Djebel Youssef, who possesses particular characteristics in terms of geographical isolation in the high Setifian plains, contains a very important floristic diversity. Currently, its plant formations were entering a phase of intense and continuous degradation, causing great distruption of the plant cover with the regression and disappearance of vulnerable and endemic species. This degradation is mainly due to anthropic action and climatic conditions, particularly recurrent periods of drought. In order to the preserve and protect this ecosystem, spatio-temporel monitoring of vegetation transformation was applied by using of landat satellite images (TM 5 and OLI 8), forming several study scenes. Knowledge of vegetation distribution and dynamics allows detecting changes in the state of vegetation cover over a 10-years period, using remote sensing and geographic information system (SIG) data. The recorded regression was estimated at 2,21%.

An ultra-short pulsed laser can be employed in the nanoscale processing and patterning for many materials such ceramics, polymers, and semi-conductors. Processing characteristics of an ultra-short pulsed laser is different from that of long-pulsed laser due to ultrahigh intensity, ultrahigh power, and ultrashort time. Nonlinear multiphoto-absorption and ablation threshold of laser fluence occurs. The ultrasmall prosessing can achieved by an ultra-short pulsed laser. This paper investigates the characteristics of photothermal processing of an ultra-short pulsed laser considering optical penetration absorption and thermal transport of solid-vapor phase. The results reveal that the variation of ablation rate with laser fluences predicted by this work agrees with the available measured data for an ultrashort pulsed laser processing for AIN and PZT. The optical ablation governs at the low laser fluences due to the optical absorption length of materials for directly incident ultrashort pulsed laser. For the high laser fluences, when the residual laser energy after the optical ablation is high enough to maintain the material temperature above the evaporation point, the thermal ablation appears.The removal rate of optical and thermal ablations is also presented in this study.

In agriculture, yield prediction is extremely challenging for yield assessment, boosting crop performance and coordination between supply/demand. To concede growing needs and make efficient use of resources it is essential to know the current and potential production of crops. Cluster bean is a minor crop with rich nutrients that has industrial and medical importance. Some industry, market and cultivation problem reported in it. For better resource management and more accurate production of cluster bean required modeling of plant growth and crop yield. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and interactive crop factors. This Research purposed to develop a model for growth and yield prediction of cluster bean under various experimental conditions using machine learning. Time series data was recorded from cultivation to harvesting after every fifteen days interval. Crop parameters were obtained from different field experiments. Prediction on each stage provides appropriate cultivation plans in accordance with agronomic factors. Ten most contributed features were selected by using Information Gain, Relief, Forward Selection, Backward Selection and Genetic Evaluation to select significant attributes towards crop growth. Machine learning computational algorithms of Decision Tree, Naive Bayes, Random Forests were used to generate models and evaluate outcomes. Result indicate that forecasting accuracy enhanced significantly from cultivation to harvesting, Accuracy of 92% was obtained with feature selection was more accurate then 86% obtained through without feature selection. Random forest performed better in all scenerio and provided an accurate model for yield prediction.

Currently, India utilizes an enormous amount of fossil fuels. The major quantity of fossil fuels is imported from other countries. It\'s a giant load on the Indian Economy. The burning of fossil fuels causes global warming. Carbon neutral, renewable fuels are essential for environmental protection and it\'s economically sustainable for India. Biofuels have more attention day by day due to a rise in energy demands and environmental concerns. Biodiesel produced from algal oil may be a possible renewable and carbon-neutral substitute to fossil fuels. The feasibility of the algal-based biodiesel industry depends on the selection of adequate species regarding commercial oil yields and oil quality. Present research work designed to bioprospecting and screening of 19 algal and blue green algal species by applying, as selective criteria, the oil percentage and the fatty acid profiles, used for analyzing the biodiesel fuel properties. Within the present research paper, we extracted oil from Tolypothrix phyllophila algal strain and compared it with another eighteen algal and blue green algal strains from different literature. We also investigated the 19 algal and blue green algal fatty acid profiles and its suitability for biodiesel production and strains selection through PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) and GAIA (geometrical analysis for interactive aid) analysis.

