Influence Of Plant Growth Regulators On Vegetative And Phenological Characters Of Okra (Abelmoschus Esculentus L. Moench) Cv. Utkal Gourav

A field experiment was conducted at All India Coordinated Research Project on Vegetable Crops, Odisha University of Agriculture and Technology, Bhubaneswar, during kharif 2021.Foliar spray of PGRs at various concentrations were given to okra crop cv. Utkal Gourav. The experiment was laid out in Randomized Block Design with three replications and eleven treatments viz., GA3 (100 ppm) (T1), GA3 (150 ppm) (T2), NAA (150ppm) (T3), NAA (200 ppm) (T4), Thiourea (250 ppm) (T5), Thiourea (500 ppm) (T6), Cycocel (200 ppm) (T7), Cycocel (250 ppm) (T8), Paclobutrazol (100 ppm) (T9), Paclobutrazol (200 ppm) (T10) and Control (T11). The foliar sprays of growth regulators were done at 15 & 30 days after sowing. All growth regulators significantly performed better as compared to control. The results revealed that NAA performed better with respect to plant height (148.66cm), internodal length (8.66 cm) and leaf area (237.60 Cm2) compared to control with 112.83 cm,5.45 cm & 174.69 Cm2 respectively. Cycocel 250 ppm recorded significantly better with respect to number of nodes per plant (21.53), number of branches per plant (3.47), number of leaves (32.74), leaf chlorophyll content (1.390 mg/100 g) and days to 50 % flowering (36.50). However the untreated control plot recorded number of nodes per plant (15.08), number of branches per plant (1.66), number of leaves (21.88), leaf chlorophyll content (1.071 mg/100 g) and days to 50 % flowering (42.10)

A Study of Factors Affecting Liberalization Policies of Jute Industries in Visakhapatnam District

The researcher had made an attempt to evaluate the most influential factor for Work environment, Grievance handling system and Participation management with the impact of liberalization polices on industrial relations by taking twenty two parameter for the jute industry of the sample districts of Visakhapatnam The researcher has personally collected the opinions of the respondents through the structured questionnaire. The collected data are analyzed through KMO test, Factor Analysis, Bivariate Correlation Matrix Reliability test. The Analysis shows that the employees are considering three parameters out of twelve of work environment, two out of five for grievance handling system and also two out of five parameters from participative management. DOI: 10.17762/ijritcc2321-8169.150518

Valence electronic structure of Mn in undoped and doped lanthanum manganites from relative K x-ray intensity studies

Relative $K$ x-ray intensities of $Mn$ in $Mn$, $MnO_{2}$, $LaMnO_{3}$ and $La_{0.7}B_{0.3}MnO_{3}$ ($B$ = $Ca$, $Sr$, and $Ce$) systems have been measured following excitation by 59.54 keV $\gamma$-rays from a 200 mCi $^{241}$Am point-source. The measured results for the compounds deviate significantly from the results of pure $Mn$. Comparison of the experimental data with the multiconfiguration Dirac-Fock (MCDF) effective atomic model calculations indicates reasonable agreement with the predictions of ionic model for the doped {manganites except} that the electron doped $La_{0.7}Ce_{0.3}MnO_{3}$ and hole doped $La_{0.7}Ca_{0.3}MnO_{3}$ compounds show some small deviations. The results of $MnO_{2}$ and $LaMnO_{3}$ deviate considerably from the predictions of the ionic model. Our measured $K\beta/K\alpha$ ratio of $Mn$ in $La_{0.7}Ca_{0.3}MnO_{3}$ cannot be explained as a linear superposition of $K\beta/K\alpha$ ratios of $Mn$ for the end members which is in contrast to the recent proposal by Tyson et al. from their $Mn$ $K\beta$ spectra.Comment: 14 pages, 4 figures. to appear in NIM-B.Please send an e-mail for figure

A short survey on fault diagnosis in wireless sensor networks

Fault diagnosis is one of the most important and demand- able issues of the network. It makes the networks reliable and robust to operate in the normal way to handle almost all types of faults or failures. Additionally, it helps sensor nodes to work smoothly and efficiently till the end of their lifetime. This short survey paper not only presents a clear picture of the recent proposed techniques, but also draws comparisons and contrasts among them to diagnose the potential faults. In addition, it proposes some potential future-work directions which would lead to open new research directions in the field of fault diagnosis

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours