Computational Techniques Research Articles

Computational fluid dynamics method for determining the rotational diffusion coefficient of cells

This work presents a straightforward computational method to estimate the rotational diffusion coefficient (Dr) of cells and particles of various sizes using the continuum fluid mechanics theory. We calculate the torque (Γ) for cells and particles immersed in fluids to find the mobility coefficient μ and then obtain the Dr by substituting Γ in the Einstein relation. Geometries are constructed using triangular mesh, and the model is solved with computational fluid dynamics techniques. This method is less intensive and more efficient than the widely used models. We simulate eight different particle geometries and compare the results with previous literature.

Heat transfer analysis for magnetohydrodynamic peristalsis of Reiner–Philippoff fluid: Application of an artificial neural network

Present communication explores a novel application of the computational intelligence technique, namely, the Levenberg–Marquardt scheme under a Backpropagated Neural Network (LM-BNN) to solve the mathematical model for the magnetohydrodynamic peristaltic transport of Reiner–Philippoff (R–Ph) pseudoplastic fluid considering the influences of Ohmic heating, mixed convection, and viscous dissipation through a symmetric channel. The R–Ph fluid model is used in this investigation to elucidate the non-Newtonian behavior of the fluid under consideration. The Reiner–Philippoff fluid model delineates the intricate relationship between stress and deformation rate within the fluid. There are a few studies available on the peristaltic transport of the Reiner–Philippoff fluid that do not incorporate Joule heating, mixed convection, and magnetic field effects. Therefore, a novel mathematical model is developed to employ an artificial neural network technique with a different approach that has not been examined before. The governing equations of the problem are simplified using long wavelength and low Reynolds number approximations, and the resulting system is numerically solved using the BVP4c scheme in MATLAB based on the shooting algorithm. Furthermore, a dataset is constructed through the BVP4c technique for the proposed LM-BNN, considering eight scenarios of peristaltic motion of the Reiner–Philippoff fluid model by varying the Bingham number, the Brinkman number, the Grashof number, the R–Ph fluid parameter, and the Hartmann number. The numerical dataset is divided into 15% for testing, 15% for training, and 70% for validation, which are utilized in LM-BNN to analyze the numerical solutions and Levenberg–Marquardt neural networks (LM-NNs) predicted results. The consistency and effectiveness of LM-BNN are validated through regression analysis, stresses at the wall, error histogram, correlation index, heat transfer, and mean squared error based fitness curves, which vary from 10−3→10−11. Variations in several flow parameters affecting temperature and velocity profiles are explained physically through graphs. Additionally, an analysis of heat transfer and stresses at the wall, including absolute errors, is provided through tables. The outcomes reveal that the improving Grashof number and the Hartmann number tend to increase the temperature profile. Tabular results indicate that rates of heat transfer improve when assigning higher values to the Hartmann number, the Brinkman number, and the Grashof number, whereas stresses at the wall decrease for the Reiner–Philippoff fluid parameter and the Bingham number. The error analysis of numerical simulations is a valuable step in determining whether the data obtained are reliable and accurate. In terms of absolute error, the disagreement between numerical solutions and those predicted by LM-NNs is approximately 10−5→10−11. It is clear from this error analysis that LM-NNs predicted results are consistent and reliable.

A Novel Decision Support System Based on Computational Intelligence and Machine Learning: Towards Zero-Defect Manufacturing in Injection Molding

Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.

Analysis of Fluid Pressure Drop through a Globe Valve using Computational Fluid Dynamics and Statistical Techniques

Fluid mechanics plays a crucial role in everyday life, enabling the selection of accessories, materials, and various components essential for a system through which fluid flows. Pressure drop stands out as one of the most relevant factors in the design of fluid flow systems. However, analytical and experimental physical methods can increase these analyses' costs and time. Hence, in this study, statistical tools are employed to carry out specific experiments supported by numerical fluid simulation, aiming to comprehend the pressure drop behavior in a fluid as it passes through a globe valve. This valve, in turn, possesses distinct operating and manufacturing characteristics. The methods employed encompass a complete factorial system of response surface as support to construct the experimental design path through computational fluid dynamics. Among the key findings, it is demonstrated that, for systems with relatively low flow rates, the valve opening percentage does not exhibit a significant relationship with fluid pressure drop. Conversely, significant effects are observed for systems with relatively high flow rates regarding the valve opening percentage and pressure drop, reaching values of up to 73% pressure drop in this study. It can be inferred that the integration of statistical experimental design techniques and computational fluid dynamics constitutes a valuable resource for studying the pressure drop of a fluid passing through a system.

