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We present the first calculations to follow the evolution of all stable isotopes (and their abundant radioactive progenitors) in a finely zoned stellar model computed from the onset of central hydrogen burning through explosion as a Type II supernova. The calculations were performed for a 15 solar mass Pop I star using the most recently available set of experimental and theoretical nuclear data, revised opacity tables, and taking into account mass loss due to stellar winds. We find the approximately solar production of proton-rich isotopes above a mass number of A=120 due to the gamma-process. We also find a weak s-process, which along with the gamma-process and explosive helium and carbon burning, produces nearly solar abundances of almost all nuclei from A=60 to 85. A few modifications of the abundances of heavy nuclei above mass 90 by the s-process are also noted and discussed. New weak rates lead to significant alteration of the properties of the presupernova core. 10 pages, 4 figures, Nuclear Astrophysics X Workshop proceedings

Performed in cooperation with the School of Mineral Engineering, University of Washington. ; Includes bibliographical references. ; Mode of access: Internet.

Yeast volatiles double starvation survival in Drosophila .

An analytical Detailed Loop Model (DLM) has been developed to analyze the performance of solar thermosiphon water heaters with heat exchangers in storage tanks. The model has been used to study the performance of thermosiphons as a function of heat exchanger characteristics, heat transfer fluids, flow resistances, tank stratification, and tank elevation relative to the collector. The results indicate that good performance can be attained with these systems compared to thermosiphons without heat exchangers.

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

Effective waste management is of paramount importance as it contributes significantly to environmental preservation, mitigates health hazards, and aids in the preservation of precious resources. Conversely, mishandling waste not only presents severe environmental risks but can also disrupt the balance of ecosystems and pose threats to biodiversity. The emission of carbon dioxide, methane, and greenhouse gases (GHGs) can constitute a significant factor in the progression of global warming and climate change, consequently giving rise to atmospheric pollution. This pollution, in turn, has the potential to exacerbate respiratory ailments, elevate the likelihood of cardiovascular disorders, and negatively impact overall public health. Hence, efficient management of trash is extremely crucial in any society. It requires integrating technology and innovative solutions, which can help eradicate this global issue. The internet of things (IoT) is a revolutionary communication paradigm with significant contributions to remote monitoring and control. IoT-based trash management aids remote garbage level monitoring but entails drawbacks like high installation and maintenance costs, increased electronic waste production (53 million metric tons in 2013), and substantial energy consumption for always-vigilant IoT devices. Our research endeavors to formulate a comprehensive model for an efficient and cost-effective waste collection system. It emphasizes the need for global commitment by policymakers, stakeholders, and civil society, working together to achieve a common goal. In order to mitigate the depletion of manpower, fuel resources, and time, our proposed method leverages quick response (QR) codes to enable the remote monitoring of waste bin capacity across diverse city locations. We propose to minimize the deployment of IoT devices, utilizing them only when absolutely necessary and thereby allocating their use exclusively to central garbage collection facilities. Our solution places the onus of monitoring garbage levels at the community level firmly on the shoulders of civilians, demonstrating that a critical aspect of any technology is its ability to interact and collaborate with humans. Within our framework, citizens will employ our proposed mobile application to scan QR codes affixed to waste bins, select the relevant garbage level, and transmit this data to the waste collection teams’ database. Subsequently, these teams will plan for optimized garbage collection procedures, considering parameters such as garbage volume and the most efficient collection routes aimed at minimizing both time and fuel consumption.