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 disruptive events and the selection of appropriate risk treatment plans. Moreover, the framework leverages a fuzzy reasoning system in conjunction with a multi-criteria decision-making method to process ambiguous information, thereby enhancing decision accuracy and reliability. The findings demonstrate that this comprehensive approach not only prioritizes risks effectively but also supports companies in refining their response strategies, ensuring the efficient delivery of services under challenging conditions. Ultimately, the study redefines resilience as a dynamic process of navigating and adapting to chaos rather than merely resisting it.
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreThis paper demonstrates the design of an algorithm to represent the design stages of fixturing system that serve in increasing the flexibility and automation of fixturing system planning for uniform polyhedral part. This system requires building a manufacturing feature recognition algorithm to present or describe inputs such as (configuration of workpiece) and built database system to represents (production plan and fixturing system exiting) to this algorithm. Also knowledge – base system was building or developed to find the best fixturing analysis (workpiece setup, constraints of workpiece and arrangement the contact on this workpiece) to workpiece.
The choice of binary Pseudonoise (PN) sequences with specific properties, having long period high complexity, randomness, minimum cross and auto- correlation which are essential for some communication systems. In this research a nonlinear PN generator is introduced . It consists of a combination of basic components like Linear Feedback Shift Register (LFSR), ?-element which is a type of RxR crossbar switches. The period and complexity of a sequence which are generated by the proposed generator are computed and the randomness properties of these sequences are measured by well-known randomness tests.
BACKGROUND: Many genetic factors are known to be related to osteoporosis, and currently the role of the glucagon-like peptide-1 receptor (GLP-1R) gene in bone health has been studied intensively. Some variation of this gene, such as rs1042044 and rs6458093, are known to be linked to metabolic diseases and lower bone mineral density, however their specific contribution to osteoporosis remains largely unexplored. Therefore, this study was conducted to investigate the combined genotypic effect of rs1042044 and rs6458093 as a genetic risk factor for osteoporosis in postmenopausal Iraqi women.METHODS: Blood samples from 75 osteoporosis patients and 75 healthy controls, aged 45-85, were collected. DNA was extracted, and a region of GLP-1R
... Show MoreOrganizations nowadays, whether public or private organizations, productive or service (such as banks), face major challenges as a result of rapid and continuous changes, and in front of these challenges traditional management operations and tools become unable to make the organization able to work, which makes it imperative for these organizations to follow the methods and management practices, including the proposed knowledge risk management and strategic direction so that it can elevate its actions at the level of developments in reality, in the sense that these organizations and in the light of environmental developments to characterize their strategic direction as an intellectual approach characterized by mo
... Show MoreThis study aim to identify the concept of web based information systems since its one of the important topics that is usually omitted by our organizations, in addition to, designing a web based information system in order to manage the customers data of Al- Rasheed bank, as a unified information system that is specialized to the banking deals of the customers with the bank, and providing a suggested model to apply the virtual private network as a tool that is to protect the transmitted data through the web based information system.
This study is considered important because it deals with one of the vital topics nowadays, namely: how to make it possible to use a distributed informat
... Show MoreIraqi insurance market need to develop products and to find new insurance policies to cover the damages of the violence and political commotions dangers and to meet the needs of the proposers.
The global insurance companies recently issued such policies to pay the losses wich caused by the violent political acts of vandalism suffered by the property and investments wich estimated billions of dollars.
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