Arithmetic Mean Method Research Articles

Prerequisites of hybridization of university financing as a tool for ensuring sustainability and strategic development

Purpose. To assess the state of budget financing of Ukrainian HEIs, determine their funding structure, and identify prerequisites for developing hybrid financing as a tool for stability and strategic development. Methodology. Dynamic and structural analysis, the Hirschman-Herfindahl index (HHI(DI) and such methods as quantitative, arithmetic mean, limit values, graphic, visualization, and system methods were used in the research process. The practical testing of research hypotheses was carried out on the basis of a comprehensive approach to the financing of HEIs of Ukraine and a panel sample of 35 universities, which takes into account rating indicators, industry and regional aspects. Findings. Transformation processes at various levels are linked to trends such as reduced budget funding, structural changes, staff and student migration, decreased demand for additional services, and military factors, creating prerequisites for hybrid university funding. The article identifies conditions for hybrid financing, highlights negative trends in HEIs budget financing –including systemic underperformance and inflation impacts – and analyzes income diversification in Ukrainian universities using the HHI(DI). Limiting and average values were determined using the quantitative method, which made it possible to classify universities into sustainable (HHI(DI)  0.55) and risky (HHI(DI) > 0.55) ones; tendencies towards a slight diversification of universities’ incomes are revealed; a conceptual model is proposed of hybrid financing of universities to expand their financial autonomy, based on cooperation between university management, stakeholders, and staff core, with the aim of creating value propositions which satisfy the needs of stakeholders and generate cash flows; a new approach to the division of hybrid financing tools into three groups is proposed: passive or image-based, active-intellectual, and passive-innovative ones. This approach is innovative in the development of financial strategies for universities. Originality. New tools for making management decisions aimed at ensuring financial stability and developing financial strategies for universities are proposed. In particular, they include an original approach to the classification of universities into sustainable and risky ones based on the value of the HHI; the conceptual model of hybrid financing of universities, which promotes the expansion of their financial autonomy; the innovative approach to the division of hybrid financing instruments into three categories: passive or image-based, active-intellectual, passive-innovative. Practical value. The study’s results can be used to make management decisions on expanding financial autonomy of universities. The introduction of the conceptual model of hybrid financing will contribute to the diversification of income, increase in financing, as well as ensuring financial stability and strategic development of universities.

Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India

Water quality is critical for public health and environmental sustainability, necessitating effective monitoring to prevent contamination. In this study, we focus on predicting and evaluating water quality in Maharashtra, India, using machine learning techniques. Groundwater contamination in Maharashtra is a significant issue due to poor waste management, yet research in this area is limited. Traditional water quality monitoring methods involve complex calculations based on fixed parameters, which can lead to errors. This study aims to streamline the monitoring process by identifying the most significant features, thereby saving time, money, and energy. We calculated the Water Quality Index (WQI) using the Weighted Arithmetic Mean method, analyzing data from 2012 to 2022 from the National Water Monitoring Program in India. The analysis identified three key parameters, BOD, pH, and Fecal Coliform, as most correlated with the WQI. Machine learning techniques, including regression and classification, were employed to predict WQI and Water Quality Classification (WQC). The results indicate that Polynomial Regression and Ridge Regression achieved high accuracy in predicting the WQI, while the Decision Tree classifier excelled in WQC classification. This research demonstrates the potential of machine learning to enhance water quality monitoring, offering a cost-effective solution for managing water resources in Maharashtra.

PENGARUH DISIPLIN KERJA TERHADAP KINERJA KARYAWAN DI PT ALAM JAYA GRAHA

Work discipline is a crucial factor in achieving a company's goals. This study aims to determine the impact of work discipline on employee performance in the Logistics Division of PT Alam Jaya Graha. The company operates in the transportation sector and is based in Bekasi, West Java. The research was conducted using arithmetic mean and simple linear regression methods, with a sample size of 71 respondents and census is adapted. The findings indicate that the average work discipline score is 3.55, which falls into the "Good" category, and the average employee performance score is 3.56, also categorized as "Good." Furthermore, the simple linear regression analysis shows that work discipline has a positive and significant effect on employee performance (0.00 0.05). The coefficient of determination obtained is 97%, indicating that work discipline has a substantial contribution to influencing employee performance. Future research is recommended to use different methods and a combination of more independent variables

