Bridging theory and practice: A comprehensive algorithm for imageless total knee arthroplasty.

Total knee arthroplasty (TKA) is a surgical procedure to treat severe knee osteoarthritis. Among several techniques available for performing TKA, imageless TKA is known for achieving precise alignment while minimizing invasiveness. This work proposes a comprehensive algorithm for imageless TKA device to calculate the varus/valgus and flexion/extension angles, as well as resection depths for cutting planes at distal femur and proximal tibia. Moreover, the algorithm calculates the hip-knee-ankle (HKA) and flexion angles of the leg. Initially, the proposed algorithm was validated in a virtual environment using a CT-scanned bone model in Solidworks. Subsequently, for the real-world validation, a SoftBone model was resected with conventional intra and extramedullary rods and cross-checked with the proposed algorithm. For the third validation, another SoftBone model was resected with the proposed algorithm and cuts were measured with a vernier caliper. During this experiment, there was an error of approximately 1 mm for both femoral and tibial resection cases when using an infrared camera with an accuracy of ±0.5 mm. However, this error could be reduced using an infrared camera with higher accuracy.

Femoral coordinate system based on articular surfaces: Implications for computer-assisted knee arthroplasty.

Osteoarthritis knee can be restored by total knee arthroplasty (TKA). Imageless TKA requires several anatomical points to construct a reference coordinate system to measure bone resections and implant placement. Inaccuracies in the definition of the coordinate system lead to malalignment and failure of the implant. While the surgical transepicondylar axis (sTEA) is a reliable anatomical axis to define the lateromedial axis for the femoral coordinate system (FCS), the presence of the collateral ligaments and deterioration of the medial sulcus (MS) make the registration of sTEA a challenging task. In this work, sTEA is assigned using the articular surfaces of the femoral condyles, independent of the lateral epicondyle (LE) and MS. A single 3D arc is marked on each condyle, which is transformed into a 2D arc to get the best-fit curve according to the profile of condyles. The turning point of each best-fit curve, when transformed back to 3D, defines an axis parallel to sTEA. The condyles-based sTEA is measured experimentally on a 3D-printed bone using an Optitrack tracking setup. Using the proposed method, the angle between the aTEA, sTEA, and Whiteside's line was (3.77, 0.55, and 92.72)°, respectively. The proposed method provides the same level of accuracy and improves the anatomical points registration efficiency, as there is no need to register the LE or MS.

Hydrothermal synthesis of CuO@MnO2 on nitrogen-doped multiwalled carbon nanotube composite electrodes for supercapacitor applications.

Nitrogen-doped multiwalled carbon nanotubes (N-MWCNTs) have been used to fabricate nanostructured materials for various energy devices, such as supercapacitors, sensors, batteries, and electrocatalysts. Nitrogen-doped carbon-based electrodes have been widely used to improve supercapacitor applications via various chemical approaches. Based on previous studies, CuO@MnO2 and CuO@MnO2/N-MWCNT composites were synthesized using a sonication-supported hydrothermal reaction process to evaluate their supercapacitor properties. The structural and morphological properties of the synthesized composite materials were characterized via Raman spectroscopy, XRD, SEM, and SEM-EDX, and the morphological properties of the composite materials were confirmed by the nanostructured composite at the nanometer scale. The CuO@MnO2 and CuO@MnO2/N-MWCNT composite electrodes were fabricated in a three-electrode configuration, and electrochemical analysis was performed via CV, GCD, and EIS. The composite electrodes exhibited the specific capacitance of ~ 184 F g-1 at 0.5 A g-1 in the presence of a 5 M KOH electrolyte for the three-electrode supercapacitor application. Furthermore, it exhibited significantly improved specific capacitances and excellent cycling stability up to 5000 GCD cycles, with a 98.5% capacity retention.

Bifunctional iron molybdate as highly effective heterogeneous electro-Fenton catalyst and Li-ion battery anode.

Three-dimensional materials have attracted considerable interest in energy and environmental remediation fields. Iron molybdate (FMO) materials have prepared via a facile hydrothermal technique with glycerol assistance, and their structural and chemical composition confirmed using various physico-chemical techniques. The prepared bi-functional material is a strong candidate for energy storage and electrocatalytic degradation of Methylene blue and Congo red. Experimental results confirmed the synthesized FMO-10 catalyst was extremely efficient for methylene blue and Congo red breakdown, achieving 91 % and 96 % degradation in 36 h, respectively. This high catalytic activity was attributed to FMO significant visible light absorption, and reactive OH formation from H2O2 synergistically triggered by both Fe3+ and MoO42-. Prepared FMO samples demonstrated excellent potential as negative electrode material for lithium ion batteries. Electrode specific capacity initially dropped then rebounded to 1265 mAh g-1 after 100 cycles at 100 mA g-1 change rate between 0.01 and 3.0 V.

