In this research, a Learning Vector Quantization neural network ((LVQ)) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means comparision, variance and statistical distribution were performed between the observed points samples data and the estimated cadmium values to evaluate the performance of the pattern recognition method. The Results showed that in training and test phase, there were no significant differences, with the confidence level of 95%, between the statsitcal parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated cadmium concentrations. The results suggest that Learning Vector Quantization ((LVQ)) neural network can learn cadmium cocentration model precisely. In addition the results also indicated that trained (LVQ) neural network had a high capability in predicting cadmium concentrations for non-sampled points. The technique showed that the (LVQ)NN could predict and map the spatial cadmium concentrations variability. Our results indicated that it is possible to discriminate different cadmium levels in soil, using (LVQ)NN.

Recent advances in precision farming technologies have triggered the need for highly flexible modelling methods to estimate, classificate and map weed population patterns for using in site-specific weed management. In this research, a Learning Vector Quantization neural network ((LVQ)NN) model was used to predict and classify the spatial distribution of Acroptilon repens L. density. This method was evaluated on data of A. repens L. density in a fallow field in Shahrood, Semnan province in 2010. Weed density assessments were performed following a 2 m × 2 m grid pattern on the field and a total of 550 sampling units on field. At each node of grid pattern, the numbers of A. repens L. seedlings were counted in the field within a permanent 50 cm by 50 cm quadrat. Some statistical tests, such as comparisions of the means, variance, statistical distribution as well as coefficient of determination in linear regression were used between the observed point sample data and the estimated weed seedling density surfaces to evaluate the performance of the pattern recognition method. Results showed that in training (LVQ)NN, test and total phase P-value was greater than 0.7, 0.8 and 1 percent respectively, indicating that there was no significant (p<0.05) difference between statsitcal parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated weed seedling density. This results suggest that (LVQ) neural network can learn weed density model very well. In addition, results indicated that trained (LVQ) neural network has a high capability in predicting weed density with recognition accuracy of 2.7 percent at unsampled points. The technique showed that the (LVQ)NN could classify and map A. repens L. spatial variability on the field. Our map showed that patchy weed distribution offers large potential for using site-specific weed control on this field.

Coastal states, have been affected by international law of the sea to develop and approve national laws that reflect their claims to the sea. These claims, are always considered by other countries, especially the neighbors and great sea powers and if these laws and claims are inconsistent with international laws and regulations or in conflict with their rights and interests at sea, they will respond to diplomatic objections. Kuwait also approved the rules in the field of territoriality at sea in the years 1949, 1967 and 2014, the Kuwaiti maritime zones law adopted in 2014 is the most complete and recent. The present study, with a descriptive-analytical approach and with documentary review, examined the law of Kuwaitchr('39')s maritime zones. Also in the Arc GIS software, the annexed map of the law has undergone cartographic analysis. Studies and analyzes indicate that the use of straight baselines at the mouth of the Kuwait Bay does not comply with the Convention on the Law of the Sea in the following cases. 1) The use of closing lines with a total length of more than 24 nautical miles in the mouth of the Kuwait Bay, 2) No official publication of the geographical coordinates of the points of straight baseline, 3) Insert the Awhah island behind the straight baseline, In addition, the cartographic analysis of the Kuwaiti Maritime zones Legislation Map, illustrates the interference and overlap between the Kuwaiti claimed maritime zones and the maritime zones of Iran. So far, the eastern boundary of the Kuwaitchr('39')s exclusive economic zone and the continental shelf has exceeded the Middle line of Persian Gulf, and even the fourth point of the Kuwait-Saudi Arabian maritime boundary (agreed in 2000). This expanding territoriality and transit through the Middle Line, violate the principles and rules of international law of the sea. Meanwhile, Iran and Kuwait have not reached an agreement on the delimitation of the continental shelf. Due to the presence of oil and gas resources and fields in this area, such acts and claims affect the process of delimiting the boundaries of the continental shelf of the two countries.

Vector Quantization (VQ) is the powerful technique in image compression. Generating a good codebook is an important part of VQ. There are various algorithms in order to generate an optimal codebook. Recently, Swarm Intelligence (SI) algorithms were adapted to obtain the near-global optimal codebook of VQ. In this paper, we proposed a new method based on a modified firefly algorithm (MFA) to construct the codebook of VQ. The proposed method merged genetic crossover operator with FA to develop the VQ. This method is called MFA model. Experimental results indicate that the reconstructed images generated by the proposed model is get higher quality than FA and it’ s about one percent, but it is no significant superiority to the PSO algorithm. Furthermore, MFA is slower than FA.

Mapping of weeds distribution patterns for using in site-specific weed management has been favored by researchers. In this study, a Learning Vector Quantization neural network ((LVQ)4) model was developed to predict and classify the spatial distribution patterns of Alhagi pseudalhagi. This method was evaluated on data of weed density counted at 550 points of a fallow field located in Faculty of Agriculture, Shahrood University of Technology, Semnan, Iran, in 2010. Some statistical tests, such as comparisions of the means, variance, statistical distribution as well as coefficient of determination in linear regression were used between the observed point sample data and the estimated weed seedling density surfaces to evaluate the performance of the pattern recognition method. Results showed that in training (LVQ)4, test and total phase P-value was greater than 0.7, 0.2 and 1.000 percent respectively, indicating that there was no significant (p<0.05) difference between statsitcal parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated weed seedling density. This results suggest that (LVQ)4 neural network can learn weed density model very well. In addition, results indicated that trained (LVQ)4 neural network has a high capability in predicting weed density with recognition accuracy less than 0.9 percent at unsampled points. The technique showed that the (LVQ)4 could classify and map A. pseudalhagi spatial variability on the field. Our map showed that patchy weed distribution offers large potential for using site-specific weed control on this field.

In this research, a Learning Vector Quantization ((LVQ)) neural network model was developed to predict the spatial distribution of Tuta absoluta in tomato fields of the city of Ramhormoz, Iran. Pest density was assessed through 10 m × 10 m grid pattern on the field with a total of 100 sampling units. Some statistical tests, such asmeans comparison, variance and statistical distribution were performed between the sampling point data and the estimated pest values in order to evaluate the performance of prediction of pest distribution. In training and testphase, there was no significant difference in average, variance, statistical distribution and coefficient of determination at 95% confidence level. The results suggest that (LVQ) neural network can learn pest density model precisely and trained (LVQ) neural network high capability (88%) of predicting pest density for non-sampled points.The (LVQ)NN successfully predicted and mapped the spatial distribution of Tuta absoluta whose aggregation distribution implied the possibility of using site-specific pest control in the field.