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Hourly Sulfur Dioxide Observations Over North America: First Retrieval Results From TEMPO
Full text linkWe present the first sulfur dioxide (SO2) retrievals from Tropospheric Emissions: Monitoring of Pollution (TEMPO), the first geostationary atmospheric composition sensor to cover North America, along with some potential applications of TEMPO SO2 data. We show that high resolution (∼10 km2) TEMPO measurements can be used to produce good quality SO2 retrievals with relatively small noise and biases. We demonstrate that hourly TEMPO data are useful for monitoring volcanic hazards, by providing frequent updates on the plume location and additional information on the plume height or winds. With the large number of measurements from TEMPO, it is also feasible to monitor diurnal changes in SO2 for relatively large sources such as the Cantarell oil field. We also show that high-cadence TEMPO measurements allow estimates of SO2 degassing from Popocatépetl volcano on sub-daily timescales. Overall, our results suggest that TEMPO can significantly enhance space-based SO2 detection and monitoring over North America
Differential effects of geographic-cluster and alliance resources on firm innovation: The moderating role of firm technological capability
No full textIn this study, we focus on two significant sources of a firm\u27s external knowledge—geographic clusters and strategic alliances—and examine whether a firm\u27s technological capability influences the effectiveness of these two sources of knowledge for firm innovation performance in different ways. We theorize that clusters and alliances differ in their knowledge flow mechanisms, leading to varying roles of internal technological capability. Specifically, we argue that a firm\u27s capability is more crucial for absorbing and integrating knowledge from clusters, where information flows in a fragmented form through informal channels. Furthermore, firms are less concerned about knowledge loss due to the nature and pattern of knowledge flows in clusters. In contrast, in alliances, where knowledge flow is more integrated and structured, technologically capable firms are generally more concerned about knowledge loss, which adversely affects reciprocity and, consequently, the flow of knowledge between partners. Moreover, since knowledge in alliances is transferred through structured mechanisms, the advantages of high internal capability in absorbing and integrating partner knowledge become less significant. Using 15 years of longitudinal data from the U.S. semiconductor industry—a sector characterized by innovation, strategic alliances, and clustering tendencies—we find that technologically stronger firms derive greater innovation benefits from clusters in enhancing the value of their innovations. In contrast, technologically weaker firms gain more from strategic alliances. Overall, our study supports our hypotheses and provides a nuanced understanding of how internal technological capability operates differently in leveraging external knowledge from clusters versus alliances
Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Fixed-Tilt Systems in the U.S. Eastern Interconnection for Weather Year 2018
No full textUsing 2018 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be 30-degree, fixed tilt systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with No Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Fixed-Tilt Systems in the U.S. Eastern Interconnection for Weather Year 2021
No full textUsing 2021 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be 30-degree, fixed tilt systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata
Convexity Helps Iterated Search in 3D
No full textInspired by the classical fractional cascading technique [13, 14], we introduce new techniques to speed up the following type of iterated search in 3D: The input is a graph G with bounded degree together with a set Hv of 3D hyperplanes associated with every vertex of v of G. The goal is to store the input such that given a query point q ∈ R3 and a connected subgraph H ⊂ G, we can decide if q is below or above the lower envelope of Hv for every v ∈ H. We show that using linear space, it is possible to answer queries in roughly O(log n +|H| √log n) time which improves trivial bound of O(|H| log n) obtained by using planar point location data structures. Our data structure can in fact answer more general queries (it combines with shallow cuttings) and it even works when H is given one vertex at a time. We show that this has a number of new applications and in particular, we give improved solutions to a set of natural data structure problems that up to our knowledge had not seen any improvements. We believe this is a very surprising result because obtaining similar results for the planar point location problem was known to be impossible [15]
Hourly Simulated Power Production Data with Snow Loss Model at Existing Utility-Scale PV Sites (\u3e5 MW) in the U.S. Eastern Interconnection in 2022
No full textProject Summary: We ran PySAM power production simulations for utility-scale (\u3e5 MW) PV sites located in the U.S. Eastern Interconnection in the year 2022. Site panel mounts (fixed-tilt or single-axis tracking), capacities, and locations (latitudes and longitudes) were extracted from Lawrence Berkeley National Laboratory\u27s Utility-Scale Solar 2024 Edition dataset. See 2022_PV_existing_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with No Snow Loss Model at Existing Utility-Scale PV Sites (\u3e5 MW) in the U.S. Eastern Interconnection in 2014
No full textProject Summary: We ran PySAM power production simulations for utility-scale (\u3e5 MW) PV sites located in the U.S. Eastern Interconnection in the year 2014. Site panel mounts (fixed-tilt or single-axis tracking), capacities, and locations (latitudes and longitudes) were extracted from Lawrence Berkeley National Laboratory\u27s Utility-Scale Solar 2024 Edition dataset. See 2014_PV_existing_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with No Snow Loss Model at Existing Utility-Scale PV Sites (\u3e5 MW) in the U.S. Eastern Interconnection in 2016
No full textProject Summary: We ran PySAM power production simulations for utility-scale (\u3e5 MW) PV sites located in the U.S. Eastern Interconnection in the year 2016. Site panel mounts (fixed-tilt or single-axis tracking), capacities, and locations (latitudes and longitudes) were extracted from Lawrence Berkeley National Laboratory\u27s Utility-Scale Solar 2024 Edition dataset. See 2016_PV_existing_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with No Snow Loss Model at Existing Utility-Scale PV Sites (\u3e5 MW) in the U.S. Eastern Interconnection in 2020
No full textProject Summary: We ran PySAM power production simulations for utility-scale (\u3e5 MW) PV sites located in the U.S. Eastern Interconnection in the year 2020. Site panel mounts (fixed-tilt or single-axis tracking), capacities, and locations (latitudes and longitudes) were extracted from Lawrence Berkeley National Laboratory\u27s Utility-Scale Solar 2024 Edition dataset. See 2020_PV_existing_site_metadata.csv file for individual site metadata
First-Principles study of high-temperature thermoelectric performance induced by hydrogenation of ZnAs and CdAs monolayers
No full textMotivated by the improved electronic properties of the isostructural hydrogenated ZnSb monolayers, we investigate the thermoelectric efficiency in the hydrogenated ZnAs and CdAs monolayers (i.e., ZnAsH and CdAsH) at temperatures of 300 K and 900 K using Boltzmann transport theory while accounting for multiple carrier scattering mechanisms. Our results reveal that hydrogenation modifies the band structures of ZnAsH and CdAsH, inducing a transition from metallic to semiconducting behavior (1.89 eV for ZnAsH and 1.23 eV for CdAsH. The cohesive energy, formation energy, phonon spectrum, ab initio molecular dynamics (AIMD), and elastic constants confirm their robust stability. The electronic transport analysis shows that p-type ZnAsH and CdAsH exhibit high Seebeck coefficient of 225.42μV/K and 409.45μV/K respectively, along with high electrical conductivity. Small group velocity, strong anharmonicity, and high scattering rates lead to ultralow lattice thermal conductivities of 3.36(3.74) W/mK for the ZnAsH monolayer and 0.23(0.35) W/mK for the CdAsH monolayer in the x(y) directions. The electronic part of the thermal conductivity is consistent with predictions from the Wiedemann–Franz law. Combining the excellent electronic transport with ultralow lattice thermal conductivity, we achieve optimal ZTs of 0.53 for ZnAsH and 3.72 for CdAsH in the x-direction. These findings suggest that hydrogenated monolayers are promising candidates for thermoelectric (TE) technology