Regulatory Mechanisms in Biosystems (E-Journal - Dnipro National University)
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New Insights Into the Seasonal Movement Patterns of Shortfin Mako Sharks in the Gulf of Mexico
No full textHighly mobile apex predators such as the shortfin mako shark (mako shark; Isurus oxyrinchus) serve an important role in the marine ecosystem, and despite their declining populations and vulnerability to overexploitation, this species is frequently harvested in high abundance in both commercial and recreational fisheries. In 2017, the North Atlantic stock was deemed overfished and to be undergoing overfishing and was recently listed in CITES Appendix II. Effective management of this species can benefit from detailed information on their movements and habitat use, which is lacking, especially in the Gulf of Mexico, a potential mating and parturition ground. In this study, we used satellite telemetry to track the movements of mako sharks in the western Gulf of Mexico between 2016 and 2020. In contrast to previous studies that have primarily tagged juvenile mako sharks (>80% juveniles), ∼80% of sharks tagged in this study (7 of 9) were presumed to be mature based on published size-at-maturity data. Sharks were tracked for durations ranging from 10 to 887 days (mean = 359 days; median = 239 days) with three mature individuals tracked for >2 years. Mako sharks tagged in this study used more of the northwestern Gulf of Mexico than reported in previous movement studies on juveniles, suggesting potential evidence of size segregation. While one mature female remained in the Gulf of Mexico over a >2-year period, predominantly on the continental shelf, two mature males demonstrated seasonal migrations ∼2,500 km from the tagging location off the Texas coast to the Caribbean Sea and northeastern United States Atlantic coast, respectively. During these migrations, mako sharks traversed at least 12 jurisdictional boundaries, which also exposed individuals to varying levels of fishing pressure and harvest regulations. Movement ecology of this species, especially for mature individuals in the western North Atlantic, has been largely unknown until recently. These data included here supplement existing information on mako shark movement ecology and potential stock structure that could help improve management of the species
Benchmark Sketch of Port O’Connor, Station Number: 877-3701, Date: 06/30/1995.
No full textBenchmark Sketch of Port O’Connor, Station Number: 877-3701, drawn by T.Z Jeffries. Date: 06/30/1995.US Army Corps of Engineers (USACE), Texas General Land Office (GLO), Texas Water Development Board (TWDB) and in collaboration and following the standards of the National Oceanic and Atmospheric Administration (NOAA)
Orange Crossandra Infundibuliformis Potted Plant
No full textClose up of Orange Crossandra Infundibuliformis Potted Plant Flower bloomin
Benchmark Sketch of FM 2918, Station Number: 877-2683, Date: 07/13/2012.
No full textBenchmark Sketch of FM 2918, Station Number: 877-2683, drawn by Sara Ussery. Date: 07/13/2012.US Army Corps of Engineers (USACE), Texas General Land Office (GLO), Texas Water Development Board (TWDB) and in collaboration and following the standards of the National Oceanic and Atmospheric Administration (NOAA)
A family photograph. Arturo and Mary Vasquez sit front center.
No full textA family photograph. Arturo and Mary Vasquez sit front center
Intelligent mobile edge computing
No full textWith the emergence of the Internet of Things (IoT), connected devices have been growing exponentially. These IoT devices typically have low resources, thus augmented resources, particularly compute and storage resources, are needed to support various IoT services. Mobile Edge Computing (MEC) deploys compute and storage resources at the network edge servers to
accommodate IoT services, where data collected by IoT devices can be processed and analyzed in proximity. Compared with conventional cloud computing, MEC can mitigate potential network congestion caused by massive data transmission and reduce service latency. However, the performance of the MEC heavily relies on the prediction accuracy of the spatiotemporal distribution of IoT traffic and intelligent resource provision. In this work, we first developed a spatiotemporal method for modeling and predicting time-varying demand from IoTs so that MEC providers can provision resources efficiently. The prediction results can help network providers find the best suitable locations to deploy edge servers. Furthermore, we develop deep learning (DL) models to learn and predict the temporal content popularity to intelligently utilize the storage resources of MEC servers for caching content. Finally, deep reinforcement learning (DRL) models have been harnessed to control computational offloading to efficiently utilize computational resources to support IoT services and reduce energy consumption. The developed
models are evaluated through simulations and real-world datasets, and the results show that our models outperform existing methods.Computing SciencesCollege of Science and Engineerin
A certificate from the League of United Latin American Citizens, appointing Arturo Vasquez as the National Treasurer of LULAC for 1966-1967.
No full textA certificate from the League of United Latin American Citizens, appointing Arturo Vasquez as the National Treasurer of LULAC for 1966-1967
Luling Watermelon Thump Float in 1965
No full textLuling Watermelon Thump Best float in civic divisio