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Background: One of the oldest cultivated legumes, chickpeas (Cicer arietinum L.), are essential for world nutrition, especially with climate change. Chickpea output is poor despite rising demand for its nutritional value. Improved genetics can enhance yields to fulfil local and global demands. By selecting better parents and potential hybrids utilising half-diallel mating, breeders can hasten the development of high-yielding, durable cultivars. Methods: This study examined 55 half-diallel mating genotypes, comprising 10 parental lines and 45 F1 hybrids. At the Acharya Narendra Deva University of Agriculture and Technology Genetics and Plant Breeding Farm in Ayodhya, U.P., these were evaluated using a randomised block design with three replications under late-sown circumstances in 2022-23 rabi. Normal agricultural procedures were used. A four-meter row of each genotype was planted with 30 cm between rows and 10 cm between plants. Genetic indices like GCV, PCV, heritability, genetic progress, correlation coefficient and path analysis were evaluated. Result: ANOVA revealed significant genetic variability across all traits. Biological yield plant-1 had the highest PCV and GCV, followed by seed yield plant-1. Heritability and genetic progress were highest for days to 50% flowering and biological yield plant-1, indicating strong genetic control. Days to 50% flowering and maturity inversely linked with most yield-related characteristics. Seed yield was closely associated with primary and secondary branches plant-1, plant height, pods per plant, seeds pod-1, 100-seed weight and biological yield. Path coefficient study showed that all characteristics except days to 50% flowering, days to maturity and plant height influenced grain yield. [ABSTRACT FROM AUTHOR]

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This study assessed genetic variability among oat genotypes for twelve traits, including yield. Significant differences were found across all traits, with high heritability and genetic advance for the number of effective tillers, 1000-seed weight, and grain yield. Hierarchical clustering using morphometric traits grouped the genotypes into four distinct clusters. We identified 665 potential microsatellites using 1000 contigs from NCBI and designed possible primer pairs to develop PCR-based markers in orphan crop like oats. Validation with a panel of 31 diverse genotypes revealed that seven of ten newly developed markers detected expected alleles, with four being polymorphic. Additionally, eight reported SSRs were used to assess genotypic differences. The markers showed a mean allele richness of 2.86 (range: 2-4) and a mean polymorphism information content (PIC) of 0.37 (range: 0.15-0.96). Cluster analysis indicated three distinct clusters with a mean dissimilarity of 0.54, demonstrating the markers’ effectiveness for genetic diversity assessment and breeding.

A half diallel crossing was carried out between six Inbreeds of maize according to the second Griffing method. The genotypes (parents and the hybrids resulting from them) were planted during the spring and fall seasons of 2022 using a randomized complete block design (R.C.B.D) with three replicates to study genetic and phenotypic correlations and their variations and the path coefficient for traits (number of silking days, plant height, leaf area, number of ears, number of rows per ear, number of grains per row, weight of 500 grains, and grain yield per plant). Analysis of Variance showed highly significant differences between the genotypes for all the studied traits for the fall and spring seasons of 2020. The results indicated that the values of the phenotypic variation coefficient were close to the values of the genetic variation coefficient for most traits. Accordingly, the genotype can be studied by taking phenotypic data, and such traits can be considered as a selection index for improving this crop. The brant yield was (0.874 and 0.6 the number of rows per ear, as they reached (0.691, 0.498, and 0.671) for the spring season, respectively, and (0.701, 0.516, and 0.689) for the fall of rows per ear in breeding programs to increase yields in yellow maize.

Abstract High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising. Graphical Abstract

High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising. [ABSTRACT FROM AUTHOR]

The present investigation was conducted during the rabi season December 2022 to April 2023 at Instructional Farm Unit 4 of the Integral Institute of Agricultural Science and Technology (IIAST), Integral University Lucknow. The objective was to assess the genetic variability, heritability, and genetic divergence among ten mustard genotypes (Brassica juncea L.) crossed in a half diallel mating design, resulting in 56 genotypes, including a standard check 'Kranti'. These genotypes were cultivated in a Randomized Complete Block Design with three replications. Key yield-affecting traits were observed, including days to 50% flowering, days to maturity, plant height, number of primary and secondary branches, number of siliqua plant-1, number of seeds silique-1, length of siliqua, thousand seed weight, and seed yield plant-1. The analysis revealed significant genetic variability for traits like the number of primary and secondary branches plant-1, number of siliqua plant-1, and thousand seed weight. High heritability was noted for plant height, days to maturity, number of secondary branches plant-1, and number of siliqua plant-1. Seed yield plant-1 showed strong positive correlations with thousand seed weight, length of siliqua, and number of siliqua plant-1. Path coefficient analysis indicated that thousand seed weights had the most substantial positive direct effect on yield. Cluster analysis demonstrated significant genetic diversity, with notable differences among clusters, which is beneficial for breeding programs. Thousand seed weight and oil content were the primary contributors to genetic divergence. [ABSTRACT FROM AUTHOR]

