Sustainability assessment of biofuel and value-added product from organic fraction of municipal solid waste.

The current study aims to examine the techno-economic and environmental assessment of biorefinery development within a circular bioeconomy context by using an organic fraction of municipal solid waste (OFMSW) by extraction of lipids, carbohydrates, and proteins with 98, 51 and 62 % by mass of conversion efficiency and yield recovery, and value-added fractions production as well. Fatty acid methyl ester (biodiesel) and glycerol (biofuel) were produced by applying transesterification process, and the remaining biomass was converted into biocrude oil by thermal liquefication. The biorefinery using 613 kg of OFMSW produced biodiesel, glycerol, and bioethanol with 126 litter, 14.3 kg, and 172 litter respectively, as well as value-added fractions, such as biocrude oil with 78 kg. The environmental impact was assessed using the life cycle assessment (LCA) framework, ReCiPe2016 Mid-point (H) approach, through 18 different environmental categories. The key findings elucidate that Terrestrial ecotoxicity, Climate change, Fossil depletion and Human toxicity were the main impact categories which are potentially contributed 9.81E+02 kg 1,4-DB eq., 1.43E+03 kg CO2 eq., 2.04E+02 kg oil eq., and 8.08E+01 kg 1,4-DB eq. The normalization (person per equivalent) analysis revealed that only categories of resource reduction (fossil and metal depletion) are the key contributors to environmental degradation. The biorefinery system's total revenue was estimated at USD 6.817,509 million annually. The calculated revenue was USD 0.026 million daily in a shift of 8 h. The Net present worth (NPW) was calculated at USD 499.97 million by assuming a discount factor of 10 % and inflation rate of 5 % for 15 years. The project is considered feasible by demonstrating 7.15 payback year. This research showcased the efficient portrayal of the biorefinery system and succinctly conveyed the significant circular bioeconomy for a greener future. Thus, it could be helpful to the stakeholder's context towards techno-economic and environmental evaluation.

Advancement in total hip implant: a comprehensive review of mechanics and performance parameters across diverse novelties.

The UK's National Joint Registry (NJR) and the American Joint Replacement Registry (AJRR) of 2022 revealed that total hip replacement (THR) is the most common orthopaedic joint procedure. The NJR also noted that 10-20% of hip implants require revision within 1 to 10 years. Most of these revisions are a result of aseptic loosening, dislocation, implant wear, implant fracture, and joint incompatibility, which are all caused by implant geometry disparity. The primary purpose of this review article is to analyze and evaluate the mechanics and performance factors of advancement in hip implants with novel geometries. The existing hip implants can be categorized based on two parts: the hip stem and the joint of the implant. Insufficient stress distribution from implants to the femur can cause stress shielding, bone loss, excessive micromotion, and ultimately, implant aseptic loosening due to inflammation. Researchers are designing hip implants with a porous lattice and functionally graded material (FGM) stems, femur resurfacing, short-stem, and collared stems, all aimed at achieving uniform stress distribution and promoting adequate bone remodeling. Designing hip implants with a porous lattice FGM structure requires maintaining stiffness, strength, isotropy, and bone development potential. Mechanical stability is still an issue with hip implants, femur resurfacing, collared stems, and short stems. Hip implants are being developed with a variety of joint geometries to decrease wear, improve an angular range of motion, and strengthen mechanical stability at the joint interface. Dual mobility and reverse femoral head-liner hip implants reduce the hip joint's dislocation limits. In addition, researchers reveal that femoral headliner joints with unidirectional motion have a lower wear rate than traditional ball-and-socket joints. Based on research findings and gaps, a hypothesis is formulated by the authors proposing a hip implant with a collared stem and porous lattice FGM structure to address stress shielding and micromotion issues. A hypothesis is also formulated by the authors suggesting that the utilization of a spiral or gear-shaped thread with a matched contact point at the tapered joint of a hip implant could be a viable option for reducing wear and enhancing stability. The literature analysis underscores substantial research opportunities in developing a hip implant joint that addresses both dislocation and increased wear rates. Finally, this review explores potential solutions to existing obstacles in developing a better hip implant system.

Design and Performance Evaluation of a Novel Spiral Head-Stem Trunnion for Hip Implants Using Finite Element Analysis.

