Similar Background Research Articles

Predictive ability of multi-population genomic prediction methods of phenotypes for reproduction traits in Chinese and Austrian pigs

BackgroundMulti-population genomic prediction can rapidly expand the size of the reference population and improve genomic prediction ability. Machine learning (ML) algorithms have shown advantages in single-population genomic prediction of phenotypes. However, few studies have explored the effectiveness of ML methods for multi-population genomic prediction.ResultsIn this study, 3720 Yorkshire pigs from Austria and four breeding farms in China were used, and single-trait genomic best linear unbiased prediction (ST-GBLUP), multitrait GBLUP (MT-GBLUP), Bayesian Horseshoe (BayesHE), and three ML methods (support vector regression (SVR), kernel ridge regression (KRR) and AdaBoost.R2) were compared to explore the optimal method for joint genomic prediction of phenotypes of Chinese and Austrian pigs through 10 replicates of fivefold cross-validation. In this study, we tested the performance of different methods in two scenarios: (i) including only one Austrian population and one Chinese pig population that were genetically linked based on principal component analysis (PCA) (designated as the “two-population scenario”) and (ii) adding reference populations that are unrelated based on PCA to the above two populations (designated as the “multi-population scenario”). Our results show that, the use of MT-GBLUP in the two-population scenario resulted in an improvement of 7.1% in predictive ability compared to ST-GBLUP, while the use of SVR and KKR yielded improvements in predictive ability of 4.5 and 5.3%, respectively, compared to MT-GBLUP. SVR and KRR also yielded lower mean square errors (MSE) in most population and trait combinations. In the multi-population scenario, improvements in predictive ability of 29.7, 24.4 and 11.1% were obtained compared to ST-GBLUP when using, respectively, SVR, KRR, and AdaBoost.R2. However, compared to MT-GBLUP, the potential of ML methods to improve predictive ability was not demonstrated.ConclusionsOur study demonstrates that ML algorithms can achieve better prediction performance than multitrait GBLUP models in multi-population genomic prediction of phenotypes when the populations have similar genetic backgrounds; however, when reference populations that are unrelated based on PCA are added, the ML methods did not show a benefit. When the number of populations increased, only MT-GBLUP improved predictive ability in both validation populations, while the other methods showed improvement in only one population.

Influence of leaf inclination angle and tillering on population transpiration, soil evaporation, and yield in winter wheat near-isogenic lines.

Leaf inclination angle (LIA) and tillering impact the winter wheat (Triticum aestivum L.) population canopy structure. Understanding their effects on water use (WU) parameters and yield can guide water-saving strategies through population control. In this study, six near-isogenic lines (NILs) and their parents were selected as materials. These special materials were characterized by varying tillering at the current sowing density, a similar genetic background, and, particularly, a gradient in mean flag leaf LIA. The investigation focused on the jointing to early grain-filling stage, the peak water requirement period of wheat crops. Population-scale transpiration (PT) and evaporation from the soil surface (E) were partitioned from total evapotranspiration (ET) by the means of micro-lysimeters. The results showed decreased PT, E, and ET with increased population density (PD) within a narrow density range derived from varying tillering across genotypes. Significant correlations existed between PD and ET, E, and PT, especially in the wettest 2017-2018 growing season. Within such narrow PD range, all the correlations between WU parameters and PD were negative, although some correlations were not statistically significant, thereby suggesting the population structure's predominant impact. No significant correlation existed between LIA and both ET and PT within the LIA range of 35°-65°. However, significant correlations occurred between LIA and E in two growing seasons. Genotypes with similar LIA but different PD produced varied ET; while with similar PD, the four pairs of genotypes with different LIA each consumed similar ET, thus highlighting PD's more crucial role in regulating ET. The yield increased with higher LIA, and showed a significant correlation, emphasizing the LIA's significant effect on yield. However, no correlation was observed with PD, indicating the minor effect of tillering at the current sowing density. Therefore these results might offer valuable insights for breeding water-saving cultivars and optimizing population structures for effective field water conservation.

