Systems For Applications Research Articles

Hybrid Nanoparticle-Hydrogel Systems for Drug Delivery Depots and Other Biomedical Applications.

Hydrogel-based depots typically tend to remain where injected and have excellent biocompatibility but are relatively poor at controlling drug release. Nanoparticles (NPs) typically have the opposite properties. The smaller the NPs are, the more likely they are to leave the site of injection. Their biocompatibility is variable depending on the material but can be poor. However, NPs can be good at controlling drug release. In these and other properties, combining NPs and hydrogels can leverage their advantages and negate their disadvantages. This review highlights the rationale for hybrid NP-hydrogel systems in drug delivery, the basic methods of producing them, and examples where combining the two systems addressed specific problems.

A Hybrid Sparse-dense Defensive DNN Accelerator Architecture against Adversarial Example Attacks

Understanding how to defend against adversarial attacks is crucial for ensuring the safety and reliability of these systems in real-world applications. Various adversarial defense methods are proposed, which aim at improving the robustness of neural networks against adversarial attacks by changing the model structure, adding detection networks, and adversarial purification network. However, deploying adversarial defense methods in existing DNN accelerators or defensive accelerators leads to many key issues. To address these challenges, this article proposes sDNNGuard , an elastic heterogeneous DNN accelerator architecture that can efficiently orchestrate the simultaneous execution of original ( target ) DNN networks and the detect algorithm or network. It not only supports for dense DNN detect algorithms, but also allows for sparse DNN defense methods and other mixed dense-sparse (e.g., dense-dense and sparse-dense) workloads to fully exploit the benefits of sparsity. sDNNGuard with a CPU core also supports the non-DNN computing and allows the special layer of the neural network, and used for the conversion for sparse storage format for weights and activation values. To reduce off-chip traffic and improve resources utilization, a new hardware abstraction with elastic on-chip buffer/computing resource management is proposed to achieve dynamical resource scheduling mechanism. We propose an extended AI instruction set for neural networks synchronization, task scheduling and efficient data interaction. Experiment results show that sDNNGuard can effectively validate the legitimacy of the input samples in parallel with the target DNN model, achieving an average 1.42× speedup compared with the state-of-the-art accelerators.

Adaptive data quality scoring operations framework using drift-aware mechanism for industrial applications

Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the reliability of the incoming data streams is an integral part of trustworthy decision-making. An approach to assess data validity is data quality scoring, which assigns a score to each data point or stream based on various quality dimensions. However, certain dimensions exhibit dynamic qualities, which require adaptation on the basis of the system’s current conditions. Existing methods often overlook this aspect, making them inefficient in dynamic production environments. In this paper, we introduce the Adaptive Data Quality Scoring Operations Framework, a novel framework developed to address the challenges posed by dynamic quality dimensions in industrial data streams. The framework introduces an innovative approach by integrating a dynamic change detector mechanism that actively monitors and adapts to changes in data quality, ensuring the relevance of quality scores. We evaluate the proposed framework performance in a real-world industrial use case. The experimental results reveal high predictive performance and efficient processing time, highlighting its effectiveness in practical quality-driven AI applications.

Specifics of Data Collection and Data Processing during Formation of RailVista Dataset for Machine Learning- and Deep Learning-Based Applications.

This paper presents the methodology and outcomes of creating the Rail Vista dataset, designed for detecting defects on railway tracks using machine and deep learning techniques. The dataset comprises 200,000 high-resolution images categorized into 19 distinct classes covering various railway infrastructure defects. The data collection involved a meticulous process including complex image capture methods, distortion techniques for data enrichment, and secure storage in a data warehouse using efficient binary file formats. This structured dataset facilitates effective training of machine/deep learning models, enhancing automated defect detection systems in railway safety and maintenance applications. The study underscores the critical role of high-quality datasets in advancing machine learning applications within the railway domain, highlighting future prospects for improving safety and reliability through automated recognition technologies.

Integrating advanced human-computer interaction and machine learning models for optimizing VR systems in educational and business applications

This paper presents the development of an advanced Human-Computer Interaction (HCI) model and algorithm integrating Natural Language Processing (NLP) to optimize Virtual Reality (VR) systems for educational and business applications. The proposed model enhances user experience and operational efficiency by addressing interface design, user engagement, real-time data processing, and accessibility. Continuous learning and contextual data integration ensure adaptive and personalized interactions, improving the functionality and applicability of VR environments. Fig.: 2. Refs.: 11 titles.

Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity.

