Basis For Optimization Research Articles

Research on Evolutionary Path of Land Development System Towards Carbon Neutrality

Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving toward a low-carbon path. Firstly, through nonlinear dynamics and bifurcation analysis, this study identifies three typical transformation paths that the system may experience: gradual, transitional, and hybrid, emphasizing the nonlinear, phased, and highly context-dependent characteristics of the transformation process. On this basis, early warning indicators and robustness analysis methods are introduced, which provide operational tools for identifying critical turning points in the system and improving the effectiveness and resilience of regulatory strategies. Furthermore, this paper proposes a multi-level regulation mechanism design framework, which combines the immediate feedback with the historical cumulative effect to achieve the refined guidance of land development patterns and carbon emission paths. The results provide a scientific basis and practical enlightenment for land use optimization, green infrastructure construction, and industrial structure adjustment under the background of realizing the “3060” dual carbon goal and the reform of territorial spatial planning in China. In the future, it is necessary to strengthen the empirical calibration of parameters, data-driven optimization, and collaborative research of multiple policy tools to further improve the applicability and decision-making reference value of the model.

Electromagnetic Field Modeling and Optimization Design of Axial Magnetic Flux Eddy Current Brake Considering Non‐Ideal Factors

Accurate analytical calculation of electromagnetic field is the basis of research and optimization of axial magnetic flux eddy current brake, and structural optimization design is the key to improving system performance and reducing engineering construction costs. In order to establish an accurate analytical calculation model of electromagnetic field of axial magnetic flux eddy current brake, based on the two‐dimensional analytical calculation model of electromagnetic field, this paper comprehensively considers the influence of non‐ideal factors such as core saturation, transverse dynamic end effect and secondary skin effect on the electromagnetic characteristics of axial magnetic flux eddy current brake, and obtains an improved analytical calculation model of electromagnetic field by introducing corresponding correction coefficients, and verifies the accuracy of the analytical calculation model of electromagnetic field by comparing with the numerical calculation results of three‐dimensional electromagnetic field finite element model. On this basis, an improved multi‐objective particle swarm optimization algorithm is adopted to optimize the system design, so as to increase the electromagnetic braking torque, reduce the electromagnetic axial force and reduce the secondary mass. Finally, the accuracy of the above electromagnetic field analytical calculation model and optimization design results is verified on the experimental research platform of axial magnetic flux eddy current brake, which can provide theoretical basis for subsequent researchers to design and optimize the electromagnetic scheme. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Study on vortex-induced vibration response of large-scale two-lay steel trusses bridge under large wind angle of attack

With the advancement of urbanization, two-lay trusses bridges are widely used because of their good traffic capacity and structural performance. However, the aerodynamic behavior of this beam type is still in the exploratory stage. The local microclimate characteristics at the bridge site in mountainous cities are obvious, and it is easy to form a large wind angle of attack, which has a significant impact on the vortex-induced vibration (VIV) performance of the bridge. Therefore, this study takes a long-span two-lay steel trusses bridge in a mountainous city as the engineering background, and uses wind tunnel test and numerical calculation methods to study the changes of the static three-component force coefficient and VIV response of the main beam in the construction and completion state under the action of high wind angle of attack. The results show that the three-component force coefficient curves under different wind speeds are close to each other, and the Reynolds number effect is not obvious. The vibration test shows that the vertical bending VIV first occurs at +3° and +5°, and then two torsional VIV with different amplitudes occur. Both vertical bending and torsional VIV are simple harmonic vibrations with a single frequency, and the vertical bending VIV frequency is locked at 2.227 Hz, and the torsional VIV frequency is locked at 4.289 Hz, which are close to the natural frequency of the test model. Compared with +3°, the maximum amplitude of vertical bending VIV under +5° increases by 30.0 %, while the maximum amplitude of torsional VIV under high and low wind speed increases by 16.6 % and 12.7 % respectively, and the locking range is longer. It can be seen that the wind angle of attack has a significant effect on the VIV response of the main beam in the completion state. Especially, the trusses beam at a large angle is more sensitive to VIV, and it is more prone to large-scale and large-amplitude VIV. The research results can provide a theoretical basis for the aerodynamic shape optimization and provide a reference for the design of related bridges.

