SAPIENS in XR: operationalizing interaction-attention in extended reality

Virtual Reality - We examine peripheral interactions in XR environments, for which we propose a conceptual space with two specialized dimensions, interaction-attention and reality-virtuality. We...     

Identifying and correcting invalid citations due to DOI errors in Crossref data

This work aims to identify classes of DOI mistakes by analysing the open bibliographic metadata available in Crossref, highlighting which publishers were responsible for such mistakes and how many of these incorrect DOIs could be corrected through automatic processes. By using a list of invalid cited DOIs gathered by OpenCitations while processing the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) in the past two years, we retrieved the citations in the January 2021 Crossref dump to such invalid DOIs. We processed these citations by keeping track of their validity and the publishers responsible for uploading the related citation data in Crossref. Finally, we identified patterns of factual errors in the invalid DOIs and the regular expressions needed to catch and correct them. The outcomes of this research show that only a few publishers were responsible for and/or affected by the majority of invalid citations. We extended the taxonomy of DOI name errors proposed in past studies and defined more elaborated regular expressions that can clean a higher number of mistakes in invalid DOIs than prior approaches. The data gathered in our study can enable investigating possible reasons for DOI mistakes from a qualitative point of view, helping publishers identify the problems underlying their production of invalid citation data. Also, the DOI cleaning mechanism we present could be integrated into the existing process (e.g. in COCI) to add citations by automatically correcting a wrong DOI. This study was run strictly following Open Science principles, and, as such, our research outcomes are fully reproducible.

     

The Interference between SARS-CoV-2 and Tyrosine Kinase Receptor Signaling in Cancer

Cancer and viruses have a long history that has evolved over many decades. Much information about the interplay between viruses and cell proliferation and metabolism has come from the history of clinical cases of patients infected with virus-induced cancer. In addition, information from viruses used to treat some types of cancer is valuable. Now, since the global coronavirus pandemic erupted almost a year ago, the scientific community has invested countless time and resources to slow down the infection rate and diminish the number of casualties produced by this highly infectious pathogen. A large percentage of cancer cases diagnosed are strongly related to dysregulations of the tyrosine kinase receptor (TKR) family and its downstream signaling pathways. As such, many therapeutic agents have been developed to strategically target these structures in order to hinder certain mechanisms pertaining to the phenotypic characteristics of cancer cells such as division, invasion or metastatic potential. Interestingly, several authors have pointed out that a correlation between coronaviruses such as the SARS-CoV-1 and -2 or MERS viruses and dysregulations of signaling pathways activated by TKRs can be established. This information may help to accelerate the repurposing of clinically developed anti-TKR cancer drugs in COVID-19 management. Because the need for treatment is critical, drug repurposing may be an advantageous choice in the search for new and efficient therapeutic compounds. This approach would be advantageous from a financial point of view as well, given that the resources used for research and development would no longer be required and can be potentially redirected towards other key projects. This review aims to provide an overview of how SARS-CoV-2 interacts with different TKRs and their respective downstream signaling pathway and how several therapeutic agents targeted against these receptors can interfere with the viral infection. Additionally, this review aims to identify if SARS-CoV-2 can be repurposed to be a potential viral vector against different cancer types.     

Estimation of regional input coefficients and output multipliers for the regions of Chile

The greatest limitation for carrying out intersectoral studies at the regional level is the lack of regional input–output tables. This study compares various indirect methods to estimate regional output multipliers in 15 regions of Chile through Monte Carlo simulation. The augmented Flegg location quotient (AFLQ) method (δ = 0.5) is chosen since it presents the best behaviour to estimate regional input coefficients and output multipliers. However, the value of δ is sensitive to the number of regions and/or sectors and to the use of simulated or real sectoral gross domestic product (GDP) in each region. So, it is recommended that when it is possible, a simulation of Monte Carlo simulation be carried out to select this parameter in each country and not simply adopt values extracted from the literature.     

