چشم انداز مدیریت دولتی

ارگانیزم‌های حکمرانی عمومی در شبکه‌های نانوبایوتک با تکنیک همگام‌سازی سیناپسی

نوع مقاله : پژوهشی (با رویکردهای آمیخته)

نویسندگان

1 دکتری مدیریت بازرگانی، دانشگاه تهران، تهران، ایران

2 دانشیار، گروه مدیریت منابع انسانی، دانشگاه علوم انتظامی امین - مدیریت دولتی

3 استادیار گروه مدیریت بازرگانی، دانشگاه پیام نور، تهران، ایران

چکیده

هدف:  هدف اصلی این پژوهش تحلیل و شناسایی ساختارهای حکمرانی عمومی در شبکه‌های نانوبایوتکنولوژی از طریق استفاده از تکنیک‌های همگام‌سازی سیناپسی است. حکمرانی عمومی فعلی یا همان سنتی، تمامی ابعاد یک حکمرانی مطلوب را در بر نمی‌گیرد و ارتباطات در این نوع حکمرانی صرفا به یک مرکز تصمیم‌گیری وابسته است یا درگیر بوروکراسی پیچیده‌ای می‌شود که در سطح ملی و بین‌المللی مشکلاتی ایجاد می‌کند. در حالی که دیدگاه حکمرانی از طریق همگام‌سازی سیناپسی در شبکه‌های نانوبایوتکنولوژی نوعی رویکرد نوین و همه‌جانبه به حکمرانی عمومی ارائه می‌دهد. این دیدگاه جدید، امکان بهبود هماهنگی، کارایی و پاسخگویی سیستم‌های حکمرانی را فراهم می‌سازد. از طریق تحلیل دقیق و سیستماتیک ساختارهای شبکه‌ای و تعاملات پیچیده بین اجزای مختلف شبکه‌های نانوبایوتک، می‌توان به شناسایی الگوهای بهینه‌سازی در فرآیندهای تصمیم‌گیری و اجرایی حکمرانی عمومی دست یافت. این پژوهش تلاش می‌کند تا با استفاده از الگوریتم‌های پیشرفته هوش مصنوعی، نقش همگام‌سازی سیناپسی را در افزایش کارایی نانوبایوتک و استفاده از فناوری‌های هوشمند  بررسی کند و راهکارهایی عملی برای بهبود ساختارهای حکمرانی عمومی ارائه دهد.
طراحی/ روش‌شناسی/ رویکرد: پژوهش حاضر از منظر پارادایم در دسته پژوهش‌های پُست‌مدرن قرار می‌گیرد چرا که از ابزارهایی مانند هوش مصنوعی، شبکه‌های پیشرفته نانوبایوتکنولوژی و الگوریتم هوشمند همگام‌سازی سیناپسی استفاده کرده است. روش تحقیق این پژوهش از منظر هدف، توسعه‌ای-کاربردی و از منظر ماهیت، آمیخته (کیفی-کمی) است که به شیوه شبه‌آزمایشگاهی با استفاده از سیمولینک‌کردن شبکه‌های حکمرانی عمومی و نانوبایوتکنولوژی با استفاده از الگوریتم‌های هوشمند همگام‌سازی سیناپسی صورت پذیرفته است. برای این کار ابتدا ابعاد حکمرانی عمومی از بخش ادبیات تحقیق و مراجعه به مقالات و کتب قبلی، استخراج گردیده است. عوامل و شبکه‌های به دست آمده، جهت تائید به پنج نفر از خبرگان ارئه گردید و مورد تائید نهایی قرار گرفت. ​سپس عوامل کلیدی و شبکه‌های نانوبایوتک قابل تطبیق با این مضامین، با استفاده از مدل‌های زبانی هوش مصنوعی مانند ChatGPT به عنوان دستیار پژوهشی شناسایی شدند. با توجه به محدودیت‌های مرجعیت علمی این ابزارها، یافته‌های حاصل پس از تأیید نهایی توسط خبرگان، برای تطبیق با مضامین شبکه‌های حکمرانی دولتی مورد استفاده قرار گرفتند.​ جهت تطبیق مضامین و شبکه‌های  حکمرانی عمومی و نانوبایوتکنولوژی، از الگوریتم‌های هوش مصنوعی همگام‌سازی سیناپسی استفاده گردید. برای این کار، نورون‌ها(گره) و سیناپس‌ها(مش‌ها) برای هر دو شبکه شناسایی شده و از طریق ارتباطات سیناپسی، با یکدیگر تطبیق پیدا می‌کند. زبان برنامه‌نویسی به کار رفته برای پیاده‌سازی الگوریتم‌ها، پایتون در محیط ژوپیتر بود.