Research on the Aerodynamics of Electric Vehicles

A major contributing factor to the growing severity of the global warming issue is greenhouse gas emissions, of which carbon dioxide from moving cars makes up a significant amount. Therefore, electric vehicles have become the focus of countries to replace conventional internal combustion engine vehicles due to their low emission characteristics. However, the weight of electric vehicles is increased by their reinforced structures and batteries, which reduces their range. In the short run, it is challenging to lower the weight of electric vehicles greatly because of the constraints of battery technology. Thus, lowering air resistance has emerged as a key strategy for extending the range of electric cars. This paper first analyses the reasons why electric vehicles need better aerodynamic design to increase range, including increased environmental demands, battery technology limitations and the high weight of electric vehicles. Next, the causes of air resistance are explored. Finally, the paper discusses ways to design active aerodynamic components to increase or decrease drag under different operating conditions. For example, a front air dam design can reduce the amount of airflow into the underbody, reducing lift and increasing grip. Variable air dams can optimize aerodynamics by automatically adjusting their position according to speed and driving conditions. In conclusion, the aim of this paper is to find and discover ways to optimize the aerodynamic performance of electric vehicles in order to solve the range problem. This paper provides an important theoretical and practical basis for aerodynamic optimization of electric vehicles

Optimization of Seed-Receiving Mechanism in Belt-Driven Seed Guide Tube Based on High-Speed Videography Experiment

During high-speed corn sowing at 10 km/h, the rapid seed discharge resulting from the high rotation speed of the seed disc escalates the impact force of seeds as they are released from the seed metering device into the seed guiding apparatus, consequently diminishing the overall seeding efficiency of the seeder. This study employed high-speed videography to conduct experiments and optimize parameters for the seed-receiving mechanism of a belt-driven seed guide tube. By changing the clamping wheel speed and seed-receiving angle, the speed change curve and displacement trajectory of seeds under different conditions were obtained and analyzed. The findings demonstrate that the seed speed fluctuation is more stable, and the seed displacement trajectory achieves greater stability at a clamping wheel speed of 560 r·min−1. When the seed-receiving angle is set at 85°, the seed speed fluctuation becomes less apparent, resulting in a smoother seed displacement trajectory. Finally, the experimental results of high-speed cameras are confirmed by field tests. The findings of this study can act as a theoretical basis for the further optimization of the experimental belt-driven seed guide tube.

Optimization and Prediction of the Mechanical Properties of Concrete with Crumb Rubber and Stainless-Steel Fibers Under Varying Temperatures

This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at various temperatures. The initial phase assessed three existing stress–strain formulas as a basis for optimization. Using the Genetic Algorithm (GA) and the Whale Optimization Algorithm (WOA), a new equation was created to simulate the stress–strain relationship while considering temperature changes and material additions. Results showed that Formula (1), optimized with the WOA, performed much better than other polynomial and exponential formulas, proving the WOA’s effectiveness over the traditional GA. A comparison of the mechanical properties from experiments and those predicted by the new formula showed a high level of accuracy. Key properties like the maximum stress, strain at maximum stress, modulus of elasticity, and toughness were well captured. The findings highlight how temperature and material composition significantly affect concrete’s mechanical behavior. Overall, this research offers important insights into the factors influencing concrete performance, providing a solid framework for future studies and practical applications in engineering and construction. The proposed formula is a reliable tool for predicting concrete’s mechanical properties under various conditions, which aids in better modeling and optimization in concrete design.

