Computational Model Research Articles

Physico-chemical properties and biological evaluation of graphene quantum dots for anticancer drug susceptibility

Graphene quantum dots (GQDs) possess unique optical and biocompatible properties, making them suitable candidates for biomedical and pharmaceutical applications. This study reports the hydrothermal synthesis of pristine-GQD and doped variants: Nitrogen-GQD and Sulfur-GQD. The materials underwent thorough characterization techniques such as UV–vis, fluorescence, XRD, FE-TEM/SEM, EDX, and Raman spectroscopy. The particle sizes of these GQDs range from 2 to 5 nm. We conducted a comprehensive study through MTT assays to evaluate the potential cytotoxic effect of GQD and the doped variants. This study demonstrated their synergistic interactions with an anti-cancer drug, methotrexate (MTX), and also improvement of cytocompatibility in the presence of folic acid (FA). Systematic MD simulations revealed a compacting effect on the dynamic behavior of GQD and its variants in the presence of drugs. Fluorescence spectroscopy and computational modeling suggest non-intercalative surface interactions between GQDs and the drugs. The cytotoxic activity of pristine GQD on HeLa cervical cancer cells is higher than that of N-GQD and S-GQD. When treated with GQD-IC50–MTX-IC50, only 5.6 % of HeLa cells remained viable. The doped variants exhibited bio-compatibility when tested on normal HEK cell lines. Overall, this study emphasizes the potential of GQDs for targeted cancer therapy through an interdisciplinary approach involving material characterization, computational modeling, and biological assays.

Modeling CSF circulation and the glymphatic system during infusion using subject specific intracranial pressures and brain geometries

BackgroundInfusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open question. Here we introduce a computational model that includes the glymphatic system and enables us to determine the importance of (1) brain geometry, (2) intracranial pressure, and (3) physiological parameters on the outcome of and response to an infusion test.MethodsWe implemented a seven-compartment multiple network porous medium model with subject specific geometries from MR images using the finite element library FEniCS. The model consists of the arterial, capillary and venous blood vessels, their corresponding perivascular spaces, and the extracellular space (ECS). Both subject specific brain geometries and subject specific infusion tests were used in the modeling of both healthy adults and iNPH patients. Furthermore, we performed a systematic study of the effect of variations in model parameters.ResultsBoth the iNPH group and the control group reached a similar steady state solution when subject specific geometries under identical boundary conditions was used in simulation. The difference in terms of average fluid pressure and velocity between the iNPH and control groups, was found to be less than 6% during all stages of infusion in all compartments. With subject specific boundary conditions, the largest computed difference was a 75% greater fluid speed in the arterial perivascular space (PVS) in the iNPH group compared to the control group. Changes to material parameters changed fluid speeds by several orders of magnitude in some scenarios. A considerable amount of the CSF pass through the glymphatic pathway in our models during infusion, i.e., 28% and 38% in the healthy and iNPH patients, respectively.ConclusionsUsing computational models, we have found the relative importance of subject specific geometries to be less important than individual differences in resistance as measured with infusion tests and model parameters such as permeability, in determining the computed pressure and flow during infusion. Model parameters are uncertain, but certain variations have large impact on the simulation results. The computations resulted in a considerable amount of the infused volume passing through the brain either through the perivascular spaces or the extracellular space.

Proliferative Vitreoretinopathy in Retinal Detachment: Perspectives on Building a Digital Twin Model Using Nintedanib.

Proliferative vitreoretinopathy (PVR) is a pathological process characterized by the formation of fibrotic membranes that contract and lead to recurrent retinal detachment. Pars plana vitrectomy (PPV) is the primary treatment, but recurrence rates remain high, as surgery does not address the underlying molecular mechanisms driving fibrosis. Despite several proposed pharmacological interventions, no approved therapies exist, partly due to challenges in conducting preclinical and in vivo studies for ethical and safety reasons. This review explores the potential of computational models and Digital Twins, which are increasingly gaining attention in medicine. These tools could enable the development of progressively complex PVR models, from basic simulations to patient-specific Digital Twins. Nintedanib, a tyrosine kinase inhibitor targeting PDGFR, VEGFR, and FGFR, is presented as a prototype for computational models to simulate its effects on fibrotic pathways in virtual patient cohorts. Although still in its early stages, the integration of computational models and Digital Twins offers promising avenues for improving PVR management through more personalized therapeutic strategies.

