Coordinates Of Points Research Articles

Revisiting the Dynamics of Two-Body Problem in the Framework of the Continued Fraction Potential

In this analytical study, a novel solving method for determining the precise coordinates of a mass point in orbit around a significantly more massive primary body, operating within the confines of the restricted two-body problem (R2BP), has been introduced. Such an approach entails the utilization of a continued fraction potential diverging from the conventional potential function used in Kepler’s formulation of the R2BP. Furthermore, a system of equations of motion has been successfully explored to identify an analytical means of representing the solution in polar coordinates. An analytical approach for obtaining the function t = t(r), incorporating an elliptic integral, is developed. Additionally, by establishing the inverse function r = r(t), further solutions can be extrapolated through quasi-periodic cycles. Consequently, the previously elusive restricted two-body problem (R2BP) with a continued fraction potential stands fully and analytically solved.

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

Estimating the phase diagrams of deep eutectic solvents within an extensive chemical space

Assessing the formation of a deep eutectic solvent (DES) necessitates a solid-liquid equilibrium phase diagram. Yet, many studies focusing on DES applications do not include this diagram because of challenges in measurement, leading to misidentified eutectic points. The present study provides a practical approach for estimating the phase diagram of any binary mixture from the structural information, utilizing machine learning and quantum chemical techniques. The selected machine learning model provides reasonably high accuracy in predicting melting point (R2 = 0.84; RMSE = 40.53 K) and fusion enthalpy (R2 = 0.84; RMSE = 4.96 kJ mol−1) of pure compounds upon evaluation by test data. By pinpointing the eutectic point coordinates within an extensive chemical space, we highlighted the impact of the mole fractions and melting properties on the eutectic temperatures. Molecular dynamics simulations of selected mixtures at the eutectic points emphasized the pivotal role of hydrogen bonds in dictating mixture behavior.

Research of measurement accuracy using GNSS RTK method for determining geodetic coordinates of vertical network points

Research of measurement accuracy using GNSS RTK method for determining geodetic coordinates of vertical network points

Comparative Analysis of Smartphones and Survey-Grade GNSS Receivers for Parcel Boundary Determination

This paper advances the existing body of knowledge on the suitability of the accuracy derivable from the use of smartphones for cadastral mapping. Zenvus App software was installed on two smartphones of a different make. A set of dual-frequency GPS Promark 3 receivers and two different smartphones of different makes were used for data acquisition. Observations were carried out at the boundaries of ten parcels of land, comprising 46 boundary points. The coordinates of these points were obtained using Differential Global Positioning System (DGPS) observation in static mode and two Android smartphones (the Samsung A70 and the Tecno Spark 3 Pro). Mean score, root mean square error, and one-way analysis of variance were used to show significant differences in the equipment used. Overall, both the accuracy (mean) and precision (RMSE) were lower than those obtained by Differential GPS. A one-way analysis of variance (ANOVA) was calculated on the values of both X and Y. For X, the analysis was not significant; F (1, 45) = 0.88, p = 0.419 and for Y, the analysis was also not significant; F (1, 45) = 0.97, p = 0.383. The total RMSE shows that the coordinates of points as obtained by the Samsung smartphone (3.368) were more precise than those obtained by Tecno (4.041). However, the two smartphones (Tecno and Samsung) were less accurate than differential GPS. This implies that there is a 95% chance that the errors in the estimates are less than 6.993m (for Tecno) and 5.848m (for Samsung), respectively. The variation in the observations obtainable with smartphones affects both linear and polygon estimates. The study concluded that the magnitude of these errors is significant in cadastral survey practices and hence not suitable for use. It is recommended that further studies be carried out on the use of the Zenvus app on centimeter grade smartphones; probably this could yield a better result suitable for cadastral mapping.

Model-Based 3D Gaze Estimation Using a TOF Camera.

Among the numerous gaze-estimation methods currently available, appearance-based methods predominantly use RGB images as input and employ convolutional neural networks (CNNs) to detect facial images to regressively obtain gaze angles or gaze points. Model-based methods require high-resolution images to obtain a clear eyeball geometric model. These methods face significant challenges in outdoor environments and practical application scenarios. This paper proposes a model-based gaze-estimation algorithm using a low-resolution 3D TOF camera. This study uses infrared images instead of RGB images as input to overcome the impact of varying illumination intensity in the environment on gaze estimation. We utilized a trained YOLOv8 neural network model to detect eye landmarks in captured facial images. Combined with the depth map from a time-of-flight (TOF) camera, we calculated the 3D coordinates of the canthus points of a single eye of the subject. Based on this, we fitted a 3D geometric model of the eyeball to determine the subject's gaze angle. Experimental validation showed that our method achieved a root mean square error of 6.03° and 4.83° in the horizontal and vertical directions, respectively, for the detection of the subject's gaze angle. We also tested the proposed method in a real car driving environment, achieving stable driver gaze detection at various locations inside the car, such as the dashboard, driver mirror, and the in-vehicle screen.

