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Multilayer thermoplastic composites offer sustainable alternatives to traditional thermoset and metal materials. However, their design is inherently complex, involving numerous interdependent parameters that render conventional processes both expensive and time‐consuming. While machine learning‐assisted methods provide a potential solution, they typically require large datasets that can be costly to obtain. This study explores a robust neural network, specifically, an Advanced Multilayer Perceptron (AdvMLP) Regressor, to predict the peel strength of multilayer thermoplastic composites. Through architectural enhancements, the AdvMLP is effectively trained on a limited yet authentic manufacturing dataset, yielding robust predictions validated by benchmark metrics and k‐fold cross‐validation. The model captures the intricate interplay between manufacturing processes and composite properties, enabling comprehensive feature importance analysis and dimensionality reduction. Overall, this study establishes a robust and generalizable machine learning‐assisted methodology to guide and accelerate the design and optimization of multilayer thermoplastic composites. This study presents a machine learning‐assisted design of multilayer thermoplastic composites, utilizing robust neural network prediction and feature importance analysis. Advanced Multilayer Perceptron Regressor is developed on limited manufacturing dataset to accurately predict peel strength. The model effectively reduced design problem's complexity from 13 parameters down to 6 critical ones, facilitating efficient and sustainable composite development in real‐world applications.

Exacerbated attack behavior has a profound socioeconomic impact and devastating social consequences; however, there is no satisfactory clinical management available for an escalated attack behavior. Social isolation (SI) is widespread during this pandemic and may exert detrimental effects on mental health, such as causing heightened attack behavior. To explore the therapeutic approaches that alleviate the SI-induced heightened attack behavior, we utilized pharmacological methods targeting the GluN2B/NO signaling pathway during the attack behavior. Ifenprodil and TAT-9C peptide targeting GluN2B showed that the inhibition of GluN2B mitigated the SI-induced escalated attack behavior and the SI-induced aberrant nitric oxide (NO) level in the brain. Additionally, the potentiation of the NO level by L-arginine reversed the effects of the inhibition of GluN2B. Moreover, we showed that high doses of L-NAME and 7-NI and subeffective doses of L-NAME in combination with ifenprodil or TAT-9C or subeffective doses of 7-NI plus ifenprodil or TAT-9C all decreased the SI-induced escalated attack behavior and reduced the NO level, further supporting the idea that GluN2B/NO signaling is a crucial modulator of the escalated attack behavior.

We present results of the synthesis of cobalt nanorods using the polyol process and the mechanism of magnetization reversal. We show that the nucleation step is significantly dependent on the nature of the ruthenium chloride used as the nucleating agent. This allows varying the diameter and aspect ratio of the cobalt nanorods independently. Co nanorods with aspect ratio, mean diameter, and mean length in the ranges ARm =3-16, Din= 7-25 nm, and Lm=30-300 nm, respectively, were produced using this method. X-ray diffraction and electron microscopy showed that a strong discrepancy between the structural coherence and morphological aspect ratio can exist because of stacking faults. The coercivity of assemblies of different nanorods was systematically measured, and the highest values were obtained for the smallest diameter and the largest structural coherence length. Micromagnetic simulations were performed to account for the dependence of the coercive field on the diameter. An important observation is that simple coherent magnetization rotation models do not apply to these magnetic nano-objects. Even for very small diameters （Dm = 5-10 nm） well below the theoretical coherent diameter Dcoh（CO）= 24 nm, we observed inhomogeneous reversal modes dominated by nucleation at the rod edges or at structural defects such as stacking faults. We conclude that, in order to produce high-coercivity materials based on nanowires, moderate aspect ratios of 5-10 are sufficient for providing a structural coherence similar to the morphological aspect ratio. Thus, the first priority should be to avoid the formation of stacking faults within the Co nanowires.

We aim at combining high coercivity magnetic nanowires in a polymer matrix in a view to fabricate rare-earth free bonded magnets. In particular, our aim is to fabricate anisotropic materials by aligning the wires in the polymer matrix. We have explored the different parameters of the fabrication process in order to produce a material with the best possible magnetic properties. We show that the choice of a proper solvent allows obtaining stable nanowire suspensions. The length and the type of the polymer chains play also an important role. Smaller chains (M w  < 10,000) provide better magnetization results. The magnetic field applied during the casting of the material plays also a role and should be of the order of a fraction of a tesla. The local order of the nanowires in the matrix has been characterized by TEM and small angle X-ray scattering. The correlation between the local order of the wires and the magnetic properties is discussed. Materials with coercivity μ 0 H c up to 0.70 T at room temperature have been obtained.

