Search

Mangifera indica peels are a rich source of diverse flavonoids and xanthonoids; however, generally these are discarded. Computational studies revealed that mangiferin significantly interacts with amino acid residues of transcriptional regulators 1IK3, 3TOP, and 4f5S. The methanolic extract of Langra variety of mangoes contained the least phenol concentrations (22.6 ± 0.32 mg/gGAE [gallic acid equivalent]) compared to the chloroform (214.8 ± 0.12 mg/gGAE) and ethyl acetate fractions (195.6 ± 0.14 mg/gGAE). Similarly, the methanolic extract of Sindhri variety contained lower phenol concentrations (42.3 ± 0.13 mg/gRUE [relative utilization efficiency]) compared with the chloroform (85.6 ± 0.15 mg/gGAE) and ethyl acetate (76.1 ± 0.32 mg/gGAE) fractions. Langra extract exhibited significant α‐glucosidase inhibition (IC50 0.06 mg/mL), whereas the ethyl acetate fraction was highly active (IC50 0.12 mg/mL) in Sindhri variety. Mangiferin exhibited significant inhibition (IC50 0.026 mg/mL). A moderate inhibition of 15‐LOX was observed in all samples, whereas mangiferin was least active. In advanced glycation end product inhibition assay, the chloroform fraction of Langra variety exhibited significant inhibition in nonoxidative (IC50 64.4 μg/mL) and oxidative modes (IC50 54.7 μg/mL). It was concluded that both Langra and Sindhri peel extracts and fractions possess significant antidiabetic activities. The results suggest the potential use of peel waste in the management and complications of diabetes. [ABSTRACT FROM AUTHOR]

The significance of this study lies in its exploration of bioactive plant extracts as a promising avenue for combating oral bacterial pathogens, offering a novel strategy for biofilm eradication that could potentially revolutionize oral health treatments. Oral bacterial infections are common in diabetic patients; however, due to the development of resistance, treatment options are limited. Considering the excellent antimicrobial properties of phenolic compounds, we investigated them against isolated oral pathogens using in silico and in vitro models. We performed antibiogram studies and minimum inhibitory concentration (MIC), antibiofilm, and antiquorum sensing activities covering phenolic compounds. Bacterial strains were isolated from female diabetic patients and identified by using 16S rRNA sequencing as Pseudomonas aeruginosa, Bacillus chungangensis, Bacillus paramycoides, and Paenibacillus dendritiformis. Antibiogram studies confirmed that all strains were resistant to most tested antibiotics except imipenem and ciprofloxacin. Molecular docking analysis revealed the significant interaction of rutin, quercetin, gallic acid, and catechin with transcription regulator genes 1RO5, 4B2O, and 5OE3. All tested molecules followed drug-likeness rules except rutin. The MIC values of the tested compounds varied from 0.0625 to 0.5 mg/mL against clinical isolates. Significant antibiofilm activity was recorded in the case of catechin (73.5% ± 1.6% inhibition against B. paramycoides), cinnamic acid (80.9% ± 1.1% inhibition against P. aeruginosa), and vanillic acid and quercetin (65.5% ± 1.7% and 87.4% ± 1.4% inhibition, respectively, against B. chungangensis) at 0.25-0.125 mg/mL. None of the phenolic compounds presented antiquorum sensing activity. It was, therefore, concluded that polyphenolic compounds may have the potential to be used against oral bacterial biofilms, and further detailed mechanistic investigations should be performed. [ABSTRACT FROM AUTHOR]

Oral bacterial biofilms are the main reason for the progression of resistance to antimicrobial agents that may lead to severe conditions, including periodontitis and gingivitis. Essential oil-based nanocomposites can be a promising treatment option. We investigated cardamom, cinnamon, and clove essential oils for their potential in the treatment of oral bacterial infections using in vitro and computational tools. A detailed analysis of the drug-likeness and physicochemical properties of all constituents was performed. Molecular docking studies revealed that the binding free energy of a Carbopol 940 and eugenol complex was -2.0 kcal/mol, of a Carbopol 940-anisaldehyde complex was -1.9 kcal/mol, and a Carbapol 940-eugenol-anisaldehyde complex was -3.4 kcal/mol. Molecular docking was performed against transcriptional regulator genes 2XCT, 1JIJ, 2Q0P, 4M81, and 3QPI. Eugenol cinnamaldehyde and cineol presented strong interaction with targets. The essential oils were analyzed against Staphylococcus aureus and Staphylococcus epidermidis isolated from the oral cavity of diabetic patients. The cinnamon and clove essential oil combination presented significant minimum inhibitory concentrations (MICs) (0.0625/0.0312 mg/mL) against S. epidermidis and S. aureus (0.0156/0.0078 mg/mL). In the anti-quorum sensing activity, the cinnamon and clove oil combination presented moderate inhibition (8 mm) against Chromobacterium voilaceum with substantial violacein inhibition (58% ± 1.2%). Likewise, a significant biofilm inhibition was recorded in the case of S. aureus (82.1% ± 0.21%) and S. epidermidis (84.2% ± 1.3%) in combination. It was concluded that a clove and cinnamon essential oil-based formulation could be employed to prepare a stable nanocomposite, and Carbapol 940 could be used as a compatible biopolymer. [ABSTRACT FROM AUTHOR]

