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One of the main challenges autonomous vehicles (AVs) will face is interacting with pedestrians, especially at unmarked midblock locations where the right-of-way is unspecified. This study investigates pedestrian crossing behavior given different roadway centerline features (i.e., undivided, two-way left-turn lane (TWLTL), and median) and various AV operational schemes portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications). This study employs virtual reality to simulate an urban unmarked midblock environment where pedestrians interact with AVs. Results demonstrate that both roadway centerline design features and AV operations and signaling significantly impact pedestrian unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. But only the roadway centerline features significantly impact the walking time. Participants in the undivided scene spent a longer time waiting at the curb and walking on the road than in the median and TWLTL scenes, but they spent a shorter time waiting in the middle. Compared to the AV without a signal, the design of yellow signal significantly reduced pedestrian waiting time at the curb and in the middle. But yellow/blue significantly increased the pedestrian waiting time. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle. For middle waiting time, yellow/blue signals had the most impact on the median road type and the least on the undivided road. Demographics, past behaviors, and walking exposure are also explored. Older individuals tend to wait longer, and pedestrian past crossing behaviors and past walking exposures do not significantly impact pedestrian walking behavior.

Monitoring noise pollution in urban areas in a more systematic manner has been gaining traction as a theme among the research community, especially with the rise of smart cities and the IoT. However, although it affects our everyday life in a profound way, monitoring indoor noise levels inside workplaces and public buildings has so far grabbed less of our attention. In this work, we report on noise levels data produced by an IoT infrastructure installed inside 5 school buildings in Greece. Our results indicate that such data can help to produce a more accurate picture of the conditions that students and educators experience every day, and also provide useful insights in terms of health risks and aural comfort. Preprint submitted to the WSACC 2023 workshop, organized in the scope of the 9th IEEE International Smart Cities Conference 2023

In order to complement the picture of the atmospheric water cycle in the Southern Ocean, we have continuously monitored water vapor isotopes since January 2020 in Amsterdam Island (37.7983 °S, 77.5378 °E) in the Indian Ocean. We present here the first 2-year-long water vapor isotopic record monitored on this site. We show that the vapor isotopic composition largely follows the vapor mixing ratio, as expected in marine boundary layers. However, we evidence 11 cold front periods of a few days where there is a strong loss of correlation between water vapor δ18O and mixing ratio. These periods are associated with abrupt negative excursions of water vapor δ18Ο, often occurring toward the end of precipitation events. Six of these events show a decrease in gaseous elemental mercury suggesting subsidence of air from higher altitude. Accurately representing the water isotopic signal during these cold fronts is a real challenge for the atmospheric components of Earth System models equipped with water isotopes. While the ECHAM6-wiso model was able to reproduce most of the sharp negative water vapor δ18O excursions, the LMDZ-iso model at 2° (3°) resolution was only able to reproduce 7 (1) of the negative excursions. Based on a detail model-data comparison, we conclude that the most plausible explanations for such isotopic excursions are rain-vapor interactions associated with subsidence at the rear of a precipitation event.

Abstract. The 79° North Glacier (Nioghalvfjerdsbrae, 79NG) is one of three remaining glaciers with a floating tongue in Greenland. Although the glacier was considered exceptionally stable in the past, earlier studies indicate that the ice tongue has thinned in recent decades. By conducting high-resolution ground-based and airborne radar measurements in conjunction with satellite remote sensing observations, we find significant changes in the geometry of 79NG. In the vicinity of the grounding line, a 500 m high subglacial channel has grown since ~2010 and caused surface lowering of up to 7.6 m a-1. Our results show extreme basal melt rates exceeding 150 m a-1 within a distance of 5 km from the grounding line, where the ice has thinned by 42 % since 1998. We found a heterogeneous distribution of melt rates likely due to variability in water column thickness and channelization of the ice base. Time series of melt rates show a decrease in basal melting since 2018, indicating an inflow of colder water into the cavity below 79NG. We discuss the processes that have led to the changes in geometry and conclude that the inflow of warm ocean currents has led to the extensive thinning of 79NG's floating ice tongue near the grounding line in the last two decades. In contrast, we hypothesize that the growth of the channel results from increased subglacial discharge due to a considerably enlarged area of summer surface melt due to the warming of the atmosphere.

