Sum Of Squares Research Articles

Invariance principle for independent random variables with infinite variance

A functional central limit theorem for self-normalized adaptive process U−1m,N ζn is considered, where Um,N is a sum of squares of block-sums of size m, as m and the number of blocks N = n/m tend to infinity.

Optimisation of artificial intelligence models and response surface methodology for predicting viscosity and relative viscosity of GNP-alumina hybrid nanofluid: incorporating the effects of mixing ratio and temperature

The viscosity properties of GNP-alumina hybrid nanofluids are of significant importance in various engineering applications. This study compares the predictive performance of response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for the viscosity (µrel) and relative viscosity (µrel) of GNP-alumina hybrid nanofluid at varying mixing ratio (0–3) and temperature (15–55 °C). The ANN and ANFIS models were optimised by varying the number and type of neurons and membership functions (MFs), respectively. In contrast, the RSM model was optimised by varying the source model. The efficacy of the models was assessed using various measures of performance metrics, including residual sum of squares, root mean square error, mean absolute error, and mean absolute percentage error (MAPE). The ANN architecture with 4 neurons exhibited exceptional proficiency in forecasting the µnf, achieving an R2 value of 0.9997 and a MAPE of 0.3100. Meanwhile, the best ANN architecture for the µrel was achieved with 5 neurons, resulting in an R2 of 0.9817 and MAPE of 0.2588. Furthermore, the ANFIS model with the difference of two sigmoidal MFs and the product of two sigmoidal MFs for µnf and Generalized Bell MFs for µrel exhibited the best performance with (3 5) and (4 5) input membership functions, respectively. An R2 value of 0.9999 and 0.9872, with a corresponding MAPE value of 0.0945 and 0.1214, were reported for the optimal ANFIS architecture of µnf and µrel, respectively. The RSM model also produced its most accurate prediction with the quadratic model for both µnf and µrel, with an R2 value of 0.9986 and 0.8835, respectively. Thus, comparative analysis across various models indicated that the ANFIS model outperformed others regarding performance metrics for both µnf and µrel. This study underscores the potential of ANN and ANFIS models in accurately forecasting the viscosity properties of GNP-alumina hybrid nanofluids, thus offering reliable tools for future applications.

Spatial and Temporal Analysis of Rainfall Variability and Trends for Improved Climate Risk Management in Kayonza District, Eastern Rwanda

The variability, intensity, and distribution of rainfall have drawn a lot of interest globally and especially in nations where rainfed agriculture is the norm. This article uses rainfall data from the Rwanda Meteorology Agency for the years 1981 to 2021 to delineate and analyze rainfall variability and trends in the Kayonza District. The time series were grouped using the K-means clustering technique based on computed Euclidean distance, the total within-cluster sum of squares, and the elbow plot technique to determine the optimal number of clusters. The coefficient of variation measures was employed to analyze rainfall variability, while Sen’s slope and the Mann–Kendall (MK) test were used, respectively, to find trends and changes in magnitude. The results indicated four near homogeneous zones named region one to four. The dry seasons indicated higher variability compared to rainy seasons and annual rainfall total with a variability of 128–142% over the southeastern part during June to August (JJA) season, while a variability of 16–48% was observed over most of the district during both annual and rainy seasons. It was further noted that the areas in the central part of the Kayonza District indicated a significant increasing trend at a significance level of 95% and above during January to February (JF), September to December (SOND), and on annual basis, while March to May (MAM) and JJA season exhibited no significant trend. The findings of this study are essential for creating adequate mitigation strategies to lessen climate change’s effects on agriculture as well as other socioeconomic sectors.

A machine learning approach to color space Euclidization

AbstractIn this work, a machine learning methodology is proposed for the issue of color space Euclidization. Given a color difference formula as reference distance law, the Euclidization task consists in finding an injective transformation from the original color space into a real vector space and the corresponding inverse transformation, such that the Euclidean distances in the embedded color space align with the reference color distances. For this, artificial neural networks are devised as function approximators for the color space transformations being sought. Training these neural networks is accomplished through unsupervised learning, making use of random sampling and gradient descent. As key disagreement measure, either the (symmetric) relative isometric disagreement or the standardized residual sum of squares (STRESS) index is considered at a time and incorporated as part of the optimization criterion into the objective function. Comparative evaluation is carried out on well‐established color distance laws, including the CIELAB‐based DE2000 color difference formula. The evaluation results indicate significant performance advantages of the proposed approach over previous contributions.

Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm

Accurate estimation of the model parameters of a fuel cell is extremely important to study its system behavior. This paper presents a parameter identification method based on improved artificial bee colony algorithm (IABC). Inspired by the cross operation of genetic algorithm and the cross and mutation operation of differential evolution algorithm, this study improves the position update formula of ABC in the parameter optimization process, thus the convergence accuracy and speed of IABC algorithm are improved. The algorithm was used to identify 3 typical standard test cases, namely 250W FC, BCS 500W and NedStack PS6 stack, and the minimum sum of squares (SSE) error between PEMFC measured voltage data and estimated voltage data was defined as the objective function. The SSE values of the three PEMFCs reached 0.29686, 0.011718 and 2.9848, respectively. The model I/V polarization curve estimated by IABC algorithm has a high fitting degree with the experimental measured curve, which indicates the accuracy of IABC algorithm. To verify the superiority of the proposed IABC algorithm, the identification results obtained by some optimization algorithms are compared and ANOVA (analysis of variance) is performed. Results show that the IABC algorithm has faster convergence speed and better robustness.

Performance Evaluation of Brinjal (Solanum melongena L.) under Mid Hill Conditions of Uttarakhand, India

Brinjal (Solanum melongena L.), belongs to the family Solanaceae, which is an important vegetable crop of sub-tropics and tropics. The present investigation was carried out at the Vegetable Research and Demonstration Block, College of Horticulture, VCSG UUHF, Bharsar, during Kharif, 2017 to evaluate diverse germplasm of brinjal under mid hill conditions. The experiment was laid out in RCBD with three replications. In this study, twenty-seven genotypes viz., DRNKV-02-104, IVBL-116-131, NBJ-19, JB-9, Aruna, JB-2, CH-1045, Swarna Shree, JB, DRNKV-104-43, RCMBL-04-04, IBH-2, Kavya, B. Dewariya, DBR-8, DMU-1, Azad Brinjal-2, Co-11, JB-67, KKM-1, BCB-464, PLR-1, JB-18, Bhagyamati, BB-58, Pusa Shyamala including one check cultivar Pant Samrat were evaluated for different horticultural traits. During the investigation, 18 growth, flowering, fruiting, yield and quality parameters were recorded. The result of variance revealed that the mean sum of square due to treatment were significant at 5% level for all the traits. The genotypes, Aruna, DRNKV-02-104, NBJ-19, JB-2, RCMBL-04-04, IVBL-116-131 and Kavya recorded higher fruit yield and also performed better for other horticultural traits than check cultivar. These genotypes need to undergo additional testing before being made available as a replacement for currently available brinjal varieties, or they can be included in additional breeding programmes to develop improved varieties or hybrids of brinjal.

Non-homogeneous Riemannian gradient equations for sum of squares of Bures–Wasserstein metric

We investigate Riemannian gradient equations on the open convex cone Pm of all m×m positive definite Hermitian matrices, for the function WA(X)=∑j=1nwjdW2(X,Aj)where dW denotes the Bures–Wasserstein metric. On the special case where the gradient of the weighted sum of squares of the Bures–Wasserstein metrics vanishes, its unique solution is known as the Wasserstein mean of A1,…,An. We discuss the existence and uniqueness of solutions for non-homogeneous Riemannian gradient equations at which vector fields are constant and congruence transformations. Furthermore, we establish some bounds for these solutions.

