Local Fitting Research Articles

Supporting Veteran's Administration Medical Center Directors' Decisions When Adopting Innovative Practices: Development and Implementation of the "QuickView" and "WishList" Tools.

Background Since 2015, the Veterans Health Administration (VHA) Diffusion of Excellence Program has supported spread of practices developed by frontline employees. Shark Tank-style competitions encourage "Sharks" nationwide (VHA medical center/regional directors) to bid for the opportunity to implement practices at their institutions. Methods The authors evaluated bidding strategies (2016-2020), developing the "QuickView" practice comparator to promote informed bidding. Program leaders distributed QuickView and revised versions in subsequent competitions. Our team utilized in-person observation, online chats after the competition, bidder interviews, and bid analysis to evaluate QuickView use. Bids were ranked based on demonstrated understanding of resources required for practice implementation. Results Sharks stated that QuickView supported preparation before the competition and suggested improvements. Our revised tool reported necessary staff time and incorporated a "WishList" from practice finalists detailing minimum requirements for successful implementation. Bids from later years reflected increased review of facilities' current states before the competition and increased understanding of the resources needed for implementation. Percentage of bids describing local need for the practice rose from 2016 to 2020: 4.7% (6/127); 62.1% (54/87); 78.3% (36/46); 80.6% (29/36); 89.7% (26/29). Percentage of bids committing specific resources rose following QuickView introduction: 81.1% (103/127) in 2016, 69.0% (60/87) in 2017, then 73.9% (34/46) in 2018, 88.9% (32/36) in 2019, and 89.7% (26/29) in 2020. Discussion In the years following QuickView/WishList implementation, bids reflected increased assessment before the competition of both local needs and available resources. Conclusion Selection of a new practice for implementation requires an understanding of local need, necessary resources, and fit. QuickView and WishList appear to support these determinations.

Normal calculation of artillery cradle before the robot arm assisted ultrasonic phased array automatic detection

This study is interested in the normal calculation of artillery cradle. When the beam axis of ultrasonic phased array probe cannot detect the workpiece surface vertically, the detection result is usually inaccurate. Thus, in order to determine the normal of the surface to be detected, a method based on visual point cloud to calculate the normal of artillery cradle is proposed in this paper. The proposed method solves two key problems. One is to sparse the point cloud while preserving the tiny information on the surface. A mesh division method along the uv direction is proposed. The disordered point cloud data in 3D space are connected into regular polygon meshes. While establishing the local relationship of point cloud data, it also maintains the overall integrity of point cloud data. The other is to calculate the normal vector of the point cloud. The local plane fitting method based on the least square method calculates the normal at a point of the point cloud. At this time, the calculated normal are nondirective. When there is no abrupt curvature change near the calculated point cloud, the direction of the normal is determined based on the same slope at two adjacent points. The calculated point cloud normal vector is compared with the actual normal vector of the mathematical model. The maximum deviation between the theoretical value and the actual value of the normal vector is 0.465°, and the average deviation is 0.302°. The experimental results show that the proposed method can be used to detect the actual normal of artillery cradle.

Envy: Differences between the West and Africa

Envy is perceived and handled differently in the West than in Africa. Defined as “a negative . . . response to someone else’s superior quality,” envy is largely considered psychological in the West, and is often suppressed to avoid the stigma of a negative “emotion.” In contrast, Africans often express envy openly, at least to outsiders. Open expression of envious desire for what the West has underlies contemporary formal education in Africa, that as a result has little local fit. Unlike in the West, envy in Africa translates into witchcraft. My experience in Africa tells me that this is not magical or supernatural in the western sense of these terms, but rather is a way of responding to powerful and potentially destructive envy. Freedom from envy comes through faith in Christ, thus making clear the need for mission, discipleship, and culturally relevant theological education. My observations on envy in Africa are based on my own experience—born and reared in a White British community but having participated in village life in East Africa using indigenous languages, from 1988 to date, in the course of missionary work. Being deeply a “westerner,” with profound exposure to Africa, enables my translating what is less known in the West (African ways of life) into the “familiar,” that is, western scholarship. This is on the basis that good translation should always be into what is “known”. Although geographically and historically my exposure to African ways of life is very limited, I am inclined to refer to “Africans” rather than more specific ethnicities (tribes) that I know well, so as not to imply that the people whom I have gotten to know over many decades through closely sharing in their lives are uniquely peculiar.

