Arman Process Journal (APJ)

Abstract Quantum genetic algorithm, which is a combination of quantum mechanics principles and evolutionary algorithms, has been proposed as a new method in the field of optimization. In recent years, numerous applications of these algorithms have been reported in solving complex biological problems such as gene and stem cell structure analysis, protein simulation, and molecular behavior prediction. This article aims to comprehensively review the performance of quantum genetic algorithm in biological problems, and reviews its theoretical foundations, functions, advantages, and challenges. Various studies have shown that quantum genetic algorithm, by utilizing quantum properties such as superposition and entanglement, has been able to improve the inefficient convergence problems of classical algorithms and provide more optimal solutions. Also, in the second part of the article, the architecture and technical mechanisms of quantum genetic algorithm are described and applied examples in biology are analyzed. Finally, the prospects of this technology in biological research are reflected by providing recommendations and perspectives for future developments.

Abstract One of the most important aspects of customer relationship management is discovering the customer's purchasing behavior pattern. The organization can act by defining more precise marketing strategies to attract similar customers. In today's competitive world, accurate knowledge of customers and the ability to respond to their needs is critical to the success of organizations. With recent advances in data mining and big data analysis, organizations are now able to use more sophisticated methods to segment customers and better understand their behavior. The novelty, frequency and financial model (RFM) as one of the prominent models in this field, provides the possibility of dividing customers based on their value for the organization. In this thesis, a customer segmentation scheme is presented using fractal clustering and AVOAGA optimization method, which is a combination of two optimization methods, African vulture and genetic method. The simulation of the proposed design was done in the Python environment and using the standard data set containing RFM of customers. Based on the results obtained from the simulation, the proposed design is improved in both compactness and dispersion indices compared to the basic design.

Abstract امروزه از چارچوب های مدیریت شبکه های توزیعی برای تهیه، بهره برداری، نگهداری و ایمن سازی زیرساخت های شبکه استفاده میشود. اساسا مدیریت صحیح شبکه های توزیعی نیازمند طیف وسیعی از فعالیت ها، روش ها، روال ها و استفاده از ابزارهای اجرایی، عملیاتی و نگهداری از سیستم های کامپیوتری می باشد. زمانی که موضوع حملات امنیتی در کمین شبکه مطرح می‌شود، اهمیت مدیریت جامع و نظارت روی داده هایی که در شبکه رد و بدل می‌شوند دوچندان خواهد بود. برای اینکه مدیران چنین شبکه هائی بتوانند به‌طور منظم روی لاگ‌های سیستم و چارچوب عملیاتی آن نظارت داشته باشد و مشکلات سخت‌افزاری، نرم‌افزاری و حملات امنیتی را شناسایی کند، نیاز به چارچوبی مدون است که مدیریت جامع و نظام مند شبکه را بر عهده بگیرد. چارچوبی که وقایع امنیتی را تجزیه‌وتحلیل کند، لاگ‌های سیستم را جمع‌آوری و دسته‌بندی کرده و در کل مدیریت و نظارت روی شبکه را به‌صورت متمرکز امکان‌پذیر کند. رویکرد مدیریت جامع شبکه های توزیعی مجموعه ای از سخت‌افزارهای ارتباطی و نرم‌افزارهای رابط کاربری و ملحقات را به صورت سامانه‌ای یکپارچه برای کنترل ابزارها و تجهیزات شبکه و نظارت بر عملکردها به خدمت میگیرد. دیدگاه مدیریت جامع شبکه های توزیعی منابع شبکه را به شکلی کارآمد، موثر و سریع در دسترس کاربران قرار میدهد. همچنین بهینه سازی شبکه با استفاده از تجزیه و تحلیل خطا و مدیریت عملکرد تضمین میشود. با استفاده از چارچوب های مدیریت جامع میتوان بهترین ابزارهای مناسب برای مدیریت، نظارت و کنترل شبکه را به کار گرفت تا در صورت نیاز، به چالش های پیش آمده در شبکه پاسخ دهند. در این مقاله قصد داریم درخصوص رویکرد دستیابی به مدیریت جامع شبکه های توزیعی و ابعاد آن تحقیق و بررسی نمائیم

Abstract One of the most important applications of the Internet of Things in the field of health is remote monitoring of patients. This technology allows doctors to check the health status of patients in real time, which is especially vital for people suffering from or prone to heart diseases. Prediction of cardiovascular diseases is known to be a complex challenge that faces low accuracy in existing models. In this research, a new recommender system for predicting cardiovascular diseases is proposed that uses a convolutional neural network to analyze physiological data of patients. Physiological data from patients are collected remotely through four biological sensors including ECG sensor, blood pressure sensor, heart rate sensor and blood sugar sensor. These data are then processed by an Arduino controller and the convolutional neural network model is used to predict cardiovascular disease. With outstanding capabilities in extracting local features and without the need for complex time sequence analysis, this model can effectively use fixed numerical data such as blood pressure, heart rate, and blood sugar to diagnose heart diseases. The experimental results showed that the convolutional neural network was able to effectively extract local and non-temporal features of the data and help the model achieve a prediction accuracy of 98.90%.

