نشریه آینده پژوهی دفاعی
پیاپی 30 (پاییز 1402)

The purpose of this research is to extract the strategic components of sustainable development with the approach of national power.

The research strategy is mixed exploratory. In the qualitative part, the strategic components of sustainable development were determined by studying thirteen high-level documents of the system with the theme analysis method. In the quantitative part, with structural-interpretive modeling and targeted sampling of experts, classification and identification of relationships between strategic components of sustainable development was done.

The components were identified in such a way that the components of diplomacy, knowledge-based defense, economic development and natural resources and environment were placed at level one. In the second level, the component of knowledge-based development was placed, and in the third level, the two components of administrative governance and national culture of development were determined.

Originality: 

Soft power components include the culture of national development, administrative governance and knowledge-based development, the basis for actualizing and exploiting the hard power components of economic development, knowledge-based defense, and resources and environment.

The main goal of this research is to achieve the maturity pattern of cyber resilience of command and control systems in dealing with future threats.

The current research is prospective and applied, and descriptive/analytical with an exploratory approach. The statistical population of the research includes 30 military and cyber experts, and the sample population is considered equal to the statistical population. In order to collect data, interview tools and questionnaires were used, and data analysis was done by factor load analysis using SmartPLS software, and to confirm the model, a significant test was used. The presence of components was used.

in the final model, for the dimensions of leadership, supervision and operation, there are 10 components in maturity level one, 12 components in maturity level two, 12 components in maturity level three, 9 components in maturity level four and 6 components for the fifth maturity level, and a total of 49 components were calculated and presented.

The cyber maturity model is a method that enables organizations to strengthen their cyber security program, prioritize cyber security actions and investments, and maintain the desired level of security throughout the life cycle of information technology systems. Based on this, in order to reach the maturity of cyber resilience, the three dimensions of leadership, monitoring and control and operation of command and control systems at 5 levels (performed, planned, managed, measured and institutionalized) should be matured.

Researchers aim to address in proposing a model for optimal talent development in the Army of the Islamic Republic of Iran.

This research is of a developmental-applied nature and was conducted using an exploratory mixed-method approach. To achieve this, four main steps were taken, including a systematic review of the literature to discover indicators of optimal talent development and qualitative data analysis using the six-stage Clarke and Braun network content analysis method. Additionally, the fuzzy Delphi method was used to validate the identified indicators. The statistical population consisted of commanders, managers, and experienced experts in the field of human resource management and talent management in the General Staff of the Islamic Republic of Iran's Army and the University of Command and Staff with a specialized doctoral degree and over 20 years of experience in the relevant field. S

The results of the network content analysis showed that the dimensions of optimal talent development can be categorized into three main categories: commitment combined with enthusiasm, competencies and capabilities, and alignment with the organization's goals and strategies. Below these dimensions, 12 organizing themes and 44 basic themes (indicators) were identified. Furthermore, using the best and worst fuzzy methods, the weight and impact of each indicator on talent optimization were determined.

The summary indicates that for the Army to have optimal talents, it is essential to develop talents in three dimensions simultaneously: alignment with the organization's goals and strategies, competencies and capabilities, and commitment combined with enthusiasm

Before starting an activity or a military mission, the military units check the weather and the ground of the region and evaluate its effect on the assigned missions. The purpose of this research is to determine the effect of 120-day winds on military activities in the northern region of Sistan and Baluchistan province and on a case-by-case basis in the city of Zabul.

Data related to wind direction and speed, horizontal visibility and the number of dust reports with a statistical period of 21 years (2000-2021) have been received from the Zabul synoptic station and subjected to statistical analysis.

In order to determine the effect of these winds on military activities, an expert interview was conducted with military experts, and in order to determine the proximity and desirability of the months of the year to the ideal desired by the experts, the TOPSIS method was used. The predominant wind is in the northwest region and the highest wind speed and the lowest visibility occurred in the summer season.

The results showed that the months of Azar, January, November, Bahman, Farvardin and Mehr are the closest to the military ideal in terms of weather (horizontal visibility, direction and wind speed) and the months of July, August, June and September are the most distant from the ideal. They have the weather for military activities in the studied area. Any military activity at this time will bring the least benefit and the most negative effect in terms of weather conditions.

Considering the importance of the South Caucasus region for Iran, it is very important to know the geopolitical factors affecting the development of Iran's strategy in this region. The purpose of this research is to develop strategies for Iran in the South Caucasus region.

This research is based on the combined model of SWOT strategic analysis and QSPM strategic planning model. The current research is considered to be descriptive-analytical in terms of applied nature and in terms of study method. The method of data collection was library and field studies (questionnaire and interview).

The analysis from the SWOT model shows that although Iran has the capacities that can make it an important player in the Caucasus region, international sanctions, the presence and influence of regional and extra-regional powers near Iran, border tensions and armed conflicts, pressure from minorities Independence and autonomy, as well as the lack of infrastructure, are the main obstacles on the way to achieving this goal for Iran.

Originality: 

At the same time, the priority table of the selected QSPM strategies shows that among the six selected strategies, the strategy of strengthening trade relations between Iran and the Caucasus countries in the field of crude oil and gas, petrochemical products, steel, etc., has an attractiveness score of 93.4 compared to others. Strategies are of high priority.

Artificial intelligence is a part of computer science that emphasizes the creation of intelligent machines in defense equipment and military equipment. Intelligent systems for automatic underwater target recognition are increasingly used in passive sonar to reduce human intervention and related challenges in accurately identifying vessels. Today, highly advanced methods of machine learning and deep learning are being used by the world's navies to identify acoustic targets.

In this article, recent works in the field of automatic underwater acoustic target recognition are reviewed, and a new method based on deep learning algorithms is presented. In this method, first, the raw audio signals are received from the hydrophones, and after performing the necessary pre-processing, using the Short-time Fourier transform, the spectrogram images related to the passive sonar acoustic data are generated and fed to the model layers for model validation and classification.

The obtained results show that the multi-layer structures of the proposed model can automatically extract several features are required for the classification of different ship classes. In this article, common deep learning algorithms are used to identify targets, which can increase identification accuracy and reduce evaluation errors by searching for the most informative features of sonar data.

The obtained results show that the recognition accuracy of the proposed model is more than 97%, and its validation loss is less than 3%. In this method, with the relative improvement of classification accuracy, the speed of target recognition has increased significantly.