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Identifying adopted Green Super Rice (GSR) under different agro-ecological locations in Pakistan is crucial to sustaining the high productivity of rice. For this purpose, the multi-location trials of GSR were conducted to evaluate the magnitude of genetic variability, heritability, and stability in eight different locations in Pakistan. The experimental trial was laid out in a randomized complete block (RCB) design with three replications at each location. The combined analysis of variance (ANOVA) manifested significant variations for tested genotypes (g), locations (L), years (Y), genotype × year (GY), and genotype × location (GL) interactions revealing the influence of environmental factors (L and Y) on yield traits. High broad-sense heritability estimates were observed for all the studied traits representing low environmental influence over the expression of traits. Noticeably, GSR 48 showed maximum stability than all other lines in the univariate model across the two years for grain yield and related traits data. Multivariate stability analysis characterized GSR 305 and GSR 252 as the highest yielding with optimum stability across the eight tested locations. Overall, Narowal, Muzaffargarh, and Swat were the most stable locations for GSR cultivation in Pakistan. In conclusion, this study revealed that G×E interactions were an important source of rice yield variation, and its AMMI and biplots analysis are efficient tools for visualizing the response of genotypes to different locations.

Specialty fragrant rice is sold at a premium price in both local and international trade because of its superior grain qualities. In this research, 40 advanced fragrant rice accessions were evaluated in different environments. The primary objective was to identify genotypes with high grain yield and high stability using multivariate (GGE biplot) and univariate analysis (regression slope, deviation from regression, Shukla’s stability variance, Wricke’s ecovalence, and Kang’s stability statistic). The field experiment trials were laid in a randomized complete block design in three replications. The analysis of variance showed highly significant differences among genotypes, locations, seasons, and the interactions between genotype, locations, and seasons. The environment significantly explained about 43.32% (37.01 and 6.31% for locations and seasons) of the total sum of squares. Based on average ranking generated from multivariate and univariate stability measured, rice accessions were classified into three major categories, viz., genotypes having high trait performance, and high stability as category 1. The second category consists of genotypes that exhibit high mean performance but low stability, while the third category includes genotypes with high stability but low trait performance. Our results showed that breeding for yield performance was possible, and the identified genotypes could be recommended for commercial cultivation.

There is a wide spread of rice blast in Africa, Asia and Latin America, leading to significant yield losses. In addition, the vagaries of climate change makes it necessary to develop a resistant variety capable of adapting to varied environmental conditions. This experiment was conducted to select superior genotypes across environments from the advanced blast resistant rice lines. Eighteen improved blast-resistant rice genotypes that were produced by crossing MR219 (susceptible) and Pongsu Seribu-2 (resistant) were assessed in four distinct environments in Malaysia (UPM Serdang; Tanjung Karang, Selangor; Kota Sarang Semut, Kedah and Tanjung Karang, Selangor). MR219 variety was included as a check variety for yield making a total of 19 genotypes evaluated. Three replications in each environment were used in the randomised complete block design experiment. Data on vegetative, yield, and yield component attributes were recorded. For most of the variables under investigation, analysis of variance showed significant differences between genotypes, environment, and GxE interaction. With the exception of the number of panicles and tillers per hill, low genetic advance was also found for every trait. Positive correlation was found between yield per hectare and other agronomic traits evaluated except days to flowering and maturity, plant height, and number of unfilled grains. Three groups of rice genotypes were identified by stability analyses utilizing both univariate ( b i , S 2 d , σ i 2 , W i 2 , YS i ) and multivariate stability statistics. The first group comprised genotypes with high mean yield traits and good stability, including G18, MR219, G17, and G11. These genotypes had broad environmental adaptations. The second group’s genotypes, including G1, G6, and G8, were characterised by low stability but high mean yield traits, making them appropriate for cultivation in a specified environment. The third group comprised genotypes such as G7, G9, and G13 that had high stability but a low mean yield characteristic. Environmental discriminate analyses using GGE biplot revealed that Tanjung Karang 1 was the most appropriate environment for most of the genotypes. Blast disease reaction after challenging with inoculum indicates that 11 genotypes proved resistant to the disease while blast disease screening under protected glass house at Mardi and UPM indicated resistance in 13 and 12 genotypes, respectively. Superior genotypes, specifically G18, G17, G11, G14, and G5, were selected from this study because of their stability and high yielding characteristics across mega environments. Breeders could utilize these genotypes for future improvement programmes.