Study on the Detection Model of Cigarettes' Dense-end Position Based on Density Probability Distribution

The density distribution of cigarettes was analyzed in order to improve the detection precision of cigarettes' dense-end position. The distribution pattern of the density under the ideal production condition was combined, and a dense-end position detection model was constructed. The model parameters were determined on the basis of data optimization. The model was validated by MW-T and cigarettes. We got conclusions as below. 1) For the measurement of dense-end with the Dense-end Position Standard Parts, the average error of the measurement was 0.1646 mm and the maximum error was -0.2735 mm. 2) The average error between the measured value of the dense-end position with ordinary cigarettes and that of the MW4420 was 0.79 mm, which meets the actual production demand (≤1 mm). The model has high accuracy and good reliability, and can provide a favorable technical support for the dense-end position detection of cigarettes. It is helpful to improve the level of quality control in cigarette production, and inject the new vigor for the sustainable development of cigarette industry.

Study of Interfacial Reaction Mechanism of Silicon Anodes with Different Surfaces by Using the In Situ Spectroscopy Technique.

The interfacial reaction of a silicon anode is very complex, which is closely related with the electrolyte components and surface elements' chemical status of the Si anode. It is crucial to elucidate the formation mechanism of the solid electrolyte interphase (SEI) on the silicon anode, which promotes the development of a stable SEI. However, the interface reaction mechanism on the silicon surface is closely related to the surface components. This work systematically investigates the interfacial reaction mechanism on silicon materials with three representative coatings of graphene, TiO2, and SiO2 by ex situ X-ray photoelectron spectroscopy (XPS) and dynamic analysis in operando attenuated total reflection-Fourier transform infrared (ATR-FTIR), in situ revealing the different ring-opening mechanisms of fluoro-ethylene carbonate (FEC) and ethylene carbonate (EC) on different silicon surfaces with varying electrical conductivities. Due to the different ring-opening mechanisms, the final decomposition product of FEC on the graphene/electrolyte interface is stable LiF, while on the oxide (native SiO2 or emerging TiO2) interface, it forms an unstable solid lithium compound •CH2CHFOCO2Li. This study demonstrates that the formation mechanism of the SEI on silicon-based electrodes is related to the electron conductivity of surface elements, providing a theoretical basis for further optimization of silicon-based composite materials.

Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks

The application of machine learning methods for predicting potential energy surface and physical properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional Multi-Layer Perceptrons. This study evaluates the impact of substituting Multi-Layer Perceptrons with KANs within four established machine learning frameworks: Allegro, Neural Equivariant Interatomic Potentials, Higher Order Equivariant Message Passing Neural Network (MACE), and the Edge-Based Tensor Prediction Graph Neural Network. Our results demonstrate that the integration of KANs enhances prediction accuracies, especially for complex datasets such as the HfO2 structures. Notably, using KANs exclusively in the output block achieves the most significant improvements, improving prediction accuracy and computational efficiency. Furthermore, employing KANs exclusively in the output block facilitates faster inference and improved computational efficiency relative to utilizing KANs throughout the entire model. The selection of optimal basis functions for KANs depends on the specific problem. Our results demonstrate the strong potential of KANs in enhancing machine learning potentials and material property predictions. Additionally, the proposed methodology offers a generalizable framework that can be applied to other ML architectures.

Spatial Distribution and Mechanisms of Groundwater Hardness in the Plain Area of Tangshan City, China

