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Ensuring the human right to water requires monitoring at national or subnational levels, but few comprehensive frameworks exist for industrialized contexts. This paper introduces a subnational-level framework - known as the California Human Right to Water Framework and Data Tool (CalHRTW) - developed by the authors at the California EPA's Office of Environmental Health Hazard Assessment. This paper has two objectives: (1) to present the theoretical foundations and methodology used to develop the first version of CalHRTW (CalHRTW 1.0) and (2) to showcase how results can be used. CalHRTW 1.0 measures three components of the human right to water: drinking water quality, accessibility and affordability for community water systems in California. Nine individual indicators grouped by component, and three indices that summarize component-level outcomes are used to quantify system-level results. CalHRTW allows users to: (1) summarize system-level conditions statewide and identify challenges, (2) explore social equity implications and (3) centralize information for planning. CalHRTW draws on approaches from existing international monitoring efforts and complements existing California efforts by being the first US effort to comprehensively and explicitly monitor the HRTW under one umbrella. This work offers other US states and countries a model to build monitoring efforts to realize the human right to water. [ABSTRACT FROM AUTHOR]

In this paper, the sensitivity of the neural network (NN) outputs to device parameter uncertainties (non-idealities) in inverter-based memristor (IM) crossbar neuromorphic circuits is mathematically modeled and verified using exhaustive circuit and system-level simulations. The NN sensitivity is obtained by modeling the sensitivity of the IM neuron output to the non-idealities of its circuit elements. The analysis reveals a higher sensitivity of the output voltage of the IM neuron to the non-idealities of the inverters compared to the conductance variation of the memristors. Among the inverter non-idealities, horizontal shift of the inverters voltage transfer characteristic (VTC) shows the highest impact on the output voltage of the neuron. To reduce the accuracy loss due to the variations, a training approach which includes a sensitivity term in the cost function of the training phase, is suggested. The achievable improvements through the said NN training approach are evaluated. In the evaluation, the California Housing, MNIST, and Fashion MNIST datasets are employed. The results show up to 50% reduction in the NN output variations in the presence of circuit elements’ non-idealities. [ABSTRACT FROM AUTHOR]

Stormwater utility fees provide dedicated revenues for managing stormwater. In setting fees, household-level affordability should be considered. Existing research does not address methods to evaluate the affordability of stormwater utility fees, especially alongside other household water costs. This paper aims to fill that gap. We present a scenario-based approach to evaluate household affordability of stormwater utility fees in a municipality, which considers other essential household water service charges. We estimate the relative contribution of stormwater utility fees to total household water costs and evaluate affordability across income brackets by integrating data for socioeconomic status, land use, water utility charges, and impervious surface cover. Using data for a case study region, results indicate that stormwater utility fees are small contributors to overall household urban water costs and comprise no more than 2% of the median household income across nearly all income quintiles. For low-income households, stormwater charges only exceed 1% of the household income for the lowestincome brackets (,$20,000) and still represent a small portion of household water costs. The analysis demonstrates a generalizable approach that can be applied everywhere when evaluating a stormwater utility fee as a funding strategy. [ABSTRACT FROM AUTHOR]

The article reports on the letters of gratitude from cadet members of the U.S. Naval Sea Cadet Corps Sacramento Division in California who received grant funding from the Navy League STEM Institute to attend the Aerospace Museum of California's (AMC) summer Science, Technology Engineering and Mathematics (STEM) Camp. The cadets share their experience and the lessons they learned including about engineering, paper rocket building, planets and stars.