scholarly journals Algorithmic Approaches for Assessing Irreversibility in Time Series: Review and Comparison

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1474
Author(s):  
Massimiliano Zanin ◽  
David Papo
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Open Source Software ◽  
Research Community ◽  
Future Research ◽  
World Systems ◽  
Software Library ◽  
Comparative Performance ◽  
Irreversible Dynamics ◽  
Algorithmic Approaches

The assessment of time irreversibility, i.e., of the lack of invariance of the statistical properties of a system under the operation of time reversal, is a topic steadily gaining attention within the research community. Irreversible dynamics have been found in many real-world systems, with alterations being connected to, for instance, pathologies in the human brain, heart and gait, or to inefficiencies in financial markets. Assessing irreversibility in time series is not an easy task, due to its many aetiologies and to the different ways it manifests in data. It is thus not surprising that several numerical methods have been proposed in the last decades, based on different principles and with different applications in mind. In this contribution we review the most important algorithmic solutions that have been proposed to test the irreversibility of time series, their underlying hypotheses, computational and practical limitations, and their comparative performance. We further provide an open-source software library that includes all tests here considered. As a final point, we show that “one size does not fit all”, as tests yield complementary, and sometimes conflicting views to the problem; and discuss some future research avenues.

SME’s Performance Through Comparative Performance Indicators, Measured by Business Statistics- Albania Case

2017 ◽  
Vol 9 (1) ◽  
pp. 361
Author(s):  
Godiva Rembeci
Keyword(s):  
Economic Performance ◽  
National Economy ◽  
Performance Indicators ◽  
Future Research ◽  
Productivity Measurement ◽  
Comparative Performance ◽  
Global Consensus ◽  
Research Activities ◽  
Business Statistics ◽  
National Economies

Now days there is a global consensus among all stakeholders that SMEs represent a driving force to the overall economic development, due to their significant contribution both on GDP and employment of national economies. SMEs also by numbers dominate the world business stage, although their contribution does vary among the countries. SMEs in Albania represents about 98% of the total enterprises with a contribution to national GDP for about 70%. The structure and the performance of national economy is depended very much on the economic performance and contribution of SME, that’s why most of the governments have strategic programmes which support the SME’s development. To measure SMEs’ performance and their ability to compete on national and international markets requires a lot of information in all aspects. Through this paper the author aims to measure and analyze the economic performance of SMEs operating in Albania. To achieve this objective, official data on business statistics published by national the statistical office (INSTAT) are used for two years period 2014-2015. In addition using an international framework addressed to the objective “improve the techniques for SMEs productivity measurement”, for the first time, a set of comparative performance indicators is established and in doing so, those results can be used as term of reference in future research activities in SMEs sector. From the results it came out that although the positive growth rate of GDP during the last years , the performance indicators of SMEs show a slightly negative trend, indicating indirectly the need for support, in order to empower their contribution in national economy.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals First record of the occurrence of sea ice in the Cordillera Darwin fjords (54°S), Chile

2021 ◽  
pp. 1-11
Author(s):  
Charles Salame ◽  
Inti Gonzalez ◽  
Rodrigo Gomez-Fell ◽  
Ricardo Jaña ◽  
Jorge Arigony-Neto
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
First Record ◽  
Ice Formation ◽  
Future Research ◽  
Landsat 8 ◽  
Surface Salinity ◽  
Air Temperatures ◽  
Aerial Reconnaissance ◽  
High Precipitation

Abstract This paper provides the first evidence for sea-ice formation in the Cordillera Darwin (CD) fjords in southern Chile, which is farther north than sea ice has previously been reported for the Southern Hemisphere. Initially observed from a passenger plane in September 2015, the presence of sea ice was then confirmed by aerial reconnaissance and subsequently identified in satellite imagery. A time series of Sentinel-1 and Landsat-8 images during austral winter 2015 was used to examine the chronology of sea-ice formation in the Cuevas fjord. A longer time series of imagery across the CD was analyzed from 2000 to 2017 and revealed that sea ice had formed in each of the 13 fjords during at least one winter and was present in some fjords during a majority of the years. Sea ice is more common in the northern end of the CD, compared to the south where sea ice is not typically present. Is suggested that surface freshening from melting glaciers and high precipitation reduces surface salinity and promotes sea-ice formation within the semi-enclosed fjord system during prolonged periods of cold air temperatures. This is a unique set of initial observations that identify questions for future research in this remote area.

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

scholarly journals Mesophotic.org: a repository for scientific information on mesophotic ecosystems

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Pim Bongaerts ◽  
Gonzalo Perez-Rosales ◽  
Veronica Z Radice ◽  
Gal Eyal ◽  
Andrea Gori ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Information ◽  
Temporal Trends ◽  
Light Availability ◽  
Research Community ◽  
Future Research ◽  
Photosynthetic Organisms ◽  
Future Research Directions ◽  
Available Information ◽  
Reduced Light