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models’ applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support “Green Chemistry” and “Green Toxicology”.

Evaluation of new scintillation crystals with MCDM methods for brain PET

Brain Positron Emission Tomography (PET) imaging has been increasingly important in recent decades for improved detection and staging of brain cancer and other brain illnesses. Many elements, including the choice of radiation detecting medium, contribute to the overall image quality used to characterize a PET system's performance. In our previous study, novel transparent optical scintillator crystals for brain PET system were simulated. Therefore, there is a need to evaluate these materials using advanced computational techniques. Hence, in this study, we evaluated and compared them using two Multi-Criteria Decision-Making (MCDM) techniques, namely Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crystals used in this study were strontium hafnate (SHO), gadolinium aluminum gallium garget (GAGG), gadolinium yttrium gallium aluminum garget (GYGAG), gadolinium lutetium gallium aluminum garget (GLuGAG), and lastly lutetium oxyorthosilicate (LSO) for comparison. Density, effective atomic number, energy resolution, light output, and decay time were selected as important criteria. Importance weights of each criteria were then assigned by considering the high resolution and high sensitivity detectors. With both MCDM methods, the results showed that SHO outranked the other scintillator materials followed by LSO and GLuGAG. GYGAG, and GAGG is revealed as the least favorable crystals, in agreement with the previous simulation studies. This study can be extended by including more scintillators as they become available in the future.

A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis.

The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA-resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost-effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through invitro and invivo experiments.

Developing and valuation for Techniques Fingerprint Recognition System

          The distinctive patterns observed on human fingertips are a result of the combination of ridges and troughs present on the skin surface. These patterns achieve their complete development during the period of pregnancy and remain stable throughout an individual's lifespan. Fingerprints refer to the impressions formed by the unique patterns present on the fingertips. Bruising resulting from accidental injuries, such as cuts and burns, can temporarily compromise the visibility of fingerprints. However, after the lesion has fully healed, the original patterns will be restored. Fingerprint recognition is a highly dynamic and extensively researched domain within the realm of biometrics. The word refers to the abbreviated form of the computational technique employed to ascertain the concordance between two distinct human fingerprints. In this naturally tough pattern recognition issue, it is imperative to minimize two error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR). These mistake rates are in competition with each other. The development of computing capabilities has facilitated the creation of Automated Fingerprint Authentication Systems (AFIS), leading to a substantial increase in research activity, particularly in the past twenty years. This article aims to provide a comprehensive review of the issue surrounding fingerprint recognition, including an analysis of its underlying design and implementation difficulties, along with an assessment of its potential future developments.

Structural, spectral analysis, reactivity, topological and pharmacological investigations on an antihistamine agent

In this work, quantum computational techniques have been applied to the title compound (Methyl [2-(pyridin-3-yl) ethyl] amine) for insights into structural, vibrational, and electronic spectral features and chemical reactivity. The observed vibrational spectra in solid-phase have been compared with the theoretical one (DFT together with B3LYP /6-311++G (d, p). For the understanding of intra-molecular hyper conjugative interactions, a natural bond orbital (NBO) study was carried out. Nonlinear optical (polarizabilities) calculations have been performed. The molecule's bioactivity is confirmed by the frontier molecular orbital energy gap, and information regarding intra- and inter-non-covalent interactions is provided by non-covalent bond interaction (NCI) analysis. Molecular electrostatic potential (MEP) can identify a molecule's electrophilic and nucleophilic areas, and it can explain a molecule's chemical implications using electron localization function (ELF). The compound's reactive sites were examined using the Fukui function computations, and the molecule's reactivity is defined by its chemical descriptors. The bioactivity of a molecule is supported by molecular docking using the Histamine 1 receptors 3RZE and 7DFL protein, and drug likeness factors were computed to understand the biological properties of MP3EA.