Computational model for policy simulation and prediction of the regulatory impact of front-of-package food labels

Non-communicable diseases (NCDs), a significant cause of global mortality, necessitate targeted policy measures to reduce exposure to risk factors, particularly nutrition-related ones. Front-of-pack food labeling aims to shape consumer behavior and decrease exposure to nutritional risk factors. There are various forms of this policy, including traffic light (TL), nutrient profile model (NPM), and reference value (RV) labels. Mathematical models incorporating experimental and dose–response data support determining the most effective form. When selecting a modeling method, it is essential to consider the uncertainty involved in estimating risk factors. This study aims to create a computational model to predict the health and economic impacts of different policy options using an improved version of the Scarborough algorithm, incorporating a validation process to address potential uncertainties. The model shows that NPM was the most effective, reducing the estimated annual mortality rate from 514,247 cases to 447,394 cases (13% reduction). The following labels were TL (11% reduction) and RV (6% reduction). Economically, the RV label affects consumer spending the least, predicting an 8% increase in monthly spending per individual from the baseline. When assessed from both health and economic perspectives, the RV label has emerged as the most advantageous front-of-pack food labeling policy. The proposed model can aid policymakers in evaluating policy alternatives and thus selecting the most advantageous policies based on health and economic outcomes while ensuring the model's accuracy and applicability for the community.

Aerodynamic study of a leading-edge rotating cylinder in a cambered aerofoil (NACA2412)

Purpose Flow separation over an aircraft’s wing beyond a specific angle of attack is challenging. Flow boundary layer manipulation has been investigated to improve aerofoil lift and mitigate flow separation difficulties including stall and drag. This is solved via active or passive flow control. Active flow control method moving surface boundary (MSB) enhances shear flow momentum, making it effective. MSB is easier than suction and blowing. Asymmetrical airfoils, which generate lift in aircraft wings, have received less MSB research than symmetrical ones. The purpose of this study is to asses the design efficacy of MSB’s NACA 2412 aerofoil. Design/methodology/approach To observe the performance of MSB in NACA 2412, a computational model has been created, and aerodynamical performance has been analyzed. Findings The study results show that the NACA 2412 with MSB has better aerodynamic efficiency than the NACA 2412 base design. It works best when it reaches its optimal speed and the delay in flow separation works well. Research limitations/implications Limitations may include specific aerofoil applicability, external factors and simulation constraints. Implications guide future research for broader insights and applications. Practical implications Improving asymmetrical aerofoil performance, mitigating stall effects, reducing drag and optimizing designs with moving surface boundary. Insights gained can enhance overall aircraft efficiency and flow control techniques. Originality/value The MSB flow control in a chambered aerofoil is less explored and not explored enough in wing-based aerofoils, and the optimal cylinder speed ratio trend has been discussed at each angle of attack studied.

In Vitro and In Vivo Biological Evaluation of Novel 1,4-Naphthoquinone Derivatives as Potential Anticancer Agents.

