Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

Objective

The objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials.

Methods

It integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments.

Results

The findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches.

Conclusions

To ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.

An Insight into the Characteristics of 3D Printed Polymer Materials for Orthoses Applications: Experimental Study

Knee orthoses assist patients with impaired gait through the amendment of knee abnormalities, restoration of mobility, alleviation of pain, shielding, and immobilization. The inevitable issues with laborious traditional plaster molding procedures for orthoses can be resolved with 3D printing. However, a number of challenges have limited the adoption of 3D printing, the most significant of which is the proper material selection for orthoses. This is so because the material used to make an orthosis affects its strength, adaptability, longevity, weight, moisture response, etc. This study intends to examine the mechanical, physical, and dimensional characteristics of three-dimensional (3D) printing materials (PLA, ABS, PETG, TPU, and PP). The aim of this investigation is to gain knowledge about these materials' potential for usage as knee orthosis materials. Tensile testing, Olympus microscope imaging, water absorption studies, and coordinate measuring machine-based dimension analysis are used to characterize the various 3D printing materials. Based on the investigation, PLA outperforms all other materials in terms of yield strength (25.98 MPa), tensile strength (30.89 MPa), and shrinkage (0.46%). PP is the least water absorbent (0.15%) and most flexible (407.99%); however, it is the most difficult to fabricate using 3D printing. When producing knee orthoses with 3D printing, PLA can be used for the orthosis frame and other structural elements, PLA or ABS for moving parts like hinges, PP for padding, and TPU or PP for the straps. This study provides useful information for scientists and medical professionals who are intrigued about various polymer materials for 3D printing and their effective utilization to fabricate knee orthoses.

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

Engineering of Multifunctional Nanocomposite Membranes for Wastewater Treatment: Oil/Water Separation and Dye Degradation

Multifunctional membrane technology has gained tremendous attention in wastewater treatment, including oil/water separation and photocatalytic activity. In the present study, a multifunctional composite nanofiber membrane is capable of removing dyes and separating oil from wastewater, as well as having antibacterial activity. The composite nanofiber membrane is composed of cellulose acetate (CA) filled with zinc oxide nanoparticles (ZnO NPs) in a polymer matrix and dipped into a solution of titanium dioxide nanoparticles (TiO2 NPs). Membrane characterization was performed using transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR), and water contact angle (WCA) studies were utilized to evaluate the introduced membranes. Results showed that membranes have adequate wettability for the separation process and antibacterial activity, which is beneficial for water disinfection from living organisms. A remarkable result of the membranes' analysis was that methylene blue (MB) dye removal occurred through the photocatalysis process with an efficiency of ~20%. Additionally, it exhibits a high separation efficiency of 45% for removing oil from a mixture of oil-water and water flux of 20.7 L.m-2 h-1 after 1 h. The developed membranes have multifunctional properties and are expected to provide numerous merits for treating complex wastewater.

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.

Deep Learning-Based Transfer Learning for Classification of Skin Cancer

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.

A Hybrid Approach of ANFIS-Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as R_a of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

Engineering and Characterization of Antibacterial Coaxial Nanofiber Membranes for Oil/Water Separation

In the present study, a coaxial nanofiber membrane was developed using the electrospinning technique. The developed membranes were fabricated from hydrophilic cellulose acetate (CA) polymer and hydrophobic polysulfone (PSf) polymer as a core and shell in an alternative way with addition of 0.1 wt.% of ZnO nanoparticles (NPs). The membranes were treated with a 2M NaOH solution to enhance hydrophilicity and thus increase water separation flux. Chemical and physical characterizations were performed, such as Fourier transform infrared (FTIR) spectroscopy, and surface wettability was measured by means of water contact angle (WCA), mechanical properties, surface morphology via field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and microscopy energy dispersive (EDS) mapping and point analysis. The results show higher mechanical properties for the coaxial nanofiber membranes which reached a tensile strength of 7.58 MPa, a Young's modulus of 0.2 MPa, and 23.4 M J.m-3 of toughness. However, treated mebranes show lower mechanical properties (tensile strength of 0.25 MPa, Young's modulus of 0.01 MPa, and 0.4 M J.m-3 of toughness). In addition, the core and shell nanofiber membranes showed a uniform distribution of coaxial nanofibers. Membranes with ZnO NPs showed a porous structure and elimination of nanofibers after treatment due to the formation of nanosheets. Interestingly, membranes changed from hydrophobic to hydrophilic (the WCA changed from 90 ± 8° to 14 ± 2°). Besides that, composite nanofiber membranes with ZnO NPs showed antibacterial activity against Escherichia coli. Furthermore, the water flux for the modified membranes was improved by 1.6 times compared to the untreated membranes.

