bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards automatic evaluation of the Quality-in-Use in context-aware software systems
Authors: Salomón Sergio , Duque Rafael , Montaña José Luis , Tenés Luis 
Published: February 2022

AbstractContext-aware systems adapt their services to the user’s intentions and environment to improve the user experience. However, how to evaluate the quality of these systems in terms of user perception and context recognition is still an open problem. Our goal in this work is to evaluate the Quality-in-Use (QinU) for context-aware software systems according to the ISO/IEC 25010 standard and in an automated manner. This evaluation is oriented to be model-based, with domain specification and log data as input, while quality metrics and representations of users’ behavior as output. In this process, we use probabilistic models to discover user patterns, heuristic metrics as QinU estimation, clustering techniques to obtain user profiles according to their QinU, and feature selection to identify relevant factors of context. We propose a framework for assessing the QinU in context-aware software systems called Framework for Assessing Quality-in-use of Software (FAQuiS). FAQuiS includes a set of models to represent all dimensions of context, a methodology to apply the quality analysis to any system, and a set of tools and metrics to support and automate the process. We seek to test the impact and ease of integration in the industry for this framework. A case study in a company allows us to validate the applicability in a real environment. We analyze the mechanisms that support the QinU evaluation in context-aware systems, the feasibility of the QinU quantification, and the suitability of the integration in companies. Compared to previous works, our proposal offers a novel data-driven approach with general-purpose and industrial viability. FAQuiS can be used as a solution to assess the QinU based on the ISO 25010 standard and the models of user behaviors in different contexts. This solution analyzes the context changes in the user interaction, can quantify the quality loss in these contexts, and does not require big efforts to be integrated into a software development process.
Government Surveillance, Privacy, and Legitimacy
Authors: Königs Peter 
Published: February 2022

AbstractThe recent decades have seen established liberal democracies expand their surveillance capacities on a massive scale. This article explores what is problematic about government surveillance by democracies. It proceeds by distinguishing three potential sources of concern: (1) the concern that governments diminish citizens’ privacy by collecting their data, (2) the concern that they diminish their privacy by accessing their data, and (3) the concern that the collected data may be used for objectionable purposes. Discussing the meaning and value of privacy, the article argues that only the latter two constitute compelling independent concerns. It then focuses particularly on the third concern, exploring the risk of government surveillance being used to enforce illegitimate laws. It discusses three legitimacy-related reasons why we should be worried about the expansion of surveillance capacities in established democracies: (1) Even established democracies might decay. There is a risk that surveillance capacities that are used for democratically legitimated purposes today will be used for poorly legitimated purposes in the future. (2) Surveillance may be used to enforce laws that lack legitimacy due to the disproportionate punishment attached to their violation. (3) The democratic procedures in established democracies fail to conform to the requirements formulated by mainstream theories of democratic legitimacy. Surveillance is thus used to enforce laws whose legitimacy is in doubt.
CTRW modeling of quantum measurement and fractional equations of quantum stochastic filtering and control
Authors: Kolokoltsov Vassili 
Published: February 2022

Abstract
Initially developed in the framework of quantum stochastic calculus, the main equations of quantum stochastic filtering were later on derived as the limits of Markov models of discrete measurements under appropriate scaling. In many branches of modern physics it became popular to extend random walk modeling to the continuous time random walk (CTRW) modeling, where the time between discrete events is taken to be non-exponential. In the present paper we apply the CTRW modeling to the continuous quantum measurements yielding the new fractional in time evolution equations of quantum filtering and thus new fractional equations of quantum mechanics of open systems. The related quantum control problems and games turn out to be described by the fractional Hamilton-Jacobi-Bellman (HJB) equations on Riemannian manifolds. By-passing we provide a full derivation of the standard quantum filtering equations, in a modified way as compared with existing texts, which (i) provides explicit rates of convergence (that are not available via the tightness of martingales approach developed previously) and (ii) allows for the direct applications of the basic results of CTRWs to deduce the final fractional filtering equations.
Application of Bone Marrow Mesenchymal Stem Cells Effectively Eliminates Endotoxemia to Protect Rat from Acute Liver Failure Induced by Thioacetamide
Authors: Jiang Ting , Xia Geng , Yang Bo , Zhang Hong-wei , Yin Yue-shan , Tang Cheng-wei , Yang Jin-hui 
Published: February 2022

