Dominant Variables Research Articles

Faktor-Faktor Yang Mempengaruhi Pendapatan Transmigran Di Kabupaten Tabalong (Studi Kasus Transmigran Di Kelurahan Argomulyo)

The problems that occur among transmigrants, namely poverty is often associated with low income or low income, therefore low income can affect the transmigration program. To measure the income of transmigrant communities, you can look for variables related to income such as level of education, capital. effort, costs, work period, and work hours. This research aims to find out what factors can influence the income of transmigrants in Argomulyo village. The analysis used in this research is multiple linear regression analysis. The data sources are primary and secondary data. The results of the research are that capital, length of service, working hours and level of education simultaneously influence the income of transmigrants in Argomulyo Village. The dominant independent variable that partially influences transmigrants' income is working hours, and the independent variable that does not partially influence income is capital.

Fast Methods for Computing Photometric Variability of Eccentric Binaries: Boosting, Lensing, and Variable Accretion

We analyze accretion-rate time series for equal-mass binaries in coplanar gaseous disks spanning a continuous range of orbital eccentricities up to 0.8 for both prograde and retrograde systems. The dominant variability timescales match those of previous investigations; the binary orbital period is dominant for prograde binaries with e ≳ 0.1, with a 5 × longer “lump” period taking over for e ≲ 0.1. This lump period fades and drops from 5 × to 4.5 × the binary period as e approaches 0.1, where it vanishes. For retrograde orbits, the binary orbital period dominates at e ≲ 0.55 and is accompanied by a 2 × longer timescale periodicity at higher eccentricities. The shape of the accretion-rate time series varies with binary eccentricity. For prograde systems, the orientation of an eccentric disk causes periodic trading of accretion between the binary components in a ratio that we report as a function of binary eccentricity. We present a publicly available tool, binlite, that can rapidly (≲0.01 s) generate templates for the accretion-rate time series onto either binary component for choice of binary eccentricity below 0.8. As an example use case, we build lightcurve models where the accretion rate through the circumbinary disk and onto each binary component sets contributions to the emitted specific flux. We combine these rest-frame, accretion-variability lightcurves with observer-dependent Doppler boosting and binary self-lensing. This allows a flexible approach to generating lightcurves over a wide range of binary and observer parameter space. We envision binlite as the access point to a living database that will be updated with state-of-the-art hydrodynamical calculations as they advance.

The Influence of Product Quality and Price on Galvalum Purchasing Decisions

The research ascertain the impact of product pricing and quality on the purchasing decision galvalum at the Sumber Rejeki Shop, Tanjunganom Nganjuk District, partially and simultaneously, and to analyze the influence of the dominant variables on the decision to purchase galvalum at the Sumber Rejeki Shop, Tanjunganom Nganjuk District. This type of research uses a quantitative. This research used a sample of 96 respondents. Sampling used a nonprobability sampling technique with the Accidental Sampling method. The test used is data analysis using SPSS 25, in this research using a questionnaire or questionnaire and Multiple linear regression analysis is employed. Based on the outcomes of test it can be concluded that partially, Product Quality (X1) and Product Price (X2) partially have a positive and significant effect on the decision to purchase galvalum at the Sumber Rejeki Shop, Tanjunganom Nganjuk District. Product quality (X1) and product price (X2) simultaneously have a positive and significant effect on the decision to purchase galvalum at the Sumber Rejeki Shop, Tanjunganom Nganjuk District. The Product Price Variable (X2) is more dominant in influencing the decision to purchase galvalum at the Sumber Rejeki Store, Tanjunganom Nganjuk District than the Product Quality Variable. Keywords: Product cost, product quality, and purchasing choices

Water quality classification using self-organizing maps and cluster analysis: Case of Meknes-El Hajeb Springs, Morocco.

