Line Density Research Articles

Droplets impact on sparse microgrooved non-wetting surfaces

Droplets impinging on sparse microgrooved polydimethylsiloxane (PDMS) surfaces with different solid fractions was experimentally investigated. First, wettability and stability of droplets on these surfaces was analyzed. The advancing and receding contact angles were found to have a large difference between in the longitudinal direction and in the transverse one, which could be attributed to the anisotropy of the micropatterned surfaces. The judgement of whether a droplet on a sparse microgrooved structure is collapsed or suspended is proposed, and it was found that the droplets were in the Cassie-Baxter wetting state when the actual contact line density is greater than the critical contact line density, while they were in the Wenzel wetting state otherwise. Second, for the case of droplets impacting on sparse microgrooved PDMS surfaces, it was found that droplets can bounce off the micro-patterned surface with a solid fraction of 0.158 when the impact velocity was in a certain range. The lower limit of impact velocity for bouncing droplets can be determined by balancing the kinetic energy of the droplets with the energy barrier due to contact angle hysteresis. The upper limit of impact velocity for bouncing droplets was predicted using a theoretical model taking into account the penetration of liquid into the cavities between microstripes.

The Mechanisms of Nano‐AlN Content in the Microstructure and Mechanical Properties of Fe–25Mn–9Al–8Ni–1C–0.2Ti Alloy

In order to design a high‐strength, tough alloy for structural components, Fe–25Mn–9Al–8Ni–1C–0.2Ti–xAlN (x = 0, 0.25, 0.5, 0.75, and 1 wt%) alloys are prepared by powder metallurgy combined with vacuum arc melting. It can be seen from microstructure that the AlN/Al preform obtained by homogenizing mixing followed by vacuum pressureless sintering is added to the arc melting in the form of raw materials, and the AlN phase can exist stably and be uniformly distributed in the matrix. The main phases of the alloys are composed of austenite, ferrite, and TiC. With the increase of AlN content, the ferrite content is decreased and the grains are refined. The distribution of ferrite grains is changed from network to needle‐like and dot‐like morphology. Mechanical property test results reveal that the addition of the nano‐AlN can maintain the stability of the tensile strength while significantly improving its elongation after fracture. Specifically, when the AlN content is 0.5 wt%, the elongation after fracture reaches the maximum value of ≈55.8%, representing ≈98.5% improvement compared to the matrix alloy. It is attributed to the promotion of slip line formation by nano‐AlN, resulting in a higher density of slip lines within the alloy.

Design and Research on the Variable Polar Distance of the Double-Sided Linear Induction Motor for Electromagnetic Catapult

According to the special technical requirements of carrier-based aircraft catapults, this paper describes the design of a variable pole distance bilateral linear induction motor. When the traditional constant pole motor is used as the catapult of carrier-based aircraft, the current frequency continues to increase during the catapult process, which greatly aggravates the burden of the motor. Therefore, we propose a variable pole length primary double-sided linear induction motor structure. Compared with the traditional constant pole motor structure, this structure can gradually increase the pole distance with an increase in speed when the current frequency remains unchanged. In contrast, the variable pole distance method with a current frequency of 200 Hz has a pole distance of 0.262 m when the displacement is 10 m, and the pole distance increases to 0.352 m when the displacement is 100 m. By maintaining a constant current frequency, this method effectively reduces the control complexity at high speed. Through the theoretical analysis and research calculation conducted on the designed motor, a finite element simulation model was also established by ANSYS 14.0, and the influence of the change in the pole distance on the performance of the motor was analyzed. The magnetic field line and magnetic density distribution of the motor are simulated and analyzed, and the validity of the theoretical calculation is verified.

Building Damage Detection Using Deep Learning Architecture with Satellite Images: The Case of the 6 February 2023 Kahramanmaraş Earthquake

Turkey is located in a region with a high density of fault lines, which makes it susceptible to a significant earthquake risk. The Kahramanmaraş earthquake on February 6, 2023, was one of the most devastating in recent years, causing extensive damage and loss. This study aims to support post-disaster rapid response and rescue operations by using deep learning techniques to detect and classify damaged and intact buildings from satellite images. Satellite images of the Kahramanmaraş and Antakya regions, with a resolution of 8192x4537, were obtained via Google Earth Pro. The images were labeled as damaged or undamaged using the Labelme editor, which generated JSON format files for the labeled images. Using Google Colab, the JSON files and unlabeled images were merged, and buildings were cropped and categorized into two classes: damaged and undamaged. As a preprocessing step, interpolation was applied, resulting in 2211 images with a size of 128x128. A Convolutional Neural Network [2] algorithm was created using TensorFlow, a Python library, via Google Colab. The performance metrics, including accuracy, loss, F1 score, ROC curve, precision, recall, and confusion matrix values, were compared based on the experiments.

