Arman Process Journal (APJ)

Abstract The rapid growth of big data, coupled with advancements in data science and artificial intelligence, has significantly accelerated the potential for developing digital twins. A digital twin is a continuously updated virtual copy that enables the analysis, simulation, and prediction of a real-world object or process. Recently, applications of digital twins have seen substantial expansion across both academic communities and diverse governmental and military industries, and the healthcare sector is no exception. The concept of the digital twin for health promises a transformation in medical systems, encompassing service management and delivery, disease treatment and prevention, health maintenance, and ultimately, the enhancement of human life. By harnessing the ability to aggregate and analyze vast datasets from multiple sources, digital twins can facilitate personalized treatment pathways tailored to individual patient characteristics, medical history, and physiological data. This enables predictive analytics, preventative interventions, and the early identification of health risks and diseases through machine learning algorithms. Furthermore, digital twins can optimize clinical operations by analyzing treatment processes and resource allocation, leading to simplified and expedited treatment protocols. This review outlines the current applications of digital twins within the healthcare sector, delineates their core components in medicine, and examines the present landscape of open research opportunities. We demonstrate how the integration of diverse enabling technologies and tools—such as artificial intelligence, large language models, and mechanistic modeling—paves the way for overcoming limitations and fostering broader clinical adoption and implementation of digital twins. This review also aims to assist data scientists, clinicians, and policymakers in developing future medical digital twins and bridging the gap between this emerging paradigm's theoretical promise and practical realization.

Abstract The convergence of the Internet of Things (IoT) and artificial intelligence (AI), as one of the most central trends in digital transformation in the last decade, has created a new landscape in various industries, including manufacturing, transportation, energy, and smart cities. Despite the revolutionary potential of these technologies, their effective adoption and implementation face numerous challenges. The aim of this study is to provide a comprehensive framework for analyzing the factors affecting the adoption of artificial intelligence in IoT systems and to provide a proposed perspective to overcome existing barriers. This study, with an analytical-descriptive approach and using a systematic literature review, examines 46 authoritative articles published between 2018 and 2025. The theoretical framework of the study is based on the combined technology-organization-environment (TOE) model and the technology acceptance model (TAM). The literature analysis shows that the key applications of artificial intelligence in IoT include predictive maintenance, anomaly detection, real-time monitoring, and automated control. Facilitating factors include 5G infrastructure, organizational readiness, and skilled workforce, and major barriers include cybersecurity challenges, data quality, integration with legacy systems, and model scalability limitations. The proposed approach emphasizes the implementation of a hybrid architecture based on edge computing, federated learning, and software-defined networking (SDN) to overcome the existing challenges. This framework can serve as a roadmap for researchers and industry players on the path to AI-IoT-based digital transformation.

Abstract The convergence of emerging technologies, including the Internet of Bio-Nano Things (IoBNT), Artificial Intelligence (AI), Digital Twins, and Nanomedicine, has opened new opportunities for the development of next-generation intelligent systems for cancer diagnosis, monitoring, and treatment. Despite significant advances in each of these fields, a comprehensive framework integrating these technologies into a unified, self-regulating, and closed-loop therapeutic ecosystem has not yet been established. This review proposes a forward-looking conceptual framework in which molecular data acquired by in vivo nanosensors are transmitted through IoBNT to a Nano-Digital Twin, analyzed using artificial intelligence models, and subsequently utilized to guide intelligent nanodrug delivery systems and therapeutic decision-making.
The principal contribution of this study is the integration of four key technologies IoBNT, Artificial Intelligence, Digital Twins, and Nanomedicine within a multilayer closed-loop therapeutic architecture that enables continuous monitoring, real-time analysis, disease progression prediction, and dynamic personalized treatment optimization. From a qualitative perspective, the proposed framework has the potential to improve diagnostic accuracy, enhance the specificity of targeted drug delivery, reduce reactive clinical decision-making, and facilitate the transition toward predictive, adaptive, and personalized medicine. From a functional perspective, the proposed architecture integrates five essential capabilities real-time monitoring, intelligent data analytics, disease progression prediction, adaptive nanodrug delivery, and continuous therapeutic feedback within a unified platform, whereas these capabilities have largely been investigated independently in previous studies.
Finally, this review discusses the major scientific, technical, and translational challenges associated with the clinical implementation of the proposed framework and presents a roadmap for the development of next-generation intelligent nanomedicine and precision medicine. The proposed conceptual framework demonstrates that the convergence of these emerging technologies has the potential to transform conventional healthcare into autonomous, intelligent, and data-driven therapeutic systems for future cancer management.