Unsupervised Anomaly Detection in Marine Diesel Engines using Transformer Neural Networks and Residual Analysis

This paper presents a novel unsupervised approach for detecting anomalies in marine diesel engines using a Transformer Neural Network based autoencoder (TAE) and residual analysis with Sequential Probability Ratio Test (SPRT) and Sum of Squares of Normalized Residuals (SSNR). The proposed method can capture temporal dependencies in normal timeseries data without the need for labeled failure data. To assess the effectiveness of the proposed approach, a dataset of faulty data is generated under the same operational profile as the normal training data. The model is trained using normal data, and the faulty data is reconstructed using the trained model. SPRT and SSNR are then used to analyze the residuals from the observed and reconstructed faulty data, with significant deviations exceeding a predefined threshold being identified as anomalous behavior. The experimental results demonstrate that the proposed approach can accurately and efficiently detect anomalies in marine diesel engines. Therefore, this approach can be considered as a promising solution for early anomaly detection, leading to timely maintenance and repair, and preventing costly downtime.

Wheat Yield and Important Traits Influenced by Interaction of Potassium and Irrigation Levels Evaluated at Number of Locations in the Country by AMMI Analysis and Non-Parametric Measures

AMMI analysis of treatments consisted of levels of potassium with irrigations observed highly significant effects of locations, treatments, and T×L interactions for wheat yield. About 53.4% of the total variations in yield values was due to locations followed by 26.3% and 10.8% by treatments and interactions effects. Further analysis found 59.7% contributed by AMMI1 while 17.2% and 9.4% by AMMI2 and AMMI3 components for thousands grain weight as total of first two components cumulative to 76.9% of the total variation. The sums of squares for G×E signal and noise were 56.7% and 43.3% of interaction effects for grains per spike as the sum of squares of T×L signal was 2.58 times of treatments effects and IPC1 alone was 3.54 times the treatments effects. Measures ASV and ASV1 recommended T6, T5, T4 for wheat yield while measures utilized 81.6% of interaction sum of squares whereas MASV and MASV1 measures based on 98.4% identified T3, T5, T8, T4 treatments. Maximum average for thousands grains weight; GAI selected T8, T9, T6 whereas as per HM values treatments T5, T2, T8 would be more desirable. Grains per spike found the measures RPGV and HMRPGV settled for T8, T9, T5 treatments. Non parametric measures for yield observed Si1 selected T3, T2, T5 as opposed to T6, T4, T1 by Si2 values. T6, T4, T1 genotypes considered by Si3 Si4 measure considered T6, T4, T1 next Si5 for T6, T3, T4 and Si6 pointed towards T6, T4, T8 genotypes while Si7 favored T6, T1, T4 genotypes. Composite measures for thousands grains weight found NPi(1) for T3,T4,T7 while as per NPi(2) for T4,T3,T7, NPi(3) T4,T3,T2, NPi (4) found T4, T5, T7 as suitable treatment combinations. Multivariate hierarchical clustering as per Ward’s method for wheat yield observed first irrigation level with three potassium levels formed a cluster and other irrigation levels with potassium application remained in other one. At the first node of demarcation for thousands grains weight IPC5 exhibited MASV with MASV1, ASV1, IPC4, ASV, Si1 Si2 Si3 Si4 Si5 Si6 Si7 NPi(1), CV in one side and mean, GAI, PRVG, IPC1, HM, IPC1, NPi(2) NPi(3) NPi (4) on other side. The performance of treatments based on AMMI and non-parametric measures would be more meaningful for identification of suitable irrigation and potassium levels for wheat sustainable production.

Genotype-by-environment interactions (GEIs) and evaluate superior sweet potato (Ipomoea batatas [L.] Lam) using combined analysis and GGE biplot

Genetic studies on yield and yield quality are becoming benchmarks for farmers and industry in selecting and developing varieties. Evaluations that combine various stability statistics can provide more accurate information to select the ideal genotype. This study aims to identify the effect of genotype by environment interactions (GEIs) for yield and yield quality, to select high yield and stable sweet potato genotypes, as well as to select superior genotypes based on yield and yield quality. Three different environments in West Java, Indonesia, were used to test the sweet potato genotypes using a randomized block design that was repeated three times. Highly significant effects of sweet potato genotypes (G), environments (E), and GEIs were observed for yield and yield quality. The Combined ANOVA showed that GEIs effect contributed 54.88% for yield, 40.01% for sweetness, 10.46% for moisture content, 68.80% for tuber diameter, and 72.57% for tuber length from the sum of square. Five most high and stable yield on sweet potato genotypes identified by all measures, includes G4, G6, G7, G31, and G32. Genotype by yield*traits (GYT) selected seven genotypes that have superior in yield and yield quality, they were G7, G15, G4, G20, G6, G31, and G14. Based on stability measurements and GYT biplots, the genotypes G4, G6, G7, and G31 are in both slices. So that the four genotypes have high, stable yields, and have a good combination of traits for yield quality. Our findings can be used for improvement cultivation involving partner companies, partner institutions, and farmers, and the selected genotypes can be release as superior varieties candidate.