The Ouislane sub-watershed is currently experiencing severe water shortages and is highly dependent on its water supply. The sub-watershed spans two communes: Meknes to the north and El Hajeb to the south. It serves as the primary water source for irrigation and drinking purposes for the local population. Consequently, it is crucial to assess the spatio-temporal variations of water quality to identify and address potential gaps; these focused on effective monitoring systems to detect contaminants, pollutants and health risks. This research project aims on the application of self-organizing map (SOM) techniques combined with cluster analysis to classify water quality in springs for drinking and irrigation purposes. The present study evaluates the water quality variations using physicochemical parameters of twelve water springs, collected during the wet and dry seasons of 2022. For this purpose, the water quality index (WQI), self-organizing map (SOM), hierarchical cluster analysis (HCA), and principal component analysis (PCA) are used as evaluation and classification methods. As a result, the SOM algorithm with a size of 5 × 5 units identified as the most suitable, based on the minimum quantization error (QE) and topographic error (TE), yielding a QE of 0.379 and a TE of 0.000. It grouped the water quality data into five distinct clusters, Cluster I represented 37.5% of the total samples, while cluster II represented 25%. Cluster III and IV each accounted for 8.33% of the samples, while 20.83% of the sampling water are classified in cluster V. Clusters I, II, and IV indicate good water suitable for drinking. However, cluster V had the highest WQI, suggesting very high contamination due to increased levels of the 10 studied physicochemical parameters. The water quality in this region (cluster V) is influenced by natural processes, such as precipitation intensity, weathering and vegetation cover, as well as anthropogenic factors like agriculture and urban concentration. PCA confirmed the clustering results obtained by SOM. However, SOM provides a more detailed classification and additional insights into the dominant variables influencing the classification processes. The results of this study suggest that SOM was an effective tool for gaining a better understanding of the patterns and processes driving water quality in the Ouislane sub-watershed and provides valuable avenues for further research to establish and monitor water quality for effective management of water resources.

How are urban design qualities associated with perceived walkability? An AI approach using street view images and deep learning

ABSTRACT This study attempted to provide further understanding of the association between walkability and urban design qualities in Xi’an, China. The novelty of this paper is to employ street view image data to predict people’s perceptions of walkability by constructing deep convolutional neural networks. In general, our results confirm the relationship between urban design qualities and perceived walkability in a mixed area of modern and traditional cityscapes and urban forms. Specifically, the most dominant variable is complexity, which has both positive and negative impacts, and imageability and transparency are negatively linked to walkability. It was also found that the impact of five urban design qualities on walking perception is clearly spatially differentiated.

#FamilyisLove: understanding extreme life experiences and psychological profile of pre-service teachers in the Philippines.

Despite the focus of studies in other countries that emphasize the significant impact of household and school experiences on child development, in the Philippines, the few studies available tend to focus on the prevalence of suicide cases, bullying, and other school experiences that impact mental health. No explicit studies relate demographic information, relationships and past experiences, personality variables, and household treatment among Filipino students with regard to their own mental wellbeing. Thus, with the primary aim of describing the psychological profile of second-year pre-service teachers taking Bachelor of Technical Vocational Teacher Education for the academic year 2019-20 and to identify possible interventions and propose policy recommendations to promote mental healthcare, wellbeing, and resilience, the Basic Personality Inventory (BPI) results and focus group discussion interview texts were utilized for this investigation. The explanatory sequential mixed methods research was employed to undertake a study of student participants (n = 91) aged 18-35 years (X¯ = 19.9 years, SD = 2.00) using descriptive statistical tools for quantitative data derived from BPI results and textual/thematic analysis (visual and content) for qualitative data. The dominant personality variables reported as areas of weakness were thinking disorder, persecutory ideas, deviation, and self-depreciation. On the other hand, the identified areas of strength were cooperativeness, capacity for self-criticism, cheerfulness, and socially responsible attitudes. Finally, familial relationships/household treatment and previous experiences at home and in school contributed to the respondents' strengths and weaknesses. For this reason, various mental health-related intervention activities, policies, and programs are recommended.

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

ECOLOGICAL NICHES AND IMPLICATIONS FOR REFORESTATION OF PTEROCARPUS ERINACEUS, KHAYA SENEGALENSIS AND ISOBERLINIA DOKA IN THE CLASSIFIED FORESTS OF NORTHERN COTE DIVOIRE

