scholarly journals Compositional Closure—Its Origin Lies Not in Mathematics but Rather in Nature Itself

Minerals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Nicholas E. Pingitore ◽  
Mark A. Engle
Keyword(s):  
Open Systems ◽  
Natural World ◽  
Data Sets ◽  
Physical Systems ◽  
Inherent Property ◽  
Petrology And Geochemistry ◽  
Log Ratio ◽  
Mathematical Operations

Compositional closure, spurious negative correlations in data sets of a fixed sum (e.g., fractions and percent), is often encountered in geostatistical analyses, particularly in mineralogy, petrology, and geochemistry. Techniques to minimize the effects of closure (e.g., log-ratio transformations) can provide consistent geostatistical results. However, such approaches do not remove these effects because closure does not result from mathematical operations but is an inherent property of the physical systems under study. The natural world causes physical closure; mathematics simply describes that closure and cannot alter it by manipulations. Here, we examine the distinct types of geologic systems and samples to determine in which situations closure (physical and mathematical) does or does not ensue and the reasons therefor. We parse compositional systems based on (1) types of components under study, immutable (e.g., elements) or reactive (minerals), and (2) whether the system is open or closed to component transfer. Further, open systems can be (1) displacive in which addition of a component physically crowds out others, or (2) accommodative in which addition or subtraction of components does not affect the others. Only displacive systems are subject to compositional closure. Accommodative systems, even with components expressed as percent or fractions, are not closed physically or, therefore, mathematically.

THE QUANTUM TRAJECTORY APPROACH TO GEOMETRIC PHASE FOR OPEN SYSTEMS

2005 ◽  
Vol 20 (22) ◽  
pp. 1635-1654 ◽  
Author(s):  
ANGELO CAROLLO
Keyword(s):  
Quantum System ◽  
Open System ◽  
Geometric Phase ◽  
Open Systems ◽  
Quantum Trajectory ◽  
Operational Method ◽  
Markovian Approximation ◽  
Physical Systems ◽  
Quantum Jump ◽  
Trajectory Approach

The quantum jump method for the calculation of geometric phase is reviewed. This is an operational method to associate a geometric phase to the evolution of a quantum system subjected to decoherence in an open system. The method is general and can be applied to many different physical systems, within the Markovian approximation. As examples, two main source of decoherence are considered: dephasing and spontaneous decay. It is shown that the geometric phase is to very large extent insensitive to the former, i.e. it is independent of the number of jumps determined by the dephasing operator.

scholarly journals Assessing Global Covid-19 Cases Data through Compositional Data Analysis(CoDa)

2020 ◽  
Author(s):  
Luis P.V. Braga ◽  
Dina Feigenbaum
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
Discrete Groups ◽  
Data Sets ◽  
Cumulative Number ◽  
Governmental Agencies ◽  
Global Pandemic ◽  
Number Of Patients ◽  
Log Ratio

AbstractBackgroundCovid-19 cases data pose an enormous challenge to any analysis. The evaluation of such a global pandemic requires matching reports that follow different procedures and even overcoming some countries’ censorship that restricts publications.MethodsThis work proposes a methodology that could assist future studies. Compositional Data Analysis (CoDa) is proposed as the proper approach as Covid-19 cases data is compositional in nature. Under this methodology, for each country three attributes were selected: cumulative number of deaths (D); cumulative number of recovered patients(R); present number of patients (A).ResultsAfter the operation called closure, with c=1, a ternary diagram and Log-Ratio plots, as well as, compositional statistics are presented. Cluster analysis is then applied, splitting the countries into discrete groups.ConclusionsThis methodology can also be applied to other data sets such as countries, cities, provinces or districts in order to help authorities and governmental agencies to improve their actions to fight against a pandemic.