Development Characteristics and Controlling Factors of Karst Aquifer Media in a Typical Peak Forest Plain: A Case Study of Zengpiyan National Archaeological Site Park, South China

The medium development characteristics and controlling factors of the karst peak forest plain water system constitute the core of analyzing the complex and variable hydrogeological environment, especially in revealing the controlling factors between the hydrological system and karst development characteristics, which is crucial for a deeper understanding of karst hydrogeological environments. This study takes Zengpiyan in Guilin as an example and conducts a dynamic clustering analysis on the advantageous occurrence of fracture development in three sampling areas. A total of 3472 karst channels and fractures were identified and measured. Our research reveals the following: (1) The high degree of development of fissures on surface rock outcrops is mainly formed by the expansion of shear joints through dissolution and erosion. The dip angles of fissures are mainly characterized by low angles, with fissures with dip angles between 18° and 80° accounting for 65.44% of the total observed fissures. The linear density of fissures is 3.64 per meter. (2) There are significant differences in the line density of cracks and fissures in different areas of the research area. For example, the line density in Sampling Area 1 is 0.99 lines per meter, while the line density in Sampling Area 3 reaches 5.02 lines per meter. In addition, the extension length of cracks is generally long, with joints with extension lengths exceeding 1.5 m accounting for 77.46% of the total observed joints and through cracks with extension lengths exceeding 5 m accounting for 23.33%. (3) The development characteristics of underground karst reveal that underground karst caves are mainly distributed at elevations of 120 to 160 m, with a drilling encounter rate of about 43.3%. It is also noted that geological structures control the horizontal distribution of karst, and geological lithology, hydrodynamic conditions, and water carbon dioxide concentrations are key factors affecting the vertical zoning of karst. This study provides an important scientific basis for understanding the development characteristics and controlling factors of karst water system media in peak forest plains and has important guiding significance for water resource management in karst areas and disaster prevention during tunnel excavation.

Low-dose lung CT: Optimizing diagnostic radiation dose – A phantom study

Background/purposeTo investigate a quantitative method for assessing image quality of low dose lung computed tomography (CT) and find the lowest exposure dose providing diagnostic images. MethodsAxial volumetric lung CT acquisitions (256 slice scanner) were performed on three different sized anthropomorphic phantoms at different dose levels. The maximum steepness of sigmoid curves fitted to line density profiles was measured at lung-to-pleura interfaces. For each phantom, image sharpness was calculated as the median of 468 measurements from 39 different locations. Diagnostic image quality for the adult and paediatric phantom was rated by three radiologists using 4-point Likert scales. The image sharpness cut-off for obtaining adequate image quality was determined from qualitative ratings. ResultsAdequate diagnostic image quality was reached at a median steepness of 713 HU/mm in the adult phantom with a corresponding CTDIvol of 0.14 mGy and an effective dose of 0.13 mSv at a dose level of 100 kVp and 10 mA. In the paediatric phantom diagnostic image quality was reached at a median steepness of 1139 HU/mm with a corresponding CTDIvol of 0.13 mGy and an effective dose of 0.08 mSv at a dose level of 100 kVp and 10 mA. ConclusionsDetermination of image sharpness on line density profiles can be used as quantitative measure for image quality of lung CT. Sufficient-quality lung CT can be achieved at effective radiation doses of 0.13 mSv (adult phantom) and 0.08 mSv (paediatric phantom). These findings suggest that substantial dose reduction is feasible without compromising diagnostic accuracy.

Rock density model of the Southern Benue trough in Nigeria from a synthetic gravity and in-situ density data

ABSTRACT The aim of this study is to identify potential mineral deposits beneath the Southern Benue Trough of Nigeria based on apparent density mapping technique of estimating lateral rock densities from modelled gravity data. In this study, we apply the inverse density deconvolution filter to a short-wavelength gravity component in order to estimate the rock densities for mineral exploration applications and tectonic studies at the Southern Benue Trough in Nigeria. The estimated rock densities vary between 2.50 and 2.74 g/cm3, with minimum density values assigned to volcano-sedimentary deposits along the Cameroon Volcanic Line and maximum density values in the eastern and southern parts associated with mafic igneous rocks. Our gravity and density maps reveal the geometry of dominant geological structures within the study area. The density map highlights carbonate-hosted metallic elements with varying intercalations. Some of these identified mineralized zones coincide with locations of past and present producing mines. Intermediate and mafic igneous rocks as well as highly compacted and volcano-sedimentary rocks are mapped as rock-forming minerals for a mineral exploration. Hence, the obtained model of mineral-bearing rocks mainly demonstrates mechanisms of tectonic events, patterns of deformation regimes, and mineral prospectivity map of the study area.

