Reliable uncertainties of tests and surveys – a data-driven approach

MSC Classification 60J10, 91Exx, 91E45, 05A18.Supplementary material are available online at: https://www.metrology-journal.org/10.1051/ijmqe/2023018/olm . The article is accompanied by supplementary information that includes proofs to the theorems that are stated within the text of the article; linking our advanced methods to extant congeneric methods in the literature; comparison of the methods discussed herein, for partitioning a set of integers into 2 subsets and presentation of results on simulated data.Policy decisions are often motivated by results attained by a cohort of responders to a survey or a test. However, erroneous identification of the reliability or the complimentary uncertainty of the test/survey instrument, will distort the data that such policy decisions are based upon. Thus, robust learning of the uncertainty of such an instrument is sought. This uncertainty is parametrised by the departure from reproducibility of the data comprising responses to questions of this instrument, given the responders. Such departure is best modelled using the distance between the data on responses to questions that comprise the two similar subtests that the given test/survey can be split into. The paper presents three fast and robust ways for learning the optimal-subtests that a given test/survey instrument can be spilt into, to allow for reliable uncertainty of the given instrument, where the response to a question is either binary, or categorical − taking values at multiple levels − and the test/survey instrument is realistically heterogeneous in the correlation structure of the questions (or items); prone to measuring multiple traits; and built of small to a very large number of items. Our methods work in the presence of such messiness of real tests and surveys that typically violate applicability of conventional methods. We illustrate our new methods, by computing uncertainty of three real tests and surveys that are large to very-large in size, subsequent to learning the optimal subtests.There is no funding to be reported

Changes in social norms during the early stages of the COVID-19 pandemic across 43 countries

Data availability: The data generated in this study have been deposited in the Open Science Framework (https://doi.org/10.17605/OSF.IO/STKFR). Non-experimental data included in our datasets (i.e., intensity of government response to COVID-19 is the Stringency Index, COVID-19 deaths and cases per million) are taken from the Oxford COVID−19 Government Response Tracker [22 Hale, T. et al. A global panel database of pandemic policies (Oxford COVID−19 Government Response Tracker). Nat. Human Behav. https://doi.org/10.1038/s41562-021-01079-8 (2021).] and Our World in Data [38 Ritchie, H. et al. Coronavirus Pandemic (COVID-19). Our World in Data. https://ourworldindata.org/coronavirus (2020).] (downloaded November 2020). Wave 0 data are from [3 Gelfand, M. J. et al. Differences between tight and loose cultures: a 33-nation study. Science 332, 1100–1104 (2011).[ and Wave 1 data are from [5 Eriksson, K. et al. Perceptions of the appropriate response to norm violation in 57 societies. Nat. Commun. 12, 1481 (2021).].Code availability: The survey and analysis code are available at the Open Science Framework (https://doi.org/10.17605/OSF.IO/STKFR).Supplementary information is available online at: https://www.nature.com/articles/s41467-024-44999-5#Sec40 .The emergence of COVID-19 dramatically changed social behavior across societies and contexts. Here we study whether social norms also changed. Specifically, we study this question for cultural tightness (the degree to which societies generally have strong norms), specific social norms (e.g. stealing, hand washing), and norms about enforcement, using survey data from 30,431 respondents in 43 countries recorded before and in the early stages following the emergence of COVID-19. Using variation in disease intensity, we shed light on the mechanisms predicting changes in social norm measures. We find evidence that, after the emergence of the COVID-19 pandemic, hand washing norms increased while tightness and punishing frequency slightly decreased but observe no evidence for a robust change in most other norms. Thus, at least in the short term, our findings suggest that cultures are largely stable to pandemic threats except in those norms, hand washing in this case, that are perceived to be directly relevant to dealing with the collective threat.Knut and Wallenberg Grant “How do human norms form and change?” 2016.0167. (G.An.). The Swedish Research Council grant “Norms & Risk: Do social norms help dealing with collective threats” 2021-06271 (G.An.). Ministero dell’Istruzione dell’Università e della Ricerca, PRIN 2017, prot. 20178TRM3F (D.B.). Universidad de Los Andes, Fondo Vicerrectoría de Investigaciones (J.-C.C.). Ministry of Innovation and Technology of Hungary, National Research, Development and Innovation Fund NKFIH-OTKA K135963 (M.F.). Grant 23-061770 S of the Czech Science Foundation (M.H. and S.G.). RVO: 68081740 of the Institute of Psychology, Czech Academy of Sciences (M.H. and S.G.). RA Science Committee, research project N.20TTSH-070 (A.Gr. and N.Khac.). Open University of Israel, 511687 (R.N.). HSE University Basic Research Program (E.O.). Project BASIC (PID2022-141802NB-I00) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” (A.Sá.). US Army Research Office Grant W911NF-19-1-910281 (B.S.). Netherlands Organisation for Scientific Research, 019.183SG.001 (E.S.). Netherlands Organisation for Scientific Research, VI.Veni.201 G.013 (E.S.). European Commission, Horizon 2020-ID 870827 (E.S.). UKRI Grant “Secret Power” No. EP/X02170X/1 awarded under the European Commission’s “European Research Council - STG” Scheme (G.A.T.)