With an expectation of an increased number of revision surgeries and patients receiving orthopedic implants in the coming years, the focus of joint replacement research needs to be on improving the mechanical properties of implants. Head-stem trunnion fixation provides superior load support and implant stability. Fretting wear is formed at the trunnion because of the dynamic load activities of patients, and this eventually causes the total hip implant system to fail. To optimize the design, multiple experiments with various trunnion geometries have been performed by researchers to examine the wear rate and associated mechanical performance characteristics of the existing head-stem trunnion. The objective of this work is to quantify and evaluate the performance parameters of smooth and novel spiral head-stem trunnion types under dynamic loading situations. This study proposes a finite element method for estimating head-stem trunnion performance characteristics, namely contact pressure and sliding distance, for both trunnion types under walking and jogging dynamic loading conditions. The wear rate for both trunnion types was computed using the Archard wear model for a standard number of gait cycles. The experimental results indicated that the spiral trunnion with a uniform contact pressure distribution achieved more fixation than the smooth trunnion. However, the average contact pressure distribution was nearly the same for both trunnion types. The maximum and average sliding distances were both shorter for the spiral trunnion; hence, the summed sliding distance was approximately 10% shorter for spiral trunnions than that of the smooth trunnion over a complete gait cycle. Owing to a lower sliding ability, hip implants with spiral trunnions achieved more stability than those with smooth trunnions. The anticipated wear rate for spiral trunnions was 0.039 mm3, which was approximately 10% lower than the smooth trunnion wear rate of 0.048 mm3 per million loading cycles. The spiral trunnion achieved superior fixation stability with a shorter sliding distance and a lower wear rate than the smooth trunnion; therefore, the spiral trunnion can be recommended for future hip implant systems.

Surface Acoustic Wave (SAW) Sensors for Hip Implant: A Numerical and Computational Feasibility Investigation Using Finite Element Methods.

In this paper, a surface acoustic wave (SAW) sensor for hip implant geometry was proposed for the application of total hip replacement. A two-port SAW device was numerically investigated for implementation with an operating frequency of 872 MHz that can be used in more common radio frequency interrogator units. A finite element analysis of the device was developed for a lithium niobate (LiNBO3) substrate with a Rayleigh velocity of 3488 m/s on COMSOL Multiphysics. The Multiphysics loading and frequency results highlighted a good uniformity with numerical results. Afterwards, a hip implant geometry was developed. The SAW sensor was mounted at two locations on the implant corresponding to two regions along the shaft of the femur bone. Three discrete conditions were studied for the feasibility of the implant with upper- and lower-body loading. The loading simulations highlighted that the stresses experienced do not exceed the yield strengths. The voltage output results indicated that the SAW sensor can be implanted in the hip implant for hip implant-loosening detection applications.

A Review of Biomaterials and Associated Performance Metrics Analysis in Pre-Clinical Finite Element Model and in Implementation Stages for Total Hip Implant System.

Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10-15 years. The material selection for the total hip implant systems is a major research field since it affects the mechanical and clinical performance of it. Stress shielding due to excessive contact stress, implant dislocation due to a large deformation, aseptic implant loosening due to the particle propagation of wear debris, decreased bone remodeling density due to the stress shielding, and adverse tissue responses due to material wear debris all contribute to the failure of hip implants. Recent research shows that pre-clinical computational finite element analysis (FEA) can be used to estimate four mechanical performance parameters of hip implants which are connected with distinct biomaterials: von Mises stress and deformation, micromotion, wear estimates, and implant fatigue. In vitro, in vivo, and clinical stages are utilized to determine the hip implant biocompatibility and the unfavorable local tissue reactions to different biomaterials during the implementation phase. This research summarizes and analyses the performance of the different biomaterials that are employed in total hip implant systems in the pre-clinical stage using FEA, as well as their performances in in vitro, in vivo, and in clinical studies, which will help researchers in gaining a better understanding of the prospects and challenges in this field.

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals.

Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images.

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

Multi-objective optimization of a biomass gasification to generate electricity and desalinated water using Grey Wolf Optimizer and artificial neural network.