Size and shape of the neurocranium of laying chicken breeds

The neurocranium in birds provides valuable insights into their morphological diversity, including adaptations related to brain size, facial shaping, and environmental factors. This study analyzes the neurocranial shape characteristics and size of chickens with similar genetic backgrounds. By examining the neurocranial shape variation in chickens of the same age and sex, the study aims to understand the factors contributing to morphological diversity within this specific group. 3D geometric morphometrics was used to analyze 235 neurocrania from four chicken breeds. The analysis revealed significant differences in centroid size among the chicken breeds. The largest neurocranium centroid size was found in Sasso chickens., which were statistically separated from Atak-S. Additionally, centroid size effectively differentiates between Lohmann Brown and Lohmann Sandy chicken breeds.The most significant shape variation concerned the width of the rostral part of the frontal bone. However, according to the PC1 value, the shape variation was observed within rather than between groups. Lohmann Sandy chickens exhibited higher variability in neurocranial shape, suggesting greater shape diversity within this breed than others. As for shape analysis, the breeds showed closer similarity to each other. Lohmann Sandy chickens are characterized by positive PC1 value, with the rostral end of the frontal region inclined more ventrally, and a more extensive basioccipital region. Sasso chickens have a more dome-shaped middle part of the frontal region than other breeds. The study also identified the most significant shape variation among the study samples, observed at the rostral part of the frontal bone. These findings contribute to understanding the genetic and environmental influences shaping neurocranial morphology in chickens. Similar studies in different bird species and subspecies offer valuable insights into avian biology and adaptation.

How does a structurally similar background language influence L3 grammars? A study on the syntax of L2/L3 Mandarin [SI 21/3]

ABSTRACT This article reports on an empirical study of L3 Mandarin, aiming to shed light on transfer effects and their interaction with other factors throughout the L3 acquisition trajectory. A fill-in-the-blank task was employed to examine L2 and L3 acquisition of three types of Mandarin sentence-final particle clusters. Participants in the study were Mandarin native speakers, L1 English-L2 Mandarin speakers, L1 English-L2 Cantonese-L3 Mandarin speakers, and L1 Cantonese-L2 English-L3 Mandarin speakers at low and high Mandarin proficiency levels. Our data support arguments in the L3 literature that transfer at the L3 initial stages is from a structurally more similar language. Facilitation takes place when the instantiation of the target property in the L3 is exactly the same as that in the transfer source language. Overall typological similarity might be helpful when transfer is from the L1, but it does not always facilitate the L3 acquisition of individual properties. For later L3 development, construction frequency and mapping paradigms have a great impact on the acquisition outcome. Moreover, a certain kind of non-facilitative transfer from an L1 is found to persist in L3 Mandarin grammars, which qualitatively supports the Cumulative Input Threshold Hypothesis.

The person-environment fit of immigrants to the United States: A registered report.

There are notable parallels between processes leading to person-environment fit (PE-fit) and processes of selection and acculturation among U.S. immigrants. Thus, a natural question is: Do immigrants benefit from fitting their new environments? PE-fit appears to have uniformly positive effects in the education, career, and personality literatures, but it is unclear whether this would be the case for immigrants. The present study evaluated the PE-fit of U.S. immigrants (N = 39,195) to their new host communities (9,925 Zip Code Tabulation Areas [ZCTAs]). PE-fit varied across immigrants. On average, immigrant PE-fit was lower (b = 0.23 and b = 0.35) than the PE-fit of U.S. natives (b = 0.47; N = 122,339 from 2,374 ZCTAs). Immigrants more closely matched their community's profile when they were older, more educated, from Western countries, or from countries with French or German as the official language. PE-fit was positively associated with immigrant traits of Honesty, Introspection, Creativity, and Industry. Immigrants experienced better PE-fit when they resided in communities with more educated residents, with residents born abroad-particularly in the same world region-or with residents with a similar ethnic background. Finally, immigrant PE-fit was associated with well-being and self-reported health. We discuss the implications for the study of U.S. immigrants and the field of acculturation and propose future directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Formation and stability of point defect color centers in 6H silicon carbide

Point defect color centers acting as single-photon emitters are promising for quantum technology applications and have been extensively studied, e.g., in the 4H polytype of silicon carbide (SiC). However, the physics of such color centers in other SiC polytypes is much less explored. Herein, we study the formation and thermal stability of such color centers in 6H-SiC using photoluminescence spectroscopy. The emissions from typical single-photon emitters, such as silicon vacancies, divacancies, and carbon antisite-vacancy pairs in 6H-SiC, were monitored as a function of the proton irradiation fluence and post-irradiation annealing, and compared to that in 4H-SiC. Overall, at the background of similarities between the emission behavior in 4H- and 6H-SiC polytypes, we observed prominent differences; e.g., for the thermal stability of the carbon antisite-vacancy pair, which exhibited maximized emissions upon 300 and 900 °C anneals in 4H- and 6H-SiC, respectively. Moreover, we observed a range of defect emission signatures not previously reported for 6H-SiC in the literature and discussed their potential origin in the context of the thermal stability. For example, among the PL-lines in 6H-SiC, we detected periodically repeatable emission signatures, resembling the so-called L-lines recently reported in 4H-SiC, even though their exact origin has not yet been settled in the literature. Thus, we use color centers comparison in different polytypes to better understand the nature of the single-photon emitters in SiC.

