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Dynamic or static stability of electrical networks is maintained not only by the internal energy of the system but also by the utilization of a hot power reserve. In networks that incorporate renewable energy sources, this reserve should be commensurate with the power capacity of the renewable energy sources. However, this leads to an increase in economic costs. Additionally, the integration of renewable energy sources results in a decrease in the dynamic stability of the system due to the unregulated changes in the supplied energy. The objective of this research is to analyze the potential impact of renewable energy sources on network stability and explore possibilities for enhancing stability through adaptive operation of smart consumers. Renewable energy generation is characterized by temporal and concentration uncertainties, which can potentially disrupt the operation of the energy system. The primary approach employed in this study involves analyzing the potential influence of various operational modes of smart consumers, both during load commutation and energy source switching. The main outcome of this work is the development of a smart consumer algorithm. The devised operating principle mitigates the effects of unstable renewable energy operation or uncertain consumer behavior.

The search for productive and stable genotypes is the main goal of breeding programs. The Genotype × Environment interaction strongly influences genotype performance, and makes the selection of new cultivars difficult. One way to take advantage of this interaction is to identify genotypes with high grain yield (GY) and stability in different environments. The objective of this study was to evaluate the consistency of correlation between GY and stability evaluation methods in multi-environment trials and identify which methods are more suitable for selecting genotypes. GY data from 11 soybean ( Glycine max [L.] Merr.) cultivars conducted in Value for Cultivation and Use trials in 10 locations in Paraná and Mato Grosso do Sul states, Brazil, in the 2013-2014, 2014-2015, and 2015-2016 crop seasons. A randomized complete block design with three replicates was used. Seven methods were applied to evaluate stability, and Spearman's correlation coefficient was used to compare methods. Positive associations were observed between GY and the harmonic mean of genotypic values (HMGV) across environments and genotype main effect + Genotype × Environment interaction effect by ideal genotype (GGE IG) methodologies, and between GY and the Lin and Binns method modified by Carneiro for general and unfavorable environments. The Eberhart and Russell, additive main effects and multiplicative interaction (AMMI1), and GGE for stability (GGE STA) methods presented no positive associations with GY in any year. Positive associations were found between the Wricke, AMMI1, and Eberhart and Russell methods because they were related to the static stability concept. The HMGV and GGE IG methods can be used together because they are consistently associated with GY and based on the dynamic stability concept.

The search for productive and stable genotypes is the main goal of breeding programs. The Genotype * Environment interaction strongly influences genotype performance, and makes the selection of new cultivars difficult. One way to take advantage of this interaction is to identify genotypes with high grain yield (GY) and stability in different environments. The objective of this study was to evaluate the consistency of correlation between GY and stability evaluation methods in multi-environment trials and identify which methods are more suitable for selecting genotypes. GY data from 11 soybean (Glycine max [L.] Merr.) cultivars conducted in Value for Cultivation and Use trials in 10 locations in Parana and Mato Grosso do Sul states, Brazil, in the 2013-2014, 2014-2015, and 2015-2016 crop seasons. A randomized complete block design with three replicates was used. Seven methods were applied to evaluate stability, and Spearman's correlation coefficient was used to compare methods. Positive associations were observed between GY and the harmonic mean of genotypic values (HMGV) across environments and genotype main effect + Genotype * Environment interaction effect by ideal genotype (GGE IG) methodologies, and between GY and the Lin and Binns method modified by Carneiro for general and unfavorable environments. The Eberhart and Russell, additive main effects and multiplicative interaction (AMMI1), and GGE for stability (GGE STA) methods presented no positive associations with GY in any year. Positive associations were found between the Wricke, AMMI1, and Eberhart and Russell methods because they were related to the static stability concept. The HMGV and GGE IG methods can be used together because they are consistently associated with GY and based on the dynamic stability concept.

