Maximum Dissimilarity Research Articles

Identification and characterization of Capsicum mutants using, biochemical, physiological, and single sequence repeat (SSR) markers

Identification and characterization of crop mutants through molecular marker analysis are imperious to develop desirable traits in mutation breeding programs. In the present study, macromolecular variations with altered morphological, quantitative, and biochemical traits were generated through chemically induced mutagenesis via alkylating agents and heavy metals. Statistical analysis based on quantitative traits indicating enhanced mean value in mutant lines selected from the M4 generation as compared to previous generations. Identification and characterization of morphology in selected mutant lines are based on altered phenotypes (e.g. tall and dwarf mutant with high yield, fruits with thick texture and bold seeds, etc.) in comparison to control populations. The useful mutations were recorded in phytochemicals (e.g. capsaicin and dihydrocapsaicin) and macro and micro nutrients profile (e.g. protein, iron, copper, cadmium and zinc) in selected mutant lines of Capsicum annuum L. Single Sequence Repeats (SSRs) markers analysis in selected mutant lines revealed genetic diversity in Capsicum. annuum L. The total of 44 alleles were observed with average number of allele 4.00. The Unweighted Pair Group Arithmetic Mean Method (UPGMA) showed maximum dissimilarity was recorded between mutant A-III and F-III followed by mutant G-III and C-III, while mutant B-III and G-III showed the lowest dissimilarity to each other followed by mutant L-III and mutant J-III. Correlation and Principal Component Analysis (PCA) revealed genetic diversity among mutant lines indicating their prioritization over other traits in indirect selection and also revealed that mutants treated with lower and medium concentrations were divergent. These mutant lines could be suitable in crop improvement programs for the broadening the genetic base of C. annuum L. Hierarchical Cluster Analysis (HCA) grouped the mutants into two clusters with variable euclidean distance indicated heterogeneous mutant lines developed from induced mutagenic treatments. Thus beneficial mutations could be induced in chilli genotypes via mutation breeding to enhance genetic variability in limited resources, period, and efforts.

Dissimilarities of the demi-plié movement trajectory in its different roles in classical ballet

ABSTRACT Demi-plié is a coordinated dance movement involving knees bent while keeping hips turned out and heels grounded. It consists of descending and ascending phases, the latter often preparing for the next move. Task goals may influence its trajectory. Ten classical dancers (eight females, two males), aged 20–45 years old performed demi-plié itself and prior to pirouette en dehors, relevé, sauté, and sissonne fermée de côté. Lower limb and hip trajectory data were collected through a motion capture system. Time-series data were aligned across all conditions using a dynamic time warping algorithm. The Euclidean distance measured the hip trajectory between each ballet movement, providing a continuous dissimilarity function over time. Descriptive analysis focused on each participant’s maximum dissimilarity values across conditions. Results showed maximum hip trajectory differences of 40% for demi-plié before sauté, 30% before sissonne, 20% before relevé, and 10% before pirouette. Dissimilarities began in the descending phase of the demi-plié. ANOVAs for repeated measures and Bonferroni’s post-hoc showed that the standard deviation of dissimilarity had the largest effect size for the demi-plié – demi-plié before sauté, and the smallest for the demi-plié – demi-plié before pirouette. Demi-plié prior to jumps demands plyometric forces and has open-loop motor planning, requiring acceleration, whereas demi-plié is closed-loop, relying on proprioceptive updating.

Exploitation of the Genetic Variability of Diverse Metric Traits of Durum Wheat (Triticum turgidum L. ssp. durum Desf.) Cultivars for Local Adaptation to Semi-Arid Regions of Algeria.