Spatial-temporal graph Transformer for object tracking against noise spoofing interference

Achieving accurate object tracking against noise spoofing interference caused by similar backgrounds, similar objects, occlusion, or illumination variations is a challenge, especially since Siamese trackers largely ignore the spatial-temporal and part-level information of object structure. To handle this problem, we present a novel object tracking network with spatial-temporal awareness, which learns the correlation of local feature changes and extracts global spatial-temporal fusion features of the object to prevent noise spoofing interference. Specifically, our tracker integrates an object graph reconstruction representation module and a spatial-temporal graph Transformer module. The graph reconstruction representation module models the object structure and search region as part-to-part graph node correspondences, propagating object information to achieve part-level feature aggregation in the search region. The proposed spatial-temporal graph Transformer module fuses the temporal and spatial features of the object to enhance the contextual features and correlation between temporal and spatial feature variations, recognizing the noise spoofing interference to enhance the tracking performance. Meanwhile, our tracker learns object motion information to improve object state awareness, enhancing tracking accuracy under similar objects interference. Extensive experiments on six public datasets validate that our tracker outperforms related state-of-the-art trackers and achieves accurate tracking results against noise spoofing interference.

Horizontal subcontracting and the global factory

The focus of this article is the examination of the factors that trigger the addition of horizontal subcontracting to the structure of global factories. Horizontal subcontracting takes place when one firm is contracted to produce a given item, fills as much of the order as they are able to based on their available capacity, and subsequently subcontracts the remainder to a qualitatively identical firm. We find that similar entrepreneurial backgrounds of contract manufacturers lead to strong business ties in the same industry and stage of production; low-cost, used production machinery and inexpensive factory rent lead to low barriers to entry; tightening labor markets, limited capital access and lumpy or uncertain demand lead to the need to avoid underused internal capacity, and the widespread use of digital sourcing platforms by buyers enables contract manufacturers to receive orders both small and much larger than their internal production capacity. These factors combine to produce many networked small manufacturers who use horizontal subcontracting to ensure sustained demand. Currently a largely China-based phenomenon, horizontal subcontracting is likely to spread beyond China, offering a new source of vitality to low-cost manufacturing in global factories, keeping global factories viable into the future.

Efficiency of construction waste and carbon reduction in the construction industry: based on improved three stage SBM-DEA model in China

PurposeReducing construction waste generation and carbon emission in the construction industry is crucial for the “dual carbon” goal. Evaluating the efficiency of reducing construction waste generation and carbon emission in the construction industry at the regional level is an important evaluation basis for the sustainable development of the construction industry. It provides a basis for formulating construction waste and carbon reduction policies tailored to local conditions and comprehensively promote the sustainable development of the construction industry.Design/methodology/approachA three stage SBM-DEA model based on non-expected outputs is proposed by combining the SBM-DEA model with the SFA method. The proposed model is used to evaluate the efficiency of construction waste and carbon reduction in the construction industry in 30 regions of China from 2010 to 2020. Moreover, the study explores the impact of environmental variables such as urbanization level, proportion of construction industry employees, resident consumption level, and technological progress.FindingsFrom 2010 to 2020, the efficiency of construction waste and carbon reduction in China’s construction industry has been increasing year by year. Provinces with higher efficiency of construction waste and carbon reduction in the construction industry are mainly concentrated in the eastern coastal areas, showing an overall pattern of “East>West>Northeast>Middle”. There is a clear correlation between the level of urbanization, the proportion of construction industry employees, residents’ consumption level, technological progress, labor input, machinery input, and capital investment. The construction waste and carbon emission efficiency of the construction industry in various provinces is greatly influenced by environmental factors.Practical implicationsThe research results provide policy makers and business managers with effective policies for reducing construction waste generation and carbon emission in the construction industry, especially circular economy policies. To provide empirical support for further understanding the connotation of construction waste and carbon reduction in the construction industry, to create innovative models for construction waste and carbon reduction, and to promote the multiple benefits of construction waste and carbon reduction in the construction industry, and to provide empirical support for countries and enterprises with similar development backgrounds in China to formulate relevant policies and decision-making.Originality/valueThe construction industry is a high investment, high energy consumption, and high pollution industry. This study uses the three stage SBM-DEA model to explore the efficiency of construction waste and carbon reduction in the construction industry, providing a new perspective for the evaluation of sustainable development in the construction industry, enriching and improving the theory of sustainable development.

