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Resilience is often treated as a single-dimension system attribute, or various dimensions of resilience are studied separately without considering multi-dimensionality. The increasing frequency of catastrophic natural or man-made disasters affecting rural areas demands holistic assessments of community vulnerability and assessment. Disproportionate effects of disasters on minorities, low-income, hard-to-reach, and vulnerable populations demand a community-oriented planning approach to address the “resilience divide.” Rural areas have many advantages, but low population density, coupled with dispersed infrastructures and community support networks, make these areas more affected by natural disasters. This paper will catalyze three key learnings from our current work in public librarians’ roles in disaster resiliency: 1) rural communities are composed of diverse sub-communities, each which experiences and responds to traumatic events differently, depending on micro-geographic and demographic drivers; 2) public libraries are central to rural life, providing a range of informational, educational, social, and personal services, especially in remote areas that lack reliable access to community resources during disasters; and 3) rural citizens tend to be very self-reliant and are committed to strengthening and sustaining community resiliency with local human capital and resources. Public libraries and their librarian leaders are often a “crown jewel” of rural areas’ community infrastructure and this paper will present a community-based design and assessment process for resiliency hubs located in and operated through rural public libraries. The core technical and social science research questions explored in the proposed paper are: 1) Who were the key beneficiaries and what did they need? 2) What was the process of designing a resiliency hub? 3) What did library resiliency hubs provide and how can they be sustained? This resiliency hub study will detail co-production of solutions and involves an inclusive collaboration among researchers, librarians, and community members to address the effects of cascading impacts of natural disasters. The novel co-design process detailed in the paper reflects 1) an in-depth understanding of the complex interactions among libraries, residents, governments, and other agencies by collecting sociotechnical hurricane-related data for Calhoun County, Florida, USA, a region devastated by Hurricane Michael (2018) and hard-hit by Covid-19; 2) analyzed data from newly-developed fusing algorithms and incorporating multiple communities; and 3) co-designed resiliency hubs sited in public libraries. This research leverages a unique opportunity for the co-development of integrated library-centered policies and technologies to establish a new paradigm for developing disaster resiliency in rural settings. Public libraries serve a diverse population who will directly benefit from practical support tailored to their needs. The project will inform efficient plans to ensure that high-need groups are not isolated in disasters. The knowledge and insight gained from disseminating the study’s results will not only improve our understanding of emergency response operations, but also will contribute to the development of new disaster-related policies and plans for public libraries, with a broader application to rural communities in many settings. 

A novel multivariate deep causal network model (MDCN) is proposed in this paper, which combines the theory of conditional variance and deep neural networks to identify the cause-effect relationship between different interdependent time-series. The MCDN validation is conducted by a double step approach. The self validation is performed by information theory - based metrics, and the cross validation is achieved by a foresting application that combines the actual interdependent electricity, transportation, and weather datasets in the City of Tallahassee, Florida, USA. 

Natural disasters have devastating effects on the infrastructure and disrupt every aspect of daily life in the regions they hit. To alleviate problems caused by these disasters, first an impact assessment is needed. As such, this paper focuses on a two-step methodology to identify the impact of Hurricane Hermine on the City of Tallahassee, the capital of Florida. The regional and socioeconomic variations in the Hermine’s impact were studied via spatially and statistically analyzing power outages. First step includes a spatial analysis to illustrate the magnitude of customers affected by power outages together with a clustering analysis. This step aims to determine whether the customers affected from outages are clustered or not. Second step involves a Bayesian spatial autoregressive model in order to identify the effects of several demographic-, socioeconomic-, and transportation-related variables on the magnitude of customers affected by power outages. Results showed that customers affected by outages are spatially clustered at particular regions rather than being dispersed. This indicates the need to pinpoint such vulnerable locations and develop strategies to reduce hurricane-induced disruptions. Furthermore, the increase in the magnitude of affected customers was found to be associated with several variables such as the power network and total generated trips as well as the demographic factors. The information gained from the findings of this study can assist emergency officials in identifying critical and/or less resilient regions, and determining those demographic and socioeconomic groups which were relatively more affected by the consequences of hurricanes than others. 

Abstract: Load forecasting plays a very crucial role in many aspects of electric power systems including the economic and social benefits. Previously, there have been many studies involving load forecasting using time series approach, including weather-load relationships. In one such approach to predict load, this paper investigates through different structures that aim to relate various daily parameters. These parameters include temperature, humidity and solar radiation that comprises the weather data. Along with natural phenomenon as weather, physical aspects such as traffic flow are also considered. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered. Electricity consumption data is collected from the City of Tallahassee utilities. Traffic count is provided by the Florida Department of Transportation. Moreover, the weather data is obtained from Tallahassee regional Airport weather station. This paper aims to study and establish a cause and effect relationship between the mentioned variables using different causality models and to forecast load based on the external variables. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered. 

A bstract A measurement of the inclusive jet production in proton-proton collisions at the LHC at $$ \sqrt{s} $$ s = 13 TeV is presented. The double-differential cross sections are measured as a function of the jet transverse momentum p T and the absolute jet rapidity |y| . The anti- k T clustering algorithm is used with distance parameter of 0.4 (0.7) in a phase space region with jet p T from 97 GeV up to 3.1 TeV and |y| < 2 . 0. Data collected with the CMS detector are used, corresponding to an integrated luminosity of 36.3 fb − 1 (33.5 fb − 1 ). The measurement is used in a comprehensive QCD analysis at next-to-next-to-leading order, which results in significant improvement in the accuracy of the parton distributions in the proton. Simultaneously, the value of the strong coupling constant at the Z boson mass is extracted as α S ( m Z ) = 0 . 1170 ± 0 . 0019. For the first time, these data are used in a standard model effective field theory analysis at next-to-leading order, where parton distributions and the QCD parameters are extracted simultaneously with imposed constraints on the Wilson coefficient c 1 of 4-quark contact interactions.