Accuracy of single-implant digital impression with various scanbody exposure levels at anterior and posterior regions

ObjectivesThis in vitro study aimed to evaluate the effect of the exposure heights of the scanbody on the accuracy of digital implant impressions at different positions. MethodsFour maxillary master models with one analog at the anterior and posterior region were fabricated by a 3-dimensional (3D) printer. The analogs were submerged from the gingival margin to ensure four exposure heights of the scanbody: 10, 8, 6, and 4 mm. . The master models were then scanned with D2000 dental laboratory scanner as the reference models. An intraoral scanner obtained ten test models for each group. After aligning the scanbody library file, the related files were imported into inspection software for superimposition by a local fit algorithm based on the adjacent teeth. Results3D trueness was significantly decreased at 6 and 4 mm scanbody exposure at the anterior region. In comparison, a significant decrease was only seen at 4 mm scanbody exposure at the posterior region. 3D precision was significantly decreased at 4 mm scanbody exposure at both anterior and posterior regions. ConclusionThe exposure height of the scanbody influenced the accuracy of the digital implant impression, according to the implant positions. Scanbody exposure of less than 6 mm at the anterior region and 4 mm scanbody exposure at the posterior region could lead to increased deviations, but still in the tolerance range. Clinical significanceThe scanbody exposure height less than 6 mm at the anterior region and 4 mm scanbody exposure height at the posterior region could lead to significantly increased deviations. Though these deviations may be still in the clinically acceptable range, caution should be taken.

Machine learning to set hyperparameters for overlapping community detection algorithms

AbstractThe Local Fitness Method (LFM) and Speaker‐Listener Label Propagation (SLPA) algorithms are widely used to detect overlapping communities in complex networks. The main problem with these two algorithms is that they are extremely sensitive to the setting of the hyperparameters. Previous methods set the hyperparameters of LFM or SLPA based on either empirical values or random choices, resulting in a large amount of computation for adjusting those parameters. To solve this problem, in this paper, a machine‐learning‐based approach is proposed to automatically set the hyperparameters of these two algorithms. Experimental results show that compared with the manual method, automatically setting the hyperparameter using machine learning models can lead to higher‐quality divisions. Furthermore, in comparison to the well‐known hyperparameter tuning method using Bayesian Optimization, the proposed predictive model‐based approach can find suitable para meters for LFM and SLPA much faster, while achieving competitive results in terms of division quality.

Wideband DOA Estimation Utilizing a Hierarchical Prior Based on Variational Bayesian Inference

The direction-of-arrival (DOA) estimation of wideband signals, based on sparse signal reconstruction, has recently been proposed, owing to its unique high-resolution performance. As a typical tool of sparse signal reconstruction, sparse Bayesian learning (SBL) enhances little sparsity in most works, leading to a non-robust local fitting. To significantly enhance sparsity, we proposed a novel hierarchical Bayesian prior framework, and deduced a novel iterative approach. It was discovered that the iterative approach had a lower computational complexity than the majority of current state-of-the-art algorithms. Besides, the proposed approach achieves a high angular estimation accuracy and sparsity performance, by utilizing the joint sparsity of the multiple measurement vector (MMV) models. Moreover, the approach stabilizes the estimated values between different frequencies or snapshots, so as to obtain a flat spatial spectrum. Extensive simulation results are presented, to demonstrate the superior performance of our method.