Effective Sequestration of Phosphate and Ammonium Ions by the Bentonite/Zeolite Na-P Composite as a Simple Technique to Control the Eutrophication Phenomenon: Realistic Studies

A bentonite/Zeolite-P (BE/ZP) composite was synthesized by controlled alkaline hydrothermal treatment of bentonite at 150 °C for 4 h for effective sequestration of phosphate and ammonium pollutants. The composite is of 512 m²/g surface area, 387 meq/100 g ion-exchange capacity, and 5.8 nm average pore diameter. The experimental investigation reflected the strong effect of the pH value in directing the uptake behavior and the best results were attained at pH 6. The kinetic properties showed an excellent agreement for phosphate and ammonium adsorption results with the pseudo-second-order model showing equilibrium intervals of 600 and 360 min, respectively, and maximum experimental capacities of 170 and 155 mg/g, respectively. Additionally, their equilibrium modeling confirmed excellent fitness with the Langmuir hypothesis, signifying homogeneous and monolayer uptake processes with a theoretical q _max of 179.4 and 166 mg/g for phosphate and ammonium, respectively. Moreover, the calculated Gaussian adsorption energies of phosphate (0.8 kJ/mol) and ammonium (0.72 kJ/mol) suggested physisorption for them with mechanisms close to the zeolitic ion-exchange process or the coulumbic attractive forces. This was supported by the assessed thermodynamic parameters which also suggested spontaneous uptake by endothermic reaction for phosphate and exothermic reaction for ammonium. The BE/ZP composite is of excellent reusability and used for eight recyclability runs achieving removal percentages of 61.5 and 74.5% for phosphate and ammonium, respectively, in run 8. Finally, the composite was applied in the purification of sewage water and groundwater, achieving complete removal for phosphate from sewage water and ammonium from groundwater and reduction of the ammonium ions in the sewage water to 2.3 mg/L.

Antibacterial activity of silver nanoparticles against field and reference strains of Mycobacterium tuberculosis, Mycobacterium bovis and multiple-drug-resistant tuberculosis strains

Tuberculosis (TB) is an endemic disease in animals and humans in Egypt. This study aims to investigate the antimycobacterial activity of silver nanoparticles(AgNPs) by determining the minimal inhibitory concentration (MIC) of AgNPs, using the microplate Alamar blue assay. The AgNPs were chemically synthesised and their form and size were characterised by ultraviolet-visible absorption spectrophotometry, transmission electron microscopy and X-ray diffraction.The reference strains of Mycobacterium bovis and Mycobacterium tuberculosisH37Rv, and one multiple-drug-resistant (MDR) strain of M. tuberculosis were tested, as well as clinical isolates of M. bovis and M. tuberculosis. The AgNPs were tetrahydral with a few spherical particles and an average particle size of 50 nm. The mycobacterial strains were varied with MICs of AgNPs. Both reference strains of M. tuberculosis and M. bovis, in addition to the MDR strain of M. tuberculosis, were successfully inhibited by AgNPs at MICs of 1 ?g/ml, 4 ?g/ml and 16 ?g/ml, respectively, whereas clinical isolates of M. bovis and M. tuberculosis were inhibited at MIC values of 4-32 ?g/ml and 1-16 ?g/ml, respectively. The AgNPs showed an in vitro chemotherapeutic effect against Mycobacterium spp.Thus, they can be used to treat TB not only in humans but also in animals, and maybe useful in TB prevention and control strategies worldwide.

