Fatty acids and stable isotopes as indicators of early-life feeding and potential maternal resource dependency in the bull shark Carcharhinus leucas

The degree of reliance of newborn sharks on energy reserves from maternal resource allocation and the timescales over which these animals develop foraging skills are critical factors towards understanding the ecological role of top predators in marine ecosystems. We used muscle tissue stable carbon isotopic composition and fatty acid analysis of bull sharks Carcharhinus leucas to investigate early-life feeding ecology in conjunction with maternal resource dependency. Values of δ13C of some young-of-the-year sharks were highly enriched, reflecting inputs from the marine-based diet and foraging locations of their mothers. This group of sharks also contained high levels of the 20:3ω9 fatty acid, which accumulates during periods of essential fatty acid deficiency, suggesting inadequate or undeveloped foraging skills and possible reliance on maternal provisioning. A loss of maternal signal in δ13C values occurred at a length of approximately 100 cm, with muscle tissue δ13C values reflecting a transition from more freshwater/estuarine-based diets to marine-based diets with increasing length. Similarly, fatty acids from sharks >100 cm indicated no signs of essential fatty acid deficiency, implying adequate foraging. By combining stable carbon isotopes and fatty acids, our results provided important constraints on the timing of the loss of maternal isotopic signal and the development of foraging skills in relation to shark size and imply that molecular markers such as fatty acids are useful for the determination of maternal resource dependency.

Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.