The Svalbard archipelago is particularly sensitive to climate change due to the relatively low altitude of its main ice fields and its geographical location in the higher North Atlantic, where the effect of Arctic amplification is more significant. The largest temperature increases have been observed during winter, but increasing summer temperatures, above the melting point, have led to increased glacier melt. Here, we evaluate the impact of this increased melt on the preservation of the oxygen isotope (δ18O) signal in firn records. δ18O is commonly used as a proxy for past atmospheric temperature reconstructions, and, when preserved, it is a crucial parameter to date and align ice cores. By comparing four different firn cores collected in 2012, 2015, 2017 and 2019 at the top of the Holtedahlfonna ice field (1100 m a.s.l.), we show a progressive deterioration of the isotope signal, and we link its degradation to the increased occurrence and intensity of melt events. Our findings indicate that, starting from 2015, there has been an escalation in melting and percolation resulting from changes in the overall atmospheric conditions. This has led to the deterioration of the climate signal preserved within the firn or ice. Our observations correspond with the model's calculations, demonstrating an increase in water percolation since 2014, potentially reaching deeper layers of the firn. Although the δ18O signal still reflects the interannual temperature trend, more frequent melting events may in the future affect the interpretation of the isotopic signal, compromising the use of Svalbard ice cores. Our findings highlight the impact and the speed at which Arctic amplification is affecting Svalbard's cryosphere.

We propose two distributed set-based observers using strip-based and set-propagation approaches for linear discrete-time dynamical systems with bounded modeling and measurement uncertainties. Both algorithms utilize a set-based diffusion step, which decreases the estimation errors and the size of estimated sets, and can be seen as a lightweight approach to achieve partial consensus between the distributed estimated sets. Every node shares its measurement with its neighbor in the measurement update step. In the diffusion step, the neighbors intersect their estimated sets using our novel lightweight zonotope intersection technique. A localization example demonstrates the applicability of our algorithms. Comment: Accepted at Journal of the Franklin Institute

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional methods, e.g., remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forest states. In this work, we combine the new-generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results and explore the influence of different calculation methods of mean leaf parameters. For the majority of the forest stands, we generated good reflectances with all levels of forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R2=0.62). This work provides a forward modeling approach for relating forest reflectance to forest characteristics. With this tool, it is possible to analyze a large set of forest stands with corresponding reflectances. This opens up the possibility to understand how reflectance is related to succession and different forest conditions.

AbstractBackgroundChronic obstructive pulmonary disease (COPD) is a complex disorder with a high degree of interindividual variability. Gastrointestinal dysfunction is common in COPD patients and has been proposed to influence the clinical progression of the disease. Using the presence of bile acid(s) (BA) in bronchoalveolar lavage fluid (BAL) as a marker of gastric aspiration, we evaluated the relationships between BAs, clinical outcomes, and bacterial lung colonisation.MethodsWe used BAL specimens from a cohort of COPD patients and healthy controls. Bile acids were profiled and quantified in BAL supernatants using mass spectrometry. Microbial DNA was extracted from BAL cell pellets and quantified using qPCR. We profiled the BAL microbiota using an amplicon sequencing approach targeting the V3-V4 region of the 16S rRNA gene.ResultsDetection of BAs in BAL was more likely at earliest clinical stages of COPD and was independent of the degree of airway obstruction. BAL specimens with BAs demonstrated higher bacterial biomass and lower diversity. Likewise, the odds of recovering bacterial cultures from BAL were higher if BAs were also detected. Detection of BAs in BAL was not associated with either inflammatory markers or clinical outcomes. We also observed different bacterial community types in BAL, which were associated with different clinical groups, levels of inflammatory markers, and the degree of airway obstruction.ConclusionDetection of BAs in BAL was associated with different parameters of airway ecology. Further studies are needed to evaluate whether BAs in BAL can be used to stratify patients and for predicting disease progression trajectories.