Turkish Military Academy

One of the most important aspects of military logistics is ammunition. Ammunition is one of the most crucial materials to consider in order to win a war. Therefore, this study examines the criteria affecting ammunition consumption. The aim is to provide war planners with an analytical perspective. Military expert opinions were consulted for the evaluation of the criteria, and fuzzy multi-criteria decision-making (FMCDM) was chosen as the solution methodology. The Level Based Weight Assessment (LBWA), commonly used in military problems in the literature, was employed as the method. The most important criterion is the number of enemy targets. This is followed by interceptions and aerial photographs containing potential targets obtained through intelligence activities. Finally, the changes in criterion weights were examined using different scenarios for the coefficient of elasticity value, and the results were validated.

Building fires are considered major disasters because of their significant effects on people, property, and the environment. This understanding has led to increased attention on developing preventive measures, particularly through the creation of effective methods for assessing fire risk. However, the effectiveness of these methods relies heavily on detailed physical and functional information of the building and data-driven decision-making. Building Information Modeling (BIM) has proven effective in representing structures, even in three dimensions. When integrated with Geographic Information Systems (GIS), it enhances spatial intelligence, leading to improved decision-making through robust multi-criteria approaches. Hence, this study develops a framework to assess fire risk in an indoor environment that deploys a BIM-based GIS and Multi-Criteria Decision-Making; this is specifically known as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The developed framework consists of four steps: identifying fire risk parameters, calculating weights, conducting spatial fire risk assessments, and visualizing the results, where the developed concepts are tested and validated. According to the significant findings, the developed framework estimates that 18% of building rooms are at moderate risk, while the compared model identifies only 1%. This considerable difference could potentially arise from the detailed data structure of BIM and the spatial insights gained from GIS. By implementing the designed framework, key fire risk factors can be identified in three dimensions, accompanied by a comprehensive quantitative evaluation platform for fire risks within indoor environments.

This paper presents the design and performance analysis of an X-band monolithic microwave integrated circuit (MMIC) high-power amplifier (HPA) using four parallel high-electron-mobility transistors (HEMTs) with dimensions of 8×125 μm2. Operating in the frequency range of 7.6–10.3 GHz, the proposed amplifier achieves a maximum small-signal gain of 11.7 dB with a gain flatness of 0.59 dB/GHz, ensuring consistent performance across the operating band. The coplanar waveguide (CPW) structure, implemented using Gallium Nitride (GaN) on a Silicon Carbide (SiC) process, eliminates the need for via-holes, reducing manufacturing complexity while improving impedance matching and compactness. Stability analyses confirm unconditional stability, with the K-factor and μ-factor exceeding 1 and Delta remaining below 1 across the entire operating frequency range. Power simulations demonstrate a saturated output power of 41.21 dBm and a peak power-added efficiency (PAE) of 39.81%, highlighting the amplifier's high efficiency and robust performance. The measurement results exhibit a remarkable consistency with the simulated data. These results demonstrate the suitability of the amplifier for demanding X-band applications including satellite communications, radar systems, and electronic warfare.

In this study, we begin by deriving a two-term strong converse inequality that characterizes the rate of approximation for generalized exponential sampling operators, utilizing the logarithmic moduli of smoothness. Subsequently, we combine the direct and converse estimates to establish the saturation property and identify the class of these approximation operators. Finally, we provide the results for the generalized exponential sampling series using specific kernels that meet the necessary assumptions. It should be noted that in the present study we follow a direct approach within the framework of Mellin analysis in the processes obtained.

Abstract In this study, the metallurgical and ballistic properties of aluminum matrix composites (AMCs) reinforced with varying ratios of boron carbide (B 4 C) and 0.5% graphene nanoplatelets (GNPs) were experimentally investigated to determine the optimal structure for armor applications. Hybrid GNP/B 4 C/AA7075 AMCs were fabricated using the stir casting method, incorporating 0.5% GNP and B 4 C in the range of 2.5–22.5%. The influence of B 4 C reinforcement and GNP addition on the AA7075 alloy was systematically analyzed through structural, crystallographic, microhardness, and ballistic impact assessments. The bullet core and AA7075 plate used in the ballistic tests were modeled in Abaqus-2022 utilizing the Johnson–Cook (J–C) damage model parameters The ballistic test results, which were obtained using the 7.62 × 39 mm API BZ (former Soviet designation for Armor-Piercing Incendiary bullet) projectile, were found to behave consistently with the numerical modelling analyses. In conclusion, the optimal performance was achieved with a composition of 15% B 4 C and 0.5% GNP. Ballistic efficiency began to decline when the B 4 C reinforcement ratio exceeded 15%.

Capability-based planning (CBP) is an approach that is increasingly gaining importance in modern defense strategies. Unlike traditional threat-focused planning, capability-based planning aims to enable countries to develop flexible and sustainable military capabilities against changing security environments. In this context, weapon system selection should be evaluated not only against current threats but also in terms of suitability for possible future operational requirements. The selection of weapon systems requires consideration of a large number of technical, operational and financial criteria. In this process, Fuzzy Multi-Criteria Decision Making (FMCDM) methods are frequently used. In addition, simulation techniques are a critical tool for testing the effectiveness of weapon systems on the battlefield by creating scenarios close to reality. Lanchester equations, in particular, are an effective tool for analyzing the combat dynamics of forces. In this study, an Anti Guided Tank Missiles selection methodology in which the Fuzzy Analytic Hierarchy Process (FAHP) is integrated with Lanchester equations is applied to an example. JCATS was used for computer assisted military experiments scenario analysis. The results show that Lanchester equations are valid.

This study addresses the problem of hand–eye calibration in robotic systems by developing Continual Learning (CL)-based approaches. Traditionally, robots require explicit models to transfer knowledge from camera observations to their hands or base. However, this poses limitations, as the hand–eye calibration parameters are typically valid only for the current camera configuration. We, therefore, propose a flexible and autonomous hand–eye calibration system that can adapt to changes in camera pose over time. Three CL-based approaches are introduced: the naive CL approach, the reservoir rehearsal approach, and the hybrid approach combining reservoir sampling with new data evaluation. The naive CL approach suffers from catastrophic forgetting, while the reservoir rehearsal approach mitigates this issue by sampling uniformly from past data. The hybrid approach further enhances performance by incorporating reservoir sampling and assessing new data for novelty. Experiments conducted in simulated and real-world environments demonstrate that the CL-based approaches, except for the naive approach, achieve competitive performance compared to traditional batch learning-based methods. This suggests that treating hand–eye calibration as a time sequence problem enables the extension of the learned space without complete retraining. The adaptability of the CL-based approaches facilitates accommodating changes in camera pose, leading to an improved hand–eye calibration system.

We give a prescription how high energy physics tools can be used to perform scalar dark matter analysis. We also present the analysis results of scalar dark matter in the context of 6 dimensional Effective Field Theory. We present the relic density and the cross sections of s scalar DM with the current results of the actual experiments.