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs. [ABSTRACT FROM AUTHOR]

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time–frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications. [ABSTRACT FROM AUTHOR]

By making use of the concept of basic (or q-) calculus, a subclass of q-starlike functions with reference to symmetric points, which is associated with the q-exponential function, is introduced in the open unit disc. Further, we derived upper bounds for the third-order Hankel determinant for the defined class. For the validity of our results, relevant connections with those in earlier works are also pointed out. [ABSTRACT FROM AUTHOR]

The present study focuses upon linguistic barriers faced by the EFL learners in learning English speaking skills at High Schools level. A survey descriptive design is used to collect data from sample of one hundred and fifty students from five high schools through an adapted questionnaire. The data analysis reveals that students' language hurdles take shape of a lack of vocabulary, errors in the word pronunciation, and other grammatical structures. These impediments mostly manifest themselves because of not placing enough emphasis upon key components of speaking and learning, or on practicing those skills. This caused other hurdles with various root reasons that affected learners' ability to speak English. The study is delimited to focus on students at high schools of Laghman, Afghanistan. The results offered significant and valuable information about research issues. According to the study, to ensure that speaking abilities are adequately taught and practiced, learning and teaching strategies should be based upon the contemporary trends. These strategies can improve English speaking skills at high and higher secondary school level. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods. [ABSTRACT FROM AUTHOR]

This work investigated the improvement of amylopectin addition on the quality of myofibrillar proteins (MP) gel damaged by high doses of epigallocatechin-3-gallate (EGCG, 80 μM/g protein). The results found that the addition of amylopectin partially alleviated the unfolding of MP induced by oxidation and EGCG, and enhanced the structural stability of MP. Amylopectin blocked the loss of the free amine group and thiol group, and increased the solubility of MP from 7.0% to 9.5%. The carbonyl analysis demonstrated that amylopectin addition did not weaken the antioxidative capacity of EGCG. It was worth noting that amylopectin significantly improved the gel properties of MP treated with a high dose of EGCG. The cooking loss was reduced from 51.2% to 35.5%, and the gel strength was reduced from 0.41 N to 0.29 N after adding high concentrations of amylopectin (A:E(8:1)). This was due to that amylopectin filled the network of MP gel after absorbing water and changed into a swelling state, and partially reduced interactions between EGCG and oxidized MP. This study indicated that amylopectin could be used to increase the polyphenol loads to provide a more lasting antioxidant effect for meat products and improve the deterioration of gel quality caused by oxidation and high doses of EGCG. [ABSTRACT FROM AUTHOR]

BACKGROUND: Myosin is the most important component of myofibrillar protein, with excellent gelling properties. To date, heating treatment remains the mainstream method for forming gel in meat products, and it has the most extensive application in the field of meat industry. However, at present, there are few reports on the effects of heating rates on myosin self‐assembly and aggregation behavior during heating treatment. RESULTS: The present study aimed to investigate the effects of different heating rates (1, 2, 3 and 5 °C min−1) on the self‐assembly behavior, physicochemical, structural and gelling properties of myosin. At the lowest heating rate of 1 °C min−1, the myosin gel had a dense microstructure, the highest elastic modulus (G′) and water holding capacity compared to higher heating rates (2, 3 and 5 °C min−1). At higher temperatures (40, 45 °C), the surface hydrophobicity, turbidity, particle size distribution and self‐assembly behavior of myosin in pre‐gelling solutions showed that myosin had sufficient time to denature, underwent full structure unfolding before aggregation at the heating rate of 1°C min−1, and formed regular and homogeneous spherical aggregates. Therefore, the myosin gel also had a better three‐dimensional network. CONCLUSION: The heating rates had an important effect on the quality of myosin gels, and had theoretical implications for improving the quality of meat gel products. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform. [ABSTRACT FROM AUTHOR]

