Mapping Model Research Articles

TTPRNet: A Real-Time and Precise Tea Tree Pest Recognition Model in Complex Tea Garden Environments

The accurate identification of tea tree pests is crucial for tea production, as it directly impacts yield and quality. In natural tea garden environments, identifying pests is challenging due to their small size, similarity in color to tea trees, and complex backgrounds. To address this issue, we propose TTPRNet, a multi-scale recognition model designed for real tea garden environments. TTPRNet introduces the ConvNext architecture into the backbone network to enhance the global feature learning capabilities and reduce the parameters, and it incorporates the coordinate attention mechanism into the feature output layer to improve the representation ability for different scales. Additionally, GSConv is employed in the neck network to reduce redundant information and enhance the effectiveness of the attention modules. The NWD loss function is used to focus on the similarity between multi-scale pests, improving recognition accuracy. The results show that TTPRNet achieves a recall of 91% and a mAP of 92.8%, representing 7.1% and 4% improvements over the original model, respectively. TTPRNet outperforms existing object detection models in recall, mAP, and recognition speed, meeting real-time requirements. Furthermore, the model integrates a counting function, enabling precise tallying of pest numbers and types and thus offering practical solutions for accurate identification in complex field conditions.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.

In-plane crashworthiness study of bio-inspired metallic lattice structure based on deep-sea glass sponge

Inspired by the double-diagonal reinforced configuration of deep-sea glass sponges, a metallic bio-inspired lattice structure (BLS) was fabricated using additive manufacturing (AM) techniques. In-plane quasi-static compression tests demonstrated the energy absorption (EA) of BLS was improved by about 60 % compared to conventional lattice designs. Subsequent experimental and numerical investigations examined the effects of wall thickness, elucidating the corresponding variations in crashworthiness indicators. The study revealed that when the wall thickness increased from 0.40 mm to 0.60 mm, the EA and mean crushing force (Pm) exhibited a substantial increment of 58.1 % and 91.1 %, respectively. The finite element (FE) simulations underwent experimental validation, exhibiting satisfactory agreement. Parametric studies using the validated FE model explored the effects of geometric factors, such as diagonal distance, unit size, unit number and graded thickness on the crushing performance of BLS. Results indicated that enhancing the EA performance of BLS was achievable by concurrently reducing the unit size and increasing the unit number. The maximum EA achieved was 3535.5 J, representing a 62.9 % improvement compared to the initial configuration, which exhibited an EA of 2170.7 J. Additionally, the introduction of graded thickness facilitated a controllable collapse pattern, thereby improving the crashworthiness performance. Lastly, the in-plane dynamic crushing simulations were conducted, identifying three distinct collapse patterns, where the U-Mode exhibiting a negative Poisson's ratio (NPR) behavior. A deformation mode map and rigid-perfectly plastic-lock (R-PP-L) model were developed to evaluate the dynamic response of BLS under varying relative density (RD) and impact velocity (V). These findings provided valuable insights for designing sponge-inspired structures with enhanced crashworthiness performance.

Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing.

Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process-property relationships. In this work, a self-driving lab is utilized that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, an underlying self-stabilizing characteristic of VTP layer thickness is discovered as an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable-driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Discovery of a major QTL for resistance to the guava root-knot nematode (Meloidogyne enterolobii) in ‘Tanzania’, an African landrace sweetpotato (Ipomoea batatas)

Sweetpotato, Ipomoea batatas (L.) Lam. (2n = 6x = 90), is among the world’s most important food crops and is North Carolina’s most important vegetable crop. The recent introduction of Meloidogyne enterolobii poses a significant economic threat to North Carolina’s sweetpotato industry and breeding resistance into new varieties has become a high priority for the US sweetpotato industry. Previous studies have shown that ‘Tanzania’, a released African landrace, is resistant to M. enterolobii. We screened the biparental sweetpotato mapping population, ‘Tanzania’ x ‘Beauregard’, for resistance to M. enterolobii by inoculating 246 full-sibs with 10,000 eggs each under greenhouse conditions. ‘Tanzania’, the female parent, was highly resistant, while ‘Beauregard’ was highly susceptible. Our bioassays exhibited strong skewing toward resistance for three measures of resistance: reproductive factor, eggs per gram of root tissue, and root gall severity ratings. A 1:1 segregation for resistance suggested a major gene conferred M. enterolobii resistance. Using a random-effect multiple interval mapping model, we identified a single major QTL, herein designated as qIbMe-4.1, on linkage group 4 that explained 70% of variation in resistance to M. enterolobii. This study provides a new understanding of the genetic basis of M. enterolobii resistance in sweetpotato and represents a major step towards the identification of selectable markers for nematode resistance breeding.

