How are fictions given? Conjoining the ‘artifactual theory’ and the ‘imaginary-object theory’

According to the so-called ‘artifactual theory’ of fiction, fictional objects are to be considered as abstract artifacts. Within this framework, fictional objects are defined on the basis of their complex dependence on literary works, authors, and readership. This theory is explicitly distinguished from other approaches to fictions, notably from the imaginary-object theory. In this article, I argue that the two approaches are not mutually exclusive but can and should be integrated. In particular, the ontology of fiction can be fruitfully supplemented by a phenomenological analysis, which allows us to clarify the defining modes of givenness of fictional objects. Likewise, based on the results of the artifactual theory, some assumptions in the imaginary-object theory, which are liable to be interpreted as laying the ground to phenomenalism, can be corrected.

Multi-landmark alignment of genomic signals reveals conserved expression patterns across transcription start sites

The prevalent one-dimensional alignment of genomic signals to a reference landmark is a cornerstone of current methods to study transcription and its DNA-dependent processes but it is prone to mask potential relations among multiple DNA elements. We developed a systematic approach to align genomic signals to multiple locations simultaneously by expanding the dimensionality of the genomic-coordinate space. We analyzed transcription in human and uncovered a complex dependence on the relative position of neighboring transcription start sites (TSSs) that is consistently conserved among cell types. The dependence ranges from enhancement to suppression of transcription depending on the relative distances to the TSSs, their intragenic position, and the transcriptional activity of the gene. Our results reveal a conserved hierarchy of alternative TSS usage within a previously unrecognized level of genomic organization and provide a general methodology to analyze complex functional relationships among multiple types of DNA elements.

Diverse response of surface ozone to COVID-19 lockdown in China

<p>Ozone (O<sub>3</sub>) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O<sub>3</sub> have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O<sub>3</sub> across China for periods before and during the lockdown. We find that daytime O<sub>3</sub> decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O<sub>3</sub> changes, with a larger impact from the former especially in central China. The southward-shifted wind with increased temperature, enhanced planetary boundary layer height, decreased cloud fraction and precipitation favored the O<sub>3</sub> increase in north and central China, while the northward-shifted wind with decreased temperature and then biogenic volatile organic compounds (VOCs) emissions, increased cloud fraction and precipitation reduced O<sub>3</sub> in south China. As for the emission reduction, the drop in nitrogen oxide (NO<sub>x</sub>) emission contributed to O<sub>3</sub> increases in populated regions, whereas the reduction in VOCs contributed to O<sub>3</sub> decreases across the country. Due to a decreasing level of NO<sub>x</sub> saturation from north to south, the emission reduction in NO<sub>x</sub> (46%) and VOC (32%) contributed to net O<sub>3</sub> increases in north China; the opposite effects of NO<sub>x</sub> decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in the two precursors contributed to net O<sub>3</sub> declines in south China. Our study highlights the complex dependence of O<sub>3</sub> on its precursors and the importance of meteorology in the short-term O<sub>3</sub> variability.</p>

Bayesian graphical models for modern biological applications

Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

As one of the five major coal mine disasters, the water inrush disaster poses a serious threat to the safety of the country and people, so the prevention work for that becomes very important. However, there is no perfect assessment system that can better solve the complex dependence relationships among disaster-causing factors of water inrush disasters. This study applied the knowledge of Complex Networks to research water inrush disaster, and based on that, the early warning evaluation system that combined ANP and Cloud model was established in order to solve the complex dependence problem and prevent the occurrence of water inrush. Moreover, this evaluation model was applied to the example Y coal mine to verify its superiority and feasibility. The results showed that the main cloud of goal was located at the yellow-strong warning level, and the first-level indicators were, respectively, at that the yellow-strong level of mining conditions, the yellow-strong warning level of hydrological factors, between the yellow-strong warning level and purple-general level of the geological structure, and among the blue-slightly weak warning level, purple-general level, and yellow-strong level of the human factor. The prediction results were consistent with the actual situation of the coal water inrush disaster in Y mine, which further proved that this early warning evaluation model is reliable. In response to the forecast results, the authors put forward relative improvements necessary to strengthen the prevention ability to disaster-causing factors among hydrological factors, mining conditions, and geological structure, which should comprehensively increase knowledge, technology, and management of workers to avoid leaving out disaster-causing factors. Meanwhile, the warning evaluation model also provides the relevant experience basis for other types of early warning assessment networks.

