Use Of Intensity Data Research Articles

Features of systems implementing data intensive domains as objects for ensuring information security using the example of automated control systems for agrotechnological processes

The current stage of development of Russian society is characterized by digital transformation of all its spheres, including economics, science, healthcare, education, culture, etc. One of the areas of such transformation is the widespread use of systems that implement data intensive domain (DID), as well as artificial intelligence technologies and systems. The increasing use of these systems and their operation as part of global networks entails information security risks that can negatively impact individuals, groups, organizations, economic sectors and society as a whole. Methods for ensuring information security require development, considering the specifics of these systems and technologies. Features of ensuring information security are considered using the example of an automated control system for agrotechnological processes in agriculture as a system that implements IID. Features of DID systems as objects of ensuring information security and factors influencing the provision of their information security and the creation of means of ensuring information security for DID are determined on the basis of the country's current regulatory and technical framework in the field of information security and taking into account the specifics of the organization of data processing and storage and application of DID systems. The identified features can be used in the development of systems that implement intensive use of data, as well as in the educational process.

Constraining earthquake fault sources through the use of intensity data and seismic scenarios: application to the Betic Cordillera (South Spain)

Macroseismic observations can be useful to study pre-instrumental earthquakes when paleoseismological analysis are not viable for various reasons (e.g., erosion or lack of sedimentation). The analysis of the distribution of the macroseismic intensity data points and the reports about geological effects has been shown as a tool that may provide meaningful information to localize the fault source of a historical earthquake. Using this approach, we have studied two earthquakes in the Betic Cordillera (South Spain), the 1804 Dalías and the 1680 Málaga events, and we have used the 2011 Lorca earthquake as a test subject to calibrate the methodologies. During the calibration process, we also find the best performing combination of ground-motion models and ground-motion-to-intensity-conversion equations for generating seismic scenarios in this area. Even though the results for the Málaga earthquake were not conclusive, our methodology successfully identified the most likely source for the Dalías earthquake: a conjunct rupture of the Loma del Viento and Llano del Águila Faults.

Capitalismo de plataforma e tipologia constitucional das plataformas digitais: entre mercados, praças públicas, anfiteatros e estradas

This text deals with a proposed classification of digital platforms, based on a constitutional typology and represented by the archetypes of the “Market”, the “Public Square”, the “Amphitheatre” and the “Roads”. The article begins by analyzing the heuristics for understanding the digital age (speed, complexity, ambivalence, and uncertainty). In sequence, the industrial and communication revolutions are covered, until the advent of the digital age. Furthermore, it is demonstrated that technological progress is not a simple deterministic march but is also subject to political and social choices. It continues with the exposition of the evolution of the concept of platform, its main characteristics (network effects, non-neutrality of prices, scalability at “almost zero” marginal cost, intensive use of data and algorithms, and tendency to concentration in quasi-natural monopolies). Moreover, the current concept of the multilateral digital platform is presented, as well as its main functions (coordination, governance, and dispute resolution). The article ends with an analysis of the main classifications already presented for digital platforms, as well as proposing the outline of a new classification, based on the aforementioned constitutional typology.

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Digital Services Tax: Challenge of International Cooperation For Harmonization

The spread of the digital economy brings about many benefits, for example in terms of growth, employment and well-being more generally. These challenges extend well beyond domestic and international tax policy and touch upon areas such as international privacy law and data protection, as well as accounting and regulation, a strategic tax policy perspective, the uptake of digital technologies may potentially constrain the options available to policymakers in relation to the overall tax mix. Technological innovation and the growth of the Internet have profoundly affected trade relations, production processes and products, and the organization of companies. The digital economy is characterized by several key elements, such as the massive expansion of intangible assets; intensive use of data (especially personal data), and widespread adoption of multi-faceted business models that exploit the value externalities of free assets. As the digital economy brings us into new territory, is fair taxation fiction or reality? Although overcoming the numerous ch a l l en g es will be a difficult task for policy makers , fair taxation of the digital e c o n o m y is a reality. A prerequisite to making real sustainable change is enthusiastic country commitment and international cooperation. Asia and the Pacific region need to pull together and push forward the necessary reforms, innovate tax structures and administration, and continue to learn from each other’s experiences. Other key enablers include a coherent strategy and strong evidence-based communication and knowledge.