Groundwater resources play a critical role in meeting the agricultural, industrial, and domestic water demands of Tangshan, a key industrial city in China. However, with the acceleration of urbanization and the overextraction of groundwater, issues related to groundwater quality have become increasingly apparent. Notably, groundwater hardness has steadily increased over the years, posing risks to human health and elevating industrial water treatment costs. This study analyzed the spatial distribution characteristics and causes of groundwater hardness using 214 groundwater quality samples collected in 2022 from the plain area of Tangshan City, employing inverse distance weighting (IDW), Gibbs diagrams, ion ratios, mineral saturation indices, and Pearson correlation analysis. The results indicate that, in horizontal distribution, high-hardness groundwater is predominantly concentrated in the southern coastal plain area, with hardness gradually decreasing from south to north. Vertically, shallow groundwater in the coastal plain exhibits significantly higher hardness than deep groundwater, with a non-compliance rate of 94.12%, while deep groundwater hardness remains markedly lower. Mid-depth groundwater (60–300 m) in the alluvial plain exhibits elevated hardness, primarily attributed to mineral dissolution and agricultural irrigation return flow. The spatial distribution pattern of groundwater hardness across the study area is predominantly governed by hydrogeochemical processes and hydrochemical environmental factors, with cation exchange adsorption and evaporation–concentration processes identified as the dominant influences. The analysis of ion sources indicates that Ca2+ and Mg2+, the primary contributors to groundwater hardness in the area, are mainly derived from the weathering and dissolution of carbonate minerals, sulfate minerals, and cation exchange processes. Therefore, an in-depth investigation into the spatial distribution and driving factors of groundwater hardness in Tangshan can provide a scientific basis for regional water resource management, pollution control, and water quality optimization. Such research also supports the development of sustainable groundwater management and optimization strategies.

Construction of college student’s volleyball functional physical training system based on 5G embedded analysis from the perspective of biomechanics

In volleyball, the movement and performance of athletes are closely related to biomechanical principles. The process of getting the ball beyond the opponent’s net and landing on the court involves complex biomechanical actions such as jumping, spiking, and blocking. These actions require appropriate muscle activation, joint movement, and force generation. Through a 5G embedded physical training system, college students’ biomechanical performance in volleyball can be effectively monitored and analyzed. For example, the Wireless Sensor Network (WSN) can be used to measure the forces exerted on the body during movements, such as the impact force on the fingers during spiking and blocking, and the stress on the lower back. By analyzing these biomechanical data, problems such as finger injuries and lower back pain can be better understood and addressed. Moreover, the 5G embedded system allows for a detailed analysis of the biomechanical characteristics of different teams and players, enabling coaches and athletes to optimize training strategies. The system can also provide visual feedback on biomechanical parameters, which helps students improve their overall coordination and muscle strength. By incorporating biomechanical analysis into the volleyball physical training system, students can better understand the scientific basis of their movements and improve their performance. Statistical analysis has been carried out effectively based on the study analysis with the existing open-source datasets, providing a quantitative basis for biomechanical research and training optimization.

A Dual Filter Based on Radial Basis Function Neural Networks and Kalman Filters with Application to Numerical Wave Prediction Models.

The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter's potential as a robust post-processing tool for environmental simulations.

Underwater image processing and target detection from particle swarm optimization algorithm

The underwater image obtained is difficult to satisfy human visual perception because of the particle scattering and water absorption phenomena when visible light propagates underwater. In underwater images, light absorption easily leads to image distortion and reduction of image contrast and brightness. Therefore, this work aims to improve the quality of underwater image processing, reduce the distortion rate of underwater images, and further improve the efficiency of underwater image extraction, processing, and tracking. This work combines intelligent blockchain technology in emerging multimedia industries with existing image processing technology to improve the target detection capability of image processing algorithms. Firstly, the theory of visual saliency analysis (VSA) is studied. The steps of image processing using VSA are analyzed. Based on the original Itti model, the visual significance detection step is optimized. Then, the theoretical basis and operation steps of particle swarm optimization (PSO) algorithm in intelligent blockchain technology are studied. VSA theory is combined with PSO to design underwater image processing algorithms and target detection optimization algorithms for underwater images. The experimental results show that: (1) the method has a higher F value and lower Mean Absolute Error. (2) Compared with the original image, the restored image entropy through this method is greatly improved, and the information in the image increases. Therefore, this method has good performance. Besides, this method performs well in image definition, color, and brightness. The quality of the restored image through this method is better than that of other algorithms. (3) Compared with similar algorithms, the relative errors of this method are reduced by 2.56%, 3.24% and 3.89%, respectively. The results show that the method has high accuracy. The research results can provide a reference for future underwater image processing and target detection research. In addition, the designed underwater image processing and target detection and tracking algorithms can improve the detection efficiency and accuracy of underwater targets and help to accurately obtain underwater target images.