Prevalence of oral infections in diabetic patients is a health challenge due to persistent hyperglycemia. However, despite great concerns, limited treatment options are available. We therefore aimed to develop nanoemulsion gel (NEG) for oral bacterial infections based on essential oils. Clove and cinnamon essential oils based nanoemulgel were prepared and characterized. Various physicochemical parameters of optimized formulation including viscosity (65311 mPa·S), spreadability (36 g·cm/s), and mucoadhesive strength 42.87 N/cm2) were within prescribed limits. The drug contents of the NEG were 94.38 ± 1.12% (cinnamaldehyde) and 92.96 ± 2.08% (clove oil). A significant concentration of clove (73.9%) and cinnamon essential oil (71.2 %) was released from a polymer matrix of the NEG till 24 h. The ex vivo goat buccal mucosa permeation profile revealed a significant (52.7–54.2%) permeation of major constituents which occurred after 24 h. When subjected to antimicrobial testing, significant inhibition was observed for several clinical strains, namely Staphylococcus aureus (19 mm), Staphylococcus epidermidis (19 mm), and Pseudomonas aeruginosa (4 mm), as well as against Bacillus chungangensis (2 mm), whereas no inhibition was detected for Bacillus paramycoides and Paenibacillus dendritiformis when NEG was utilized. Likewise promising antifungal (Candida albicans) and antiquorum sensing activities were observed. It was therefore concluded that cinnamon and clove oil-based NEG formulation presented significant antibacterial-, antifungal, and antiquorum sensing activities. [ABSTRACT FROM AUTHOR]

A hybrid dielectric barrier discharge (DBD) plasma‐catalysis system is an effective way to catalyze the conversion of small molecules such as carbon dioxide. Until now, ABO3 perovskite catalyst used for CO2 hydrogenation to light hydrocarbons reaction has been rarely reported. Herein, the lanthanum nickel perovskite‐type catalysts named as La0.8A'0.2NiO3±δ (A' represents for Ca, Ce, Pr, Sr) are prepared by a modified nitrate citric acid method, and the influence of A‐site substitution on catalytic performance is investigated. The reactivity results show that the substitution of A‐site can improve CO2 conversion and C2–4 (hydrocarbons containing C2–C4) selectivity in varying degrees. Compared to LaNiO3 catalyst, the La0.8Ca0.2NiO3±δ catalyst has CO2 conversion increased from 72.6% to 82.3%, and C2–4 selectivity from 5.8% to 8.5%. The superior performance can be put down to the formation of smaller Ni particle size on La0.8Ca0.2NiO3±δ catalyst, which promotes the activation of H2. Additionally, the sufficient oxygen vacancies on La0.8Ca0.2NiO3±δ provide more active sites for CO2 adsorption and then enhance CO2 conversion. The hybrid DBD plasma‐catalysis system is proved effective for CO2 hydrogenation, and the idea of using A‐site substitution would serve as a guide for the modification of perovskite‐type ABO3 catalysts. [ABSTRACT FROM AUTHOR]

Pakistan is an Islamic democratic state but still its economic system is based on usury. Due to usury, multiple important and fundamental problems have arisen in the country's economy. Which have negative economic and social effects on the country Usury or Interest has been prevalent in human society for thousands of years and is also considered as the backbone of Capitalism. It has adverse effects to the Pakistan economy too. Islam has declared Usury as a forbidden, unbalanced and exploitative economic factor. The evils of usury have divided the society into rich and poor classes by fostering the psychology of selfishness and wider losses. In this article, the negative effects of usury have been pointed out, in order to eliminate usury from the economy of Pakistan. The abrogation of Interest from Pakistanis economy will help in promoting the Islamic economic system on one hand and public welfare will be achieved on the other hand. [ABSTRACT FROM AUTHOR]

The extractive summarization approach involves selecting the source document's salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model. [ABSTRACT FROM AUTHOR]

