bluuebunny/crossref_metadata_2025_split huggingface dataset:
Information Measures and Concomitants of k-Record Values Based on Sarmanov Family of Bivariate Distributions
Authors: Alawady M. A. , Barakat H. M. , Mansour G. M. , Husseiny I. A. 
Published: November 2022

AbstractIn this paper, the marginal distributions of concomitants ofk-record values based on Sarmanov family of bivariate distributions are obtained, as an extension of several recent papers. Besides, we derive the joint distribution of concomitants ofk-record values for this family. Furthermore, some new and useful properties of information measures, namely, the extropy, Shannon entropy, inaccuracy measure, cumulative entropy, cumulative residual entropy, and cumulative residual Fisher information are studied. Finally, we offered various examples accompanied by numerical investigations that backed up the theoretical findings.
Late Bloomer Offending Patterns: Towards a Harder Empirical Definition—Commentary on Matsuda et al. (2022)
Authors: Skardhamar Torbjørn 
Published: November 2022

Abstract
Matsuda et al. (2022) show that late bloomers also exist in self-report data. They use a “dynamic” definition of late bloomer offending pattern as they have concerns with definitions based on arbitrary age cutoff points. But the approach does not make the groups less arbitrary than hard definitions. In this commentary, I argue that a hard definition has important advantages and demonstrates an alternative using a hard definition while exploring heterogeneity within the late onset group.
Digital ergonomics and digital work planning in university education: experiences from Germany and Austria
Authors: Schlund Sebastian , Kamusella Christiane , Knott Verena , Löffler Thomas , Engel Lutz , Fischer Clara , Rupprecht Patrick , Bengler Klaus , Bullinger-Hoffmann Angelika , Kaiser André , Kögel Alexander 
Published: November 2022

AbstractThe publication presents an overview of the use of digital human models (DHM) in academic education at five exemplary universities in Germany and Austria. In addition to the presentation of different human models, the integration of them into the respective lectures is discussed. The teaching concepts of the individual courses of the universities, exercise examples and scenarios are presented. Experience shows that the active and independent use of digital ergonomics tools gives students pleasure and motivates them to deal intensively with complex tasks in terms of time and content. Feedback is consistently positive over all the involved lectures and universities. As a consequence of the recent Covid-19 pandemic, universities significantly increased online and blended learning. Based on the experience with the use of digital human models, the paper derives recommendations for future developments.Practical RelevanceTo sustain global value chains, companies are increasingly planning trans-regionally adapted products and production processes. Tools for digital ergonomics contribute to increasing competitiveness by using prospective working methods. Companies increasingly need experts with the corresponding know-how. Firmly anchoring the topic of digital ergonomics in relevant subjects of university teaching is therefore a prerequisite for this transfer of trained graduates.
First report of watermelon crinkle leaf associated virus-1 (WCLaV-1) in watermelon (Citrullus lanatus) in Australia
Authors: Mulholland S. , Wildman O. , Kinoti W. M. , Constable F. , Daly A. , Tesoriero L. , Maina S. , Chapman T. A. 
Published: November 2022

AbstractSurveys of key cucurbit production regions in New South Wales, Australia were undertaken to catalogue the diversity of viral pathogens affecting field grown cropping systems. Analysis of the collected isolates confirmed a detection of watermelon crinkle leaf associated virus-1 (WCLaV-1) in watermelon constituting the first report of WCLaV-1 in Australia. A mixed infection of WCLaV-1 and watermelon mosaic virus (WMV) was also detected constituting the first report of this type of pathogen combination.
Dynamic population mapping with AutoGluon
Authors: Song Yimeng , Xu Yong , Chen Bin , He Qingqing , Tu Ying , Wang Fei , Cai Jixuan 
Published: November 2022

AbstractTimely and accurate population mapping plays an essential role in a wide range of critical applications. Benefiting from the emergence of multi-source geospatial datasets and the development of spatial statistics and machine learning, multi-scale population mapping with high temporal resolutions has been made possible. However, the over-complex models and the strict data requirement resulting from the constant quest for increased accuracy pose challenges to the repeatability of many population spatialization frameworks. Therefore, in this study, using limited publicly available datasets and an automatic ensemble learning model (AutoGluon), we presented an efficient framework to simplify the model training and prediction process. The proposed framework was applied to estimate county-level population density in China and received a good result with an r2 of 0.974 and an RMSD of 427.61, which is better than the performances of current mainstream population mapping frameworks in terms of estimation accuracy. Furthermore, the derived monthly population maps and the revealed spatial pattern of population dynamics in China are consistent with earlier studies, suggesting the robustness of the proposed framework in cross-time mapping. To our best knowledge, this study is the first work to apply AutoGluon in population mapping, and the framework’s efficient and automated modeling capabilities will contribute to larger-scale and finer spatial-temporal population spatialization studies.
ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India
Authors: Mishra Sharma Sumanta Chandra , Mitra Adway 
Published: November 2022

