Integration of AI-Based Nano Synergy in Bayesian Uncertainty Quantification for Advanced Engineering Design DOI Open Access
S. N. Deepa,

Dr.Meenakshipatil,

Padmini Kaji

et al.

Nanotechnology Perceptions, Journal Year: 2024, Volume and Issue: unknown, P. 77 - 89

Published: Dec. 1, 2024

The advancement in artificial intelligence and nanotechnology has provided new solutions for tackling problems enhanced engineering design. This research focuses on both AI assisted observational methodologies Bayesian uncertainty quantification (BUQ) improving the predictive models, material properties, design procedures. Four complex techniques of estimating managing are following: Neural Networks (BNN), Gaussian Processes (GP), Monte Carlo Dropout (MCD), Ensemble Learning (EL). Numerical studies revealed that forecast accuracy proposed framework is 94.6% with BNN 93.1% GP, which makes excellent improvements over prior arts up to 15% quantification. Besides, computational resources less by 20% EL compared standalone approaches, while incorporation nanoscale information increase AT RT 17%. To demonstrate AI-driven BUQ addresses limitations existing a comparative discussion provided. results reinforce its viability providing sustainable efficient under conditions risk. work may be used as platform subsequent synergies between AI, nanotechnology, advanced materials systems drive progress well

Language: Английский

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration DOI Creative Commons
James C. L. Chow

Biomolecules, Journal Year: 2025, Volume and Issue: 15(3), P. 444 - 444

Published: March 20, 2025

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional modalities. Recent advancements material engineering have enabled the development of nanoparticles tailored for various techniques, including magnetic resonance (MRI), computed tomography (CT), positron emission (PET), ultrasound (US). These nanoscale agents improve sensitivity specificity, enabling early detection precise tumor characterization. Monte Carlo (MC) simulations play a pivotal role optimizing nanomaterial-based modeling their interactions with biological tissues, predicting contrast enhancement, refining dosimetry radiation-based techniques. computational methods provide valuable insights into nanoparticle behavior, aiding design more effective agents. Moreover, artificial intelligence (AI) machine learning (ML) approaches are transforming enhancing image reconstruction, automating segmentation, improving diagnostic accuracy. AI-driven models can also optimize MC-based accelerating data analysis through predictive modeling. This review explores latest imaging, highlighting synergy between nanotechnology, MC simulations, innovations. By integrating these interdisciplinary approaches, future technologies achieve unprecedented precision, paving way diagnostics personalized treatment strategies.

Language: Английский

Citations

2
Smart biomaterials in healthcare: Breakthroughs in tissue engineering, immunomodulation, patient-specific therapies, and biosensor applications DOI Creative Commons
Ansheed Raheem, Kalpana Mandal, Swarup Biswas

et al.

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 1, 2025

Smart biomaterials have significantly impacted human healthcare by advancing the development of medical devices designed to function within tissue, mimicking behavior natural tissues. While intelligence has evolved from inert active over past few decades, smart take this a step further making their surfaces or bulk respond based on interactions with surrounding tissues, imparting outcomes similar tissue functions. This interaction helps in creating stimuli-responsive biomaterials, which can be useful engineering, regenerative medicine, autonomous drug delivery, orthopedics, and much more. Traditionally, material engineering focused refining static properties accommodate them body without evoking an immune response, was major obstacle unrestricted operation. review highlights explains various approaches currently under research for developing that tune responses bodily factors like temperature, pH, ion concentration external magnetism, light, conductivity. Applications soft hard 4D printing, scaffold design are also discussed. The advanced application microfluidics, organ-on-a-chip models, extensively benefits intrinsic discussed below. elaborates how biomaterial could revolutionize biosensor applications, thereby improving patient care quality. We delineate limitations key challenges associated providing insights into path forward outlining future directions next-generation will facilitate clinical translation.

Language: Английский

Citations

1
Bio-Hybrid Films from Chirich Tuber Starch: A Sustainable Approach with Machine Learning-Driven Optimization DOI Open Access
Eyyüp Karaoğul, Gencay Sarıışık, Ahmet Sabri Öğütlü

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 1935 - 1935

Published: Feb. 24, 2025

This study investigates the potential of Chirich (Asphodelus aestivus) tuber, one Turkey’s natural resources, for sustainable bio-hybrid film production. Bio-hybrid films developed from tuber starch in composite form with polyvinyl alcohol (PVOH) were thoroughly examined their physical, mechanical, and barrier properties. During production process, twin-screw extrusion hydraulic hot pressing methods employed; films’ optical, chemical, performances analyzed through FT-IR spectroscopy, water vapor permeability, solubility, mechanical tests. To evaluate durability against environmental factors model properties, advanced computational algorithms such as Gradient Boosting Regression (GBR), Random Forest (RFR), AdaBoost (ABR) utilized. The results showed that GBR algorithm achieved highest accuracy 99.92% R2 presented most robust terms sensitivity to factors. indicate tuber-based exhibit significantly enhanced strength performance compared conventional corn starch-based biodegradable polymers. These superior properties make them particularly suitable industrial applications food packaging medical materials, where durability, moisture resistance, gas characteristics are critical. Moreover, biodegradability integration into circular economy frameworks underscore sustainability, offering a viable alternative petroleum-derived plastics. incorporation ML-driven optimization not only facilitates precise property prediction but also enhances scalability By introducing an innovative, data-driven approach material design, this contributes advancement bio-based polymers applications, supporting global efforts mitigate plastic waste promote environmentally responsible manufacturing practices.

