have a 2.5-dimensional structure. An FWNAV comprises a micro piezoelectric drive system and a pair of micro thin flexible wings. The drive system includes a micro transmission and a piezoelectric bimorph actuator. The flight performance of the designed FWNAV is evaluated using a hierarchical coupling approach, where the whole system is decomposed into a micro wing and a micro piezoelectric drive system. The coupling between these parts is modelled as one-way coupling and that between the micro wings and the surrounding air is modelled as strong coupling. In the one-way coupling analysis, nonlinear structural dynamic analysis is conducted for the micro piezoelectric drive system, where the dynamic response is transmitted to the micro wings via the Dirichlet boundary condition. In the strong coupling analysis, strongly coupled fluid-structure interaction analysis is conducted for the micro wings and the surrounding air to consider their strong coupling. The optimisation of flight performance is conducted using fluid-structure interaction analysis to achieve sufficient lift force to support the weight of an FWNAV. The design of a tethered and flyable FWNAV with a size of 10 mm or smaller is demonstrated. This FWNAV can be easily fabricated using polymer micromachining.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial—88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

A Mini Review on Cancer Epigenetics

Cancer epigenetics is the study of epigenetic changes to cancer cells' DNA that don't involve a change in the nucleotide sequence but instead affect how the genetic code is expressed. The complicated disease of cancer is brought on by genetic and epigenetic changes in the regulation of cell division. Our knowledge of the molecular etiology of cancer has substantially advanced and also the discoveries in the fields of cancer genomics and epigenomics, which have improved our comprehension of the development and evolution of tumorigenic processes. The interaction between genetic and epigenetic mutations and their interaction with environmental factors, including our microbiome, that influences cellular metabolism and proliferation rates, must therefore be taken into account in any modern perspective on cancer research. Future genetic and epigenetic therapeutics as well as diagnostics and prognosis will all benefit from the integration and increased understanding of these processes. Here, we tried to give a general summary of the disrupted epigenetic processes in cancer and how they affect the beginning and development of the illness. In conclusion, we talked about how advanced experimental methods and computational tools, such as fresh methods for utilizing enormous data sets, could help us better understand and cure cancer.

Investigations on shear band formation in metallic nanolayered composites

While metallic nanolayered composites exhibit ultrahigh strength, they can fail due to shear bands propagation. Shear bands are affected by many factors, such as layer thickness and stacking fault energy. There is a growing demand to simultaneously prevent shear bands while harnessing the high strength potential derived from the dense interface nanostructures. The mechanisms of shear band formation vary among different nanolayered composites, and some of these mechanisms are investigated. The importance of utilizing advanced computational tools to understand shear band formation is highlighted. This review comprehensively addresses the influencing factors of shear band formation, strategies for shear band suppression, and the underlying mechanisms of shear band formation within metallic nanolayered composites.

UCSF ChimeraX: Tools for structure building and analysis.

Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen in electron microscopy, often at challenging resolutions of 3-4 Å. We describe new methods in the UCSF ChimeraX molecular modeling package that take advantage of machine-learning structure predictions, provide likelihood-based fitting in maps, and compute per-residue scores to identify modeling errors. Additional model-building tools assist analysis of mutations, post-translational modifications, and interactions with ligands. We present the latest ChimeraX model-building capabilities, including several community-developed extensions. ChimeraX is available free of charge for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.