یافته­‌های پژوهش:  یافته‌های این پژوهش نشان می‌دهد که استفاده از انواع الگوریتم‌های هوش مصنوعی نظیر الگوریتم‌های ژنتیک، الگوریتم‌های ازدحام ذرات  (PSO)، شبکه‌های عصبی پیچشی  (CNN)، الگوریتم‌های یادگیری تقویتی (RL) و الگوریتم‌های تصادفی برای همگام‌سازی سیناپسی، بهترین نتایج را از نظر دقت، صحت و قابلیت اطمینان فراهم کرده است. با استفاده از این الگوریتم‌ها، دقت و صحت در تشخیص و تحلیل داده‌های حکمرانی بهبود یافته و مدل‌های بهینه‌ای برای تصمیم‌گیری و مدیریت در زمینه‌های مختلف حکمرانی، از جمله بهداشت و درمان، سیاست خارجی، نظام اداری، نظام اقتصادی، آموزش و پژوهش، زیرساخت‌ها و حمل و نقل، حوزه محیط زیست، امنیت و دفاع، دیجیتال و فناوری اطلاعات، فرهنگ و هنر، حقوق بشر و عدالت اجتماعی، گردشگری و میراث فرهنگی، کشاورزی و امنیت غذایی استخراج شده‌اند.
محدودیت‌­ها و پیامدها: از محدودیت‌های این پژوهش می‌توان به محدودیت در دسترسی به برخی از داده‌های حساس و محرمانه دولتی و نیز پیچیدگی‌های موجود در تحلیل شبکه‌های بزرگ و پیچیده اشاره کرد. پیامدهای این محدودیت‌ها می‌تواند شامل کاهش دقت برخی از تحلیل‌ها و مدل‌ها باشد. همچنین، به‌کارگیری نتایج این پژوهش در سطوح مختلف حکومتی نیازمند تغییرات ساختاری و فرهنگی قابل توجهی است که ممکن است در کوتاه‌مدت چالش‌برانگیز باشد.
پیامدهای عملی: پیامدهای عملی این پژوهش می‌تواند به دولت‌ها کمک کند تا با بهره‌گیری از فناوری‌های نوین و مدل‌های پیشرفته، به بهبود فرآیندهای تصمیم‌گیری، افزایش پاسخگویی و کارآمدی در ارائه خدمات عمومی بپردازند. همچنین، این پژوهش می‌تواند به عنوان یک نقشه راه برای سیاست‌گذاران و مدیران دولتی در جهت توسعه سیستم‌های حکمرانی نوین و کارآمد مورد استفاده قرار گیرد.
ابتکار یا ارزش مقاله: ارزش این مقاله در ارائه یک چارچوب نظری و عملی برای بهبود سیستم‌های حکمرانی از طریق بهره‌گیری از مدل‌های پیشرفته و فناوری‌های نوین نهفته هوشمند است. این پژوهش با ترکیب مفاهیم نظری و تکنیک‌های عملی، یک دیدگاه جامع و نوین برای تحلیل و بهبود سیستم‌های حکمرانی ارائه می‌دهد که می‌تواند در سطح بین‌المللی نیز مورد توجه و استفاده قرار گیرد.
نوع مقاله: مقاله پژوهشی
 

کلیدواژه‌ها

عنوان مقاله [English]

Public Governance Organisms in Nano-Biotech Networks with Synaptic Synchronization Technique

نویسندگان [English]

  • Mohammad Amin Torabi 1
  • hajieh rajabi farjad 2
  • Orkideh Hamedi 3

1 Ph.D. in Business Administration, University of Tehran, Tehran, Iran

2 Associate Professor, Department of Human Resources Management, Amin police University - Public Administration

3 Assistant Professor, Department of Business Administration, Payam Noor University, Tehran, Iran

چکیده [English]

