Reconciliation Process in Afghanistan and Pakistan as Cardinal Player

Afghanistan is in state of war for almost 40 long years but the political dynamics got changed after 9/11. Since the war on terror started facets of peace talks launched without meaningful end. The reconciliation process in Afghanistan had immediately started after establishment of Afghan interim government in 2001. Till 2016 mid, four different reconciliation /reintegration processes were launched by the USA and Hamid Karzai government with Afghan Taliban. The new elected president Ashraf Ghani ended all such processes in 2016 but showed willingness again in 2018 for direct peace talks between USA and Taliban clerics. In the current political scenario, Afghan reconciliation process is back on track with direct peace talks between USA, Taliban and Afghan Government along other international actors. However, Taliban were not ready to sit with the Afghan Government and this is where the Pakistan played as key to Afghan peace process. The focus of this research article is to find the challenges and opportunities of this peace process; Pakistan’s role as cardinal player can consolidate the peace process in Afghanistan. Furthermore, this research will give future recommendations for the peace process in Afghanistan.

Automated scratch detection syseem for the automative industry.

Following the manufacturing process within an automotive industry, a vehicle is prone to defects being present on its body. In the Pakistani industry, one of the largest contributors to these defects are scratches. The current measure being taken to detect scratches on vehicles involve the presence of trained specialists who inspect the body under specific lighting conditions. For this very reason, a low cost and effective automated system is desired which is capable of detection scratches on the front bumper of vehicles on the production line of industries. The main features of this system should its ability to pinpoint a range of areas where the scratches originate from in the manufacturing process as well as to help improve the current manual inspection system in place. To tackle the problem at hand, we propose a scratch detection device, based on a microcontroller interfaced with a distance sensor and a camera module, set up on a robotic arm which is mapped to the front bumper of a vehicle. The system is placed on the manufacturing line. It detects the presence of a vehicle through the threshold being broken on the distance sensor. It then begins the mapping process and takes images of the bumper which it then sends to the image processor located on the microcontroller of the device. The image processor loads the images and begins to preprocess them. This involves the removal of unwanted objects, the removal of reflections of surfaces, etc. It then passes the preprocessed image onto a trained machine learning model which outputs a decision stating whether a scratch is present or not. This data is then logged onto a spreadsheet online which is accessible by the human inspector. The trained machine learning model is based on a Convolutional Neural Network that is trained on a image dataset of around 1500 images of scratches and non-scratches. This model has an accuracy rate of 90% and is capable of telling whether a scratch is present in an image or not. The Robotic Arm has 6 degrees of freedom, allowing it to move in all four directions in a spacial plane as well as extend further into space. This is done to take into account the curved body of a front bumper. The proposed implementation is to place two such Automated Scratch Detection Systems on two sides of the vehicle at a distance of 16 centimeters from the body. This ensures optimal processing time as well as detection accuracy. Each of the systems map towards one half of the bumper and perform the image processing task in a total of 12 minutes