Evaluation of Genetic Diversity in Sorghum Genotypes via Simple Sequence Repeat (SSR) Markers

This study aimed to evaluate the genetic diversity of 20 sorghum [Sorghum bicolor (L.) Moench] genotypes via simple sequence repeat (SSR) markers, with a focus on identifying drought-tolerant varieties. Leaf samples were collected from three-week-old seedlings, and genomic DNA was extracted via the CTAB method, a widely recognized protocol for obtaining high-quality DNA. The DNA samples were quantified to ensure consistent template concentrations for amplification via 25 SSR primers. PCR amplification was performed, followed by agarose gel electrophoresis to separate and visualize the SSR band patterns, which were then recorded in a binary matrix format on the basis of the presence or absence of bands. Among the SSR primers used, nine were polymorphic, generating 13 scorable markers, highlighting the genetic variability among the genotypes. The polymorphic information content (PIC) values ranged across the SSR loci, with the Xtxp145 locus exhibiting the highest PIC value of 0.998, indicating its high discriminatory power and informativeness in differentiating between genotypes. Genetic similarity indices were calculated via Jaccard's similarity coefficient [1], and the data were subjected to cluster analysis via the unweighted pair group method with arithmetic mean (UPGMA) method. The resulting dendrogram grouped the genotypes into seven main clusters at a 50% similarity threshold, underscoring the genetic diversity present within the sorghum genotypes studied. Cluster I contained a single genotype, SVD-1272R, whereas, Cluster II included seven genotypes with subcluster formation. Cluster III comprised one ungrouped genotype, SPV-486. Cluster IV included eight genotypes, whereas Clusters V, VI, and VII each contained a single ungrouped genotype. This dendrogram illustrates the genetic diversity and relationships among the sorghum genotypes on the basis of similarity indices. The findings of this study confirmed the efficacy of SSR markers in assessing genetic diversity and emphasized their potential utility in breeding programs aimed at improving drought tolerance.

Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods.

Background: Fold change is a common metric in biomedical research for quantifying group differences in omics variables. However, inconsistent calculation methods and inadequate reporting lead to discrepancies in results. This study evaluated various fold-change calculation methods aiming at a recommendation of a preferred approach. Methods: The primary distinction in fold-change calculations lies in defining group expected values for log ratio computation. To challenge method interchangeability in a "stress test" scenario, we generated diverse artificial data sets with varying distributions (identity, uniform, normal, log-normal, and a mixture of these) and compared calculated fold-changes to known values. Additionally, we analyzed a multi-omics biomedical data set to estimate to what extent the findings apply to real-world data. Results: Using arithmetic means as expected values for treatment and reference groups yielded inaccurate fold-change values more frequently than other methods, particularly when subgroup distributions and/or standard deviations differed significantly. Conclusions: The arithmetic mean method, often perceived as standard or picked without considering alternatives, is inferior to other definitions of the group expected value. Methods using median, geometric mean, or paired fold-change combinations are more robust against violations of equal variances or dissimilar group distributions. Adhering to methods less sensitive to data distribution without trade-offs and accurately reporting calculation methods in scientific reports is a reasonable practice to ensure correct interpretation and reproducibility.

Genetic diversity and population structure of a modified three-way tomato hybrids for determining fruit size traits

The assessment of genetic diversity and genetic structure of crops has a vital impact on the plant breeding program, including the characterization, use and conservation of genetic material. The genetic diversity and genetic structure of a set of 94 F3 tomato hybrids were assessed with 25 polymorphic SNP markers of quantitative trait loci (QTLs) underlying fruit size in tomatoes using Sequenom Mass ARRAY system. A total of 50 alleles were amplified and the average polymorphic information content (PIC) was 0.2220. Nei's genetic distance ranged from 0 to 0.62. Therefore, single nucleotide sequence (SNP) markers detected a significantly high degree of polymorphism in tomato hybrids. Cluster analysis using the unweighted pair group method with arithmetic mean methods (UPMA) indicated that the tomato hybrids studied are grouped into four main groups, which is to some extent consistent with the size, shape and number of locules of the tomatoes studied. Analysis of population structure with SNP markers revealed three subpopulations. Association mapping using 25 SNP markers detected 9 markers with a significant association with mean fruit weight, fruit length, fruit diameter, number of locules, and fruit shape index. The nine markers detected in this study are recommended for the tomato fruit size improvement breeding program.

