Exploring the Potential of Artificial Intelligence for Hydrogel Development—A Short Review

Gels, Journal Year: 2023, Volume and Issue: 9(11), P. 845 - 845

Published: Oct. 25, 2023

AI and ML have emerged as transformative tools in various scientific domains, including hydrogel design. This work explores the integration of techniques realm development, highlighting their significance enhancing design, characterisation, optimisation hydrogels for diverse applications. We introduced concept train underscoring its potential to decode intricate relationships between compositions, structures, properties from complex data sets. In this work, we outlined classical physical chemical setting stage AI/ML advancements. These methods provide a foundational understanding subsequent AI-driven innovations. Numerical analytical empowered by were also included. computational enable predictive simulations behaviour under varying conditions, aiding property customisation. emphasised AI’s impact, elucidating role rapid material discovery, precise predictions, optimal like neural networks support vector machines that expedite pattern recognition modelling using vast datasets, advancing formulation discovery are presented. ML’s influence on revolutionised design expediting optimising properties, reducing costs, enabling technologies address pressing healthcare biomedical challenges, offering innovative solutions drug delivery, tissue engineering, wound healing, more. By harmonising insights with techniques, researchers can unlock unprecedented potentials, tailoring

Language: Английский

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Efficient Adsorption Capacity of MgFe-Layered Double Hydroxide Loaded on Pomelo Peel Biochar for Cd (II) from Aqueous Solutions: Adsorption Behaviour and Mechanism

Molecules, Journal Year: 2023, Volume and Issue: 28(11), P. 4538 - 4538

Published: June 3, 2023

A novel pomelo peel biochar/MgFe-layered double hydroxide composite (PPBC/MgFe-LDH) was synthesised using a facile coprecipitation approach and applied to remove cadmium ions (Cd (II)). The adsorption isotherm demonstrated that the Cd (II) by PPBC/MgFe-LDH fit Langmuir model well, behaviour monolayer chemisorption. maximum capacity of determined be 448.961 (±12.3) mg·g-1 from model, which close actual experimental 448.302 (±1.41) mg·g-1. results also chemical controlled rate reaction in process PPBC/MgFe-LDH. Piecewise fitting intra-particle diffusion revealed multi-linearity during process. Through associative characterization analysis, mechanism involved (i) formation or carbonate precipitation; (ii) an isomorphic substitution Fe (III) (II); (iii) surface complexation functional groups (-OH); (iv) electrostatic attraction. great potential for removing wastewater, with advantages synthesis excellent capacity.

Language: Английский

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Uncovering Key Factors in Graphene Aerogel-Based Electrocatalysts for Sustainable Hydrogen Production: An Unsupervised Machine Learning Approach

Gels, Journal Year: 2024, Volume and Issue: 10(1), P. 57 - 57

Published: Jan. 12, 2024

The application of principal component analysis (PCA) as an unsupervised learning method has been used in uncovering correlations among diverse features aerogel-based electrocatalysts. This analytical approach facilitates a comprehensive exploration catalytic activity, revealing intricate relationships with various physical and electrochemical properties. first two components (PCs), collectively capturing nearly 70% the total variance, attested reliability efficacy PCA unveiling meaningful patterns. study challenges conventional understanding that material's reactivity is solely dictated by quantity catalyst loaded. Instead, it unveils complex perspective, highlighting intricately influenced overall design structure. bi-plot uncovers between pH Tafel slope, suggesting interdependence these variables providing valuable insights into interactions slope stands to be positively correlated PC

Language: Английский

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Application of Principal Component Analysis for the Elucidation of Operational Features for Pervaporation Desalination Performance of PVA-Based TFC Membrane

Processes, Journal Year: 2024, Volume and Issue: 12(7), P. 1502 - 1502

Published: July 17, 2024

Principal Component Analysis (PCA) serves as a valuable tool for analyzing membrane processes, offering insights into complex datasets, identifying crucial factors influencing performance, aiding in design and optimization, facilitating monitoring fault diagnosis. In this study, PCA is applied to understand operational features affecting pervaporation desalination performance of PVA-based TFC membranes. PCA-biplot representation reveals that the first two principal components (PCs) accounted 62.34% total variance, with normalized permeation selective layer thickness (Pnorm), water flux (P), temperature (T) contributing significantly PC1, while salt rejection dominates PC2. Membrane clustering indicates distinct influences, membranes grouped based on correlation factors. Excluding outliers increases variance 74.15%, showing altered arrangements. Interestingly, adopted strategy showed high discrepancy between P Pnorm, indicating relevance comparing PVA specific layers those none. results Pnorm more important than features, highlighting its significance both research practical applications. Our findings show even know remains key property; critical developing high-performance, efficient, economically viable Subsequent without (M1 M6) (M7 M11) highlights higher influence variables, understanding membranes’ behavior suitability under different conditions. Overall, effectively delineates characteristics potential applications This study would confirm applicability approach efficiency via these

Language: Английский

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Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model

Mathematics, Journal Year: 2024, Volume and Issue: 12(7), P. 1020 - 1020

Published: March 28, 2024

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences results. In this study, authors consider process assessing business climate depending on realized foreign investments. Due to expected difference assessment using approaches, goal paper is create an optimization model ensemble that uses both types with aim creating a symmetrical approach achieves better results than each type method individually. The proposed solution simultaneously analyzes impact factors investments order determine most important thus enable local government ensure best possible process. innovative idea study inclusion classification feature selection machine learning fulfill set goal. Our research, focused specific case various cities across Republic Serbia, evaluated effectiveness This extends previous research confirms published results, highlighting advantages newly model.