A novel library of naphthoquinone derivatives (3-5 aa) was synthesized and evaluated for their anticancer properties. Specifically, compounds 5 i, 5 l, 5 o, 5 q, 5 r, 5 s, 5 t, and 5 v demonstrated superior cytotoxic activity against the cancer cell lines that were studied. All the studied compounds exhibited a higher selectivity index (SI) and a favourable safety profile than the standard drug doxorubicin. Notably, compound 5 v displayed a greater cytotoxic effect on MCF-7 cells (IC50=1.2 μM, and 0.9 μM at 24 h and 48 h, respectively) compared to the standard drug doxorubicin (IC50=2.4 μM, and 2.1 μM at 24 h and 48 h, respectively). To further investigate the mechanism of cytotoxic effect, additional anticancer studies were conducted with 5 v in MCF-7 cells. The studies are including morphological changes, AO/EB (acridine orange/ethidium bromide) double staining, apoptosis analysis, cell colony assay, SDS-PAGE and Western blotting, cell cycle analysis, and detecting reactive oxygen species (ROS) assay. The findings showed that 5 v triggered cytotoxic effects in MCF-7 cells through the initiation of cell cycle arrest at the G1/S phase and necrosis. In vivo ecotoxicity studies indicated that 5 v had lower toxicity towards zebrafish larvae (LC50=50.15 μM) and had an insignificant impact on cardiac functions. In vivo xenotransplantation of MCF-7 cells in zebrafish larvae demonstrated a significant reduction in tumour volume in the xenograft. Approximately 95 % of the zebrafish larvae with 5 v xenografts survived after 10 days of the treatment. Finally, a computational modelling study was conducted on four protein receptors, namely ER, EFGR, BRCA1, and VEFGR2. The findings highlight the importance of the aminonaphthoquinone moiety, amide linkage, and propyl thio moiety in enhancing the anticancer properties. 5 v exhibited superior drug-likeness features and docking scores (-9.1, -7.1, -8.9, and -10.9 kcal/mol) compared to doxorubicin (-7.2, -6.1, -6.9, and -7.3 kcal/mol) against ER, EFGR, BRCA1, and VEGFR2 receptors, respectively. Therefore, the notable antitumor effects of naphthoquinone derivatives (3-5 aa) suggest that these molecular frameworks may play a role in the development of promising anticancer agents for cancer treatment.

From online hate speech to offline hate crime: the role of inflammatory language in forecasting violence against migrant and LGBT communities

Social media messages often provide insights into offline behaviors. Although hate speech proliferates rapidly across social media platforms, it is rarely recognized as a cybercrime, even when it may be linked to offline hate crimes that typically involve physical violence. This paper aims to anticipate violent acts by analyzing online hate speech (hatred, toxicity, and sentiment) and comparing it to offline hate crime. The dataset for this preregistered study included social media posts from X (previously called Twitter) and Facebook and internal police records of hate crimes reported in Spain between 2016 and 2018. After conducting preliminary data analysis to check the moderate temporal correlation, we used time series analysis to develop computational models (VAR, GLMNet, and XGBTree) to predict four time periods of these rare events on a daily and weekly basis. Forty-eight models were run to forecast two types of offline hate crimes, those against migrants and those against the LGBT community. The best model for migrant crime achieved an R2 of 64%, while that for LGBT crime reached 53%. According to the best ML models, the weekly aggregations outperformed the daily aggregations, the national models outperformed those geolocated in Madrid, and those about migration were more effective than those about LGBT people. Moreover, toxic language outperformed hatred and sentiment analysis, Facebook posts were better predictors than tweets, and in most cases, speech temporally preceded crime. Although we do not make any claims about causation, we conclude that online inflammatory language could be a leading indicator for detecting potential hate crimes acts and that these models can have practical applications for preventing these crimes.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Brighter nights and darker days predict higher mortality risk: A prospective analysis of personal light exposure in >88,000 individuals

Light enhances or disrupts circadian rhythms, depending on the timing of exposure. Circadian disruption contributes to poor health outcomes that increase mortality risk. Whether personal light exposure predicts mortality risk has not been established. We therefore investigated whether personal day and night light, and light patterns that disrupt circadian rhythms, predicted mortality risk. UK Biobank participants (N = 88,905, 62.4 ± 7.8 y, 57% female) wore light sensors for 1 wk. Day and night light exposures were defined by factor analysis of 24-h light profiles. A computational model of the human circadian pacemaker was applied to model circadian amplitude and phase from light data. Cause-specific mortality was recorded in 3,750 participants across a mean (±SD) follow-up period of 8.0 ± 1.0 y. Individuals with brighter day light had incrementally lower all-cause mortality risk (adjusted-HR ranges: 0.84 to 0.90 [50 to 70th light exposure percentiles], 0.74 to 0.84 [70 to 90th], and 0.66 to 0.83 [90 to 100th]), and those with brighter night light had incrementally higher all-cause mortality risk (aHR ranges: 1.15 to 1.18 [70 to 90th], and 1.21 to 1.34 [90 to 100th]), compared to individuals in darker environments (0 to 50th percentiles). Individuals with lower circadian amplitude (aHR range: 0.90 to 0.96 per SD), earlier circadian phase (aHR range: 1.16 to 1.30), or later circadian phase (aHR range: 1.13 to 1.20) had higher all-cause mortality risks. Day light, night light, and circadian amplitude predicted cardiometabolic mortality, with larger hazard ratios than for mortality by other causes. Findings were robust to adjustment for age, sex, ethnicity, photoperiod, and sociodemographic and lifestyle factors. Minimizing night light, maximizing day light, and keeping regular light-dark patterns that enhance circadian rhythms may promote cardiometabolic health and longevity.