Abiotic stresses pose significant challenges to wheat farming, yet exploiting the genetic variability within germplasm collections offers an opportunity to effectively address these challenges. In this study, we investigated the genetic diversity of key agronomic traits among twenty durum wheat cultivars, with the intention to pinpoint those better suited to semi-arid conditions. Field trials were conducted at the ITGC-FDPS Institute, Setif, Algeria, during the winter season of 2021/22. A completely randomized design was used with three replicates. Statistical analyses revealed significant variation among the genotypes for most of the studied traits, with some cultivars exhibiting a superior performance in a stressful environment. Notably, traits like the number of grains per spike (NGS) and the grain yield (GY) displayed high genotypic coefficients of variation (CVg). Except for membrane thermostability (MT) and biological yield (BY), the majority of the assessed traits exhibited moderate-to-high heritability estimates. Genotypic and phenotypic correlation studies have confirmed the importance of many yield-related traits in the expression of GY. The harvest index (HI) underscored the highest genotypic direct effect on GY, followed closely by spike number (SN), serving as consistent pathways through which most of the measured traits indirectly influenced GY. The cluster analysis categorized the durum wheat cultivars into seven distinct clusters. The largest inter-cluster distance was observed between clusters G3 and G4 (D2 = 6145.86), reflecting maximum dissimilarity between the individuals of these clusters. Hybridizing divergent clusters may benefit future breeding programs aiming to develop potential durum wheat varieties through cross combinations. This study's findings contribute to sustainable agriculture efforts by facilitating the selection of genotypes with enhanced resilience and productivity, particularly for cultivation in challenging semi-arid regions.

Using the divergent association task to measure divergent thinking in Chinese elementary school students

The Divergent Association Task (DAT), published in July 2021, is a psychological test designed to measure an individual's divergent thinking. The test requires participants to name ten nouns that exhibit maximum dissimilarity from each other. The semantic distance between these nouns is then calculated to indicate the person's level of divergent thinking. In this study, we explored the applicability of the DAT for elementary school students in Chinese contexts, given that it was not initially designed for this specific population and was available only in English. We recruited a total of 348 students who were asked to complete three creativity tasks: the DAT, the Alternative Uses Task (AUT), and the Bridge-the-Associative-Gap Task (BAG). We examined the associations between DAT and the scores of the AUT and BAG tests. Moreover, we tested the accuracy of the DAT using varying numbers of nouns and different natural language processing models to calculate the semantic distance between nouns. Our findings supported the suitability of using the DAT to measure divergent thinking in elementary school students within Chinese contexts. We also found that using only eight nouns, instead of ten, could achieve a relatively high accuracy in measuring divergent thinking based on the DAT method. The language model of Word2Vec performed better than the BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) models when calculating semantic distances between nouns. This study has methodological and practical implications.

Gene sequence analysis model construction based on k-mer statistics.

With the rapid development of biotechnology, gene sequencing methods are gradually improved. The structure of gene sequences is also more complex. However, the traditional sequence alignment method is difficult to deal with the complex gene sequence alignment work. In order to improve the efficiency of gene sequence analysis, D2 series method of k-mer statistics is selected to build the model of gene sequence alignment analysis. According to the structure of the foreground sequence, the sequence to be aligned can be cut by different lengths and divided into multiple subsequences. Finally, according to the selected subsequences, the maximum dissimilarity in the alignment results is determined as the statistical result. At the same time, the research also designed an application system for the sequence alignment analysis of the model. The experimental results showed that the statistical power of the sequence alignment analysis model was directly proportional to the sequence coverage and cutting length, and inversely proportional to the K value and module length. At the same time, the model was applied to the system designed in this paper. The maximum storage capacity of the system was 71 GB, the maximum disk capacity was 135 GB, and the running time was less than 2.0s. Therefore, the k-mer statistic sequence alignment model and system proposed in this study have considerable application value in gene alignment analysis.

Computational Performance Enhancement Strategies for Risk-Averse Two-Stage Stochastic Generation and Transmission Network Expansion Planning

This paper proposes a new acceleration technique and a representative day aggregation procedure for the risk-averse two-stage stochastic generation and transmission network expansion planning problem, in which the conditional value-at-risk is used. We use a finite set of scenarios to model uncertainty in the peak demand level of loads, along with the capacity and marginal production cost of generating units. Moreover, we use representative days to model the operational variability of the electrical demand and renewable generation. The combination of scenarios and representative days involves many variables and constraints, which may lead to computationally intractable problems. Therefore, we propose a new relaxed version of the constraint generation-based algorithm that reduces the computational time of the problem. We additionally present a two-stage aggregation procedure that combines the modified maximum dissimilarity algorithm and the priority chronological time-period clustering in order to reduce the resolution of the representative days and to pay attention to extreme conditions. The numerical results of modified versions of the IEEE 24-bus Reliability Test System and the IEEE 118-bus Test System show reductions in the computational time of more than 89% for the relaxed constraint generation-based algorithm, and of more than 94% for the two-stage aggregation procedure.