Twenty parametric and non-parametric measures derived from grain yield of 15 advanced durum genotypes evaluated across 12 variable environments during the 2004-2006 growing seasons were used to assess performance stability and adaptability of the genotypes and to study interrelationship among these measures. The combined ANOVA and the non-parametric tests of Genotype x environment interaction indicated the presence of significant crossover and non-crossover interactions, and of significant differences among genotypes. Principal component analysis based on the rank correlation matrix indicated that most non-parametric measures were significantly inter-correlated with parametric measures and therefore can be used as alternatives. The results also revealed that stability measures can be classified into three groups based on static and dynamic concepts of stability. The group related to the dynamic concept and strongly correlated with mean grain yield of stability included the parameters of TOP (proportion of environments in which a genotype ranked in the top third), superiority index (P-i) and geometric adaptability index. The second group reflecting the concept of static stability included, Wricke's ecovalence, the variance in regression deviation (S-di(2)), AMMI stability value, the Huehn's parameters [S-i((1)), S-i((2))], Tennarasua's parameter [NPi(1)], Kang's parameter ( RS) and yield reliability index (I-i) which were not correlated with mean grain yield. The third group influenced simultaneously by grain yield and stability included the measures S-i((3)), S-i((6)), NPi(2), NPi(3), environmental variance (S-xi(2)), coefficient of variability and coefficient of regression (b(i)). Based on the concept of dynamic stability, genotypes G6, G4, and G3 were found to be the most adapted to favorable environments, whereas genotypes G8, G9, and G12 were more stable and are related to the concept of static stability.

The objective of this study was to compare nonparametric stability procedures and apply different nonparametric tests for genotype × environment (G × E) interactions on grain yields of 15 durum wheat genotypes selected from Iran/ICARDA joint project grown in 12 environments during 2004-2006 in Iran. Results of nonparametric tests of G × E interaction and a combined ANOVA across environments indicated the presence of both crossover and noncrossover interactions, and genotypes varied significantly for grain yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of sum of ranks of mean grain yield and Shukla's stability variance (rank-sum) were associated with high mean yield. The other nonparametric stability methods were not positively correlated with mean yield but they characterized a static concept of stability. The results of correlation analysis indicated that only TOP and rank-sum methods would be useful for simultaneous selection for high yield and stability. These two methods identified lines Mrb3/Mna-1, Syrian-4 and Mna-1/Rfm-7 as genotypes with dynamic stability and wide adaptation. According to static stability parameters, the genotypes 12A-Mar8081 and 19A-Mar8081 with lowest grain yield were selected as genotypes with the highest stability.[PUBLICATION ABSTRACT]

Landslide susceptibility (LSI) modeling of Darjeeling–Sikkim Himalaya is performed by integrating 28 causative factors on 28C28 combinations on Geographical Information System (GIS) following analytic hierarchy process (AHP)-based multicriteria decision protocol, logistic regression (LR)-based multivariate technique, machine learning data-driven random forest (RF) and artificial neural network (ANN) methods wherein the terrain is classified into ‘None’ (with: 0.0 < LSI ≤ 0.17), ‘Low’ (with: 0.17 < LSI ≤ 0.34), ‘Moderate’ (with: 0.34 < LSI ≤ 0.51), ‘High’ (with: 0.51 < LSI ≤ 0.68),‘Very High’ (with: 0.68 < LSI ≤ 0.85) and ‘Severe’ (with: 0.85 < LSI ≤ 1.00) susceptible zones as validated through standard statistical accuracy tests and direct cross-correlation analysis of all the susceptible zonation maps generated by drawing comparison with the 30% landslide inventory test data. The best integrated thematic RF-based LSI vector layer with an accuracy level of 0.871, in turn, on integration with the vulnerability components like population density, number of households, building types, building height and building density has demarketed approximately 21% of the region under ‘Very High’ to ‘Severe’ socioeconomic risk zone while about 36% area are classified under ‘Very High’ to ‘Severe’ structural risk zone as implicated by devastating landslide hazards in the region. Ground Penetrating Radar Survey has been conducted on all the slopes in the ‘Very High to Severe’ landslide susceptible zones wherein near-surface lithologic setting, presence of paleo-slopes and microstructural features like fractures/faults and poorly stratified debris flow have been imaged that provided favorable subsurface conditions for slope failure. Finite element method-based slope failure analysis for Newmark displacement estimates factor of safety (FoS) value that acts as the proxy in defining the degree of slope instability is seen to vary between 1.905 and 2.357 in the ‘Low to Moderate’ landslide susceptible zone while it ranges between 1.051 and 1.652 in the ‘High’ landslide susceptible zone and between 0.649 and 1.349 in the ‘Very High to Severe’ landslide inventory subset along the slopes under both gravity loading and seismic shaking in the terrain. The slope stability analysis puts the yield acceleration between 0.0012 and 0.11984 m/s2 and the total deformation between 0.0027 and 1.4484 m. All these parameters in the classified landslide susceptible zones in unison demonstrate how unstable are the terrain slopes in the ‘High to Severe’ landslide susceptible zones.