Be-dataHIVE: a base editing database

Base editing is an enhanced gene editing approach that enables the precise transformation of single nucleotides and has the potential to cure rare diseases. The design process of base editors is labour-intensive and outcomes are not easily predictable. For any clinical use, base editing has to be accurate and efficient. Thus, any bystander mutations have to be minimized. In recent years, computational models to predict base editing outcomes have been developed. However, the overall robustness and performance of those models is limited. One way to improve the performance is to train models on a diverse, feature-rich, and large dataset, which does not exist for the base editing field. Hence, we develop BE-dataHIVE, a mySQL database that covers over 460,000 gRNA target combinations. The current version of BE-dataHIVE consists of data from five studies and is enriched with melting temperatures and energy terms. Furthermore, multiple different data structures for machine learning were computed and are directly available. The database can be accessed via our website https://be-datahive.com/ or API and is therefore suitable for practitioners and machine learning researchers.

Challenges and perspectives in using unspecific peroxygenases for organic synthesis

In the past 20 years, unspecific peroxygenases (UPOs) have emerged as promising biocatalysts for various organic transformations. Particularly, we have witnessed great attention being paid to the screening of new enzymes and expansion of the substrates/products. However, challenges such as enzyme stability, low turnover numbers, and substrate specificity hinder their widespread utilization in practical organic synthesis. This review article provides a concrete and mini-overview of the challenges associated with using UPOs in organic synthesis and discusses strategies for enzyme engineering to overcome these limitations. The article highlights recent advancements in UPO research and presents potential solutions to enhance their catalytic efficiency, stability, substrate specificity, and regioselectivity. Additionally, the review outlines the current methodologies employed for directed evolution and protein engineering of UPOs, along with computational modeling approaches for rational enzyme design. By addressing the challenges and exploring avenues for enzyme engineering, this review aims to shed light on the prospects of UPOs in organic synthesis.

Towards the development of offshore wind farms in the Mediterranean Sea: A techno‐economic analysis including green hydrogen production during curtailments

AbstractBringing floating offshore wind turbines (FOWTs) to a real industrial maturity and reducing the levelized cost of floating wind energy are key to significantly increasing the penetration of renewables in the energy mix of Mediterranean countries, especially if in combination with suitable energy storage systems, such as those involving green hydrogen production. The present study analyses techno‐economic aspects of some of the technologies related to FOWTs and hydrogen production by means of offshore‐generated energy, aiming to evaluate the potential of a floating wind farm integrated with a power‐to‐gas energy storage system in a specific installation site near the Sardinian shores. In comparison to the pioneering studies to date, a more detailed computational model is used, able to account for several critical factors like a better description of metocean conditions, constraints on grid capacity, and a state‐of‐the‐art model to define the farm layout. Concerning hydrogen production, a comparison between the statistical approach, which is commonly used in the field, and a fully time‐dependent method is performed. Proposed results obtained with the statistic and the time‐dependent approach show values ranging between 3.79 and 5.47€/kg, respectively. These outcomes are thought to provide an interesting comparison between different fidelity approaches and realistic reference values for the levelized cost of hydrogen by floating wind in the Mediterranean Sea.