Language: Английский

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Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis

Nutrition Metabolism and Cardiovascular Diseases, Journal Year: 2024, Volume and Issue: 34(6), P. 1389 - 1398

Published: Jan. 21, 2024

Language: Английский

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Investigating the Physical and Operational Characteristics of Manufacturing Processes for MFI-Type Zeolite Membranes for Ethanol/Water Separation via Principal Component Analysis

Processes, Journal Year: 2024, Volume and Issue: 12(6), P. 1145 - 1145

Published: June 1, 2024

In this study, Principal Component Analysis (PCA) was applied to discern the underlying trends for 31 distinct MFI (Mobil No. 5)-zeolite membranes of 11 textural, chemical, and operational factors related manufacturing processes. Initially, a comprehensive PCA approach employed entire dataset, revealing moderate influence first two principal components (PCs), which collectively accounted around 38% variance. Membrane samples exhibited close proximity, prevented formation any clusters. To address limitation, subset acquisition strategy followed, based on findings dataset. This resulted in an enhanced overall contribution revelation diverse patterns among considered (total variance between 55% 77%). The segmentation data unveiled robust correlation silica (SiO2) concentration pervaporation conditions. Additionally, notable clustering chemical compositions preparation solutions underscored their significant efficacy zeolite membranes. On other hand, exclusive composition solution noticed. highlighted high efficiency coupling with experimental results can provide data-driven enhancement MFI-type used ethanol/water separation.

Language: Английский

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Machine Learning Techniques to Analyze the Influence of Silica on the Physio-Chemical Properties of Aerogels

Gels, Journal Year: 2024, Volume and Issue: 10(9), P. 554 - 554

Published: Aug. 27, 2024

This study explores the application of machine learning techniques, specifically principal component analysis (PCA), to analyze influence silica content on physical and chemical properties aerogels. Silica aerogels are renowned for their exceptional properties, including high porosity, large surface area, low thermal conductivity, but mechanical brittleness poses significant challenges. The initially utilized cross-correlation examine relationships between key such as Brunauer-Emmett-Teller (BET) pore volume, density, conductivity. However, weak correlations prompted PCA uncover deeper insights into data. results demonstrated that has a impact aerogel with first (PC1) showing strong positive correlation (R

Language: Английский

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Unveiling Precision Medicine with Data Mining: Discovering Patient Subgroups and Patterns

2021 IEEE Symposium Series on Computational Intelligence (SSCI), Journal Year: 2023, Volume and Issue: unknown, P. 1304 - 1309

Published: Dec. 5, 2023

Data mining techniques, prominently clustering, assume a pivotal role in fortifying precision medicine by facilitating the revelation of patient subgroups that share common attributes. By harnessing clustering for analysis data behavior within realm medicine, distinctive disease patterns, and progression dynamics are unveiled, thereby contributing to formulation precisely tailored treatment strategies. This paper aims present outcomes derived from applied diverse clinical datasets encompassing critical facets such as vital signs, laboratory exams, medications, sepsis, Glasgow Coma Scale, procedures, interventions, diagnostics, admission/discharge records. compilation pertains cohort seventy patients. The resultant uncovers intrinsic patterns relationships residing intricate datasets. Executed following rigorous CRISP-DM methodology, this discovery study identified three distinct clusters group similar characteristics, both categorical numerical data, resulted major groups: patients with stable health conditions, recovery stage, at risk. outcome catalyzes future endeavors, including classification tasks aimed identifying new specific classes, advancing horizons medicine.

Language: Английский

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Towards Understanding Aerogels’ Efficiency for Oil Removal—A Principal Component Analysis Approach

Gels, Journal Year: 2023, Volume and Issue: 9(6), P. 465 - 465

Published: June 6, 2023

In this study, our aim was to estimate the adsorption potential of three families aerogels: nanocellulose (NC), chitosan (CS), and graphene (G) oxide-based aerogels. The emphasized efficiency seek here concerns oil organic contaminant removal. order achieve goal, principal component analysis (PCA) used as a data mining tool. PCA showed hidden patterns that were not possible by bi-dimensional conventional perspective. fact, higher total variance scored in study compared with previous findings (an increase nearly 15%). Different approaches pre-treatments have provided different for PCA. When whole dataset taken into consideration, able reveal discrepancy between nanocellulose-based aerogel from one part chitosan-based graphene-based aerogels another part. overcome bias yielded outliers probably degree representativeness, separation individuals adopted. This approach allowed an 64.02% (for dataset) 69.42% (outliers excluded 79.82% only dataset). reveals effectiveness followed high outliers.

Language: Английский

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