Deforestation in Cote dIvoire, particularly in the Pale and Pouniakele classified forests, threatens biodiversity and essential ecosystem services. This study explores the potential of Pterocarpus erinaceus, Khaya senegalensis, and Isoberliniadoka to restore degraded ecosystems through reforestation projects, using maximum entropy modeling. The objective is to analyze the ecological niches of these species in order to optimize reforestation strategies. The results show that Pterocarpus erinaceus has a high tolerance to climate variations, with an average AUC of 0.878, and a concentrated distribution in the central and northern savannahs. The main factors influencing its presence are the standard deviation of temperatures (47%) and humidity (15.4%). Khaya senegalensis, on the other hand, is well adapted to open environments with low vegetation cover and high climate variability. Its mean AUC of 0.893 indicates a robust prediction, with areas of presence in the central-eastern part of the country. Major contributions include temperature standard deviation (21.4%) and NDVI (18.5%). Finally, Isoberliniadoka shows greater sensitivity to stable climatic conditions, with a mean AUC of 0.848 and a presence limited to northern regions, where forest cover (32.1%) and mean temperature (39.7%) are the dominant variables. Ecological differences are observed between species, mainly influenced by climate variability and vegetation density. This implies the importance of adapting reforestation programs to the environmental specificities of each species. This study offers practical recommendations to improve the sustainability of reforestation projects in Cote dIvoire, taking into account local ecological challenges.

Analysis of the Determinants for the Islamic Social Responsibility

This research aims to determine the influence of firm size, profitability, leverage, and board size on Islamic social responsibility. The population in this research are companies listed on the Indonesian Stock Exchange in 2020–2022. In this research, we will see the simultaneous and partial influence and the dominant variables using data analysis tools in the form of multiple linear regression, coefficient of determination, hypothesis testing using the F test and t-test, and the classical assumption test. Based on the research results, it can be concluded as follows: Simultaneously, firm size, profitability, leverage, and board size influence Islamic social responsibility. Partially, it can be seen that not all variables have a significant effect on Islamic social responsibility. Firm size and leverage influence Islamic social responsibility, while profitability and board size have no influence.

Development of Polymorphic Index Model for Assessing Subtropical Secondary Natural Oak Forest Site Quality Under Complex Site and Climate Variables

Site and climate conditions are the key determinants controlling dominant height growth and forest productivity, both independently and interactively. Secondary natural oak forests are a typical forest type in China, especially in Hunan Province, but little is known about the site index of this forest under the complex site and climate variables in the subtropics. Based on survey data of dominant trees and site variables from 101 plots in Hunan oak natural secondary forests and climate data obtained using spatial interpolation, we used the random forest method, correlation analysis, and the analysis of variance to determine the main site and climate factors affecting oak forest dominant height and proposed a modeling method of an oak natural secondary forest site index based on the random effect of site–climate interaction type. Of the site variables, elevation affected stand dominant height the most, followed by slope direction and position. Winter precipitation and summer mean maximum temperature had the greatest impact on stand dominant height. To develop the modeling method, we created 10 popular base models but found low performance (R2 ranged from 0.1731 to 0.2030). The optimal base model was Mitscherlich form M3 (R2 = 0.1940) based on parameter significance tests. Since site and climate factors affect the site index curve, the dominant site and climate factors were combined into site types and climate types, respectively, and a nonlinear mixed-effects approach was used to simulate different site types, climate types, site–climate interaction types, and their combinations as random effects. Site–climate interaction type as a random factor enhanced model (M3.4) performance and prediction accuracy (R2 from 0.1940 to 0.8220) compared to the optimum base model. After clustering the 62 site–climate interaction types into three, five, and eight groups using hierarchical clustering, a mixed-effects model with the random effects of eight groups improved model performance (R2 = 0.8265) and applicability. The modeling method developed in this study could be used to assess a regional secondary natural oak forest site index under complex site and climate variables to evaluate the forest productivity.

A comparative study of the effects of urban morphology on land surface temperature in Chengdu and Chongqing, China

Urbanization combined with global climate change, exacerbates the urban thermal environment and hinders sustainable urban development. However, the complex relationships between land surface temperature (LST) and urban morphology are being further understood, particularly in relation to different urban development patterns, distinct topography, and 3D building morphology. Thus, this study conducted a comparative study in Chengdu and Chongqing, Southwest China. We explored the impact of comprehensive factors (including socio-economic factos, topography, land use composition, and building morphology) on LST by employing the methods of linear regression, geographical detector model, and the boosted regression trees. Our results suggest that (1) high LST was mainly observed in the central part of Chengdu but it presented multicenter aggregation trend in Chongqing; (2) Socio-economic factors were the dominant variables affecting LST in both cities; (3) land use composition and building morphology showed distinct contributions to LST among the two cities; and (4) 3D building management was more effective in Chengdu than in Chongqing. A better understanding of the impact of various influencing factors on LST will enable policy makers and planners to develop appropriate strategies for constructing climate-adaptive cities and mitigating urban heat.

