This article aims to study the energy that can separate potatoes from the soil through an experimental study supported by theoretical equations and interpretations of the results obtained. The aim of this work is to show the relation between the water content of the soil and the extraction force of the potato. The empirical relationship between these factors will guide the correct design and sizing of harvesting tools; in order to obtain a correlation between the required resistance and humidity, these tests are based on two types of potatoes most consumed in Tunisia (sponta-dafla) and one type of clay in the north of the country (beja- majez al.bab), It is also more compact compared to others which will soon facilitate generalized studies, since the cultivation of potatoes presents a solution to secure the world diet. minimized damage to the potato necessarily gives a competitive product on the market, and we must also develop the efficiency of the processes. In order to extract data for designers to create them and translate them correctly into technical solutions to target at least two points, the first is to improve the mechanical power to operate the various components of the systems, of course, the second is better economic management. In addition, a good decision can be made to implement the harvesting process because due to climate change the rainfall levels in Tunisia, As happens in the world, goes through two of the higher levels Values to the lower ones ie drought in summer and sometimes high levels in winter.This forces us to take into account the climate and the diversity of precipitation seasons, and the presence of water in the soil to translate it into energy according to the effort.

Background/aim:The reduction in effectiveness and efficiency of orthodox drugs in malaria treatments due to resistance has resulted in extensive use of plants for the treatment of periodic fevers and malaria in endemic countries. A standardized fractionated acetone-water extract from leaves(IRAB), of the neem tree (Azadirachta indica) has previously been reported to have strong antimalarial activity against Plasmodium falciparum in an in-vitro assays.However, there are no reports of its antimalarial activity in-vivo. We therefore, tested the efficacy of IRAB in-vivo in mice infected with Plasmodium yoelii.
Methods: Using the 4–day antimalarial suppressive method, two experiments were carried out using four groups of 8 mice each. They were inoculated intraperitoneally, on day 0, with 0.2 mL of infected blood containing 107 Plasmodium yoelii parasitized erythrocytes.Two hours post-parasite inoculation, IRAB was administered to three groups of mice by oral gavage, twice daily for four consecutive days as follows; A(10mg/kg); B(30mg/kg); C(90mg/kg) IRAB.The control group (D) was similarly treated with phosphate buffered saline (pH 6.5).
Results:Treatment with IRAB significantly reduced (p<0.0001) the parasitaemia in a dose-dependent manner.The control group had a mean parasitaemia of 16.7±4.5 while parasitaemia in groups treated with 10mg/kg, 30mg/kg and 90mg/kg IRAB was 11.9±4.7, 7.1±3.9, and 4.5±1.7 with corresponding parasite suppression of 29.0%, 57.5%,and 73.4%,respectively. In both experiments, seven mice survived in the control group (D) while in the groups treated with IRAB (A, B, C), 11, 13, and 16 mice survived, respectively.
Conclusion:These findings demonstrate that IRAB suppressed parasitaemia and enhanced the survival rate of Plasmodium yoelii infected mice.

Cardiac MR Images are the vital resources for the detection of various cardiac diseases. Considering the anatomy of heart, left ventricle is responsible for pumping oxygenated blood all over the body. Thus to spot cardiac disorders, identification of left ventricle (LV) is a pre-requisite in the clinical diagnosis. Automated segmentation of ventricle promotes accurate and reliable classification of the normal and abnormal ones. The method followed in this work for segmentation of Cardiac MR Images involves deep convolution neural network and deformation model. The Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 challenge database is taken to serve as the cardiac MR dataset for this work. The deep convolution network is employed to localize LV and to identify the region of interest from the entire cardiac MRI. Then deformation model based on contours is used in order to provide the exact LV boundary. Two techniques of deformable model are implemented and evaluated metrics from that are calculated from the segmented LV. The performance of both the models is compared and the Region Based Geometric model deformable technique is found to outperform the Chan Vese Active Contour deformable model