Periodical time-delay scenario is often encountered in industrial manufacturing processes. However, the presence of time delays and periodical coefficients brings challenges to controller design and system analysis, w...
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This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep n...
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Grid modernization has increased the reliance of power networks on cyber networks within distribution systems (DSs), heightening their vulnerability to disasters. Communication network failures significantly impede DS...
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The mission of engineering technology education is to produce technologists who can work with current technology. Therefore, engineering curricula must be a combination of applied engineering theory and hands-on instr...
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Although construction is one of the oldest sectors of the global economy, the digital innovation and application of artificial intelligence (AI) in the industry are still insignificant. For the past several years, how...
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The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO)...
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The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO) using fuzzy modelling and non-dominated sorting genetic algorithm-II (NSGA-II). The optimisation process focuses on key input parameters: methanol quantity, reaction temperature, reaction time, and catalyst concentration, which were normalised and represented using linguistic variables. Fuzzy logic was employed to predict biodiesel yield, expressed in terms of linguistic variables, and defuzzified to yield crisp output values. The developed model achieved a high R2 value of 96.34%, demonstrating a strong correlation between input variables and biodiesel yield. The NSGA-II algorithm was utilised for multi-objective optimisation, determining the optimal conditions for biodiesel production: 150 ml of methanol, a reaction temperature of 62 °C, a reaction time of 63 min, and a catalyst concentration of 7.5 g. These parameters resulted in a maximum biodiesel yield of 97.36%. The Box-Behnken experimental design validated the model’s efficiency, achieving a yield of 96.88%. This study emphasises the practical implications of optimised biodiesel production, such as reducing environmental pollution by recycling WCO and minimising reliance on fossil fuels. The optimised process meets ASTM standards and exhibits scalability potential for industrial-level production with minor modifications. The model’s robustness makes it suitable for integration into intelligent manufacturing systems, ensuring consistent biodiesel quality and yield through automated monitoring and control mechanisms. Despite its success, challenges such as feedstock variability and initial setup costs must be addressed. Future studies should focus on adaptive models and energy-efficient processing technologies to enhance scalability and sustainability. This research demonstrates a significant ste
This research paper presents the design and implementation of an automated hydroponic system capable of managing both closed and open setups in indoor and outdoor environments. The system integrates sensors and microc...
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The fueling process for haul trucks in open-pit mining operations is traditionally manual, leading to inefficiencies, operational delays, and increased costs. This paper presents the design of an automated robotic fue...
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This study evaluates the Fuzzy Analytical Hierarchy process(FAHP)as a multi-criteria decision(MCD)support tool for selecting appropriate additive manufacturing(AM)techniques that align with cleaner production and envi...
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This study evaluates the Fuzzy Analytical Hierarchy process(FAHP)as a multi-criteria decision(MCD)support tool for selecting appropriate additive manufacturing(AM)techniques that align with cleaner production and environmental *** FAHP model was validated using an example of the production of aircraft components(specifically fuselage)employing AM technologies such as Wire Arc Additive Manufacturing(WAAM),laser powder bed fusion(L-PBF),Binder Jetting(BJ),Selective Laser Sintering(SLS),and Laser Metal Deposition(LMD).The selection criteria prioritized eco-friendly manufacturing considerations,including the quality and properties of the final product(e.g.,surface finish,high strength,and corrosion resistance),service and functional requirements,weight reduction for improved energy efficiency(lightweight structures),and environmental *** metrics,such as cost-effectiveness,material efficiency,waste minimization,and environmental impact,are central to the evaluation process.A computer-aided modeling approach was also used to simulate the performance of aluminum(AA7075 T6),steel(304),and titanium alloy(Ti6Al4V)for fuselage *** results demonstrate that MCD approaches such as FAHP can effectively guide the selection of AM technologies that meet functional and technical requirements while minimizing environmental degradation ***,the aluminumalloy outperformed the other materials investigated in the simulation with the lowest stress concentration and least *** study contributes to advancing cleaner production practices by providing a decision-making framework for sustainable and eco-friendly manufacturing,enabling manufacturers to adopt AM technologies that promote environmental responsibility and sustainable development,while maintaining product quality and performance.
This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T(CAR-T) cells in treating cancer. The pr...
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This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T(CAR-T) cells in treating cancer. The proposed real-time control strategy aims to maximize tumor regression while ensuring the safety of the treatment. A dynamic growth model of cancer cells under the influence of cellular immunotherapy is established for the first time, which aligns with clinical experimental *** the backstepping method, a novel consensus reference model is designed to consider the characteristics of cancer cell changes during the treatment process and conform to clinical rules. The model is segmented and continuous, with cancer cells expected to decrease in a step-like manner. Furthermore, a prescribed performance mechanism is constructed to maintain the therapeutic effect of the proposed scheme while ensuring the transient performance of the system. Through the analysis of Lyapunov stability, all signals within the closed-loop system are proven to be semiglobally uniformly ultimately bounded(SGUUB). Simulation results demonstrate the effectiveness of the proposed control strategy, highlighting its potential for clinical application in cancer treatment.
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