Using RGB Image Processing for Designing an Alginate Edible Film

The use of edible films to coat food products is a technique that allows for an extended shelf-life. One of the most widely used polymers is calcium alginate. However, this polymer can modify the original food color and the perception by consumers. The objective was to design an alginate film based principally on color changes using a RGB color model. Edible films were prepared with sodium alginate and glycerol as plasticizer, cross-linking the polymer with calcium. Dry and hydrated states of the edible films were studied. Film thickness was directly proportional to surface concentration and increased with hydration. There is a zone in which the color does not change with alginate surface concentration and another where the color is directly proportional to it. This latter scenario is not a consequence of structural changes or the degree of hydration. Results showed a range where the color was not modified by the alginate concentration; hence, an optimal surface concentration was determined as a design parameter. Edible films made using the optimal surface concentration would not mask microbial contamination and have good physical properties (water vapor transmission and swelling) compared with other surface concentrations. In addition, it was possible to model alginate surface concentration as a function of surface color using mathematical tools (clustering, linear regression, and support vector machine), allowing one to study the optimal use of the edible films.     

Chemical Graphs. Part 51.1 Enumeration of Nonbranched Catafusenes According to the Numbers of Benzenoid Rings in the Catafusene and in its Longest Linearly Condensed Portion

The number N(h,s) of nonbranched cata-condensed benzenoids, including helicenes and other out-of-plane systems, is discussed in terms of two parameters: total number h of benzenoid rings, and number s of benzenoid rings in the longest linearly condensed portion. Recurrence formulas for N(h,s) are demonstrated by Theorems 1 and 2 and implemented by means of a computer program. For h ≤ 2s ? 1, an explicit formula for N(h,s) is proved by Theorem 3; in this case, N(h,s) no longer depends separately on h and s, but only on the difference h ? s.     

Geometrical modelling of angle warp interlock fabrics

A geometrical modelling approach has been developed which predicts all the necessary geometrical parameters for multilayer angle warp interlock weaves. The model requires tow and weaver data as input and gives fabric thickness, warp and weft crimp angle, areal weight and fibre volume fraction (FVF) as outputs. In order to validate the model we have woven three angle warp interlock woven reinforcements, having same number of total layers, on a conventional loom, using carbon multifilament tows in warp and glass multifilament tows in weft. The depth of the binder (maximum number of layers traversed by the binding warp in vertical plane) was maximum for the first variant (5). The binder tow traversed all the five layers so that this variant is termed as through-the-thickness angle interlock. For the second variant it was reduced to an intermediate level (3), whereas for the third one it was minimum (2) so as to conceive a layer-to-layer interlock structure. The geometry of such woven reinforcements can be categorised in terms of crimp amplitude and cross-sectional shape of the warp and weft tows. These two vary with the structure of the woven fabric and weaving parameters, ultimately influencing the areal weight, size of the unit cell and FVF of the fabric reinforcement. Results obtained show that the modelling approach can be successfully applied to calculate necessary fabric geometry parameters from minimum number of manufacturer and weaver data.     

Applying different decomposition schemes using the progressive hedging algorithm to the operation planning problem of a hydrothermal system

This paper addresses the solution of the Medium-Term Operation Planning (MTOP) problem. This operation scheduling problem aims to define the output of each power plant to minimize the expected production cost over a medium-term planning horizon. In hydrothermal systems, the MTOP is strongly influenced by the amount of water inflow to the reservoirs of hydroelectric plants, which is uncertain. Thus, the System Operator (SO) must consider these uncertainties in the problem resolution, which can be solved by means of Stochastic Programming (SP) techniques, such as the Progressive Hedging (PH) proposed in this paper. This paper presents suitable decomposition schemes to reduce the CPU time, such that it is possible to use a detailed model for the problem. Additionally, a parallel computational approach based on the PH algorithm is also implemented. To demonstrate the efficiency of the proposed schemes, a large hydrothermal system is investigated in the case studies.