Conceptual framework of recommendation system with hybrid method

Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.

Is Okun’s Law Valid in Bangladesh? A Time Series Analysis

Using the data from 1991 to 2019, this study attempts to verify Okun’s law by utilizing both ‘The difference and the dynamic versions’ of Okun’s law in Bangladesh and tries to find the value of Okun’s coefficient. The autoregressive-distribution lag (ARDL) bounds test is applied to determine the existence of a long-term relationship between the variables, and then the ordinary least squares (OLS) approach is used to estimate the findings. The findings show that Okun’s coefficient is –0.058 for the difference version of Okun’s law, which is statistically insignificant. In addition, for the dynamic version of Okun’s law, findings show that the effect of the ‘immediate past economic growth rate’ on ‘current change in the unemployment rate’ is 0.15, and it is statistically insignificant too. However, the effect of the ‘current economic growth rate’ on ‘current change in the unemployment rate’ is –0.22, and it is statistically significant at 10% significance level. In both versions of Okun’s law, R2 values, F-statistic and cumulative sum of squares show the instability of the models. Due to statistically insignificant coefficients and model instability, it can be concluded that Okun’s law is invalid in Bangladesh. JEL Codes: E24, C22, B23, C87

Performance and Genetic Evaluation of Okra (Abelmoschus esculentus (L.) Moench) Genotypes for Agromorphological Traits

Okra or Bhendi is a crucial vegetable farmed in India. The state of Tamil Nadu contains a smaller cultivable area and less okra production. As a result, the investigative study was conducted at Sivapuri, Chidambaram, Tamil Nadu to estimate variability metrics such as PCV, GCV, and GA% mean for crop advancement studies. The experiment involved 48 okra genotypes and 12 qualitative variables. In accordance with ANOVA, the mean sum of squares of 48 genotypes was highly significant for all parameters tested. For the twelve studied traits, values of PCV was higher than values of GCV by a little margin, showing the influence of environmental influences on genotypes. High heritability was found in all characters except days to 50% flowering and days to first harvest. High genetic advance % of mean was perceived in all the traits except for days to the 50% flowering, the harvest period and days to first harvest. High heritability combined with high GA was recorded in traits like primary branches per plant, fruit girth, no. of fruits per plant, fruit length, and fruit yield per plant. If genotypes are chosen based on these traits, selection for genetic improvement programs would be highly efficient.

Prediction of concrete residual compressive strength under elevated temperatures: Response surface methodology (RSM) approach

Exposure of concrete to elevated temperatures causes irreversible damage to the concrete structure and poses a serious threat to its service life. Due to the importance of concrete fire performance, extensive research has been conducted to investigate its behavior under different conditions of elevated temperatures. The properties of concrete are significantly affected by various factors, including heating temperatures, heating durations, and cooling methods. Among these factors, the residual compressive strength of concrete is considered the most crucial characteristic after exposure to elevated temperatures. This paper aims to develop mathematical models for analyzing and predicting the relative residual compressive strength of concrete at high temperatures. Three independent factors were identified in this study: heating temperatures, heating duration, and cooling method. Two groups of datasets on the relative residual compressive strength of concrete under elevated temperatures were reviewed and collected from previous studies, serving as the benchmark dataset and validation dataset, respectively. Response Surface Methodology (RSM) was employed to analyze the datasets. The results of various statistical parameters, such as the coefficient of determination, sum of squares, F-value, and P-value, indicate the significance of the predicted model for estimating concrete's relative residual compressive strength under elevated temperatures. The RSM analysis reveals that heating temperatures have the most significant effect on the relative residual compressive strength of concrete. In summary, the RSM model shows a strong correlation with the validation datasets, with a coefficient of determination (R2) of 0.7869.