Pharmaceutical development and practical approach to synthesis of tropantiol radiopharmaceutical ([99mTc]Tc-TRODAT-1) for diagnostic of neurodegenerative diseases (review)

Introduction. The symptoms of Parkinson's disease are mainly associated with the formation of intraneuronal protein inclusions with Lewy bodies, and the progressive loss of dopaminergic neurons of the Substantia nigra and their axons. Existing diagnostic criteria for the diagnosis of Parkinson's disease often take into account symptoms occurring in the later stages of the disease. Thus, for a more accurate diagnosis in the early stages, it is necessary to confirm pathologic changes in brain tissue by molecular imaging methods such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). At the same time SPECT is a more accessible method of diagnostics of neurodegenerative diseases in comparison with PET, because of the possibility to obtain medical radionuclides for SPECT imaging using mobile generator systems, in particular 99Mo/99mTc generator. Among the formulations based on 99mTc and tropane derivatives proposed for dopamine transporter (DAT) imaging, [99mTc]Tc-TRODAT-1 (technetium-99m-labeled tropantiol) is the most effective. Currently, various compositions of the freeze-dried kits for the synthesis of [99mTc]Tc-TRODAT-1 have been proposed, facilitating the process of its production in situ, which, together with the availability of technetium-99m generator in a healthcare facility, as well as favorable pharmacokinetics, makes [99mTc]Tc-TRODAT-1 a drug of choice for routine use in clinical practice.Text. In this review, various approaches to design and optimize the composition of the freeze-dried kits for the synthesis of [99mTc]Tc-TRODAT-1, including the amount and ratio of active ingredient and excipients, synthesis conditions, in particular the temperature regime, synthesis time and pH of the reaction mixture, have been considered.Conclusion. Development and optimization of the composition of the freeze-dried kits for the synthesis of [99mTc]Tc-TRODAT-1 is an urgent task in the context of improving its use in clinical practice. Based on the published data, clear dependencies can be traced, which may form the basis for further development and optimization of the composition of the freeze-dried kits for [99mTc]Tc-TRODAT-1 synthesis for the diagnosis of Parkinson's disease and other neurodegenerative diseases by SPECT in the Russian Federation.

Modelling heart rate dynamics in relation to speed and power output in sprint kayaking as a basis for training evaluation and optimisation.

With the development of power output sensors in the field of paddle sports and the ongoing advancements in dynamical analysis of exercise data, this study aims to model the measurements of external training intensity in relation to heart rate (HR) time-series during flat-water kayak sprint. Nine elite athletes performed a total of 47 interval training sessions with incremental intensity (light to (sub-) maximal effort levels). The data of HR, speed and power output were measured continuously and rating of perceived exertion and blood lactate concentration ([BLa]) were sampled at the end of each interval stage. Different autoregressive-exogenous (ARX) modelling configurations are tested, and we report on which combination of input (speed or power), model order (1st or 2nd), parameter estimation method (time-(in)variant) and training conditions (ergometer or on-water) is best suited for linking external to internal measures. Average model R2 values varied between 0.60 and 0.97, with corresponding average root mean square error values of 15.6 and 3.2 bpm. 1st order models with time-varying (TV) parameter estimates yield the best model performance (average R2=0.94). At the level of the individual athlete, the TV modelling features (i.e., the model parameters and derivatives such as time constant values) show significant repeated measure correlations in relation to measures of exercise intensity. In conclusion, the study provides a comprehensive description of how the dynamic relationship between external load and HR for sprint kayaking training data can be modelled. Such models can be used as a basis for improving training evaluation and optimisation.