A straightforward feature selection method based on mean ratio for classifiers

2021 ◽  
pp. 1-12
Author(s):  
Emmanuel Tavares ◽  
Alisson Marques Silva ◽  
Gray Farias Moita ◽  
Rodrigo Tomas Nogueira Cardoso
Keyword(s):  
Feature Selection ◽  
Computational Cost ◽  
Feature Selection Method ◽  
Predictive Ability ◽  
Large Data ◽  
Research Area ◽  
Data Sets ◽  
Promising Alternative ◽  
The Mean ◽  
Mathematical Operations

Feature Selection (FS) is currently a very important and prominent research area. The focus of FS is to identify and to remove irrelevant and redundant features from large data sets in order to reduced processing time and to improve the predictive ability of the algorithms. Thus, this work presents a straightforward and efficient FS method based on the mean ratio of the attributes (features) associated with each class. The proposed filtering method, here called MRFS (Mean Ratio Feature Selection), has only equations with low computational cost and with basic mathematical operations such as addition, division, and comparison. Initially, in the MRFS method, the average from the data sets associated with the different outputs is computed for each attribute. Then, the calculation of the ratio between the averages extracted from each attribute is performed. Finally, the attributes are ordered based on the mean ratio, from the smallest to the largest value. The attributes that have the lowest values are more relevant to the classification algorithms. The proposed method is evaluated and compared with three state-of-the-art methods in classification using four classifiers and ten data sets. Computational experiments and their comparisons against other feature selection methods show that MRFS is accurate and that it is a promising alternative in classification tasks.

scholarly journals A Methodology for Network Analysis to Improve the Cyber-Physicals Communications in Next-Generation Networks

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2247
Author(s):  
David Cortés-Polo ◽  
Luis Ignacio Jimenez Gil ◽  
José-Luis González-Sánchez ◽  
Jesús Calle-Cancho
Keyword(s):  
Real Data ◽  
Data Sets ◽  
New Paradigm ◽  
Physical Systems ◽  
Network Information ◽  
First Case ◽  
Network Analytics ◽  
A New Technique ◽  
New Applications ◽  
Single Cluster

Cyber-physical systems allow creating new applications and services which will bring people, data, processes, and things together. The network is the backbone that interconnects this new paradigm, especially 5G networks that will expand the coverage, reduce the latency, and enhance the data rate. In this sense, network analytics will increase the knowledge about the network and its interconnected devices, being a key feature especially with the increment in the number of physical things (sensors, actuators, smartphones, tablets, and so on). With this increment, the usage of online networking services and applications will grow, and network operators require to detect and analyze all issues related to the network. In this article, a methodology to analyze real network information provided by a network operator and acquire knowledge of the communications is presented. Various real data sets, provided by Telecom Italia, are analyzed to compare two different zones: one located in the urban area of Milan, Italy, and its surroundings, and the second in the province of Trento, Italy. These data sets describe different areas and shapes that cover a metropolitan area in the first case and a mainly rural area in the second case, which implies that these areas will have different comportments. To compare these comportments and group them in a single cluster set, a new technique is presented in this paper to establish a relationship between them and reduce those that could be similar.

scholarly journals Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data

2020 ◽  
Author(s):  
Thomas P. Quinn ◽  
Ionas Erb
Keyword(s):  
Dimension Reduction ◽  
Domain Knowledge ◽  
Compositional Data ◽  
Real Data ◽  
R Package ◽  
Data Driven ◽  
Alternative Methods ◽  
Compositional Data Analysis ◽  
Data Sets ◽  
Log Ratio

AbstractIn the health sciences, many data sets produced by next-generation sequencing (NGS) only contain relative information because of biological and technical factors that limit the total number of nucleotides observed for a given sample. As mutually dependent elements, it is not possible to interpret any component in isolation, at least without normalization. The field of compositional data analysis (CoDA) has emerged with alternative methods for relative data based on log-ratio transforms. However, NGS data often contain many more features than samples, and thus require creative new ways to reduce the dimensionality of the data without sacrificing interpretability. The summation of parts, called amalgamation, is a practical way of reducing dimensionality, but can introduce a non-linear distortion to the data. We exploit this non-linearity to propose a powerful yet interpretable dimension reduction method. In this report, we present data-driven amalgamation as a new method and conceptual framework for reducing the dimensionality of compositional data. Unlike expert-driven amalgamation which requires prior domain knowledge, our data-driven amalgamation method uses a genetic algorithm to answer the question, “What is the best way to amalgamate the data to achieve the user-defined objective?”. We present a user-friendly R package, called amalgam, that can quickly find the optimal amalgamation to (a) preserve the distance between samples, or (b) classify samples as diseased or not. Our benchmark on 13 real data sets confirm that these amalgamations compete with the state-of-the-art unsupervised and supervised dimension reduction methods in terms of performance, but result in new variables that are much easier to understand: they are groups of features added together.

