AbstractDigitally networked and new, unconventional activities allow citizens to participate politically in activities that are low in the effort and risks they bear. At the same time, low-effort types of participation are more loosely connected to democratic political systems, thereby challenging established modes of political decision-making. This can set in motion two competing dynamics: While some citizens move closer to the political system in their activities (upstream effects), others engage in political activities more distant from it (downstream effects). This study investigates non-electoral participation trajectories and tests intra-individual change in political participation types over time, exploring whether such dynamics depend on citizens' exposure to political information. Utilizing a three-wave panel survey (n = 3490) and random intercept cross-lagged panel models with SEM, we find more evidence for downstream effects but detect overall diverse participation trajectories over time and a potentially crucial role of elections for non-electoral participation trajectories.
Being popular makes it easier for dictators to govern. A growing body of scholarship therefore focuses on the factors that influence authoritarian popularity. However, it is possible that the perception of popularity itself affects incumbent approval in autocracies. We use framing experiments embedded in four surveys in Russia to examine this phenomenon. These experiments reveal that manipulating information—and thereby perceptions—about Russian President Vladimir Putin's popularity can significantly affect respondents' support for him. Additional analyses, which rely on a novel combination of framing and list experiments, indicate that these changes in support are not due to preference falsification, but are in fact genuine. This study has implications for research on support for authoritarian leaders and defection cascades in nondemocratic regimes.
AbstractThis research investigates medium‐scale disruptive events to understand how these events influence communication and coordination between two interdependent systems (i.e., the water system and the public health system). Medium‐scale events are events that are often overlooked as routine as they occur with more frequency than large‐scale events, yet they have the potential to provide important information about the state and vulnerability of systems, and, if not managed appropriately, can cascade into larger‐scale crises. A survey of US public drinking water systems (N = 471) shows that medium‐scale events promote coordination, especially when those events have a public dimension. Findings also reveal that several features of water systems including surface water sources, system size, and ownership types are associated with higher levels of interaction with the public health systems. Additionally, a network analysis identifies three distinct subnetworks that engage in emergency response activities. The strength of the working relationship was strongly associated with coordinated emergency responses, coordinated public responses, planning, and technical assistance. Findings have implications for both theory and crisis management.
Front Cover -- Series Page -- Title Page -- Copyright -- Contents -- Contributors -- Preface -- Chapter One: The interaction between social and communication skills in individuals with intellectual disability -- 1 Introduction -- 2 The interdependence between social interaction and communication -- 2.1 What are social and communication skills? -- 2.2 Theoretical framework from neurotypical development: Social information processing -- 2.3 Applications to ID: Developmental cascades -- 3 Social cognition: Examples from development and approaches to assessment -- 3.1 Joint attention -- 3.1.1 Defining joint attention through assessment -- 3.1.2 Implications for research on ID -- 3.2 Theory of mind and perspective-taking -- 3.2.1 Defining theory of mind through assessment -- 3.2.2 Implications for research on ID -- 3.3 Pragmatic language -- 3.3.1 Defining pragmatic language through assessment -- 3.3.2 Implications for research on ID -- 4 Broader measures of social and communicative functioning -- 4.1 Implications for research on ID -- 5 Recommendations for researchers: Challenges and opportunities -- 6 Conclusions -- References -- Chapter Two: Novel approaches for characterizing social communication and language development of young children with neurogenetic syndromesSocial communication and language development -- 1 The trajectory of communication development -- 2 Communication development in neurogenetic syndrome populations -- 3 The importance of appropriate communication assessment tools -- 4 Limitations of existing communication assessment tools -- 5 Advancing communication assessment for young children with neurogenetic syndromes -- 6 Novel approaches for adapting existing communication assessment tools -- 7 Novel communication assessment methods-remote naturalistic assessment -- 7.1 Remote assessments -- 7.2 Naturalistic assessments.