In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize the synthesis formulation of enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM)2 (eIM = 2-ethylimidazolate) was selected as the model ZIF to test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, we efficiently discovered GOx/ZIF (G151) and HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents the product of encapsulation efficiency (E in %), retained enzymatic activity (A in %), and thermal stability (T in %)) at least 1.3 times higher than those in systematic seed data studies. Furthermore, advanced statistical methods derived from the trained random forest model qualitatively and quantitatively reveal the relationship among synthesis, structure, and performance in the enzyme/ZIF system, offering valuable guidance for future studies on enzyme/ZIFs. Overall, our proposed ML-assisted design strategy holds promise for accelerating the development of enzyme/ZIFs and other enzyme immobilization systems for biocatalysis applications and beyond, including drug delivery and sensing, among others.

Tuning equations for sliding mode controllers: An optimal multi-objective approach for non-minimum phase systems

This article proposes an approach to create tuning equations based on optimal multi-objective design specifically for Sliding Mode controllers applied to non-minimum phase processes that can be approximated to a First-Order-Plus-Deadtime class of systems. This is achieved by taking into account various performance criteria, including tracking, controller action, and transient response. The proposed tuning equations aim to achieve a well-balanced trade-off among competing objectives. The paper presents a systematic framework for formulating the multi-objective optimization problem and derives analytical expressions for the tuning parameters based on the desired performance specifications. The effectiveness of the proposed method is demonstrated through simulations in three dynamic processes: a high-order linear system with dominant delay, a high-order plus inverse response system, and a nonlinear process with variable parameters and dominant deadtime. A comparative analysis with an existing SMC tuning technique shows superior performance results. This paper provides valuable information on the optimization procedure for obtaining analytical tuning equations for controllers, offering a promising avenue for improving the performance of control systems in practical applications.

In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification

This study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived from skeletal data, offering a new approach that diverges from traditional silhouette-based methods. We thoroughly examine each module of GAF-Net and explore various fusion methods at the both score and feature levels, extending beyond initial simple concatenation. Comprehensive evaluations on the iLIDS-VID and MARS datasets demonstrate GAF-Net’s effectiveness across scenarios. GAF-Net achieves state-of-the-art 93.2% rank-1 accuracy on iLIDS-VID’s long sequences, while MARS results (86.09% mAP, 89.78% rank-1) reveal challenges with shorter, variable sequences in complex real-world settings. We demonstrate that integrating skeleton-based gait features consistently improves Re-ID performance, particularly with long, more informative sequences. This research provides crucial insights into multi-modal feature integration in Re-ID tasks, laying a foundation for the advancement of multi-modal biometric systems for diverse computer vision applications.

Advancements in Cancer Therapeutics: Current Paradigms in Applying Biodegradable Polymer Nanoparticles for Enhanced Management

Abstract: The development of novel colloidal formulations that can regulate the pharmacological and biological properties of medications has been made possible by the developments of nanotechnology. Biodegradable nanoparticles were exploited as drug delivery methods because of their high bioavailability, improved encapsulation, controlled release, and less toxic characteristics. Over the past few decades, a variety of synthetic polymers have been investigated for application in nanomedicine, particularly in drug delivery systems. Drug delivery polymers need to be environmentally friendly, biodegradable, and biocompatible. As they have the ability to provide targeted delivery to a specified site, polymeric nanoparticles have the potential to increase the effectiveness of cancer therapies significantly. It is possible to modify the physical and chemical characteristics of polymers to provide delivery across the many biological barriers needed to reach different cell subsets. The use of biodegradable polymers as nanocarriers is particularly appealing since these materials can be developed to show triggered functionality at certain locations or activated by an external source in addition to degrading under physiological circumstances. Biodegradable polymers can be developed as easy drug-delivery systems that specifically target the tumour microenvironment. This is because these nanomedicines can directly target cancer cells, as well as blood vessels that supply the nutrition and oxygen required for tumour growth and immune cells that support anti-cancer immunotherapy. With the advancements in nanotechnology-based drug delivery systems for pharmaceutical applications, it is exciting to examine and highlight the significance of polymeric nanocarrier systems for drug delivery in chemotherapy.