Abstract The integration of artificial intelligence into strategic decision-making processes enhances accuracy, speed, and efficiency; however, it simultaneously introduces unprecedented challenges to the traditional accountability framework within corporate governance. Employing a critical analytical approach and drawing upon findings from authoritative research in the fields of artificial intelligence and corporate governance, this article argues that existing legal and ethical systems are ill-prepared to address three structural gaps arising from algorithmic decision-making at the board level.
Accountability is defined as a tripartite relationship among an agent, the board of directors, and the general assembly of shareholders, wherein the agent is obligated to justify and explain their conduct, while the assembly holds the authority to question, judge, evaluate performance, decide on the continuation of cooperation, and take legal action if necessary. The first gap concerns the absence of a clear rule for determining responsibility when AI systems make errors, as responsibility is a "bundle" concept comprising multiple components, and merely attributing it cannot resolve ethical dilemmas. This gap manifests acutely in augmented intelligence and reinforcement learning contexts. The second gap involves the lack of a mechanism to monitor the board itself in its capacity as the overseer of AI systems—referred to herein as the "monitor of the monitor" dilemma. The third gap stems from the temporal misalignment between board decision-making cycles and AI system processing speeds, which undermines effective oversight and timely accountability.
This study proposes three policy-oriented solutions: the non-delegable rule of ultimate human responsibility; the establishment of a specialized AI committee within the board, endowed with continuous auditing authority; and the redefinition of fiduciary duty such that algorithmic literacy and oversight over the lifecycle of AI models become integral components of directors' legal responsibilities. These three solutions are analyzed within the framework of accountability's four objectives: compliance, reporting, monitoring, and enforcement.

Abstract Nowadays, due to the complexity of medical decisions, the use of intelligent information systems and decision support to support these decisions has increased in various operational areas. Meanwhile, the role of intelligent and decision support systems in helping doctors and the field of medical sciences and medical engineering is prominent. Equipping medical science with smart tools in the diagnosis and treatment of diseases can reduce the mistakes of doctors and the loss of life and money and create countless achievements. In this article, the applications and capabilities of these systems in medical sciences have been investigated and the most important challenges of using these systems have been discussed. Also, in this article, the applications of neural networks in the medical field have been investigated. We have tried to make the article useful both for artificial intelligence researchers and for medicine.

Abstract In the digital age we are witnessing, artificial intelligence (AI) has emerged as a key factor in the evolution of e-commerce. This article examines how to use AI to improve the experience of using data-driven web apps and combine it with artificial intelligence packages, as well as a more dynamic user interface with smart and dynamic parts and create a competitive advantage for businesses. For this research, we used forms that are designed dynamically and collect data from the user, and finally analyze the data and display it in the form of charts and graphs. Our findings show that AI has been able to Increase accuracy of predictions. Using data and advanced algorithms, AI is able to predict customer behavior and market demand with high accuracy. Automation of processes and reduction of the need for manpower has reduced operating costs. Recommender systems and intelligent customer support have led to faster and more personalized service. It has emerged as a powerful e-commerce tool that can help businesses succeed in today's competitive marketplace. As technology continues to advance, we can expect AI to play an even greater role in this industry.

Abstract Recently, recommender systems have expanded more and more as a new and fundamental technology to support users in choosing the right resources. These systems provide a personalized environment for selecting the desired resources by examining the past interactions of users and identifying interests. Of course, user behavior modeling and the recommendation mechanism are fundamental and decisive issues in the efficiency of recommender systems. In the field of e-commerce, the use of recommender systems plays an essential role in improving the user experience, attracting potential customers, increasing sales, and optimizing the efficiency of related service systems. Therefore, considering the importance of these systems in today's electronic businesses, knowing the functional dimensions of recommender systems is of particular importance. In this article, we are going to review the basic dimensions of recommender systems in the field of e-commerce and introduce some practical tools in this field. Certainly, by moving towards e-commerce systems based on intelligent recommender systems, we will witness huge and revolutionary changes in the infrastructure of the digital economy and related services.

Abstract Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short of providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems are centralized, manual, and highly disintegrated, which make them vulnerable to manipulation and the single point of failure problem. Emerging technologies such as the Internet of Things (IoT), fog computing, cloud computing, and block chain can play a vital role in boosting the operational efficiency of the oil and gas industry. In this paper, we explore the potential opportunities and applications of Artificial Intelligence technology in managing the exploration, production, and supply chain and logistics operations in the Natural Gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. This research highlights the use cases of AI in decentralized Block chain with smart contracts, the company’s trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. Results spotlight the use of AI in decision accuracy for the developed smart contract-based Natural Gas Industry, thereby qualitatively limiting the threshold level of costs, energy and other control functions in procurement, production and distribution.

Abstract Artificial intelligence is a branch of the science of producing and studying machines that aim to stimulate human intelligence processes. The main goal of artificial intelligence is to optimize routine processes, improve their speed and efficiency. As a result, the number of functions and services that use artificial intelligence continues to increase worldwide. Today, artificial intelligence has entered the field of education and training and is rapidly replacing traditional methods with modern methods. In this article, we discuss the functions of artificial intelligence in the field of education and electronic knowledge transfer. Considering the increasing development of virtual education in today's world, artificial intelligence can play an effective role in the field of education and electronic knowledge transfer. Artificial intelligence as a powerful tool can be used to improve the quality of education, reduce costs and increase the speed of education. This technology allows students and teachers to provide the best solutions to improve the quality of teaching and learning through intelligent and data-based systems. With the use of artificial intelligence, virtual education can become an effective and affordable method. In general conclusion, it can be said that this technology is very useful in all stages of education, including lesson planning and design, content presentation, exercises and evaluation, and it will soon become an integral part of education.