Optimization of land-use pattern based on suitability and trade-offs between land development and protection: A case study of the Hohhot-Baotou-Ordos (HBO) area in Inner Mongolia, China

Determining comprehensive land-use suitability is crucial for effective zoning management. However, limited research on land-use multi-suitability and the trade-offs between land development and protection hinder optimal environmental management. This study focuses on the Hohhot-Baotou-Ordos (HBO) area in Inner Mongolia, China, known for its unique geographical attributes, including abundant energy resources, potential arable land, and vulnerable ecological environment. The objective is to determine comprehensive land-use suitability for ecological protection, agricultural production, and urban construction, while addressing the trade-offs involved in land-use multi-suitability. Multi-scenarios were conducted based on multi-suitability using quantitative indicators, such as per capita space occupancy, patch density, and conductance index. The most suitable scenario, identified through these comparisons, was subsequently optimized for the HBO area by addressing land conflict zones using the InVEST model. The results revealed that: (1) Extremely suitable areas for ecological protection, agricultural production, and urban construction accounted for 47.75%, 18.19%, and 8.61% of the HBO area, respectively, indicating significant potential for ecological conservation. (2) Trade-offs were categorized into six scenarios based on land-use multi-suitability. The ecological-agricultural priority scenario demonstrated reasonable space allocation, the highest landscape agglomeration and connectivity, making it the most suitable option for optimizing the land-use pattern. (3) The optimized zoning delineated the ecological protection red line (EPR), which accounted for the highest percentage at 40.71%, while the urban reserve area (URA) was minimal, constituting only 1.28%. The primary goal of zoning management is to curb urban expansion and alleviate ecological degradation through effective strategies. Consequently, regulatory strategies focusing on management zoning were proposed. Overall, this study developed a comprehensive index system for land-use suitability, providing a method for balancing land development and protection. The findings offer a scientific reference for sustainable development in the HBO area and other regions with similar natural environmental backgrounds.

An upgraded Myxococcus xanthus chassis with enhanced growth characteristics for efficient genetic manipulation

Myxobacteria are well known for multicellular social behaviors and valued for biosynthesis of natural products. Myxobacteria social behaviors such as clumping growth severely hamper strain cultivation and genetic manipulation. Using Myxococcus xanthus DK1622, we engineered Hu04, which is deficient in multicellular behavior and pigmentation. Hu04, while maintaining nutritional growth and a similar metabolic background, exhibits improved dispersed growth, streamlining operational procedures. It achieves high cell densities in culture and is promising for synthetic biology applications.

Comparative Analysis of Energy Characteristics of Two Southwest Vortices in Sichuan Under Similar Circulation Backgrounds

Comparative Analysis of Energy Characteristics of Two Southwest Vortices in Sichuan Under Similar Circulation Backgrounds

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5–11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.

Implementing artificial intelligence across task types: constraints of automation and affordances of augmentation

PurposeThis study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.Design/methodology/approachQualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.FindingsWe identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.Research limitations/implicationsWhile the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.Practical implicationsOur research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.Originality/valueOur granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

Construction of Rules for Recognizing Landslides Along Highways Induced By Rainfall

After the "7-20" concentrated rainfall, many landslides occurred along the highways in Jiaozuo City, which seriously threatened the transportation safety and brought serious economic losses to the government and the residents along the highways. In order to investigate the development pattern and distribution characteristics of landslides along highways under extreme weather, we took Jiaozuo City Highway as the research object, based on the Sentinel-2 remote sensing data, and on the basis of analyzing the spectral characteristics of landslide hazards, we used the band ratio method to establish the identification rules of loess landslides along highways; based on the topography, geology, human impacts, and meteorological and hydrological selected 11 impact factors, and used geoprospectors to investigate the The driving factors of landslides. The following conclusions and understandings were obtained: (1) a total of 256 landslides along the highway in Jiaozuo City were identified, and the overall identification accuracy of 69.9% was obtained through visual interpretation and on-site research; (2) the ratio of the surface true reflectance of red and green light fused with the slope (>15°) can be used to carry out the identification of landslides in large-scale post-disaster situations; (3) the human activities and topography have a greater impact on landslide development; (4) the identification rule has a greater impact on the development of landslides with similar geological backgrounds. rule is useful for the identification and prevention of landslides along highways in low-hill loess-covered areas with similar geologic backgrounds.

A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry.