Regulated Interfacial Proton and Water Activity Enhances Mn2+/MnO2 Platform Voltage and Energy Efficiency

Aqueous zinc battery (AZB) is one of the most promising energy storage systems for large-scale applications due to its high safety, low cost, and environmental friendliness. To match the high capacity of zinc metal (~820 mAh g-1), various cathode materials, such as oxides, open structure analogues, chalcogens, and halogens, have been explored for AZBs. Among them, manganese dioxide (MnO2) has been considered to be one of the most attractive candidates due to its high capacity (~308 mAh g-1 for one-electron transfer) and adequate redox potential (~0.70 V vs. SHE). Recently, the MnO2 dissolution/deposition charge storage mechanism has been demonstrated to further increase the voltage and capacity of Mn-based AZBs. The dissolution/deposition (Mn2+/MnO2) is a two-electron transfer process, which doubles the theoretical capacity of MnO2 cathode to ~616 mAh g-1 and considerably increases the redox potential (~1.229 V vs. SHE). The charge storage via the dissolution/deposition mechanism pushes the aqueous-based energy density even higher, to over 1000 Wh kg-1.However, this charge storage mechanism involves liquid-solid transition, as well as the thermodynamics and kinetics of this reaction are highly dependent on the acidity and interfacial environment. Yet, the opposite effect of interfacial H+ on the dissolution/deposition processes and the role of interfacial H2O are rarely discussed. Here we introduce tetrafluoroborate anion (BF4 -) into the sulfate-based electrolyte to regulate the interfacial H+ and H2O activity. First, BF4 - hydrolysis increases the electrolyte’s acidity, promoting MnO2 dissolution. Second, BF4 - forms an H-bond network with interfacial H2O that assists H+ diffusion while retaining sufficient H2O supply to facilitate MnO2 deposition. As a result, the cathode-free Zn//MnO2 electrolytic cell achieves a high plateau of ~1.92 V and energy efficiency of ~84.23 % in the BF4 - containing electrolyte. Significantly, the cell delivers 1000 cycles at 1 C with ~100 % Coulombic efficiency and a high energy efficiency retention of 93.65 %. Our findings disclose a new strategy to promote Mn2+/MnO2 platform voltage and energy efficiency for future large-scale energy storage systems.

Enabling Downstream Processing of Lignin Effluent from Cosolvent Enhanced Lignocellulosic Fractionation (CELF) Process through Controlled Electrocatalytic Oxidation and Reduction

Lignin which is a complex organic polymer is abundant in nature. But due to its recalcitrant nature, it can't be converted to value added products easily. Previous studies have found that 40–60 million tons of lignin wastes are generated from the pulp and paper industry globally [1]. The sustainability of these industries can be ensured by converting these wastes into valuable products through environmentally friendly processes. Currently, there are many prevalent catalytic technologies for converting lignin to valuable fuels and chemicals. But these technologies use large amounts of energy and harsh reaction conditions. As a result, catalyst recovery and decomposition often become difficult under such harsh conditions. Besides, bonds between lignin precursors are cleaved unselectively and the selectivity of aromatic products is reduced. An electrocatalytic approach may allow improved control, and few studies have evaluated the electrocatalytic oxidation and reduction of lignin in organic solvents.Organic solvents like tetrahydrofuran (THF) are of interest for lignin electrocatalysis due to their use in the promising cosolvent enhanced lignocellulosic fractionation (CELF) process. Also, THF is a renewable solvent as it can be produced sustainably from the conversion of lignocellulosic biomass. In CELF process, high purity technical lignin can be produced at milder temperatures (1800C) from a mixture of THF and sulfuric acid. Thus, this process has the capability to overcome biomass recalcitrance by breaking β-O-4 aryl ether interunit linkages. Using electrocatalytic conversion processes, additional β-O-4, interunit linkages can be broken down selectively. As a result, if these two processes are integrated, a high amount of phenolic hydroxyl groups with low-content aryl ether linkages will be produced, making the product suitable for the development of biofuels and other chemicals. This study shows that by using controlled electrocatalytic oxidation and reduction in THF/aqueous acidic electrolytes in the ratio of 2:1, nonpolar β-O-4 linkages can be cleaved [2]. This ratio was chosen to mimic the electrolyte composition environment of the CELF pretreatment. The results from both attenuated total reflection-infrared spectroscopy (ATR-IR) and NMR characterization are consistent and show that β-O-4 interlinkage bonds were broken. Quantitative calculations from NMR show that during controlled oxidative potential holds (constant potential oxidation), the presence of aromatic structural components of the lignin polymer increased by 28.67% and aliphatic structural components decreased by 32.73%. On the other hand, during controlled reductive potential holds (constant potential reduction), the presence of aromatic structural compounds decreased by 33.50% and aliphatic structural compounds decreased by 78.43%. These results indicate that electrooxidation and electroreduction may be used strategically to cleave interunit linkages. Finally, it can be said, if electrochemical degradation of lignin is used as a secondary treatment along with the CELF process, it will substantially increase the possibility of transforming lignin into value-added products.References Collins, M. N., Nechifor, M., Tanasă, F., Zănoagă, M., McLoughlin, A., Stróżyk, M. A., Culebras,M., & Teacă, C. A. (2019). Valorization of lignin in polymer and composite systems for advanced engineering applications–a review. International journal of biological macromolecules, 131, 828-849.Hasan, M., Akbari, A., & Greenlee, L. F. (2023). Combined Electrocatalytic Oxidation and Reduction to Selectively Cleave β-O-4 Linkage of Lignin over Platinum Electrode in Organic Solvent: Secondary Treatment Opportunity for CELF Process. ACS Sustainable Chemistry & Engineering. Figure 1

