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In recent years, the global tuna fishing and aquaculture industry has encountered significant challenges in balancing operational efficiency with sustainable resource management. This study introduces an innovative approach utilizing an advanced computer vision model, PA-YOLOv8, specifically adapted for drones, to enhance the monitoring and management of tuna populations. PA-YOLOv8 leverages the capabilities of YOLOv8, a state-of-the-art object detection system known for its precision and speed, tailored to address the unique demands of aerial surveillance in marine environments. Through comprehensive modifications including downsampling techniques, feature fusion enhancements, and the integration of the Global Attention Module (GAM), the model significantly improves the detection accuracy of small and juvenile tuna within complex aquatic landscapes. Experimental results using the Tuna dataset from Roboflow demonstrate marked improvements in detection metrics such as precision, recall, and mean average precision (mAP), affirming the model’s effectiveness. This study underscores the potential of integrating cutting-edge technologies like UAVs and computer vision in promoting sustainable practices in the aquaculture sector, setting a new standard for technological applications in environmental and resource management. The advancements presented here provide a scalable and efficient solution for real-time monitoring, contributing to the long-term sustainability of marine ecosystems.

This study presents a comprehensive analysis of combustion mechanisms and emission formation in marine diesel engines using biodiesel blends through experimental validation and computational fluid dynamics simulation using Matlab 2024a. Two marine engines were tested—YANMAR 6HAL2-DTN (200 kW, 1200 rpm) and Niigatta Engineering 6L34HX (2471 kW, 600 rpm)—with biodiesel ratios B0, B20, B50, and B100 at loads from 10% to 100%. The methodology combines detailed experimental measurements of exhaust emissions, fuel consumption, and engine performance with three-dimensional CFD simulations employing k-ε RNG turbulence model, Kelvin–Helmholtz–Rayleigh–Taylor droplet breakup model, and extended Zeldovich mechanism for NOx formation modeling. Key findings demonstrate that biodiesel’s oxygen content (10–12% by mass) increases maximum combustion temperature by 25 °C at 50% load, resulting in NOx emissions increase of 5–13% across all loads. Conversely, CO emissions decrease by 7–10% due to enhanced oxidation reactions. CFD analysis reveals that B100 exhibits 12% greater spray penetration depth, 20% larger Sauter Mean Diameter, and 20–25% slower evaporation rate compared to B0. The thermal Zeldovich mechanism dominates NOx formation (>90%), with prompt-NO and fuel-NO contributions increasing from 6.5% and 0.3% for B0 to 7.2% and 1.3% for B100, respectively, at 25% load. Optimal injection timing varies with biodiesel ratio: 13–15° BTDC for B0 reducing to 10–12° BTDC for B100. These quantitative insights enable evidence-based optimization of marine diesel engines for improved environmental performance while maintaining operational efficiency.

The sliding mode controller stands out for its exceptional stability, even when the system experiences noise or undergoes time-varying parameter changes. However, designing a sliding mode controller necessitates precise knowledge of the object’s exact model, which is often unattainable in practical scenarios. Furthermore, if the sliding control law’s amplitude becomes excessive, it can lead to undesirable chattering phenomena near the sliding surface. This article presents a new method that uses a special kind of computer program (Radial Basis Function Neural Network) to quickly calculate complex relationships in a robot’s control system. This calculation is combined with a technique called Sliding Mode Control, and Fuzzy Logic is used to measure the size of the control action, all while making sure the system stays stable using Lyapunov stability theory. We tested this new method on a robot arm that can move in three different ways at the same time, showing that it can handle complex, multiple-input, multiple-output systems. In addition, applying LPV combined with Kalman helps reduce noise and the system operates more stably. The manipulator’s response under this controller exhibits controlled overshoot (Rad), with a rise time of approximately 5 ± 3% seconds and a settling error of around 1%. These control results are rigorously validated through simulations conducted using MATLAB/Simulink software version 2022b. This research contributes to the advancement of control strategies for robotic manipulators, offering improved stability and adaptability in scenarios where precise system modeling is challenging.

In recent years, the research on drones has increased rapidly because of its high applicability in many fields and its great development potential. In the maritime sector too, especially marine rescue, a Drone with a compact size and fast flight speed is an effective solution in search and surveillance, giving quick results and being very convenient. When operating at sea, marine rescue drones are often affected by the environment, especially wind, which leads to turbulence that causes the drone to deviate from its predetermined flight trajectory. To overcome the above problem, the author has proposed the application of a Neural-Fuzzy controller for unmanned marine rescue aircraft presented in this paper introduces a controller that combines neural networks and fuzzy controllers that enhance the efficiency of the drone’s trajectory tracking. The paper presents the mathematics of a quadcopter described by the Newton-Euler equations. Presentation on stable flight control and trajectory control of marine rescue drones. In this paper, Matlab/Simulink is used to describe the operation of the Drone, and the characteristics obtained after using the simulation are used to compare, test, and analyze the system. The obtained results show that the Neural-Fuzzy controller is much more sensitive, more resistant to turbulence, and can be used on different sizes, weights, and configurations of drones without adjusting PID gain.

The operation of the robot underwater is done by a control system. Dynamic re-search is needed, it plays an important role in the research, operation, and development of underwater robots. Modeling the dynamics of the underwater robot with the highest possible accuracy is essential for the design of the robot controller. This task requires not only defining a mathematical model of the robot, but also describing the interaction between the robot and the water surrounding it. The equation of motion of the underwater robot is established by applying Newton Euler's equation to a freely moving solid and taking into account the interaction between the liquid and the structure. Many factors used in dynamic modeling of underwater robots have only relative accuracy. In this study, the author will introduce the dynamic calculations of ROV. In addition, the design of simple controllers for dynamic testing is also the foundation for the development of intelligent controllers. All simulation and testing of the results in this study were con-ducted by Matlab/Simulink.

In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV’s performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter’s presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms.

Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R2 < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R2 = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation.

Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency.

This paper addresses the issue of prescribed-time control for a specific class of uncertain nonlinear systems. Initially, a stability theorem based on prescribed time is introduced, utilizing adaptive techniques for the first time. Building on this theorem, a novel state feedback control approach is presented, employing the backstepping method for high-order nonlinear systems with unknown parameters to ensure convergence within the prescribed time. Furthermore, the proposed prescribed-time controller is derived in the form of continuous time-varying feedback, enabling all system states to converge to zero within the specified time. Notably, the prescribed time is independent of the system’s initial conditions, allowing it to be set arbitrarily within the physical constraints. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study presents an intelligent control approach, titled “Designing a Ship Autopilot System for Operation in a Disturbed Environment using the Adaptive Neural Fuzzy Inference System”, combining a neural network and fuzzy logic control to steer ships. A 6DOF dynamic model is constructed, simulating ship operations with noise signals. The ANFIS controller comprises six layers, with a distinct composition rule expressing conclusions as linear equations of input variables. Layer 1 has two input signals, layer 2 represents fuzzy rules with six nodes, and layers 3, 4, and 5 contain nine nodes each. Layer 6 combines output signals from layer 5, following the first-order Takagi–Sugeno fuzzy logic control model. Simulation results using MATLAB/Simulink demonstrate the superiority of the ANFIS controller over the PID controller, significantly improving stability and trajectory accuracy.