Search for: All records

Abstract We present an SNP-based crossover map for Drosophila mauritiana. Using females derived by crossing 2 different strains of D. mauritiana, we analyzed crossing over on all 5 major chromosome arms. Analysis of 105 male progeny allowed us to identify 327 crossover chromatids bearing single, double, or triple crossover events, representing 398 crossover events. We mapped the crossovers along these 5 chromosome arms using a genome sequence map that includes the euchromatin-heterochromatin boundary. Confirming previous studies, we show that the overall crossover frequency in D. mauritiana is higher than is seen in Drosophila melanogaster. Much of the increase in exchange frequency in D. mauritiana is due to a greatly diminished centromere effect. Using larval neuroblast metaphases from D. mauritiana—D. melanogaster hybrids we show that the lengths of the pericentromeric heterochromatin do not differ substantially between the species, and thus cannot explain the observed differences in crossover distribution. Using a new and robust maximum likelihood estimation tool for obtaining Weinstein tetrad distributions, we observed an increase in bivalents with 2 or more crossovers when compared with D. melanogaster. This increase in crossing over along the arms of D. mauritiana likely reflects an expansion of the crossover-available euchromatin caused by a difference in the strength of the centromere effect. The crossover pattern in D. mauritiana conflicts with the commonly accepted view of centromeres as strong polar suppressors of exchange (whose intensity is buffered by sequence nonspecific heterochromatin) and demonstrates the importance of expanding such studies into other species of Drosophila. 

Thermal radiation directionality (TRD) characterizes the anisotropic signature of most surface targets in the thermal infrared domain. It causes significant uncertainties in estimating surface upward longwave radiation (SULR) from space observations. In this regard, kernel-driven models (KDMs) are suitable to remove TRD effects from remote sensing dataset as they are computationally efficient. However, KDMs requires simultaneous multiangle observations as inputs to be well calibrated, which yields a difficulty with geostationary satellites as they can only provide a single-angle observation. To overcome this issue, we proposed a six-parameter time-evolving KDM that combines a four-parameter SULR diurnal variation model and a two-parameter TRD amplitude model to correct the TRD effect for single-angle estimated SULR dataset of geostationary satellites. The significant daytime TRD effect when solar zenith angle is within 60cm can be effectively eliminated. The modeling accuracy of the time-evolving KDM is evaluated using a simulated SULR dataset generated by the 3D Discrete Anisotropic Radiative Transfer (DART) model; the TRD correction method based on the new time-evolving KDM is validated using a two-year single-angle estimated SULR dataset derived from data of the Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-16 (GOES-16) against in situ measurements at 20 AmeriFlux sites. Results show that the proposed time-evolving KDM has a high accuracy with an R2 > 0.999 and a small RMSE = 1.5 W/m2; the TRD correction method based on the time-evolving KDM can greatly reduce the GOES-16 SULR uncertainty caused by the TRD effect with an RMSE decrease of 4.5 W/m2 (22.1%) and mean bias error decrease of 7.9 W/m2 (62.7%). Hence, the proposed TRD correction method is practically efficient for the operational TRD correction of SULR products generated from the geostationary satellites (e.g., GOES-16, FY-4A, Himawari-8, MSG). 

Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification. 

Wave energy has been studied and explored because of its enormous potential to supply electricity for human activities. However, the uncertainty of its spatial and temporal variations increases the difficulty of harvesting wave energy commercially. There are no large-scale wave converters in commercial operation yet. A thorough understanding of wave energy dynamic behaviors will definitely contribute to the acceleration of wave energy harvesting. In this paper, about 40 years of meteorological data from the Gulf of Mexico were obtained, visualized, and analyzed to reveal the wave power density hotspot distribution pattern, and its correlation with ocean surface water temperatures and salinities. The collected geospatial data were first visualized in MATLAB. The visualized data were analyzed using the deep learning method to identify the wave power density hotspots in the Gulf of Mexico. By adjusting the temporal and spatial resolutions of the different datasets, the correlations between the number of hotspots and their strength levels and the surface temperatures and salinities are revealed. The R value of the correlation between the wave power density hotspots and the salinity changes from −0.371 to −0.885 in a negative direction, and from 0.219 to 0.771 in a positive direction. For the sea surface temperatures, the R values range from −0.474 to 0.393. Certain areas within the Gulf of Mexico show relatively strong correlations, which may be useful for predicting the wave energy behavior and change patterns. 

The complexity and variability of ocean waves make wave energy harvesting very challenging. Previous research has indicated that wave energy was mainly generated and transferred by wind, but the detailed correlation between wind and wave energy has not been discovered. Wave energy in the Gulf of Mexico (GoM) has high variability with distinct seasonal behavior. However, the underlying reasons for this unique behavior have not been discussed and discovered yet. In this paper, a computer animation-based dynamic visualization method was created to conduct exploratory and explanatory analyses of 36 years of meteorological data in the GoM from the WaveWatch III system to identify preliminary patterns and underlying reasons for the unique behavior of wave energy in the GoM. These preliminary patterns and underlying reasons were further analyzed using Energy Events and Breaks concepts. During both high and low levels wave energy periods, the detailed correlation between wave energy and the wind was analyzed and determined. High level wave power in the GoM was mainly generated by the local inland wind from northern weather patterns, while low level wave power was mainly generated by swells from the Caribbean and the Atlantic oceans, which entered the GoM through the two narrow pathways, the Straits of Yucatan and the Florida Straits. The results from this paper will also be able to help the design, placement, and operation of future wave energy converters to improve their efficiency in harvesting wave energy in the GoM.