Tracking Structure and Composition Changes in Oxide Derived Cu-Sn Catalysts During CO2 Electroreduction Using X-Ray Spectroscopy

<div> <p>In the field of electrochemical CO₂ conversion, the development of earth-abundant catalysts which are selective for a single product is a central challenge. Cu-Sn bimetallic catalysts have been reported to yield selective CO₂reduction towards either carbon monoxide or formate. To advance the understanding of possible synergetic effects between Cu and Sn which direct product selectivity, a thorough investigation of the catalyst structure and composition in its active state is desired. We present an X-ray spectroscopy investigation of oxide-derived Cu-Sn catalysts prepared by functionalization of Cu(OH)₂ nanowire arrays with ultrathin SnO₂ overlayers. This method allows precisely tunable Sn composition, which enables synthesis of composite catalysts with high selectivity toward either CO or formate. Under CO₂ reduction conditions, the materials undergo significant transformations before reaching their catalytically active forms. Complementary information on the electrocatalysts’ dynamic bulk and surface structure was revealed via correlating observations from multiple X-ray spectroscopy methods. <i>In situ</i> investigations of Cu K-edge revealed that in the bulk Cu is fully reduced from Cu²⁺ to Cu° after a pre reduction step. <i>Quasi in situ</i> XPS demonstrated that, at the catalyst surface, Cu is also present exclusively as Cu°, whereas significant differences in Sn quantification and speciation were observed between the CO- and formate-selective catalysts. After CO<a>₂</a> electrolysis, CO-selective catalysts exhibited a surface Sn content of 13 at. % predominantly present as Sn oxide, while the formate-selective catalysts had a Sn content of ~70 at. % consisting of both metallic Sn° and Sn oxidespecies. Our study reveals the complex dependence of catalyst structure, composition, and speciation with applied electrochemical bias in Sn-functionalized nanostructured Cu catalysts.</p></div>

Tracking Structure and Composition Changes in Oxide Derived Cu-Sn Catalysts During CO2 Electroreduction Using X-Ray Spectroscopy

<div> <p>In the field of electrochemical CO₂ conversion, the development of earth-abundant catalysts which are selective for a single product is a central challenge. Cu-Sn bimetallic catalysts have been reported to yield selective CO₂reduction towards either carbon monoxide or formate. To advance the understanding of possible synergetic effects between Cu and Sn which direct product selectivity, a thorough investigation of the catalyst structure and composition in its active state is desired. We present an X-ray spectroscopy investigation of oxide-derived Cu-Sn catalysts prepared by functionalization of Cu(OH)₂ nanowire arrays with ultrathin SnO₂ overlayers. This method allows precisely tunable Sn composition, which enables synthesis of composite catalysts with high selectivity toward either CO or formate. Under CO₂ reduction conditions, the materials undergo significant transformations before reaching their catalytically active forms. Complementary information on the electrocatalysts’ dynamic bulk and surface structure was revealed via correlating observations from multiple X-ray spectroscopy methods. <i>In situ</i> investigations of Cu K-edge revealed that in the bulk Cu is fully reduced from Cu²⁺ to Cu° after a pre reduction step. <i>Quasi in situ</i> XPS demonstrated that, at the catalyst surface, Cu is also present exclusively as Cu°, whereas significant differences in Sn quantification and speciation were observed between the CO- and formate-selective catalysts. After CO<a>₂</a> electrolysis, CO-selective catalysts exhibited a surface Sn content of 13 at. % predominantly present as Sn oxide, while the formate-selective catalysts had a Sn content of ~70 at. % consisting of both metallic Sn° and Sn oxidespecies. Our study reveals the complex dependence of catalyst structure, composition, and speciation with applied electrochemical bias in Sn-functionalized nanostructured Cu catalysts.</p></div>

TO THE CALCULATION OF LENGTH OF GEOPHYSICAL OBJECTS OF HYPERBOLIC OUTLINE

Hyperbolic curves are used in various theoretical and practical studies, including in the field of water management and environmental construction when calculating various geophysical objects with hyperbolic outlines (surfaces of coastal slopes, sliding lines of landslide massifs, directing dams, spillway surfaces of watersheds, water free fall trajectories, etc.). The exact determination of the length of the hyperbola arc is represented by a rather complex dependence based on “unbreakable” incomplete elliptic integrals, which makes it difficult to carry out analytical calculations and involves the use of tabular data with a time-consuming cross and non-linear interpolation of them, etc. Elementary dependencies are proposed to determine the length of the hyperbola arc, which give a very close approximation (up to 1%) to exact values. The obtained calculated analytical dependencies for determining the length of the hyperbola arc are recommended for practical use in theoretical and applied research in various fi elds of science and technology.

Socio-Cultural Impacts of COVID-19 in Nepal

The COVID-19 pandemic has considerably distorted the social and cultural life of people on a global scale. It has a profound impact on the dynamics of human mobility, in & out - migration, economy, and socio-cultural correlation that underpin population diversity. It seen that some of these effects are short-lived, but others will have long-lasting implications that can see in the future. The COVID-19 crisis is exposing the fragility of all our systems, our complex dependence upon one another, livelihood patterns and health, and healthcare as the most basic of human rights. Health security is one of the essential parts of social safety, which encompasses material or economic security; the security of life, and protection from violence and conflict, and these all are apprehensive and questionable in this pandemic. Like other developed and developing countries, Nepal is also facing the challenges contrived by the pandemic. This article describes the impacts of COVID-19 in socio-culture sectors and the diverse categories of the people of Nepal.