Abstract
In the twenty-first century, machine learning and deep learning have been successfully used to find hidden information from coarse-grained data in various domains. In Computer Vision, scientists have used neural networks to identify hidden pixel-level information from low-resolution (LR) image data. This approach of estimating high-resolution (HR) information from LR data is called the super-resolution (SR) approach. This approach has been borrowed by climate scientists to downscale coarse-level measurements of climate variables to obtain their local-scale projections. Climate variables are spatial in nature and can be represented as images where each pixel denotes a grid point where the variables can be measured. We can apply the deep learning-based SR techniques on such “images” for statistical downscaling of such variables. This approach of downscaling can be termed as deep downscaling. In this work, we have tried to make HR projection of the Indian summer monsoon rainfall by using a novel deep residual network called ResDeepD. The aim is to downscale the 10 × 10 low LR precipitation data to get the values at 0.250 × 0.250 resolution. The proposed model uses a series of skip connections across residual blocks to give better results as compared to the existing models like super-resolution convolutional neural network, DeepSD, and Nest-UNet that have been used previously for this task. We have also examined the model’s performance for downscaling rainfall during some extreme climatic events like cyclonic storms and deep depression and found that the model performs better than the existing models.
ON THE PRIMES IN FLOOR FUNCTION SETS
Authors: MA RONG , WU JIE 
Published: November 2022

AbstractLet
$[t]$
be the integral part of the real number t and let
$\mathbb {1}_{{\mathbb P}}$
be the characteristic function of the primes. Denote by
$\pi _{\mathcal {S}}(x)$
the number of primes in the floor function set
$\mathcal {S}(x) := \{[{x}/{n}] : 1\leqslant n\leqslant x\}$
and by
$S_{\mathbb {1}_{{\mathbb P}}}(x)$
the number of primes in the sequence
$\{[{x}/{n}]\}_{n\geqslant 1}$
. Improving a result of Heyman [‘Primes in floor function sets’, Integers22 (2022), Article no. A59], we show
$$ \begin{align*} \pi_{\mathcal{S}}(x) = \int_2^{\sqrt{x}} \frac{d t}{\log t} + \int_2^{\sqrt{x}} \frac{d t}{\log(x/t)} + O(\sqrt{x}\,\mathrm{e}^{-c(\log x)^{3/5}(\log\log x)^{-1/5}}) \quad\mbox{and}\quad S_{\mathbb{1}_{{\mathbb P}}}(x) = C_{\mathbb{1}_{{\mathbb P}}} x + O_{\varepsilon}(x^{9/19+\varepsilon}) \end{align*} $$
for
$x\to \infty $
, where
$C_{\mathbb {1}_{{\mathbb P}}} := \sum _{p} {1}/{p(p+1)}$
,
$c>0$
is a positive constant and
$\varepsilon $
is an arbitrarily small positive number.
The Religions of Human Rights
Authors: Loeffler James 
Published: November 2022

AbstractThe modern human rights movement arose during a moment of unprecedented encounter between global religions in the mid-twentieth century. Yet attempts to parse the historical relationship between human rights and religious thought have almost exclusively taken the form of case studies of individual religious traditions. This focus on intellectual genealogies obscures the fact that much of human rights doctrine emerged from interreligious contacts and conflicts between Judaism and Christianity, particularly in the context of the decolonizing Middle East. This article retraces this interreligious encounter through the writings of Amnesty International founder Peter Benenson, diplomat and theologian Charles Malik, and rabbi and activist Maurice Perlzweig. Together they represent three different theopolitical responses to the problem of religious pluralism after global empire: minoritarian human rights, majoritarian human rights, and cosmopolitan human rights. Recovering these interrelated human rights conceptions exposes the frames of religious difference embedded in the modern Western human rights imagination.
Low-carbon Frontier: Renewable Energy and the New Resource Boom in Western China
Authors: Harlan Tyler 
Published: November 2022

