Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components (i.e., sucrose, yeast extract, and urea) were optimized for biomass...
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Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components (i.e., sucrose, yeast extract, and urea) were optimized for biomass growth and extracellular EPS production in Haloferax mediterranei DSM 1411 using Box-Behnken design. In a multi-objective optimization framework, response surface methodology (RSM) and genetic algorithm (GA)-optimized artificial neural network (ANN) were used to minimize biomass growth while increasing EPS production. The performance of the selected ANN model for the prediction of biomass and EPS (R-2: 0.964 and 0.975, respectively) was found to be better than that of the multiple regression model (R-2: 0.818, 0.963, respectively). The main effect of sucrose and its interaction with urea appears to have a significant effect on both responses. The ANN model projects an increase in EPS production from 4.49 to 18.2 g l(-1) while shifting the priority from biomass to biopolymer. The optimized condition predicted a maximum biomass and EPS production of 17.27 g l(-1) and 17.80 g l(-1), respectively, at concentrations of sucrose (19.98 g l(-1)), yeast extract (1.97 g l(-1)), and urea (1.99 g l(-1)). Based on multi-objective optimization, the GA-ANN model predicted an increase in the EPS to biomass ratio for increasing the EPS and associated biomass production. The extracted EPS, identified as Gellan gum through NMR spectroscopy, was further characterized for surface and elemental composition using SEM-EDX analysis.
The localization of unmanned aerial vehicles is an important topic due to several threats near sensitive sites. Localization based on their sounds has been a particular point of interest in past studies for many years...
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The localization of unmanned aerial vehicles is an important topic due to several threats near sensitive sites. Localization based on their sounds has been a particular point of interest in past studies for many years. It requires the use of a microphone array. The positioning of the various microphones making up an antenna defines the intrinsic directivity of the array. In this study, a genetic algorithm is used to determine the microphone positions that optimize directivity in a focus direction and for a frequency, by favoring the narrowness of the main lobe and the reduction of the secondary lobes. The optimization leads to several antennas with a 3D structure similar to that designed in a previous study. A method estimating the direction of arrival of a drone was also presented in that study making use of its acoustic signature to enhance the signal-to-noise ratio and thus improving the estimations. In this paper, an improvement to the method is proposed for tracking the drone's trajectory. Measurements were conducted to compare the drone locations given by the first designed antenna and the one optimized by the genetic algorithm. Performance on the direction of arrival found is characterized in terms of mean error, standard deviation and root mean square error relative to the GPS reference onboard the UAV. An experiment with the optimized antenna has also been conducted with the drone at a great distance to the antenna to characterize the maximal distance for possible estimations of the direction of arrival. Results show that the method used for the direction of arrival estimation can give a mean error below 10 degrees in azimuth and 5 degrees in elevation. The maximum distance between the antenna and the drone for which the method is able to give estimations is between 240 and 340 m.
Efficient ground motion intensity measures can significantly reduce the variability in predicting structural response, making the selection of appropriate measures a critical step in seismic vulnerability analysis. Th...
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Efficient ground motion intensity measures can significantly reduce the variability in predicting structural response, making the selection of appropriate measures a critical step in seismic vulnerability analysis. This study conducts vulnerability analyses on a six-story reinforced concrete column-steel beam (RCS) frame under three damage limit states: immediate occupancy (IO), life safety (LS), and collapse prevention (CP). The structural model is developed in the open-source software OpenSees, simulating both shear deformation and vertical bearing failure at beam-column joints. To account for the characteristics of seismic motions, two sets of ground motions-far-field and near-field-are selected. The efficiency of 22 chosen intensity measures (IMs) is evaluated and compared using the log-normal standard deviation beta RTR in vulnerability analysis. Results indicate that velocity-related measures, specifically Housner Intensity (HI) and Velocity Spectrum Intensity (VSI), perform well. To further enhance the HI measure's effectiveness across damage states, an optimized ground motion intensity measure, HIIMP, is proposed using the global optimization capabilities of a genetic algorithm (GA). As the damage limit state deepens, the proposed HIIMP measure achieves higher upper integration limits, increasing the influence of the softening period. Finally, the applicability of HIIMP to RCS structures is demonstrated from the perspectives of sufficiency and scaling robustness.
