Modeling Of Effects Research Articles

Long short-term memory based modeling of heat treatment and trigger mechanism effect on thin-walled aluminum 6063 T5 for crashworthiness

ABSTRACT Vehicle safety relies on the capacity of vehicle systems to decrease the likelihood of biomechanical injuries to both vehicle occupants and pedestrians. This can take the form of active or passive measures applied to the materials and their geometry in the event of an impact. Nevertheless, due to the nonlinear behaviour of materials and deformation, a distinct collapse process emerges that requires analysis; this analysis can be virtual or through experimental tests. While observed performance can be ascertained through experimental designs, executing such a design for every parameter combination can be time-consuming and costly. This study employs a Long Short-Term Memory (LSTM) to predict the energy absorbed and crushing force of thin-walled aluminium 6063 T5 tubes subject to different collapse triggers and heat treatments. The LSTM model is constructed using experimental data derived from experiments that consider trigger shape, area, trigger position, furnace duration, cooling temperature, and heat treatment soaking method. The LSTM model achieved a Root Mean Square Error (RSME) of 0.56 and 0.0025 for crushing force and energy absorption, respectively. LSTM proves to be a valuable tool for predicting results in nonlinear analysis, particularly in the context of crush behaviour. Also, a comparison of the LSTM and finite element analysis (FEA) predictive performance is presented.

Data-driven disease progression model of Parkinson's disease and effect of sex and genetic variants.

As Parkinson's disease (PD) progresses, there are multiple biomarker changes, and sex and genetic variants may influence the rate of progression. Data-driven, long-term disease progression model analysis may provide precise knowledge of the relationships between these risk factors and progression and would allow for the selection of appropriate diagnosis and treatment according to disease progression. To construct a long-term disease progression model of PD based on multiple biomarkers and evaluate the effects of sex and leucine-rich repeat kinase 2 (LRRK2) mutations, a technique derived from the nonlinear mixed-effects model (Statistical Restoration of Fragmented Time course [SReFT]) was applied to datasets of patients provided by the Parkinson's Progression Markers Initiative. Four biomarkers, including the Unified PD Rating Scale, were used, and a covariate analysis was performed to investigate the effects of sex and LRRK2-related mutations. A model of disease progression over ~30 years was successfully developed using patient data with a median of 6 years. Covariate analysis suggested that female sex and LRRK2 G2019S mutations were associated with 21.6% and 25.4% significantly slower progression, respectively. LRRK2 rs76904798 mutation also tended to delay disease progression by 10.4% but the difference was not significant. In conclusion, a long-term PD progression model was successfully constructed using SReFT from relatively short-term individual patient observations and depicted nonlinear changes in relevant biomarkers and their covariates, including sex and genetic variants.

A Paradoxical Study of the Influence of Underdog Expectations on Employees' Work Status

While prior studies have demonstrated that underdog expectations (UEs) can benefit or harm performance, research of whether they are detrimental or beneficial to employees' work status has not been much explored. Based on the two-mode models of self-regulation theory, the authors develop a model of the double-edged sword effect of UEs on employees' work status and their boundary conditions. Data from 357 employees showed that: (1) UEs positively affect affective rumination (AR), AR positively affects work alienation (WA) and thus mediates between UEs and WA; (2) UEs positively affect problem-solving pondering (PSP), PSP positively affects work engagement (WE) and thus mediates between UEs and WE; (3) Mindfulness moderated the relationship between UEs and AR, thereby moderating the mediating effects of AR and PSP; (4) Mindfulness moderated the relationship between UEs and PSP, thereby moderating the mediating effects of PSP. This study advances the UEs research. It also provides suggestions on how to leverage the positive effects of UEs with the alertness to mitigate its negative effects.

The effects of online interruption pace and richness on task performance

ABSTRACT This study investigates the impact of online interruption pace and information richness on managerial task performance. A computer game simulating external interruptions online was used to expose participants to either a slow or rapid pace of interruptions and provide them with either lean or rich information during the task. Task performance was measured based on the points scored in the game. Results indicate that the pace of interruptions significantly affected task performance, with participants experiencing a slow pace of interruptions performing better than those experiencing a rapid one. Information richness did not significantly affect task performance. However, the interaction between the two variables significantly affected task performance, with optimal performance occurring with fewer interruptions and richer information. The growing prevalence of remote work and its potential for more frequent and varied interruptions calls for investigating a more complex model of interruption effects, helping managers better understand interruptions in work environments.