The incidence of ulcerative colitis (UC) has increased globally. As a complex disease, the genetic predisposition for UC could be estimated by the polygenic risk score (PRS), which aggregates the effects of a large number of genetic variants in a single quantity and shows promise in identifying individuals at higher lifetime risk of UC. Here, based on a cohort of 2869 UC cases and 2900 controls with genotype array datasets, we used PRSice-2 to calculate PRS, and systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, and impact of variants. After leveraging a stepwise condition analysis, we eventually established the best PRS model, achieving an AUC of 0.713. Meanwhile, samples in the top 20% of the PRS distribution had a risk of UC more than ten times higher than samples in the lowest 20% (OR = 10.435, 95% CI 8.571-12.703). Our analyses demonstrated that including population-enriched, more disease-associated SNPs and using GWAS summary statistics from similar ethnic background can improve the power of PRS. Strictly following the principle of focusing on one population in all aspects of generating PRS can be a cost-effective way to apply genotype-array-derived PRS to practical risk estimation.

Study of distinguishable method for mixed images with similar background

Study of distinguishable method for mixed images with similar background

Liver Disease Associated with Inflammatory Bowel Disease

Inflammatory Bowel Disease (IBD) is a chronic condition characterized by persistent inflammation of the gastrointestinal tract, comprising ulcerative colitis (UC) and Crohn’s disease (CD). While the current global incidence of IBD exceeds 0.3% in Western nations, Indonesia reports a comparatively lower prevalence however with projections indicate a potential rise in incidence from 2020 to 2050. Hepatobiliary manifestation is one of the most common extraintestinal manifestations of IBD. Some of the liver disease associated with IBD had similar background immune response, inflammatory reaction, similar risk factor, or due to the treatment of IBD. Primary sclerosing cholangitis (PSC) has close association with IBD and both affect the prognostic and disease progression of each other. Non-alcoholic fatty liver disease (NAFLD) shares common metabolic risk factors. Several drugs used as the treatment of IBD might cause hepatic injury especially due to the long treatment duration of IBD. Hepatitis B reactivation is another concerning event found after prolonged used of immunosuppressive drugs of IBD. Therefore, close monitoring of liver function test periodically is a mandatory test to screen liver disease in IBD patients. Keyword: Inflammatory bowel disease, liver disease, autoimmune hepatitis, drug-induced liver injury

The Study of Exploring the Policy of Seed Management Systems (PSMS)

The goal of this research is to describe and analyze the Kurdistan Region's seed system development. Also, to examine why and how the formal seed system has been prioritized over other alternatives (farmers’ and community-based seed systems). It further illustrates how the growing seed systems development debate generates challenges for policymakers and governments using the KRG case. Seed sector development policies have been formulated and implemented. The reason is to motivate the new seed policy bill and new agenda for future farmer seed production, with the different actors’ interests influencing seed policy formulation and implementation. The study was conducted from February to June 2023. Primary data for this study were gathered through focus group discussions (FGDs), which were defined as gatherings of individuals with similar experiences or backgrounds to investigate a specific topic of interest. The extensive utilization method used by qualitative researchers to examine qualitative data is a thematic analysis, which typically consists of descriptive information. These themes encompass various aspects related to seed, including seed origin and seed transparency, sustainable agriculture and food security, cooperation with stakeholders, regulation, and the governance gap.

Research on moving object tracking with a large number of outliers based on TRESAC++ algorithm

In contrast to the method based on motion equations, image registration proves effective for tracking moving objects in missions where establishing a motion model is unfeasible. However, in cases where the object is captured at varying imaging angles or against a similar background, a large number of outliers may arise during the process of image feature extraction. Consequently, when confronted with a large number of outliers, image registration methods exhibit a drawback in accurate outlier filtering, especially when spatial constraints on feature matching pairs are constrained. Ultimately, these factors can render object tracking inaccurate or even unattainable. In light of the aforementioned circumstances, this paper proposes TRESAC++, which based on a triplet relation feature matching method to effectively filter outliers. To enhance the operational efficiency of the algorithm, Principal Component Analysis is employed for dimensionality reduction, aiming to decrease the incidence of triple matches and subsequently mitigate execution time. To fortify the algorithm's robustness, a triplet relation linked with an initial data subset selection strategy is additionally proposed. Experimental results on five challenging experiments show that TRESAC++ rivals current state-of-the-art techniques in terms of computational cost, while the implementation exhibits better outlier filtering. Additionally, Experiment 4 and Experiment 5 use moving object datasets to confirm TRESAC++'s superior capability in precise object tracking.