Optimal dimensioning of elastic-link manipulators regarding lifetime estimation

Resourceful operation and design of robots is key for sustainable industrial automation. This will be enabled by lightweight design along with time and energy optimal control of robotic manipulators. Design and control of such systems is intertwined as the control must take into account inherent mechanical compliance while the design must accommodate the dynamic requirements demanded by the control. As basis for such design optimization, a method for estimating the lifetime of elastic link robotic manipulators is presented. This is applied to the geometry optimization of flexible serial manipulators performing pick-and-place operations, where the optimization objective is a combination of overall weight and vibration amplitudes. The lifetime estimation draws from a fatigue analysis combining the rainflow counting algorithm and the method of critical cutting plane. Tresca hypothesis is used to formulate an equivalent stress, and linear damage accumulation is assumed. The final robot geometry is selected from a Pareto front as a tradeoff of lifetime and vibration characteristic. The method is illustrated for a three degrees of freedom articulated robotic manipulator.

Land Use Changes and Its Impact on Ecological Environment Quality in Bayannur City

As an important ecological protective screen in northern China, monitoring and evaluating the dynamic changes in habitat quality and their relationship with land use changes in Bayannur played a notable role in maintaining regional ecological balance and species diversity. Based on the Google earth engine （GEE） platform, this study employed multi-period remote sensing image coupling to construct a remote sensing ecological index （RSEI）, analyzed the changes in habitat quality in Bayannur City from 2000 to 2022, and explored its relationship with land use changes. The results showed that： ① From 2000 to 2022, although the habitat quality of Bayannur improved slowly, the overall quality was poor. The annual change rate was 0.000 8 a-1 （P>0.05）, showing an insignificant upward trend, and the habitat quality in the south was significantly higher than that in the north. ② The changes of land types in Bayannur mainly occurred in Urad Middle Banner and Urad Back Banner. The changes in grassland area were the largest, followed by those of wasteland and cultivated land. ③ From 2000 to 2010, the increase in habitat quality in the study area was mainly related to the conversion of other land types to cultivated land and the conversion of wasteland and some cultivated land to grassland. From 2010 to 2022, the decrease in habitat quality was related to the shrinkage of grassland areas and the conversion of large areas to wastelands. ④ Grassland had the greatest impact on the rising and falling areas of habitat quality. For every 10% increase in grassland area, the rising area increased by 6.33%, and the falling area decreased by 4.58%. The research could provide a scientific theoretical basis for urban land pattern optimization, ecological environment management, and ecological civilization city construction in arid and semi-arid areas.

Design of CO2 Huff-n-Puff Parameters for Fractured Tight Oil Reservoirs Considering Geomechanical Effects

CO2-Huff-n-Puff (CO2-HnP) is an effective method for improving oil recovery in conventional reservoirs and has been widely applied to tight oil reservoirs. Recently, there has been a series of studies published on the oil increase mechanism and huff-n-puff parameter optimization of CO2-HnP. However, the understanding of the influence of fracture characterization, threshold pressure gradient (TPG), and geomechanical effects on CO2-HnP in fractured tight oil reservoirs is still limited. In this paper, a numerical model based on the embedded discrete fracture model (EDFM) was constructed to investigate the impact of TPG and geomechanical effects on cumulative oil production (COP). The effects of various huff-n-puff parameters, including bottomhole pressure, oil recovery rate, total CO2 injection amount, number of huff-n-puff cycles, timing of production transfer injection, production time, injection time, CO2 injection rate, and soaking time on the COP and oil replacement ratio were also explored in the paper. The results include the following: (1) The TPG and geomechanical effects led to significantly reduced COP. (2) A positive correlation with COP was found for parameters such as timing of production transfer injection and production time, while negative correlations were found for cycles, soaking time, and injection rate. For oil replacement ratio, soaking time and injection rate were positively correlated, while CO2 injection amount and number of cycles showed negative correlation. (3) With a constant injection volume, it is crucial to avoid an excessive number of cycles that reduce COP. On the basis of this parameter optimization, the oil replacement ratio can be enhanced by advancing the production transfer injection, shortening the injection time, and extending the soaking time. The findings can help optimize CO2-HnP strategies to improve oil recovery and economic benefits from the reservoir. This paper provides an effective numerical simulation method for CO2-HnP in fractured tight oil reservoirs, which has certain reference value.

