Search Results (186)

In this work, the development of Al-based metal matrix composites (MMCs) is achieved using hybrid SiC and ZrO₂ reinforcement particles for automotive applications. Powder metallurgy (PM) is employed with various combinations of important process parameters for the fabrication of MMCs. MMCs were characterized using scanning electron microscopy (SEM), X-ray diffractometry (XRD), and a microhardness study. All XRD graphs adequately exhibit Al, SiC, and ZrO₂ peaks, indicating that the hybrid MMC products were satisfactorily fabricated with appropriate mixing and sintering at all the considered fabrication conditions. Also, no impurity peaks were observed, confirming high composite purity. MMC products in all the XRD patterns, suitable for the desired applications. According to the SEM investigation, SiC and ZrO₂ reinforcement components are uniformly scattered throughout Al matrix in all produced MMC products. The occurrence of Al, Si, C, Zr, and O in EDS spectra demonstrates the effectiveness of composite ball milling and sintering under all manufacturing conditions. Moreover, an increase in interfacial bonding of fabricated composites at a higher sintering temperature indicated improved physical properties of the developed MMCs. The highest microhardness value is 86.6 HVN amid all the fabricated composites at 7% silica, 14% zirconium dioxide, 500° sintering temperature, 90 min sintering time, and 60 min milling time. An integrated Particle Swarm Optimization–Support Vector Machine (PSO-SVM) model was developed to predict microhardness based on the input parameters. The model demonstrated strong predictive performance, as evidenced by low values of various statistical metrics for both training and testing datasets, highlighting the PSO-SVM model’s robustness and generalization capability. Specifically, the model achieved a coefficient of determination of 0.995 and a root mean square error of 0.920 on the training set, while on the testing set, it attained a coefficient of determination of 0.982 and a root mean square error of 1.557. These results underscore the potential of the PSO-SVM framework, which can be effectively leveraged to optimize process parameters for achieving targeted microhardness levels for the developed Al-SiC-ZrO₂ Composites. Full article

Bamboo, as a forest product material with good mechanical properties, is considered to be a future timber alternative due to its fast growth and accelerated reforestation potential. The use of OSB panels has significantly increased in the market and OSB has replaced traditional panels. Three different OSB panels coded Type 1, Type 2, and Type 3 were produced using bamboo and some mechanical properties were evaluated. Based on the results, Type 2 OSB panels yielded statistically higher bending strength values than Type 1 and Type 3 panels. There were no significant differences between the Type 1 and the Type 3 OSB panels. When the internal bonding (IB) values of the panels were examined, Type 3 yielded the highest values, followed by Type 2 and Type 1. However, it was observed that these resistance differences were not statistically significant. The only type of failure mode observed was brush-shaped separation from the center of the panels. The load–displacement graph of the OSB bamboo panels under bending load indicated a similar load-displacement curve of typical wood under bending load. Full article

Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes. Full article

Power transformers are a key piece of equipment located between the points of supply and consumption of electrical energy. Due to their continuous exposure to the environment, they may be subject to failure. Thus, the modeling of transformers subject to incipient faults using a bond graph approach is presented in this study. In particular, incipient faults in the primary and secondary windings with respect to ground and a turn-to-turn fault in the primary winding are modeled. In order to develop a mathematical model capturing the incipient faults in transformers including magnetic saturation effects, a junction structure for the system applied to the bond graph model is proposed. The steady-state responses of the faulted transformer models using a bond graph approach are presented, leading to the proposal of a method for fault analysis in transformers with DC supply sources. Simulation results for the transformers with the different faults are presented, validating the results obtained according to expressions derived from the bond graph models. Full article