Feature selection is a significant part of many machine learning applications dealing with small-sample and high-dimensional data. Choosing the most important features is an essential step for knowledge discovery in many areas of biomedical informatics. The increased popularity of feature selection methods and their frequent utilisation raise challenging new questions about the interpretability and stability of feature selection techniques. In this study, we compared the behaviour of ten state-of-the-art filter methods for feature selection in terms of their stability, similarity, and influence on prediction performance. All of the experiments were conducted on eight two-class datasets from biomedical areas. While entropy-based feature selection appears to be the most stable, the feature selection techniques yielding the highest prediction performance are minimum redundance maximum relevance method and feature selection based on Bhattacharyya distance. In general, univariate feature selection techniques perform similarly to or even better than more complex multivariate feature selection techniques with high-dimensional datasets. However, with more complex and smaller datasets multivariate methods slightly outperform univariate techniques. •Ten feature selection methods are compared using stability and similarity measures.•Univariate FS perform better than multivariate FS for high dimensional datasets.•Multivariate FS slightly outperform univariate FS for complex and smaller datasets.•Most stable appears to be entropy based FS.•FS yielding the highest prediction performance are MRMR and Bhattacharyya distance.

The study was conducted to evaluate the performance of 10 sunflower genotypes in 12 environments during 2017 and 2018 cropping seasons with RCBD design. Twenty-nine parametric and non-parametric measures were compared for the stability of seed yield. Highly significant (p 0.001) interactions were found in the combined analyses of variance, with environmental factors contributing to 46.5% of the total variation. The Spearman correlation analysis determines the existence of a positive and significant (p < 0.001) association with AMMI values, but a non-significant association was observed between most of the non-parametric statistics and seed yield. The AMMI values show a positive and significant correlation and multiple overlaps in the biplot with CV, Var, W2, and Sij, indicating that comparable outcomes will be obtained if one of these parameters is used. Seven clusters were visible in the biplot, and R 2 and Gai appeared in a similar group with seed yields, demonstrating their close relationship. G1, G4, and G6 are identified as the most favorable genotypes in terms of stability and seed yield. This study confirms that the tested parameters are informative in identifying desirable genotypes. However, it is essential to identify stability parameters that are more reliable and informative by eliminating duplication.

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.

The expansion of human activities across natural environments is now well known. This includes agricultural activities that effectively render many former natural environments sterile habitats for animals. Very often, what remains of the natural habitat are hedgerows that serve as habitat or pathways for movement between habitats for many species, including reptiles. In this study, we describe population changes in the western green lizard, Lacerta bilineata, in a hedgerow system in western France. The results are derived from a univariate diversity analysis of photographic data to identify individual lizards over a 4-year study period. Lizards were sighted from March April to October early November but there was a midsummer gap in sightings during July–August. The annual presence of individual lizards was low, both between and within years, but based on the diversity analysis, the overall stability of the population was high. Female numbers varied and were highest in 2020, but juveniles were highest in 2023; the numbers of males present each year were approximately the same. Individual lizards that were present before the midsummer gap were mostly absent after the midsummer gap and were replaced by new individuals. Incidences of autotomy were low in males and juveniles and were not recorded in females. In general, the results suggest that the lizards move through hedgerow systems but remain in a specific section for reproduction from March to July. Through this study, we also highlight the importance of univariate diversity formulas to obtain robust results in investigations of the demographic aspects of animal populations that are easy to monitor.