Remote sensing image feature matching via graph classification with local motion consistency

ABSTRACT Feature matching is a classic challenge in the computer vision field. In this paper, we propose an innovative graph classification method based on neighborhood motion consistency to eliminate erroneous matches. Specifically, we transform the coordinates of feature matching points into vectors on a unified scale. For a given match, we construct a graph centered around the match and incorporating neighboring matches. Node attributes are designed to represent the similarity between the vector of the central node and those of its neighbors. To facilitate this, we develop a lightweight graph attention neural network dedicated to graph property classification, thereby predicting the accuracy of the match under consideration. To effectively train the model, we employ a random cropping strategy to generate a plethora of diverse graphs for classifier training. We evaluate our method on datasets encompassing translational remote sensing data, rotational and scaled remote sensing imagery produced via random cropping, and nonrigid fisheye datasets. Our algorithm demonstrates superior performance to current state-of-the-art methods.

On the Optimal Point of the Weighted Simpson Index

In this short communication, following a brief introduction, we undertake a comprehensive analytical study of the weighted Simpson index. Our primary emphasis concerns the precise determination of the optimal point (minimizer) coordinates and of the minimum value of the index, a differentiable convex function, which is related to the harmonic mean concept. Furthermore, we address and solve the inversion problem and show the tight connection between both approaches. Last, we give some insights and final remarks on this subject.

Physiological state recognition model of small silkworm based on improved YOLOv5.

Silkworm breeding, as a pivotal economic activity across various regions of China, plays a crucial role in promoting rural revitalization. Notably, the early stage of silkworm development, during which the larvae are most vulnerable and environmentally sensitive, poses significant challenges due to their high pathogenicity and mortality rates. To enhance the efficiency of silkworm breeding, it is imperative to accurately and rapidly identify the physiological state of these small silkworms, ensuring timely feedback to farmers. By using the manually labeled data set, we trained a neural network model to identify the age of the small silkworm through the external characteristics and body length of different instars, and the model used the output center point coordinates to evaluate whether the silkworm entered the dormancy period. If the small silkworm enters the dormant period, the small silkworm will not move. By comparing the maximum difference of the coordinates of the center point of the small silkworm in the experimental group during the dormant period and the feeding period, a certain threshold is set. If the maximum difference of the coordinates of the center point is less than the threshold, the small silkworm is judged to enter the dormant period. To further enhance the model's performance, we introduced an improved target detection network model, building upon the established YOLOv5 architecture. This enhanced model integrates the C3-SE attention mechanism, enabling the network to focus more intently on the target of interest, thus improving detection accuracy. Additionally, we replaced the CIoU loss function in the original target detection network model with the Focal-EIoU loss function. This adjustment effectively mitigates the issue of imbalanced positive and negative samples, accelerating the convergence speed of the network and ultimately enhancing the model's accuracy and recall rate. To validate the accuracy of the proposed model, we randomly selected sample pictures from the curated small silkworm dataset, constituting the test and verification sets. This dataset comprised images and videos capturing different developmental stages of small silkworms. The test results demonstrate that the improved YOLOv5 model achieves an average accuracy of 92.2%, surpassing the preimproved network model by 2.29%. Specifically, the model exhibits a 0.3% increase in accuracy, a 3.4% improvement in recall rate, and a significant 7.7% enhancement in frames per second. These findings indicate that the enhanced YOLOv5 model is capable of accurately and efficiently identifying the physiological state of small silkworms.

A method for fast reconstruction of closed section workpiece surface using point clouds

Parameterized reconstruction of workpiece surfaces is a critical step for advanced manufacturing applications, such as the solving of machining parameters, and the expression of workpiece in situ measurement. Generally, the point clouds model is easy to obtain and can be seen as the source model of the workpiece, and the B-spline surface is always taken as the ideal target parameterized model. Existing research focuses on reconstructing the single-valued surface, which can be projected onto a spatial plane without overlapping. However, the dual-valued surface, which overlaps at least twice, is also commonly encountered in industry, and has few investigations. Therefore, this paper proposes a fast reconstruction method based on reasonable segmentation and parameter domain mapping. In this approach, first, the dual-valued point clouds model is segmented into multiple single-valued point clouds models according to the sought edges in the optimal attitude. Then, the grid point coordinates of the single-valued point clouds model are calculated using the parametric-domain moving least squares method. Finally, the B-spline surface is generated by interpolating these grid points. The proposed algorithm is applied to single-bending closed-section workpieces such as the turbine blade to illustrate the effectiveness of this method. The contrast results with existing method demonstrate that the proposed approach can reconstruct the workpiece with multi-valued surfaces more quickly and more accurately, which is significant in advanced manufacturing.