The Influence Of Motivation, Work Environment, And Competency On Asn Performance In The Public Works, Spatial Planning, Housing And Settlement Area (Putrpkp) Enviroment Of Takalar District

The Influence of Motivation, Work Environment, and Competence on the Performance of Civil Servants in the Public Works, Spatial Planning, Housing and Settlement Areas Office (PUTRPKP) of Takalar Regency, guided by Andi Mappatompo Badawi and Dg. Maklassa. This study aims to analyze and explain the Influence of Work Motivation, Work Environment, and Competence on the Performance of Civil Servants in the Public Works, Spatial Planning, Housing and Settlement Areas Office (PUTRPKP) of Takalar Regency. This type of research is quantitative research, which is research based on data that can be calculated to produce a solid assessment using SPSS. This study used 68 respondents using multiple linear analysis techniques. The results of this study show that work motivation has a significant positive influence on ASN performance with P = 0.007 < 0.05 with a coefficient value of 0.380. The work environment has a positive and insignificant influence on ASN performance with P = 0.502 > 0.05 with a coefficient value of 0.118. Competence has a significant positive influence on ASN performance with P = 0.024 < 0.05 with a coefficient value of 0.280. The Work Motivation variable is the most dominant variable that affects ASN Performance.

Pengaruh Faktor Pengembangan Sumber Daya Manusia terhadap Tingkat Produktivitas Kerja Pegawai Negeri Sipil pada Badan Perencanaan Pembangunan Daerah Kabupaten Fakfak

Human resource development both those working in the public sector (Civil Servants, SOE Employees, BUMD Employees) and sectors. Talking about organizational problems is inseparable from the element of human resources. The definition of human resources is people in an organization who participate and contribute according to their respective fields of duty. Human resource development will indeed not see results quickly, but in the long term, this investment will yield results that meet the expectations of the organization.In this sense, the term development will include the meaning of training and education, namely as a means of improving skills and general knowledge for human resources, in this case Civil Servants who work at the Fakfak Development Planning Agency. In the work unit at the Fakfak Development Planning Agency, it can be achieved well if there is a Civil Servant development program.This study aims to find out the influence of human resource training and development factors on the productivity of civil servants on the Fakfak Regency BAPPEDA and to find out the most dominant variables of human resource training and development factors affecting the productivity of civil servants in the Fakfak Regency BAPPEDA.The research methods used are quantitative and qualitative research methods. Based on the results of important findings of the Civil Servant Development Method research, there are 2 (two) methods of employee development, namely the on the job training method and the off the job training method.

Analysis and Suppression of Oscillations in Doubly Fed Variable Speed Pumped Storage Hydropower Plants Considering the Water Conveyance System

The doubly fed variable speed pumped storage (DFVSPS) system is a hydraulically, mechanically, and electrically coupled system, and the characteristics of the components from the water conveyance system to the transmission line need to be fully considered in the oscillation analysis. Hence, the model of the water conveyance system is included to investigate the oscillation characteristics of the DFVSPS connecting to the grid via a series-compensated line. A small-signal state-space model of the DFVSPS system in the generation mode is first established. The oscillation characteristics of the DFVSPS are studied, and the dominant state variables for each oscillation mode are identified. The impact of system parameters on oscillations is further studied, and simulations are carried out to validate the accuracy of the model. The results indicate the oscillation mode of the DFVSPS comprises the electrical sub-synchronous oscillation (SSO) mode and the hydraulically, mechanically coupled low-frequency mechanical oscillation modes. When the series compensation level is high, the SSO becomes divergent, and the system is more likely to be unstable. Optimizing the rotor-side control parameters and the governor control parameters, sub-synchronous and low-frequency oscillations could be effectively suppressed, respectively. This study provides reference suggestions for the development and use of the future DFVSPS system.