Rotating Bending Fatigue of Laser Powder Bed Fused 316L Stainless Steel at Various Stress Levels: Microstructural Evaluation and Predictive Modeling

ABSTRACTThis research studies the effect of variable stress levels on the rotating bending fatigue (RBF) tests of 316L stainless steel fabricated by the laser powder bed fusion (LPBF) method. The mechanical properties and fatigue behavior were evaluated to ascertain the correlation between the applied stress levels and microstructural changes that occurred during the fatigue experiments. These relationships were further investigated using a classical model developed on the Python platform. The microstructural analysis demonstrated that the face‐centered cubic structure was maintained throughout the application of stresses, varying from 255 to 402 MPa along with no phase changes. Nevertheless, the density of shear lines on the surface was substantially influenced by variations in stress levels as demonstrated by high‐stress areas in Kernel average misorientation maps. At lower stress levels, the model analysis exhibited a higher degree of reliability, with R2 values of 96.25% at lower stress rather than 89.30% at higher stress.

Kinetic of the development of a quantum vortex tangle in superfluids under the influence of thermal activation

The study explores the development of a thermally equilibrated quantum vortex tangle in superfluid liquids under the influence of thermal activation. This problem is of interest to both applied and fundamental research and has been investigated by many authors in various aspects. Despite the important and impressive results obtained, a significant part of the process, namely, the kinetics of processes leading to equilibrium state, remained unexplored. In this article, we conduct a study of kinetic phenomena and focus our attention on the evolution of the vortex line density (VLD) L(t), the total length of the filament per unit volume. The initial development of VLD is due to random thermal fluctuations. The increase in the vortex line length L(t) can be obtained based on the famous Novikov–Furutsu theorem, which shows that the growth rate of L(t) is proportional to a random force correlator. As the length of the vortex filaments increases, the interaction between the vortices becomes significant and affects the dynamics process. At this point, we turn to the phenomenological Feynman–Vinen theory, which offers various models for the evolution of the quantity L(t). Next, we examine the evolution of a vortex tangle as a combination of growth due to random thermal excitations and decay in the Feynman–Vinen theory. Several applications leading to significant and remarkable results are considered.

Quantum turbulence in superfluid helium: Decay and energy spectrum

The paper presents a comprehensive numerical study of the free decay of vortex tangle in superfluid helium. The initial vortex tangle represents one of the stationary configurations of loops in a counterflow with a laminar normal fluid component. The calculations are carried out in the framework of the vortex line method using the full Biot–Savart law over a wide range of temperatures. The aim of the study is to identify the role of various factors introduced into the numerical procedures (removal of small loops and segments during reconnections, the addition of and exclusion of vortex points on loops) and to determine the evolution of energy spectrum during the decay of quantum turbulence. A statistical approach is used to calculate the kinetic energy distribution on length scales. The calculations are carried out using periodic boundary conditions in a cube. The results show that, in agreement with the Feynman–Vinen theory, initially the rates of reduction in vortex line density at different temperatures are the same. However, when the vortex structure becomes rarefied, the influence of the mutual friction force becomes apparent, in agreement with Schwarz's theory. Statistical method for determining the energy spectrum is used. The Kolmogorov spectrum is not observed during decays at any temperature.

Enhanced Structural, Optical, and photovoltaic properties of Sm-Doped MAPbI2Br perovskite solar cells

Samarium-enhanced methylammonium lead iodide bromide (Sm-MAPbI2Br) thin films were applied onto FTO-glass substrates. X-ray diffraction (XRD) analysis revealed an enlargement in grain size to 29.09 nm, a decrease in dislocation line density to 4.32 x 1015 m−2, an expansion in d-spacing to 5.18 Å, a reduction in lattice constant to 1.59 Å, and a diminished volume in the cubic crystal lattice of MAPbI2Br. The incorporation of Sm2+ ions resulted in a narrowed band gap energy (1.95 eV), an augmented refractive index (2.65), and a decreased extinction coefficient (2.24). The solar cell constructed with a 5 % Sm2+ doping level demonstrated an enhanced open-circuit voltage of 1.05 V, a notable increase in short-circuit current density to 9.87 mA/cm2, a fill factor of 0.80, and an elevated efficiency reaching 9.32 %. Comparative analyses suggest that the cell with 5 % Sm2+ doping experiences a lower rate of recombination compared to undoped MAPbI2Br-based cells. EIS is used to investigate enhanced charge transport in hybrid MAPbI2Br devices, emphasizing the impact of a Sm-doped thin layer on carrier dynamics. The robustness of the open-circuit voltage highlights the potential of layering MAPbI2Br in the development of highly efficient solar cells.

