Integrated Satellite System for Fire Detection and Prioritization

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment.

Emerging Technologies and their Impact on the Future of Tourism and Hospitality Industry

The evolution in technology has become now a strategic choice to develop every organization and its existence in the future. The tourism industry is not an exception. This study highlights the development of technologies and the impact of their integration in the field of tourism. Furthermore, it discusses their influence on the quality of the touristic products. This study focuses on how does the emerging technology can improve the tourism industry, and the most usable information systems that are used in this domain. The purposed model has been designed to investigate the effect of adopting the technology among tourism agencies. A sample of 72 tourism agencies in Jordan has been surveyed and discussed by using structural equation modeling. The results reveal that efficiency, productivity, profitability, effectiveness, and marketing are improved after employing new technologies. Therefore, it can be concluded that the integration of technology in tourism is unavoidable for the continued existence of service providers in the market.

A Survey of Data Mining Activities in Distributed Systems

Distributed systems, which may be utilized to do computations, are being developed as a result of the fast growth of sharing resources. Data mining, which has a huge range of real applications, provides significant techniques for extracting meaningful and usable information from massive amounts of data. Traditional data mining methods, on the other hand, suppose that the data is gathered centrally, stored in memory, and is static. Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. This becomes increasingly costly to centralize them at a single location. Furthermore, traditional data mining methods typically have several issues and limitations, such as memory restrictions, limited processing ability, and insufficient hard drive space, among others. To overcome the following issues, distributed data mining's have emerged as a beneficial option in several applications According to several authors, this research provides a study of state-of-the-art distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection distributed data mining. Furthermore, each work is evaluated and compared to the others.

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS), which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy — i.e., failure, cause of failure, and recommendations — as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.

Survey of Process of Data Discovery and Environmental Decision Support Systems

The process of data discovery is an approach to extracting knowledge, valid, and usable information from large amounts of data, using automatic or semi-automatic methods. This article is an inventory of the different information extraction processes encountered in the literature for different fields of application and for the development of environmental informatics. Following an analysis between the different models, we can summarize the existing models with a proposal for a process that exploits the strengths of the different processes.

The Influence of Geomagnetic Storms on Calculating Magnetotelluric Impedance

Magnetotelluric (MT) field data contain natural electromagnetic signals and artificial noise sources (instrumental, anthropogenic, etc.). Not all available time-series data contain usable information about the electrical conductivity distribution at depth, particularly when the signal-to-noise ratio (SNR) is low. Geomagnetic storms represent temporary disturbances of the Earth's magnetosphere caused by solar wind-shock wave interacts with Earth's magnetic field. The variation of the electromagnetic signal increases dramatically in the presence of a strong geomagnetic storm. Using the data observed during a strong geomagnetic storm may overcome the locale noise and bring a reliable MT impedance at contaminated sites. Three case studies are presented to show the positive effect of geomagnetic storms on MT field data. A more reliable and interpretable impedance calculated from a survey line contaminated by strong noise is obtained using the data observed during a strong geomagnetic storm.

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

New affordable therapeutic protocols for COVID-19 are urgently needed despite the increasing number of effective vaccines and monoclonal antibodies. To this end, there is increasing attention towards computational methods for drug repositioning and de novo drug design. Here, we systematically integrated multiple data-driven computational approaches to perform virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the set of prioritized drugs, we selected a subset of representative candidates to test in human cells. Two compounds, 7-hydroxystaurosporine and bafetinib, showed synergistic antiviral effects in our in vitro experiments, and strongly inhibited viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, we extracted and prioritized the chemical substructures of the identified drugs, providing a chemical vocabulary that may help to design new effective drugs.

Deep neural networks as surrogate models for time-efficient manufacturing process optimisation

Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). Results reveal an improved predictive accuracy as more process-relevant information from the supplied simulations can be extracted. Application of the DNN in an SBO- framework for blank holder optimisation shows improved convergence compared to classical evolutionary algorithms. Thus, DNNs are a promising option for future surrogates in SBO.

Marketing for Nonprofit Organizations

Marketing for Nonprofit Organizations: Insights and Innovations, Second Edition, is a comprehensive overview of the marketing process written specifically for nonprofit and social impact organizations. This book covers topics important to nonprofit professionals: branding; target audience selection; strategy; promotional tactics, including social media; and marketing evaluation. The “Insights” sections are based primarily on academic research that has been published and now translated into usable information for marketing professionals. The “Innovations” sections highlight organizations that are doing things in a different way and topics that are relatively new to the field. This second edition includes many updated examples as well as new information on several topics such as social enterprise, design thinking, collective impact, and narratives in nonprofits. Readers will find an organized, easy-to-read overview of important considerations in marketing for new and established nonprofit organizations and foundations.

BiH Info-portals and Commentary Section During Info/Pan/Demic: Searching for Values

This paper deals with the analysis of the effects and perception of infoemia both on social networks and in the sphere of civic journalism during the second wave of the COVID-19 pandemic. Comparative analysis of the perception of the COVID-19 virus pandemic in developed democratic societies and its repercussions (to the audience above all) and comparison with the informational values of comments on the most visited online portals in Bosnia and Herzegovina are the essence of this work. In pursuing these goals, the work is divided into two parts. The first part of the paper analyzes the existential dissemination of information about the first and second wave of COVID-19 pandemics in the world with a focus on social networks and citizen writing – an audience about pandemics that has undoubtedly taken all the notes of infodemium where it is difficult to determine the information value of comments or writing prosumer. Web 2.0 as the dominant dissemination tool of all types of media content and the general approach and freedom of commenting have influenced the experience and – consecutive – perception of the available information. The informative cacophony affected the audience (which is also a generator of “informative” content) in several ways mentioned in the first part of the paper. The second part of the work deals exclusively with the analysis of the content of comments on several of the most visited informational portals of Bosnia and Herzegovina. Most comments have been found to lack usable information value and contribute to media cacophony, and from the point of view of the value of news usually do not provide useful information or at best provide information whose credibility (although they may seem plausible and/or argumentative) cannot be easily verified. The conclusion of the paper is that the information value of comments from visitors to bh information portals is not particularly high in the slightest regard from the content level with rare examples of (semi)quality information of usable content.