Sustainable mangrove management strategy in Sumber Nadi Village, Ketapang District, South Lampung Regency

The sustainability of mangrove forests really depends on the involvement of communities in the areas surrounding the mangrove forests. If the mangrove environment is to be used in the long term, it must be managed and preserved. This research aims to determine strategies for sustainable mangrove management in Sumber Nadi Village, Ketapang District, South Lampung Regency. The method used in this research is a qualitative approach. Data was obtained through in-depth interviews involving key informants who were selected and determined using snowball sampling and analyzed using SWOT analysis of Strengths, Weaknesses, Opportunities and Threats, which includes Natural Resources and Human Resources. The results of this research show that the total value of the influence of internal strategies has a value difference of 0.32. Likewise, the external value has a difference of 0.11. Then, combining the two values of the difference between strengths and weaknesses and opportunities against threats, a coordinate point is obtained, namely the coordinate point (0.32; 0.11). The strategy that must be implemented in Sumber Nadi Village, Ketapang, is to support aggressive growth policies (growth-oriented strategy). Mangrove forest management strategies, as in the quadrant above, show a favorable situation. The relevant agencies, management and the community should support this management development strategy so that it can maintain the potential of existing mangroves

Case mapping and evaluation of the stunting reduction acceleration program in Kendari, Indonesia

Stunting is a child's growth and development disorder due to chronic malnutrition and recurrent infections. In 2022, the prevalence of stunting in Kendari was 19.5%, a decrease of 4.5% from 2021. However, this prevalence still exceeds the national target (14%). This study aimed to determine the distribution of stunting cases and evaluate the Acceleration of Stunting Reduction Program in Kendari. A mixed methods with concurrent design was carried out in August-November 2023 in Kendari. In the mapping distribution cases, the coordinate point of all cases was domiciled in 15 Focus Location Villages collected then analyzed by Quantum Geographic Information System (QGIS). For program evaluation, 10 informants were selected by purposive sampling technique. Data was collected through in-depth interviews and then analyzed using reduction techniques, data presentation and drawing conclusions. There were 82 cases of stunting spread across 15 villages, with the most cases being in Punggaloba Village with 20 cases. In implementing the acceleration reduction stunting program, it was supported with guidelines in each level, the sensitive and specific interventions from National Population and Family Planning Agency have been carried out. Several cross-programs and cross-sectors from sub-district to provincial levels play a role in implementing the program. Obstacles faced include limited operational funds, sectoral egos and low public awareness and non-acceptance of the status of stunted toddlers. Cross-sector commitment in efforts to implement the acceleration program needs to be strengthened and socialization to increase public awareness and acceptance of the community.

Application of holography method for the restoration of the Williams series near the crack tip

The article describes the processing of the results of a series of experiments performed by the interference-optical method of holographic interferometry (holographic photoelasticity) aimed at computing the amplitude coefficients of the M. Williams series constituting the stress and displacement fields at the crack tip for several cracked configurations. The main objective of this study is the experimental and computational determination of the coefficients of the M. Williams series for the stress, strain and displacement fields associated with the crack tip in an isotropic linearly elastic medium taking into account regular (non-singular) terms in the multiparameter Williams series. These coefficients are named generalized stress intensity factors. The method of holographic interferometry is shown to be a convenient and efficacious tool for reconstructing the stress field near the tip of the crack, because during the experiment it is possible to obtain two families of interference fringe patterns: absolute retardation fringes (isodromics) for vertical and horizontal polarizations. Experimental outcomes were thoroughly processed using the developed digital application allowing us to accumulate the isodromics orders and coordinates of points belonging to absolute retardations. In this work, absolute retardation fringes in a plate with a central horizontal crack and a crack inclined at different angles are obtained. For each type of experimental sample, the coefficients of the Williams series were calculated taking into account non-singular terms (in the representation of M. Williams, fifteen terms were preserved). A procedure for linearization of nonlinear algebraic equations following from the relations of Favre’s law is proposed. By solving the obtained overdeterministic system of linear algebraic equations, the generalized stress intensity factors (coefficients of the M. Williams series) are estimated. Conjointly, the finite element analysis of the specimens with same geometry was effectuated. The experimentally determined values of the Williams series are compared with the results of the finite element calculation of the stress-strain state performed in the SIMULIA Abaqus software.The results of the numerical and experimental studies were found to be quite consistent. It is lucidly shown that it is imperative to keep the higher order terms in the Williams series expansions for the fields associated with the crack tip.