Designing breezeways to enhance wind environments in high-density cities: A comprehensive analysis of ten morphological parameters

To advance urban breezeway designs, this paper presents a pioneering and comprehensive study of breezeway morphological parameters. Ten parameters, identified through extensive literature review, include coverage ratio, porosity, line density, sinuosity, rotation angle, width, length, average height, height variation, and aspect ratio. Regression analysis, utilizing over 200 data points collected from wind tunnel experiments in Hong Kong, established correlations between these parameters and pedestrian-level wind velocity ratio (VRpoint). Results reveal that among the 2D parameters, width, length, line density, and coverage ratio exhibit the strongest correlations with VRpoint, while aspect ratio and porosity emerge as significant factors among the 3D parameters. Notably, simple 2D parameters, coverage ratio and width, can effectively substitute for their 3D counterparts, porosity and aspect ratio, in high-density urban environments. Furthermore, the results highlight the relative contributions of different parameters to urban ventilation. From a street-level perspective, VRpoint is primarily influenced by configurations of street segments (width, 80 %) and street intersections (rotation angle, 20 %). From a neighborhood-level perspective, permeability (coverage ratio, 35 %), fragmentation (line density, 30 %), and roughness (average height, 35 %) are critical factors. Illustrative examples are provided to help translate these findings into spatial analysis tools and design guidelines, aiding planners and decision-makers in improving urban living environments.

Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping

AbstractAccurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. Currently, neural network‐based approaches dominate deep learning (DL), but they lack interpretability and have high modeling complexity, making them less effective for complex problems and time‐consuming. Deep Forest, an innovative DL paradigm, addresses these issues by dynamically adjusting complexity and providing importance assessments for predictive factors. This study focuses on the North American Cordillera, known for its rich geological data and potential for porphyry copper deposits (PCDs). Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. Feature importance analysis shows that geological structure and magmatism significantly influence PCD prediction. Elevated levels of elements like Al, Co, and Cr in stream sediments help identify mineralization‐related alterations. These findings underscore Deep Forest's capability to accurately and efficiently guide mineral exploration, highlighting its potential as a promising approach for mineral prospecting.

Mechanism and application of drill pipe bending induced borehole collapse in soft coal seam drilling

AbstractGas extraction drilling is a necessary measure for managing gas hazards. For soft coal seams where gas extraction drilling holes are prone to collapse, it is believed that drill rod disturbance is the main cause of hole collapse. This study proposes a research approach to reduce wall stress by optimizing the drill rod structure. Through theoretical analysis, numerical simulation, and industrial tests, a stress model for the drill rod inside the hole was established, and a wall stress equation was derived. The effects of various parameters on wall stress were analyzed. The study suggests optimizing the drill rod structure to reduce the disturbance‐induced wall stress. SolidWorks was used for drilling stress simulation, and a four‐winged concave groove drill rod was developed. After strength verification, comparative industrial tests were conducted. The research results show that as the line density increases, the wall stress of the drilling hole increases. As the length of the suspended section increases, the wall stress initially decreases and then increases. With increasing drilling thrust, wall stress increases linearly, and the growth rate is greater with a larger diameter difference between the drill hole and the drill rod. Numerical simulation results indicate that the critical point maximum stress at the hole entrance, the critical point maximum stress at the hole bottom, and the average stress at the bottom section of the four‐winged concave groove drill rod with a concavity of 5 are significantly reduced compared to those of circular and grooved drill rods. Industrial test results show that using the four‐winged concave groove drill rod significantly reduces the extent of hole collapse. This study provides a reference for addressing the issue of hole collapse in gas extraction drilling for soft coal seams.

Identifying restoration priorities for habitat defragmentation: a case study in Alberta’s oil sands

