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Background: The research focused on investigating the impact of genotype-environment interaction on the yield stability and performance of eight chickpea genotypes across three different locations (Bhatapara, Kawardha and Raipur). Methods: The study employed a randomized block design with two replications in each environment in Rabi season during 2021-22. Analysis of variance (ANOVA) revealed significant variations between environments and genotype-environment interaction (G x E) based on AMMI and GGE biplot. Result: To visualize the genotype-environment relationship, a two-dimensional GGE biplot was created using the first two principal components, which accounted for 87.8% and 11.5% of the interaction's variation. The GGE biplot indicated that genotype g4 (ICCX 161100-B-B-B-B) in environment e2 (Kawardha) and g8 (JG-24) in environment e1 (Bhatapara) exhibited high yield. Genotype g3 (ICCV 201112) in environment e3 (Raipur) also showed high yield, while genotype g5 (JG 2020-10) demonstrated stability across all three environments. Furthermore, the AMMI analysis identified genotype g2 (ICCV 201103) as a superior performer in terms of yield stability across environments. By employing the GGE biplot and AMMI analysis, the study categorized the genotypes into three groups, facilitating simplified visual evaluations. The GGE biplot highlighted that genotype g8 (JG-24) exhibited better seed index in environment e3 (Raipur), while genotype g1 (ICCV 201210) performed well in environment e1 (Bhatapara). Genotype g2 (ICCV 201103) demonstrated a good seed index in all three environments. The AMMI analysis indicated that genotype g3 (ICCV 201112) had a slightly lower seed index but maintained stability across all environments. Overall, the findings of this investigation, as represented by the GGE and AMMI biplots, visually illustrated the relationship between genotypes and environments. The identified genotypes hold potential for improving yield production and further research and breeding efforts can be based on these findings. [ABSTRACT FROM AUTHOR]

Introduction: Volume Controlled Ventilation (VCV) is traditionally used during One Lung Ventilation (OLV); however, it is associated with complications such as volutrauma and barotrauma. On the other hand, Pressure Controlled Ventilation (PCV) allows the delivery of a required tidal volume at lower airway pressures, leading to enhanced oxygenation and ventilation. Aim: To compare VCV and PCV modes for OLV in patients undergoing surgery for empyema thoracis. Materials and Methods: A randomised controlled trial was conducted among 50 patients requiring OLV. The participants were divided into two groups, namely Group-V and Group-P, with each group receiving VCV and PCV, respectively. The two groups were compared based on the partial pressure of oxygen (during the intraoperative and post-operative period), peak and plateau airway pressures, lung compliance, and complications. The groups were analysed using the Chi-square test, and the threshold of statistical significance was set at a p-value <0.05. Results: Fifty participants were divided into two groups: VCV (n=25) and PCV (n=25). Both study groups were found to be comparable in terms of demographic details, haemodynamic parameters, and duration of surgery. The mean age of the patients was 27.80 years in Group-V and 31.04 years in Group-P. The authors observed improved PaO2 levels, lung compliance, and reduced peak pressures during OLV in the PCV group. After lung isolation, PaO2 levels of Group-P patients (93.64±5.154 mmHg) were higher compared to Group-V (81.38±7.975 mmHg) at 50% FiO2 (p-value <0.001). Similarly, post-extubation PaO2 levels were better in Group-P (99.24±18.58 mmHg) than in Group-V (84.35±7.677 mmHg) at 36% FiO2 (p-value <0.001). The mean peak pressures were lower in Group-P (25.17±4.34 cm H2O) than in Group-V (28.22±4.51 cm H2O). Additionally, there was a statistically significant improvement in lung compliance among Group-P patients (p-value=0.0144). Conclusion: Thus, it can be inferred that PCV improves oxygenation and reduces airway pressures during OLV. However, there was no significant difference seen between the two modes in terms of post-operative pulmonary complications. [ABSTRACT FROM AUTHOR]

In recent times, large-scale infrastructural projects are being constructed on varieties of soil, especially in highly compressible marine clays and soft soil. The coefficient of consolidation (cv) is one of the most important technical parameters used to estimate the consolidation characteristics of the soil. The experimental laboratory techniques used to obtain cv are time-consuming and possess different practical limitations. In this study, a reliable method for predicting cv is presented based on machine learning (ML). The study considered 11 inherent soil variables, among which the least significant variables are discarded using univariate feature selection technique. Different ML models were developed like the random forest, artificial neural network, and support vector machine for nonlinear mapping of the cv using dimensionally reduced independent variables. Verification against experimental data demonstrates that the Random Forest model accurately predicts the cv (with MAE = 0.0231, MSE= 0.00148, and RMSE = 0.03854). Further, a comparative study of the proposed model is presented with available empirical equations and numerically simulated data. Moreover, the strengths and shortcomings of different ML algorithms are also discussed in detail. [ABSTRACT FROM AUTHOR]