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km2 at low elevations in spring and 86.5 km2 at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year−1 in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research.

Molecular genetic divergence analysis amongst high curcumin lines of Golden Crop (Curcuma longa L.) using SSR marker and use in trait-specific breeding

Curcuma longa L., is recognized worldwide as a medicinally and economically important plant species due to its curcumin content which is an industrially important compound. In this study, a total of 329 accessions were collected from four states of India and planted in the experimental farm of CSIR-NEIST, Jorhat, India, in augmented design. Among these, 152 high curcumin (> 1.50%) accessions were screened for molecular divergence study using 39 SSR primers. The primers showed the most efficient outcome with 2–8 allele/ loci and a total 163 number of alleles with 100% polymorphism. Cluster analysis revealed the construction of three clusters, out of which one cluster was geographically dependent, and germplasm was particularly from Assam state. Jaccard's pairwise coefficient showed maximum genetic dissimilarity of (0.75) between accession RRLJCL 3 and RRLJCL 126, indicating high variation as it was from two different states viz Arunachal Pradesh and Nagaland respectively and minimum genetic dissimilarity of (0.09) between RRLJCL 58 and RRLJCL 59 indicating significantly less variation as the two accessions were from same state, i.e., Arunachal Pradesh. Analysis of Molecular Variance (AMOVA) revealed high molecular variation within the population (87%) and significantly less variation among the population (13%). Additionally, Neighbour Joining dendrogram, Principal Component Analysis (PCA), and bar plot structure revealed similar clustering of germplasm. This diversity assessment will help in selecting the trait-specific genotypes, crop improvement program, conservation of gene pool, marker-assisted breeding, and quantitative trait loci identification. Moreover, to the best of our knowledge, it is the first molecular diversity report among 152 high curcumin lines of C. longa from North East India using 39 SSR primers.

Generation of Dissimilar Alternative Product Formulations Using Graphs

In this work, alternative product formulations are represented as a bipartite graph, and mixed-integer programming is used to find a graph partitioning that identifies the most dissimilar formulations within a potentially large design space. This involves fully integrating available models for product properties and known heuristic rules for product formulation. The set of dissimilar alternative formulations thus found constitutes the best exploratory plan of experiments in the face of the available knowledge. Also in this work, a cosmetic emulsion example is explored, where some ingredients (in a non-specified number) have to be chosen from a pool of 32 possible. The number of possible combinations of ingredients is around half a million. With the new tools proposed herein, one is able to identify small sets of alternative formulations that adhere to available models and known heuristics rules, have maximum dissimilarity, and are optimal regarding a specific product design objective (cost or other), in a few minutes of computational time.

AUTOMATIC WAVE MODEL CALIBRATION USING SURROGATE MODELS

Alonso and Solari (2017) presented an approach for the automatic calibration of a third-generation wave model, using the significant wave height error as the objective function. Alonso and Solari (2021) extended the methodology, incorporating a spectral error as the objective function and the use of the maximum dissimilarity algorithm to minimize the number of sea states used for calibration without losing representativeness. It was found that the use of the spectral error does not necessarily guarantee an improvement in Hs results. Furthermore, calculation times were still too long for the general application of the method. The objective of this work is twofold: (1) to introduce the use of surrogate models in the automatic calibration algorithm to speed up computation times, and (2) to explore a wide spectrum of objective functions for model calibration.