Efektifitas Pedoman Program Latihan Kebugaran Berbasis Kearifan Lokal Malind Anim Sebagai Media Literasi Dalam Meningkatkan Pola Hidup Sehat Pada Korban Penyalahgunaan Napza Di Perbatasan RI-PNG

This study aims to determine: The Effectiveness of the Guidelines for the Malind Anim Local Wisdom-Based Fitness Exercise Program as a Literacy Media In Improving Healthy Lifestyles in Drug Abuse Victims at the RI-PNG Border, and differences in people's fitness levels, between the use of Malind Anim Local Wisdom-Based Fitness Exercise Program Guidelines as Media Literacy in Improving Healthy Lifestyles with the use of random material sources.
 The method used in this study is an experimental method, in the form of a Quasi Experiment, namely the Nonrandomized Control Group Pretest-Posttest Design. The subject of this research is the people of Kampung Yanggandur who are included in the research criteria. The methods used to collect data are test and non-test instruments. This research will answer the differences in community fitness levels, between the use of Malind Anim Local Wisdom-Based Fitness Exercise Program Guidelines as Media Literacy in Improving Healthy Lifestyles and the use of random material sources. The effectiveness of the use of the Guidelines for the Malind Anim Local Wisdom-Based Fitness Exercise Program on Drug Abuse Victims at the RI-PNG Border is not yet used by the community.
 Based on the results of posttest data calculations, the researchers concluded that an increase in VO2 Max could be increased by using the Cooper test. Besides that, it can also be seen by the results of calculating a significance value of 0.000. It can be concluded that the product in the form of a fitness training program guideline for drug abuse victims that has been developed is effective in increasing the fitness of drug abuse victims.
 Keywords: Exercise Guidelines, Drugs, OGPZ

NeAF: Learning Neural Angle Fields for Point Normal Estimation

Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks. However, these methods are not generalized well to unseen scenarios and are sensitive to parameter settings. To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF). Instead of directly predicting the normal of an input point, we predict the angle offset between the ground truth normal and a randomly sampled query normal. This strategy pushes the network to observe more diverse samples, which leads to higher prediction accuracy in a more robust manner. To predict normals from the learned angle fields at inference time, we randomly sample query vectors in a unit spherical space and take the vectors with minimal angle values as the predicted normals. To further leverage the prior learned by NeAF, we propose to refine the predicted normal vectors by minimizing the angle offsets. The experimental results with synthetic data and real scans show significant improvements over the state-of-the-art under widely used benchmarks. Project page: https://lisj575.github.io/NeAF/.

Leucopterin, the white pigment in butterfly wings: structural analysis by PDF fit, FIDEL fit, Rietveld refinement, solid-state NMR and DFT-D.

Leucopterin (C6H5N5O3) is the white pigment in the wings of Pieris brassicae butterflies, and other butterflies; it can also be found in wasps and other insects. Its crystal structure and its tautomeric form in the solid state were hitherto unknown. Leucopterin turned out to be a variable hydrate, with 0.5 to about 0.1 molecules of water per leucopterin molecule. Under ambient conditions, the preferred state is the hemihydrate. Initially, all attempts to grow single crystals suitable for X-ray diffraction were to no avail. Attempts to determine the crystal structure by powder diffraction using the direct-space method failed, because the trials did not include the correct, but rare, space group P2/c. Attempts were made to solve the crystal structure by a global fit to the pair distribution function (PDF-Global-Fit), as described by Prill and co-workers [Schlesinger et al. (2021). J. Appl. Cryst. 54, 776-786]. The approach worked well, but the correct structure was not found, because again the correct space group was not included. Finally, tiny single crystals of the hemihydrate could be obtained, which allowed at least the determination of the crystal symmetry and the positions of the C, N and O atoms. The tautomeric state of the hemihydrate was assessed by multinuclear solid-state NMR spectroscopy. 15N CPMAS spectra showed the presence of one NH2 and three NH groups, and one unprotonated N atom, which agreed with the 1H MAS and 13C CPMAS spectra. Independently, the tautomeric state was investigated by lattice-energy minimizations with dispersion-corrected density functional theory (DFT-D) on 17 different possible tautomers, which also included the prediction of the corresponding 1H, 13C and 15N chemical shifts in the solid. All methods showed the presence of the 2-amino-3,5,8-H tautomer. The DFT-D calculations also confirmed the crystal structure. Heating of the hemihydrate results in a slow release of water between 130 and 250 °C, as shown by differential thermal analysis and thermogravimetry (DTA-TG). Temperature-dependent powder X-ray diffraction (PXRD) showed an irreversible continuous shift of the reflections upon heating, which reveals that leucopterin is a variable hydrate. This observation was also confirmed by PXRD of samples obtained under various synthetic and drying conditions. The crystal structure of a sample with about 0.2 molecules of water per leucopterin was solved by a fit with deviating lattice parameters (FIDEL), as described by Habermehl et al. [Acta Cryst. (2022), B78, 195-213]. A local fit, starting from the structure of the hemihydrate, as well as a global fit, starting from random structures, were performed, followed by Rietveld refinements. Despite dehydration, the space group remains P2/c. In both structures (hemihydrate and variable hydrate), the leucopterin molecules are connected by 2-4 hydrogen bonds into chains, which are connected by further hydrogen bonds to neighbouring chains. The molecular packing is very efficient. The density of leucopterin hemihydrate is as high as 1.909 kg dm-3, which is one of the highest densities for organic compounds consisting of C, H, N and O only. The high density might explain the good light-scattering and opacity properties of the wings of Pieris brassicae and other butterflies.