Experimental Analysis on the Influence and Optimization of μ-RUM Parameters in Machining Alumina Bioceramic

Fabrication of precise micro-features in bioceramic materials is still a challenging task. This is because of the inherent properties of bioceramics, such as low fracture toughness, high hardness, and brittleness. This paper places an emphasis on investigating the multi-objective optimization of fabrication of microchannels in alumina (Al₂O₃) bioceramics by using rotary ultrasonic machining (RUM). The influence of five major input parameters, namely vibration frequency, vibration amplitude, spindle speed, depth of cut, and feed rate on the surface quality, edge chipping, and dimensional accuracy of the milled microchannels was analyzed. Surface morphology and microstructure of the machined microchannels were also evaluated and analyzed. Unlike in previous studies, the effect of vibration frequency on the surface morphology and roughness is discussed in detail. A set of designed experiments based on central composite design (CCD) method was carried out. Main effect plots and surface plots were analyzed to detect the significance of RUM input parameters on the outputs. Later, a multi-objective genetic algorithm (MOGA) was employed to determine the optimal parametric conditions for minimizing the surface roughness, edge chipping, and dimensional errors of the machined microchannels. The optimized values of the surface roughness (Ra and Rt), side edge chipping (SEC), bed edge chipping (BEC), depth error (DE), and width error (WE) achieved through the multi-objective optimization were 0.27 μm, 2.7 μm, 8.7 μm, 8 μm, 5%, and 5.2%, respectively.

Biological evaluation of pyrazinamide liposomes for treatment of Mycobacterium tuberculosis

Pyrazinamide liposomes were prepared employing the phospholipid molar ratios; dipalmitoyl phosphatidyl choline (7):cholesterol (2) neutral and dipalmitoyl phosphatidyl choline (7):cholesterol (2):dicetyl phosphate (1) negatively charged. Swelling at 52 degrees C led to higher trapping efficiencies. An optimum sterilizing dose of 25 kGy was exhibited by gamma (gamma)-irradiation. Neutral pyrazinamide liposomes (7:2), swollen for 24 h, were employed in biological evaluation for treatment of mice infected by Mycobacterium tuberculosis. Liposomal pyrazinamide could effect highly significant reduction in bacterial counts (colony forming units/g lung), 10, 20 and 30 days after the last treatment dose. Histopathological examination of mice lungs showed highest severity of infection in drug-free liposomes (control) group > pyrazinamide liposomes > free pyrazinamide 6 days/week. The results indicate high therapeutic efficacy of pyrazinamide liposomes, injected twice weekly, in treatment of M. tuberculosis in mice.

Cloning and expression of recombinant MPB70 protein antigen from Mycobacterium bovis BCG for diagnosis of tuberculosis

In a search for developing new skin test reagents, MPB70 protein antigen was a candidate antigen for the Diagnosis of bovine tuberculosis. First M. bovis BCG genomic DNA was extracted purified and the mpb70 gene was amplified by PCR. The gene was then ligated to an expression vector, PQE. After transformation of the expression E. coil M15 host strain with the PQE plasmid, the expression was induced using 10 mM of IPTG. Two bands were seen in the SDS-PAGE analysis the 44 and 50 KDa represents the dimmers of the nonglycosylated and glycosylated form of the reMPB70 antigen. The His-tagged reMPB70 antigen was then purified by metal affinity chromatography using Ni-NTA agarose. Protein refolding was done by the use of the co solvent Polyethylene glycol MW 3000. The diagnostic potential of the re-MPB70 was evaluated using sera from experimentally sensitized guinea pigs with different strains of mycobacteria (M. bovis BCG, M. tuberculosis, M. kansasii and M. intracellular) using ELISA test. The results indicated the efficiency of MPB70 but not bovine PPD to discriminate between M. bovis sensitized guinea pigs and those sensitized with other mycobacterial strains at serum dilution of 1150. In a field trials to using reMPB70 antigen for the serodiagnosis of bovine tuberculosis using ELISA test. Fifty serum samples from tuberculin +ve and 6 from tuberculin -ve cattle were used as well as 10 tuberculin +ve buffaloes. All +ve animals were confirmed to be M. bovis infected by P/M analysis, bacteriological examination. ELISA results revealed that reMPB70 could recognize the tuberculin +ve infected animals at serum dilution of 1/50 and that it could diagnose tuberculosis in cattle as well as buffaloes.