Optimizing Camera Exposure Time for Automotive Applications.

Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. Exposure time, the period during which the camera sensor is exposed to light, directly influences the amount of information captured. In dynamic scenarios, such as those encountered in typical driving scenarios, optimizing exposure time becomes challenging due to the inherent trade-off between Signal-to-Noise Ratio (SNR) and motion blur, i.e., extending exposure time to maximize information capture increases SNR, but also increases the risk of motion blur and overexposure, particularly in low-light conditions where objects may not be fully illuminated. The study introduces a comprehensive methodology for exposure time optimization under various lighting conditions, examining its impact on image quality and computer vision performance. Traditional image quality metrics show a poor correlation with computer vision performance, highlighting the need for newer metrics that demonstrate improved correlation. The research presented in this paper offers guidance into the enhancement of single-exposure camera-based systems for automotive applications. By addressing the balance between exposure time, image quality, and computer vision performance, the findings provide a road map for optimizing camera settings for ADAS and autonomous driving technologies, contributing to safety and performance advancements in the automotive landscape.

Field-Programmable Gate Array Architecture for the Discrete Orthonormal Stockwell Transform (DOST) Hardware Implementation

Time–frequency analysis is critical in studying linear and non-linear signals that exhibit variations across both time and frequency domains. Such analysis not only facilitates the identification of transient events and extraction of key features but also aids in displaying signal properties and pattern recognition. Recently, the Discrete Orthonormal Stockwell Transform (DOST) has become increasingly utilized in many specialized signal processing tasks. Given its growing importance, this work proposes a reconfigurable field-programmable gate array (FPGA) architecture designed to efficiently implement the DOST algorithm on cost-effective FPGA chips. An accompanying MATLAB app enables the automatic configuration of the DOST method for varying sizes (64, 128, 256, 512, and 1024 points). For the implementation, a Cyclone V series FPGA device from Intel Altera, featuring the 5CSEMA5F31C6N chip, is used. To provide a complete hardware solution, the proposed DOST core has been integrated into a hybrid ARM-HPS (Advanced RISC Machine–Hard Processor System) control unit, which allows the control of different peripherals, such as communication protocols and VGA-based displays. Results show that less than 5% of the chip’s resources are occupied, indicating a low-cost architecture that can be easily integrated into other FPGA structures or hardware systems for diverse applications. Moreover, the accuracy of the proposed FPGA-based implementation is underscored by a root mean squared error of 6.0155 × 10−3 when compared with results from floating-point processors, highlighting its accuracy.

Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications.

Recognizing Handwritten Digits on MNIST Dataset using KNN Algorithm

Handwritten digit recognition is a critical job in computer vision and is used as a frequent benchmark for testing machine learning algorithms. This work describes the creation of a recognition system utilizing the K-Nearest Neighbors (KNN) method, which was chosen for its simplicity and ease of understanding. The system is built on the MNIST dataset, which contains a vast number of photographs of handwritten digits. The process begins with data collection and investigation, which involves analyzing the content and properties of the MNIST dataset to better comprehend the range of handwriting styles. This is followed by data preparation, which consists of standardizing pixel values to increase algorithm performance and dividing the dataset into training and testing sets. This split aids in assessing the model's generalization capabilities. KNN was selected as the model due to its non-parametric nature, meaning it does not make assumptions about the data's underlying distribution. This characteristic makes it versatile and effective for classification tasks like digit recognition. The model is trained in a unique way compared to other machine learning methods: instead of learning parameters, KNN stores all the training data and uses it during the prediction phase to classify new data points based on the nearest neighbors. The performance of the model was evaluated using accuracy as a metric, which measures the percentage of correctly classified digits. Different values of kkk, the number of neighbors, were tested to find the optimal setting. The results showed that KNN could effectively classify handwritten digits with significant accuracy. Finally, the paper discusses future work and deployment considerations. It highlights the potential for deploying KNN-based digit recognition systems in real-world applications and suggests further research to refine preprocessing techniques, optimize model parameters, and explore more advanced models, such as neural networks, to enhance accuracy and efficiency. This study demonstrates the practical applicability of KNN in digit recognition and outlines a path for future improvements.