The temperature control of silicon diodes as actuators was studied from both theoretical and experimental perspectives. genetic algorithms were employed to optimize diode distributions for effective temperature regula...
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The temperature control of silicon diodes as actuators was studied from both theoretical and experimental perspectives. genetic algorithms were employed to optimize diode distributions for effective temperature regulation. Temperature fluctuations can significantly affect the performance of high-precision electronic equipment, necessitating robust temperature control methods. By integrating series diodes into the temperature control system alongside the traditional proportional-integral-differential (PID) control mode, precise regulation of high-precision component temperatures in power supplies was achieved. The diodes' minimal voltage variation ensures linear power and current characteristics, enabling effective temperature rise control through current adjustments and mitigating temperature overshoot issues. Diodes also offer advantages such as ease of installation and high safety margins against open circuits. Simulation-validated optimization of diode positions using genetic algorithms demonstrated their effectiveness in achieving optimal configurations through selection, crossover, and mutation operations. This approach not only reduces the number of diodes but also meets diverse temperature control requirements, enhancing system responsiveness and power output stability. The study underscores the potential of diodes as temperature control actuators, particularly in regulating high-precision power components in power supplies.
As fundamental quantum mechanical descriptors of crystalline lattice vibrational properties, phonons play a critical role in determining numerous macroscopic physical characteristics spanning thermal transport behavio...
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As fundamental quantum mechanical descriptors of crystalline lattice vibrational properties, phonons play a critical role in determining numerous macroscopic physical characteristics spanning thermal transport behavior and thermodynamic response functions. The precise determination of complete phonon spectra and their corresponding interatomic force constants continues to present substantial computational challenges, particularly in architecturally complex material systems. In this study, using graphene as a prototypical system, theoretical derivation of the phonon dispersion relations is presented through rigorous lattice dynamics formalism. The first- through eighth-nearest-neighbor force constants in the dynamical matrix are systematically determined via a self-consistent iterative genetic algorithm optimization framework. These derived parameters are further systematically validated through density functional theory simulations. The optimized interatomic force constants demonstrate remarkable fidelity in reproducing both the acoustic and optical phonon branches across the entire Brillouin zone, thereby establishing a comprehensive theoretical foundation for predictive calculations of temperature-dependent thermodynamic properties. The developed genetic algorithm optimization methodology shows significant transferability to diverse material systems, enabling precise alignment with inelastic neutron scattering and Raman spectroscopy measurements. This advancement provides a generalized computational tool for investigating lattice dynamics in complex material systems.
Hardness serves as a crucial indicator for assessing the success of quenching treatment in the steel and iron industry, impacting the processability and wear properties of materials. In the present study, a dataset co...
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Hardness serves as a crucial indicator for assessing the success of quenching treatment in the steel and iron industry, impacting the processability and wear properties of materials. In the present study, a dataset comprising 125 hardness values of the QT500-7 sample subjected to various austempering heat treatment parameters was utilised to train a neural network model for predicting the hardness of austempered ductile iron (ADI). The established model based on a genetic algorithm and error backpropagation algorithm demonstrates high accuracy in predicting the hardness of ADI if given heat treatment parameters. The mean square error of the model was about 1.019, indicating the reliability and precision of the model in predicting the hardness of ADI based on the specified heat treatment parameters.
Grain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even...
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Grain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even intelligent grain leveling robot for the precise operation of grain silos. In this paper, we propose an area classification method based on target leveling height for the special working mode of truss-type grain leveling robot, simplify the 3D map to a 2D map, reduce the difficulty of path planning, and improve the working efficiency. Based on multi-level path planning and genetic algorithm to achieve the planning of the working path of the grain-leveling robot, it solves the problem of a large number of useless trips under the adoption of full-coverage operation. It saves a lot of time, improves grain-leveling efficiency, reduces energy consumption, optimizes the effect of grain-leveling, and helps realize the precise storage operation of grain silos. The experiment of rough leveling operation was carried out through the grain leveling robot prototype and the simulated experimental warehouse, and the results show that the height difference between the peak of the grain surface and the target leveling height is within 5 cm, which verifies that the path planning method in this paper is feasible, and shows that the grain leveling robot can complete the task of grain leveling and the effect is good.