UNet and Variants for Medical Image Segmentation

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Renewable energy sources integration via machine learning modelling: A systematic literature review

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided

Into the purple ocean: The formation and dynamics of a transcultural fandom as a result of cultural diffusion through K-pop

Within the context of cultural exchange, research into the impact of Korean pop (K-pop) music and its idols largely focuses on their marketability rather than the psychological effects this exchange may have on their fandom. The aim of this research, therefore, is to investigate the formation and dynamics of a transcultural fandom as a result of cultural diffusion through K-pop through the relationship between Bangtan Sonyeondan (a K-pop group) and their fandom, Adorable Representative Master of Ceremonies for Youth (ARMY). Through this research we hope to examine the formation of a new transcultural fandom “in-group” as a function of cultural diffusion, as well as the role of the idol in this process. A quantitative design was employed, consisting of a cross-sectional survey with 116 participants completing measures of identification with all of humanity, universal values, online group identification, knowledge of in-group norms, and remote acculturation levels. Results revealed that stronger identification with the in-group, ARMY, was a significant predictor of ability to detect and use in-group cues to predict target identities successfully, increased remote acculturation, and increased identification with all of humanity. Overall, the research provides insights into the relationships between idol and fan, the levels of remote acculturation experienced by ARMY, and the subsequent identities that are constructed within the new transcultural context of their global community

Corporate credit default swap systematic factors

Data Availability: All data used is obtained from third-party data providers. Data will be made available on request with the permission of the data providers.JEL: G12, G13, G23.Supporting Information is available online at: https://onlinelibrary.wiley.com/doi/10.1002/fut.22505#support-information-section .We examine a comprehensive set of systematic and firm-specific determinants of the credit default swap (CDS), using a two-step approach to explore the factor's effect on CDS spread changes. We show that systematic factors are important and account for the most changes in the CDS spreads (with average R2 of 35%), while firm-specific factors are limited (with R2 of 5% in panel regression) with only 4 out of 28 firm-specific factors being significant. It implies that the systematic factors are overlooked in the literature, and they can provide many implications for practitioners in CDS pricing and the firm's credit risk management

Groundwater Age and Origin and Its Relation with Anthropogenic and Climatic Factors

Data Availability Statement: The data used in this study includes spatial data, isotopic data of precipitation, and water bodies. This is freely available and can be accessed from the websites given in the data section of the manuscript. The isotopic data on groundwater is the property of the Center of Excellence in Water Resources Engineering (CEWRE), Lahore, and the Pakistan Council of Research in Water Resources (PCRWR), which can be retrieved by making a request to the corresponding author. However, the climatic data is the property of the Pakistan Meteorological Department (PMD) and can be requested via official channels.Groundwater plays a major role in addressing the worldwide problem of water scarcity and food security. With a growing population and increasing urbanization, there is a rising demand for groundwater to meet agricultural and domestic water needs. A variety of advanced approaches are necessary to sustain groundwater management. This study investigated the age and origin of groundwater, as well as its relationship with anthropogenic and climatic factors. Stable isotopes were used, namely oxygen-18 (18O) and deuterium (2H) for the estimation of groundwater origin and radioactive isotopes of Tritium (3H) for the estimation of its age. The investigation of stable isotopes revealed that the aquifer is predominantly influenced by river water, with a minor contribution from rainwater. Furthermore, the analysis of radioactive isotopes revealed that the groundwater age ranges from 5 to 50 years old in most areas. Older groundwater is predominantly found in urban areas, while younger groundwater is present in agricultural and woodland regions. However, the presence of “old” water in the upper groundwater layers in urban areas is attributed to over-abstraction and limited natural recharge. The primary climatic factor that governs the age and origin of groundwater is rainfall upstream of the study area, which directly contributes to the river flows. The rainfall is high in the east but, due to urbanization, recharge is decreased. Consequently, old and river recharge groundwater is found in this area. These observations underscore the unsustainable and alarming use of groundwater in urban areas.Center of Excellence in Water Resources Engineering; Pakistan Institute of Nuclear Science and Technology (PINSTECH), Pakistan; Pakistan Council of Research in Water Resources