Heterologous Biosynthesis of Taxifolin in Yarrowia lipolytica: Metabolic Engineering and Genome-Scale Metabolic Modeling.

Taxifolin, also known as dihydroquercetin (DHQ), is a flavonoid recognized for its potent antioxidant properties and a wide range of biological activities, including anti-tumor, antiviral, and immunomodulatory effects. Conventional extraction and chemical synthesis methods for taxifolin are often limited by low yields and associated environmental concerns. In this study, we investigated the heterologous biosynthesis of taxifolin in Yarrowia lipolytica through a combination of metabolic engineering and genome-scale metabolic modeling (GSM), complemented by flux balance analysis (FBA). We engineered Yarrowia lipolytica by introducing key biosynthetic genes and successfully synthesized taxifolin using naringenin (NAR) as a substrate, chosen for its low cost. Fermentation experiments demonstrated an optimal taxifolin yield of 10% at a substrate concentration of 200mg/L naringenin, with a maximum yield of 26.4mg/L taxifolin at 1g/L naringenin. To further enhance production, we applied a marker-free Cre-loxP-based gene integration method, allowing stable genomic integration of key genes, which increased taxifolin yield to 34.9mg/L at 1g/L naringenin. Additionally, intermediate metabolites eriodictyol (ERI) and dihydrokaempferol (DHK) accumulated to concentrations of 89.2mg/L and 21.7mg/L, respectively. Furthermore, we integrated metabolic data into a GSM and applied FBA to optimize the taxifolin biosynthetic pathway. Through Pareto frontier analysis, sensitivity analysis, flux variability analysis, and single gene deletion simulations, we identified key genetic modifications that significantly enhanced taxifolin yield. Overexpression of GND1 and IDP2 increased yields by 94% and 155%, respectively, while knockout of LIP2 led to a 46% increase. Using tri-baffled shake flasks to improve oxygen supply resulted in a 120% yield increase, whereas YPG medium decreased yield by 59%, validating our model's accuracy. To ensure stable and efficient gene expression, we integrated multi-copy constructs into the ribosomal DNA (rDNA) locus of Yarrowia lipolytica, doubling taxifolin production. These results demonstrate the effectiveness of GSM and FBA in addressing bottlenecks in microbial taxifolin biosynthesis and provide a basis for future optimization and large-scale production.

Efficient distribution network based on photovoltaic fed electric vehicle charging station using WSO-RBFNN approach

An integral and prevalent aspect of modern life is the electric vehicle (EV).EV charging networks struggle with power losses and high energy costs, particularly as demand rises leading to inefficiencies and potential system overloads. A hybrid WSO-RBFNN approach is proposed for the distribution network's photovoltaic (PV) fed electric vehicle charging stations. The performance of the proposed hybrid strategy is a combination of war strategy optimization (WSO) and Radial basis function neural network (RBFNN). It is hence referred to as the WSO-RBFNN technique. WSO optimizes the distribution network by minimizing power loss, improving voltage sensitivity, and reducing costs. Meanwhile, the RBFNN predicts the load demand. This innovative technique WSO-RBFNN identifies the nearest charging spots that minimize power loss and RBFNN optimizes power flow predicts charging demands and addresses both environmental and electrical grid stability concerns. The proposed technique is implemented in MATLAB. MATLAB is powerful computational software widely used in different fields like numerical computation, visualization, and algorithm development. MATLAB provides powerful tools for data visualization and plotting. The results are compared to various existing Heap-based optimizer (HBO), Wild horse optimizer (WHO), and Salp Swarm Algorithm (SSA) techniques. The proposed approach contributes only 0.59 % of a power loss it is less and the cost of energy is 0.18$ which is lesser and the voltage deviation is 6.6 pu which is less than the existing techniques.