Graphs are one of the dynamic tools used to solve network-related problems and real-time application models. The stability of the network plays a crucial role in ensuring uninterrupted data flow. A network becomes vulnerable when a node or a link becomes non-functional. To maintain a stable network connection, it is essential for the nodes to be able to interact with each other. The vulnerability of a network can be defined as the level of resistance it exhibits following the failure of communication links. Graphs serve as vital tools for depicting molecular structures, where atoms are shown as vertices and bonds as edges. The domination number quantifies the least number of atoms (vertices) required to dominate the entire molecular framework. Domination integrity reflects the impact of removing specific atoms on the overall molecular structure. This concept is valuable for forecasting fragmentation and decomposition pathways. In contrast to the domination number, domination integrity evaluates the extent to which the molecule remains intact following the removal of reactive or controlling atoms. It aids in assessing stability, particularly in the contexts of drug design, polymer analysis, or catalytic systems. This work focuses on the vulnerability parameter, specifically examining the domination integrity of a specific group of bidegreed hexagonal chemical network systems such as pyrene $PY\left(p\right)$, prolate rectangle $R_{p,q}$, honeycomb $HC\left(p\right)$, and hexabenzocoronene $HBC\left(p\right)$. This work also extends to the calculation of the domination integrity value for Cyclic Silicate $C{C}_p$ and Chain Silicate $C{S}_p$ chemical structure networks. Full article

Knee exoskeletons are sophisticated wearable devices engineered to aid or augment human movement, especially in rehabilitation and mobility assistance contexts. To address reliability concerns, the proposed knee exoskeleton incorporates a fault-tolerant control system using a fault detection, isolation and reconfiguration (FDI) technique. This system enables the exoskeleton to continue functioning even if one of the actuators experiences a fault, ensuring user safety and continuous operation. For actuator fault detection, analytical redundancy relations (ARRs) are derived from the bond graph model of the knee exoskeleton. ARRs are monitored for actuator fault detection and isolation. In this work, there is no fault initially; after some time, a fault is created in the rotary actuator; finally, the faulty actuator is reconfigured by another rotary actuator. Simulation findings illustrate the suggested FDI system’s effectiveness in improving the robustness of knee exoskeletons during the sit-to-stand motion. The proposed system successfully reconfigures itself in response to faults. Full article

The effects of proline co-solvent on helix folding are explored through the single discrete coordinate of the number of helical hydrogen bonds. The analysis is based on multi-microsecond length molecular dynamics simulations of alanine-based helix-forming peptides, (ALA)n, of length n = 4, 8, 15 and 21 residues, in an aqueous solution with 2 M concentration of proline. The effects of addition of proline on the free energy landscape for helix folding were analyzed using the graph-based Dijkstra algorithm, Optimal Dimensionality Reduction kinetic coarse graining, committor functions, as well as through the diffusion of the helix boundary. Viewed at a sufficiently long time-scale, helix folding in the coarse-grained hydrogen bond space follows a consecutive mechanism, with well-defined initiation and propagation phases, and an interesting set of intermediates. Proline addition slows down the folding relaxation of all four peptides, increases helix content and induces subtle mechanistic changes compared to pure water solvation. A general trend is for transition state shift towards earlier stages of folding in proline relative to water. For ALA5 and ALA8 direct folding is dominant. In ALA8 and ALA15 multiple pathways appear possible. For ALA21 a simple mechanism emerges, with a single path from helix to coil through a set of intermediates. Overall, this work provides new insights into effects of proline co-solvent on helix folding, complementary to more standard approaches based on three-dimensional molecular structures. Full article

Rigid–flexible coupled robots have problems such as vibration and elastic deformation caused by the flexibility of the members during the motion process, which significantly impacts the system’s motion accuracy and dynamics performance. To address the above problems, a dynamic modeling method based on a vector bond graph is proposed, and a multi-energy domain global dynamic model of Delta-type rigid–flexible coupled parallel robot considering rod flexibility is established. The coupled vibration of the control part and mechanical part of the system is analyzed, and model simulation is verified by 20-sim 4.4 software and ADAMS software (instructional version), which verifies the validity and reasonableness of the modeling method and provides a reference for the modeling of other rigid–flexible coupled systems with parallel systems in space. Full article