Think Human: exploring the exhibition of ergonomics to enhance education and engagement: establishing the CIEHF 75th anniversary exhibition

The field of ergonomics and human factors (E/HF) has an urgent need for increased uptake of specialists to meet the growing demands of the sector and related industries employing ergonomists. In the absence of E/HF being part of the school curriculum, one way of achieving this is through public engagement activities. Specialists in E/HF are motivated by understanding people and interactions, inherent qualities in outreach and engagement, so we are well placed to practice what we preach by understanding our own users, the consumers of our science. In this paper, we present a curriculum map and museum exhibition model as part of ongoing work to develop a public exhibition, called ‘Think Human’, hosted at the Museum of Making in Derby, England, from September 2024, as part of ongoing work in timely celebration of the 75th anniversary of the UK’s Chartered Institute of Ergonomics and Human Factors (CIEHF).

Heliostat field aiming strategy based on deterministic optimization: An experimental validation

In Solar Power Tower plants, the aiming strategy plays a key role in the performance and interaction between heliostat field and receiver. This work presents an aiming strategy based on a deterministic optimization that maximizes the flux uniformity and minimizes the spillage losses. The algorithm can be fed either by a flux mapping model or by the synthesis – superposition – of experimental images. Both approaches are experimentally tested at PROTEAS research facility, aiming 16 heliostats at a flat Lambertian target. In the comparison between experimental and computed flux maps, the cross-correlation coefficient exceeds 97%. The spillage loss factors are underestimated by the computations: 9.2 percentage points by the model-based and 6.7 points by the synthesis-based approach.

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Mapping models of V1 and V2 selectivity with local spectral reverse correlation

Mapping models of V1 and V2 selectivity with local spectral reverse correlation

Combining Predictive Soil Mapping and Process Models to Estimate Future Carbon Sequestration Potential Under No-Till

Combining Predictive Soil Mapping and Process Models to Estimate Future Carbon Sequestration Potential Under No-Till

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

Performance of a Five-Layer ANN Model for Earthquake Magnitude Prediction and Spatial Risk Mapping in Turkey

Turkey faces significant seismic risks, necessitating accurate earthquake forecasting for effective disaster preparedness. This study employs advanced Artificial Neural Networks (ANN) to predict earthquake magnitudes and assess risks specific to Turkey. Focusing on a distinct segment of the USGS earthquake catalogue from January 2014 to August 2023, the research tailors ANN algorithms with five layers to Turkey's seismic challenges. Rigorous dataset cleaning and processing ensure accuracy, with the ANN model demonstrating exceptional alignment with earthquake data ( RMSE: 0.078, R2: 0.89). Comparative evaluations highlight the effectiveness of ANN models in forecasting earthquake magnitudes in Turkey. The study explores the spatial distribution of earthquake risk across Turkey through an ANN-based map, emphasizing the critical window for preventive measures in this seismically active region. The analysis ensures further enhancement of model accuracy in seismic-prone areas globally. This study advances earthquake prediction by showcasing the high accuracy of our five-layer ANN model in forecasting magnitudes and spatial risk, significantly improving disaster preparedness and risk management in regions such as Turkey.

Super-resolution reconstruction improves multishell diffusion: using radiomics to predict adult-type diffuse glioma IDH and grade.