A numerical model for chemo-thermo-mechanical coupling at large strains with an application to thermoresponsive hydrogels

The aim of this work is the derivation and examination of a material model, accounting for large elastic deformations, coupled with species diffusion and thermal effects. This chemo-thermo-mechanical material model shows three key aspects regarding its numerical formulation. Firstly, a multiplicative split of the deformation gradient into a mechanical, a swelling and a thermal part. Secondly, temperature-scaled gradients for a numerical design comprising symmetric tangents and, thirdly, dissipation potentials for the modelling of dissipative effects. Additionally, the derived general material model is specialised to thermoresponsive hydrogels to study its predictive capabilities for a relevant example material class. An appropriate finite element formulation is established and its implementation discussed. Numerical examples are investigated, including phase transition and stability phenomena, to verify the ability of the derived chemo-thermo-mechanical material model to predict relevant physical effects properly. We compare our results to established models in the literature and discuss emerging deviations.

Chiral Induced Spin Selectivity.

Since the initial landmark study on the chiral induced spin selectivity (CISS) effect in 1999, considerable experimental and theoretical efforts have been made to understand the physical underpinnings and mechanistic features of this interesting phenomenon. As first formulated, the CISS effect refers to the innate ability of chiral materials to act as spin filters for electron transport; however, more recent experiments demonstrate that displacement currents arising from charge polarization of chiral molecules lead to spin polarization without the need for net charge flow. With its identification of a fundamental connection between chiral symmetry and electron spin in molecules and materials, CISS promises profound and ubiquitous implications for existing technologies and new approaches to answering age old questions, such as the homochiral nature of life. This review begins with a discussion of the different methods for measuring CISS and then provides a comprehensive overview of molecules and materials known to exhibit CISS-based phenomena before proceeding to identify structure-property relations and to delineate the leading theoretical models for the CISS effect. Next, it identifies some implications of CISS in physics, chemistry, and biology. The discussion ends with a critical assessment of the CISS field and some comments on its future outlook.

Network analysis of depression, cognitive functions, and suicidal ideation in patients with diabetes: an epidemiological study in Iran.

The main aim of this study was to assess the prevalence of suicidal ideation and previous suicide attempts among Iranian patients diagnosed with Type-1 diabetes (T1D) and Type-2 diabetes (T2D). Additionally, the study sought to estimate the network structure of depressive symptoms and cognitive functions. 1073 patients participated in the current study. We used Patient Health Questionnaire-9 (PHQ-9), Ask Suicide-Screening Questionnaire, diabetes-related factors, and a battery of cognitive functions tasks to estimate network structures. Also, suicidal ideations and suicide attempts prevalence have been estimated. Statistical analyses were performed using R-studio software, including mixed-graphical models (MGMs) for undirected effects and Directed Acyclic Graphs (DAGs) for directed effects. The prevalence of suicidal ideation was 29.97% in T1D and 26.81% in T2D (p < 0.05). The history of suicide attempts was higher in T1D (10.78%) compared to T2D (8.36%) (p < 0.01). In the MRF networks for T1D, suicidal ideation was directly linked to 'feeling guilt (PHQ.6)', 'Suicide (PHQ.9)', HbA1c, and FBS, while the Inhibition node was directly related to suicidal ideation. The DAGs suggested connections between 'depression', HbA1c, and 'inhibition' with suicidal ideation, along with a link between the current family history of suicide attempts and the patient's history of suicide attempts. For T2D, the MRF networks indicated direct links between suicidal ideation and 'anhedonia (PHQ.1)', 'suicide (PHQ.9)', age, being female, and BMI, with inhibition also being directly related to suicidal ideation. The DAGs revealed connections between 'depression', age, and 'inhibition' with suicidal ideation, as well as links between being female or single/divorced and the patient's history of suicide attempts. The findings suggest that suicide ideation is highly prevalent in patients with diabetes, and these symptoms should be carefully monitored in these patients.

Experimental and Modelling of Shape Memory Effect of Shape Memory Natural Rubber

Shape memory natural rubber (SMNR) is a form of smart material that can memorise its permanent shape in response to temperature. In this article, a phenomenological constitutive model was adopted to predict the stress-strain evolution during the shape memory process of SMNR to understand the behavior of SMNR. A standard linear solid (SLS) model with Kelvin Voigt element was extended with two Mooney Rivlin models to account for the mechanical response, while a thermal strain model represented the change of length during the programming process and recovery process. An external temperature law was constructed to govern the volume fraction change between the soft active and frozen phase. The proposed constitutive model is capable of capturing the stress-strain behaviour of each shape memory step.