Finger millet is an important cereal owing to its nutritive qualities. It is highly self-pollinated, allotetraploid crop with extremely small-sized flower makes hybridization a bottleneck to exploit the variability. Partial male sterile and virescence lines are of greater importance in finger millet breeding since, it facilitates easy identification of true F1 s in seedling stage with the help of virescence maker. Selection of appropriate parents is key to success of hybridization and crop improvement. In this back drop the present study was conducted during kharif, 2021 and kharif, 2022 utilizing twenty-three finger millet lines comprising 11 partial male sterile, six partial male sterile with virescence marker and six virescent lines to unravel the genetic variability parameters. Analysis of variance indicated the selection of experimental material was appropriate. High phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) was observed for number of productive tillers, grain yield per plant, ear head weight and threshing percentage in both seasons. The line 20-12 was earliest flowering, 3-3-1 had long finger length and highest productive tillers and the virescent line vir early had highest yield per plant. Days to fifty per cent flowering showed low PCV and GCV in both the seasons. High heritability and high GAM were observed for traits like peduncle length, ear length, finger length, finger width, finger number, productive tillers, test weight, grain yield per plant and ear head weight in kharif, 2021 and traits viz., finger number, grain yield per plant and threshing percentage in kharif, 2022. These results suggest that the trait expression is less influenced by the environment and selection would be effective. Partial male sterile and virescence lines have substantial amount of variability and most of the traits under study exhibit high heritability and genetic advance and could be exploited in crop improvement program. [ABSTRACT FROM AUTHOR]

Foxtail millet (Setaria italica (L.) Beauv) is one of the earliest cultivated millet for grain, hay and silage. There is wide genetic diversity available in foxtail millet and characterization of available germplasm is a pre-requisite for the genetic improvement of any crop. Keeping the above fact in view 30 foxtail germplasm lines were evaluated during kharif 2020 and 2021 for morphological traits, stover yield, dry fodder yield and seed yield under rainfed conditions of Haryana. The results (based on pooled data of two years) revealed that enough variability was present among germplasm lines for further exploitation. High heritability and positive correlation was reported between traits like plant height, leaf length, leaf stem ratio, stover yield, grain yield etc. Principal component analysis also revealed that PC1, PC2, PC3, PC4 and PC5 contributed for 31.8%, 15.05%, 12.31%, 11.43% and 10.14%, respectively to the total variation. Five foxtail accessions viz., SEA8, SEA12, SEA48, SEJ184 and SEA37 were identified promising for yield and related traits under rainfed conditions of Haryana.

A panel of 25 nutmeg genotypes were evaluated at the Horticulture Research Station, Pechiparai, Kanyakumari district from 2019 to 2022 for the pattern of genetic divergence and relationship among them based on seven morphological traits by cluster and path analysis. Notable difference was observed for yield characteristics of genotypes, in which the genotype MF-4 was observed to record high plant height, stem girth, leaf length and fruit production. Genetic variability analysis revealed that traits such as stem girth, number of fruits and mace yield/tree were found to possess high genotypic coefficient of variation along with high heritability and genetic advance as percent of mean which implied that these traits had higher variability among the studied genotypes. The 25 genotypes were grouped in to four clusters based on cluster analysis. Path analysis revealed that traits such as number of fruits, single fruit weight and plant height had a favourable direct impact on the mace yield. These traits might be used as selection indicators in future breeding programmes.

An investigation was carried out with 101 finger millet genotypes for assessment of genetic variability and heritability in four environments during Kharif 2021. Variation due to genotypes for all the traits under study was found significant suggesting high amount of variability among different genotypes for different traits providing ample scope for selection and further evaluation as well as improvement in the seed yield and its component traits. Grain yield per plant (g) had moderate heritability estimates along with high genetic advance expressed as per cent of mean which implied that the character is controlled by genes having additive nature. High heritability combined with high genetic advance expressed as per cent of mean was noted for the characters viz., main ear head length, finger length, 1000-seed weight, protein content, calcium content, iron content and zinc content. Simple selection for such characters would be rewarding.

