Exchange Rates, Money and Relative Prices: The Dollar-Pound in the 1920's

This paper applies the analytical framework of the monetary approach to exchange rate determination to the analysis of the Dollar/Pound exchange rate during the first part of the 1920's. The analysis uses monthly data up to the return of Britain to gold in 1925. The equilibrium exchange rate is shown to be influenced by both real and monetary factors which operate through their influence on the relative demands and supplies of monies. Special attention is given to examination of the relationship between exchange rates and the relative price of traded to non-traded goods. In the empirical work the prices of traded goods are proxied by the wholesale price indices and the prices of non-traded are proxied by wages. One of the key findings of the paper is the estimate of the elasticity of the exchange rate with respect to the relative price of traded to non-traded goods. This elasticity is estimated with high precision and is shown to be .415 which provides an independent measure of the relative share of spending on non-traded goods. This estimate is consistent with other estimates obtained in studies of expenditure shares. The paper concluded with a dynamic simulation which indicates the satisfactory quality of the predictive ability of the model.

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Martian Atmospheric Dust Mitigation for ISRU Intakes via Electrostatic Precipitation

The Mars 2020 and Mars Sample Return missions expected to fly to Mars within the next ten years will each include an In Situ Resource Utilization (ISRU) system. They convert carbon dioxide in the Martian atmosphere into consumable oxygen at 1% and 20% of the rate required by a full scale human exploration Mars mission, respectively. The ISRU systems will need to draw in the surrounding atmosphere at a rate of 110L/min and 550L/min, respectively, in order to meet their oxygen production goals. Over the duration of each respective mission, a total atmospheric dust mass of 4.86g and 243g will be drawn into each system, respectively. Ingestion of large quantities of dust may interfere with ISRU operations, so a dust mitigation device will be required. The atmospheric volume and dust mass flow rates above will be utilized to simulate Martian environmental conditions in a laboratory electrostatic precipitator being developed to provide active dust mitigation support for atmospheric ISRU systems such as these

Riparian Nitrate and Phosphate Removal Under Future Climate Scenarios

Riparian buffer zones are specially managed zones that lie between agricultural fields and rivers, lakes, or wetlands. They are crucial for protecting water quality, human health, and ecosystem function. Critical ecosystem services of riparian soils include nitrogen removal via denitrification and phosphorus retention through sorption on mineral surfaces. Soil moisture influences these processes by controlling the rate of oxygen diffusion and, therefore, the soil’s redox potential. However, soils are predicted to be drier as climate change progresses, and these changes in soil moisture conditions will alter nitrogen cycle dynamics and phosphorus removal in riparian systems. We conducted a lab experiment to investigate potential changes in riparian ecosystem services brought on by climate change. We hypothesized that climate-induced shifts in moisture dynamics would enhance phosphorus removal but hinder denitrification due to increased oxygen diffusion caused by lower soil moisture conditions. We collected forty-eight soil cores (5 cm diameter, 15 cm height), and we collected additional samples for particle size, bulk density, and powder X-ray diffraction (XRD) analyses. We applied soil treatments in a fully factorial design, considering soil texture (sandy loam versus silty clay loam), antecedent soil moisture (field capacity versus drought), water application (flooding versus capillary rise), and pollutant quantity (simulated agricultural runoff versus deionized water). We primarily performed colorimetric assays on soil porewater and soil samples to determine NO3- and PO43- availability and movement. We also performed elemental analyses to complement the colorimetric assays. Our porewater chemistry and mass balance results showed significant changes in nitrogen cycle dynamics, showing evidence of denitrification, Dissimilatory Nitrate Reduction to Ammonium (DNRA), nitrogen fixation, and nitrogen mineralization. Statistical analyses of the data, primarily through generalized additive mixed-effects models (GAMMs), indicate significant individual and combined positive and negative effects (p<0.05) of the simulated treatments on porewater nitrate, ammonium, and phosphate concentrations, along with porewater pH and ARQ (CO2/O2). Critically, Moisture Regime and Water Application, our two climate proxies, both individually and collectively, significantly affected porewater nitrate, ammonium, and phosphate concentrations. Nitrate porewater concentrations are higher in decreased moisture conditions and changing precipitation as predicted under future climate scenarios. Phosphate porewater concentrations were lower in sandier soils, drought conditions, and capillary rise water application. However, phosphate leached out of the soil during simulated intense precipitation, highlighting the complexities of how predicted climate scenarios will be partially beneficial for phosphate sorption. XRD analysis revealed a mixed clay mineralogy, including a mixed-layer illite-montmorillonite, IS70R1. Additionally, clay mineralogy in clay-rich soil plays a statistically significant role in moderating the soil nitrogen cycle. Correlated extractable iron and nitrogen data indicate evidence of the Ferrous Wheel Hypothesis, especially in temporarily anoxic soils flooded by intense precipitation. Our research demonstrates that future climate scenarios affect key riparian biogeochemical processes and should be researched more thoroughly as the average worldwide temperature climbs above 1.5°C

Murakami Haruki's Short Fiction and the Japanese Consumer Society

This thesis seeks to describe the Japanese novelist Murakami Haruki’s continuing critique of Japan’s modern consumer-oriented society in his fiction. The first chapter provides a brief history of Japan’s consumer-oriented society, beginning with the Meiji Restoration and continuing to the 21st Century. A literature review of critical works on Murakami’s fiction, especially those on themes of identity and consumerism, makes up the second chapter. Finally, the third chapter introduces three of Murakami Haruki’s short stories. These short stories, though taken from three different periods of Murakami’s career, can be taken together to show a legacy of critiquing Japan’s consumer-oriented society

Sensitivity-Informed Augmentation for Robust Segmentation

Segmentation is an integral module in many visual computing applications such as virtual try-on, medical imaging, autonomous driving, and agricultural automation. These applications often involve either widespread consumer use or highly variable environments, both of which can degrade the quality of visual sensor data, whether from a common mobile phone or an expensive satellite imaging camera. In addition to external noises like user difference or weather conditions, internal noises such as variations in camera quality or lens distortion can affect the performance of segmentation models during both development and deployment. In this work, we present an efficient, adaptable, and gradient-free method to enhance the robustness of learning-based segmentation models across training. First, we introduce a novel adaptive sensitivity analysis (ASA) using Kernel Inception Distance (KID) on basis perturbations to benchmark perturbation sensitivity of pre-trained segmentation models. Then, we model the sensitivity curve using the adaptive SA and sample perturbation hyperparameter values accordingly. Finally, we conduct adversarial training with the selected perturbation values and dynamically re-evaluate robustness during online training. Our method, implemented end-to-end with minimal fine-tuning required, consistently outperforms state-of-the-art data augmentation techniques for segmentation. It shows significant improvement in both clean data evaluation and real-world adverse scenario evaluation across various segmentation datasets used in visual computing and computer graphics applications.Comment: 10 page

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Letter to the Editor