Wave-driven sediment transport potential on a tropical coast: Implications for the northeastern Australian sediment budget

Coastal dynamics are governed by the interface of complex, multidimensional processes that result from the interaction between offshore wave climate and local geomorphology. Nearshore waves are usually the primary driver of sediment transport in the coastal zone. However, nearshore wave processes in sub-tropical to tropical regions are often poorly understood, since deep-water waves are often substantially transformed, via interaction with coral reefs, as they propagate towards the nearshore zone. Here, we produce a suite of nearshore wave conditions using a discrete set of representative deep-water wave parameters to determine the potential for large-scale, wave-driven longshore sediment transport along the sub-tropical to coral reef mediated tropical northeastern coast of Australia (15-28oS). Representative conditions at deep-water source points, determined using a Maximum Dissimilarity Algorithm (MDA), were used as boundary conditions for a coastal wave model. Potential longshore sediment transport is largely dependent on offshore wave direction and the presence (or lack thereof) of the Great Barrier Reef (GBR). There is a decrease in mean, wave-driven transport potential from >500,000 m3/year in southern Queensland to <50,000 m3/year north of Fraser Island and along the section of the coast regulated by wave transformation through the GBR. Seasonal changes in the deep-water wave regime control patterns of sediment transport along the entire coast, even in the low wave energy regions. We also show that there are complex sediment transport pathways on this tropical coast with large variability in both direction (up-coast vs. down-coast transport) and magnitude in the nearshore. Our results provide insights into the potential volumes, pathways, and complexity of sediment transport on tropical and sub-tropical coasts and a methodological approach that can be applied globally to efficiently construct wave-driven sediment flux estimates on tropical coastlines where coral reefs influence nearshore wave processes.

A generalizable normalization for assessing plant functional diversity metrics across scales from remote sensing

Abstract Remote sensing (RS) increasingly seeks to produce global‐coverage maps of plant functional diversity (PFD) across scales. PFD can be quantified with metrics assessing field or RS data dissimilarity. However, their comparison suffers from the lack of normalization approaches that (1) correct for differences in the number and correlation of traits and spectral variables and (2) do not require comparing all the available samples to estimate the maximum trait's dissimilarity (unfeasible in RS). We propose a generalizable normalization (GN) based on the maximum potential dissimilarity for the traits and spectral data considered and compare it to more traditional approaches (e.g. the maximum dissimilarity within datasets). To do so, we simulated plant communities with radiative transfer models and compared RS‐based diversity measurements across spatial scales (α‐ and β‐diversity components). Specifically, we assessed the capability of different normalization approaches (GN, local, none) to provide PFD estimates comparable between (1) RS and plant traits and (2) estimates from different RS missions. Unlike the other approaches, GN provides diversity component estimates that are directly comparable between field data and RS missions with different spectral configurations by removing the effect of differences in the number of traits or bands and the maximum dissimilarity across datasets. Therefore, GN enables the separated analysis of RS images from different sensors to produce comparable global‐coverage cartography. We suggest GN is necessary to validate RS approaches and develop interpretable maps of PFD using different RS missions.

Molecular Characterization and Genetic Variation in Ceratocystis fimbriata Ell. and Halst. on Pomegranate

Fifteen isolates of Ceratocystis fimbriata collected from different locations in Karnataka were characterized using ITS gene technology. It produced an amplification size of 600−650 bp, which indicated that all the isolates belong to the genus Ceratocystis, thus confirming the identity of the pathogenic isolates. To test genetic variability, isolates were analyzed using microsatellite markers. An UPGMA dendrogram for genetic variation among the isolates showed that all the isolates fell into two major clusters. The first cluster consisted of isolate Cf-10 and the second cluster was further divided into two sub-clusters. Sub-cluster one consisted of isolate Cf-2. Sub-cluster two was again divided into five groups. The first group included isolate Cf-13, the second group consisted of isolate Cf-14, the third group included isolates Cf-1, Cf-4, Cf-6, Cf-7, Cf-8 and Cf-9, the fourth group included Cf-5 and Cf-11, and the fifth group consisted of Cf-3, Cf-12 and Cf-15. The dissimilarity coefficient ranged from 0.00 to 0.20 among the isolates. Isolates Cf-1, Cf-3, Cf-4, Cf-5 Cf-6, Cf-7, Cf-8, Cf-9, Cf-11, Cf-12 and Cf-15 were found to be highly similar, as their dissimilarity coefficient was zero. Maximum dissimilarity (0.20) was found between isolate Cf-10 and all the other isolates, suggesting they were genetically distinct.