Nanorod-like Structure of ZnO Nanoparticles and Zn8O8 Clusters Using 4-Dimethylamino Benzaldehyde Liquid to Study the Physicochemical and Antimicrobial Properties of Pathogenic Bacteria.

To study their physicochemical and antimicrobial properties, zinc oxide nanoparticles were synthesized using a simple chemical route and 4-dimethylaminobenzaldehyde (4DB) as an organic additive. ZnO nanoparticles were characterized with XRD analysis, which confirmed the presence of a hexagonal wurtzite structure with different crystalline sizes. The SEM morphology of the synthesized nanoparticles confirmed the presence of nanorods in both modifications of ZnO nanoparticles. EDS analysis proved the chemical composition of the synthesized samples via different chemical approaches. In addition, the optical absorption results indicated that the use of 4DB increased the band gap energy of the synthesized nanoparticles. The synthesized Zn8O8 and Zn8O8:4DB clusters were subjected to HOMO-LUMO analysis, and their ionization energy (I), electron affinity (A), global hardness (η), chemical potential (σ), global electrophilicity index (ω), dipole moment (µ), polarizability (αtot), first-order hyperpolarizability (ßtot), and other thermodynamic properties were determined. Furthermore, the antimicrobial properties of the ZnO nanoparticles were studied against G+ (S. aureus and B. subtilis) and G- (K. pneumoniae and E. coli) bacteria in a nutrient agar according to guidelines of the Clinical and Laboratory Standards Institute (CLSI).

Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning.

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

Numerical Investigation of Ferrofluid Preparation during In-Vitro Culture of Cancer Therapy for Magnetic Nanoparticle Hyperthermia.

Recently, in-vitro studies of magnetic nanoparticle (MNP) hyperthermia have attracted significant attention because of the severity of this cancer therapy for in-vivo culture. Accurate temperature evaluation is one of the key challenges of MNP hyperthermia. Hence, numerical studies play a crucial role in evaluating the thermal behavior of ferrofluids. As a result, the optimum therapeutic conditions can be achieved. The presented research work aims to develop a comprehensive numerical model that directly correlates the MNP hyperthermia parameters to the thermal response of the in-vitro model using optimization through linear response theory (LRT). For that purpose, the ferrofluid solution is evaluated based on various parameters, and the temperature distribution of the system is estimated in space and time. Consequently, the optimum conditions for the ferrofluid preparation are estimated based on experimental and mathematical findings. The reliability of the presented model is evaluated via the correlation analysis between magnetic and calorimetric methods for the specific loss power (SLP) and intrinsic loss power (ILP) calculations. Besides, the presented numerical model is verified with our experimental setup. In summary, the proposed model offers a novel approach to investigate the thermal diffusion of a non-adiabatic ferrofluid sample intended for MNP hyperthermia in cancer treatment.

Hexagonal nanostructured cobalt oxide @ nitrogen doped multiwalled carbon nanotubes/polypyyrole composite for supercapacitor and electrochemical glucose sensor.

Hexagonal nanostructured cobalt oxide @ N-doped MWCNT /polypyyrole (Co3O4/PPy@N-MWCNT) composite was produced by an ultrasonication-mediated solvothermal method for electrochemical supercapacitor and glucose sensor applications. The structural and electrochemical properties of the Co3O4/PPy@N-MWCNT were confirmed by various spectroscopic and microscopic techniques. The as-prepared electrode showed an excellent capacitance of ∼872 F/g at 0.5 A/g with a capacitance retention of 96.8 %, even after 10,000 cycles. In addition, analysis of the sensing activity of the composite materials towards the glucose detection showed excellent electrochemical sensing performance that includes the glucose linear limit of (10 to 0.15) µm, detection sensitivity of 195.72 µA/cm2/mM, and lower detection value of S = 0.07327 µA/cm2 @ R2 = 0.99. The as-prepared composite material can be a promising candidate for the electrochemical supercapacitor and the efficient sensing of glucose.

Ultrasonication-mediated nitrogen-doped multiwalled carbon nanotubes involving carboxy methylcellulose composite for solid-state supercapacitor applications.