Abstract In today's technology based era, information and communication technology is creating a new revolution all over the world, the importance of which is no less than the industrial revolution. In fact, we are on the threshold of a technological revolution that will completely change the way we live, work and communicate. Such a transformation with this scale, scope and complexity is unlike any previous human experience. In line with this transformation, the response of governments, organizations and individuals should be integrated and comprehensive and include all stakeholders, from global governments and public and private sectors to academics and civil society. With the emergence of different technologies, many businesses took advantage of them and promoted and developed business models and related matters. Information and communication technology in organizations affect the indicators that are all the basic conditions of success for organizations. One of the important areas that can receive many effects from this technology are business and industry. Today, information and communication technology has become the basic factor of transformation and modernization of industries, organizations and businesses. What the fourth industrial revolution refers to as digital transformation means that organizations should contribute to the benefits created by new technologies in an intelligent and effective way to increase organizational productivity by transforming their leadership methods, strategic thinking and operational activities. Realizing organizational goals and improving the working and interactive experience between employees, customers, suppliers, partners and stakeholders with the organization. In Iran, with the introduction of technology, they were greatly welcomed and many businesses achieved tremendous success by using new technologies. The purpose of this research is to investigate the effects of information technology on the organizational changes of business and industry.

Abstract Over the last decade, offensive cyber operations have evolved from intelligence-oriented campaigns toward destructive attacks targeting the availability and integrity of critical data and digital infrastructures. Among these threats, wiper malware has emerged as one of the most destructive cyber weapons due to its capability to permanently erase or corrupt information and disrupt organizational operations. This review paper aims to provide a comprehensive technical analysis of the design, implementation, and evolution of wiper malware by examining more than twenty documented real-world incidents, including Shamoon, NotPetya, WhisperGate, and AcidRain. Based on this analysis, a unified framework is proposed to classify data destruction techniques into three categories: physical-level destruction through storage controller commands such as ATA Secure Erase, block-level overwriting using fixed or random patterns, and logical-level corruption of metadata, partition tables, and file system structures. The study further analyzes the common architecture of modern wiper malware, including persistence mechanisms, command-and-control strategies, self-propagation techniques, parallel destruction modules, and anti-forensic capabilities. Comparative analysis demonstrates the gradual evolution of wiper malware from simple disk destruction toward sophisticated attacks targeting cloud infrastructures, virtualization platforms, and distributed storage environments. The review also identifies major technical challenges associated with forensic recovery, snapshot-based backup systems, and replicated cloud storage. As a practical contribution, the paper proposes a multilayer defensive framework based on Data Immutability, Write Once Read Many (WORM) hardware technologies, offline backup architectures, and kernel-level I/O anomaly detection to improve cyber resilience against destructive attacks. Overall, the findings highlight current technological trends, identify existing research gaps, and provide practical recommendations for developing more resilient storage architectures and effective protection mechanisms against future generations of destructive cyber threats.

Abstract The present study aims to identify and explain the key factors affecting the performance of the Golrang brand, with an emphasis on the mediating role of brand innovation, focusing on Pakshoo Company in the detergent industry. Based on the resource‑based view and the dynamic capabilities approach, market orientation, organizational learning, and internationalization were considered as organizational and strategic drivers, brand innovation as the mediating mechanism, and brand performance as the final outcome of the model.

In terms of purpose, this research is applied, and in terms of method, it is descriptive–survey and correlational. The statistical population consisted of managers and employees of Pakshoo Company located in the Alborz Industrial City of Qazvin. Out of 260 individuals, 152 respondents were selected using simple random sampling based on the Morgan table. Data were collected through a standardized questionnaire adapted from validated studies and localized for the detergent industry. The content validity of the questionnaire was confirmed by experts, and its reliability was verified using Cronbach’s alpha; the alpha coefficients for the constructs ranged from 0.712 to 0.946.

Data were analyzed using descriptive statistics, the Kolmogorov–Smirnov test, linear regression, and the Preacher and Hayes mediation model in SPSS software. The findings indicated that market orientation, organizational learning, and internationalization have a positive and significant effect on brand innovation, and brand innovation in turn positively and significantly improves brand performance. Furthermore, the mediating role of brand innovation in the relationships between the three drivers and brand performance was confirmed.

The results suggest that brand innovation acts as a connecting mechanism between organizational capabilities and developmental strategies with superior brand performance. It can serve as a lever for differentiation, market responsiveness, and strengthening competitive advantage in the detergent industry.