Radical pair model for magnetic field effects on NMDA receptor activity

The N-methyl-D-aspartate receptor is a prominent player in brain development and functioning. Perturbations to its functioning through external stimuli like magnetic fields can potentially affect the brain in numerous ways. Various studies have shown that magnetic fields of varying strengths affect these receptors. We propose that the radical pair mechanism, a quantum mechanical process, could explain some of these field effects. Radicals of the form [RO·Mg(H2O)n+·]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[\ ext{RO}^\\cdot \ ext{Mg(H}_2\ ext{O})_n^{+\\cdot }]$$\\end{document}, where R is a protein residue that can be Serine or Tyrosine, are considered for this study. The variation in the singlet fractional yield of the radical pairs, as a function of magnetic field strength, is calculated to understand how the magnetic field affects the products of the radical pair reactions. Based on the results, the radical pair mechanism is a likely candidate for explaining the magnetic field effects observed on the receptor activity. The model predicts changes in the behaviour of the system as magnetic field strength is varied and also predicts certain isotope effects. The results further suggest that similar effects on radical pairs could be a plausible explanation for various magnetic field effects within the brain.

Мемристивные биосенсоры: классификация и энергоинформационная модель

The potential of memristive biosensors as an effective and dynamic link between engineering and biology, providing direct and functional communication for extracting information about biological processes in the human body, is discussed. Memristors can be part of a processing chain and, in the future, combine signal conversion with subsequent processing, acting as intelligent sensors. An energy-informational memristor model describing this nonlinear physical and technical effect and a parametric block diagram for describing such nonlinearity are proposed. To obtain a model of the nonlinear physico-technical effect of a memristor within the framework of the energy-informational model of circuits, a special functional dependence in the “charge-pulse” plane was revealed. It is noted that the memristive effect is observed not only in electrical circuits, but is also described for mechanical, thermal, diffusion, and optical circuits, which are well represented in terms of an energy-informational circuit model. The presented model of the memristive effect will expand the knowledge base of the computer-aided design system by including passports of memristive physical and technical effects. A classification of biosensors based on memristive effects is proposed, which will make it possible to supplement the knowledge bases of the computer-aided design system with passports of memristive physical and technical effects in accordance with this classification and parametric structural schemes of memristive physical and technical effects. The systematization of knowledge based on the identification of the characteristics and features of biosensors, as well as the classification of various types of memristors, will automate the process of choosing the most appropriate type of memristor, taking into account the required characteristics and features of the biosensor, which will lead to an increase in the efficiency of synthesis of new designs of memristive biosensors.

Exploratory empirical model of combined effects of COVID-19 and climate change on youth mental health

Exploratory empirical model of combined effects of COVID-19 and climate change on youth mental health

A complete deterministic model of point contact mixed lubrication coupling dual-mode effects of surface fractal characteristics

For mixed lubrication under point contact, a complete model is proposed including two modes of direct effects of surface roughness on the oil film thickness and rough solid contact characteristic. A new method to describe initial oil film thickness is established based on the effects of the holistic interface roughness. The real contact area of the interface is directly obtained by the sum of all solid nodes in a discrete grid, based on which, the solid contact characteristics are calculated by the fractal method considering the surface roughness. This fills the gap in mixed lubrication where the calculation of the solid part has no basis since solid contact force is unknown. The total load balance is taken as the convergence condition, and film thickness and pressure distribution of mixed lubrication are solved ultimately. The direct influences of fractal factors on lubrication characteristics are analyzed, and a comparison between the classical statistical method and the fractal method is conducted.

A new characterization model of FinFET self-heating effect based on FinFET characteristic parameter

The characterization of the self-heating effect (SHE) has been an important research topic in advanced technology, but the existing characterizations are few and the characterization process is relatively complex. In this research, a SHE characterization model is established based on the relationship between output transconductance variation (∆gm), gate source voltage (VGS) and temperature variation (∆T) caused by SHE through machine learning, and then the model is validated by theoretical analyses and experimental simulation. The characterization model is capable of directly calculating the ∆T caused by SHE during I - V testing and simplifying the SHE characterization steps while ensuring characterization accuracy (∆T difference < 1 °C), thus saving costs. It is also found that the model can expand the characterization range (VGS: 0.3–0.7 V) of SHE and conducts quantitative characterization with model calculation under different VGS, realizing a high characterization resolution of VGS: 0.01 V. The circuit level application proves that the method can be effectively applied to the characterization of the SHE and solves the problem of the characterization of the circuit level SHE.

