CemoMemo

Digitizing cemeteries and gravestones aids cultural preservation, genealogical search, dark tourism, and historical analysis. CemoMemo, an app and associated website, enables bottom-up crowd-sourced digitization of cemeteries, categorizing and indexing of gravestone data and metadata, and offering powerful full-text and numerical search. To date, CemoMemo has nearly 5,000 graves from over 130 cemeteries in 10 countries with the majority being Jewish graves in Israel and the USA. We detail CemoMemo's deployment and component models, technical attributes, and user models. CemoMemo went through two design iterations and architectures. We detail its initial architecture and the reasons that led to the change in architecture. To show its utility, we use CemoMemo's data for a historical analysis of two Jewish cemeteries from a similar period, eliciting cultural and ethnological difference between them. We present lessons learned from developing and operating CemoMemo for over 1 year and point to future directions of development.

Factorisation Path Based Refactorisation for High-Performance LU Decomposition in Real-Time Power System Simulation

The integration of renewable energy sources into modern power systems requires simulations with smaller step sizes, larger network models and the incorporation of complex nonlinear component models. These features make it more difficult to meet computation time requirements in real-time simulations and have motivated the development of high-performance LU decomposition methods. Since nonlinear component models cause numerical variations in the system matrix between simulation steps, this paper places a particular focus on the recomputation of LU decomposition, i.e., on the refactorisation step. The main contribution is the adoption of a factorisation path algorithm for partial refactorisation, which takes into account that only a subset of matrix entries change their values. The approach is integrated into the modern LU decomposition method NICSLU and benchmarked against the methods SuperLU and KLU. A performance analysis was carried out considering benchmark as well as real power systems. The results show the significant speedup of refactorisation computation times in use cases involving system matrices of different sizes, a variety of sparsity patterns and different ratios of numerically varying matrix entries. Consequently, the presented high-performance LU decomposition method can assist in meeting computation time requirements in real-time simulations of modern power systems.

Investigation on Vanadium Chemistry in Basic-Oxygen-Furnace (BOF) Slags—A First Approach

Basic oxygen furnace (BOF) slag accounts for the majority of all residual materials produced during steelmaking and may typically contain certain transition metals. Vanadium, in particular, came into focus in recent years because of its potential environmental toxicity as well as its economic value. This study addresses the vanadium chemistry in BOF slags to better understand its recovery and save handling of the waste stream. The experimental results from the electron probe microanalysis (EPMA) study show that vanadium is preferably incorporated in calcium orthosilicate-like compounds (COS), with two variations occurring, a low vanadium COS (COS-Si) (approx. 1 wt.%), and a high vanadium COS (COS-V) (up to 18 wt.%). Additionally, vanadium is incorporated in dicalcium ferrite-like compounds (DFS) with an average amount of 3 wt.%. Using powder x-ray diffraction analysis (PXRD), EPMA, and virtual component models, stoichiometric formulas of the main vanadium-bearing phases were postulated. The stoichiometries give an estimate of the oxidation states of vanadium in the respective hosts. According to these results, trivalent vanadium is incorporated on the Fe-position in dicalcium ferrite solid solution (DFS), and V4+ and V5+ are incorporated on the Si-position of the COS.

A Performance-Based Approach to Quantify Atmospheric River Flood Risk

Atmospheric rivers (ARs) are a class of meteorologic phenomena that cause significant precipitation and flooding on the US West Coast. This work presents a new Performance-based Atmospheric River Risk Analysis (PARRA) framework that adapts existing concepts from probabilistic risk analysis and performance-based engineering for application in the context of AR-driven fluvial flooding. The PARRA framework is a chain of physically based models that link the atmospheric forcings, hydrologic impacts, and economic consequences of AR-driven fluvial flood risk together at consistent “pinch point” variables. Organizing around these pinch points makes the framework modular, in that models between pinch points can be updated without affecting the rest of the model chain, and it produces a probabilistic result that quantifies the uncertainty in the underlying system states. The PARRA framework can produce results beyond analyses of individual scenario events and can look towards prospective assessment of events or system changes that have not been seen in the historic record. The utility of the PARRA framework is demonstrated through a series of analyses in Sonoma County, California. Evaluation of a February 2019 case study AR event shows that the individual component models produce simulated distributions that capture the observed precipitation, streamflow, inundation, and damage. The component models are then run in sequence to generate a first-of-its-kind AR flood loss exceedance curve for Sonoma County. The prospective capabilities of the PARRA framework are presented through the evaluation of a hypothetical mitigation action. It was found elevating 150 homes, selected based on their proximity to the Russian River, was able to reduce the average annual loss by half. The loss results from the mitigated building portfolio are compared against the original case. While expected benefits were minimal for the smallest events, the larger, more damaging ARs were expected to see loss reductions of approximately $50 million per event. These results indicate the potential of the PARRA framework for examining other changes to flood risk at the community level, including future changes to the hazard, through climate change; exposure, through development; and/or vulnerability, through flood mitigation investments.