In this study, a novel nanohybrid composite containing nitrogen-doped multiwalled carbon nanotubes/carboxymethylcellulose (N-MWCNT/CMC) was synthesized for supercapacitor applications. The synthesized composite materials were subjected to an ultrasonication-mediated solvothermal hydrothermal reaction. The synthesized nanohybrid composite electrode material was characterized using analytical methods to confirm its structure and morphology. The electrochemical properties of the composite electrode were investigated using cyclic voltammetry (CV), galvanic charge-discharge, and electrochemical impedance spectroscopy (EIS) using a 3 M KOH electrolyte. The fabricated composite material exhibited unique electrochemical properties by delivering a maximum specific capacitance of approximately 274 F g-1 at a current density of 2 A g-1. The composite electrode displayed high cycling stability of 96% after 4000 cycles at 2 A g-1, indicating that it is favorable for supercapacitor applications.

Parametric Investigations of Magnetic Nanoparticles Hyperthermia in Ferrofluid using Finite Element Analysis.

Recently, magnetic nanoparticles (MNPs) based hyperthermia therapy has gained much attention due to its therapeutic potential in biomedical applications. This necessitates the development of numerical models that can reliably predict the temporal and spatial changes of temperature during the therapy. The objective of this study is to develop a comprehensive numerical model for quantitatively estimating temperature distribution in the ferrofluid system. The reliability of the numerical model was validated by comparative analysis of temperature distribution between experimental measurements and numerical analysis based on finite element method. Our analysis showed that appropriate incorporation of the heat effects of electromagnetic energy dissipation as well as thermal radiation from the ferrofluid system to the surrounding in the modeling resulted in the estimation of temperature distribution that is in close agreement with the experimental results. In summary, our developed numerical model is useful to evaluate the thermal behavior of the ferrofluid system during the process of magnetic fluid hyperthermia.

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM).

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection.

Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model's effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

A review on numerical modeling for magnetic nanoparticle hyperthermia: Progress and challenges.

Recent progress in nanotechnology has advanced the development of magnetic nanoparticle (MNP) hyperthermia as a potential therapeutic platform for treating diseases. Due to the challenges in reliably predicting the spatiotemporal distribution of temperature in the living tissue during the therapy of MNP hyperthermia, critical for ensuring the safety as well as efficacy of the therapy, the development of effective and reliable numerical models is warranted. This article provides a comprehensive review on the various mathematical methods for determining specific loss power (SLP), a parameter used to quantify the heat generation capability of MNPs, as well as bio-heat models for predicting heat transfer phenomena and temperature distribution in living tissue upon the application of MNP hyperthermia. This article also discusses potential applications of the bio-heat models of MNP hyperthermia for therapeutic purposes, particularly for cancer treatment, along with their limitations that could be overcome.

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates.

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

Novel approach to synthesize NiCo2S4 composite for high-performance supercapacitor application with different molar ratio of Ni and Co.

Here, we developed a new approach to synthesize NiCo2S4 thin films for supercapacitor application using the successive ionic layer adsorption and reaction (SILAR) method on Ni mesh with different molar ratios of Ni and Co precursors. The five different NiCo2S4 electrodes affect the electrochemical performance of the supercapacitor. The NiCo2S4 thin films demonstrate superior supercapacitance performance with a significantly higher specific capacitance of 1427 F g-1 at a scan rate of 20 mV s-1. These results indicate that ternary NiCo2S4 thin films are more effective electrodes compared to binary metal oxides and metal sulfides.

Porous materials of nitrogen doped graphene oxide@SnO2 electrode for capable supercapacitor application.

The porous materials of SnO2@NGO composite was synthesized by thermal reduction process at 550 °C in presence ammonia and urea as catalyst. In this process, the higher electrostatic attraction between the SnO2@NGO nanoparticles were anchored via thermal reduction reaction. These synthesized SnO2@ NGO composites were confirmed by Raman, XRD, XPS, HR-TEM, and EDX results. The SnO2 nanoparticles were anchored in the NGO composite in the controlled nanometer scale proved by FE-TEM and BET analysis. The SnO2@NGO composite was used to study the electrochemical properties of CV, GCD, and EIS analysis for supercapacitor application. The electrochemical properties of SnO2@NGO exhibited the specific capacitance (~378 F/g at a current density of 4 A/g) and increasing the cycle stability up to 5000 cycles. Therefore, the electrochemical results of SnO2@NGO composite could be promising for high-performance supercapacitor applications.