Language: Английский

Citations

0
Analyzing the effects of recycled aggregates on the workability and mechanical characteristics of concrete through mixture design and optimization techniques DOI

Messaouda Bensmail,

Rebih Zaitri,

Mostefa Hani

et al.

World Journal of Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Purpose This paper aims to report the results of an experimental program using a statistical modeling technique enhance formulation ordinary concrete with recycled aggregates (RCA) derived from demolition trash in Biskra region, Algeria. Design/methodology/approach The valorized materials consist coarse dry and presaturated (SRCA), available two granular fractions (3 / 8 mm 16 mm), obtained through crushing screening operations. These partially substitute natural (NCA). A three-factor design was used evaluate effects RCA, SRCA NCA on fresh hardened properties conventional concrete. research effectively created recognized mathematical models that most accurately describe findings. Findings demonstrate notably improves workability due its presaturation, which reduces water absorption elevates availability free water. In contrast, mechanical strength (compressive at 14 28 days) is highest when content maximal (100%), but increasing proportion RCA leads progressive reduction strength. Furthermore, flexural days increases higher aggregates; but, days, deflection more pronounced combinations high concentration NCA. Originality/value optimization validation confirmed predicted values error margin under 8%, emphasizing feasibility as sustainable construction material. findings offer significant insights into effective utilization design, enabling their incorporation practical applications while maintaining structural performance sustainability.

Language: Английский

Citations

0
3D‐Printed Scaffolds for Cranial Bone Regeneration: A Systematic Review of Design, Materials, and Computational Optimization DOI

Elnaz Khorasani,

Bahman Vahidi

Biotechnology and Bioengineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 27, 2025

ABSTRACT Cranial bone defects from trauma, congenital conditions, or surgery are challenging to treat due the skull's limited regeneration. Traditional methods like autografts and allografts have drawbacks, including donor site issues poor integration. 3D‐printed scaffolds provide a patient‐specific alternative, improving regeneration This review evaluates advancements in for cranial regeneration, focusing on fabrication techniques, material innovations, structural optimization while assessing their preclinical clinical potential. A systematic literature search (2014–2024) was conducted using PubMed other databases. Studies addressing scaffold properties such as porosity, pore interconnectivity, mechanical stability were included, non‐cranial studies excluded. Advances 3D printing enabled with optimized architecture enhance support, nutrient transport. Bioceramics, polymers, composites mimic native properties, bioactive coatings further improve osteogenesis. However, translation insufficient customization remain challenges. Further trials crucial overcoming barriers fabrication, bridging gap between research applications.

Language: Английский

Citations

0
BAYESIAN NEURAL NETWORKS FOR PROBABILISTIC MODELING OF THERMAL DYNAMICS IN MULTISCALE TISSUE ENGINEERING SCAFFOLDS DOI
Janjhyam Venkata Naga Ramesh, Abhilash Sonker, G. Indumathi

et al.

Journal of Thermal Biology, Journal Year: 2025, Volume and Issue: 130, P. 104134 - 104134

Published: May 1, 2025

Language: Английский

Citations

0
Economic, technological and environmental drivers of the circular economy in the European Union: a panel data analysis DOI Creative Commons
L. Georgescu, Costinela Forțea, Valentin Marian Antohi

et al.

Environmental Sciences Europe, Journal Year: 2025, Volume and Issue: 37(1)

Published: May 24, 2025

Language: Английский

Citations

0
Integration of AI-Based Nano Synergy in Bayesian Uncertainty Quantification for Advanced Engineering Design DOI Open Access
S. N. Deepa,

Dr.Meenakshipatil,

Padmini Kaji

et al.

Nanotechnology Perceptions, Journal Year: 2024, Volume and Issue: unknown, P. 77 - 89

Published: Dec. 1, 2024

The advancement in artificial intelligence and nanotechnology has provided new solutions for tackling problems enhanced engineering design. This research focuses on both AI assisted observational methodologies Bayesian uncertainty quantification (BUQ) improving the predictive models, material properties, design procedures. Four complex techniques of estimating managing are following: Neural Networks (BNN), Gaussian Processes (GP), Monte Carlo Dropout (MCD), Ensemble Learning (EL). Numerical studies revealed that forecast accuracy proposed framework is 94.6% with BNN 93.1% GP, which makes excellent improvements over prior arts up to 15% quantification. Besides, computational resources less by 20% EL compared standalone approaches, while incorporation nanoscale information increase AT RT 17%. To demonstrate AI-driven BUQ addresses limitations existing a comparative discussion provided. results reinforce its viability providing sustainable efficient under conditions risk. work may be used as platform subsequent synergies between AI, nanotechnology, advanced materials systems drive progress well

Language: Английский

Citations

0