Numerical investigation of blast loading effects on a thin-walled cylindrical steel storage tank

Cylindrical storage vessels with thin walls are extensively utilized across various industrial sectors, including oil, gas, and petrochemicals, serving as crucial repositories for substances relevant to these industries. However, their susceptibility to fire and explosions underscores the need for comprehensive understanding and mitigation strategies. Amidst the escalating frequency of subversive attacks, regional conflicts, and other destabilizing events, a thorough comprehension of blast loading dynamics becomes paramount. These imperatives are compounded by recent occurrences, notably the cataclysmic explosion at the Beirut port in 2020 and the conflagration at a chemical plant in Texas in 2019, which starkly emphasize the pressing necessity for enhanced safety protocols. Thin-walled structures are particularly vulnerable to explosions, making the prediction of their response crucial. This study focuses on developing a computational model tailored for a horizontally oriented, simply supported, thin-walled water storage tank, utilizing advanced Abaqus/Explicit software. The primary objective is to investigate the impact of water when exposed to the explosive load generated by a 50 kg-TNT detonation at a distance of 0.50 m. The novelty of this work lies in the integration of advanced computational techniques, including the use of ConWEP 2.0, a sophisticated software framework developed by the U.S. Army, which integrates established formulations outlined by Kingery and Bulmash, as well as the employment of the Jones-Wilkins-Lee (JWL) Equation-of-State (EOS) alongside a sophisticated Smoothed-Particle Hydrodynamics (SPH) technique. The results reveal a significant reduction in deformation and damage inflicted upon the storage tank when subjected to explosive forces, attributed to the presence of water. This crucial finding highlights the effectiveness of water inclusion in enhancing the structural resilience of these tanks against potentially catastrophic blasts, thereby providing valuable insights for improved safety and durability in industrial environments.

Identifying and characterising promising small molecule inhibitors of kinesin spindle protein using ligand-based virtual screening, molecular docking, molecular dynamics and MM‑GBSA calculations

The kinesin spindle protein (Eg5) is a mitotic protein that plays an essential role in the formation of the bipolar spindles during the mitotic phase. Eg5 protein controls the segregation of the chromosomes in mitosis which renders it a vital target for cancer treatment. In this study our approach to identifying novel scaffold for Eg5 inhibitors is based on targeting the novel allosteric pocket (α4/α6/L11). Extensive computational techniques were applied using ligand-based virtual screening and molecular docking by two approaches, MOE and AutoDock, to screen a library of commercial compounds. We identified compound 8-(3-(1H-imidazol-1-ylpropylamino)-3-methyl-7-((naphthalen-3-yl)methyl)-1H-purine-2, 6 (3H,7H)-dione (compound 5) as a novel scaffold for Eg5 inhibitors. This compound inhibited cancer cell Eg5 ATPase at 2.37 ± 0.15 µM. The molecular dynamics simulations revealed that the identified compound formed stable interactions in the allosteric pocket (α4/α6/L11) of the receptor, indicating its potential as a novel Eg5 inhibitor.Graphical

Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes. This condition, known as hot-hole blasting, is dangerous due to the increased possibility of premature explosions in loaded blastholes. Thus, it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes. Additionally, it will help achieve the desired fragment size. This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam. Data on blast design, rock mass, and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), variance account for (VAF) and coefficient of efficiency in percentage (CE) were calculated to validate the results. It indicates that the random forest algorithm (RFA) outperforms the artificial neural network (ANN), response surface method (RSM), and decision tree (DT). The values of R2, RMSE, MAE, MSE, VAF, and CE for RFA are 0.94, 0.034, 0.027, 0.001, 93.58, and 93.01, respectively. Multiple parametric sensitivity analyses (MPSAs) of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size. The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting. The optimum factor values for RFA of S/B, ld/B and ls/ld are 1.03, 1.85 and 0.7, respectively.