scholarly journals Transparency-by-design as a foundation for open government

2017 ◽  
Vol 11 (1) ◽  
pp. 2-8 ◽  
Author(s):  
Marijn Janssen ◽  
Ricardo Matheus ◽  
Justin Longo ◽  
Vishanth Weerakkody
Keyword(s):  
Open Systems ◽  
System Development ◽  
Open Government ◽  
Data Sets ◽  
Content Type ◽  
The Public ◽  
Design Concepts ◽  
Intuitive Concept ◽  
New Research ◽  
Public Collaboration

Purpose Many governments are working toward a vision of government-wide transformation that strives to achieve an open, transparent and accountable government while providing responsive services. The purpose of this paper is to clarify the concept of transparency-by-design to advance open government. Design/methodology/approach The opening of data, the deployment of tools and instruments to engage the public, collaboration among public organizations and between governments and the public are important drivers for open government. The authors review transparency-by-design concepts. Findings To successfully achieve open government, fundamental changes in practice and new research on governments as open systems are needed. In particular, the creation of “transparency-by-design” is a key aspect in which transparency is a key system development requirement, and the systems ensure that data are disclosed to the public for creating transparency. Research limitations/implications Although transparency-by-design is an intuitive concept, more research is needed in what constitutes information and communication technology-mediated transparency and how it can be realized. Practical implications Governments should embrace transparency-by-design to open more data sets and come closer to achieving open government. Originality/value Transparency-by-design is a new concept that has not given any attention yet in the literature.

SMDI: An Index for Measuring Subgingival Microbial Dysbiosis

2021 ◽  
pp. 002203452110357
Author(s):  
T. Chen ◽  
P.D. Marsh ◽  
N.N. Al-Hebshi
Keyword(s):  
Test Data ◽  
Training Data ◽  
Response To Treatment ◽  
Data Sets ◽  
Sequencing Data ◽  
Characteristic Analysis ◽  
Data Set ◽  
Microbial Dysbiosis ◽  
Log Ratio

An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival microbial dysbiosis index (SMDI) based on machine learning analysis of published periodontitis/health 16S microbiome data. The raw sequencing data, split into training and test sets, were quality filtered, taxonomically assigned to the species level, and centered log-ratio transformed. The training data set was subject to random forest analysis to identify discriminating species (DS) between periodontitis and health. DS lists, compiled by various “Gini” importance score cutoffs, were used to compute the SMDI for samples in the training and test data sets as the mean centered log-ratio abundance of periodontitis-associated species subtracted by that of health-associated ones. Diagnostic accuracy was assessed with receiver operating characteristic analysis. An SMDI based on 49 DS provided the highest accuracy with areas under the curve of 0.96 and 0.92 in the training and test data sets, respectively, and ranged from −6 (most normobiotic) to 5 (most dysbiotic) with a value around zero discriminating most of the periodontitis and healthy samples. The top periodontitis-associated DS were Treponema denticola, Mogibacterium timidum, Fretibacterium spp., and Tannerella forsythia, while Actinomyces naeslundii and Streptococcus sanguinis were the top health-associated DS. The index was highly reproducible by hypervariable region. Applying the index to additional test data sets in which nitrate had been used to modulate the microbiome demonstrated that nitrate has dysbiosis-lowering properties in vitro and in vivo. Finally, 3 genera ( Treponema, Fretibacterium, and Actinomyces) were identified that could be used for calculation of a simplified SMDI with comparable accuracy. In conclusion, we have developed a nonbiased, reproducible, and easy-to-interpret index that can be used to identify patients/sites at risk of periodontitis, to assess the microbial response to treatment, and, importantly, as a quantitative tool in microbiome modulation studies.

scholarly journals Global areas of low human impact (‘Low Impact Areas’) and fragmentation of the natural world

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrew P. Jacobson ◽  
Jason Riggio ◽  
Alexander M. Tait ◽  
Jonathan E. M. Baillie
Keyword(s):  
Habitat Fragmentation ◽  
Human Impact ◽  
Habitat Loss ◽  
Protected Area ◽  
Human Impacts ◽  
Natural World ◽  
Primary Response ◽  
Natural State ◽  
Data Sets ◽  
Critical Question