FDM printing of microneedles based on biocompatible and biodegradable thermoplastic polymers

AbstractMicroneedles are an emerging class of transdermal drug delivery systems for biomedical applications. These devices allow minimally invasive and painless drug delivery, making them an attractive alternative to hypodermic injections. This paper focuses on the study of polymer‐based solid microneedles fabrication process using the fused deposition modeling (FDM) method. Biodegradable and biocompatible polymers are selected as printing materials. For this study, the thermoplastic polylactic acid (PLA), currently used for FDM microneedle preparation was selected. To overcome the potential brittleness of PLA during transdermal application, two other materials are also studied: polycaprolactone (PCL) and a PLA/PCL blend. The effect of 3D printing parameters on the dimensional accuracy of the microneedles is studied to determine the optimum printing conditions for each filament. In addition, the dimensions and/or geometry of the microneedles are modified to print its with minimal dimensions. Then, a post‐fabrication chemical etching treatment is used to improve both, size and shape of the printed solid microneedles.

Fluorescent Nanodiamonds for High-Resolution Thermometry in Biology.

Optically active color centers in diamond and nanodiamonds can be utilized as quantum sensors for measuring various physical parameters, particularly magnetic and electric fields, as well as temperature. Due to their small size and possible surface functionalization, fluorescent nanodiamonds are extremely attractive systems for biological and medical applications since they can be used for intracellular experiments. This review focuses on fluorescent nanodiamonds for thermometry with high sensitivity and a nanoscale spatial resolution for the investigation of living systems. The current state of the art, possible further development, and potential limitations of fluorescent nanodiamonds as thermometers will be discussed here.

Advanced Denoising and Meta-Learning Techniques for Enhancing Smart Health Monitoring Using Wearable Sensors

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.

Innovations in Deep Learning and Intelligent Systems for Healthcare and Engineering Applications

Innovations in Deep Learning and Intelligent Systems for Healthcare and Engineering Applications

Towards Design Optimization of Compliant Mechanisms: A Hybrid Pseudo-Rigid-Body Model-Finite Element Method Approach and an Accurate Empirical Compliance Equation for Circular Flexure Hinges.

Innovative designs such as morphing wings and terrain adaptive landing systems are examples of biomimicry and innovations inspired by nature, which are actively being investigated by aerospace designers. Morphing wing designs based on Variable Geometry Truss Manipulators (VGTMs) and articulated helicopter robotic landing gear (RLG) have drawn a great deal of attention from industry. Compliant mechanisms have become increasingly popular due to their advantages over conventional rigid-body systems, and the research team led by the second author at Toronto Metropolitan University (TMU) has set their long-term goal to be exploiting these systems in the above aerospace applications. To gain a deeper insight into the design and optimization of compliant mechanisms and their potential application as alternatives to VGTM and RLG systems, this study conducted a thorough analysis of the design of flexible hinges, and single-, four-, and multi-bar configurations as a part of more complex, flexible mechanisms. The investigation highlighted the flexibility and compliance of mechanisms incorporating circular flexure hinges (CFHs), showcasing their capacity to withstand forces and moments. Despite a discrepancy between the results obtained from previously published Pseudo-Rigid-Body Model (PRBM) equations and FEM-based analyses, the mechanisms exhibited predictable linear behavior and acceptable fatigue testing results, affirming their suitability for diverse applications. While including additional linkages perpendicular to the applied force direction in a compliant mechanism with N vertical linkages led to improved factors of safety, the associated increase in system weight necessitates careful consideration. It is shown herein that, in this case, adding one vertical bar increased the safety factor by 100N percent. The present study also addressed solutions for the precise modeling of CFHs through the derivation of an empirical polynomial torsional stiffness/compliance equation related to geometric dimensions and material properties. The effectiveness of the presented empirical polynomial compliance equation was validated against FEA results, revealing a generally accurate prediction with an average error of 1.74%. It is expected that the present investigation will open new avenues to higher precision in the design of CFHs, ensuring reliability and efficiency in various practical applications, and enhancing the optimization design of compliant mechanisms comprised of such hinges. A specific focus was put on ABS plastic and aluminum alloy 7075, as they are the materials of choice for non-load-bearing and load-bearing structural components, respectively.