The experiment was conducted during September, 2023 to February, 2024 at the Department of Biochemistry, Anand Agricultural University, Anand, Gujarat, India. Total 28 genotype originating from a cross between a little leaf resistance parent AB 15-06 (S. melongena) and a susceptible parent GRB 5 (S. melongena). Disease incidence recorded from 14 each of highly resistant and susceptible genotypes and their parents AB 15-06 (Resistance against little leaf disease) and GRB 5 (Susceptible against little leaf disease) were selected for biochemical attributes comprised of chlorophyll (0.73-0.98 mg g-1), moisture (76.77-97.43%), phenol (0.43-3.26 mg g-1), total soluble sugar (2.36-4.57 mg g-1), membrane injury (24.56-69.87%). Correlation analysis of biochemical characters like chlorophyll and moisture were observed negatively correlated with disease incidence while rest of biochemical parameters total soluble solids, membrane injury and phenol content were positively correlated. Membrane injury (0.254), moisture (0.026), TSS (0.030), and phenol content (0.016) showed positive skewness whereas chlorophyll (-1.318), membrane injury (-1.540), moisture (-1.543), total soluble sugar (-1.160) and phenol content (-1.626) detected negative kurtosis. GCV, PCV and heritability analysis for all biochemical characters recorded were chlorophyll (36.46%, 36.5% and 99.82%), membrane injury (61.52%, 61.96% and 98.59%), moisture (7.55%, 8.73% and 13.44%), total soluble sugar (99.39%) and phenol content (46.76%, 46.83% and 99.67%). [ABSTRACT FROM AUTHOR]

A single faba bean (Vicia faba L.) population derived from the cross Sakha 3 x Giza 2 was subjected to two cycles of pedigree line selection (PSM). Selection criteria included seed yield/ plant (SY/P), number of pods/ plant (NP/P), and seed index (SI). Analysis of variance revealed significant differences among families within the F3 and F4 generations for all studied traits, indicating successful selection progress. Notably, mean values for all traits, except days to flowering, increased from cycle 1 to cycle 2. Days to flowering exhibited the opposite trend, with a higher mean value observed in cycle 1. The expected gain from selection (EGS%) for SY/P in the F2 generation was 29.52 and increased in the F3 and F4 generations in values of 34.41 and 2.06, respectively. The realized gain from selection (RGS%) for SY/P in the F3 generation was 23.06, then decreased in successive generation which amounted to 9.56 in F4. Multiple promising faba bean genotypes were identified within Families 1, 2, 13, 56, and 60. These families exhibited superior performance in relative yield traits compared to the better parent, check variety, and bulk samples. Additionally, they displayed favorable values for most characters assessed. These findings suggest their significant potential for incorporation into breeding programs to enhance faba bean yield. [ABSTRACT FROM AUTHOR]

Background: Assessment of genetic variability and inter-relationship between the characters can be used in the breeding programme to evolve new varieties with wide genetic diversity to maximize the yield potential in crop improvement programmes. Eighty-four field pea genotypes were evaluated in an augmented block design for thirteen quantitative traits to study variance components, heritability, genetic advance and inter-relationship between the yield and yield contributing traits. Methods: The extent of phenotypic and genotypic variation that exist in a character was calculated by the formula suggested by Burton and de Vane (1953). Heritability in broad sense and genetic advance as per cent of mean for each character was computed using the formula suggested by Hanson et al. (1956) and Johnson et al. (1955), respectively. Correlation coefficient was calculated by method suggested by Searle (1961) and path coefficient analysis done as per method of Wright (1921) and elaborated by Dewey and Lu (1959). Result: Significant differences observed among the genotypes tested for the yield characters indicated the presence of variability. High heritability coupled with high genetic advance as percent of mean was observed for the traits viz., plant height, effective pods plant-1, harvest index and seed yield plant-1 were governed by additive gene effects which will aid in effective selection. Correlation coefficient analysis revealed that seed yield plant-1 had highly significant and positive correlation with biological yield plant-1, effective pods plant-1, harvest index, seeds pods-1 and effective nodes plant-1, indicating that these traits are strongly associated with seed yield in field pea. Path coefficient analysis identified biological yield plant-1 followed by harvest index, seed pod-1, effective nodes plant-1, 100-seed weight and day to 50% flowering as highly desirable components with great direct effects on seed yield. [ABSTRACT FROM AUTHOR]