The ecological causes of functional distinctiveness in communities.

Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages.

Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm

Abstract Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperparameters at a reasonable time to improve the performance of CNNs has always been a complex problem. To solve this problem, this paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. To avoid the influence of complicated parameter adjustment of LACHS algorithm on its performance, a parameter dynamic adjustment strategy is adopted, which makes the pitch adjustment probability PAR and step factor BW dynamically adjust according to the actual situation. To strengthen the fine search of neighborhood space and reduce the possibility of falling into local optima for a long time, an autonomous decision-making search strategy based on the optimal state is designed. To help the algorithm jump out of the local fitting situation, this paper proposes a local competition mechanism to make the new sound competes with the worst harmonic progression of local selection. In addition, an evaluation function is proposed, which integrates the training times and recognition accuracy. To achieve the purpose of saving the calculation cost without affecting the search result, it makes the training time for each model depending on the learning rate and batch size. In order to prove the feasibility of LACHS algorithm in configuring CNN superparameters, the classification of the Fashion-MNIST dataset and CIFAR10 dataset is tested. The comparison is made between CNN based on empirical configuration and CNN based on classical algorithms to optimize hyperparameters automatically. The results show that the performance of CNN based on the LACHS algorithm has been improved effectively, so this algorithm has certain advantages in hyperparametric optimization. In addition, this paper applies the LACHS algorithm to expression recognition. Experiments show that the performance of CNN optimized based on the LACHS algorithm is better than that of the same type of artificially designed CNN. Therefore, the method proposed in this paper is feasible in practical application.

Fitness-based mobility enhances the maintenance of biodiversity in the spatial system of cyclic competition

In ecosystems, species are willing to stay longer when they are in good environments and, in this regard, explaining biodiversity and understanding the relative effect of species’ behavior is a fundamental issue in ecological sciences. Staying or moving stands for the movement of the population, and mobility has been accepted as one of the important mechanisms in systems of cyclic competition. In this paper, we investigate the biodiversity in the system of cyclic competition when an individual’s mobility is affected by environmental fitness. We found that fitness-based mobility, which is defined by the ratio of the difference between predator and prey among species, can lead to the slow behavior of species when local fitness is higher than the average fitness and enhance the biodiversity of species. The fitness-based mobility behavior is successful in reducing the extinction probability on the lattice network. However, when the average degree of the network increases, the extinction probability increases significantly. In addition, when we consider the same model on heterogeneous networks, the extinction probability is always 1 regardless of the sensitivity to the fitness. The finding indicates that the heterogeneous network destroys species coexistence. Through extensive numerical investigations, we provided solid evidence supporting the great importance of fitness-based mobility in maintaining biodiversity. We further highlighted the significant relationship among fitness, the fraction of empty sites, and the tunable parameter by investigating the correlation that the relationship is not influenced by mobility.

Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation

Due to the curse of dimensionality and the limitation on training data, approximating high-dimensional functions is a very challenging task even for powerful deep neural networks. Inspired by the Nonlinear Level-set Learning (NLL) method, which uses the reversible residual network (RevNet), in this paper, we propose a new method of Dimension Reduction via Learning Level Sets (DRiLLS) for function approximation. Our method contains two major components: one is the pseudoreversible neural network (PRNN) module, which effectively transforms high-dimensional input variables to low-dimensional active variables, and the other is the synthesized regression module for approximating function values based on the transformed data in the low-dimensional space. The PRNN not only relaxes the invertibility constraint of the nonlinear transformation present in the NLL method due to the use of RevNet but also adaptively weights the influence of each sample and controls the sensitivity of the function to the learned active variables. The synthesized regression uses Euclidean distance in the input space to select neighboring samples whose projections on the space of active variables are used to perform local least-squares polynomial fitting. This helps to resolve numerical oscillation issues present in traditional local and global regressions. Extensive experimental results demonstrate that our DRiLLS method outperforms both the NLL and active subspace methods, especially when the target function possesses critical points in the interior of its input domain.

Cover Picture: Astron. Nachr. 05/2023

Oscillation power spectrum of the Kepler red giant KIC 1027337. The left panel shows the raw spectrum, where the frequency of the maximum oscillation power, νmax, is indicated by two almost coincident vertical lines corresponding to the reference measurement (dashed red and the measurement with the new FRA pipeline (solid black), respectively. The right panel shows the optimally smoothed power spectrum, together with a local fit of oscillations centered around νmax, represented by the orange line. The new Fast, Robust, and Automated (FRA) tool for the detection and measurement of solar-like oscillations in time-series photometry of red-giant stars is described, tested, and validated in detail in the paper by Gehan et al., this issue, e220090.

Distance Education Research in South Africa: A Longitudinal Study into the Research Levels of ODL Journal Articles

The measure of an academic field lies in the richness and depth of its published research, especially within the ever-developing field of distance education, which is relatively new. The University of South Africa is one of the oldest open distance learning (ODL) higher education institutes globally, which has given rise to its status internationally as a leader of distance education. It is prudent to analyse and reflect on the research outputs published by South African academics, particularly regarding the levels of research that are conducted. This article follows the research published by Roberts, which analysed South African distance learning research levels and sublevels from articles published between 2011 and 2015. This longitudinal study applied a thematic content analysis of the titles and abstracts of all ODL-related papers published by South African authors. The findings compare ODL trends for the five-year periods from 2010 to 2014 and 2015 to 2019. The data were obtained from the Scopus and SABINET databases, using the same search criteria employed by Roberts. The levels of research publications were analysed according to the open distance learning research framework of Zawacki-Richter presented through descriptive statistics. The results indicate that although the number of published open distance learning research articles has more than doubled, the research levels have not shown any significant change from the previous five years. The South African ODL publications should give attention to meso- and macro-level research to enhance the ODL development within Southern Africa and create local trends fit for purpose.

Reconstructing charged-particle trajectories in the PANDA Straw Tube Tracker using the LOcal Track Finder (LOTF) algorithm