Paper presents the behaviour of three-layered annular plates subjected to loads acting in plate plane. Plates are composed of laminated fibre-reinforced composite facings and foam core. The static and dynamic parameters of plate critical state were evaluated. The sensitivity of composite structure of plate to the acting of quickly increasing in time loads is shown. The problem has been solved numerically using the finite element method. Results have been compared with ones obtained for plate models with isotropic layers. These plate models have also been calculated solving formulated task analytically and numerically by means of the finite difference method. Solutions to the problem concern the axisymmetrical and asymmetrical plate buckling modes. Numerous presented tables and figures create the image of the stability behaviour of examined composite plates.

For wing-in-ground vehicles, ground effect increases lift force, which is mostly affected by wingspan. Four rectangular NACA4412 wings with varied aspect ratios were chosen to explore the influence of wingspan on aerodynamic properties in ground effect, and their aerodynamics were studied using CFD modeling. To simulate the flow around the wing in ground effect, SST k – ω model was utilized. The overset technique was used to achieve varied pitch angle and altitude states where the wing operates. Results demonstrate that as wingspan increases, the variation rate of lift coefficient with respect to pitch angle or altitude increases, especially when pitch angle is less than 2 ∘ and relative altitude is less than 0.3. While a smaller wingspan causes a greater varying rate of downwash angle with pitch angle or altitude, when a wing has a smaller span and its relative altitude is less than 0.3, the development of the wingtip vortex becomes a stronger restriction for the block of ground, significantly reducing the downwash angle. The distance between the aerodynamic centers of altitude and pitch angle decreases as the wingspan expands, and the maximum magnitudes of change in chord length reach 0.045 and 0.2749 of chord length when the aspect ratio shifts from 1 to 4. The aspect ratio has no discernible effect on the boundary of static stability. When the aspect ratio increases from 1 to 4, the center of gravity must be moved upstream to ensure motion stability, and the maximum distance is 0.098 of the chord length.

To investigate the effect of recess structures on the static and dynamic performance of aerostatic thrust bearings and to explore superior designs, this study analyzes the load-capacity theoretical model, identifying that the throttling effect and pressure-holding effect of the recess are the key factors determining the bearings’ static performance. Computational fluid dynamics (CFD) was used to evaluate three types of recess structures: a simple-orifice recess (SOR), a rectangular-compound recess (RCR), and a bionic-compound recess (BCR). The results indicate that the BCR structure demonstrates efficient transmission performance by reducing flow resistance and diverting air, while ensuring a reasonable pressure drop as the radial ratio αi changes. Additionally, the smaller air capacity of the BCR structure contributes to enhanced bearing stability, showing clear advantages in both static and dynamic performance. This research illustrates the practical application of bionics in mechanical design and provides new theoretical foundations and design strategies for improving aerostatic bearing performance.

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S2di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha−1 for breeding lines and modern cultivar versus 2,041 kg ha−1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.