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.

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.

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%.

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.

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.

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.

Electro-Active Paper as a Flexible Mechanical Sensor, Actuator and Energy Harvesting Transducer: A Review.

Electro-active paper (EAPap) is a cellulose-based smart material that has shown promising results in a variety of smart applications (e.g., vibration sensor, piezo-speaker, bending actuator) with the merits of being flexible, lightweight, fracture tolerant, biodegradable, naturally abundant, cheap, biocompatible, and with the ability to form hybrid nanocomposites. This paper presents a review of the characterization and application of EAPap as a flexible mechanical vibration/strain sensor, bending actuator, and vibration energy harvester. The working mechanism of EAPap is explained along with the various parameters and factors that influence the sensing, actuation, and energy harvesting capabilities of EAPap. Although the piezoelectricity of EAPap is comparable to that of commercially available polyvinylidene fluoride (PVDF), EAPap has the preferable merits in terms of natural abundance and ample capacity of chemical modification. The article would provide guidelines for the characterization and application of EAPap in mechanical sensing, actuation, and vibration energy scavenging, along with the possible limitations and future research prospects.

Recent Progress on Cellulose-Based Electro-Active Paper, Its Hybrid Nanocomposites and Applications.

We report on the recent progress and development of research into cellulose-based electro-active paper for bending actuators, bioelectronics devices, and electromechanical transducers. The cellulose electro-active paper is characterized in terms of its biodegradability, chirality, ample chemically modifying capacity, light weight, actuation capability, and ability to form hybrid nanocomposites. The mechanical, electrical, and chemical characterizations of the cellulose-based electro-active paper and its hybrid composites such as blends or coatings with synthetic polymers, biopolymers, carbon nanotubes, chitosan, and metal oxides, are explained. In addition, the integration of cellulose electro-active paper is highlighted to form various functional devices including but not limited to bending actuators, flexible speaker, strain sensors, energy harvesting transducers, biosensors, chemical sensors and transistors for electronic applications. The frontiers in cellulose paper devices are reviewed together with the strategies and perspectives of cellulose electro-active paper and cellulose nanocomposite research and applications.

Transient analysis of biocomposite laminates with delamination.

The transient analysis of smart biocomposite laminates with delamination at ply interfaces is investigated, by using an electro-mechanical coupled improved layerwise theory. The piezoelectric coupling effect was modeled using higher order electric potential. Four-node plate elements were used for finite element implementation. Linear Lagrange interpolation functions were used for in-plane structural unknowns and electric unknowns, and a Hermite cubic interpolation function was used for out-of-plane structural unknowns. Single delamination was seeded in the biocomposite laminates at three different locations, to study the effects of delamination. Numerical results were obtained by using the Newmark-beta algorithm. The results showed that the time history of nodal displacement and sensor output didn't give enough information of delamination. However, the power spectral density of the time history provided clear information of the delamination effects. From the results, it is expected that the proposed theory has the potential to predict the dynamic characteristics and delamination effects in smart biocomposite laminates.

Free-edge stress analysis of functionally graded material layered biocomposite laminates.

A stress function based theory is proposed to obtain free-edge stress distributions for three-dimensional, orthotropic, linearly elastic rectangular biocomposite laminates with surface-bonded functionally graded materials (FGM). The assumed stress fields automatically satisfy the pointwise equilibrium equation, as well as traction-free and free edge boundary conditions. The complementary virtual work principle, followed by the general eigenvalue solution procedure, is used to obtain 3-D free edge stress states. A typical stacking sequence of composite laminate is used as numerical investigation with surface bonded FGMs. It is shown that with proper exponential factor of FGMs, the interlaminar stresses at the FGM layer interface can be reduced significantly, in return to prevent debonding of FGM layers. This approach can be useful in the design of functionally graded material layered biocomposite structures.

Damage analysis of biocomposite laminates using model order reduction.

The transient quasi-static Ritz vector method (TQSRV) is applied to efficiently calculate the transient response of a delaminated biocomposite laminate. Delamination of the laminated biocomposite structure was modeled using an improved layerwise displacement field. The piezoelectric coupling effect was modeled using higher order electric potential. One piezoelectric actuator was used to excite the laminated biocomposite plate, and one piezoelectric sensor was used to detect the transient structural response of the plate. Single discrete delamination was seeded in the laminated biocomposite plate, to investigate the effect of delamination. Three different locations of delamination through the thickness direction were considered, to study the effects of delamination on structural response. The Newmark-beta algorithm and the model order reduction (MOR) method were used, to obtain transient response of the delaminated composite plate under impulse loading. The effects of delamination were clearly observed in the power spectral density of the piezoelectric sensor output. From the results, it is concluded that the MOR is a very efficient method in predicting the damage effects of delaminated biocomposite structures.