Hadley Circulation in the Present and Future Climate Simulations of the K-ACE Model

Hadley circulation (HC) is a planetary-scale overturning circulation in the tropics that transports momentum, heat, and moisture poleward. In this study, we evaluate the strength and extent of the HC in the historical and future climate simulations of the Korean Meteorological Administration (KMA) Advanced Community Earth system model (K-ACE), which was recently developed by the National Institute of Meteorological Sciences of Korea. Compared with a reanalysis product, the overall structure of the HC is reasonably reproduced by the K-ACE. At the same time, it is also found that the Northern Hemisphere HC in the K-ACE is shifted southward by a few degrees, while the strength of the Southern Hemisphere (SH) HC is under-represented by approximately 20%. These biases in the strength and extent of the HC can be explained by biases in the eddy momentum flux and precipitation in the tropics. In the future climate simulations under the Shared Socioeconomic Pathway 5-Representative Concentration Pathway 8.5 scenario, the HCs in the K-ACE show a weakening and widening trend in both hemispheres, which is consistent with the projections of many Coupled Model Intercomparison Project Phase 6 models. A notable feature of the K-ACE is the widening of the SH HC, which takes place at a rate that is about double the multi-model mean. Climate models that share the component models with the K-ACE, such as UKESM, HadGEM3-GC31-LL, and ACCESS-CM2/ESM1, also show enhanced poleward expansion of the HC in the SH. This strong expansion is shown to be dominated by the expansion of the regional HC over the Pacific.

Intrinsic Rewards in Human Curiosity-Driven Exploration: An Empirical Study

Despite their apparent importance for the acquisition of full-fledged human intelligence, mechanisms of intrinsically motivated autonomous learning are poorly understood. How do humans identify useful sources of knowledge and decide which learning situations to approach in the absence of external rewards? While the recognition of this important problem has grown in psychological sciences over the recent years, an intriguing proposition for the possible mechanism comes from artificial intelligence, where efficient autonomous learning is achieved by programming agents to follow the heuristic of maximizing learning progress (LP) during exploration. In this study, we set out to examine the empirical evidence for this idea. Using computational modeling, we demonstrate that humans show signs of following LP while they freely explore and practice a set of multiple learning activities of varying difficulty, including an activity that is impossible to learn. Different approaches to operationalizing the notion of LP and their plausibility in light of empirical data are also discussed. We also show that models combining several types of intrinsic rewards fit better human exploration data than single component models considered so far in theoretical accounts.

Phase Transitions in a 2D Ising Model of Agent Expectations in Financial Markets: Analytics in One- and Two-Dimensional Network Topologies

Phase transitions between ordered and disordered states of interactive agents have been recognized as integral to dynamics in a range of economic and social processes. Several theorists in the study of financial markets have directly linked phase transitions between disordered and ordered states of agents to a critical point in the dynamics of market price. To date, phase transitions in the dynamics of price in financial markets have been demonstrated with numerical methods. In an application to a financial market, we propose a multicomponent in which a first component is in bounded rationality and a second component is in behavior that generates herding in financial markets. A transition function defines the relative weight of components. We extend conditions of Onsager (1944) for phase transitions in a 2D Ising model and analytically demonstrate that the proposed model evidences phase transitions. Generalizations of the results to other multi-component models are noted.

Petroelastic Modeling of Complex Lithology Fields: Case Study

The present-day growth of petroleum reserves, maintaining and enhancing of oil production is associated with involving of complex fields into production (Uspenskaya, 2014). The complex structure of the study areas of the Urai Region (facies variability, formation replacement, fracturing, crushing and cleavage zones, complicated structure of the pore space) makes conventional forecast of reservoir properties ineffective. Moreover, the pay formations are highly exhausted with high production water cut. Therefore, the search of prospective targets is relevant in the Urai Region. The article shows generalized results of petroelastic modeling of several fields of the Urai Region for solving the following tasks: ○ Lithology, saturation and poroperm properties forecast of reservoir rocks from log and seismic data; ○ Development of 1D mechanical earth models (MEM) of horizontal well sections and their maintenance while drilling horizontal wells. The approach is based on the principles of integrating different-scale geological and geophysical studies. The lithological division of rocks was carried out, the saturation behavior and point-by-point interpretation of the data of geophysical methods of well logging (WL) were identified. 3D component models were built with taking into account the lithological features of the section using core sample studies. Effective models for the environment of sediments of interest were selected and petroelastic modeling was carried out.