In the current research, an innovative biomass-based energy system is proposed for power and desalinated water production. The plant's primary components consist of a gasifier, a compressor, a heat exchanger, a gas turbine, a combustion chamber, and a Multi-effect desalination with thermal vapor compression (MED-TVC) unit. A comprehensive thermodynamic and thermoeconomic assessment is conducted on the proposed system. Besides, a parametric study is conducted to determine the effect of primary decision variables on the system performance. Multiple objective optimization using the multi-objective grey wolf optimizer (MOGWO) algorithm is applied to obtain the optimal solution with the highest exergy efficiency and the minimum amount of total cost rate. The artificial neural network (ANN) has an intermediary role in the optimization process to decrease computational time and enhance optimization speed. The relation between the objective function and decision variables is investigated, employing ANN to determine the energy system's optimum point. The generation rate for power and freshwater at the optimal point is equal to 5127 kW and 38.6 kg/s, respectively. Besides, the optimum value of the exergy efficiency and total cost rate are computed as 15.61% and 206.78 $/h, respectively. The results also revealed that the number of effects of the desalination unit does not affect the carbon dioxide emissions. Moreover, the scatter distribution of the key decision variable indicates that the air compressor pressure ratio is not a sensible variable, and their optimum points are distributed across the entire domain.

Angle-insensitive co-polarized metamaterial absorber based on equivalent circuit analysis for dual band WiFi applications.

A novel and systematic procedure to design a co-polarized electromagnetic metamaterial (MM) absorber with desired outputs and resonance frequencies for dual-band WiFi signal absorption is presented. The desired resonance frequencies with expected S parameters' values were first designed as an equivalent circuit with extensive analysis and then implemented into frequency-selective MM absorber by numerical simulation with precise LRC elements, satisfying least unit cell area (0.08λ), substrate thickness (0.01λ) and maximum effective medium ratio (12.49). The absorber was simulated for the maximum angle of incidence for both the normal and oblique incidences at co-polarization. The absorptions at the desired resonance frequencies were found at a satisfactory level by both simulation and practical measurement along with a single negative value to ensure metamaterial characteristics. The proposed equivalent circuit analysis approach can help researchers design and engineering co-polarization insensitive MM absorbers using conventional split-ring resonators, with perfection in output and desired resonance frequencies without the necessity of lumped elements or multilayer substrates. The proposed metamaterial can be utilized for SAR reduction, crowdsensing, and other WiFi-related practical applications.

Review on Medical Implantable Antenna Technology and Imminent Research Challenges.

Implantable antennas are mandatory to transfer data from implants to the external world wirelessly. Smart implants can be used to monitor and diagnose the medical conditions of the patient. The dispersion of the dielectric constant of the tissues and variability of organ structures of the human body absorb most of the antenna radiation. Consequently, implanting an antenna inside the human body is a very challenging task. The design of the antenna is required to fulfill several conditions, such as miniaturization of the antenna dimension, biocompatibility, the satisfaction of the Specific Absorption Rate (SAR), and efficient radiation characteristics. The asymmetric hostile human body environment makes implant antenna technology even more challenging. This paper aims to summarize the recent implantable antenna technologies for medical applications and highlight the major research challenges. Also, it highlights the required technology and the frequency band, and the factors that can affect the radio frequency propagation through human body tissue. It includes a demonstration of a parametric literature investigation of the implantable antennas developed. Furthermore, fabrication and implantation methods of the antenna inside the human body are summarized elaborately. This extensive summary of the medical implantable antenna technology will help in understanding the prospects and challenges of this technology.

Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning.

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

Solar still desalination system equipped with paraffin as phase change material: exergoeconomic analysis and multi-objective optimization.

The current work is about analysis and multi-objective optimization (MOO) of weir-type solar still systems equipped with phase change material (PCM) regarding the exergetic and economic performance. To do so, the energetic and exergetic modeling of the suggested system is conducted then the substantial economic factors is applied to obtain the total cost rate of the considered SSDS. The total exergetic efficiency and total annual cost (TAC) is considered objective functions. Four parameters include mass of the PCM (mPCM), inlet brine water flow rate ([Formula: see text]), gap distance (d), and insulation width (xins) is chosen as decision variables. Moreover, a genetic algorithm-based MOO was applied to find the optimum states of evaluated solar still unit. The outputs represented that increasing the brine feed water mass flow rate does not affect the TAC while decreasing distilled water production rate. The scattered distribution of optimum states infers that the optimum value of PCM mass is about 1 kg. In addition, applied MOO reveals that with optimization of the studied system, the exergy efficiency increases about 1.47% and the annual distilled water increases 4.35% compared with the non-optimized system. The suggested system is capable to produce fresh water in remote areas without any pollution as well as in a low cost rate.