Accuracy of additively manufactured and steam sterilized surgical guides by means of continuous liquid interface production, stereolithography, digital light processing, and fused filament fabrication

Different printing technologies can be used for prosthetically oriented implant placement, however the influence of different printing orientations and steam sterilization remains unclear. In particular, no data is available for the novel technology Continuous Liquid Interface Production. The objective was to evaluate the dimensional accuracy of surgical guides manufactured with different printing techniques in vertical and horizontal printing orientation before and after steam sterilization. A total of 80 surgical guides were manufactured by means of continuous liquid interface production (CLIP; material: Keyguide, Keyprint), digital light processing (DLP; material: Luxaprint Ortho, DMG), stereolithography (SLA; Surgical guide, Formlabs), and fused filament fabrication (FFF; material: Clear Base Support, Arfona) in vertical and horizontal printing orientation (n = 10 per subgroup). Spheres were included in the design to determine the coordinates of 17 reference points. Each specimen was digitized with a laboratory scanner after additive manufacturing (AM) and after steam sterilization (134 °C). To determine the accuracy, root mean square values (RMS) were calculated and coordinates of the reference points were recorded. Based on the measured coordinates, deviations of the reference points and relevant distances were calculated. Paired t-tests and one-way ANOVA were applied for statistical analysis (significance p < 0.05). After AM, all printing technologies showed comparable high accuracy, with an increased deviation in z-axis when printed horizontally. After sterilization, FFF printed surgical guides showed distinct warpage. The other subgroups showed no significant differences regarding the RMS of the corpus after steam sterilization (p > 0.05). Regarding reference points and distances, CLIP showed larger deviations compared to SLA in both printing orientations after steam sterilization, while DLP manufactured guides were the most dimensionally stable. In conclusion, the different printing technologies and orientations had little effect on the manufacturing accuracy of the surgical guides before sterilization. However, after sterilization, FFF surgical guides exhibited significant deformation making their clinical use impossible. CLIP showed larger deformations due to steam sterilization than the other photopolymerizing techniques, however, discrepancies may be considered within the range of clinical acceptance. The influence on the implant position remains to be evaluated.

A Comparison Study of Edge Line Estimation Algorithms for Dimensional Quality Assessment of Precast Concrete Slabs

Point cloud data-based edge line extraction is an important task for accurate geometrical inspection of precast concrete (PC) elements in the construction industry. Although a few edge extraction algorithms have been developed so far based on point cloud data, little attention has been paid on which edge extraction algorithm performs the best in terms of edge estimation accuracy. To tackle the research gap, this study aims to evaluate currently available edge extraction algorithms in order to determine optimal algorithm for precise geometrical inspection of PC elements. To do this, simulated scan points are first generated and used for algorithm performance analysis using a geometrical model and a measurement noise modeling that determine the coordinates of simulated scan points. For validation of the simulation approach, comparison tests with experimental data are performed and the results show that the simulation approach has a high similarity of more than 90% compared to experimental data in terms of the number of scan points, scan pattern, and scan density, proving the effectiveness of the simulation-based evaluation method. In addition, it shows that a least square regression (LSR) algorithm provides the best performance with an edge extraction accuracy of 1.56 and 2.71 mm for simulated and experimental scan points, respectively. The contributions of this study are (1) development of the geometrical model and noise modeling based on actual scan data and (2) validation of simulated-based evaluation method on the lab-scale PC slabs.