Abstract
Background:
Endotoxemia is related to worse clinical outcomes in acute liver failure (ALF), but its management remains unsatisfactory. In this study, we aimed to assess whether the application of bone marrow mesenchymal stem cells (BMSCs) could eliminate endotoxemia and protect rats against ALF induced by thioacetamide (TAA).

Methods:
BMSCs were isolated from rats and identified by the specific morphology, differentiation potential, and surface markers. The optimal dose of TAA for this study was explored and TAA-induced ALF rats were randomized to three groups: the normal control group (Saline), ALF group (TAA + Saline), and BMSCs-treated group (TAA + BMSCs). The intestinal migration and differentiation of BMSCs was tracked in vivo, and intestinal permeability, endotoxin and inflammatory cytokines, histology, and mortality were analyzed. Moreover, we added the inhibitor of the PI3K/AKT/mTOR signaling pathway into the co-culture system of BMSCs with enterocytes and then performed CK and Villin expression experiments to assess the role of PI3K/AKT/mTOR signal pathway in the intestinal differentiation of BMSCs.

Results:
BMSCs migrated to the intestinal injury sites and differentiated into enterocytes, intestinal permeability was decreased compared with the ALF group. The higher expression of endotoxin and inflammatory cytokines were reversed after BMSCs transplantation in rats with ALF. Mortality and intestinal lesion were significantly decreased. Blocking the PI3K/AKT/mTOR signal pathway inhibited BMSCs’ intestinal differentiation in vitro.

Conclusion:
BMSCs can eliminate endotoxemia and reduce mortality in rats with ALF, and the PI3K/AKT/mTOR signal pathway is involved in intestinal differentiation. BMSCs transplantation could be a potential candidate for the treatment of endotoxemia in ALF.
Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing
Authors: Kwon Ki Chang , Baek Ji Hyun , Hong Kootak , Kim Soo Young , Jang Ho Won 
Published: February 2022

AbstractTwo-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.
Global modes of viscous heated jets with real gas effects
Authors: Demange S. , Qadri U.A. , Juniper M.P. , Pinna F. 
Published: February 2022

This study investigates the stability features of spatially spreading heated jets in the viscous regime with real gas effects, using both unsteady two-dimensional axisymmetric simulations and linear analyses of the steady state and time-averaged states. At a Reynolds number of

$400$

, the heated jets are found to undergo a subcritical Hopf bifurcation, marking the start of self-sustained oscillations when decreasing the temperature ratio

$S=T_{\infty }/T_{{c}}$

, which highly depends on the thermodynamic and transport property assumptions imposed in the simulations. Once the flow enters a limit cycle past the Hopf bifurcation, the linear analyses over the steady base state are unable to capture the oscillation's frequency. Nevertheless, this study confirms that including real gas effects in the stability equations has a strong effect on the growth rate of the global mode once the centreline temperature of the jet reaches that of the dissociation reaction onset, which is

$T=2800$

K for air at

$p_s=100$

mbar. The linear global analyses over the time-averaged states lead to a satisfying prediction of the oscillation's frequency for the cases studied, and the baroclinic torque obtained from the resulting global mode matches well with that of the simulations.
Proper Lie automorphisms of incidence algebras
Authors: Fornaroli Érica Z. , Khrypchenko Mykola , Santulo Ednei A. 
Published: February 2022