Multishell diffusion scanning is limited by low spatial resolution. We sought to improve the resolution of multishell diffusion images through deep learning-based super-resolution reconstruction (SR) and subsequently develop and validate a prediction model for adult-type diffuse glioma, isocitrate dehydrogenase status and grade 2/3 tumors. A simple diffusion model (DTI) and three advanced diffusion models (DKI, MAP, and NODDI) were constructed based on multishell diffusion scanning. Migration was performed with a generative adversarial network based on deep residual channel attention networks, after which images with 2x and 4x resolution improvements were generated. Radiomic features were used as inputs, and diagnostic models were subsequently constructed via multiple pipelines. This prospective study included 90 instances (median age, 54.5 years; 39 men) diagnosed with adult-type diffuse glioma. Images with both 2x- and 4x-improved resolution were visually superior to the original images, and the 2x-improved images allowed better predictions than did the 4x-improved images (P<.001). A comparison of the areas under the curve among the multiple pipeline-constructed models revealed that the advanced diffusion models did not have greater diagnostic performance than the simple diffusion model (P>.05). The NODDI model constructed with 2x-improved images had the best performance in predicting isocitrate dehydrogenase status (AUC_validation=0.877; Brier score=0.132). The MAP model constructed with the original images performed best in classifying grade 2 and grade 3 tumors (AUC_validation=0.806; Brier score=0.168). SR improves the resolution of multishell diffusion images and has different advantages in achieving different goals and creating different target diffusion models.

The Wheel–Rail Contact Force for a Heavy-Load Train Can Be Measured Using a Collaborative Calibration Algorithm

The wheel–rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel–rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel–rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel–rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car’s wheel–rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car’s examined wheelset. Second, based on actual measured data on the wheel–rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel–rail contact force obtained from the railway inspection car with the wheel–rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel–rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel–rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel–rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%; for the working condition of a curved-line section, the calibration error was 2.344 kN, and the RMSE was 184.72 N.

MATERIALIZING THE LANDSCAPE: DRONES, GEODESY, AND 3D PRINTERS IN A VIABLE AND ECONOMIC SOLUTION FOR 3D MODELING: DRONES, GEODESY, AND 3D PRINTERS IN A VIABLE AND ECONOMIC SOLUTION FOR 3D MODELING

In recent years, environmental modeling has become increasingly crucial for understanding and mitigating the impacts of human activities on the environment. However, traditional methods for environmental mapping, primarily those of physical understanding, face challenges related to accuracy, time, and cost. In this sense, this work aimed to evaluate the technical feasibility of altimetric mapping and 3D model construction using drones, precision geodesy, and 3D printing as an extremely effective and low-cost alternative for environmental modeling. The study focused spatially on the boundaries of the IFRN/Campus Macau terrain. The study was conducted in five stages: I) Literature review; II) Geodetic survey (GNSS); III) Aerophotogrammetric survey; IV) Processing of geodetic and aerophotogrammetric data; and V) 3D printing. A protocol was obtained for the elaboration with high vertical and horizontal accuracy of the Digital Surface Model (DSM), orthomosaic, and 3D model (virtual and physical) of the mapped environment. Regarding the 3D model, its printing allowed for a perfect understanding of the geomorphology of the mapped environment and the existing physical structures. The applied technique could be useful in various areas, such as enhancing the planning stage in civil construction, architecture, and environmental studies. Also becoming a great ally in future geomorphological and tactile cartography studies.

An efficient inducible model for the control of gene expression in renin cells.

Fate mapping and genetic manipulation of renin cells have relied on either noninducible Cre lines that can introduce the developmental effects of gene deletion or bacterial artificial chromosome transgene-based inducible models that may be prone to spurious and/or ectopic gene expression. To circumvent these problems, we generated an inducible mouse model in which CreERT2 is under the control of the endogenous Akr1b7 gene, an independent marker of renin cells that is expressed in a few extrarenal tissues. We confirmed the proper expression of Cre using Akr1b7CreERT2/+;R26RmTmG/+ mice in which Akr1b7+/renin+ cells become green fluorescent protein (GFP)+ upon tamoxifen administration. In embryos and neonates, GFP was found in juxtaglomerular cells, along the arterioles, and in the mesangium, and in adults, GFP was present mainly in juxtaglomerular cells. In mice treated with captopril and a low-salt diet to induce recruitment of renin cells, GFP extended along the afferent arterioles and in the mesangium. We generated Akr1b7CreERT2/+;Ren1cFl/-;R26RmTmG/+ mice to conditionally delete renin in adult mice and found a marked reduction in kidney renin mRNA and protein and mean arterial pressure in mutant animals. When subjected to a homeostatic threat, mutant mice were unable to recruit renin+ cells. Most importantly, these mice developed concentric vascular hypertrophy ruling out potential developmental effects on the vasculature due to the lack of renin. We conclude that Akr1b7CreERT2 mice constitute an excellent model for the fate mapping of renin cells and for the spatial and temporal control of gene expression in renin cells.NEW & NOTEWORTHY Fate mapping and genetic manipulation are important tools to study the identity of renin cells. Here, we report on a novel Cre mouse model, Akr1b7CreERT2, for the spatial and temporal regulation of gene expression in renin cells. Cre is properly expressed in renin cells during development and in the adult under basal conditions and under physiological stress. Moreover, renin can be efficiently deleted in the adult, leading to the development of concentric vascular hypertrophy.