This work presents a complete energy model for graphene flakes’ growth with the fewest possible dangling bonds. The model is based on a simple equation that describes the binding energy of graphene flakes consisting of up to 10,000 carbon atoms. Moreover, we demonstrate that the model can accurately calculate the binding energy of a topologically and geometrically diverse array of graphene flakes. According to our calculations, the model can predict the binding energy of a graphene flake with a deviation error of about 2–3%. Hence, we envision that the complete energy model for graphene flakes presented here could be utilized as a novel alternative to conventional Monte Carlo simulation methods used to study graphene growth. Full article

This paper proposes a novel approach to the virtual 3D modeling of articulated mechanisms. It follows the widespread use of XML (eXtensible Markup Language) for various applications and defines a version of XML that is specially designed for the description of 3D geometric models of articulated bodies. In addition, it shows how the 3D geometric model of a mechanism can be gradually developed through the use of suitably defined elements and stored in a corresponding XML file. The developed XML model is processed, and using a powerful VTK (Visualization Toolkit) library, the corresponding virtual model is built and shown on the computer screen. To drive the virtual model, the dynamic model of the mechanism is developed using Bond Graph modeling techniques. Virtual 3D geometric and dynamic models are created using the corresponding software packages: BonSim3D 2023 Visual and BondSim 2023. The models are interconnected by a two-way named pipe. During the simulation of the dynamic model, the parameters necessary to drive the virtual model (e.g., the joint displacements) are collected and sent to the virtual model over the pipe. When the virtual model receives a package, the computer screen is updated by showing the new state of the mechanism. The approach is demonstrated using the example of a holonomic omnidirectional mobile robot. Full article

New assembly methods for aircraft structural parts, such as skins and stringers, are being investigated to address issues like galvanic corrosion, stress concentration, and weight. For this, many researchers are examining the mechanical and fracture properties of adhesively bonded parts through experimental testing and numerical modelling methods, including Cohesive Zone Modelling (CZM), Compliance-Based Beam Method (CBBM), Double Cantilever Beam (DCB), and End Notched Flexural (ENF) tests. In this study, similarly, DCB and ENF tests were conducted on skin and beam parts bonded with AF163-2K adhesive using CBBM and then modelled and analysed in ABAQUS CAE 2018 software. Four different skin–stringer connection models were analysed, respectively, using only adhesive, only rivets, both adhesive and rivets, and also a reduced number of rivets in the adhesively bonded joint. This study found that adhesive increased initial strength, while rivets improved strength after the adhesive began to crack. Using a hybrid connection that combines both rivets and adhesive has been observed to enhance the overall strength and durability of the assembly. Then, experimental results were compared, and four numerical models for skin–stringer connections (adhesive only, rivets only, adhesive and rivets, and adhesive with reduced rivets) were analysed and discussed. In this context, the results were supported and reported with graphs, tables, and analysis images. Full article

Allostery is a fundamental biological phenomenon that occurs when a molecule binds to a protein’s allosteric site, triggering conformational changes that regulate the protein’s activity. However, allostery in antibodies remains largely unexplored, and only a few reports have focused on allostery from antigen-binding fragments (Fab) to crystallizable fragments (Fc). But this study, using anti-phenobarbital antibodies—which are widely applied for detecting the potential health food adulterant phenobarbital—as a model and employing multiple computational methods, is the first to identify a cyclopeptide (cyclo[Link-M-WFRHY-K]) that induces allostery from Fc to Fab in antibody and elucidates the underlying antibody allostery mechanism. The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. Meanwhile, molecular dynamics simulations combined with other analytical methods demonstrated that cyclopeptide induces global conformational shifts in the antibody, which ultimately alter the Fab domain and enhance its antigen-binding activity from Fc to Fab. This result will enable cyclopeptides as a potential Fab-targeted allosteric modulator to provide a new strategy for the regulation of antigen-binding activity and contribute to the construction of novel immunoassays for food safety and other applications using allosteric antibodies as the core technology. Furthermore, graph theory analysis further revealed a common allosteric signaling pathway within the antibody, involving residues Q123, S207, S326, C455, A558, Q778, D838, R975, R1102, P1146, V1200, and K1286, which will be very important for the engineering design of the anti-phenobarbital antibodies and other highly homologous antibodies. Finally, the non-covalent interaction analysis showed that allostery from Fc to Fab primarily involves residue signal transduction driven by hydrogen bonds and hydrophobic interactions. Full article

