Akram Fadhel Mahdi

Background: The length and depth of global supply networks have been rising over time, causing permanent problems in visibility, protection, and performance. Blockchain technology has come out as a disruptive technology to tackle all these problems, by providing decentralized, safe and transparent systems.  Objective: This article examines the use of blockchain technology within the context of supply chain and specifically digs deeper into the area of increased transparency, security and subsequent efficient supply chain transactions. The aim is to show that it is possible to transform resource supply chain operations through blockchain and engender trust amongst stakeholders. Methods: Permissioned blockchain system has been design and implemented using proof of Authority (PoA) consensus algorithm. IoT sensors were deployed to obtain data in real-time, and smart contracts were incorporated to perform the tasks of product evaluation, and payment authorization. The performance of the system was assessed depending on the indicators including the number of transactions per time or volume per time, time taken for each transaction, auditability and workability. Results: The combination of high transaction throughput with low latency made the blockchain system scalable as well as operationally stable. Smart contracts were able to minimize the time taken and mistakes, and improve the integration of IoT in relation to tracing transactions in real time. The system also proved that it had the ability to withstand cyber assaults and no data were compromised.  Conclusion: Based on the analysis of supply chain problems, blockchain technology can be used to transform supply chain management. Furthermore, more studies should be done on the future compatibility of the usage of such a system with new technology trends and also its implementation in multiple regions supply chain to harness this valuable system.

Background: Neuromorphic computing is a newly developed technology that is based on data-flow architectures similar to the brain, which has the potential to power energy-constrained, latency-sensitive, and large-scale applications. The lack of flexibility in energy consumption and response time of traditional systems is a problem where neuromorphic platforms shine in real-time applications like robotics, IoT and autonomous systems. Objective: The article aims to assess the capabilities of neuromorphic computing platforms with respect to conventional schemes, both quantitatively and qualitatively, in terms of energy consumption, response time, modularity, and application-dependent adaptability, and to determine the drawbacks and application prospects for its further development. Methods: The study uses a comparative analysis approach to compare the identified factors and make statistical comparisons of the performance measures. The performance of the neuromorphic platforms as compared to non-neuromorphic platforms like Intel Loihi, IBM TrueNorth, NVIDIA Tesla V100, and Google TPU is compared based on its applications in robotics, IoT, and especially in healthcare. Data is derived from the experimental assessments of knowledge and theoretical paradigms encountered in prior research studies. Results: Neuromorphic systems showed better energy consumption, system size, and delay characteristics. Nevertheless, that the algorithm so excellently solves particular tasks does not mean that it can successfully be used regardless of its purpose, or can be adapted freely to new, further-reaching trends, such as quantum computing. Regression results demonstrate a high degree of dependency between these measures as well as their potential for real time data processing. Conclusion: Neuromorphic computing can be regarded as a new paradigm of energy-efficient and scalable AI and is especially promising for latency-sensitive deployment. Their shortcomings have been discussed earlier, yet it is worth stating that extension of these approaches by hybrid systems and more sophisticated integration frameworks might open new opportunities and eventually promote them as a foundation for new-generation computation models.