Disaster Coverage Model (DCM): Six Dimensions to Confront Activities and Workflows for Journalists and News Departments

This work aims to identify, classify, and categorize relevant activities regarding professional journalistic work in major disaster coverage, and develop a conceptual model that organizes them theoretically. We conducted a series of empirical data collection stages (background gathering through in-depth interviews and content analysis) and later applied the theory-building block approach that uses concepts to create and operationalize constructs. The main result is a six-dimension model based on the traditional questions of the journalistic process: How, why, who, when, what, and where. It comprehensively addresses the multiple aspects involved in disaster coverage: emotional, logistic, professional, and ethical challenges, as well as timing, key actors/roles, and their needs and demands according to the disaster type and stage they face. The model also brings together a group of potential activities journalists must confront and carry out when covering major disasters or highly significant social crises. Its main contribution is to make a useful theoretical tool available to academia and the media, striving for a versatile matrix management approach.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Path coverage enhancement in wireless sensor networks based on CVT model

In order to overcome the problems of low detection probability, low coverage uniformity and low coverage of current path coverage enhancement methods in wireless sensor networks, a new path coverage enhancement method based on CVT model is proposed in this paper. Firstly, the node perception model and network coverage model are constructed. On the basis of the node awareness model and network coverage model, CVT model is used to adjust the connection mode, density and location of nodes in wireless sensor networks, so as to improve the coverage performance of nodes in the detection area in wireless sensor networks, and realize the effective enhancement of path coverage in wireless sensor networks. Experimental results show that, compared with the traditional methods, the proposed method has high detection probability, high coverage uniformity and coverage rate, and the highest coverage rate reaches 97%, which has higher practical application performance.

WSN Deployment Strategy for Real 3D Terrain Coverage Based on Greedy Algorithm with DEM Probability Coverage Model

The key to the study of node deployment in Wireless Sensor Networks (WSN) is to find the appropriate location of the WSN nodes and reduce the cost of network deployment while meeting the monitoring requirements in the covered area. This paper proposes a WSN node deployment algorithm based on real 3D terrain, which provides an effective solution to the surface-covering problem. First of all, actual geographic elevation data is adopted to conduct surface modeling. The model can vividly reflect the real terrain characteristics of the area to be deployed and make the deployment plan more visible and easy to adjust. Secondly, a probabilistic coverage model based on DEM (Digital Elevation Model) data is proposed. Based on the traditional spherical coverage model, the influence of signal attenuation and terrain occlusion on the coverage model is added to make the deployment model closer to reality. Finally, the Greedy algorithm based on grid scanning is used to deploy nodes. Simulation results show that the proposed algorithm can effectively improve the coverage rate, reduce the deployment cost, and reduce the time and space complexity in solving the WSN node deployment problem under the complex 3D land surface model, which verifies the effectiveness of the proposed algorithm.

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

ReSeq simulates realistic Illumina high-throughput sequencing data

In high-throughput sequencing data, performance comparisons between computational tools are essential for making informed decisions at each step of a project. Simulations are a critical part of method comparisons, but for standard Illumina sequencing of genomic DNA, they are often oversimplified, which leads to optimistic results for most tools. ReSeq improves the authenticity of synthetic data by extracting and reproducing key components from real data. Major advancements are the inclusion of systematic errors, a fragment-based coverage model and sampling-matrix estimates based on two-dimensional margins. These improvements lead to more faithful performance evaluations. ReSeq is available at https://github.com/schmeing/ReSeq.

Participant Training Improves Adherence with Reporting Timeframe for Momentary Subjective Experiences in Ecological Momentary Assessment (Preprint)

BACKGROUND Ecological Momentary Assessment (EMA) has the potential to minimize recall bias by having people report on their experiences in the moment (momentary model) or over short periods of time (coverage model). This potential hinges on the assumption that participants provide ratings based on the reporting timeframe instructions prescribed in the EMA items. However, it is unclear what timeframes participants are actually using when they answer EMA questions and whether participant training improves participants’ adherence to the reporting instructions. OBJECTIVE The objectives of this study are to investigate the reporting timeframes participants used when answering EMA questions and whether participant training improves participants’ adherence to the EMA reporting timeframe instructions. METHODS This study used telephone-based cognitive interviews to investigate this question. In a 2x2 factorial design, participants (n=100) were assigned to receive either basic or enhanced EMA training and also randomized to rate their experiences using a momentary (at the moment you were called) or coverage (since the last phone call) model. Participants received 5 calls over the course of one day to provide ratings; after each rating, participants were immediately interviewed about the timeframe that they used to answer the EMA questions. Two raters independently coded the momentary interview responses into timeframe categories (Cohen’s kappa = 0.64 (95%CI: 0.55-0.73)). RESULTS Results from the momentary conditions showed that most of the calls referred to the period during the call (28.6%) or just before the call (49.2%) to provide ratings; the remainder were from longer reporting periods. Multinomial logistic regression results indicated a significant training effect (χ2 (1, 199)=16.61, p<0.001), where the enhanced training condition yielded more reports within the intended reporting timeframes for momentary EMA reports. Cognitive interview data from the coverage model did not lend themselves to reliable coding and were not analyzed. CONCLUSIONS These findings provide the first evidence about adherence to EMA instructions to reporting periods, and that enhanced participant training improves adherence to the timeframe specified in momentary EMA studies.

Site Selection and Layout of Material Reserve Based on Emergency Demand Graduation under Large-Scale Earthquake

Reasonable siting layout of reserve emergency supplies plays a critical role in rapid response and accurate rescue after disaster. People’s life safety and health, as well as the psychological satisfaction brought by the government’s excellent emergency rescue level, is an important guarantee for maintaining social stability and sustainable development. Based on the coverage model, considering demand graduation, this paper develops a bi-objective optimization model to determine the optimal location plan of graduated supplies by maximizing the rescue satisfaction and minimizing the number of warehouses. A heuristic multi-center clustering location algorithm is designed to solve the model. This model is applied to the prepositioning of emergency supplies in an earthquake affected area in Sichuan province, China to verify the effectiveness of the model and algorithm. Finally, the paper discusses the influence of demand graduation on the location of emergency supplies. The results show that reasonable location planning of different levels of supplies can effectively improve the rescue satisfaction.