Estimation of genetic components and assessing heterosis association with molecular markers in half diallel derived rice hybrids developed with landraces of NE India

Combining ability and heterosis were estimated in 45 rice hybrids generated by mating 10 parents, comprising both land races and advance breeding lines, in half diallel fashion. Hayman’s graphical analysis showed that all the twelve morphological traits studied had significant positive dominance variance. Narrow sense heritability was high for Days to fifty percentage flowering, Plant height, Panicle length and Test weight (> 30 %), and low for Grain yield per plant. According to the VrWr graph, traits like GYPP, PL, PH, TN and plot yield showed partial dominance, whereas traits like Blast disease index showed overdominance. A significant improvement over check was observed for heterosis and the crosses Mynri x CAUS103, Joha x CAUS107, CAUS 126 x CAUS107 and CAUS103 x CP, exhibited significantly positive standard heterosis for GYPP (>80 %) and PY (>100 %). Genetic distance based on fourteen markers revealed significant positive association with mid parent and better parent heterosis for Spikelet’s per plant, Filled grains per plant, Harvest index, Grain yield per plant, Panicle number and Plot yield. Therefore, it is evident that crosses with larger genetic distance between the parents gave hybrids with higher heterosis. Keywords: Landraces, Hayman’s graphical analysis, Heterosis, Genetic distance

Examine the Role of Indo-Pacific Sea Surface Temperatures in Recent Meteorological Drought in Sudan

Drought poses a serious threat to Sudan, causing water shortages, crop failures, hunger, and conflict. The relationships between Indo-Pacific teleconnections and drought events in Sudan are examined based on the Standardized Precipitation Index (SPI), anomalies, Empirical Orthogonal Function (EOF), correlation, composite analysis, sequential Mann–Kendall test, and MK-trend test during the period of 1993–2022. The results of the SPI-1 values indicate that the extreme drought in Sudan in 2004 was an exceptional case that affected the entire region, with an SPI-1 value of −2 indicating extremely dry conditions. In addition, Sudan experienced moderate to severe drought conditions for several years (1993, 2002, 2008, 2009 and 2015). The Empirical Orthogonal Function showed that the first EOF mode (42.2%) was the dominant variability mode and had positive loading over most of the country, indicating consistent rainfall variation in the central, eastern, and western regions. Correlation analysis showed a strong significant relationship between June–September rainfall and Indian Ocean sea surface temperature (SST) (r ≤ 0.5). Furthermore, a weak positive influence of the Indian Ocean Dipole (IOD) on JJAS precipitation was observed (r ≤ 0.14). Various time lags in the range of ±12 months were examined, with the highest correlation (0.6) found at 9 month among the time lags of ±12 months. This study contributes to a better understanding of drought dynamics and provides essential information for effective drought management in Sudan. Further research is needed to explore the specific mechanisms driving these interactions and to develop tailored strategies to mitigate the impacts of drought events in the future.

Comparative analysis of advanced deep learning models for predicting evapotranspiration based on meteorological data in bangladesh.

Evapotranspiration is one of the crucial elements in water balance equations and plays a pivotal role in the water and energy cycle of an area. An accurate and precise estimation and prediction of reference evapotranspiration (ETo) is necessary for regional management of water resources and irrigation scheduling. The challenge of predicting daily evapotranspiration with limited meteorological data in Bangladesh. This study aims to predict daily evapotranspiration using limited meteorological data of Bangladesh by three deep learning (CNN, GRU, LSTM) and one hybrid (CNN-GRU) model. The novel method of hybrid CNN-GRU models, which have not been commonly used for this purpose. The performance of models was evaluated by five accuracy matrices R2, RMSE, MAE, MAPE, and CE and comparison is visualized by radar graphs. The study's novelty lies in the use of hybrid CNN-GRU models to estimate reference evapotranspiration, as this algorithm has not been commonly used for this purpose. In the case of the Rangpur station, the hybrid CNN-GRU algorithm outperformed other models, achieving the best values across various statistical metrics during both the training and testing phases. The highest correlation coefficient values of approximately 0.994 and 0.995. Moreover, during training and testing stages, the hybrid model had the lowest MAE (0.076, 0.068) and RMSE (0.138, 0.106) at the Rangpur station. Additionally, in the Sreemangal station, it can be notable that the statistical parameter RSME found superior results in the hybrid model around 0.225 and 0.174, respectively. In addition, the highest R2 and CE values were noted as 0.986, 0.987 and 0.985, 0.986 during the training and testing phases, respectively. The comparison suggests that the hybrid model will be best suited for prediction with the limited meteorological data. The outcome of the present research signifies the ability of deep learning methods in the prediction of evapotranspiration and the dominant variables affecting the changes the in context of Bangladesh.