OLEDs are playing an important role in flexible displays, smart wearable, in-vehicle displays, and other fields. Foldable OLED panels consist of multiple layers of film. To ensure the reliability, some key films such ...
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OLEDs are playing an important role in flexible displays, smart wearable, in-vehicle displays, and other fields. Foldable OLED panels consist of multiple layers of film. To ensure the reliability, some key films such as the OLED should be placed on the neutral plane, which is usually achieved by adjusting the thickness of each layer. In this paper, genetic algorithm is introduced to find the optimum configurations of the thickness of each layer. Based on the mathematical express of the stress happening on each film when an OLED panel is folded, the objective function can be defined and the optical thicknesses of some layers can be got. The simulation results show that with such optical thickness configuration, the stresses on key films can effectively reduce. The method proposed in the paper can help improve the folding performance of a foldable OLED panel.
Passive radiative cooling is a promising path to tackle worsening energy crisis and global warming. Despite advancements in cooling mechanisms, material design, preparation technologies, and practical applications, th...
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Passive radiative cooling is a promising path to tackle worsening energy crisis and global warming. Despite advancements in cooling mechanisms, material design, preparation technologies, and practical applications, the traditional white or silver appearance fails to meet both aesthetic and functional requirements, and the lack of transparency limits their applicability in scenarios where optical clarity is crucial. In this work, a genetic algorithm is employed to optimally design a dielectric/metal/dielectric/metal/dielectric stacked multilayer structure as a near-infrared (NIR) reflector, which is integrated with a plain glass substrate and an infrared high-emission PDMS layer on the outermost layer to form a transparent-colored radiative cooler (TCRC). By integrating a Fabry-Perot resonant cavity within the NIR reflector, we achieved customization of TCRC with varying colors. A grey TCRC exhibits optimal performance with visible transmissivity of 0.63, high NIR reflectivity of 0.88, and atmospheric transparency window emissivity of 0.95, all demonstrating angular independence (<60 degrees). In outdoor experiments during midday, TCRC achieves a room temperature reduction of 17.6 degrees C compared to the original glass. Additionally, TCRC exhibits an extraordinary potential for building energy-saving in most climate zones. This work provides a valuable reference for the further development of radiative cooling and the design of metamaterials.
This study introduces a Takagi-Sugeno (T-S) fuzzy modeling framework for kinematic modeling of mecanum wheeled mobile robot (MWMR). T-S fuzzy systems are particularly effective in capturing complex nonlinear dynamics ...
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This study introduces a Takagi-Sugeno (T-S) fuzzy modeling framework for kinematic modeling of mecanum wheeled mobile robot (MWMR). T-S fuzzy systems are particularly effective in capturing complex nonlinear dynamics and unmodeled subsystems inherent to MWMR architectures. Optimization of parameters within the T-S structure is achieved through a genetic algorithm (GA), enabling precise alignment between the T-S derived model and physical system behavior. Notably, the proposed methodology achieves convergence to optimal T-S model within 200 generations of the GA, without necessitating an explicit analytical formulation of the complete MWMR dynamics. Validation experiments reveal the optimized T-S model achieves 0.015 m/s a mean squared error (MSE) difference relative to empirical velocity profiles from the MWMR platform. Rigorous numerical assessment demonstrates the formulated T-S model achieves exceptional dynamic congruence with the physical MWMR platform, manifesting peak velocity discrepancies of 57 x 10-4 m/s accompanied by standard deviations of 0.027 m/s across experimental trials. Comparative evaluation against conventional probabilistic modeling techniques highlights superior predictive accuracy and dynamic fidelity of the proposed T-S framework. Observed results substantiate the model's capacity to replicate nonlinear kinematic interactions and transient velocity characteristics under experimentally validated boundary conditions, corroborating theoretical expectations through empirical system identification.
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