Possibility of cellulose-based electro-active paper energy scavenging transducer.

In this paper, a cellulose-based Electro-Active Paper (EAPap) energy scavenging transducer is presented. Cellulose is proven as a smart material, and exhibits piezoelectric effect. Specimens were prepared by coating gold electrodes on both sides of cellulose film. The fabricated specimens were tested by a base excited aluminum cantilever beam at resonant frequency. Different tests were performed with single and multiple parallel connected electrodes coated on the cellulose film. A maximum of 131 mV output voltage was measured, when three electrodes were connected in parallel. It was observed that voltage output increases significantly with the area of electrodes. From these results, it can be concluded that the piezoelectricity of cellulose-based EAPap can be used in energy transduction application.

Cellulose-silica/gold nanomaterials for electronic applications.

Cellulose and one dimensional nano-material composite has been investigated for various industrial applications due to their optical, mechanical and electrical properties. In present investigation, cellulose/silica and silica-gold hybrid biomaterials were prepared by sol-gel covalent cross-linking process. The tetraethoxysiliane (TEOS) and gold precursors and γ-aminopropyltriethoxysilane (γ-APTES) as coupling agent were used for sol-gel cross-linking process. The chemical and morphological properties of cellulose/silica and cellulose/silica-gold nano-materials via covalent cross-linking hybrids were confirmed by FTIR, XRD, SEM, and TEM analysis. In the sol-gel process, the inorganic particles were dispersed in the cellulose host matrix at the nanometer scale, bonding to the cellulose through the covalent bonds.

Reduction of free edge peeling stress of laminated composites using active piezoelectric layers.

An analytical approach is proposed in the reduction of free edge peeling stresses of laminated composites using active piezoelectric layers. The approach is the extended Kantorovich method which is an iterative method. Multiterms of trial function are employed and governing equations are derived by taking the principle of complementary virtual work. The solutions are obtained by solving a generalized eigenvalue problem. By this approach, the stresses automatically satisfy not only the traction-free boundary conditions, but also the free edge boundary conditions. Through the iteration processes, the free edge stresses converge very quickly. It is found that the peeling stresses generated by mechanical loadings are significantly reduced by applying a proper electric field to the piezoelectric actuators.

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.

Energy Storage Application of CaO/Graphite Nanocomposite Powder Obtained from Waste Eggshells and Used Lithium-Ion Batteries as a Sustainable Development Approach.

The reuse of waste materials has recently become appealing due to pollution and cost reduction factors. Using waste materials can reduce environmental pollution and product costs, thus promoting sustainability. Approximately 95% of calcium carbonate-containing waste eggshells end up in landfills, unused. These eggshells, a form of bio-waste, can be repurposed as catalytic electrode material for various applications, including supercapacitors, after being converted into CaO. Similarly, used waste battery electrode materials pose environmental hazards if not properly recycled. Various types of batteries, particularly lithium-ion batteries, are extensively used worldwide. The recycling of used lithium-ion batteries has become less important considering its low economic benefits. This necessitates finding alternative methods to recover and reuse the graphite rods of spent batteries. Therefore, this study reports the conversion of waste eggshell into calcium oxide by high-temperature calcination and extraction of nanographite from spent batteries for application in energy storage fields. Both CaO and CaO/graphite were characterized for their structural, morphological, and chemical compositions using XRD, SEM, TEM, and XPS techniques. The prepared CaO/graphite nanocomposite material was evaluated for its efficiency in electrochemical supercapacitor applications. CaO and its composite with graphite powder obtained from used lithium-ion batteries demonstrated improved performance compared to CaO alone for energy storage applications. Using these waste materials for electrochemical energy storage and conversion devices results in cheaper, greener, and sustainable processes. This approach not only aids in energy storage but also promotes sustainability through waste management by reducing landfills.

Reduction of the radiating sound of a submerged finite cylindrical shell structure by active vibration control.

In this work, active vibration control of an underwater cylindrical shell structure was investigated, to suppress structural vibration and structure-borne noise in water. Finite element modeling of the submerged cylindrical shell structure was developed, and experimentally evaluated. Modal reduction was conducted to obtain the reduced system equation for the active feedback control algorithm. Three Macro Fiber Composites (MFCs) were used as actuators and sensors. One MFC was used as an exciter. The optimum control algorithm was designed based on the reduced system equations. The active control performance was then evaluated using the lab scale underwater cylindrical shell structure. Structural vibration and structure-borne noise of the underwater cylindrical shell structure were reduced significantly by activating the optimal controller associated with the MFC actuators. The results provide that active vibration control of the underwater structure is a useful means to reduce structure-borne noise in water.

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.