Geospatial analysis of Plasmodium falciparum serological indicators: school versus community sampling in a low-transmission malaria setting

BackgroundDue to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling.MethodsThis study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti.ResultsConsistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman’s rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09–0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35–0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47–0.49).ConclusionsIn settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

Research on Measurement and Prediction of Well Blowout Fluid Flow Rate based on Machine Vision

A machine vision-based method for measuring and predicting well blowout fluid flow rate is proposed to solve the problems of limited quantitative identification of well blowout fluid flow rate, inability to install traditional measurement equipment in the field, and inability to accurately measure and predict blowout fluid flow rate. This method establishes an image processing model based on ORB-BF-RANSAC for blowout sequence images, feature extraction and feature identification are performed on the blowout images from consecutive frames in order to obtain the coordinates of the feature points within the well blowout images. The pixel displacement of the feature point pairs is then calculated. Subsequently, the true displacement of the feature points is calculated using the pinhole imaging principle. Finally, the true displacement is divided by the number of frames to calculate the blowout fluid flow rate. Compared to the speed of mass flow meter calibration, the method achieves a measurement accuracy of 91%. Based on the above method, a well blowout fluid flow rate prediction method is designed by introducing Spatial Attention Mechanism (SAM) to improve the Graph Convolutional Networks (GCN). The prediction model proposed in this paper is more effective compared with other prediction methods, and the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are 12.39%, 18.72% and 9.16%.

Application of thermodynamics at different scales to describe the behaviour of fast reacting binary mixtures in vapour-liquid equilibrium

The use of reactive working fluids in thermodynamic cycles is currently being considered as an alternative to inert working fluids, because of the preliminarily attested higher energy-efficiency potential. The current needs to simulate their use in thermodynamic cycles, which may operate in liquid, vapour or vapour-liquid state, are an accurate real-fluid equation of state and ideal gas thermochemical properties of each molecule constituting the mixture, to calculate the equilibrium constant. To this end, the appeal to a multi-scale theoretical methodology is paramount and its definition represents the objective of the present work. This methodology is applied and validated on the system N2O4 ⇌ 2NO2. Firstly, the equations solved for simultaneous two-phase and reaction equilibrium are presented. Secondly, ideal gas thermochemical properties of N2O4 and NO2 are computed at atomic scale by quantum mechanics simulations. Then, to apply the selected cubic equation of state, pure-component properties of the species forming the reactive mixture (critical point coordinates and acentric factor) are required as input. However, these properties are not measurable, since NO2 and N2O4 do not exist in nature as pure components. To get around this difficulty, the methodology relies on molecular Monte Carlo simulations of the pure N2O4 and NO2, as well as on the reactive N2O4 ⇌ 2NO2, enabling the determination of those missing pure-component properties and thus the calculation, on a macroscopic scale, of the reactive mixture properties. Finally, the comparison of calculated mixture properties with available experimental data leads to validate the accuracy of the proposed methodology.

Penerapan Sistem Informasi Geografis Dalam Pemetaan Toko Oleh - Oleh Khas Lombok

This research aims to produce a geographic information system with spatial data visualization containing the location of East Lombok souvenir shops, shop information and directions that will be presented to visitors or users. The research targets were carried out in several sub-districts in the East Lombok Regency area which have souvenir shops. This is an effort to introduce craft products and food and drinks that are characteristic of the East Lombok Regency area. The data used in this research is by collecting map data of the East Lombok Regency area using Google Map API and Google Map Direction Service, interviews with shop owners, data collection on coordinate points and several other supporting data. The result of this research is that a web-based geographic information system for East Lombok souvenir shops has been built using the Google Maps API which makes it easy for web users to find out the position of East Lombok souvenir shops, where there is shop information and shop position. , items for sale and directions to the desired store.

Prawn morphometrics and weight estimation from images using deep learning for landmark localization

Accurate morphometric analyses and weight estimation are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breeding, grading processes, and monitoring the health status of production animals. However, the collection of phenotypic data through traditional manual approaches at industrial scales and in real-time is time-consuming, labour-intensive, and prone to errors. Digital imaging of individuals and subsequent training of prediction models using Deep Learning (DL) has the potential to rapidly and accurately acquire phenotypic data from aquaculture species. In this study, we applied a novel DL approach to automate morphometric analysis and weight estimation using the black tiger prawn (Penaeus monodon) as a model crustacean. The DL approach comprises two main components: a feature extraction module that efficiently combines low-level and high-level features using the Kronecker product operation; followed by a landmark localization module that then uses these features to predict the coordinates of key morphological points (landmarks) on the prawn body. Once these landmarks were extracted, weight was estimated using a weight regression module based on the extracted landmarks using a fully connected network. For morphometric analyses, we utilized the detected landmarks to derive five important prawn traits. Principal Component Analysis (PCA) was also used to identify landmark-derived distances, which were found to be highly correlated with shape features such as body length, and width. We evaluated our approach on a large dataset of 8164 images of the Black tiger prawn (Penaeus monodon) collected from Australian farms. Our experimental results demonstrate that the novel DL approach outperforms existing DL methods in terms of accuracy, robustness, and efficiency.