Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

Characterization of neuropeptides which control cerebellar granule cell survival, migration and differentiation.

Abstract: During cerebellar development, granule cell precursors are produced from a secondary germinative zone forming the external granule cell layer (EGL). Immature granule neurons from the inner part of the EGL then start a tangential migration followed by a centripetal inward radial migration across the molecular and Purkinje cell layers to reach their final destination at the bottom of the forming internal granule cell layer (IGL). This complex migratory process is highly regulated and takes about 2 days in rodents and it is essential for the proper formation of the cortical layers forming the mature cerebellum. In the IGL, granule cells differentiate to establish functional excitatory synapses with GABAergic neurons including Purkinje, basket, stellate and Golgi cells, or die. Some neurotrophins and neurotransmitters have been shown to be involved in the control of cerebellar granule cell survival, migration and differentiation. Initially, when I started my carrier as a researcher, we used to claim that very few neuropeptides were produced in the cerebellum. Nevertheless, we now know that this was wrong as we have recently identified by mass spectrometry over 70 peptides expressed in the cerebellum during development. Over the years, the involvement of some of these peptides such as somatostatin, PACAP or ODN, has been established in the control of cerebellar granule cell survival, migration and differentiation as will illustrate my presentation.

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Abstract: Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always nonstationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it's flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.

Erratum to: “Design of an explicit expression of the Poincaré map for the passive dynamic walking of the compass-gait biped model”

Abstract: Some missing interesting references related to our quoted paper are added. The difference between our analytical approach and that presented in these references is also briefly described.