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This erratum corrects a typographical error in Fig. 2 of our published paper [ Opt. Express 29 , 27554 ( 2021 ) 10.1364/OE.434751 ].
In this paper, we present a simple cascaded Fabry-Perot interferometer (FPI) that can be used to measure in real-time the refractive index (RI) and length variation in silica optical fibers caused due to external physical parameters, such as temperature, strain, and radiation. As a proof-of-concept, we experimentally demonstrate real-time monitoring of temperature effects on the RI and length and measure the thermo-optic coefficient (TOC) and thermal expansion coefficient (TEC) by using the cascaded FPI within a temperature range of 21–486°C. The experimental results provide a TEC of 5.53 × 10 −7 /°C and TOC of 4.28 × 10 −6 /°C within the specified temperature range. Such a simple cascaded FPI structure will enable the design of optical sensors to correct for measurement errors by understanding the change in RI and length of optical fiber caused by environment parameters.
We correct a minor error in Table 1 and a related minor error in the simulations of the improved system. These revisions result in minor changes to Fig. 6 and Table 1 in [ Opt. Express 32 , 7521 ( 2024 ) 10.1364/OE.510787 ]. These changes do not affect the experimental measurements or the conclusions of the work.
This erratum corrects a typographic error that appears in Table 1 of our earlier paper [ Optica 8 , 539 ( 2021 ) OPTIC8 2334-2536 10.1364/OPTICA.418984 ].
This collection is the data-centric appendices for the report Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile. It consists of Appendix A: Additional Modeling Parameters [spreadsheet]; Appendix B: Water Burdens [spreadsheet]; Appendix D: Simulation of Liquids Unloading [python script and spreadsheet]; Appendix E: Detailed GHG Results for All Scenarios [spreadsheet]; Appendix F: Full Inventory Results [spreadsheet]; and Appendix I: Stage-Level Natural Gas Loss and Consumption Rates [spreadsheet]. These results have been updated from the previous version (https://edx.netl.doe.gov/dataset/2020-natural-gas-lca-data-appendices) to correct a modeling error where the same post-processing natural gas composition was used instead of the intended regional compositions.
This is the excel-based life cycle model that contains all the parameters and Monte Carlo simulation capabilities to model the techno-regions contained in the report: Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile. This is an update to a previous version (https://edx.netl.doe.gov/dataset/netl-natural-gas-lifecycle-model) and corrects a modeling error in the post-processing natural gas composition.
CCSI Toolset 3.25 Release Highlights The copyright in the CCSI Toolset was updated to include the year 2025. The code and notes within it were revised to correct minor typographical errors and clarify meanings of variables. The configuration of the FOQUS documentation via Read the Docs was updated to explicitly set the path.
Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach which allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we first propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.
The overall objective for this project was to develop and demonstrate a set of methods for solving the transmission investment problem for a large network considering many possible scenarios of future conditions and multiple decision points when investments can be made. Project sub-objectives achieved this goal through a succession of extending the methods to apply to problems with increasing complexity or additional features, including the number of decision points, whether generation and transmission are co-optimized, and whether AC or DC power flow is used. A transmission model was developed for the Western Electric Coordinating Council (WECC) region, the high-voltage transmission system that serves the western third of the continental U.S. Using a dataset provided by WECC and by researchers from John Hopkins University, we have validated and demonstrated the model and used it to compare the new method for solving multi-stage stochastic transmission planning to several state-of-the-art techniques. The project has resulted in several key outcomes and achievements: The covariance-based method for choosing a small set of hours to represent short-term variability has superior performance in terms of accuracy to existing methods, including K-means clustering and Importance Sampling; The combined partitioning method for long-term uncertainty with the nested clustering approach for choosing representative hours for each long-term group has superior accuracy for equivalent computational effort compared with existing methods; Using the partitioning/clustering method combined with Sample Average Approximation provides both statistical bounds on the quality of the solution and at the same time, a complete investment plan for all contingencies in the full uncertainty set; no existing methods can provide both at the same time; The method is demonstrated to work well for choosing both transmission and generation investments; A variant on the method allows for both scenario selection and simultaneous correction for the error from the DC power flow approximation to provide a tractable method for AC power flow-based transmission planning under uncertainty; The method applied to the WECC case study demonstrates the additional value to the system operator and the consumer of identifying flexible investment options in the near-term decisions. In particular, the case study exhibits significant option value in postponing some transmission additions that appear useful but in some long-term system states create new congestion problems.
This analysis expands upon previous life cycle analyses (LCAs) of natural gas systems performed by the National Energy Technology Laboratory (NETL). It provides a complete inventory of emissions to air and water, water consumption, and land use change. These environmental burdens are detailed for all supply chain steps from natural gas production through natural gas distribution. This package includes the report, the NETL Natural Gas Model, and appendices that include several Excel workbooks and a python script to provide transparent access to the calculations and resulting data. This is revision 1 of the 2024 study (published December 17, 2024 and updated on January 24, 2025) and corrects a modeling error in natural gas composition. See the errata on page 2 for more information.
