pax view sti 12 mpcr ulfa kit

pax view sti 12 mpcr ulfa kit

The outlawed United Liberation Front of Asom-Independent or ULFA (I) has released one of the two employees of a New Delhi-based private oil exploration firm its members had abducted from Arunachal Pradesh more than three months ago.

The police in Changlang district of Arunachal Pradesh found Assam-based Pranab Kumar Gogoi from Longvi village near the India-Myanmar border. A border patrol team of the Assam Rifles had intercepted him as he was crossing over from Myanmar.

“There is no trace yet of Ram Kumar, the other employee abducted with this person we located,” Changlang’s SP Mihin Gambo told The Hindu.

Mr. Gogoi, 51, was engaged as a drilling superintendent at the Quippo Oil and Gas Infrastructure Limited at the Kumchaika drilling site in Changlang district. Mr. Kumar (35) from Bihar was the radio operator.

“We are interrogating the released person. But he has been unable to say where his colleague is, as they were being kept in separate locations for a month,” Mr. Gambo said.

Members of the ULFA (I) had abducted the two Quippo employees on December 21, 2020 and had set a February 16 deadline for payment of ₹20 crore for their release. Outfits’ commander-in-chief Paresh Baruah later threatened to take the “ultimate step” against them after the firm declined to pay the ransom.

A backlash from intellectuals and common people in Assam made the ULFA (I) tone down its death threat and set Quippo a new condition for their release — creation of 5,000 jobs for Assamese youth.

The ULFA (I) has neither announced Mr. Gogoi’s release nor said a word about Mr. Kumar’s whereabouts.

pax view sti 12 mpcr ulfa kit
pax view sti 12 mpcr ulfa kit

Full Text Available China has over 271 million villages and less than the number in ten years ago in which there are 363 million villages. New rural construction indeed do some good for common villages but still destroy hundreds and thousands traditional village which contain great cultural, science, artistic values. In addition, traditional villages can’t meet the increasing needs in more convenient and comfortable living conditions. Increasing population also makes traditional villages out of control in construction. With the background of this, we have to set up in traditional village protection. This article put forward an idea in protection which make use of landscape localization to pursue the sustainable development and vernacular landscape protection. Tangyin Town is a famous trade center in history and left many cultural heritage, especially historical buildings. Take Tangyin as a case study to apply the localization method which could guide other similar villages to achieve same goals.

Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields.

By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

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