The effects of flux on the radiation-induced embrittlement of reactor pressure vessel steels: review of current understanding and application to high fluences

It is necessary to quantify the effects of flux on reactor pressure vessel steel embrittlement under neutron irradiation, if surveillance or high-flux test reactor data is used to predict vessel embrittlement occurring at lower fluxes. This is particularly important when considering embrittlement occurring during extended (60–80 years) operation for which there is no direct experience. Dedicated investigations are time-consuming and expensive even when only small flux-fluence ranges are investigated, so collating data from multiple campaigns is necessary to provide sufficient information to cover the wide range of fluxes required for vessel assessment in the long term. This paper collates and reviews such data. The review finds that flux dependences probably differ in sign and strength in different regimes (low flux and fluence, intermediate flux at low and high fluence, high flux at low and high fluence) with the regime limits affected by composition and temperature. The current understanding of diffusion processes and microstructural development are invaluable in interpreting the trends and limits. Many contradictory data sets were found, however, and not all contradictions could be dismissed as resulting from poor quality data. Suggestions are made for investigations to clarify the uncertainties. One wide-ranging model of flux effects, based on an extensive data set, is used to compare high-fluence data from different sources, to assess whether embrittlement rates accelerate after a high, threshold fluence. The model helps to identify experiments which investigated comparable flux-fluence-temperature regimes. The comparable data are split evenly between data sets supporting acceleration after a particular fluence and data sets contradicting it. The model identifies regimes in which further campaigns would clarify the causes of these contrasting observations.

Hexasodium fytate exposure-response correlations in a randomized, placebo-controlled study of patients on dialysis with cardiovascular calcification.

Background: Patients receiving dialysis have high cardiovascular risk in part due to extensive vascular calcification. In the CaLIPSO study, infusion of hexasodium fytate (SNF472), the hexasodium salt of inositol hexaphosphate, for 52weeks thrice weekly during hemodialysis significantly reduced progression of coronary artery calcification (CAC). This report examines pharmacokinetic/pharmacodynamic (PK/PD) and exposure-efficacy in CaLIPSO. Methods: We measured hexasodium fytate plasma concentrations (PK) by validated liquid chromatography-mass spectroscopy, and hydroxyapatite crystallization in plasma (PD) by validated spectrophotometry. Analyses included patients evaluable for PK, PD, and CAC change (per-protocol analysis). We developed a simple Emax model for maximum concentration (Cmax) and PD effect, and linear and non-linear Emax models for exposure-efficacy among individual average Cmax and absolute and percent changes in CAC score from baseline to week 52. Results: Among evaluable patients receiving placebo (n = 15), 300mg (n = 20), or 600mg (n = 20), average Cmax across visits was not quantifiable (<0.76μM), 15μM, and 46μM, respectively. These results suggest a more-than-proportional increase, without accumulation, with a Cmax ratio of approximately 3 for the doses administered. Average inhibition of hydroxyapatite crystallization was 15%, 61%, and 75%, respectively, and similar across visits. Simple Emax models described 80% maximal effect at exposures >21.9µM and a plateau in exposure-efficacy above the third quartile of Cmax (≥32µM). Conclusion: Hexasodium fytate has exposure-dependent effects on hydroxyapatite crystallization and progression of cardiovascular calcification. Simple Emax models show robust relations among exposure, inhibition of hydroxyapatite crystallization, and change in CAC volume. Clinical Trial Registration: https://www.clinicaltrials.gov; identifier NCT02966028.

Computational Modelling and Analysis of Effect of Viscoelastic Materials on Damping and Vibrational Behaviors of Composite Structures—An Extensive Review

Computational Modelling and Analysis of Effect of Viscoelastic Materials on Damping and Vibrational Behaviors of Composite Structures—An Extensive Review

Analytical modeling of magnetocaloric effect in dense nanoparticle systems

AbstractDetermining the magnetocaloric effect (MCE) in dense nanoparticle systems (DNSs) poses a challenge due to the increased complexity of matter at the nanoscale. Given the interparticle magnetic interactions, diverse particle size and shape distributions, and the presence of inhomogeneous magnetic phases, selecting a suitable phenomenological model is essential to describe the temperature dependence of magnetic behavior in DNSs. Herein, we chose a cost‐effective Ni100‐xCrx DNS with adjustable magnetic transitions to showcase the resilience of the MCE across a broad temperature range (147–614 K). While the hyperbolic tangent model appears more fitting for materials with a single Curie temperature (TC), such as its parent bulk alloys, in the presence of a TC distribution a Gaussian distribution model proves to be better suited for DNSs. The latter model yields a magnetic entropy change, ΔSmax = 0.09–0.15 J kg‐K−1 in the DNS at a tiny field of 0.1T. The correlations between the broadening of the MCE peak and TC distribution are attributed to the particle size distribution and chemical inhomogeneity present in the DNS, paving the way for fine‐tuning MCE‐related properties such as the relative cooling power (13.17–33.45 J kg−1) and adiabatic temperature change (0.03–0.17 K). Our methodology not only enhances the potential for designing innovative MCE materials with broader operating ranges but also validates the universality of our phenomenological model for other families of nanocrystalline/nanogranular oxides/alloys thin films.

Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022.

As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture. We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density. The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.

Analytical modelling of scanning strategy effect on temperature field and melt track dimensions in laser powder bed fusion

The manufacture of defect-free and dimensionally accurate parts in laser powder bed fusion (LPBF) is influenced by temperature field, deposited track geometry, and process-induced thermomechanical stress. The selection of an appropriate scanning strategy is key to achieving this goal. Well-tested numerical models of heat transfer and thermal stress are possible routes to design for the LPBF process, but these models are computationally expensive and arduous for practicing engineers. Here, we introduce an analytical heat transfer model tailored for part-scale LPBF simulations, encompassing widely used scanning strategies such as linear, circular, spiral, and circular beam oscillation paths. Notably, our model integrates exact curvilinear trajectories of the laser beam, enhancing fidelity in representing non-linear scanning paths. The computed melt track profiles and thermal cycles are tested rigorously with the corresponding experimentally measured independent results. The computational times for various scanning strategies are examined. A unique temperature non-uniformity metric is defined as the sum of the normalized deviations between the computed temperature field in a layer and the average layer temperature at any time instance. The computed temperature non-uniformity metric is shown to work well as a susceptibility factor for the thermal stress along a layer. Ultimately, the work underscores the potential of an efficient analytical heat transfer model, reducing trial-and-error tests and helping to select optimal scanning strategies in part-scale LPBF.

Novel imidazole based ionic liquid as anti-corrosion additive for aluminum alloy: Combined experimental, DFT/MD simulation and soft computing approach

The anti-corrosion effectiveness of novel 1‑butyl‑3-methylimidazolium tetrachloroindate ionic liquid ([C4MIM][InCl4] (IL)) for aluminum-silicon-titanium (Al-Si-Ti) based aluminum alloy in 1mole (M) potassium hydroxide (KOH) electrolyte at 303–343 K was explored in the current study. To realize this, standard methods such as weight loss, electrochemical investigation, density functional theory (DFT)/molecular dynamics simulation (MD-simulation), scanning electron microscope (SEM), and scanning force microscopy (SFM), were employed to scrutinize the anti-corrosion successfulness of [C4MIM][InCl4] for aluminum alloy in KOH solution. From our findings, the ionic liquid mitigated the corrosion of Al-Si-Ti aluminum alloy, and the inhibition efficiency (IE%) is enhanced with improved ionic liquid concentration. The inhibition efficiencies obtained at 0.8 g/L [C4MIM][InCl4] concentration were 88.46%, 82%, and 82.35%, for gravimetric, potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) procedures, respectively. PDP result disclosed [C4MIM][InCl4] performed like a mixed-type inhibitor of a cathodic predominance. The SEM/SFM examination proved that the ionic liquid developed a shield coat on the metal alloy surface. The thermodynamic probe disclosed [C4MIM][InCl4] molecules fastened onto Al-Si-Ti aluminum alloy surface by physisorption mechanism and best fitted the Frumkin adsorption isotherm model. The DFT/MD-simulation procedure confirmed the adsorption configuration and orientation of [C4MIM][InCl4] molecules in gas and aqueous phase which is in harmony with the experimental discovering. Simulated neural network (SNN), and the adaptive neuro-fuzzy inference system (ANFIS) were deployed for a robust training, forecast and modeling of the interactive effects of the input parameters and the expected feedback, Herein, training via the ANN and ANFIS designs without (GA), as well as computing the statistical indices such as the mean squared error (MSE), hybrid fractional error function (HYBRID%), absolute average relative error (AARE), Marquardt's percentage standard deviation (MPSED%) and r-squared (R2) were employed to appraise the models capability. The optimal IE% forecasted was 88.4842% and 89.0643%, for the ANN and ANFIS, respectively. Based on the numerical values of the ANN and ANFIS parameters calculated much acceptance was accorded to the ANFIS model over the ANN due its high degree of precision and robustness. The aftermath of this study furnishes additional information on systematic plan of corrosion mitigation, and proffer useful instructions for the logical use of [C4MIM][InCl4] as anti-corrosion additive for Al-Si-Ti aluminum alloy threatened by alkaline solution.