Ethical Dimensions of the GDPR, AI Regulation, and Beyond

Our digital society is changing rapidly, with emerging new technologies such as artificial intelligence (AI) and machine learning, robotics, and the internet of things. These changes trigger new fundamental ethical questions relating to privacy, data protection and other values, including human rights and the way they are affected by the extensive and intensive use of data for analytical and practical innovations. This article explores these ethical dimensions and the extent to which the European Union’s General Data Protection Regulation (GDPR) of 2018 takes ethics into account in relation to these socio-technical developments. More briefly, it looks similarly but more selectively at the EU’s proposed AI Act of 2021, which aims to regulate AI in relation to levels of risk.It concludes with some observations on desirable institutional arrangements for making and applying ethical judgements in the regulation of advanced technologies that use personal data.

Six-Sigma Quality Management of Additive Manufacturing.

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

V International Conference «Information Technologies in Earth Sciences and Applications for Geology, Mining And Economy. Ites&Mp-2019»

The materials presented at the Conference describe the results of recent years in the following areas: Open access to scientific data and knowledge in Earth Sciences; Data peculiarities in Earth Sciences: new concepts and methods, tools for their collection, integration and processing in different information systems, including systems with intensive use of data; Data mining and mathematical simulation of natural processes in Earth Sciences. Evolution of classical GIS-applications in Earth Sciences; Application to Critical Raw Materials (CRM); social aspects of mining (e.g., the Social Licence to Operate [SLO]); predictive mapping and applications to exploration, landuse and search for extensions of known deposits; Intelligent data analysis, elicitation of facts and knowledge from scientific publications. Thesauruses, ontologies and conceptual modeling. Semantic WEB, linked data. Services. Content semantic structuring. Applications for geosciences, e.g., Ontology-based Dynamic Decision Graphs for Expert systems and decision-aid tools; Application of methods and technologies of the remote sensing in Earth Sciences: from satellites to unmanned aerial vehicles; Information technologies for demonstration and popularization of scientific achievements in Earth Sciences; Applications: environmental risks including mining wastes, natural hazards, water resource management, etc.

ANÁLISE DO SISTEMA DE CURRÍCULO LATTES SEGUNDO O MODELO CESM: PERSPECTIVAS PARA UM SISTEMA DE INFORMAÇÃO PARA A E-SCIENCE

The emerging e-science, with its intensive use of data and computing, is an opportunity for the Lattes Curriculum System, which exceeds six million curricula at 20 years of existence. Seizing the opportunity requires knowledge and mobilization of the actors and human and technological factors involved, as well as their bonds. In that sense, the objective of this article is to describe the Lattes Curriculum System according to the composition-environment-structure-mechanism system metamodel (or CESM, by Mario Bunge) that accounts for those actors, factors, and bonds. The procedures include the first two of the general methodological rules of Bunge's systemic research: placing social facts in their broader context (or system) and dividing the system into its composition, environment, and structure. Information sources for model abstraction include texts from the scientific literature on the Lattes Platform and Curriculum System, as well as articles that cite Lattes curricula as information sources, and official web pages on the Lattes Platform. The resulting model is presented in graphic and in textual form. We discuss the potential use of the model in informing the modernization plan of the Lattes Platform. We conclude by drawing on those potentialities and outlining aspects of the social control mechanism, which, as the study of other mechanisms of this system, is an indication for future work.

Teachers’ Use of Assessment Data in Secondary Schools in Kenya: Opportunities and Threats

Governments around the world are pushing educators to adopt assessment data as an objective basis for initiating changes in curriculum and instruction. Recent research findings indicate that teachers' effective and consistent use of assessment data in planning curriculum, implementation and monitoring of teaching improves the learner's overall academic achievement. This paper is a product of a desktop review of the limited cognate literature present in Kenya on the utility of assessment data for decision making in secondary schools. Although findings reveal that Kenyan teachers use assessment data to among others; identify weak learners for individual remediation programs, inform parents of their children’s academic progress and for career selection, the instructor’s intensive use of data in classrooms to drive meaningful positive change faces pervasive bottlenecks. Teachers need to exploit the available opportunities created by the government, such as in-service training on information communications technology, to enhance the use of assessment data to promote learning.