ContextAnthropogenic habitat loss and fragmentation are major threats to ecosystems and a focus in conservation. However, conservation is often limited to considerations of site-level processes neglecting the effect of the surrounding landscape that might limit the effectiveness of restoration efforts.ObjectivesUsing seismic lines in Alberta’s oil sands as a case study, we demonstrate an approach that integrates spatial configuration of anthropogenic footprints to prioritize habitat defragmentation.MethodsWe quantified the effects of seismic line density and configuration on functional footprints for caribou, butterfly diversity, and vascular plant diversity, to predict whether edge effects are more pronounced under different line densities and configurations. We then estimated the portion of the original functional footprint that would persist in the landscape due to the co-occurrence with other anthropogenic activities.ResultsWe found that functional footprint for caribou grows rapidly as habitat loss increases. In contrast, butterflies and plants exhibited a more gradual and linear growth in functional footprints at more local scales. This effect varies based on configuration of lines, either suppressing or facilitating the effect of habitat loss on functional habitats. Finally, restoration of all seismic lines without considering other footprints would reduce the original functional footprint by only 57% for caribou.ConclusionsRestoration efforts for habitat defragmentation rarely consider the spatial configuration of linear features, particularly as it relates to the co-occurrence of other footprints that are not being restored. Our functional approach to defragmentation of habitat encompasses different spatial concepts related to anthropogenic forest fragmentation and allows up to a 25-fold gain in cost-effectiveness for seismic lines restoration.

Informed sampling and recommendation of cycling routes: leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation

Attractive cycling routes can effectively promote active mobility, thus reducing the twin pressures of the population boom and the greenhouse effect. However, the existing approaches for cycling route recommendation primarily concentrate on identifying the most efficient routes while ignoring the urban spatial context, which is essential to meet the user’s particular preferences. This article proposes a novel method for informed sampling and recommending cycling routes leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation (WLDA). Precisely, spatial context mapping, incorporating a weighting mechanism into LDA, latent topics mining, and cycling route recommendation based on informed sampling are introduced. We collected 1,016 cycling trajectories around Cologne, Germany, for experimental analysis. The experimental results show that the three latent topics within the trajectories, leisure, city, and green tours, are clearly presented in the line density analysis. The insightful recommendation for unfamiliar cyclists could also be actively sampled upon the WLDA model. These findings suggest that our approach could shift the route recommendation paradigm from GIS analysis to a semantic mining perspective, yielding highly interpretable results and offering novel research avenues for applying machine learning in route planning.

The Improvement of Luminous Uniformity of Large-Area Organic Light-Emitting Diodes by Using Auxiliary Electrodes

The study developed a large emission area of flexible blue organic light-emitting diodes (BOLED) on a polyethylene terephthalate/ Indium tin oxide (PET/ITO) substrate using a polycyclic skeleton ν-DABNA Thermally Activated Delayed Fluorescence (TADF) material. Initially, a 1 × 1 cm2 blue OLED was fabricated to optimize the layer thickness. The blue OLED structure consisted of PET/ITO/HATCN/TAPC/UBH-21:ν-DABNA/TPBi/LiF/Al. However, as the emission area increased to 3.5 × 3.5 cm2, the current density decreased due to the resistance of PET/ITO, leading to luminance non-uniformity. To address this issue, auxiliary Au lines were added to the ITO anode to enhance current injection. Despite this, when the Au lines reached a thickness of 30 nm, average light emission was disrupted. To improve the luminescence characteristics of large-area PET/ITO OLEDs, a capping and planarization layer of PEDOT:PSS was applied. Grid uniformity revealed a significant increase in overall luminance uniformity from 74.1% to 87.4% with the addition of auxiliary Au lines. Further increases in grid line density slightly reduced uniformity but enhanced brightness, resulting in brighter, flexible, large-area blue OLED lighting panels.

Droplet impinging on sparse micropillar-arrayed non-wetting surfaces

Wettability of droplets and droplet impinging on sparse micropillar-arrayed polydimethylsiloxane (PDMS) surfaces were experimentally investigated. For droplets wetting on these surfaces, the contact line density model combining stability factor and droplet sagging depth was developed to predict whether the droplets were in the Wenzel or Cassie–Baxter wetting state. It was found that droplets on the sparser micropillar-arrayed PDMS surfaces were in the Wenzel wetting state, indicating that a complete rebound cannot happen for droplets impinging on these surfaces. For the case of droplets impinging on sparse micropillar-arrayed PDMS surfaces, it was found that there existed a range of impact velocity for bouncing droplets on the micropatterned surfaces with a solid fraction of 0.022. To predict the upper limit of impact velocity for bouncing droplets, a theoretical model considering the immersion depth of liquid into the micropillar structure was established to make the prediction, and the lower limit of impact velocity for bouncing droplets can be obtained by balancing kinetic energy with energy barrier due to contact angle hysteresis. In addition, the droplet maximum spreading parameter was fitted and found to follow the scale law of We1/4.

Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research.

To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods. Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores. A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR (P<0.05). ERD decreased with increasing angle as the level of DLIR increased. High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.

Spatial Predictive Modeling of Liver Fluke Opisthorchis viverrine (OV) Infection under the Mathematical Models in Hexagonal Symmetrical Shapes Using Machine Learning-Based Forest Classification Regression

Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process.