The present research was conducted to examine different parameters of variability, correlations, and path coefficients associated with yield and yield-related traits in aerobic rice. The analysis of variance indicated noteworthy variations among the genotypes for all the traits investigated. Among them, flag leaf area and alkali spreading value showed high PCV and GCV. Except for days to fifty per cent flowering, days to maturity, and amylose content, the remaining traits demonstrated substantial heritability combined with high genetic advance as percentage of mean. The correlation analysis revealed significant positive association between grain yield per plant and several traits, namely effective tillers per plant, test weight, chlorophyll content, and length breadth ratio. Traits such as test weight, effective tillers per plant, and flag leaf area showed a positive direct effect on grain yield per plant. Therefore, it is crucial to give priority to traits that exhibit significant positive correlations and substantial direct effects during selection process. This approach will yield rewarding results in the development of high-yielding cultivars suitable for aerobic conditions.

This article, published in the journal "Environment & Ecology," presents a study on the genetic variability and nutrient content of different genotypes of the amaranthus plant. The research found a wide range of major nutrients in both leaves and grains of the amaranthus genotypes, with high heritability observed for all traits. The study suggests that amaranthus has the potential to be a valuable source of nutrition due to its genetic variability and nutrient content. The document provides detailed data on the quality parameters of different amaranthus genotypes, including information on leaf fiber, iron, moisture, oxalates, vitamin C, stem fiber, and color. The authors highlight the nutritional potential of amaranthus as a source of essential nutrients in the human diet and reference other studies that support these findings. [Extracted from the article]

Citrus is an economically important fruit crop with a long history of cultivation worldwide, and many citrus varieties are grown extensively in Morocco, both for domestic consumption and export. Nevertheless, the genetic diversity of this genus in Morocco has not been reported upon. Our objective was to explore phenotypic variations in grapefruit and pomelo genotypes for targeted breeding, enhancing key traits, and identifying opportunities for genetic improvement. We emphasize practical implications and outline future research directions for developing improved citrus varieties. This present study physiochemically analyzes eight grapefruit (Citrus paradisi) and two pomelo (Citrus maxima) cultivars to assess the genetic variability of these fruits' physicochemical characteristics. The results revealed significant variance among the genotypes for all traits. For example, the phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for all the traits. Moreover, a high degree of heritability was recorded for seed number, β-carotene, vitamin C, juice yield (%), total soluble solids, titratable acidity, maturity index, fruit diameter, and fruit weight, whereas limited heritability was observed for fruit length, peel thickness, and segment number per fruit. The high genetic advance scores corresponded to the high degree of heritability estimates for seed number per fruit, β-carotene, juice yield, and vitamin C, suggesting the existence of additive genetic effects. Our principal component analysis (PCA) identified four components with cumulative variance of 93.20%, while hierarchical cluster analysis grouped the collection into five clusters. In conclusion, phenotypic distinctions among grapefruit and pomelo genotypes reveal opportunities for genetic enhancement through selection or hybridization. Varied traits, including seed count, vitamin C, beta-carotene, and juice yield, across genotypes suggest potential avenues for targeted breeding and practical applications in diverse citrus varieties. [ABSTRACT FROM AUTHOR]

In the present paper, we prove a new theorem, resulting in an update formula for linear regression model residuals calculating the exact k-fold cross-validation residuals for any choice of cross-validation strategy without model refitting. The required matrix inversions are limited by the cross-validation segment sizes and can be executed with high efficiency in parallel. The well-known formula for leave-one-out cross-validation follows as a special case of the theorem. In situations where the cross-validation segments consist of small groups of repeated measurements, we suggest a heuristic strategy for fast serial approximations of the cross-validated residuals and associated Predicted Residual Sum of Squares ( $PRESS$ ) statistic. We also suggest strategies for efficient estimation of the minimum $PRESS$ value and full $PRESS$ function over a selected interval of regularisation values. The computational effectiveness of the parameter selection for Ridge- and Tikhonov regression modelling resulting from our theoretical findings and heuristic arguments is demonstrated in several applications with real and highly multivariate datasets.