Abstract Mesophotic coral ecosystems (MCEs) and temperate mesophotic ecosystems (TMEs) occur at depths of roughly 30–150 m depth and are characterized by the presence of photosynthetic organisms despite reduced light availability. Exploration of these ecosystems dates back several decades, but our knowledge remained extremely limited until about a decade ago, when a renewed interest resulted in the establishment of a rapidly growing research community. Here, we present the ‘mesophotic.org’ database, a comprehensive and curated repository of scientific literature on mesophotic ecosystems. Through both manually curated and automatically extracted metadata, the repository facilitates rapid retrieval of available information about particular topics (e.g. taxa or geographic regions), exploration of spatial/temporal trends in research and identification of knowledge gaps. The repository can be queried to comprehensively obtain available data to address large-scale questions and guide future research directions. Overall, the ‘mesophotic.org’ repository provides an independent and open-source platform for the ever-growing research community working on MCEs and TMEs to collate and expedite our understanding of the occurrence, composition and functioning of these ecosystems. Database URL: http://mesophotic.org/

scholarly journals Value-added and Transparent Experiments

2021 ◽  
pp. 147892992110594
Author(s):  
Peter John Loewen ◽  
Daniel Rubenson
Keyword(s):  
Experimental Research ◽  
Large Range ◽  
Value Added ◽  
Research Community ◽  
Future Research ◽  
Political Elites ◽  
Important Work ◽  
Simple Rules ◽  
Combined Features ◽  
Public Scrutiny

Experimental research by political scientists on elites has grown dramatically in recent years. Experimenting on and with elites raises important questions, both practical and ethical. Elites are busy people, doing important work under public scrutiny. Therefore, any experiments that use up political elites’ time, risk impairing their ability to do their jobs as well as possible, or put at risk the larger research community’s access to elites should be avoided. Nevertheless, despite these risks and challenges, we argue experimenting with elites has enough benefits both to the research community and to elites themselves, that it should still be done. The relevant question then becomes how should we think about doing experiments with political elites? We propose a framework of value-added and transparent experiments. Our framework is guided by the following two simple rules: Elite subjects should individually benefit from the process of doing the experiment. It should add value to their role as representatives. Second, the identity of the researchers and purposes of the experiment should be transparent. As we argue, these two combined features can still accommodate a large range of experiments, can creatively spark researchers to think up new designs and can protect access to elites for future research. We review two such examples at the end of this essay.

scholarly journals AI-Enabled Sensing and Decision-Making for IoT Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hao Qinxia ◽  
Shah Nazir ◽  
Ma Li ◽  
Habib Ullah ◽  
Wang Lianlian ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Life ◽  
Research Community ◽  
Smart Sensors ◽  
Future Research ◽  
New Paradigm ◽  
Exclusion Criteria ◽  
Intelligent Devices ◽  
Research Questions

The influential stage of Internet of Things (IoT) has reformed all fields of life in general but specifically with the emergence of artificial intelligence (AI) has drawn the attention of researchers into a new paradigm of life standard. This revolution has been accepted around the globe for making life easier with the use of intelligent devices such as smart sensors, actuators, and many other devices. AI-enabled devices are more intelligent and capable of doing a specific task which saves a lot of resources and time. Different approaches are available in the existing literature to tackle diverse issues of real life based on AI and IoT systems. The role of decision-making has its own importance in the AI-enabled and IoT systems. In-depth knowledge of the existing literature is dire need of the research community to summarize the literature in effective way by which practitioners and researchers can benefit from the prevailing proofs and suggest new solutions for solving a particular problem of AI-enabled sensing and decision-making for the IoT system. To facilitate research community, the proposed study presents a systematic literature review of the existing literature, organizes the evidences in a systematic way, and then analyzes it for future research. The study reported the literature of the last 5 years based on the research questions, inclusion and exclusion criteria, and quality assessment of the selected study. Finally, derivations are drawn from the included paper for future research.

scholarly journals Graphical User Interface for Shuffling of Sections based on Student Profile

2021 ◽  
Vol 9 (VI) ◽  
pp. 2752-2757
Author(s):  
Kotawar Ashwitha
Keyword(s):  
Time Series ◽  
Data Structures ◽  
Graphical User Interface ◽  
Software Library ◽  
Male And Female ◽  
Data Manipulation ◽  
Student Profile ◽  
Python Language ◽  
Python Programming ◽  
Excel File

This project GUI for shuffling of sections is done to automate the hectic work of shuffling students into sections has been programed in python using open source module using pandas and tkinter the overall result achieved to this program is that students got shuffled into sections with same ratio of male and female in all section, and average of ranks of students of all sections are similar as to maintain equality and integrity. This program gives a GUI for the administrator to access the file with data of students stored to manipulate that data. In this project we will implement using python programming language .in python, we will use module pandas, TKinter. Pandas to manipulate data of students from an excel file through python program, TKinter is used to add GUI to the program to select the file to be manipulated pandas is a software library written for the python language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series In particular, it offers data structures and operations for manipulating numerical tables and time series. TKINTER is a software library for creating library for creating GUI using python language.

Analysis of Time Series Based on a New Entropy Plane by Using Weighted Dispersion Pattern

2021 ◽  
Vol 31 (09) ◽  
pp. 2150128
Author(s):  
Guyue Qin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Stock Markets ◽  
Simulated Data ◽  
Tsallis Entropy ◽  
Dispersion Pattern ◽  
Complex Time ◽  
Plane Method ◽  
Stock Indexes ◽  
Analysis Of Time Series

Complexity is an important feature of complex time series. In this paper, we construct a weighted dispersion pattern and propose a new entropy plane using past Tsallis entropy and past Rényi entropy by using weighted dispersion pattern (PTEWD and PREWD, respectively), to quantify the complexity of time series. Through analyzing simulated data and actual data, we have verified the reliability of the entropy plane method. This entropy plane successfully distinguishes American and Chinese stock indexes, as well as developed and emergent stock markets. We introduce PTEWD and PREWD into multiscale settings, which could also well distinguish different stock markets. The results show that the new entropy plane could be used as an effective tool to distinguish financial markets.

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