AbstractLet X be a finite connected poset and K a field. We study the question, when all Lie automorphisms of the incidence algebra I(X, K) are proper. Without any restriction on the length of X, we find only a sufficient condition involving certain equivalence relation on the set of maximal chains of X. For some classes of posets of length one, such as finite connected crownless posets (i.e., without weak crown subposets), crowns, and ordinal sums of two anti-chains, we give a complete answer.
The discursive construction of new citizen identities in Singapore
Authors: Vitorio Raymund 
Published: February 2022

AbstractThis article investigates how new citizens reconfigure dominant indexes of citizenship to claim status as legitimate new citizens of Singapore. New citizens are expected to resolve a tension that underpins public discourses in Singapore society: while the statal narrative of multiculturalism countenances new citizens to have perceivable markers of difference, everyday discourses expect new citizens to assimilate into the ‘Singapore core’—a term used in Singapore that denotes a homogeneous understanding of what it means to be Singaporean. By adopting a metapragmatic approach, this article identifies three common indexes of citizenship that new citizens negotiate to resolve this contradiction: language, loyalty, and legacy. By reconfiguring common markers of citizenship in Singapore, new citizens are able to discursively construct a type of citizenship that they can legitimately claim and contribute to. This expands common understandings of the notion of citizenship in Singapore society. (Citizenship, language ideologies, multiculturalism, metapragmatics, Singapore)*
Anthropogenic Influence on Recent Severe Autumn Fire Weather in the West Coast of the United States
Authors: Hawkins Linnia R. , Abatzoglou John T. , Li Sihan , Rupp David E. 
Published: February 2022

AbstractExtreme wind‐driven autumn wildfires are hazardous to life and property, due to their rapid rate of spread. Recent catastrophic autumn wildfires in the western United States co‐occurred with record‐ or near‐record autumn fire weather indices that are a byproduct of extreme fuel dryness and strong offshore dry winds. Here, we use a formal, probabilistic, extreme event attribution analysis to investigate the anthropogenic influence on extreme autumn fire weather in 2017 and 2018. We show that while present‐day anthropogenic climate change has slightly decreased the prevalence of strong offshore downslope winds, it has increased the likelihood of extreme fire weather indices by 40% in areas where recent autumn wind‐driven fires have occurred in northern California and Oregon. The increase was primarily through increased autumn fuel aridity and warmer temperatures during dry wind events. These findings illustrate that anthropogenic climate change is exacerbating autumn fire weather extremes that contribute to high‐impact catastrophic fires in populated regions of the western US.
Climate Change Impacts on the Patagonian Shelf Break Front
Authors: Franco B. C. , Ruiz‐Etcheverry L. A. , Marrari M. , Piola A. R. , Matano R. P. 
Published: February 2022

AbstractWe characterize long‐term trends of sea surface temperature (SST), absolute dynamic topography, and chlorophyll‐a (CHL) in the Patagonian shelf break front (SBF) using 27 years (1993–2019) of satellite data. Warming of the Argentinean shelf waters and the southwestward displacement of the Brazil‐Malvinas Confluence (BMC) impact the northernmost extension of the SBF. Cooling of the Malvinas Current (MC) and the concurrent warming of the adjacent shelf waters lead to a significant increase of SST gradients along the outer shelf. The southwestward displacement of the BMC implies a similar shift of the SBF. An increase in CHL trend appears to be associated with southerly wind anomalies along the shelf break. We estimate a southward shift of the northernmost penetration of the MC of −0.11 ± 0.076°/decade.
Reconstructing High Resolution ESM Data Through a Novel Fast Super Resolution Convolutional Neural Network (FSRCNN)
Authors: Passarella Linsey S. , Mahajan Salil , Pal Anikesh , Norman Matthew R. 
Published: February 2022

AbstractWe present the first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over‐smoothing, and reducing computational costs. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN‐ESM. We use high‐resolution (∼0.25°) monthly averaged model output of five surface variables over North America from the US Department of Energy's Energy Exascale Earth System Model's control simulation. These high‐resolution and corresponding coarsened low‐resolution (∼1°) pairs of images are used to train the FSRCNN‐ESM and evaluate its use as a downscaling approach. We find that FSRCNN‐ESM outperforms FSRCNN and other super‐resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes, and precipitation.
An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs
Authors: Aden‐Antoniów F. , Frank W. B. , Seydoux L. 
Published: February 2022