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma.

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

A Method for Extracting the Tree Feature Parameters of Populus tomentosa in the Leafy Stage

With the advancement of 3D information collection technology, such as LiDAR scanning, information regarding the trees growing on large, complex landscapes can be obtained increasingly more efficiently. Such forestry data can play a key role in the cultivation, monitoring, and utilization of artificially planted forests. Studying the tree growth of artificially planted trees during the leafy period is an important part of forestry and ecology research; the extraction of tree feature parameters from the point clouds of leafy trees, obtained via terrestrial laser scanning (TLS), is an important area of research. The separation of foliage and stem point clouds is an important step in extracting tree feature parameters from data collected via TLS. By modeling the separated stem point clouds, we can obtain parameters such as a tree’s diameter at breast height (DBH), the number of branches, and the relationship between these and other parameters. However, there are always problems with the collected foliated tree point clouds; it is difficult to separate the point clouds into foliage and stems, yielding poor separation results. To address this challenge, the current study uses a deep learning-based method to train a mixture of non-foliated and foliated point clouds from artificially planted trees to semantically segment the foliage labels from the stem labels of these trees. And this study focused on a Chinese white poplar (Populus tomentosa Carr.) plantation stand. At the same time, the method of this study greatly reduces the workload of labeling foliated point clouds and training models; an overall segmentation accuracy of 0.839 was achieved for the foliated Populus tomentosa point clouds. By building the Quantitative Susceptibility Mapping (QSM) model of the segmented point clouds, a mean value of 0.125 m for the tree diameter at breast height, and a mean value of 14.498 m for the height of the trees was obtained for the test set. The residual sum of squares for the diameter at breast height was 0.003 m, which was achieved by comparing the calculated value with the measured value. This study employed a semantic segmentation method that is applicable to the foliated point clouds of Populus tomentosa trees, which solves the difficulties of labeling and training models for the point clouds and improves the segmentation precision of stem-based point clouds. It offers an efficient and reliable way to obtain the characteristic parameters and stem analyses of Populus tomentosa trees.

Perspectives on teaching performance after COVID-19

The evaluation of a teacher's performance is conducted subjectively rather than objectively to determine the students' educational experience and the demands placed on them by the university which provides teachers with a backup plan. The objective of this research is to analyze and describe the perspectives on teaching performance after COVID-19. A conventional, anonymous and voluntary sample of 2263 university students from state and private universities was used in the quantitative approach of a descriptive correlational type. Teaching performance was assessed using a questionnaire with 20 items that were divided into five categories: explanation of criteria, illustration, supervision of practice and learning activities, feedback and evaluation. The findings of the research show that there is good virtual teaching performance. The university teacher shows that they are able to teach in a hybrid way. A Cronbach's alpha of 0.946 was obtained and several statistical tests were performed such as total mean (3.733), standard deviation (1.064), ANOVA with Turkey's non-additivity test (sum of squares 81.102, gl=19, F=12. 035), Hoteling’s t-squared test (283.464, F=14.800, Sig. 0.000), KMO and Bartlett's test (KMO=0.977), Bartlett's test of sphericity with chi-square 52808.872, gl=190, Sig. 0.000. University professors demonstrated a good teaching performance in front of students, having empathy with their students and managing to place themselves among people with digital competences and within the society of the digital world.

