The aim of this study is to develop a novel framework for managing risks in smart supply chains by enhancing business continuity and resilience against potential disruptions. This research addresses the growing uncertainty in supply chain environments, driven by both natural phenomena-such as pandemics and earthquakes—and human-induced events, including wars, political upheavals, and societal transformations. Recognizing that traditional risk management approaches are insufficient in such dynamic contexts, the study proposes an adaptive framework that integrates proactive and remedial measures for effective risk mitigation. A fuzzy risk matrix is employed to assess and analyze uncertainties, facilitating the identification of disr

OpenStreetMap (OSM) is the world’s biggest publicly licensed geographic data collection. Because OSM is rapidly being used in a wide range of applications, researchers have focused their efforts on determining its quality. The OSM buildings data quality is still ambiguous, due to the limitations, and a few researchers have evaluated the OSM buildings data quality through difficulties where the authoritative data are not obtainable. The focus of this research is to analyze and assess the accuracy of OSM buildings including completeness, and positional accuracy methods. Two different study areas in Baghdad city-Iraq have been investigated: Al-Rasheed and Al-Karrada. The process of the (OSM) data evaluation involved identifying the correspon

<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in

Manufacturing industry is constantly looking for ways to improve the efficiency of production processes so as to lower production costs, maintain product quality, and improve production planning. This study aims to provide an integrated optimization and forecasting framework that combines Cutting Optimization Pro5, linear programming, and an Enhanced N-BEATS deep learning model to minimize material waste and manufacturing cost and to predict the number of production items with a price-reduction strategy. The real industrial case study is based on production and pricing data from the Akad factory of the General Company for Electrical and Electronic Products in Iraq for the period from 2014 to 2025, concerning the manufacture of 80 L

In this paper the use of a circular array antenna with adaptive system in conjunction with modified Linearly Constrained Minimum Variance Beam forming (LCMVB) algorithm is proposed to meet the requirement of Angle of Arrival (AOA) estimation in 2-D as well as the Signal to Noise Ratio (SNR) of estimated sources (Three Dimensional 3-D estimation), rather than interference cancelation as it is used for. The proposed system was simulated, tested and compared with the modified Multiple Signal Classification (MUSIC) technique for 2-D estimation. The results show the system has exhibited astonishing results for simultaneously estimating 3-D parameters with accuracy approximately equivalent to the MUSIC technique (for estimating elevation and a