AbstractEarthquake catalogs are essential to analyze the evolution of active fault systems. The background seismicity rate, or rate of earthquakes that are not directly triggered by other earthquakes, directly relates to the stressing rate, a crucial quantity for understanding the seismic hazards. Determining the background seismicity rate is challenging because aftershock sequences may dominate the seismicity rate. Classifying these events in earthquake catalogs—known as catalog declustering—is a common practice and most declustering solutions rely on spatiotemporal distances between events, such as the nearest‐neighbor‐distance algorithm, widely used in various contexts. This algorithm assumes that the nearest‐neighbor distance (NND) follows a bimodal distribution related to the background seismicity and to the aftershocks. Constraining these two distributions is crucial to distinguish the aftershocks from the background events accurately. Recent work often uses linear splitting based on the NND, ignoring the potential overlap between the two populations and resulting in a biased identification of background earthquakes and aftershock sequences. We revisit this problem with machine‐learning algorithms. After testing several popular algorithms, we show that a random forest trained with various synthetic catalogs generated by an Epidemic‐Type Aftershock Sequence model outperforms approaches such as k‐means, Gaussian‐mixture models, and support vector machine classification. We apply our model to two different earthquake catalogs: the relocated Southern California earthquake center catalog and the GeoNet catalog of New Zealand. Our model capably adapts to these two different tectonic contexts, highlighting the differences in aftershock productivity between crustal and intermediate‐depth seismicity.
Air‐Water Mixing in Vegetated Supercritical Flow: Effects of Vegetation Roughness and Water Temperature on Flow Self‐Aeration
Authors: Bai Ruidi , Bai Zhongtian , Wang Hang , Liu Shanjun 
Published: February 2022

AbstractVegetated flow is rarely investigated for supercritical flow regime, in which self‐aeration occurs and modifies the flow dynamics, energy dissipation, and mass transfer between the water body and ambient air environment. In this study, natural river water was used at two flow rates in combination with six bottom vegetation conditions to investigate the air‐water flow development in an 18‐m‐long sloping chute. The vegetation configurations included two artificial turfs with different grass lengths and three flexible plant covers with different canopy densities, in addition to a reference case of smooth solid boundary, providing flow resistance and turbulent disturbance with different orders of equivalent roughness heights. Physical measurements were focused on the air concentration, free‐surface splashing, turbulent velocity, bubble count frequency, and bubble size distributions from the upstream non‐aerated flow to the far‐field fully developed aeration region. Compared to the velocity developing to uniform equilibrium shortly downstream the inception of aeration, the flow self‐aeration reached air‐concentration equilibrium at further downstream positions, while the bubble count and air‐water interfacial area kept increasing. The vegetation roughness conditions affected the inception location, equivalent bubble rise velocity, superfacial splashing height, velocity gradient, and maximum bubble count near the free surface, whereas the equilibrium air concentration in the fully developed region depended only upon the slope. The vegetation type influenced the percentage of submillimetre bubbles. Little differences were shown in the air‐water flow characteristics by repeating the summer experiments in winter with a 25.5°C drop in water temperature, despite the variation in fluid viscosity thus the Reynolds number under identical discharge.
Inactivation of EGLN3 hydroxylase facilitates Erk3 degradation via autophagy and impedes lung cancer growth
Authors: Jin Ying , Pan Yamu , Zheng Shuang , Liu Yao , Xu Jie , Peng Yazhi , Zhang Zemei , Wang Yadong , Xiong Yulian , Xu Lei , Mu Kaiyu , Chen Suwen , Zheng Fei , Yuan Ye , Fu Jian 
Published: February 2022

