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Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

Quantum-dot cellular automata (QCA) has attracted computer scientists as new emerging nanotechnology for replacement the current CMOS technology because it has unique characteristics such as high frequency, extremely small feature size and low power consumption. The main building blocks in QCA are the majority gate and inverter so any Boolean function can be represented using these gates. Many important circuits were the target for implemented in this technology in an optimal form, such as random-access memory (RAM) cell. QCA-RAM cells were introduced in literature with different forms but most of them are not optimized enough. This paper aims to demonstrate QCA inherent capabilities that can facilitate the design of many important gates such as the XOR gate and multiplexer (MUX) without following any Boolean function to get an optimum design in terms of complexity and delay.

In this paper, a novel structure of QCA-MUX in an optimal form will be used to design two unique structures of a RAM cell. The proposed RAM cells are the lowest cost required compared with different counterparts. The presented RAM cells used a new approach that follows the new suggested block diagram. The presented circuits are simulated and tested with QCADesigner and QCAPro tools.

The comparison of the proposed circuits with the previously reported in the literature show noticeable improvements in speed, area, and the number of cells. The cost function analysis results for the proposed RAM cells show significant improvement compared to older circuits.

A novel structure of QCA-MUX in an optimal form will be used to design two unique structures of a RAM cell.

Quantum-dot Cellular Automata (QCA) is a new nano-scale transistor-less computing model. To address the scaling limitations of complementary-metal-oxide-semiconductor technology, QCA seeks to produce general computation with better results in terms of size, switching speed, energy and fault-tolerant at the nano-scale. Currently, binary information is interpreted in this technology, relying on the distribution of the arrangement of electrons in chemical molecules. Using the coplanar topology in the design of a fault-tolerant digital comparator can improve the comparator’s performance. This paper aims to present the coplanar design of a fault-tolerant digital comparator based on the majority and inverter gate in the QCA.

As the digital comparator is one of the essential digital circuits, in the present study, a new fault-tolerant architecture is proposed for a digital comparator based on QCA. The proposed coplanar design is realized using coplanar inverters and majority gates. The QCADesigner 2.0.3 simulator is used to simulate the suggested new fault-tolerant coplanar digital comparator.

Four elements, including cell misalignment, cell missing, extra cell and cell dislocation, are evaluated and analyzed in QCADesigner 2.0.3. The outcomes of the study demonstrate that the logical function of the built circuit is accurate. In the presence of a single missed defect, this fault-tolerant digital comparator architecture will achieve 100% fault tolerance. Also, this comparator is above 90% fault-tolerant under single-cell displacement faults and is above 95% fault-tolerant under single-cell missing defects.

A novel structure for the fault-tolerant digital comparator in the QCA technology was proposed used by coplanar majority and inverter. Also, the performance metrics and obtained results establish that the coplanar design can be used in the QCA circuits to produce optimized and fault-tolerant circuits.

This paper aims to investigate the flow of two-layered non-Newtonian fluids with different viscosities in an axisymmetric elastic tube.

A mathematical model was considered for this study to describe the flow characteristics of two-layered non- Newtonian Jeffrey fluids in an elastic tube. Because Jeffrey fluid model is a better model for the description of physiological fluid motion. Further, this model is a significant generalization of Newtonian fluid model. Analytical expressions for flux, stream functions, velocities and interface velocity have been derived in terms of elastic parameters, inlet, outlet and external pressures. The effects of various pertinent parameters on the flow behavior have been studied.

The volumetric flow rate was calculated by different models of Mazumdar (1992) and Rubinow and Keller (1972); from this it was found that the flux of Jeffrey fluid is more in the case of Rubinow and Keller model than Mazumdar. A comparative study is made between single-fluid and two-fluid models of Jeffrey fluid flows and it was observed that more flux and higher velocities were observed in the case of two-fluid model rather than single-fluid model. Furthermore, when both the Jeffrey parameter tends to zero and ratios of viscosities and radii are unity, the results in this study agree with those of Rubinow and Keller (1972).

To describe the fluid flow in an elastic tube with two-layered systems, the models and solutions developed here are very important. These results will be highly suitable in analyzing the rheological characteristics of blood flow in a small blood vessel because of their elastic nature.

It is argued that COVID-19 patients show various neuropsychiatric symptoms, including fatigue, depression and anxiety. On the other hand, epidemiological and experimental evidence indicated that green tea could potentially have antiviral effects and ameliorate psychiatric disorders. However, there is a lack of clinical evidence. The purpose of this study was to investigate whether drinking green tea can clinically improve psychiatric complications of COVID-19 infection.

This study included 40 patients with laboratory confirmed mild-to-moderate COVID-19 disorder in the current randomized open-label controlled trial. Patients were instructed to include three cups/day of green tea (intervention) or black tea (control) to their usual diet for four weeks immediately after diagnosis of the disease. At the study baseline and after the intervention, the enrolled patients’ fatigue, depression and anxiety were assessed by the Chalder Fatigue Scale, Beck Depression Inventory-Fast Screen and State-Trait Anxiety Inventory questionnaires.

A total of 19 COVID-19 cases in the intervention group (mean age = 52 years) and 14 cases (mean age = 50 years) in the control group completed the study. Analysis of covariance adjusted for baseline levels, and confounders revealed that those who consumed three cups/day of green tea compared to the patients who received black tea experienced significantly lower fatigue, depression and state and trait anxiety levels (adjusted means for fatigue = 12.3 vs 16.2 (P = 0.03), depression = 0.53 vs 1.8 (P = 0.01), 37.4 vs 45.5 (P < 0.01) and 37.9 vs 45.2 (P < 0.01)).

The open-label design may bias the evaluation of the self-reported status of fatigue, depression or anxiety as the main outcomes assessed. Moreover, as this study did not include patients with severe COVID-19, this might affect the generalizability of the present results. Thus, the recommendation of daily drinking green tea may be limited to the subjects diagnosed with mild-to-moderate type of infection or those with long-term neuropsychiatric complications owing to COVID-19. Besides, considering the ethical issues, this study could not exclude the drug therapy’s confounding effects; thereby, this point should be considered when interpreting the current results. Besides, it is worth noting that Guilan province in the north of Iran is recognized as a tea (and particularly green tea) producing region; thereby, it is an available and relatively inexpensive product. Considering this issue, the recommendation to consume this medicinal plant in adjunct to the routine treatment approach among patients with mild-to-moderate COVID-19 based on its beneficial effects may be widely accepted.

Green tea consumption could be considered an option to combat COVID-19 associated psychological complications, including fatigue, depression and anxiety among patients suffering from mild-to-moderate type of this viral infection.

To the best of the authors’ knowledge, in this study, for the first time, the effects of green tea compared to black tea on COVID-19 associated fatigue, depression and anxiety status within an open-label controlled trial have been investigated.