Vanadium dioxide/aluminum composites for adaptive infrared stealth

Vanadium dioxide (VO2) is assumed as a promising dynamic infrared stealth material owing to its tunable emissivity. However, the relatively high emissivity associated with VO2-based adaptive infrared stealth materials poses a constraint on their practical applications. To address this issue, this study proposed a VO2/Al composite endowed with a smart infrared stealth function. With 50 % VO2, the composite achieved a low emissivity value of 0.54 at 30 °C, while it exhibited a reversible variation to 0.43 at 100 °C, demonstrating a change up to 0.11. When the temperature of the sample remained at 100 °C, the surface temperature detected by an infrared camera was only 53.4 °C, indicating a promising infrared stealth performance. Meanwhile, the metal-insulator transition (MIT) effect and thermal insulation performance were investigated under the framework of the composition effect of composite materials. The design strategy of this metal composite material paves the way for novel approaches in designing dynamic infrared stealth materials with low emissivity.National Key Research and Development Programs – Intergovernmental International Scientific and Technological Innovation and Cooperation Program (2022YFE0138000); National Natural Science Foundation of China (51604173)

Research on Self-adaptive Reinforcement Plug-in of Prefabricated Concrete Component Based on BIM

Prefabricated concrete structure has the advantages of faster construction, labor saving, pollution reduction, and enhanced quality. It has been more widely adopted in recent years, but the cost is usually higher at its design or pre-construction stages due to component disassembly and detailed design. Building information modeling (BIM) could improve the design efficiency and reduce design cost, hence promoting the development of prefabricated buildings. However, due to the complexity of reinforcement modeling, existing BIM authoring tools still have low operation efficiency. Even though the latest technologies can achieve rapid reinforcement modeling, it often fails to realize the adaptive adjustment of reinforcement. In the case of changes in prefabricated components, the internal reinforcement needs to be remodeled, with reduced efficiency and increased the risk of errors. Therefore, this research proposes a self-adaptive reinforcement plug-in based on Autodesk Revit for prefabricated concrete components. The research achieved fast modeling and adaptive reinforcement. Firstly, the parametric modeling of column, beam and slab components was completed by using Excel to drive family parameters. Then, by adopting the Revit Application Program Interface (API), programming language C# and programming platform Visual Studio (VS), the secondary development within Revit achieved the rapid adaptive configuration of reinforcement. At the same time, the user interface was developed based on Windows Presentation Foundation (WPF), and the Ribbon function was adopted to expand the Revit function area to realize the visual regulation of key parameters of reinforcement, such as the reinforced type, cover thickness, spacing, and reinforcement ratio, etc. Finally, the program was developed by integrating different modules and plug-ins were established. A typical prefabricated frame structure office building was used as a case study to test the self-adaptive reinforcement plug-in. The results showed that the modeling efficiency of the developed plug-in was nearly 3 times higher than that of manual modeling. After changing the section size parameters of the members, the reinforcement could successfully achieve self-adaptive adjustment, hence significantly saving the modeling steps and time. The developed self-adaptive reinforcement plug-in contributed to the fully interoperable and multi-disciplinary coordination in prefabricated building design.Program of China Postdoctoral Science Foundation (Grant No.2021M690264 and 2021T140031); State Key Laboratory of Special Functional Waterproof Materials (Grant No.SKWL-2021KF10); Youth Talent Cultivation Program of Jiangsu University

Optimal path planning in a real-world radioactive environment: A comparative study of A-star and Dijkstra algorithms

Data availability: Data will be made available on request.Navigating complex radioactive environments while minimizing radiation exposure to workers is a critical challenge faced by the nuclear industry. Although various shortest-path algorithms and radiation dose calculation techniques have been employed for optimal path finding, most existing models are based on simulations that do not accurately represent real-world environments. To address this limitation, this study presents a path-planning experiment conducted on a naturally radioactive slag dump, Slag Dump No. 48, also known as Black Mountain, in Zambia. The experiment utilizes the Radiation Detection Backpack System (RDBS) and Geolocation Application for Radiation Monitoring (GARM) in conjunction with the Dijkstra and A-star algorithms to search for an optimal walking path on the slag dump. The distances between neighboring nodes and heuristic values, derived from gamma dose rates, are experimentally obtained from the GARM software. This research contributes to the field by: (1) performing a real-world path planning experiment on a radioactive slag dump, (2) applying RDBS for measuring gamma radiation from a naturally radioactive slag, (3) investigating the combined use of RDBS, GARM, Dijkstra, and A-star algorithms for optimal pathfinding, (4) generating heuristic values and node distances experimentally for path planning in an actual radioactive environment, and (5) comparing the performance of state-of-the-art minimum dose walking path algorithms on dose rate-based and node distance-based weighted graphs. The results of this study and the proposed future work provide valuable insights for enhancing radiation protection and optimizing path planning in radioactive environments.Fundamental Research Funds for the Central Universities (NO. 3072022TS1501); the project of Institute of Computer Application, China Academy of Engineering Physics (NO. HT-2022-115); National Natural Science Foundation for Young Scientists of China (Grant No. 12205065); the project of China Institute for Radiation Protection (NO. CIRP-CNNCRPTKLJJ003); Heilongjiang Natural Science Foundation (joint guidance) (NO. LH2021F002); Fundamental Research Funds for the Central Universities (NO. 3072022JC0404)