امیرهمایون جعفری

Air traffic control is a very complex process, including multiple human-machine interactions. Human mental workload plays an important role in this process. Nowadays, electroencephalography indexes are considered as new indicators in the field of assessment of mental workload. The purpose of the present study was to investigate the relationship between EEG theta power and mental workload in air traffic control simulation.

Fourteen air traffic controllers participated in this study. Controllers carried out two scenarios, including low and high workload, based on task load factors in an air traffic control simulator. Mental workload was assessed in these two scenarios by the NASA-TLX questionnaire. EEG signals were continuously recorded during air traffic control tasks. Afterward, absolute theta power was extracted from participants’ EEG using Fast Fourier Transform (FFT) by the MATLAB software and was compared with each other in terms of high and low workload.

The results showed a significant relationship in absolute theta power during low and high workload scenarios in all regions of the brain (p < 0.05). Absolute theta power increased primarily in the frontal region during the high workload scenario. Also, there was a significant increase in the relationship between work experience and absolute theta power at the F3 region during the high workload scenario (P=0.021, r=0.607).

Absolute theta power provides a good parameter to assess mental workload at different levels of air traffic control tasks. Therefore, it can be used as a tool for the design of human-machine complex systems.

The use of exoskeletons as a new ergonomics intervention to reduce musculoskeletal disorders risk factors and increase human performance has emerged in the fourth-generation industrial revolution. The aim of this study was to assess the cervical exoskeleton effect on the neck and shoulder muscles electrical activity.

In this experimental study, 14 male participants were asked to perform the simulated overhead work with and without using the cervical exoskeleton at two neck postural angles randomly. During the task, electromyography of the target muscles in the neck and shoulders was recorded. In addition, at the end of each task, participants completed a perceived discomfort questionnaire. Electromyographic signals were processed with Matlab 2017b software and the level of the electrical activity of the target muscles was normalized to the maximum muscle activity. Data analysis was performed using Random intercept mix model in STATA 14 software.

Mean perceived discomfort in the neck and shoulders regions significantly reduced by the exoskeleton device, but there was no statistically significant difference in other areas. Also, the mean activity level of sternocleidomastoid and splenius capitis muscles on the right and left was significantly decreased during the use of the cervical exoskeleton. However, this difference showed a significant increase in trapezius muscles.

The use of the neck exoskeleton was associated with a reduction of muscle electrical activity and the perceived discomfort in the neck area. These results may be related to transferring neck and head weight by the exoskeleton retaining jack during the neck extension to other areas of the body. The exoskeleton design did not provide support for the shoulder and arm area, which explains the reason for the non-significant results in the shoulder area. Using the exoskeleton with the additional support in shoulder area could be considered as an ergonomic intervention in such overhead works.

The cell cycle network is responsible of control, growth and proliferation of cells. The relationship between the cell cycle network and cancer emergence, and the complex reciprocal interactions between genes/proteins calls for computational models to analyze this regulatory network. Ample experimental data confirm the existence of random behaviors in the interactions between genes and proteins in gene regulatory networks. Genetic factors, regulatory dynamics at the microscopic level, transcription rates of genes, and many other factors that depend on variable environmental conditions cause random behaviors in the cell cycle network.

The aim of this study was to present a stochastic Markov model to simulate interactions between proteins in a complex network of fission yeast cell cycle and to predict the dynamics of proteins. We used local sensitivity analysis to investigate the relationship between the weight of protein / gene interactions with the probabilities of phase transition in the cell cycle.

Using this model, the probability of transition between different phases of the cell cycle in the presence of different levels of noise was investigated and it was proved that the cell cycle path has the highest probability among all possible pathways for the cell. By performing sensitivity analysis, the correlation between the weight of interactions between proteins and the probability of transition between different phases of the cell cycle was calculated.

Our local sensitivity analysis revealed that how perturbation on parameters affect the transition probabilities between subsequent cell cycle phases, so it suggests testable hypotheses in the experimental environments. Also, the model of this study proves the stability of the cell cycle in the presence of moderate levels of noise.

Using nonlinear signal processing methods is critical in processing biological signals due to their nonlinear dynamics. Recurrence plots are one of these nonlinear methods that provide qualitative and graphical representation of inherent dynamic of signal. Reaching movement is one of the important skill movements during human life. Despite of nonlinear methods capability to analyze the electromyogram signals during reaching movement, these methods are less considered. Therefore, the current manuscript investigates the classification of reaching movements at different speeds in horizontal plane. To achieve this, some quantitative indicators of recurrence plot analysis and nonlinear quantifiers including Lyapunov exponent and Higuchi fractal dimension are used. Based on multivariate analysis of variance, most discriminative features in the separation of different speeds of reaching movement are selected. Results show Recurrence rate, determinism, laminarity and Higuchi fractal dimension are best indicators to describe the recorded signals. The accuracy of KNN is 96.67%, SVM is 100%, linear discriminant analysis is 100%, and decision tree is 90%.

According to cancer’s global statistics, there will be 27.5 million new cases of cancer each year by 2040, therefore, it is crucial to achieve a deeper understanding of the cancer progression mechanisems and immune system functions in response to it. Nowadays, Computational models are widely used to capture dynamics of the tumor-immune system (TIS). The proposed model on this manuscript is on the basis of the ordinary differential equations which mechanistically models the interactions of tumor cells, CTLs, NKs and MDSCs. CTLs and NK cells are the most important cells of adaptive and innate immune system, respectively that encounter with tumor cells, while MDSCs as immature immune cells suppress the immune responses in the inflammatory environments. Due to the error of the in vivo/in vitro experiments, vagueness, imprecise information, incomplete data and natural variability of the tumor-immune system emerges between different individuals, the kinetic parameters of computational models are uncertain that this uncertainty can be captured by fuzzy sets. Hence, we assign fuzzy numbers with triangular membership functions instead of crisp numbers to some kinetic parameters of the tumor–immune system model. In fact, the uncertainty in the kinetic parameters of the ordinary differential equations affects the dynamic of the system species. In this essay, for the first time, a fuzzy number has been used to model the uncertainty of the parameters of the ODE model. Our data reveals that increasing/decreasing the uncertainty region of the model's fuzzy parameters increases/decreases the uncertainty region of dynamics of species. Furtheremore, the simulations of the model in the crisp setting of parameters show that the repition of 5-FU treatment for inhibition of MDSCs dramatically inhibits tumor cells and eradicate tumor.