An Empirical Analysis of Factors Motivating Unemployed Individuals to Engage in Digital Entrepreneurship in Oman: Focus on Technological Infrastructure

Technological infrastructure plays a critical role in a country’s development and progress, enabling economic growth, innovation, and improving citizens’ quality of life. This study explores the potential of digital entrepreneurship and digital transformation in reducing unemployment in Oman. With the increasing reliance on digital technologies, governments need to invest in technological infrastructure that can meet the evolving needs of society. This study investigates the potential of digital entrepreneurship and digital transformation in reducing unemployment in Oman. This study highlights the importance of technological infrastructure in enabling economic growth and improving citizens’ quality of life. The findings indicate that digital entrepreneurship offers equal opportunities for both genders, potentially empowering women in entrepreneurship and addressing unemployment challenges. Additionally, the study shows a positive relationship between age and digital entrepreneurship aspirations. To foster entrepreneurship, the study suggests empowering young job seekers with digital knowledge and enabling graduates to create new opportunities for businesses and entrepreneurs. The study found significant effects of “Unemployment in the Sultanate of Oman (reasons and solutions)” on occupation, as indicated by a between-group sum of squares of 8.773, two degrees of freedom, and a mean square of 4.387, with an F value of 11.274, and a significance level of 0.000. On the other hand, the “Digital Entrepreneurship Sector” significantly affected occupation, with a between-groups sum of squares of 4.394, two degrees of freedom, and a mean square of 2.197, and an F value of 10.267 with a significance level of 0.000. Similarly, for the “Entrepreneurship Digital Infrastructure” variable, there was no significant difference between male and female mean scores (t = −1.516, df = 206, p = 0.131). Overall, the results indicate that the digital entrepreneurship sector significantly impacts occupation, while the entrepreneurship digital infrastructure has no significant effect. Additionally, female respondents showed a higher mean score in perceptions of “Unemployment in the Sultanate of Oman (reasons and solutions)” compared to male respondents.

Performance assessment of wheat genotypes based on the superiority index using additive main and multiplicative interaction effects and BLUP analysis

The simultaneous use of additive main and multiplicative interaction effects (AMMI) and best linear unbiased predictors (BLUP) has been reflected in the multi-location evaluation of trials for number of crops. The additional advantages of both these approaches would be combined in superiority index (SI) to have an edge over the commonly used approaches. The promising wheat genotypes had been considered under multi location trails in Peninsular zone of India during the cropping seasons of 2018-2019 and 2019-2020. The highly significant environmental effects contributed 44.1% & 35.3% of total sum of squares in the AMMI analysis, 20.6% & 26.2% were augmented by G × E interaction, while 10.8% & 7.5% were contributed by the genotypes.Wheat genotypes of UAS3001, MACS6222, GW322, and DDW48 expressed their superiority in BLUP values. Superiority indexes and adaptability measures had identified WHD964 and DDW48 genotypes for the second year of study. More than 75% variations among the considered measures were due to the first two interaction principal components (IPCA’s) under Biplot analysis. Number of superiority index measures were clustered with adaptability measures in the same quadrant. Superiority index, the weighted measure of yield and consistent performance of genotypes would be more appropriate for stability and adaptabilities studies.

Genotype sensitivity and adaptability for fruit yield in red and green okra on environmental change

Abstract. Abdillah SM, Syukur M, Suwarno WB, Ritonga AW, Wahyudi A. 2023. Genotype sensitivity and adaptability for fruit yield in red and green okra on environmental change. Biodiversitas 24: 4289-4298. Okra is vegetable plant consumed in immature and beneficial plants of various health. For breeders, this crop had a big chance to develop a high-yield genotype in various conditions. To develop a new variety must be known adaptability and stability in environment tests. This research aimed to evaluate the fruit yield response, sensitivity, and adaptability of okra genotypes in four environment trials. A randomized complete block design with three replications was used in each environment, with ten okra genotypes as the treatment. The AMMI-1, AMMI-2 and GGE biplot was used to illustrate genotype stability and their adaptability. Genotype by environment interaction significantly affected fruit yield, with the environment having a large contribution, with SWR01 being the highest yield. The rate of rainfall and PC1 were explained as 36.98% and 70.17% to the GEI sum of squares, respectively. ‘ZAHIRA’ and ‘GSO03’ showed sensitivities to the rate of rainfall. Genotypes ‘SWR01’ and ‘GSO03’ were the most favorable genotype according to ASV and yield performance across environments The AMMI-1, AMMI-2 and GGE biplot confirmed that both were the most suitable genotype for developing a recent variety of okra in a wide environment.