AbstractEGLN3 is critically important for growth of various cancers including lung cancer. However, virtually nothing is known about the role and mechanism for EGLN3 hydroxylase activity in cancers. EGLN3 catalyzes the hydroxylation of extracellular signal-regulated kinase 3 (Erk3), a potent driver of cancers. The role and mechanism for EGLN3-induced stabilization of Erk3 remain to be defined. Here, we show that Erk3 interacts with heat shock cognate protein of 70 kDa (HSC70) and lysosome-associated membrane protein type 2 A (LAMP2A), two core components of chaperone-mediated autophagy (CMA). As a consequence, Erk3 is degraded by the CMA-lysosome pathway. EGLN3-catalyzed hydroxylation antagonizes CMA-dependent destruction of Erk3. Mechanistically, hydroxylation blunts the interaction of Erk3 with LAMP2A, thereby blocking lysosomal decay of Erk3. EGLN3 inactivation inhibits macrophage migration, efferocytosis, and M2 polarization. Studies using EGLN3 catalytically inactive knock-in mice indicate that inactivation of EGLN3 hydroxylase in host cells ameliorates LLC cancer growth through reprogramming the tumor microenvironment (TME). Adoptive transfer of macrophages with inactivated EGLN3 restrains tumor growth by mounting anti-tumor immunity and restricting angiogenesis. Administration of EGLN3 hydroxylase pharmacologic inhibitor to mice bearing LLC carcinoma impedes cancer growth by targeting the TME. LLC cells harboring inactivated EGLN3 exhibit reduced tumor burden via mitigating immunosuppressive milieu and inducing cancer senescence. This study provides novel insights into the role of CMA in regulating Erk3 stability and the mechanism behind EGLN3-enhanced stability of Erk3. This work demonstrates that inactivation of EGLN3 in malignant and stromal cells suppresses tumor by orchestrating reciprocal interplays between cancer cells and the TME. This work sheds new light on the role and mechanism for EGLN3 catalytic activity in regulating cancer growth. Manipulating EGLN3 activity holds promise for cancer treatment.
Active demethylation upregulates CD147 expression promoting non-small cell lung cancer invasion and metastasis
Authors: Liao Cheng-Gong , Liang Xiao-Hua , Ke Yuan , Yao Li , Liu Man , Liu Ze-Kun , He Lin , Guo Yi-Xiao , Bian Huijie , Chen Zhi-Nan , Kong Ling-Min 
Published: February 2022

AbstractNon-small cell lung cancer (NSCLC) is a fatal disease, and its metastatic process is poorly understood. Although aberrant methylation is involved in tumor progression, the mechanisms underlying dynamic DNA methylation remain to be elucidated. It is significant to study the molecular mechanism of NSCLC metastasis and identify new biomarkers for NSCLC early diagnosis. Here, we performed MeDIP-seq and hMeDIP-seq analyses to detect the genes regulated by dynamic DNA methylation. Comparison of the 5mC and 5hmC sites revealed that the CD147 gene underwent active demethylation in NSCLC tissues compared with normal tissues, and this demethylation upregulated CD147 expression. Significantly high levels of CD147 expression and low levels of promoter methylation were observed in NSCLC tissues. Then, we identified the CD147 promoter as a target of KLF6, MeCP2, and DNMT3A. Treatment of cells with TGF-β triggered active demethylation involving loss of KLF6/MeCP2/DNMT3A and recruitment of Sp1, Tet1, TDG, and SMAD2/3 transcription complexes. A dCas9-SunTag-DNMAT3A-sgCD147-targeted methylation system was constructed to reverse CD147 expression. The targeted methylation system downregulated CD147 expression and inhibited NSCLC proliferation and metastasis in vitro and in vivo. Accordingly, we used cfDNA to detect the levels of CD147 methylation in NSCLC tissues and found that the CD147 methylation levels exhibited an inverse relationship with tumor size, lymphatic metastasis, and TNM stage. In conclusion, this study clarified the mechanism of active demethylation of CD147 and suggested that the targeted methylation of CD147 could inhibit NSCLC invasion and metastasis, providing a highly promising therapeutic target for NSCLC.