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The present study was carried out at the Experimental Farm, Horticulture Department, Faculty of Agriculture, Suez Canal University during two seasons of 2017/2018 and 2018/2019 to study the effect of foliar application of seaweeds extract at 2, 4 and 6 ml/l, chitosan at 2, 4 and 6 ml/l and potassium silicate at 6, 9 and 12 ml/l as well as the interaction between seaweeds extract and chitosan on growth, biochemical, and essential oil characteristics of rosemary plants grown in a sandy soil. These compounds were applied as foliar spray three times after 15 days from planting. The results showed that foliar applications of seaweeds extract at 6 ml/l, chitosan at 6 ml/l and potassium silicate at 12 ml/l significantly affected all studied parameters compared with control. The results clearly indicated that the different applied treatments increased the measured growth characteristics i.e. plant height, number of branches/plant, fresh and dry weights as well parallel increase of photosynthetic pigments. Also, constituents of N, P, K, total carbohydrates, percentage of essential oil and the G.L.C. of essential oil of plant age were existed in the two assigned seasons. In addition, the most effective combined treatment was that of seaweed at 6 ml/l + chitosan at 6 ml/l in this respect.

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The problem studied is that of controlling a finite Markov chain so as to maximize the long-run expected reward per unit time. The chain's transition probabilities depend upon an unknown parameter taking values in a subset [ a, b ] of Rn . A control policy is defined as the probability of selecting a control action for each state of the chain. Derived is a Taylor-like expansion formula for the expected reward in terms of policy variations. Based on that result, a recursive stochastic gradient algorithm is presented for the adaptation of the control policy at consecutive times. The gradient depends on the estimated transition parameter which is also recursively updated using the gradient of the likelihood function. Convergence with probability 1 is proved for the control and estimation algorithms.