Human Anthropometric Indicators for the Preparation of Simulation Models

Normal human anthropometric parameters are used in the development of medical rehabilitation techniques, in the performance of surgical techniques used for normal human functioning. In order to perform these operations successfully, it is necessary to model normal human physical characteristics and, based on these models, perform the operations correctly. Normal anatomical characteristics have a positive effect on the physiological state of the person. The relevance of the research stimulates the development of the science of valeology and increases the relevance and practical significance of the topic. Prosthetics of organs lost in wars and accidents are also performed on the basis of computer models. The science of digital anthropology is modeling human anthropometrics, and practically important predictions are being made based on this modeling. Digital anthropology is based on the research of modern technology to compile a database of the most diverse anthropological indicators of people. Anthropology occupies a special place in the system of scientific knowledge about the world, because it is a field of science that studies the origin, development and role of humans in nature and society. In modern medicine, mathematical and graphic modeling of human anatomical and anthropological indicators is required in all fields. Anthropometric indicators of humans are taken as the basis in surgery, military science, sports. The structure of shoulders, diaphragm, hips, face, and the age of a human being can also be defined by these anthropometric indicators.

Influence of Printing Parameters on the Morphological Characteristics of Plasma Directed Energy-Deposited Stainless Steel

This study investigated the influence of printing parameters and strategies on the morphological characteristics of austenitic stainless steel beads deposited on carbon steel substrates, using plasma directed energy deposition (DED). The experimental setup varied the welding current, wire feed speed, and torch travel speed, and we analyzed three printing strategies: simple-linear, overlapping, and oscillating. Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. The results showed that the computational model developed by using parabolic assumptions accurately predicted the geometric outcomes of the overlapping beads. The oscillating printing strategy was the one that showed improved morphological uniformity and bead substrate wettability, so these features were used for multi-layer component manufacturing. The use of equivalent wavelength–amplitude values resulted in maximum combinations of bead height and width. Moreover, cost-effective carbon steel substrates were feasibly used in microstructural and elemental analyses, with the latter ones confirming the alignment of the bead composition with the wire-fed material. Overall, this study provides practical insights for optimizing plasma DED processes, thus enhancing the efficiency and quality of metal component manufacturing.

Promising tools for future drug discovery and development in antiarrhythmic therapy.

Arrhythmia refers to irregularities in the rate and rhythm of the heart, with symptoms spanning from mild palpitations to life-threatening arrhythmias and sudden cardiac death (SCD). The complex molecular nature of arrhythmias complicates the selection of appropriate treatment. Current therapies involve the use of antiarrhythmic drugs (class I-IV) with limited efficacy and dangerous side effects and implantable pacemakers and cardioverter-defibrillators with hardware-related complications and inappropriate shocks. The number of novel antiarrhythmic drug in the development pipeline has decreased substantially during the last decade and underscores uncertainties regarding future developments in this field. Consequently, arrhythmia treatment poses significant challenges, prompting the need for alternative approaches. Remarkably, innovative drug discovery and development technologies show promise in helping advance antiarrhythmic therapies. Here, we review unique characteristics and the transformative potential of emerging technologies that offer unprecedented opportunities for transitioning from traditional antiarrhythmics to next-generation therapies. We assess stem cell technology, emphasizing the utility of innovative cell profiling using multi-omics, high-throughput screening, and advanced computational modeling in developing treatments tailored precisely to individual genetic and physiological profiles. We offer insights into gene therapy, peptide and peptibody approaches for drug delivery. We finally discuss potential strengths and weaknesses of such techniques in reducing adverse effects and enhancing overall treatment outcomes, leading to more effective, specific, and safer therapies. Altogether, this comprehensive overview introduces innovative avenues for personalized rhythm therapy, with particular emphasis on drug discovery, aiming to advance the arrhythmia treatment landscape and the prevention of SCD. Significance Statement Arrhythmias and sudden cardiac death account for 15-20% of deaths worldwide. However, current antiarrhythmic therapies are ineffective and with dangerous side effects. Here, we review the field of arrhythmia treatment underscoring the slow progress in advancing the cardiac rhythm therapy pipeline and the uncertainties regarding evolution of this field. We provide information on how emerging technological and experimental tools can help accelerate progress and address the limitations of antiarrhythmic drug discovery.