Exploration of Anethum graveolens diversity from North Western Himalayan

Anethum graveolens L., an aromatic plant grown during winter season belongs to the family Apiaceae. The leaves and seeds of the plants are widely consumed in India. The seeds of the plants are rich in aroma, due to the richness of essential oils whereas the leaves hold immense potential due to their antioxidant and heavy metal stress alleviation properties. However, the genetic diversity of A. graveolens from North Western Himalayan has not been explored so far. Therefore, the genetic diversity of A. graveolens was investigated using 33 random amplified polymorphic (RAPD) and 27 inter sequence specific region (ISSR) markers. Out of the 33 random primers, only 4 RAPD markers resulted in PCR amplification whereas 18 ISSR markers resulted in reproducible PCR amplification. Out of these ISSR markers, 11 markers accounted for 75–100 % of the polymorphism across all the accessions. Additionally, protein profiling was also used to investigate the diversity of A. graveolens landraces. Overall, the cluster analysis using Neighbor joining (N-J) and unweighted pair group method with arithmetic mean (UPGMA) method followed by principal component analysis (PCA) revealed that the accessions of A. graveolens irrespective to their geographic distribution shared the common group. Furthermore, the antioxidant potential of the local landraces was also investigated based on the free radical scavenging potential using 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. The present current study on exploration of A. graveolens diversity can be helpful in developing conservation strategies of its local germplasm prevalent in North Western Himalayan region.

A clustering machine learning approach for improving concrete compressive strength prediction

AbstractThis study investigates the application of clustering techniques to enhance the accuracy of hierarchical classification and regression (HCR) models for predicting concrete compressive strength (CCS). Following the hypothesis that integrating clustering at the initial levels of model hierarchy reduces classification errors and prevents their propagation to subsequent levels, HCR models were developed utilizing both the unweighted pair group method with arithmetic mean (UPGMA) and hard clustering (HC) methods. Findings demonstrate that models using UPGMA significantly outperform those based on HC. Furthermore, it was demonstrated that further hierarchical clustering allows for multilayered HCR models that improve predictive performance by further leveraging parent–child relationships within data clusters. Overall, this study demonstrates that the proposed methodology can be introduced in the model development pipeline to enhance the prediction accuracy of CCS models.

Comparison of the missing rainfall data method in enhancing discharge estimation

Abstract Missing rainfall data can cause the results of an analysis to be inaccurate. This study aims to compare the performance of different methods in estimating missing rainfall data and to evaluate the estimated missing rainfall data method on hydrograph estimation. The methods used to estimate missing rainfall data in three selected stations include the arithmetic mean (AM) method, the inverse squared distance (ISD) method, and replacing missing value with zero value (ZERO). The study employs root mean square error (RMSE) to assess the accuracy and reliability of the methods. The estimated rainfall data are then utilized in Stormwater Management Modelling Software 5 (SWMM5) to evaluate the data impact on the discharge estimation. The results indicated that the AM method is the best method to estimate missing rainfall data by considering the lowest RMSE value equal to 1.185. In conclusion, the comparison of missing rainfall data methods in this study has shed light on their performance in enhancing discharge estimation.

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

Genetic diversity and morpho-physiological assessment of drought tolerance in rapeseed (Brassica napus L.) cultivars

AbstractWater deficit is one of the most important abiotic stresses constraining crop production in rapeseed. Understanding the mechanisms of adaptation to this stress is essential for the development and production of drought-tolerant genotypes. For this reason, this research study aims to investigate the importance of genetic diversity in identifying genotypes with a high degree of drought tolerance through assessing effectiveness of inter simple sequence repeat (ISSR) markers on 14 genotypes of rapeseed in a factorial design. Morphological and physiological characteristics were studied after the early stages of growth; in order to evaluate the genetic diversity among genotypes, 18 different ISSR markers were used. A total of 106 clear and scalable loci were amplified, of which 60 bands (56.6%) were polymorphic. The highest polymorphism information content belonged to marker number 9 with the amount of 0.365 (85.7%). Gene variation ranged from 0.081 to 0.365 and the rapeseed genotypes were divided into three groups by cluster analysis (unweighted pair group method with arithmetic mean method). The analysis of molecular variance showed that 70% of the total variation was observed within populations and 30% of this variation occurred among populations. In addition, t-test was used for comparing oil content percentage among different genotypes in control and stress levels. Adriana had the highest amount of seed oil with 36.47%, whereas Karaj 2 had the lowest amount with 27.28 and Cooper had the highest decrease in oil content percentage under stress conditions. Overall, the genotypes Likord, Hyola 401 and Sarigol 32 were identified as the most drought-tolerant.