AbstractChina's west has long been framed as an undeveloped frontier, set apart by poverty and a resource-based economy. Since the 2000s, however, utility-scale renewable energy infrastructure has expanded rapidly in western China, promising local economic benefits tied to national low-carbon transition. This paper contends that these benefits have been precarious and unevenly distributed. I argue that utility-scale renewable energy has remade western China as a “low-carbon frontier,” a resource-rich region that generates low-carbon value for the national green economy. I highlight three features of low-carbon frontiers: they are constructed as spaces of exploitable low-carbon resources, creating an investment boom; they are enclosed through new land arrangements and infrastructure construction, rapidly and with little coordination; and they are reliant on external markets and policy decisions, entrenching dependency. These conditions make it difficult for frontier regions to capture sustained economic development benefits from the boom in the absence of persistent central state supports. I analyse these features by comparing two sets of technologies with similar, but ultimately diverging, trajectories: small and large hydropower in China's south-west, and solar and wind in the north-west.
Cultural influence on clock drawing test: A systematic review
Authors: Maestri Giorgia , Nicotra Alessia , Pomati Simone , Canevelli Marco , Pantoni Leonardo , Cova Ilaria 
Published: November 2022

AbstractObjective:Dementia among migrants is an emerging phenomenon worldwide and the development of neuropsychological tests sensitive to cultural differences is increasingly regarded as a priority. The Clock Drawing Test (CDT) is one of the most used screening tools for the detection of cognitive decline. Nevertheless, there is still a debate about its adoption as a cross-cultural assessment.Methods:To identify cultural variables influencing performance at CDT, we performed a systematic review of literature on three databases of all studies considering the role of at least one of the following: (1) language; (2) education; (3) literacy; (4) acculturation; and (5) ethnicity.Results:We extrapolated 160 analyses from 105 studies. Overall, an influence of cultural determinants on performance at CDT was found in 127 analyses (79.4%). Regarding specific cultural factors, 22 analyses investigated the effect of ethnicity on CDT scores, reporting conflicting results. Only two scoring systems turned out to be sufficiently accurate in a multicultural population. Language influenced performance in only 1 out of 8 analyses. A higher level of education positively influenced test performance in 118 out of 154 analyses (76.6%), and a better quality of education in 1 analysis out of 2. A negative influence of illiteracy on CDT performance emerged in 9 out of 10 analyses. Acculturation affected performances at CDT in 1 out of 2 studies.Conclusions:Based on the present findings, caution is needed when using CDT in a multicultural context, even if it requires limited linguistic competence.
Energy Consumption of IoT Devices: An Accurate Evaluation to Better Predict Battery Lifetime
Authors: Faye Ibrahima , Fam Pape Abdoulaye , Ndiaye Mamadou Lamine 
Published: November 2022

AbstractLoRa to say Long Range is one of the Low Power Wide Area Network technologies suitable for Internet of Things due to its low power consumption. In this paper we evaluate the energy consumption of a LoRa network using the LoRaWAN protocol which is based on the pure Aloha access method. We define a reference scenario (scenario 0), which consists in neglecting the energy consumption related to the different states of the end‐devices after transmission of a packet that does not require downlink acknowledgment and then we compare this scenario 0 to scenario 1, scenario 2 and scenario 3. Indeed the scenario 1 consists in considering the states of the end‐devices after transmission in uplink of a packet without acknowledgment, in the evaluation of the energy consumption. However, scenario 2 takes into account the different states of the end‐devices in scenario 1 with an acknowledgment on the first reception window. While in scenario 3 the acknowledgment is done on the second reception window always taking into account the end‐devices states. The simulation results with the LoRaSim tool have shown that the network energy consumption (NEC) increases significantly when considering the different states of the end‐device after uplink transmission of a packet without acknowledgment, but also that the increase of the NEC related to end‐device states is even more important if packets are acknowledged. Thus for a rigorous evaluation of the NEC, it is important to take into account the states of the end‐device after transmission of a packet but also the eventual acknowledgments in order to better predict the battery lifetime.
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Authors: Vionnet V. , Verville M. , Fortin V. , Brugman M. , Abrahamowicz M. , Lemay F. , Thériault J. M. , Lafaysse M. , Milbrandt J. A. 
Published: November 2022

AbstractIn mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed.
How Mesoscale Eddies Masquerade as Rossby Waves in Merged Altimetric Products
Authors: Cao Chuanchuan , Chen Ge , Wang Xuan 
Published: November 2022