Advancements in Steroidal Pt(II) & Pt(IV) Derivatives for Targeted Chemotherapy (2000-2023)

Advancements in Steroidal Pt(II) & Pt(IV) Derivatives for Targeted Chemotherapy (2000-2023)

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

تحوير وتشخيص بعض مشتقات الليفوفلوكساسين الحلقية غير المتجانسة كمثبطات إنزيم COX-2 وتقييم فاعليتها الحركية الدوائية عن طريق الالتحام الجزيئي ودراسات ADME السويسرية.

من أجل التنبؤ بالنشاط المضاد للالتهابات ضد انزيمات (COX-2) وتوقع إمكاناتها المضادة للسرطان ، حضرت مشتقات جديدة 1،2،4-تريازول-5-ثايول ، أوكسادايازول   وإيمايد.  تم استخدام الخصائص الفيزيائية ، H1-NMR ، C13-NMR ، التحليل الطيفي FT-IR ، وطرق أخرى لتوصيف وتحديد المركبات المنتجة.  استندت المركبات التي تم إنشاؤها حديثًا (1-10) جزئيًا إلى الأدوية المعروفة المضادة للالتهابات الليفوفلوكساسين.  تم استخدام برنامج دراسة  ADMET السويسري لإجراء تقنيات حسابية المختص في علم الادوية والعقاقير ويصف كيفية تعامل جسم الكائن الحي مع المركبات الصيدلانية بعد تناولها وكذلك التنبؤ بالحركة الدوائية للعقاقير الجديدة و لتقييم انتقائية المركبات التي تم تحضيرها اتجاه إنزيم COX-2 أظهرت النتائج أن جميع المركبات تتوافق مع قاعدة Lipinski الصيدلانية للمركبات المحضرة. درست قيم طاقة الارتباط PLP أعلى من المركب المرجعي من خلال دراسات الالتحام الجزيئي لتفاعلات الترابط مع بروتين COX-2 للارتباط بالأحماض الأمينية في الدواء الاساسي (Levofloxacin). وكذلك تم التنبؤ عن تحسس الشحمية واستقطاب الجزيئات الصغيرة من خلال نموذج البيضة المسلوقة التي تقترح طريقة تغلغل الدماغ والامعاء بالإضافة الى النظام الغذائي لنفس الخصائص الكيميائية والفيزيائية وترجمتها المباشرة في تصميم جزيئة الدواء.  

Optimization of Cabin Virus Transmission Suppression Technology Based on Hanging Curtain Physical Isolation

This study presents an innovative physical isolation measure for commercial scenarios, namely, hanging curtains, to prevent the spread of respiratory infections. Using computational fluid dynamics simulation techniques, the closed spaces within cruise cabins were modeled and numerically analyzed, focusing on the dispersion characteristics of droplets. Additionally, orthogonal methods were employed to investigate various arrangements of hanging curtains and their effects on droplet dispersion based on spatial positioning. The research findings indicated that hanging curtains can effectively alter the airflow within a space, realizing the innovative concept of localized pollutant containment. It was found that the spatial partitioning method based on the location of individuals contributes more to reducing droplet dispersion than other methods. Moreover, the sag height of curtains emerges as the most influential factor on individual infection risk, while the scheme for hanging curtain positions has the least impact. Finally, the optimal configuration recommendation is provided: a curtain bottom coordinate of Z = 2.3 m and a top coordinate of Z = 2.8 m when the infection source was positioned at the center of the space. This configuration has also been validated by varying the location of the infection source. The research findings provide valuable insights for formulating preventive measures for passengers on cruise ships and for pandemic control in similar scenarios.