The present investigations were carried out on fifty three rice genotypes to select the better lines for yield traits in randomized block design with three replications during kharif 2023. The variance studies showed significant variations among all the genotypes, suggesting significant genetic variation in the material. The phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for all the traits. The yield traits which showed high values for genetic advance as per cent of mean along with high heritability were number of unfilled grains per panicle followed by number of filled grains per panicle, number of total grains per panicle, grain yield per plant, number of panicles per plant, number of total tillers per plant, harvest index, test weight, plant height, biological yield per plant and panicle length, revealing the prevalence of additive gene action, thus providing scope for further improvement through selection. [ABSTRACT FROM AUTHOR]

Background: Blackgram is an important pulse crop grown in India. The creation of new variability through hybridization or mutation breeding will produce novel segregants for important yield attributing traits. The selection from the population will be effective if the traits have good genetic variability estimates. Methods: The present investigation was conducted on analysing genetic variability parameters in F2 populations of the blackgram crosses CO 5 × VBN 9 and CO 6 × VBN 11 which were evaluated during rabi, 2022. Result: Based on the coefficient of variations, it was found that sufficient amount of genetic variability and high to moderate heritability with high genetic advance were present in the populations for the traits viz., plant height, number of branches per plant, number of clusters per plant, number of pods per cluster, number of pods per plant and seed yield per plant indicates the presence of additive gene action which can be improved by simple selection in the earlier generations. The positive skewness was observed for the traits viz., number of branches per plant, number of pods per cluster, number of pods per plant, hundred seed weight and seed yield per plant in F2 population of CO 5 × VBN 9 and number of clusters per plant, number of pods per cluster, number of pods per plant and seed yield per plant in the F2 population of CO 6 × VBN 11. The above traits could be improved by intensive selection for rapid improvement. The leptokurtic nature of the distribution for the trait hundred seed weight in CO 5 × VBN 9 population and the traits viz., number of clusters per plant, number of pods per plant, pod length, number of seeds per pod and seed yield per plant in CO 6 × VBN 11 population indicates that these traits are controlled by few minor genes. Hence, the good segregants could be selected from these two populations for yield improvement. [ABSTRACT FROM AUTHOR]

This study was aimed to identify efficiency of recurrent selection program to improve maize population. This program was applied to var. Buhooth 106 with var. 5012 Using RGCA with the inbred Zm7 for the production of RSCA. The recurrent selection program was implemented for four consecutive seasons (2020 - 2021) at the Fields Station of the College of Agriculture - University of Anbar. In the spring season 2020, 100 top crosses were obtained from both varieties and progeny. During the fall season 2020, an experiment was carried out to compare the top crosses of each variety RGCA and RSCA using a 10 × 10 partially balanced lattice design.. Six genotypes from each genetic population as well as Buhooth 106 and 5012 and Zm7 inbreds). The genotype-S1-2 RSCA achieved the lowest female flowering period for females 61.50 days, the highest number of rows per ear 17.80 rows, and the highest grain yield 177.82 g. The genotype-S1-3 RSCA achieved the highest leaf area 0.593 m² and the highest number of kernels per row 43.11 kernel. The broad sense heritability of the traits of the number of rows and the number of kernels in the row and the yield of the plant reached 53.23%, 73.85% and 41.94%, respectively, and high genetic advance for kernels per row and grain yield of 4.79 and 15.52 respectively. [ABSTRACT FROM AUTHOR]

Twenty-six okra genotypes were studied for morphological characterization, genetic variability, heritability and genetic advance at the Vegetable Research Farm, JNKVV, Jabalpur, during 2022-23. Significant differences were found among genotypes, indicating substantial morphological variation. The PCV was higher than the GCV for all traits. The highest PCV and GCV were recorded for internodal length (35.86, 33.39), fruit diameter (35.72, 33.59), fruit weight (35.67, 35.19) and number of primary branches per plant (35.61, 32.95), suggesting potential for yield improvement through genetic modification. High heritability and genetic advance for traits like fruit weight (97.31, 71.50), fruit yield per plant (91.97, 58.19), number of primary branches per plant (85.62, 62.81), fruit diameter (88.43, 65.08) and internodal length (86.68, 64.04) indicated that additive genes controlled these traits and might be improved through simple selection. [ABSTRACT FROM AUTHOR]

The article discusses a study conducted in India that examined the correlation and genetic variability of different traits in three crosses of okra plants. The researchers found that certain traits, such as the number of fruits per plant and fruit weight, were highly correlated with fruit yield. They also observed high heritability and genetic advance in two of the crosses, indicating the influence of additive gene action. The study suggests that selecting plants based on these traits could lead to improved yield in okra breeding programs. [Extracted from the article]