I had a need to develop a real, physical, analog filter with real RLC components. Having spent a career in theory, I had only ever designed filters for signal processing. I needed only the a and b coefficients. But converting a and b coefficients into RLC values was beyond me. Web searches returned nothing useful until I found Dr. Aaron Danner’s Designing Passive Butterworth Filters (10 - Passive Filters), part of an excellent YouTube series which introduced me to the Cauer prototype for Butterworth filters and Cauer topologies. Failing to find documentation on Cauer filter prototypes, I asked Anthropic’s Claude AI for assistance. Claude returned a few errors which I corrected. This is a comprehensive report on what I learned.
In this research, we present a data set over 100 photovoltaic (PV) sites in TX, which have been automatically geotagged via a fully autonomous deep learning (DL) pipeline. Specifically, locations of inverters, tracker/fixed tilt rows, batteries, and substations are labeled algorithmically. To ensure high data quality, all systems have been reviewed manually and any deep learning errors have been corrected. This public data set, as well as the open-sourced pipeline used to generate it, is valuable for site planning, modelling, and insurance purposes. Given time and resources, we hope to extend the data set to additional states/regions in the US.
Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.
We report new photometric observations of the transiting exoplanetary system HAT-P-20, obtained using CCD cameras at Yunnan Observatories and Ho Koon Nature Education cum Astronomical Centre, China, from 2010 to 2013, and Observatori Ca l’Ou, Sant Marti Sesgueioles, Spain, from 2013 to 2015. The observed data are corrected for systematic errors according to the coarse de-correlation and SYSREM algorithms, so as to enhance the signal of the transit events. In order to consistently model the star spots and transits of this exoplanetary system, we develop a highly efficient tool STMT based on the analytic models of Mandel and Agol and Montalto et al. The physical parameters of HAT-P-20 are refined by homogeneously analyzing our new data, the radial velocity data, and the earlier photometric data in the literature with the Markov chain Monte Carlo technique. New radii and masses of both host star and planet are larger than those in the discovery paper due to the discrepancy of the radius among K-dwarfs between predicted values by standard stellar models and empirical calibration from observations. Through the analysis of all available mid-transit times calculated with the normal model and spotted model, we conclude that the periodic transit timing variations in these transit events revealed by employing the normal model are probably induced by spot crossing events. From the analysis of the distribution of occulted spots by HAT-P-20b, we constrain the misaligned architecture between the planetary orbit and the spin of the host star.
Abstract The Vera C. Rubin Observatory is a unique facility for survey astronomy that will soon be commissioned and begin operations. Crucial to many of its scientific goals is the achievement of sustained high image quality, limited only by the seeing at the site. This will be maintained through an active optics system that controls optical element misalignments and corrects mirror figure error to minimize aberrations caused by both thermal and gravitational effects. However, the large number of adjustment degrees of freedom available on the Rubin Observatory introduces a range of degeneracies, including many that are induced by noise due to imperfect measurement of the wave-front errors. We present a structured methodology for identifying these degeneracies through an analysis of image noise level. We also present a novel scaling strategy based on truncated singular value decomposition that mitigates the degeneracy and optimally distributes the adjustment over the available degrees of freedom. Our approach ensures the attainment of optimal image quality, while avoiding excursions around the noise-induced subspace of degeneracies, marking a significant improvement over the previous techniques adopted for Rubin, which were based on an optimal integral controller. This new approach is likely to also yield significant benefits for all telescopes that incorporate large numbers of degrees of freedom of adjustment.
An error in the gravitational force that the source of gravity induces on itself (a self-force error) violates both the conservation of linear momentum and the conservation of energy. If such errors are present in a self-gravitating system and are not sufficiently random to average out, the obtained numerical solution will become progressively more unphysical with time: the system will acquire or lose momentum and energy due to numerical effects. In this paper, we demonstrate how self-force errors can arise in the case where self-gravity is solved on an adaptively refined mesh when the refinement is nonuniform. Here, we provide the analytical expression for the self-force error and numerical examples that demonstrate such self-force errors in idealized settings. We also show how these errors can be corrected to an arbitrary order by straightforward addition of correction terms at the refinement boundaries.
TRACER-MAP operated Jul - Aug 2022 as an ASR and ARM project. TRACER-MAP measured and mapped aerosol, volatile organic compounds, trace gas and select meteorological observations across the Houston domain during TRACER. The dataset has been corrected for truncation error using Bond et al, 2009.
Redundant-channel system automatically corrects any single error in a set of three binary signal channels. This system is especially applicable to digital computers where data is transmitted in parallel channels.