Oral bacterial infections are fairly common in patients with diabetes mellitus; however, due to limited treatment options, herbal medicines are considered an alternate solution. This study aimed to formulate a stable essential-oil-loaded nanoemulsion for the treatment of oral bacterial infections. Essential oils from edible sources including coriander, clove, cinnamon and cardamom were extracted by hydrodistillation. The response surface methodology was used to optimize the nanoemulsion formulation by applying the Box–Behnken design. The oil concentration, surfactant concentration and stirring speed were three independent factors, and particle size and polydispersity index were two responses. The particle size, polydispersity index and zeta potential of the optimized formulation were 130 mm, 0.222 and −22.9, respectively. The ATR-FTIR analysis revealed that there was no incompatibility between the active ingredients and the excipients. A significant release profile in active ingredients of nanoemulsion, i.e., 88.75% of the cinnamaldehyde and 89.33% of eugenol, was recorded after 24 h. In the ex vivo goat mucosal permeation study, 71.67% of the cinnamaldehyde permeated and that of the eugenol 70.75% from the nanoemulsion. The optimized formulation of the essential-oil-loaded nanoemulsion showed a 9 mm zone of inhibition against Staphylococcus aureus and Staphylococcus epidermidis, whereas in anti-quorum sensing analysis, the optimized nanoemulsion formulation showed an 18 mm zone of inhibition. It was concluded that formulated essential-oil-loaded nanoemulsion can be used against S. epidermidis and S. aureus infections in oral cavity. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a type of eye disease that may be caused in individuals suffering from diabetes which results in vision loss. DR identification and routine diagnosis is a challenging task and may need several screenings. Early identification of DR has the potential to prevent or delay vision loss. For real-time applications, an automated DR identification approach is required to assist and reduce possible human mistakes. In this research work, we propose a deep neural network and genetic algorithm-based feature selection approach. Five advanced convolutional neural network architectures are used to extract features from the fundus images, i.e., AlexNet, NASNet-Large, VGG-19, Inception V3, and ShuffleNet, followed by genetic algorithm for feature selection and ranking features into high rank (optimal) and lower rank (unsatisfactory). The nonoptimal feature attributes from the training and validation feature vectors are then dropped. Support vector machine- (SVM-) based classification model is used to develop diabetic retinopathy recognition model. The model performance is evaluated using accuracy, precision, recall, and F1 score. The proposed model is tested on three different datasets: the Kaggle dataset, a self-generated custom dataset, and an enhanced custom dataset with 97.9%, 94.76%, and 96.4% accuracy, respectively. In the enhanced custom dataset, data augmentation has been performed due to the smaller size of the dataset and to eliminate the noise in fundus images. [ABSTRACT FROM AUTHOR]

Medical imaging is the process of visual representation of different tissues and organs of the human body to monitor the normal and abnormal anatomy and physiology of the body. There are many medical imaging techniques used for this purpose such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), digital mammography, and diagnostic sonography. These advanced medical imaging techniques have many applications in the diagnosis of myocardial diseases, cancer of different tissues, neurological disorders, congenital heart disease, abdominal illnesses, complex bone fractures, and other serious medical conditions. There are benefits as well as some risks to every imaging technique. There are some steps for minimizing the radiation exposure risks from imaging techniques. Advance medical imaging modalities such as PET/CT hybrid, three-dimensional ultrasound computed tomography (3D USCT), and simultaneous PET/MRI give high resolution, better reliability, and safety to diagnose, treat, and manage complex patient abnormalities. These techniques ensure the production of new accurate imaging tools with improving resolution, sensitivity, and specificity. In the future, with mounting innovations and advancements in technology systems, the medical diagnostic field will become a field of regular measurement of various complex diseases and will provide healthcare solutions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has brought about various global changes, as all devices will be connected. This article examines the latest 5G solutions for enabling a massive cellular network. It further explored the gaps in previously published articles, demonstrating that to deal with the new challenges. The mobile network must use massive multiple input and output (MIMO), nonorthogonal multiple access (NOMA), orthogonal multiple access (OMA), signal interference cancellation (SIC), channel state information (CSI), and clustering. Furthermore, this article has two objectives such as (1) to introduce the cluster base NOMA to reduce the computational complexity by applying SIC on a cluster, which ultimately results in faster communication and (2) to achieve massive connectivity by proposing massive MIMO with NOMA and OMA. The proposed NOMA clustering technique working principle pairs the close user with the far user; thus, it will reduce computational complexity, which was one such big dilemma in the existing articles. This will specifically help those users that are far away from the base station by maintaining the connectivity. Despite NOMA's extraordinary benefits, one cannot deny the significance of the OMA; hence, the other objective of the proposed work is to introduce OMA with MIMO in small areas where the user is low in number, it is already in use, and quite cheap. The next important aspect of the proposed work is SIC, which helps remove interference and leads to enhancement in network performance. The simulation result has clearly stated that NOMA has gained a higher rate than OMA: current NOMA users' power requirement (weak signal user 0.06, strong signal user 0.07), spectral efficiency ratio for P-NOMA and C-NOMA (21%, 5%), signal-to-noise ratio OMA, P-NOMA, C-NOMA (28, 40, 55%), and user rate pairs NOMA, OMA (7, 3), C-NOMA, and massive MIMO NOMA SINR (4.0, 2.5). [ABSTRACT FROM AUTHOR]