Genetic Diversity Based on Nutrient Concentrations in Different Organs of Robusta Coffee

The objective of this study was to evaluate nutrient concentrations in the flowers, leaves (pre-flowering and grain-filling period), grains, and husks of Robusta coffee genotypes cultivated in the Amazon region, as well as to identify their genetic diversity. This experiment was carried out in Alta Floresta D’Oeste, Rondônia, Brazil, in randomized blocks with three replications; for the leaves, a factorial experimental design of sampling periods was included. The nutrient concentrations of the different evaluated organs were subjected to analysis of variance by the F test (p &lt; 0.01), and the genetic parameters were estimated. To determine the genetic diversity, the genotypes were grouped by the UPGMA hierarchical method, and to predict it the relative importance of traits was analyzed. Genetic divergence among Coffea canephora genotypes was indicated by the leaf nutrient concentrations. At a maximum dissimilarity threshold of 82% for the genotypes, the UPGMA method formed six groups. Concentrations of nitrogen (N) and phosphorus (P) in the leaf sampling periods of pre-flowering and grain filling were not influenced by genotypes. The leaf and flower iron (Fe) concentrations contributed most to genetic divergence. For a nutritional diagnosis of Robusta coffee, it is important to take into account the comparisons of genetic diversity as well as the nutritional requirements during the flowering and grain-filling periods.

Phenotypic divergence for morphological and yield-related traits in black oat (Avena strigosa)

This study aims to evaluate the phenotypic divergence among 46 accessions of A. strigosa from Mesoregions Northwest and Northeast of Rio Grande do Sul. The experiment was carried out in the field, in a randomized block design, with three replications. The germplasm was characterized using 40 morphoagronomic, 21 of which were either binary or multicategoric, while 19 were quantitative traits as cycle (days to heading), plant length, peduncle, glume and lemma, and production of biomass and grains. The data were submitted to descriptive, univariate, and multivariate statistical analysis. The accessions were monomorphic in 17 out of 21 among qualitative characters. Vegetative habit, diameter, length, and wall thickness of culm were the main quantitative traits in the discrimination of the accessions. The maximum and minimum dissimilarity, expressed by the generalized Mahalanobis distance, ranged from 2.98 up to 55.71. Based on the magnitude of divergence and performance towards agronomic traits in attributes, some accessions may be incorporated into breeding programs of the species

A probabilistic framework for behavioral identification from animal-borne accelerometers

Many studies of animal distributions use habitat and climactic variables to explain patterns of observed space use. However, without behavioral information, we can only speculate as to why and how these characteristics are important to species persistence.Animal-borne accelerometer and magnetometer data loggers can be used to detect behaviors and when coupled with telemetry improve our understanding of animal space use and habitat requirements. However, these loggers collect tremendous quantities of data requiring automated machine learning techniques to identify patterns in the data. Supervised machine learning requires a set of training signals with known behaviors to train the model to identify the unique signal characteristics associated with each behavior. In contrast, unsupervised approaches aggregate unlabeled signals into groups based purely on signal similarity but, without additional information, do not identify specific behaviors.In this paper, we propose a probabilistic framework for interpreting uncertainty in machine learning techniques—the probability profile—and demonstrate how to post hoc identify behaviors within signal groups. We assess model performance using a matrix-based measure of dissimilarity. We used a Random Forest (RF) and a clustered self-organizing map (CSOM) for comparison and demonstrate the use of a behavioral profile for each using a data set of high-frequency accelerometer and magnetometer data collected from 7 captive wild pigs (Sus scrofa) moving in a 1 ha outdoor enclosure.We found that the RF had more discrimination than the CSOM which had fewer clusters associated with high probabilities of a single behavior (>50%). The leave-p-out cross validation statistic of the probability matrix (L1¯) indicated that there was an average maximum dissimilarity of 20% and 65% between the training and test data sets for the RF and CSOM methods, respectively.Using a probability profile to describe groups predicted from machine learning allows the variation and error inherent in behavioral prediction to be incorporated directly into the model to better reflect the nuances of behavior derived from accelerometer and/or magnetometer signals. We discuss the data requirements of this framework, demonstrate its application to field data, highlight critical assumptions and caveats, and examine how it may be used to generate new ecological inference.