Bifurcation and Stability of a Haptotaxis Mathematical Model for Complex MAP

This paper studies the bifurcation and stability of a parabolic–ordinary-parabolic system to a model with three reactive species: molecular Anchors [Formula: see text], Nanoparticulates [Formula: see text] and Matrix constituents [Formula: see text] ([Formula: see text]). Global asymptotic stability is studied for both the ODE system and the reaction–diffusion system. The results show that the positive equilibrium is globally asymptotically stable in both the ODE and the corresponding reaction–diffusion systems. Furthermore, by selecting the haptotaxis coefficient as a bifurcation factor, we analyze the existence, direction, and stability of the Hopf bifurcation. The results also reveal that haptotaxis is crucial in determining the stability and bifurcation behavior of the model, suggesting that it has a destabilizing effect and can undergo Hopf bifurcation. Moreover, numerical simulations are presented to verify and illustrate the theoretical results.

Identifying disproportionate impacts in Arkansas: New considerations for environmental justice mapping and implications for leaders

In 2021, Charles Lee defined disproportionate environmental and public health impacts and offered an approach for environmental justice (EJ) leaders to identify disproportionate impacts. However, Lee's model has not yet been compared to current mapping models, nor has it been applied to the highly disadvantaged landscape of Arkansas. In this study, we extend Lee's model and offer a complementary theory of disadvantage to identify, characterize, and describe disproportionate impacts in Arkansas. We ask: What communities are most disproportionately impacted? What is the spatial distribution of risk to environmental burdens and population vulnerabilities? How do these burdens and vulnerabilities cluster together? How might the theory of disadvantage assist in disproportionate impacts research? We deploy a descriptive, mixed-methods design using data from national mapping tools. Findings reveal regions of Arkansas that are disproportionately impacted. However, no region is devoid of high risk. Minority populations are the most impacted groups, and populations living in disproportionately impacted communities tend to suffer from specific burdens and vulnerabilities that erode well-being. The theory of disadvantage is found to be useful for disproportionate impacts research. These results offer EJ leaders a new lens to view and design mapping tools and they clarify intervention points.

Electromagnetic Fields Calculation and Optimization of Structural Parameters for Axial and Radial Helical Air-Core Inductors

To improve the current density distribution and electromagnetic performance of air-core inductors, a structural optimization method combining back-propagation(BP) neural network and genetic algorithm(GA) is proposed for the study of axial and radial spiral multi-winding inductors. The Monte Carlo method was used to extract the structural size samples of the inductors, and the training dataset was obtained through the finite element calculation of electromagnetic fields. Based on BP neural networks, nonlinear mapping models between the inductance value, volumetric inductance density, current distribution non-uniformity coefficient, and inductor structural parameters were constructed. A sensitivity analysis of the inductor inductance value affected by the structural parameters was conducted using the Sobol index calculation. Using the current distribution non-uniformity coefficient as the fitness function and the volumetric inductance density as the constraint condition, a genetic algorithm was applied to globally optimize the structural parameters of the inductor. The optimization results were verified through a finite element comparison. The results show that, under the requirement of satisfying the volumetric inductance density, the current distribution non-uniformity coefficient of the Axial Helical Inductor (AHI)-type inductor was reduced by 4.57% compared with the best sample in the sampling, while that of the Radial Helical Inductor (RHI)-type inductor was reduced by 5.33%, demonstrating the practicality of the BP-GA joint algorithm in the structural optimization design of inductors.