Elastic Wave Characteristics of Graphene Reinforced Polymer Nanocomposite Curved Beams Including Thickness Stretching Effect.

This work aims at analyzing elastic wave characteristics in a polymeric nanocomposite curved beam reinforced by graphene nanoplatelets (GNPs). GNPs are adopted as a nanofiller inside the matrix to enhance the effective properties, which are approximated through Halpin-Tasi model and a modified rule of mixture. A higher-order shear deformation theory accounting for thickness stretching and the general strain gradient model to have both nonlocality and strain gradient size-dependency phenomena are adopted to model the nanobeam. A virtual work of Hamilton statement is utilized to get the governing motion equations and is solved in conjunction with the harmonic solution procedure. A comparative study shows the effects of small-scale coefficients, opening angle, weight fraction, the total number of layers in GNPs, and wave numbers on the propagation of waves in reinforced nanocomposite curved beams. This work is also developed for two different distribution of GNPs in a polymeric matrix, namely uniformly distribution and functionally graded one.

Finite Element Analysis of the Mechanism of Traumatic Aortic Rupture (TAR).

As many as 80% of patients with TAR die on the spot while out of those reaching a hospital, 30% would die within 24 hours. Thus, it is essential to better understand and prevent this injury. The exact mechanics of TAR are unknown. Although most researchers approve it as a common-sense deceleration injury, the exact detailed mechanism of TRA still remains unidentified. In this work, a deceleration mechanism of TAR was carried out using finite element analysis (FEA). The FE analysis aimed to predict internal kinematics of the aorta and assist to comprehend the mechanism of aorta injury. The model contains the heart, lungs, thoracic aorta vessel, and rib cage. High-resolution computerized tomography (HR CT scan) was used to provide pictures that were reconstructed by MIMICS software. ANSYS FE simulation was carried out to investigate the behavior of the aorta in the thoracic interior after deceleration occurred during a car crash. The finite element analysis indicated that maximum stress and strain applied to the aorta were from 5.4819e5 to 2.614e6 Pa and 0.21048 to 0.62676, respectively, in the Y-direction when the initial velocity increased from 10 to 25 m/s. Furthermore, in the X-direction when the velocity changed from 15 to 25 m/s, the stress and strain values increased from 5.17771e5 to 2.3128e6 and from 0.22445 to 0.618, respectively.

Supercapacitor Performance of Nickel-Cobalt Sulfide Nanotubes Decorated Using Ni Co-Layered Double Hydroxide Nanosheets Grown in Situ on Ni Foam.

In this study, to fabricate a non-binder electrode, we grew nickel-cobalt sulfide (NCS) nanotubes (NTs) on a Ni foam substrate using a hydrothermal method through a two-step approach, namely in situ growth and an anion-exchange reaction. This was followed by the electrodeposition of double-layered nickel-cobalt hydroxide (NCOH) over a nanotube-coated substrate to fabricate NCOH core-shell nanotubes. The final product is called NCS@NCOH herein. Structural and morphological analyses of the synthesized electrode materials were conducted via SEM and XRD. Different electrodeposition times were selected, including 10, 20, 40, and 80 s. The results indicate that the NCSNTs electrodeposited with NCOH nanosheets for 40 s have the highest specific capacitance (SC), cycling stability (2105 Fg-1 at a current density of 2 Ag-1), and capacitance retention (65.1% after 3,000 cycles), in comparison with those electrodeposited for 10, 20, and 80 s. Furthermore, for practical applications, a device with negative and positive electrodes made of active carbon and NCS@NCOH was fabricated, achieving a high-energy density of 23.73 Whkg-1 at a power density of 400 Wkg-1.