The global surface mass variations obtained by the Gravity Recovery and Climate Experiment (GRACE) satellite Level-2 products show significant north-south strip noise, which seriously affects the estimation of regional surface mass variations. Most existing filtering methods are based on the processing of spherical harmonic basis functions to remove stripes. However, because spherical harmonic basis functions are only orthogonal globally, they may not be orthogonal in regions, which hinders the effective constraint of quality changes in specific regions. Therefore, this paper converts the spherical harmonic base of GRACE Level-2 to the Slepian base of the interested region and truncates it based on the Shannon number of the region. Even though the signals are concentrated in the region, there are still many stripes present. In order to remove the stripes, it is necessary to introduce the regularization and consider the statistical information of the spherical harmonic coefficient. The Tikhonov regularization matrix of the Slepian coefficient is obtained, combined with a power-law model to construct the prior covariance matrix of the signal and the optimal regularization coefficient is selected by using the generalized cross-validation (GCV) method, which is represented as the statistically optimal Slepian method (SO-Slepian) in this study. The results show that the ability of SO-Slepian and decorrelation and denoising Kernel (DDK) filtering with the same regularization parameters as SO-Slepian to remove stripes and retain signals in the selected region is comparable. The line chart comparing the differences between SO-Slepian and DDK not only demonstrates the close similarity in results between SO- Slepian and traditional DDK filtering in the regional domain but also emphasizes the logical application of regularization during the Slepian modeling stage. It further supports the rationality of developing a regularization scheme for GRACE data processing based on Slepian. [ABSTRACT FROM AUTHOR]

Background: Canna lily (Canna indica L.) is an edible herbaceous perennial plant in which flowers and rhizomes were commonly used as a food additive and its potentiality was not fully utilized in India. To stun these hindrances, morphological characterizations are needed to determine the genetic variability to improve flower quality and rhizome yield in canna lily. Methods: In the present research, a field experiment was conducted during 2022-2023 in Botanical Garden, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu. Twelve accessions were investigated by using a randomized block design with 3 replications. All the observations for 23 quantitative traits were carried out at 0.05 and 0.01 probability. Result: A wide range of genetic variability was observed, a high genetic coefficient of variation ranged from 8.49 to 75.62% and a phenotypic coefficient of variation ranged from 8.83 to 76.50% was observed in 20 traits except in days to fist bloom, rhizome diameter, number of nodes per rhizome. High heritability coupled with high genetic advance as a percent of mean was observed for 21 traits except in days to fist bloom and number of nodes per rhizome. Character association analysis revealed that stem diameter and inflorescence length showed highly significant and positively correlated with flower yield and rhizome yield per plant. Path analysis specified that the number of leaves per clump had a very high and positive direct effect on fresh flower yield per plant. [ABSTRACT FROM AUTHOR]

We propose MMAe, a modified MMA based on penalized Mallow's Cp. A weighted elastic net penalty is added to handle the inevitable collinearity among models, which is beneficial to high‐dimensional data modelling. We proved the sparsity, the asymptotic optimality of its weight solution and also proved that its candidate model set can be exponentially enlarged under Gaussian noises. We further proved that an MMAe adjusted by generalized cross validation (GCV) has an asymptotically lower risk than MMA under more relaxed conditions. Our approach can be implemented efficiently by convex optimization algorithms. In simulation and real‐life analysis, MMAe achieves higher prediction accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

The research work was conducted at the BCKV, research station in Mohanpur, Nadia, West Bengal in 2020, thirty jute genotypes were evaluated for fibre yield and related traits. It was revealed that 14 yieldrelated characters, such as basal diameter, bark thickness after 120 DAS, plant height, green weight plant-1, node number plant-1, and internodes length, showed moderate genetic advance and high heritability. These traits were found to be significantly different. Plant height, node number plant-1, internode length, basal diameter, petiole length, leaf area, bark thickness, green weight plant-1, chlorophylls 'a' and 'b', and total chlorophyll, as well as dry stick weight plant-1, all displayed a significantly positive correlation with dry fibre weight at both the phenotypic and genotypic levels. The path coefficient analysis results showed that node number plant-1, internode length, plant height, basal diameter, petiole length, bark thickness both at 90 DAS and 120 DAS, chlorophyll 'b' and total chlorophyll had significant direct effects on dry fibre yield. These characters should be taken into consideration as important selection criteria to increase the yield of fibre in tossa jute. [ABSTRACT FROM AUTHOR]