We present the LOcal Track Finder (lotf) algorithm, a method that performs charged-particle trajectory reconstruction using the Straw Tube Tracker, one of the central trackers of the antiProton ANnihilation at DArmstadt (PANDA) detector. The algorithm builds upon the neighboring relations of the tubes to connect individual hits and form track candidates. In addition, it uses a local fitting procedure to handle regions where several tracks overlap and utilizes a system of virtual nodes to reconstruct the z-information of the particle trajectories. We generated 30,000 events to assess the performance of our approach and compared its global track assignment efficiency with respect to two other track reconstruction methods. lotf has (1) an average of 85% of found tracks, (2) the largest number of Fully Pure tracks, (3) the lowest amount of incorrect reconstructions, and (4) is significantly faster than the other two approaches. Further, we compared the z-reconstruction performance with one of the two alternative methods and show that lotf improves the median z-error by a factor of 8.7. Finally, we tested our method using 3750 data sets composed of 4 events each, demonstrating that our approach handles cases in which events are mixed. The raw (without parallelization) average reconstruction rate is about 68,000 hits/s, which makes the present algorithm promising for online data selection and processing.

Explosion characteristics of shale gas in air

In this work, explosion characteristics of stoichiometric shale gas were studied employing a standard 20-L explosion bomb. Shale gas was represented by methane/ethane, methane/propane, and methane/ethane/propane mixtures, and the effect of fuel compositions, the volume fraction of ethane (or propane) increases from 0 to 0.42, on explosion parameters was examined. Results show that the maximum explosion pressure, the maximum explosion pressure rising rate, and the laminar flame speed increase with the volume fraction of ethane (or propane). The reason is that the increment of ethane (or propane) volume fraction increases the mole fraction of [H + O + OH]max. For a given volume fraction, propane provides more heat and H, O, and OH radicals, and consequently, propane shows a more robust incentive function on explosion parameters. Finally, the CH ratio, which is defined as the ratio of total carbon atoms and hydrogen atoms, is used to further study the explosion characteristics of various fuel compositions. There is a robust linear relationship between the CH ratio and explosion parameters. For laminar flame speed, however, the global fit curve provides poor accuracy while the local fitting curves coincide well with the data. This indicates that the laminar flame speed is more sensitive to the gas species.

AWL-GCN: Branch Parameter Identification Considering Grid Spatial Structure Constraints

Parameter identification plays an important role in power system. The existing parameter identification methods usually have two limitations: (1) The existing methods only consider the information of single branch and ignore the influence of adjacent branch information, and do not effectively use the topological structure constraints of the power grid. (2) The measurement data has the problems of poor numerical stability, numerical divergence and noise interference. Data contamination has become an important factor restricting the prediction accuracy of branch parameters. In order to solve these problems, this work proposes an Automatic Weighted Loss-Graph Convolution Networks model combined with the spatial structure of power grid. Based on the graphical modeling, the correlation between power grid branches is effectively used, and the adjacent branches are used to provide more effective information for the parameter identification of a branch, which improves the identification accuracy. In addition, the model enhances the local characteristics through the power grid structure information constraints, realizes the local optimal fitting, and constructs a self tradeoff loss function to weaken the impact of pollution data (data noise and data loss) on the model. The test results show that compared with the traditional method, this method has higher accuracy and stronger robustness.

A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems

A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems

Improvement of normal estimation for point clouds via simplifying surface fitting

During the last decade, stimulated by the roaring success of neural networks in the realm of point clouds, learning-based normal estimation has been a dominating direction and it has attained outstanding performance. The introduction of modern neural networks significantly enhances the robustness of algorithms to noise levels and sampling scales. A method that combines least-squares surface fitting with weight-learning neural networks is particularly prominent. Despite persistent efforts, there is room for improvement. We observed that a simplified surface-fitting process can significantly improve the accuracy of this type of hybrid approach. In this study, two simple yet effective strategies are proposed to boost the accuracy of normal estimation. Specifically, a dynamic Top-K selection strategy is presented to focus on the critical points of a given patch. The selected point hammer fits a local surface using a simple tangent plane, which improves the normal estimation accuracy when encountering patches with sharp corners or complex patterns. Moreover, to further improve the quality of the estimation, a point-update strategy was delicately designed to smooth the sharp boundaries of the patches before the local surface fitting process. Thus, our method can reduce the fitting distortion while simplifying the surface-fitting process. Extensive experiments and ablation studies demonstrated the superiority of our proposed method over contemporary state-of-the-art approaches.