The advancement of multilayer thermoplastic composites necessitates the development of robust adhesive materials that can withstand high temperatures and diverse mechanical stresses. This thesis presents a comprehensive approach to enhancing thermoplastic adhesives through material innovations and machine learning modeling, aiming to improve the performance and reliability of multilayer composites in demanding applications. First in this study, an immiscible blend adhesive comprising Polyethylene of Raised Temperature, Polyamide 12 was developed. By optimizing the adhesive layer composition, the resulting trilayer composite demonstrated significantly enhanced barrier properties, and mechanical strength in Young’s modulus, creep resistance, and impact absorption, highlighting the blend's suitability for high-temperature, high-pressure applications. Secondly, the incorporation of carbon fibers into adhesive matrix was investigated to address weak adhesive properties at elevated temperatures. Utilizing a novel T-peel test under controlled conditions, CF reinforcement achieved remarkable increases in peel strength. The enhancement mechanisms were elucidated through macro-level improvements such as an expanded peel zone and elimination of crazing, and micro-level factors including stress transfer and energy dispersion into micro peel zones, thereby significantly boosting the adhesive performance under thermal stress. Thirdly, the interface between carbon fibers and thermoplastic matrices was strengthened through nanostructure surface modification by graphene nanoplatelet coating. The coated carbon fibers exhibited an improvement in interfacial shear strength with polyethylene matrices, while a reduction with PA6 due to differing failure mechanisms. Comprehensive morphological, chemical, and wettability analyses, supported by machine learning-based image segmentation, X-ray photoelectron spectroscopy, and contact-angle measurements, provided a detailed understanding of the interfacial enhancements at the micro and nanoscale. Lastly, an Advanced Multilayer Perceptron Regressor model was developed to predict the peel strength of coextruded multilayer thermoplastic composites. This machine learning approach effectively captured the complex relationships between various input parameters and composite properties, despite being trained on a limited dataset. The model demonstrated robust predictive capabilities, validated through benchmark metrics and k-fold cross-validation. Additionally, feature importance analysis and dimensionality reduction facilitated a deeper insight into the key factors influencing adhesive strength, thereby enabling optimized design strategies for multilayer composite manufacturing. This thesis integrates material science innovations with advanced machine learning techniques to develop high-performance thermoplastic adhesives for multilayer composites. The synergistic enhancements in adhesive formulations, fiber interfaces, and predictive modeling contribute to the creation of composites with superior properties. These findings provide a solid foundation for future advancements in the design and optimization of thermoplastic composite materials for various industrial applications.

Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP-T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates.

Selecting H-60 PVC foam, four-axis E-glass non-woven fabric and vinyl resin, a type of innovative reinforced sandwich composite as grooved perforation sandwich （GPS） were fabricated by VIMP. The interfacial structure between the face and core of the sandwich is innovative because of the acuminate grooves in both sides of foam core and the holes perforated along core’s height. The fabrication results show that VIMP is a high-speed and cost-effective manufacturing method. The mechanical properties of the reinforced foam core were tested. The typical flexural failure modes of sandwich specimens were observed. The flexural stiffness and ultimate bearing capacity of sandwich were studied by ordinary sandwich beam theory and finite element method.

We aim at combining high coercivity magnetic nanowires in a polymer matrix in a view to fabricate rare--earth free bonded magnets. In particular, our aim is to fabricate anisotropic materials by aligning the wires in the polymer matrix. We have explored the different parameters of the fabrication process in order to produce a material with the best possible magnetic properties. We show that the choice of a proper solvent allows obtaining stable nanowire suspensions. The length and the type of the polymer chains play also an important role. Smaller chains (\\(M_w < 10000\\)) provide better magnetization results. The magnetic field applied during the casting of the material plays also a role and should be of the order of a fraction of a tesla. The local order of the nanowires in the matrix has been characterized by TEM and Small Angle Neutron Scattering. The correlation between the local order of the wires and the magnetic properties is discussed. Materials with coercivity \\(\\mu_0 H_c\\) up to 0.70 \\(T\\) at room temperature have been obtained.

We investigate the role of dipolar interactions on the magnetic properties of nanowires aggregates. Micromagnetic simulations show that dipolar interactions between wires are not detrimental to the high coercivity properties of magnetic nanowires composites even in very dense aggregates. This is confirmed by experimental magnetization measurements and Henkel plots which show that the dipolar interactions are small. Indeed, we show that misalignment of the nanowires in aggregates leads to a coercivity reduction of only 30%. Direct dipolar interactions between nanowires, even as close as 2 nm, have small effects (maximum coercivity reduction of ~15%) and are very sensitive to the detailed geometrical arrangement of wires. These results strenghten the potential of magnetic composite materials based on elongated single domain particles for the fabrication of permanent magnetic materials.

Background The triglyceride-glucose index (TyG index) has been proposed as a simple and reliable alternative insulin resistance (IR) marker, while the homeostasis model assessment for IR (HOMA-IR) is the most frequently used index. Few studies have evaluated the role of IR assessed by the TyG index and HOMA-IR on arterial stiffness in a type 2 diabetes (T2D) population with a high risk of increased arterial stiffness. We aimed to investigate the association of the TyG index and HOMA-IR with arterial stiffness in patients with T2D. Methods We recruited 3185 patients with T2D, who underwent brachial-ankle pulse wave velocity (baPWV), an indicator of arterial stiffness, but without previous cardiovascular disease. Increased arterial stiffness was defined as a baPWV value greater than the 75th percentile (18.15 m/s) in the present study. The TyG index was determined as ln(fasting triglycerides [mg/dL] × fasting glucose [mg/dL]/2), and the HOMA-IR was calculated as (fasting insulin [μIU/mL] × fasting glucose [mmol/L])/22.5. Results The mean age of the study participants was 54.6 ± 12.0 years, and 1954 (61.4%) were men. Seemingly unrelated regression estimation analysis demonstrated that the TyG index had stronger associations with baPWV than the HOMA-IR (all P < 0.001). In the multivariable logistic analyses, each one-unit increase in the TyG index was associated with a 1.40-fold (95% CI 1.16–1.70, P < 0.001) higher prevalence of increased arterial stiffness, but the prominent association of the HOMA-IR with the prevalence of increased arterial stiffness was not observed. Subgroup analyses showed that a more significant association between the TyG index and the prevalence of increased arterial stiffness was detected in older patients with a longer duration of diabetes and poor glycaemic control (all P < 0.05). Conclusions Compared with the HOMA-IR, the TyG index is independently and more strongly associated with arterial stiffness in patients with T2D.