AbstractWave‐like signals formed by mesoscale‐eddy propagation, in particular, overlap with actual waves, resulting in Rossby waves being misidentified or overestimated. This study revisited the puzzle of wave‐eddy aliasing in state‐of‐the‐art altimetric products, with up to six altimetry satellites operating simultaneously after 2016. Spatiotemporal scales and westward‐propagating speeds are the primary factors in diagnosing a low‐latitude Rossby wave regime from a high‐latitude geostrophic turbulence regime, confirming the coexistence of Rossby waves and eddies in wave‐like components. Long‐lived westward‐propagating eddies (prevailing in oceans at latitudes 10°–40°) with weaker meridional deflection are good candidates for masquerading as Rossby waves. The focus here is therefore on the long, first baroclinic mode Rossby waves, which are quantified in several periodic bands by removing eddy signals. From 2016 to 2020, energetic biannual Rossby waves can explain more than 10% of observed sea level anomalies at latitudes equatorward of ±25°, ±20°, and ±15° in the Pacific, Indian, and Atlantic Oceans, respectively. Numerical modeling results support similar assessments, indicating an intrinsic property of eddies masquerading as Rossby waves. This study provides a more accurate assessment of oceanic Rossby waves, which can deepen insight into the energy cascade of the geostrophic features and their dynamic effects on biogeochemical cycles.
Solar X-ray and EUV imager on board the FY-3E satellite
Authors: Chen Bo , Zhang Xiao-Xin , He Ling-Ping , Song Ke-Fei , Liu Shi-Jie , Ding Guang-Xing , Dun Jin-Ping , Li Jia-Wei , Li Zhao-Hui , Guo Quan-Feng , Wang Hai-Feng , Wang Xiao-Dong , Wang Yun-Qi , Zhang Hong-Ji , Zhang Guang , Han Zhen-Wei , Dai Shuang , Zhang Pei-Jie , Sun Liang , Liu Yang , Wang Peng , Wu Kun , Tao Chen , Mao Shi-Lei , Mei Gui , Yang Liang , Chen Li-Heng , Han Chun-Yang , Huang Bin , Liu Yang , Ren Shuai , Zhou Peng , Wei Ze-Xi , Zhang Xiao-Xue , Zhang Yue , Zheng Xin , Wang Yang , Chen Ya , Xie Jing-Jiang , He Fei , Song Qiao , Zong Wei-Guo , Hu Xiu-Qing , Zhang Peng , Wang Jing-Song , Yang Zhong-Dong 
Published: November 2022

AbstractThe solar X-ray and Extreme Ultraviolet Imager (X-EUVI), developed by the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), is the first space-based solar X-ray and Extreme ultraviolet (EUV) imager of China loaded on the Fengyun-3E (FY-3E) satellite supported by the China Meteorological Administration (CMA) for solar observation. Since started work on July 11, 2021, X-EUVI has obtained many solar images. The instrument employs an innovative dual-band design to monitor a much larger temperature range on the Sun, which covers 0.6–8.0 nm in the X-ray region with six channels and 19.5 nm in the EUV region. X-EUVI has a field of view of 42′, an angular resolution of 2.5″ per pixel in the EUV band and an angular resolution of 4.1″ per pixel in the X-ray band. The instrument also includes an X-ray and EUV irradiance sensor (X-EUVS) with the same bands as its imaging optics, which measures the solar irradiance and regularly calibrates the solar images. The radiometric calibration of X-EUVS on the ground has been completed, with a calibration accuracy of 12%. X-EUVI is loaded on the FY-3E satellite and rotates relative to the Sun at a uniform rate. Flat-field calibration is conducted by utilizing successive rotation solar images. The agreement between preliminarily processed X-EUVI images and SDO/AIA and Hinode/XRT images indicates that X-EUVI and the data processing algorithm operate properly and that the data from X-EUVI can be applied to the space weather forecast system of CMA and scientific investigations on solar activity.
Neurodevelopmental disorders—high-resolution rethinking of disease modeling
Authors: Khodosevich Konstantin , Sellgren Carl M. 
Published: November 2022

AbstractNeurodevelopmental disorders arise due to various risk factors that can perturb different stages of brain development, and a combinatorial impact of these risk factors programs the phenotype in adulthood. While modeling the complete phenotype of a neurodevelopmental disorder is challenging, individual developmental perturbations can be successfully modeled in vivo in animals and in vitro in human cellular models. Nevertheless, our limited knowledge of human brain development restricts modeling strategies and has raised questions of how well a model corresponds to human in vivo brain development. Recent progress in high-resolution analysis of human tissue with single-cell and spatial omics techniques has enhanced our understanding of the complex events that govern the development of the human brain in health and disease. This new knowledge can be utilized to improve modeling of neurodevelopmental disorders and pave the way to more accurately portraying the relevant developmental perturbations in disease models.