Despite the economic importance of buffalo as a main source of milk and meat, only little attention has been directed to its immune and reproductive performance. The early diagnosis of subclinical endometritis may reduce the economic loss of buffalo’s production. The difference in expression profiles of immunity-related genes has an important role in the early detection of subclinical endometritis. This study aimed to assess the expression of five immunity-related genes; TGFBR1, PTGER2, PTGER4, HP and CXCL5 in endometritis-infected buffaloes. Total RNA was extracted from 120 buffalo uteri samples; 60 infected with endometritis and 60 healthy ones. Qt-PCR was performed on cDNA synthesized from extracted RNA using Sybr green and GAPDH as a house-keeping gene. \nThe results showed the up-regulation of two tested genes; TGFBR1 and CXCL5 in endometritis-infected buffalo compared to healthy animals by 7.9 and 4.3 folds, respectively at a significance level of p<0.05. The other three tested genes; PTGER2, PTGER4 and HP were down-regulated in buffalo during endometritis infection at different levels; PTGER2 and HP (0.6 folds, p<0.05) and PTGER4 (0.4 fold, p= 0.2). It is to be concluded that the assessment of expression of inflammation-related immunity genes may have an effective role on the detection of endometritis infection in buffalo during its early stages and this early diagnosis can reduce the economic loss of buffalo production and reproduction.

Mineral fertilization, farm manure and home compost were tested on tomato hybrid variety cultivated in open field (Lycopersicon esculentum Mill.) evaluating the effect on tomato plant growth, yield components and nutritional parameters of tomato fruit. Tomato variety was grown on clay-loam soil poor in potassium and nitrogen but rich in phosphor and fairly well provided with organic matter. The test was carried out in a randomized complete block design with four replications. Results showed that supply of home compost followed by farm manure revealed to be better on most of parameters measured compared with mineral fertilization. Fruit yield was positively affected by home compost whereas the values of lycopene content, vitamin C, refractive index of fruit juice, final height of tomato plant and dry matter of fruit per plant were higher with farm manure than by mineral fertilization and home compost. The variance analysis showed at least a significant difference with home compost followed by farm manure on numerous parameters such as: number of trusses per plant, number of flowers per plant, average size of fruit per plant, average weight of fruit per plant, fruit weight per plant and sugar content of fruit juice. However, the content of calcium, phosphor and potassium in fruit juice were higher with mineral fertilization than by the other treatments. For this purpose, farm manure promotes soil enrichment in mineral elements, improves fruit yield, and their quality, in addition, it guarantees sustainable agriculture through the preservation of soil structure and keep clean our environmental soils nape.

Soil erosion is a prime issue arising from agricultural intensification, land degradation, construction, mining, and other human activities. To conserve and plan watersheds it is necessary to estimate soil erosion. The modeling of soil erosion can provide a quantitative approach to estimate soil erosion. Remote Sensing data covers a large area within a dataset. This study compares the soil erosion model Revised Universal Soil Loss Equation (RUSLE) and Modified Soil Loss Equation (MUSLE) through Arc Map-based Soil and Water Assessment Tool (SWAT). These models are used to estimate soil erosion in the Barakar river basin situated in the state of Jharkhand of India. The remote sensing data is used to calculate the parameters required for the RUSLE model and SWAT tool. The Arc GIS is used to create maps of parameters adapted by the soil erosion models. The RUSLE model provided an output of sedimentation 6 t ha-1 yr-1 and the SWAT tool provided an output of sedimentation 26 t ha-1 yr-1. A suitable soil erosion model is recommended by comparing the two models’ outputs. The results of the models can aid in the execution of soil management and conservation practices to reduce soil erosion in the Barakar river basin.