Predicting the potential geographical distribution of Zingiber striolatum Diels (Zingiberaceae), a medicine food homology plant in China.

Zingiber striolatum Diels is a unique medicine food homology plant native to China. In recent years, due to severe habitat destruction, studying the impact of climate change on the distribution of wild resources is of great significance for the ecological conservation and artificial cultivation of Z. striolatum. This study collected 141 valid species distribution records, and 37 environmental variables, and projected two future climate scenarios (SSP126 and SSP585) for two periods (2050s and 2090s). By employing Pearson analysis, Maximum Entropy Model (MaxEnt), and Geographic Information System (ArcGIS), we predicted the potential suitable habitats for Z. striolatum under present and future climates, as well as identified the dominant environmental variables influencing its distribution. The results indicated that the MaxEnt model performed well (AUC > 0.9) with high accuracy and reliability. The dominant environmental factors included Precipitation of driest quarter (39.0 ~ 473.8mm), Precipitation of wettest quarter (593.2 ~ 1269.4mm), Temperature annual range (9.8 ~ 28.6℃), and Mean diurnal range (6.5 ~ 9.6℃). The highly suitable areas for Z. striolatum were mainly distributed in western and southern Yunnan, northern and western Guangxi, Guangdong, Fujian, and central Hainan. Under future climate change, the centroid of the total suitable area for Z. striolatum is projected to shift towards the southwest (Yungui Plateau) at higher elevations.

MULTILEVEL MODAL VALUE ANALYSIS FOR INTERPRETING CATEGORICAL K-MEDOIDS CLUSTERS DATA

Consumer segmentation plays a crucial role for business owners in developing their enterprises. K-Medoid is commonly used for segmentation functions due to its low computational complexity. However, K-Medoid has limitations, such as the variability in cluster sizes across different iterations and the challenge of determining the optimal number of clusters. The Davies-Bouldin Index (DBI) is a metric used to evaluate the number of clusters by calculating the ratio between the within-cluster distance and the between-cluster distance. Most segmentation studies typically stop at the formation of clusters without further interpretation, particularly when dealing with categorical data. This study aims to modify the use of K-Medoid and propose a method for interpreting clusters with categorical data. The research began with questionnaire design and the data collecting from 100 respondents, which was normalized in the second stage. Clustering used K-Medoid with variations K values from K=2 to K=10, with each K value tested 10 times. The clustering results were evaluated using the DBI to select the optimal clusters. Data interpretation conducted using modal values, calculated as the ratio of the number of times a specific attribute variable was selected by respondents to the total number of data points in the cluster. Utilization and hierarchical visualization of modal values proposed in this study offer insights into the dominant variables within an attribute and also depict the relationships between attributes based on the ranking of modal values. These advantages facilitate business analysts in labeling clusters for developing consumer-driven business strategies.

Evaluation of Proximate Composition, Multielement, and Bioactive Phenolics Contents of Different Coix Seed Varieties using Multivariate Analysis Techniques

Coix lacryma-jobi L. is a plant that serves as a source of food, medicine, cosmetics, and forage in Asian countries. Due to the distinct geographic environments, Coix seed germplasm resources are extremely diverse. In this study, we evaluated the proximate composition, multi-elemental content by ICP-OES, and phenolic bioactives by UPLC-QTOF/MS of five varieties of Coix seeds from different Asian countries, including China, Thailand, Indonesia, and Malaysia. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were used to classify the different varieties of Coix seeds. The C. lacryma-jobi var. ma-yuen seeds from Origin 1 (China) and Origin 2 (Thailand) contained high levels of energy, total fat, and calcium, while C. lacryma-jobi var. agrotis (Indonesia) and C. lacryma-jobi var. lacryma-jobi (Malaysia) had high levels of crude fiber and carbohydrates. Twenty-three phenolic compounds were identified. Protein, carbohydrate, crude fiber, magnesium, zinc, meliadanoside, and 3,4-dihydroxyphenethyl-3-O-β-D-glucopyranoside were the dominant variables and contributed the most to data variability in PCA. The HCA results were consistent with the PCA, classifying the samples into three groups: those rich in nutrients, those rich in phenolics, and those with a mixture of nutrients and phenolics. This comprehensive analysis provides valuable insights into the nutritional and bioactive composition of Coix seed varieties, with potential applications in nutrition, food science, and pharmaceuticals.