Abstract Habitat loss and fragmentation due to human activities is the leading cause of the loss of biodiversity and ecosystem services. Protected areas are the primary response to this challenge and are the cornerstone of biodiversity conservation efforts. Roughly 15% of land is currently protected although there is momentum to dramatically raise protected area targets towards 50%. But, how much land remains in a natural state? We answer this critical question by using open-access, frequently updated data sets on terrestrial human impacts to create a new categorical map of global human influence (‘Low Impact Areas’) at a 1 km2 resolution. We found that 56% of the terrestrial surface, minus permanent ice and snow, currently has low human impact. This suggests that increased protected area targets could be met in areas minimally impacted by people, although there is substantial variation across ecoregions and biomes. While habitat loss is well documented, habitat fragmentation and differences in fragmentation rates between biomes has received little attention. Low Impact Areas uniquely enabled us to calculate global fragmentation rates across biomes, and we compared these to an idealized globe with no human-caused fragmentation. The land in Low Impact Areas is heavily fragmented, compromised by reduced patch size and core area, and exposed to edge effects. Tropical dry forests and temperate grasslands are the world’s most impacted biomes. We demonstrate that when habitat fragmentation is considered in addition to habitat loss, the world’s species, ecosystems and associated services are in worse condition than previously reported.

scholarly journals IUCN World Heritage Outlook 3

2020 ◽  
Author(s):  
Elena Osipova ◽  
Matthew Emslie-Smith ◽  
Matea Osti ◽  
Mizuki Murai ◽  
Ulrika Åberg ◽  
...  
Keyword(s):  
World Heritage ◽  
Natural World ◽  
Data Sets ◽  
World Heritage Sites ◽  
Heritage Sites ◽  
Global Biodiversity ◽  
High Standards ◽  
Litmus Test ◽  
Positive Outlook ◽  
Natural World Heritage Sites

IUCN World Heritage Outlook 3 builds on three cycles of Conservation Outlook Assessments undertaken since 2014. It presents the main results for 2020, but also some longer-term trends based on a comparison of three data sets now available. As such, it can ser ve as an indicator of the effectiveness of protected and conserved areas at a time when the international community seeks to measure progress towards global biodiversity targets and defines the Post-2020 Global Biodiversity Framework. Focusing on the natural values for which sites are inscribed, threats to these values, and the effectiveness of actions to protect them, the IUCN World Heritage Outlook assesses the conservation prospects of all natural World Heritage sites. These sites are globally recognised as the most significant natural areas on Earth and their conservation must meet the high standards of the World Heritage Convention. Our ability to conserve these sites is thus a litmus test for the broader success of conservation worldwide. Securing a positive outlook for these sites is a priority, as expressed in the Promise of Sydney carried by IUCN’s World Parks Congress in 2014.

scholarly journals Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6807
Author(s):  
Yong Xie ◽  
Yili Guo ◽  
Sheng Yang ◽  
Jian Zhou ◽  
Xiaobai Chen
Keyword(s):  
Heuristic Algorithm ◽  
Design Space Exploration ◽  
State Of The Art ◽  
Real Data ◽  
Cyber Physical Systems ◽  
Data Sets ◽  
Efficient Design ◽  
Delay Constraint ◽  
Hardware Cost ◽  
Physical Systems

The introduction of various networks into automotive cyber-physical systems (ACPS) brings great challenges on security protection of ACPS functions, the auto industry recommends to adopt the hardware security module (HSM)-based multicore ECU to secure in-vehicle networks while meeting the delay constraint. However, this approach incurs significant hardware cost. Consequently, this paper aims to reduce security enhancing-related hardware cost by proposing two efficient design space exploration (DSE) algorithms, namely, stepwise decreasing-based heuristic algorithm (SDH) and interference balancing-based heuristic algorithm (IBH), which explore the task assignment, task scheduling, and message scheduling to minimize the number of required HSMs. Experiments on both synthetical and real data sets show that the proposed SDH and IBH are superior than state-of-the-art algorithm, and the advantage of SDH and IBH becomes more obvious as the increase about the percentage of security-critical tasks. For synthetic data sets, the hardware cost can be reduced by 61.4% and 45.6% averagely for IBH and SDH, respectively; for real data sets, the hardware cost can be reduced by 64.3% and 54.4% on average for IBH and SDH, respectively. Furthermore, IBH is better than SDH in most cases, and the runtime of IBH is two or three orders of magnitude smaller than SDH and state-of-the-art algorithm.

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