Decoding paradoxical identities: The discourse construction of left-behind children in news reports

This paper presents an empirical corpus-based study on the identity of left-behind children in China, employing the discourse-historical approach (DHA) framework. It is the first study to combine Latent Dirichlet Allocation (LDA) topic modeling and reported speech analysis to examine how China Daily constructs the identity of left-behind children and explore the cultural and rhetorical factors influencing this identity construction. By integrating computational techniques and qualitative analysis methods, this study provides a comprehensive understanding of the intricate nature of identity construction for left-behind children. This interdisciplinary approach strengthens the theoretical foundations of media analysis and offers fresh insights into the dynamics of media discourse. The findings reveal that China Daily portrays left-behind children in a multifaceted and diverse manner, encompassing both positive and negative aspects, while placing emphasis on their vulnerability and passivity. Furthermore, the media employs communication techniques such as identification by sympathy, antithesis, and inaccuracy to establish emotional resonance and foster audience identification, ultimately influencing the audience’s perspectives on these children.

Unlocking Secrets: Bioinformatics' Impact on Forensic Bio-Examinations

Bioinformatics (BI) is a diverse and interdisciplinary domain that studies biological data through computational techniques. Its rapid technological progress has led to a significant increase in biological data production, sparking a notable expansion in BI research and application. In recent years, the convergence between BI and forensic science has become increasingly evident, with the growing use of BI tools and techniques in forensic investigations. BI is not only extensively used in biomedical research to analyse biological data and forecast gene regulation but also in the development of drugs targeting disorders linked to epigenetic alterations. It merges principles from various fields like biology, computer science, and statistics to process extensive omics data, create databases, predict structures, and model metabolic processes. The impact of BI on biomedical research is substantial, aiding in data processing, prediction, and visualisation. Moreover, BI is increasingly important in analysing data generated through Sanger-based electrophoresis methods and massively parallel sequencing in forensic DNA analysis, demonstrating its practical application in the field. Therefore, BI plays a critical role in forensic identification, searching databases side by side, forensic DNA phenotyping and biographic ancestry, forensic genetic genealogy, and forensic epigenomics. An overview of different bioinformatic methodologies employed in forensic applications, exploring approaches for managing data and investigating trend analysis strategies within forensic contexts, is discussed. Additionally, it investigates data management strategies. The primary objective was to explore how BI facilitates forensic BI's use, integration, and impact. Utilising a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from critical databases like Google Scholar and Scopus, focusing on publications from 2010 to 2024. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalised therapeutics. Forensic scientists can improve forensic exams' accuracy, reliability, and efficiency by using BI tools, metrics, and efficient data management procedures.

Machine learning heralding a new development phase in molecular dynamics simulations

Molecular dynamics (MD) simulations are a key computational chemistry technique that provide dynamic insight into the underlying atomic-level processes in the system under study. These insights not only improve our understanding of the molecular world, but also aid in the design of experiments and targeted interventions. Currently, MD is associated with several limitations, the most important of which are: insufficient sampling, inadequate accuracy of the atomistic models, and challenges with proper analysis and interpretation of the obtained trajectories. Although numerous efforts have been made to address these limitations, more effective solutions are still needed. The recent development of artificial intelligence, particularly machine learning (ML), offers exciting opportunities to address the challenges of MD. In this review we aim to familiarize readers with the basics of MD while highlighting its limitations. The main focus is on exploring the integration of deep learning with MD simulations. The advancements made by ML are systematically outlined, including the development of ML-based force fields, techniques for improved conformational space sampling, and innovative methods for trajectory analysis. Additionally, the challenges and implications associated with the integration of ML and artificial intelligence are discussed. While the potential of ML-MD fusion is clearly established, further applications are needed to confirm its superiority over traditional methods. This comprehensive overview of the new perspectives of MD, which ML has opened up, serves as a gentle introduction to the exciting phase of MD development.

A Novel Numerical Scheme for a Class of Singularly Perturbed Differential-Difference Equations with a Fixed Large Delay

A trigonometric spline based computational technique is suggested for the numerical solution of layer behavior differential-difference equations with a fixed large delay. The continuity of the first order derivative of the trigonometric spline at the interior mesh point is used to develop the system of difference equations. With the help of singular perturbation theory, a fitting parameter is inserted into the difference scheme to minimize the error in the solution. The method is examined for convergence. We have also discussed the impact of shift or delay on the boundary layer. The maximum absolute errors in comparison to other approaches in the literature are tallied, and layer behavior is displayed in graphs, to demonstrate the feasibility of the suggested numerical method.