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The chemical perturbation data are obtained from the L1000 project, which provides information on the up-regulation and down-regulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction. Full article

This research explores the synthesis of carboxymethyl cellulose (CMC) for the development of a cost-effective bioplastic film that can serve as a sustainable alternative to synthetic plastic. Replacing plastic packaging with CMC-based films offers a solution for mitigating environmental pollution, although the inherent hydrophilicity and low mechanical strength of CMC present significant challenges. To address these limitations, zinc oxide nanoparticles (ZnO NPs) were employed as a biocompatible and non-toxic reinforcement filler to improve CMC’s properties. A solution casting method which incorporated varying concentrations of ZnO NPs (0%, 5%, 10%, 15%, 20%, and 25%) into the CMC matrix allowed for the preparation of composite bioplastic films, the physicochemical properties of which were analyzed using scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. The results revealed that the ZnO NPs were well-integrated into the CMC matrix, thereby improving the film’s crystallinity, with a significant shift from amorphousness to the crystalline phase. The uniform dispersion of ZnO NPs and the development of hydrogen bonding between ZnO and the CMC matrix resulted in enhanced mechanical properties, with the film CZ₂₀ exhibiting the greatest tensile strength—15.12 ± 1.28 MPa. This film (CZ₂₀) was primarily discussed and compared with the control film in additional comparison graphs. Thermal stability, assessed via thermogravimetric analysis, improved with an increasing percentage of ZnO Nps, while a substantial decrease in water vapor permeability and oil permeability coefficients was observed. In addition, such water-related properties as water contact angle, moisture content, and moisture absorption were also markedly improved. Furthermore, biodegradability studies demonstrated that the films decomposed by 71.43% to 100% within 7 days under ambient conditions when buried in soil. Thus, CMC-based eco-friendly composite films have the clear potential to become viable replacements for conventional plastics in the packaging industry. Full article

This study proposes an innovative mathematical model for assessing microcirculation in patients with diabetic ulcers, using the ankle–brachial index (ABI). The methodology combines Bond Graph (BG) modeling and Particle Swarm Optimization (PSO), enabling a detailed analysis of hemodynamic patterns in a pilot sample of three patients. The results revealed a correlation between ulcer size and reduced ABI values, suggesting that deficits in microcirculation directly impact the severity of lesions. Furthermore, despite variations in ABI values and arterial pressures, all patients exhibited high capillary resistance, indicating difficulties in microcirculatory blood flow. The PSO-optimized parameters for the capillary equivalent circuit were found to be $R_1=89.784\Omega$, $R_2=426.55\Omega$, $L=27.506\mathrm{H}$, and $C=0.00040675\mathrm{F}$, which confirms the presence of high vascular resistance and reduced compliance in the microvascular system of patients with diabetic foot ulcers. This quantitative analysis, made possible through mathematical modeling, is crucial for detecting subtle changes in microcirculatory dynamics, which may not be easily identified through conventional pressure measurements alone. The increased capillary resistance observed may serve as a key indicator of vascular impairment, potentially guiding early intervention strategies and optimizing diabetic ulcer treatment. We acknowledge that the sample size of three patients represents a limitation of the study, but this number was intentionally chosen to allow for a detailed and controlled analysis of the variables involved. Although the findings are promising, additional experimental validations are necessary to confirm the clinical applicability of the model in a larger patient sample, thus solidifying its relevance in clinical practice. Full article