A Study of Sustainability Concepts for Developing Green Universities in Thailand

Developing universities to become green and sustainable universities is important. This is in line with the world’s sustainable development guidelines. In developing a university towards being green and sustainable, different sustainability assessment criteria have been used, including the UI GreenMetric, GRI, STARS, AUN, THE’S Impact Ranking, and AISHE. Each criterion is designed in a broad-spectrum manner that is not specific. Therefore, this research aims to develop sustainable and green university assessment indicators that suit the national context in Thailand, both in terms of the size and location of universities. Based on the criteria from the UI GreenMetric, there are six categories, totaling 51 indicators. Thus, this study aims to analyze the weaknesses and strengths of each indicator using a questionnaire distributed by the Sustainable University Network of Thailand to all 36 universities and to organize a meeting with sustainability experts (focus group) in each area. To obtain appropriate indicators and to prioritize each indicator, the analytic hierarchy process (AHP) and weighted arithmetic mean (WAM) method were used to develop an index of sustainability assessment criteria (GU SI) suitable for the Thai context. The results of this study show that the newly developed GU SI comprises seven aspects, including a total of 27 indicators. These are criteria that can be used to assess the sustainability of small, medium, and large universities. They are not complex, are straightforward to use, and more importantly reflect the Thai context. By applying these new sustainability assessment criteria, it was found that the universities sampled in this study yielded significantly high scores. This study is limited by its application of sustainability assessments to only universities in Thailand. Though there are different contexts across different Thai universities, the proposed indicators can still be used to evaluate the sustainability of universities in Thailand.

Reverse calculation and quantitative estimation of JRC3D for rough rock fracture surfaces in various shear directions

The quantitative evaluation of the three-dimensional roughness feature of natural rock fracture surface is essential for predicting shear strength. This article presents an effective approach to estimate the three-dimensional roughness of natural fracture surfaces from the two-dimensional roughness of fracture profile. Firstly, four types of rough fracture samples were created using rock-like materials, followed by conducting direct shear tests of the fracture surfaces under 4 different normal pressures and 4 shear directions. The results of the shear strength tests exhibited a distinct discrete pattern and consistently increased with higher normal pressures. Furthermore, the distribution of the damage area on the fracture surface after shear was significantly impacted by the distribution and size of asperities, as well as the shear direction. The three-dimensional roughness of four fracture surfaces in different directions was first reverse calculated using the shear strength model. Using the weighted positive angle, the estimation of the three-dimensional roughness in different directions of the fracture surface was then determined from the two-dimensional roughness of fracture profiles using both the arithmetic mean method and the weighted average method. The weighted average method effectively captures the contribution of the two-dimensional roughness of fracture profiles to the three-dimensional roughness of fracture surfaces, and exhibits a lower estimation error compared to the arithmetic mean method. Compared to other roughness indicators, the three-dimensional roughness evaluated through the weighted positive angle indicator are closest to the experimental result. Finally, the influence of transverse spacing on the estimated three-dimensional roughness was discussed.

Combining subjective and objective weights considerations to solve the emergency location selection problems under spherical fuzzy environments

The development of an emergency will cause difficulties in the selection of emergency locations. The COVID-19 pandemic has caused the establishment of emergency medical facilities to become more important and urgent. However, the selection of emergency locations usually involves unclear assessment information, uncertain information, and unacquirable partial information. Due to incomplete evaluation information, traditional fuzzy set and intuitionistic fuzzy set methods cannot effectively deal with the selection problem of emergency locations. In order to effectively solve the selection problem of emergency locations during an emergency, this paper integrated the spherical fuzzy set and subjective and objective weight considerations to provide correct and reasonable evaluation results for the selection of emergency locations. For the numerical verification, this paper applied the selected location for an emergency temporary hospital during the COVID-19 pandemic location in Istanbul, Turkey as an illustrative example and compared the differences in the calculation results among the proposed flexible emergency location selection method, the spherical weighted arithmetic mean method and the spherical aggregation operator method. The numerical verification assessment results demonstrated that the proposed flexible emergency location selection method not only can deal with unclear, uncertain, and even unacquirable partial information at the same time, but also can consider the subjective and objective weights of assessment data, which could provide effective and correct emergency location selections under spherical fuzzy environments.