A susceptibility map for an area, which is representative in terms of both geologic setting and slope instability phenomena of large sectors of the Sicilian Apennines, was produced using slope units and a multiparametric univariate model. The study area, extending for approximately 90 km 2 , was partitioned into 774 slope units, whose expected landslide occurrence was estimated by averaging seven susceptibility values, determined for the selected controlling factors: lithology, mean slope gradient, stream power index at the foot, mean topographic wetness index and profile curvature, slope unit length, and altitude range. Each of the recognized 490 landslides was represented by its centroid point. On the basis of conditional analysis, the susceptibility function here adopted is the density of landslides, computed for each class. Univariate susceptibility models were prepared for each of the controlling factors, and their predictive performance was estimated by prediction rate curves and effectiveness ratio applied to the susceptibility classes. This procedure allowed us to discriminate between effective and non-effective factors, so that only the former was subsequently combined in a multiparametric model, which was used to produce the final susceptibility map. The validation of this map latter enabled us to verify the reliability and predictive performance of the model. Slope unit altitude range and length, lithology and, subordinately, stream power index at the foot of the slope unit demonstrated to be the main controlling factors of landslides, while mean slope gradient, profile curvature, and topographic wetness index gave unsatisfactory results.

Continuous structural health monitoring of long-span bridges, such as suspension bridges, is essential for ensuring stability, and various efforts have been undertaken towards this goal. The hanger cables of suspension bridges play a crucial role in transmitting the main loads within the cable support structure. Therefore, monitoring the tension of hanger cables is vital for maintaining structural stability. Traditionally, the vibration method has been utilized for operational suspension bridges to indirectly estimate the tension of suspension bridge hanger cables. This method involves measuring the natural frequencies for vibration modes from the cables and their geometric conditions. In this study, digital camcorders and tripods were employed to measure the hanger cable response conveniently and efficiently. The response measured by digital camcorders is displacement based, posing challenges in measuring the natural frequencies for high-order modes required by the vibration method. Typically, systems for measuring structural response incorporate white noise components across all frequency domains, complicating the identification of cable frequency component characteristics when the response frequency measured by a digital camcorder is lower than the white noise frequency. Furthermore, measurements taken with a digital camcorder may suffer from low resolution due to long distances and optical limitations, hindering the measurement of high-frequency response components. To address these challenges, this study introduced a back analysis method to estimate tension using the natural frequencies of low-order modes. The method defines the difference between measured natural frequencies and those predicted by finite-element analysis as the error function. Optimization was then conducted using the univariate search method to minimize this error function. The findings suggest that the tension estimated by applying only the first natural frequency of hanger cables through the back analysis method closely aligns with estimates obtained using the vibration method. This research highlights the potential of using standard digital camcorders as an accurate and cost-effective means for estimating hanger cable tension.

The basic reproduction number R0 is a concept which originated in population dynamics, mathematical epidemiology, and ecology and is closely related to the mean number of children in branching processes (reflecting the fact that the phenomena of interest are well approximated via branching processes, at their inception). Despite the very extensive literature around R0 for deterministic epidemic models, we believe there are still aspects which are not fully understood. Foremost is the fact that R0 is not a function of the original ODE model, unless we also include in it a certain (F,V) gradient decomposition, which is not unique. This is related to the specification of the “infected compartments”, which is also not unique. A second interesting question is whether the extinction probabilities of the natural continuous time Markovian chain approximation of an ODE model around boundary points (disease-free equilibrium and invasion points) are also related to the (F,V) gradient decomposition. We offer below several new contributions to the literature: (1) A universal algorithmic definition of a (F,V) gradient decomposition (and hence of the resulting R0). (2) A fixed point equation for the extinction probabilities of a stochastic model associated to a deterministic ODE model, which may be expressed in terms of the (F,V) decomposition. Last but not least, we offer Mathematica scripts and implement them for a large variety of examples, which illustrate that our recipe offers always reasonable results, but that sometimes other reasonable (F,V) decompositions are available as well.