Ni(OH)2-decorated nitrogen doped MWCNT nanosheets as an efficient electrode for high performance supercapacitors.

In this study, nickel hydroxide nanoparticles (NPs) decorated with nitrogen doped multiwalled carbon nanotubes (N-MWCNT) hybrid composite was synthesized by thermal reduction process in the presence of cetyl ammonium bromide (CTAB) and urea. The as-synthesized Ni(OH)2@N-MWCNT hybrid composite was characterized by FTIR, Raman, XRD, BET, BJH and FE-TEM analyses. These prepared porous carbon hybrid composite materials possessed high specific surface area and sheet like morphology useful for active electrode materials. The maximum specific capacitance of Ni(OH)2@N-MWCNT hybrid nanocomposite in the two electrode system showed 350 Fg-1 at 0.5 A/g,energy density ~43.75 Wkg-1 and corresponds to power density 1500 W kg-1 with excellent capacity retention after 5000 cycles. The results suggest that the prepared two-dimensional hybrid composite is a promising electrode material for electrochemical energy storage applications.

Active Vibration Control of a Piezo-Bonded Laminated Composite in the Presence of Sensor Partial Debonding and Structural Delaminations.

In this paper, the active vibration control of a piezo-bonded laminated composite is investigated in the presence of sensor partial debonding and structural delamination. Improved layerwise theory, higher-order electric potential field, and the finite-element method were employed to develop an electromechanically coupled model for the two types of damage (i.e., sensor partial debonding and delamination). The developed model was numerically implemented on a single-input-multi-output (SIMO) system to demonstrate the effects of sensor partial debonding and structural delamination on the ability of a constant gain velocity feedback (CGVF) controller to attenuate vibration. The two types of damage were assessed in terms of controlled outputs of the sensors, nodal displacements, and control input signals being applied to the actuator to suppress vibrations. The obtained results showed that the sensor partial debonding and structural delamination have opposite effects on the vibration-attenuation characteristics of the CGVF controller. The presence of partial debonding in the sensor made the controller less able to suppress vibrations because of a spurious sensing signal, whereas structural delamination increased the control authority of the controller because of the loss of structural stiffness that results from structural delamination.

Facile Synthesis of Triphenylamine Based Hyperbranched Polymer for Organic Field Effect Transistors.

In this study, we reported the synthesis and characterization of a novel hyperbranched polymer (HBPs) tris[(4-phenyl)amino-alt-4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b']dithiophene] (PTPABDT) composed of benzo[1,2-b:4,5-b']dithiophene (BDT) and triphenyleamine (TPA) constituent subunits by A3 + B2 type Stille's reaction. An estimated optical band gap of 1.69 eV with HOMO and LUMO levels of -5.29 eV and -3.60 eV, respectively, as well as a high thermal stability up to 398 °C were characterized for the synthesized polymer. PTPABDT fabricated as an encapsulated top gate/bottom contact (TGBC), organic field effect transistors (OFET) exhibited a p-type behavior with maximum field-effect mobility (µmax) and an on/off ratio of 1.22 × 10-3 cm2 V-1 s-1 and 7.47 × 102, respectively.

Hierarchical Flowerlike 3D nanostructure of Co3O4@MnO2/N-doped Graphene oxide (NGO) hybrid composite for a high-performance supercapacitor.

The present study investigates the fabrication of hierarchical 3D nanostructures with multi-component metal oxides in the presence of highly-porous graphene and characterized for its applications in high-performance supercapacitors. A hierarchical flowers like 3D nanostructure of Co3O4 @MnO2 on nitrogen-doped graphene oxide (NGO) hybrid composite was synthesized by thermal reduction process at 650 °C in the presence of ammonia and urea. The synthesized Co3O4@MnO2/NGO hybrid composites were studied via Raman, XRD, X-ray XPS, FE-SEM, FE-SEM with EDX, FE-TEM and BET analyses. The electrochemical analysis of Co3O4@MnO2/NGO hybrid composite electrode was investigated using cyclic voltammetry, chronopotentiometry and electrochemical impedance measurements. The hybrid composite electrode showed significant specific capacitance results of up to 347 F/g at 0.5 A/g and a corresponding energy density of 34.83 Wh kg-1 with better rate performance and excellent long-term cycling stability were achieved for 10,000 cycles. The obtained electrochemical results paved a way to utilize Co3O4@MnO2/NGO composite electrode as a promising electrode material in high performance supercapacitors.