Automated detection of the content of images remains a challenging problem in artificial intelligence. Hence, continuous manual monitoring of restricted development zones is critical to maintaining territorial integrity and national security. In this regard, local governments of the Republic of Korea conduct four periodic inspections per year to preserve national territories from illegal encroachments and unauthorized developments in restricted zones. The considerable expense makes responding to illegal developments difficult for local governments. To address this challenge, we propose a deep-learning-based Cascade Mask region-based convolutional neural network (R-CNN) algorithm designed to perform automated detection of greenhouses in aerial photographs for efficient and continuous monitoring of restricted development zones in the Republic of Korea. Our proposed model is regional-based because it was optimized for the Republic of Korea via transfer learning and hyperparameter tuning, which improved the efficiency of the automated detection of greenhouse facilities. The experimental results demonstrated that the mAP value of the proposed Cascade Mask R-CNN model was 83.6, which was 12.83 higher than baseline mask R-CNN, and 0.9 higher than Mask R-CNN with hyperparameter tuning and transfer learning considered. Similarly, the F1-score of the proposed Cascade Mask R-CNN model was 62.07, which outperformed those of the baseline mask R-CNN and the Mask R-CNN with hyperparameter tuning and transfer learning considered (i.e., the F1-score 52.33 and 59.13, respectively). The proposed improved Cascade Mask R-CNN model is expected to facilitate efficient and continuous monitoring of restricted development zones through routine screening procedures. Moreover, this work provides a baseline for developing an integrated management system for national-scale land-use planning and development infrastructure by synergizing geographical information systems, remote sensing, and deep learning models.
Automated detection of the content of images remains a challenging problem in artificial intelligence. Hence, continuous manual monitoring of restricted development zones is critical to maintaining territorial integrity and national security. In this regard, local governments of the Republic of Korea conduct four periodic inspections per year to preserve national territories from illegal encroachments and unauthorized developments in restricted zones. The considerable expense makes responding to illegal developments difficult for local governments. To address this challenge, we propose a deep-learning-based Cascade Mask region-based convolutional neural network (R-CNN) algorithm designed to perform automated detection of greenhouses in aerial photographs for efficient and continuous monitoring of restricted development zones in the Republic of Korea. Our proposed model is regional-based because it was optimized for the Republic of Korea via transfer learning and hyperparameter tuning, which improved the efficiency of the automated detection of greenhouse facilities. The experimental results demonstrated that the mAP value of the proposed Cascade Mask R-CNN model was 83.6, which was 12.83 higher than baseline mask R-CNN, and 0.9 higher than Mask R-CNN with hyperparameter tuning and transfer learning considered. Similarly, the F1-score of the proposed Cascade Mask R-CNN model was 62.07, which outperformed those of the baseline mask R-CNN and the Mask R-CNN with hyperparameter tuning and transfer learning considered (i.e., the F1-score 52.33 and 59.13, respectively). The proposed improved Cascade Mask R-CNN model is expected to facilitate efficient and continuous monitoring of restricted development zones through routine screening procedures. Moreover, this work provides a baseline for developing an integrated management system for national-scale land-use planning and development infrastructure by synergizing geographical information systems, remote sensing, and deep learning models.
13 pages, 5 figures, supplementary information https://doi.org/10.1038/s41598-022-05230-x.-- This is a contribution from the Marine Biogeochemistry and Global Change group from the Generalitat de Catalunya (2017SGR1011) ; Upon injury, the homeostatic balance that ensures tissue function is disrupted. Wound-induced signaling triggers the recovery of tissue integrity and offers a context to understand the molecular mechanisms for restoring tissue homeostasis upon disturbances. Marine sessile animals are particularly vulnerable to chronic wounds caused by grazers that can compromise prey's health. Yet, in comparison to other stressors like warming or acidification, we know little on how marine animals respond to grazing. Marine sponges (Phylum Porifera) are among the earliest-diverging animals and play key roles in the ecosystem; but they remain largely understudied. Here, we investigated the transcriptomic responses to injury caused by a specialist spongivorous opisthobranch (i.e., grazing treatment) or by clipping with a scalpel (i.e., mechanical damage treatment), in comparison to control sponges. We collected samples 3 h, 1 d, and 6 d post-treatment for differential gene expression analysis on RNA-seq data. Both grazing and mechanical damage activated a similar transcriptomic response, including a clotting-like cascade (e.g., with genes annotated as transglutaminases, metalloproteases, and integrins), calcium signaling, and Wnt and mitogen-activated protein kinase signaling pathways. Wound-induced gene expression signature in sponges resembles the initial steps of whole-body regeneration in other animals. Also, the set of genes responding to wounding in sponges included putative orthologs of cancer-related human genes. Further insights can be gained from taking sponge wound healing as an experimental system to understand how ancient genes and regulatory networks determine healthy animal tissues ; Open Access funding enabled and organized by Projekt DEAL. LP was awarded a postdoctoral fellowship from Alexander von Humboldt Foundation, which was sponsored by The Future Ocean Cluster of Excellence. LP is currently at the Institute of Marine Science (ICM-CSIC) thanks to a fellowship supported by "la Caixa" Foundation (ID 10010434) and from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie gran agreement No 847648), fellowship code is 104855. Financial support was provided by the Spanish Government through the Grant (RTI2018-094187-B-100) and the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S) to MR ; Peer reviewed