Кластерний аналіз як метод оцінки тяжкості перебігу неалкогольної жирової хвороби печінки у пацієнтів з гіпертонічною хворобою

The purpose of the study was to determine the possibilities of cluster analysis as a method for assessing the severity of Non-alcoholic fatty liver disease in its comorbidity with hypertension. Materials and methods. In the study, we examined 63 patients with non-alcoholic steatohepatitis and hypertension, 62 patients with isolated non-alcoholic steatohepatitis and 20 healthy individuals as a control group. All patients underwent anthropometric examinations, including determination of waist circumference, measuring systolic and diastolic blood pressure, performing routine laboratory tests, revealing plasma levels of kallistatin, interleukin-1β and interleukin-10 by enzyme-linked immunosorbent assay. Abdominal ultrasonography was performed to determine the thickness of the right, left and caudal lobes of the liver, and 2-dimensional shear wave elastography with calculating the liver parenchyma stiffness (E, kPa) was performed to determine the fibrotic changes of the liver. Cluster analysis was performed in the program "STATISTICA 8.0." using a hierarchical method and k-means algorithm with pre-standardized data. Results and discussion. According to the results of clustering, 3 subgroups of patients with non-alcoholic fatty liver disease and hypertension were identified. Cluster 1 was characterized by low systolic and diastolic blood pressure, waist circumference, low levels of alanine aminotransferase and aspartate aminotransferase, moderate interleukin-1β levels, low activity of kallistatin and interleukin-10, ultrasound signs of hepatomegaly and increased liver parenchyma stiffness. Cluster 2 included patients with the highest systolic and diastolic blood pressure, moderate waist circumference, highest alanine aminotransferase, aspartate aminotransferase and interleukin-1β levels, low activity of kallistatin and interleukin-10, the most pronounced ultrasound hepatomegaly signs and the worst liver elastography results. Cluster 3 was characterized by moderate systolic and diastolic blood, minimum alanine aminotransferase, aspartate aminotransferase and interleukin-1β content, highest kallistatin and interleukin-10 levels, minimum thickness of liver lobes and lowest liver parenchymal stiffness. Clusters differed significantly from the control group in all parameters, and from the isolated non-alcoholic fatty liver disease group, significant differences were found in all characteristics except waist circumference and interleukin-1β levels. The clusters varied in at least 4 parameters, and the maximum dissimilarity was found between clusters 2 and 3, which differed by 9 indicators. Conclusion. Thus, the clustering of examination results of the patients with non-alcoholic fatty liver disease and hypertension showed a statistically significant distribution of groups according to the severity of liver fibrosis. The elevated systolic and diastolic blood pressure, increased alanine aminotransferase and aspartate aminotransferase, decreased activity of kallistatin and interleukin-10 and signs of hepatomegaly according to ultrasound data could be considered as markers of severe liver fibrosis. The cluster distribution of patients with non-alcoholic fatty liver disease and hypertension opens prospects for the development of new integrated methods in assessment of liver parenchyma changes severity in patients with this comorbidity

Genetic diversity, allelic variation and marker trait associations in gamma irradiated mutants of rice (Oryza sativa L.)

Purpose Rice is a prime staple crop for more than half of the world population. Improved White Ponni (IWP) is a premium quality grain rice variety that is fetching a good price and is increasingly popular among the consumers of Tamil Nadu. Tall plant stature of IWP makes them susceptible to lodging and medium duration are two undesirable traits in the variety increases yield losses in the field and also productivity. In this context, we aimed to generate a large mutant population of IWP irradiated with various doses of gamma irradiation to recover putative mutants for semi-dwarfism and earliness. Materials and methods Totally, 34 putative mutants (22 early, 11 semi-dwarf and early and 1 Narrow-leaf dwarf mutant) were phenotyped for nine morphological traits and genotyped using 34 microsatellite markers linked to a trait of interest of earliness and semi-dwarfism. Trait variability, allelic variations, genetic structure and marker-trait associations in gamma-irradiated putative mutants of Improved White Ponni (IWP) rice were investigated in this study. Results The hierarchical clustering of morphological data produced five clusters with a dissimilarity coefficient of 1.39. A minimum dissimilarity coefficient of 0.23 was observed between the mutants IWPM9 and IWPM20 and a maximum dissimilarity coefficient of 2.55 was observed between IWPM1 and IWPM25. In cluster analysis with molecular marker data, five clusters with a similarity coefficient of 0.67 were observed. The mutant IWPM29 exhibited the most divergence from the wild type at the genotype level. The first principal component explained 50.99% of the total variability and the majority of the traits were contributed positively. The single-marker analysis revealed the strong association of SSR marker RM3912 with the traits plant height, panicle length and number of grains per panicle with an R2 value of 0.235, 0.235 and 0.250 respectively. Conclusions The study identified semi-dwarf and short-duration rice mutants of IWP that can be utilized as potential breeding stocks. The trait-linked SSR markers can improve selection cycles in advanced breeding programs.