Nuclear mass predictions with the naive Bayesian model averaging method

A naive Bayesian model averaging (NBMA) method is developed to predict nuclear masses. In the NBMA method, the weights of different models may be different for each nucleus, which are sensitive to the model accuracies to describe the nuclear masses of the isotopes and isotones with the same proton and neutron numbers of that nucleus. Therefore, there are remarkable local structures for the weights of different models on the nuclear chart, which well eliminates the local deviations between the model predictions and the experimental masses and thus achieves better accuracy of mass predictions than the traditional arithmetic mean method (AMM) and weighted average method (WAM). Based on the latest atomic mass evaluation of AME2020, the root-mean-square (rms) mass deviation of the NBMA method is 0.293 MeV, while the rms deviations of AMM and WAM are 0.634 and 0.361 MeV, respectively. This accuracy of the NBMA method is even 28% better than the best accuracy of the mass models used in the NBMA method. The extrapolation ability of the NBMA method is also verified with the experimental nuclear masses which are not used in the training of the NBMA method.

Spatial patterns of congruence or mismatch between taxonomic, functional, and phylogenetic diversity and endemism of perennial flora along the aridity gradient of Chile.

Understanding the relationships between taxonomic, functional, and phylogenetic diversity and endemism across environmental gradients is essential for elucidating the eco-evolutionary mechanisms that shape local plant communities. A database was compiled from field surveys, national herbarium records, and virtual records of perennial plant specimens collected in the aridity gradient of northern Chile, between 18 and 32°S. A large-scale dated phylogeny of available perennial plants was used, and 11 functional traits were selected to construct a dendrogram using the Unweighted Pair-Group Method with Arithmetic Mean (UPGMA) method for the species present in our database. We calculated spatial patterns of a-diversity, including taxonomic (TD), functional (FD), and phylogenetic (PD) diversity, as well as weighted (WE), functional (FE), and phylogenetic (PE) endemism. We used multiscale geographically weighted regression (MGWR) to identify spatial congruencies and discrepancies among these dimensions and to test different eco-evolutionary processes. The diversity indices TD, FD and PD showed similar geographic patterns (R2 > 0.93), with lower diversity observed in absolute desert regions. The pattern of weighted endemism (WE) showed a weak association with functional endemism (FE) and phylogenetic endemism (PE) (local R2 < 0.48). The regions with lower FD or PD than expected given the TD (i.e. FD<TD and PD<TD) are mainly located in desert areas, as well as in high Andean areas influenced by the Atacama Desert, suggesting communities with associated in situ speciation processes, as well as a limitation of morpho-functional trait diversity in response to extreme environmental conditions (environmental filter hypothesis). Similarly, where FE and PE values are higher than expected given the WE (i.e. FE>WE and PE>WE), they are found in arid, high Andean and transitional zones, at different altitudes, which would indicate a greater presence of phylogenetic lineages and species with morpho-functional traits related to extreme environmental conditions and transitional biomes (arid-semiarid). These spatial discrepancies suggest different eco-evolutionary drivers between the dimensions of diversity and endemism (taxonomic, functional, and phylogenetic). Areas of high diversity and high endemism do not necessarily coincide, and both should be addressed by conservation efforts.

