Circadian rhythms occupy an important role in daily biological activities of living species. Circadian disorder is a phenomenon of circadian rhythms which occurs when internal rhythms cannot keep up with the changes of external environment rhythms. Changes of environmental rhythms, presented by the change of light/dark cycles or by irregular rhythms, result in phase shifts between internal and external rhythms. The existence of these phase shifts in longer term has negative effect to health. Therefore, in biological study of circadian rhythms, finding a method to recover the shifted phases to their normal rhythms, which is also the treatment of circadian disorder, is an important required task. In this paper, we propose a control design method to reset the circadian phases. The phase restoration is carried out by the synchronization of trajectories generated from a controlled model with the trajectories of a reference system via nonlinear control design using only one measurement. Both reference and controlled systems are based on a given 3rd order model of Neurospora circadian rhythms. The two other unknown states are estimated using a recently developed nonlinear observer for the output-feedback control.

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The most useful feature of ultrasound tomography founded on the inverse scattering theory is that it can detect small structures below the wavelength of the pressure wave. A popular method introduced in ultrasound tomography is the Distorted Born Iterative Method (DBIM). Recently, the dual-frequency combination technique has been utilized to improve the reconstruction quality and increase the convergence rate of the DBIM. This method uses two frequencies, f 1 (low) and f 2 (high), to estimate the sound contrast in N f 1 and N f 2 iterations, respectively. However, the influence of these iteration parameters on the overall performance of the system is not yet known. In this paper, it is shown by using the simulation technique that if we do not pay attention to the choice of these parameters, the reconstruction quality might be worse than that using a single frequency. Furthermore, we focus on the best way to select the parameters in order to improve the reconstruction quality of ultrasound tomography. Given a fixed sum N iter of N f 1 and N f 2 , simulation results show that the best value of N f 1 is N iter /2; this choice of parameters offers a normalized error that reduces by 67.6%, compared to the conventional DBIM using a single frequency.

<span>Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.</span>

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