Evaluation of Naked Barley Landraces for Agro-morphological Traits

Naked barley (Hordeum vulgare var. nudum L.) is a traditional, culturally important, climate-resilient winter cereal crop of Nepal. Evaluation of the naked barely genotypes for yield and disease is fundamental for their efficient utilization in plant breeding schemes and effective conservation programs. Therefore, to identify high yielding and yellow rust resistant landraces of naked barley for hilly and mountainous agro-ecosystem, twenty naked barley landraces collected from different locations of Nepal, were evaluated in randomized complete block design (RCBD) with three replications during winter season of 2016 and 2017 at Khumaltar, Lalitpur, Nepal. Combined analysis of variances revealed that NGRC04902 (3.46 t/ha), NGRC00886 (3.28 t/ha), NGRC02309 (3.21 t/ha) and NGRC06026 (3.10 t/ha) were the high yielding landraces and statistically at par with the released variety 'Solu Uwa' (3.15 t/ha). The landraces namely NGRC00837 (ACI Value: 1.86) was found resistant to yellow rust diseases. Landraces NGRC06034 (131.7 days) and NGRC02363 (130.8 days) were found early maturing and NGRC02306 (94.36 cm) was found dwarf landraces among tested genotypes. These landraces having higher yield and better resistance to yellow rust need to be deployed to farmers' field to diversify the varietal options and used in resistant breeding program to improve the productivity of naked barley for Nepalese farmers

High harmonic generation from Bloch electrons in solids

We study the generation of high harmonic radiation by Bloch electrons in a model transparent solid driven by a strong mid-infrared laser field. We solve the single-electron time-dependent Schr\"odinger equation (TDSE) using a velocity-gauge method [New J. Phys. 15, 013006 (2013)] that is numerically stable as the laser intensity and number of energy bands are increased. The resulting harmonic spectrum exhibits a primary plateau due to the coupling of the valence band to the first conduction band, with a cutoff energy that scales linearly with field strength and laser wavelength. We also find a weaker second plateau due to coupling to higher-lying conduction bands, with a cutoff that is also approximately linear in the field strength. To facilitate the analysis of the time-frequency characteristics of the emitted harmonics, we also solve the TDSE in a time-dependent basis set, the Houston states [Phys. Rev. B 33, 5494 (1986)], which allows us to separate inter-band and intra-band contributions to the time-dependent current. We find that the inter-band and intra-band contributions display very different time-frequency characteristics. We show that solutions in these two bases are equivalent under an unitary transformation but that, unlike the velocity gauge method, the Houston state treatment is numerically unstable when more than a few low lying energy bands are used

Fluxon Dynamics of a Long Josephson Junction with Two-gap Superconductors

We investigate the phase dynamics of a long Josephson junction (LJJ) with two-gap superconductors. In this junction, two channels for tunneling between the adjacent superconductor (S) layers as well as one interband channel within each S layer are available for a Cooper pair. Due to the interplay between the conventional and interband Josephson effects, the LJJ can exhibit unusual phase dynamics. Accounting for excitation of a stable 2$\pi$-phase texture arising from the interband Josephson effect, we find that the critical current between the S layers may become both spatially and temporally modulated. The spatial critical current modulation behaves as either a potential well or barrier, depending on the symmetry of superconducting order parameter, and modifies the Josephson vortex trajectories. We find that these changes in phase dynamics result in emission of electromagnetic waves as the Josephson vortex passes through the region of the 2$\pi$-phase texture. We discuss the effects of this radiation emission on the current-voltage characteristics of the junction.Comment: 14 pages, 6 figure

A Professional Competency Development Model: Implications for Extension Educators

Professional development refers to continuing education designed to enhance competencies, skills, and knowledge. There is a need for a professional development model based on the educational processes used by educators of adults. A professional competency development model was constructed from a study grounded on four educational process areas in Extension. In this study, 441 randomly selected Extension educators in the North Central Region of the United States participated through an online survey. The proposed model has implications for designing professional competency development programs in the areas of needs assessment/program development, teaching and learning methods, delivery strategies, and evaluation methods. It also indicates the best time and place for Extension educators to develop the competencies and suggests a mechanism to continuously identify the knowledge and skills needed to obtain the best results. This model could be used to develop educational programs in a variety of national and international settings

Fire-induced Carbon Emissions and Regrowth Uptake in Western U.S. Forests: Documenting Variation Across Forest Types, Fire Severity, and Climate Regions

The forest area in the western United States that burns annually is increasing with warmer temperatures, more frequent droughts, and higher fuel densities. Studies that examine fire effects for regional carbon balances have tended to either focus on individual fires as examples or adopt generalizations without considering how forest type, fire severity, and regional climate influence carbon legacies. This study provides a more detailed characterization of fire effects and quantifies the full carbon impacts in relation to direct emissions, slow release of fire-killed biomass, and net carbon uptake from forest regrowth. We find important variations in fire-induced mortality and combustion across carbon pools (leaf, live wood, dead wood, litter, and duff) and across low- to high-severity classes. This corresponds to fire-induced direct emissions from 1984 to 2008 averaging 4 TgC/yr and biomass killed averaging 10.5 TgC/yr, with average burn area of 2723 sq km/yr across the western United States. These direct emission and biomass killed rates were 1.4 and 3.7 times higher, respectively, for high-severity fires than those for low-severity fires. The results show that forest regrowth varies greatly by forest type and with severity and that these factors impose a sustained carbon uptake legacy. The western U.S. fires between 1984 and 2008 imposed a net source of 12.3 TgC/yr in 2008, accounting for both direct fire emissions (9.5 TgC/yr) and heterotrophic decomposition of fire-killed biomass (6.1 TgC yr1) as well as contemporary regrowth sinks (3.3 TgC/yr). A sizeable trend exists toward increasing emissions as a larger area burns annually

Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models