Purpose: The primary objective of this research is to analyze and identify public governance structures within nanobiotechnology networks through the application of synaptic synchronization techniques. Traditional public governance does not fully encompass all aspects of an optimal governance framework; rather, it relies on a centralized decision-making entity or becomes entangled in complex bureaucratic processes, which create challenges at both national and international levels. In contrast, governance through synaptic synchronization in nanobiotechnology networks introduces a novel and holistic approach to public governance. This perspective enhances coordination, efficiency, and accountability in governance systems. By conducting a systematic and detailed analysis of network structures and the intricate interactions among different components of nanobiotech networks, this study seeks to identify optimization patterns in public governance decision-making and execution. The research further explores how advanced artificial intelligence (AI) algorithms can leverage synaptic synchronization to enhance the efficiency of nanobiotechnology applications and the integration of intelligent technologies into governance models.
Design/ methodology/ approach:  This study falls under the postmodern research paradigm, as it incorporates AI-driven methodologies, advanced nanobiotechnology networks, and intelligent synaptic synchronization algorithms. From a methodological perspective, the research is applied-developmental in terms of its objective and mixed-method (qualitative-quantitative) in nature. The study adopts a quasi-experimental approach, simulating public governance networks and nanobiotechnology frameworks using intelligent synaptic synchronization algorithms.
To achieve this, the dimensions of public governance were first extracted through a comprehensive literature review of previous studies and academic sources. The identified governance factors and network structures were then presented to five subject-matter experts for validation, ensuring their accuracy and relevance. Following expert approval, key factors and nanobiotech networks compatible with these governance concepts were identified using AI-based language models such as ChatGPT as a research assistant. Given the scientific validity limitations of these AI tools, all findings were subjected to final expert validation before being applied to public governance networks.
For aligning governance frameworks with nanobiotechnology networks, synaptic synchronization AI algorithms were utilized. Within this process, neurons (nodes) and synapses (edges) of both networks were identified and synchronized through synaptic connections. The Python programming language, implemented within the Jupyter environment, was used to develop and execute the intelligent algorithms.
Research Findings: The findings indicate that using various AI algorithms, such as genetic algorithms, particle swarm optimization (PSO), convolutional neural networks (CNN), reinforcement learning (RL), and random algorithms for synaptic synchronization, provided the best results in terms of accuracy, precision, and reliability. These techniques enabled the extraction of nanobiotech model-based networks for each dimension of public governance. Using these algorithms improved the accuracy and precision in diagnosing and analyzing governance data and produced optimal models for decision-making and management in various governance areas, including healthcare, foreign policy, administrative system, economic system, education and research, infrastructure and transportation, environment, security and defense, digital and IT, culture and arts, human rights and social justice, tourism and cultural heritage, and agriculture and food security. The results suggest that nanobiotech networks, leveraging synaptic synchronization techniques, can serve as an inspiring model for developing efficient and responsive governance systems.
Limitations & Consequences: Limitations include restricted access to some sensitive and confidential government data and the complexity of analyzing large and intricate networks. These limitations may reduce the accuracy of some analyses and models. Implementing the findings across various government levels may also require significant structural and cultural changes, which could be challenging in the short term.
Practical Consequences: This research can help governments enhance their decision-making processes, increase accountability, and improve efficiency in delivering public services by utilizing advanced technologies and models. It can also serve as a roadmap for policymakers and government managers to develop modern and efficient governance systems.
Innovation or value of the Article: The value of this paper lies in providing a theoretical and practical framework for improving governance systems through advanced models and smart technologies. By combining theoretical concepts and practical techniques, this research provides a comprehensive and innovative perspective on analyzing and enhancing governance systems, which has international interest and application.
 
Paper Type: Original Paper
 

کلیدواژه‌ها [English]

  • Public Governance
  • Nano-Biotech Networks
  • Synaptic Synchronization
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چشم انداز مدیریت دولتی
دوره 16، شماره 1 - شماره پیاپی 61
فروردین 1404
صفحه 147-179