Introducing multi-round clustering on capturing the multidirectional extreme current profile for deep-water ROV operation

ROVs (Remotely Operated Vehicles) are playing an important role in installation, construction, and maintenance of subsea systems. During operation, ROVs are usually connected to surface facilities through umbilical lines where the entire umbilical-ROV system is affected by vessel motions, wave, and current induced loads. During the design process, it is commonly assumed that the current profile that leads to the extreme response of an offshore structural system has a typical profile with a uniform direction. However, in deep water, the variation of current direction along water depth becomes more apparent. Hence, the effect of direction variation cannot be neglected when establishing the extreme current profile for deep-water ROV operation.The study focuses on how to efficiently acquire the multidirectional extreme current profiles through a new multi-round clustering method. Firstly, static analysis of a typical deep-water umbilical-ROV system was performed under thousands of multidirectional current profiles extracted from a database of one-year continuously recorded ocean current. Different responses of the system such as ROV offset, umbilical lateral displacement, and maximum tension in the umbilical were investigated and set as a benchmark for further process. Then, a multi-round clustering procedure using a modified maximum dissimilarity algorithm for ROV operation was introduced and applied to extract the extreme multidirectional current profile more efficiently.It is shown that the proposed multi-round clustering methodology can be implemented efficiently and accurately in capturing the extreme vertical current profiles from a current database for ROV operation in different working depths. The study also showed that the effect of bottom current on deep-water ROV operation is more significant than the surface current. Therefore, for ROV operation in deep waters, more precautions shall be made on the bottom current profiles.

A predictive equation for residual strength using a hybrid of subset selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming

More accurate and reliable estimation of residual strength friction angle (ϕr) of clay is crucial in many geotechnical engineering applications, including riverbank stability analysis, design, and assessment of earthen dam slope stabilities. However, a general predictive equation for ϕr, with applicability in a wide range of effective parameters, remains an important research gap. The goal of this study is to develop a more accurate equation for ϕr using the Pareto Optimal Multi-gene Genetic Programming (POMGGP) approach by evaluating a comprehensive dataset of 290 experiments compiled from published literature databases worldwide. A new framework for integrated equation derivation proposed that hybridizes the Subset Selection of Maximum Dissimilarity Method (SSMD) with Multi-gene Genetic Programming (MGP) and Pareto-optimality (PO) to find an accurate equation for ϕr with wide range applicability. The final predictive equation resulted from POMGGP modeling was assessed in comparison with some previously published machine learning-based equations using statistical error analysis criteria, Taylor diagram, revised discrepancy ratio (RDR), and scatter plots. Base on the results, the POMGGP has the lowest uncertainty with U95 = 2.25, when compared with Artificial Neural Network (ANN) (U95 = 2.3), Bayesian Regularization Neural Network (BRNN) (U95 = 2.94), Levenberg-Marquardt Neural Network (LMNN) (U95 = 3.3), and Differential Evolution Neural Network (DENN) (U95 = 2.37). The more reliable results in estimation of ϕr derived by POMGGP with reliability 59.3%, and resiliency 60% in comparison with ANN (reliability = 30.23%, resiliency = 28.33%), BRNN (reliability = 10.47%, resiliency = 10.39%), LMNN (reliability = 19.77%, resiliency = 20.29%) and DENN (reliability = 27.91%, resiliency = 24.19%). Besides the simplicity and ease of application of the new POMGGP equation to a broad range of conditions, using the uncertainty, reliability, and resilience analysis confirmed that the derived equation for ϕr significantly outperformed other existing machine learning methods, including the ANN, BRNN, LMNN, and DENN equations.