Preliminary Study on Multi-Scale Modeling of Asphalt Materials: Evaluation of Material Behavior through an RVE-Based Approach

This study employs a microstructure-based finite element modeling approach to understand the mechanical behavior of asphalt mixtures across different length scales. Specifically, this work aims to develop a multi-scale modeling approach employing representative volume elements (RVEs) of optimal size; this is a key issue in asphalt modeling for high-fidelity fracture modeling of heterogeneous asphalt mixtures. To determine the optimal RVE size, a convergence analysis of homogenized elastic properties is conducted using two types of RVEs, one made with polydisperse spherical inclusions, and another made with polydisperse truncated cylindrical inclusions, each aligned with the American Association of State Highway and Transportation Official’s maximum density gradation curve for a 12.5 mm Nominal Maximum Aggregate Size (NMAS). The minimum RVE lengths for this NMAS were found to be in the range of 32–34 mm. After the optimal RVE size for each inclusion shape is obtained, computational models of heterogeneous Indirect Tensile Asphalt Cracking Test samples are then generated. These models include the components of viscoelastic mastic, linear elastic aggregates, and cohesive zone modeling to simulate the rate-dependent failure evolution from micro- to macro-cracking. Examination of load-displacement responses at multiple loading rates shows that both heterogeneous models replicate experimentally measured data satisfactorily. Through micro- and macro-level analyses, this study enhances our understanding of the composition-performance relationships in asphalt pavement materials. The procedure proposed in this study allows us to identify the optimal RVE sizes that preserve computational efficiency without significantly compromising their ability to capture the asphalt material behavior under specific operational conditions.

Cell Cycle Complexity: Exploring the Structure of Persistent Subsystems in 414 Models.

Background: The regulation of cellular proliferation and genomic integrity is controlled by complex surveillance mechanisms known as cell cycle checkpoints. Disruptions in these checkpoints can lead to developmental defects and tumorigenesis. Methods: To better understand these mechanisms, computational modeling has been employed, resulting in a dataset of 414 mathematical models in the BioModels database. These models vary significantly in detail and simulated processes, necessitating a robust analytical approach. Results: In this study, we apply the chemical organization theory (COT) to these models to gain insights into their dynamic behaviors. COT, which handles both ordinary and partial differential equations (ODEs and PDEs), is utilized to analyze the compartmentalized structures of these models. COT's framework allows for the examination of persistent subsystems within these models, even when detailed kinetic parameters are unavailable. By computing and analyzing the lattice of organizations, we can compare and rank models based on their structural features and dynamic behavior. Conclusions: Our application of the COT reveals that models with compartmentalized organizations exhibit distinctive structural features that facilitate the understanding of phenomena such as periodicity in the cell cycle. This approach provides valuable insights into the dynamics of cell cycle control mechanisms, refining existing models and potentially guiding future research in this area.

Human motor learning dynamics in high-dimensional tasks.

Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.

Research on the Impact of Geographical Environment on English Learning Based on Machine Learning

College English course is an important basic course in colleges and universities. The purpose of this study is to better understand the influence mechanism of geographical environment on English learning and provide scientific basis for formulating targeted English education policies. This study uses the method of machine learning to analyze the collected data and explore the influence of geographical environment on college students' English learning. This study constructs a comprehensive data set including students' English learning achievements, academic background and geographical location, and obtains the results through computer model analysis. The results show that geographical environment has an influence on college English learning, which will lead to differences in students' English learning achievements. This study suggests that geographical factors should be considered when formulating English education policies. This study is of great significance to promoting the development of English education and improving students' learning effect.

Computational study of micromagnetorotation on the micropolar flow characteristics in a rectangular channel with continuous applied shear

The aim of this investigation is to computationally analyse the effect of micromagnetorotation on flow characteristics within magneto-micropolar flows. To the authors' knowledge such consideration has not been discussed in the literature in present context. The assumption in literature that the magnetization is parallel to the applied magnetic field is incorrect due to the anisotropic nature of micropolar flows. This allows to re-investigate the micropolar flows in the present of micromagnetorotation. To this end, the theory of magneto-micropolar flows is briefly described and a computational model governing the system dynamics is presented. Finite element code in FreeFem++ is developed and validated through an exact solution. It turns out that the numerical findings obtained are in strong agreement with the analytical results. The results obtained are significant because they can assist researchers in allied fields of electronics, materials science and biomedical engineering in finding solutions to a variety of real-world issues.