Explore the vast range of titles available.

Mobile networks have a great challenge by serving the expected billions of Internet of Things (IoT) devices in the upcoming years. Due to the limited simultaneous access in the mobile networks, the devices should compete between each other for resource allocation during a Random-Access procedure. This contention provokes a non-depreciable delay during the device’s registration because of the great number of collisions experienced. To overcome such a problem, a framework called Random-Access Accelerator (RAA) is proposed in this work, in order to speed up network access in massive Machine Type Communication (mMTC). RAA exploits Device-To-Device (D2D) communications, where devices with already assigned resources act like relays for the rest of devices trying to gain access in the network. The simulation results show an acceleration in the registration procedure of 99%, and a freed space of the allocated spectrum until 74% in comparison with the conventional Random-Access procedure. Besides, it preserves the same device’s energy consumption compared with legacy networks by using a custom version of Bluetooth as a wireless technology for D2D communications. The proposed framework can be taken into account for the standardization of mMTC in Fifth-Generation-New Radio (5G NR).

Stakeholders and their dynamics are often neglected in innovation system literature. The importance of the bioeconomy is growing due to its implications for addressing environmental challenges, shaping economic decisions, markets, and sustainable development. This paper analyses stakeholders’ dynamics for knowledge creation and innovation to transit from unsustainable practices to the sustainable use of biological resources—the bioeconomy. The originality of this paper is the creation of an analytical framework to characterise the interactions of stakeholders and how these interactions reshape innovation systems to create a new narrative and knowledge-base platform for innovation. Using a qualitative approach, data were collected through surveys between 2022 and 2024. We explored the dynamics of 29 stakeholders involved and collaborating in R&D activities from the biotechnology sector in Caldas, Colombia. Our findings show that dynamics towards the bioeconomy are occurring only at the discursive level. Stakeholders carry out research activities to generate income rather than for innovative purposes, overlooking informal interactions that create novel ideas that could translate into solutions, services, and products. We conclude that the bioeconomy transition needs a systemic disequilibrium with a new institutional infrastructure that enables stakeholders, including civil society, to create a structural change for embracing innovation dynamics.

In response to the SARS-CoV-2 outbreak, the Mexican government implemented lockdown and business closure measures to minimize the spread of the disease. This graphic shows whether under those measures the clusters of economic complexity - a measure of the diversification of economic activities, particularly those that are highly sophisticated - have changed relative to the period before the Covid-19 pandemic. The visualization shows a notable reduction in economic complexity clusters during 2020 and a rapid increase in 2021.

Objective: The objective of this study is to examine the sensitivity of particle retention processes in artificial porous media to variations in fluid injection velocity, particle size, injection rate, and surface roughness. Similarly, this investigation contributes to the understanding of the mechanisms governing particle transport and retention, as well as supports the optimization of filtration systems across various applications.   Theoretical Framework: The research builds upon the established theories of porous media flow, particle transport, and interfacial phenomena, particularly focusing on the application of Computational Fluid Dynamics (CFD) simulations to the study of particulate matter retention in water.   Method: In this work, a sensitivity analysis was conducted using a computational model implemented in ANSYS-CFX software, which allows for the study of water-particle mixture percolation in artificial porous media. The main parameters analyzed included flow velocity, particle size, surface roughness, and injection rate. Prior to simulations, X-ray computed tomography (μCT-XR) was employed to obtain detailed geometric information of the porous media, which was used to generate realistic computational models.   Results and Discussion: The results obtained revealed article retention in porous media is influenced by flow velocity, particle size, and media roughness. Higher velocities and larger particles promote deposition. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section.   Research Implications: These findings provide valuable insights to understand the limits of applicability of computational CFD when applied to the optimization of barrier and filter construction which have significant implications for various applications, such as water filtration, soil contamination, and reservoir engineering.   Originality/Value: This study contributes to the literature by providing valuable insights about key factors influencing particle retention. The relevance and value of this research are evident in the potential application of CFD simulations, which, through sensitivity analyses, provide valuable understanding about optimizing filter design and mitigating water contamination.

We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices.

The use of spatialmodels for inferring members' preferences from voting data has become widespread in the study of deliberative bodies, such as legislatures. Most established spatial voting models assume that ideal points belong to a Euclidean policy space. However, the geometry of Euclidean spaces (even multidimensional ones) cannot fully accommodate situations in which members at the opposite ends of the ideological spectrum reveal similar preferences by voting together against the rest of the legislature. This kind of voting behavior can arise, for example, when extreme conservatives oppose a measure because they see it as being too costly, while extreme liberals oppose it for not going far enough for them. This paper introduces a new class of spatial voting models in which preferences live in a circular policy space. Such geometry for the latent space is motivated by both theoretical (the socalled \"horseshoe theory\" of political thinking) and empirical (goodness of fit) considerations. Furthermore, the circular model is flexible and can approximate the one-dimensional version of the Euclidean voting model when the data supports it. We apply our circular model to roll-call voting data from the U.S. Congress between 1988 and 2019 and demonstrate that, starting with the 112th House of Representatives, circular policy spaces consistently provide a better explanation of legislators's behavior than Euclidean ones and that legislators's rankings, generated through the use of the circular geometry, tend to be more consistent with those implied by their stated policy positions.

The objective of the study was to determine the influence of personality disorders on the cognitive distortions of the aggressor. The research methodology was carried out under a quantitative approach, type of basic study, hypothetical deductive method, the technique was the survey and the questionnaire was the instrument for personality disorders (IPD E) and for cognitive distortions (IPDMUV-R). ). The results showed that the personality disorder influences 100% of the cognitive distortions. In that sense, the personality disorder is determined by the dimensions for a better relationship with distorted thoughts. It is concluded that cognitive distortions are characterized by frequently presenting psychological alterations due to personality disorders, lack of control over anger, difficulties in expressing emotions, deficits in communication skills, problem solving and low self-esteem.

Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.