Towards smart farming: Systems, frameworks and exploitation of multiple sources

Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status.This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture.

A Comparison of three Fluorophores in Langmuir Monolayers

Phase separated monolayers regularly utilize fluorescent probes as contrast agents to image two phase systems at the air-water interface. Langmuir monolayers have long been a model system for the lipid packing in individual leaflets. Imaging techniques are often used to study micron scale domain morphology as a simple binary system. However, fluorescence intensity likely offers further insights into monolayer packing and lipid environments. Monolayer fluorescence intensities containing DMPC with varying concentrations of Texas Red DHPE, 16:0 NBD PE, and 16:0-06:0 NBD PE have been measured while under compression. Experimental data is compared to idealized models of concentration vs. fluorescence intensity. Pressures-area isotherms will provide complementary information to the fluorescence intensity and packing density. The use of intensity data within monolayer studies offers the potential to offer new experimental approaches to traditional monolayer imaging studies.

Nurses in the Kanban: are there news meanings of professional practice in innovative tools for hospital care management?

Kanban is a care management tool that values multi-professional work and intensive use of data and has been growingly used in Brazil to address overcrowding in hospital emergency services (HES). The researchers monitored the Kanban for ten months in multiple wards of a municipal HES, and their observations were recorded in field diaries and discussed in biweekly research team meetings. The empirical material was organized from two questions: Are there changes in "traditional attributions" of Kanban-operating nursing? Are Medicine-Nursing interprofessional relationships transformed? A strong nurse adherence to this tool was observed, coupled with greater specialization and fragmentation of their work: nurses working as diarists assume more traditional administrative functions, while those on-call develop more direct assistance to patients. Nurses consider that clinical decisions are still in the doctors' hands, although Kanban provides them with a stronger influence on such decisions. Nurses' role in the management of significant mass of clinical and operational data, central to Kanban's operationalization, strengthens their professional authority.

METHODS FROM INFORMATION EXTRACTION FROM LIDAR INTENSITY DATA AND MULTISPECTRAL LIDAR TECHNOLOGY

Abstract. LiDAR is a consolidated technology for topographic mapping and 3D reconstruction, which is implemented in several platforms On the other hand, the exploitation of the geometric information has been coupled by the use of laser intensity, which may provide additional data for multiple purposes. This option has been emphasized by the availability of sensors working on different wavelength, thus able to provide additional information for classification of surfaces and objects. Several applications ofmonochromatic and multi-spectral LiDAR data have been already developed in different fields: geosciences, agriculture, forestry, building and cultural heritage. The use of intensity data to extract measures of point cloud quality has been also developed. The paper would like to give an overview on the state-of-the-art of these techniques, and to present the modern technologies for the acquisition of multispectral LiDAR data. In addition, the ISPRS WG III/5 on ‘Information Extraction from LiDAR Intensity Data’ has collected and made available a few open data sets to support scholars to do research on this field. This service is presented and data sets delivered so far as are described.

Big Data: Reflexões Epistemológicas e Impactos nos Estudos de Finanças e Mercado de Capitais

Objetivo e método: O acesso a séries de dados tem um papel central na área de Finanças. A crescente disponibilidade de grandes volumes de dados, em diferentes formatos e em alta frequência, combinada aos avanços tecnológicos nas ferramentas de armazenamento e processamento desses dados, têm criado um novo cenário nas pesquisas acadêmicas em geral, e em Finanças em particular, gerando novas oportunidade e desafios. Entre esses desafios emergem questões metodológicas, vastamente discutidas por pesquisadores de diferentes áreas, mas também questões epistemológicas que merecem maior espaço de discussão. Assim, o objetivo deste ensaio teórico é analisar o aspecto conceitual e epistemológico da utilização de dados intensivos e seus reflexos para a área de Finanças.
 
 Resultados e contribuições: Consideramos que o método hipotético-dedutivo de pesquisas empíricas, que é o mais recorrente, limita a construção do conhecimento na dita ‘era Big data’, uma vez que tal abordagem parte de uma teoria estabelecida e restringe as pesquisas ao teste à(s) hipótese(s) proposta(s). Defendemos aqui a apropriação de uma abordagem abdutiva, como defendida em Haig (2005), que tem convergência com as ideias da grounded theorye que parece ser a abordagem mais adequada para esse novo contexto, por possibilitar a ampliação da capacidade de se obter informações de valor dos dados.

Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise

ABSTRACTAirborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose. One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region. Though recent researches have proposed physical and empirical approaches for intensity data correction, the effect of striping noise has not yet been resolved. This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap. The normalization technique is built based on a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The proposed method was tested with two individual LiDAR datasets collected by Teledyne Optech's Gemini (1064 nm) and Orion (1550 nm) sensors. The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification. The coefficient of variation of five selected land cover features was reduced by 19–65%, where a 9–18% accuracy improvement was achieved in different classification scenarios. With the proven capability of the proposed method, both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.

Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods

The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this paper, we study the range and incidence angle effects on the intensity measurements and search for practical correction methods for different TLS instruments and targets. We find that the range (distance) effect is strongly dominated by instrumental factors, whereas the incidence angle effect is mainly caused by the target surface properties. Correction for both effects is possible, but more studies are needed for physical interpretation and more efficient use of intensity data for target characterization.

E-Science and Data Management Resources on the Web

The way research is conducted has changed over time, from simple experiments to computer modeling and simulation, from individuals working in isolated laboratories to global networks of researchers collaborating on a single topic. Often, this new paradigm results in the generation of staggering amounts of data. The intensive use of data and the existence of networks of researchers characterize e-Science. The role of libraries and librarians in e-Science has been a topic of interest for some time now. This column looks at tools, resources, and projects that demonstrate successful collaborations between libraries and researchers in e-Science.

Terrestrial laser scanning intensity data applied to damage detection for historical buildings

This paper presents a methodology to combine the technology of the terrestrial laser scanner with the techniques of digital image processing in order to study damages on stony materials that constitute historical buildings. This methodology has been tested using intensity data from three terrestrial laser scanners with different technical specifications. The unsupervised classification algorithms were used for the classification of 2D intensity images derived from the 3D information obtained from laser scanner equipment. The results obtained show the potential of the use of intensity data from terrestrial laser scanner for the recognition and characterization of certain pathologies in building materials that constitute historical buildings.

Fundamental aspects of energy conservation policy

The formulation of a governmental energy conservation policy requires that the issues involved be fundamentally analyzed. Information transfer, more intensive use of data, and good housekeeping can all contribute to reduced energy use. Our major choice, however, is between producing the present mix of materials, commodities, and services more efficiently or decreasing demand for them. The first option is referred to as the “technical fix”, the second one as “change of lifestyle”. If the first option fails, changes in life-style might become mandatory. This paper deals with the technical fix approach. Higher capital investment can lead to a decrease in direct use of energy. Both the cost and the energy involved in these investments are analyzed along a conservation path, and a limited number of constants is used to describe the changes along this path. These constants can also be used to feed technological information into macroeconomic analysis. The time scales involved pose the major problem to achieving conservation by means of a technological fix. An increase in the price of energy will lead to higher capital investments in accordance with the economic lifecycles in the different sectors. For applications with a short life-time, such as in the transport sector, energy conservation will mainly take place through the construction of new equipment. In sectors with long lifetime investments (e.g. buildings), retrofitting will be important. Including the indirect energy requirements in the conservation study leads to some important conclusions. It can be demonstrated that the thermodynamic limit is not the ultimate limit for conservation. An energy minimum is obtained, which corresponds to a use of energy higher than the thermodynamic limit. It is also impossible to reduce or eliminate some components of the present energy supply system and at the same time to introduce a new decentralized supply system. A crucial aspect of the technological fix approach is that within the present rules, capital investments for saving direct energy can only be made after the increase in energy price has taken place or when it can be firmly anticipated on a short term. At this point, however, the time needed for making these capital investments is lacking and adverse economic consequences can be expected. It is therefore the major task of governmental policy to induce conservation before it is economically acceptable or possible. This requires some form of government interaction. The theory developed in this paper provides a method by which to rank the options according to the energy saved per dollar of public funds invested. This measures the difference between the objectives of the private sector and those of society. The longer this policy is postponed, the larger is the risk that conservation will have to be achieved in an emergency program. In that situation the indirect energy necessary for the capital construction might jeopardize the short-term goals.