Comparative Sensitivity Analysis of Some Fuzzy AHP Methods

A precise evaluation of the actual situation is a significant aspect of making a correct and informed decision. Due to the bounded accuracy and elements of uncertainty in the data itself, a point estimate may be less adjusted and rough than an estimate based on fuzzy set theory. The stability of the Fuzzy AHP Arithmetic mean, Geometric mean, Extent analysis, and Lambda Max methods, widely used in practice, is verified. Three stages of verification are considered, investigating the impact of the following: (a) the scale applied; (b) methods of aggregation of the AHP matrices into the FAHP matrix; and (c) methods of combining several FAHP judgments. Slight changes in experts’ estimates are programmatically simulated tens of thousands of times to track changes in ranking and deviations of results from the initial estimate. This continues the study of FAHP’s stability due to the ambiguous results of such verification by the method of extent analysis. As a result of a comparative analysis of the listed evaluation methods, their specific features and advantages are identified.

A multi-sensor data fusion algorithm based on consistency preprocessing and adaptive weighting

ABSTRACT In the data collection of a multi-sensor system, there are problems with large errors, conflicts, and redundancy. To solve the above problem, a multi-sensor data fusion algorithm based on anomaly data preprocessing and adaptive weighted estimation is proposed. To improve the reliability of the algorithm, first, for a single sensor measurement signal sequence, a consistency preprocessing using the off-centre distance method is performed, and the weighting factor of each measurement data is calculated. Then, the measurement signal sequence is weighted and fused; Secondly, in response to the uneven distribution of measurement errors among multiple sensors in different directions, an adaptive weighted data fusion method based on the principle of optimal weight allocation is proposed. The proposed method was compared with the adaptive weighting method and arithmetic mean method. The simulation results showed that the total mean square error of the data fusion results obtained using the proposed algorithm is smaller. The proposed algorithm can effectively improve the accuracy of data measurement, reduce redundancy, and improve the stability of data measurement.

Evaluating Suitability of Fishing Areas for Squid-Jigging Vessels in the Northwest Pacific Ocean Derived from AIS Data

Understanding the spatial distribution of fishing activity and suitable fishing areas is important for improving sustainable fisheries management and protecting vulnerable fish stocks. To identify climate-related habitat changes and variations in the distribution of fishing activity for squid-jigging vessels in the Northwest Pacific Ocean, two types (weighted arithmetic mean method, weighted-AMM; weighted geometric mean method, weighted-GMM) of habitat suitability index (HSI) models were developed in this study with marine environmental data at different depths. The boosted regression tree (BRT) model was adopted to access the monthly important environmental variables and the relative influence of the corresponding variables. The results showed that the weighted-AMM has better prediction performance than the weighted-GMM. The suitable fishing areas showed significant seasonal changes in both spatial location and coverage area. The hotspot map showed that the suitable fishing area for squid-jigging vessels was located in the scope of 42∘ N∼44∘ N, 155∘ E∼170∘ E throughout the year during 2012∼2019, which suggests that high squid-jigging fishing pressure should be given more attention in fishery management. The HSI model also had good prediction performance for the fishery data of Chinese companies, except for June and July. Additionally, fishing efforts could be used as alternative data for fishery research. The study has also suggested that fishery data are restricted by spatial and temporal distribution and fishing experience, which probably biases the results of the research.

Genetic variability of Tachigali vulgaris trees based on seed morphophysiological traits

Tachigali vulgaris is a tree with high potential for energy purposes in Brazil. However, there is little information about genetic variability that can be applied to plant breeding. Here, we quantified genetic variability based on biometric and emergence traits of T. vulgaris seeds from the native population of the Brazilian Amazon. The biometric aspects, moisture content, emergence, emergence speed index, and average emergence time of the seeds were evaluated. Genotypic values and genetic parameters were analysed using the mixed model methodology in a completely randomised design with 12 genotypes and four replications of 25 seeds. All biometric and emergence traits of seeds showed phenotypic and genetic variabilities. Heritability was high for all traits evaluated (0.66 to 0.97), and genetic accuracy showed high experimental precision and reliable results (≥ 0.90). Grouping by the Tocher, unweighted pair group with arithmetic mean and principal component analysis methods based on genetic traits returned four groups: Group 1 by Genotypes 1, 4, 5, 6, 7, 8, 9, 10 and 12; Group 2 by Genotype 3; Group 3 by Genotype 2; and Group 4 by Genotype 11. In addition, Genotypes 1, 3–10 and 12 displayed seeds with superior physical and physiological traits and a high probability of genetic gain. Our findings can be used in the selection and marking of trees with a broad genetic base for seed production and plant breeding of this species.