Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw
Important spreaders in networks: exact results on small graphsPetter Holme
To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.
Important spreaders in networks: Exact results for small graphsPetter Holme
The document discusses exact calculations of node importance in network epidemiology for small graphs. It examines three types of node importance: influence maximization, vaccination, and sentinel surveillance. The goal is to find the smallest graph where all three notions of importance differ for different nodes. Symbolic algebra and fast computational methods are used to efficiently calculate outbreak probabilities and expected times for all small graph topologies to identify cases where the important nodes are not the same.
Spreading processes on temporal networksPetter Holme
This document discusses temporal networks and how temporal structures can impact dynamical processes on networks. It begins by describing different types of temporal networks including person-to-person communication, information dissemination, physical proximity, and cellular biology networks. It then discusses methods for analyzing temporal network structures like inter-event times and how bursty or heavy-tailed distributions can slow spreading compared to memory-less processes. The document also presents examples of how neutralizing temporal structures like inter-event times or beginning/end times can impact spreading simulations. Finally, it discusses how different temporal network datasets exhibit diverse temporal structures.
Temporal Networks of Human InteractionPetter Holme
Temporal networks provide a framework for modeling systems of interactions that occur between nodes over time. These networks capture both the topological structure of connections as well as the timing of interactions. Three key aspects of temporal networks discussed in the document are:
1) Temporal networks can be represented using contact sequences that capture when interactions occur between nodes, unlike static networks which only represent connections.
2) The temporal structure of interactions, such as patterns in the timing of contacts, can impact dynamical processes unfolding on the network like information or disease spreading.
3) Randomizing the timing of contacts in empirical temporal network data can alter dynamical processes, highlighting the importance of temporal structure beyond just topology.
This document describes an adaptive model for the spread of infection on networks. It begins by introducing network analysis and percolation models. It then presents the basic SIS (Susceptible-Infected-Susceptible) model for modeling infection spread and derives the steady-state infection rates. It introduces the concept of network adaptation and reviews models of financial contagion that lack adaptation. It then presents a new popularity-based network model and develops an adaptive SIS model that incorporates network changes in response to infection spread. Computational analysis shows the existence of a phase boundary for this adaptive model.
Complex Networks Analysis @ Universita Roma TreMatteo Moci
This document discusses complex networks and their analysis. It provides a brief history of network analysis starting in the 18th century with Euler's work on the Seven Bridges of Königsberg problem. It then covers key topics like different types of networks, graph modeling approaches, measures to analyze networks, and applications of network analysis to domains like the web, social networks, and disease spreading. The document emphasizes that understanding network structure and interactions is important for studying complex systems and influences within networks.
Optimizing sentinel surveillance in static and temporal networksPetter Holme
The document discusses objective measures for comparing different approaches to sentinel surveillance in networks. It runs simulations of disease spread using the SIR model on empirical temporal and static networks. It finds that nodes identified as important by different objective measures like time to detection, time to detection or extinction, and frequency of detection do not always coincide. The correlations between measures depend on network structure, with stronger correlations in temporal networks. Centrality metrics are also correlated with objective measures, but not perfectly. The best objective measure depends on the aspect of sentinel surveillance that is most important.
Important spreaders in networks: exact results on small graphsPetter Holme
To be able to control spreading phenomena (like the spreading of diseases and information) in networks it is important to identify influential spreaders. What "important" means depends on what is spreading and what kind of countermeasures that are available. In this work, we let the susceptible-infected-removed (SIR) model represent the spreading dynamics and contrast three different definitions of importance: Influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all connected graphs of three to seven nodes. We obtain the smallest graphs where the optimal node sets are not overlapping. We find that: node separation is more important than centrality for more than one active node, that vaccination and influence maximization are the most different aspects of importance, and that the three aspects are more similar when the infection rate is low. Furthermore, we discuss similar approaches to study the extinction times in the susceptible-infected- susceptible model.
Important spreaders in networks: Exact results for small graphsPetter Holme
The document discusses exact calculations of node importance in network epidemiology for small graphs. It examines three types of node importance: influence maximization, vaccination, and sentinel surveillance. The goal is to find the smallest graph where all three notions of importance differ for different nodes. Symbolic algebra and fast computational methods are used to efficiently calculate outbreak probabilities and expected times for all small graph topologies to identify cases where the important nodes are not the same.
Spreading processes on temporal networksPetter Holme
This document discusses temporal networks and how temporal structures can impact dynamical processes on networks. It begins by describing different types of temporal networks including person-to-person communication, information dissemination, physical proximity, and cellular biology networks. It then discusses methods for analyzing temporal network structures like inter-event times and how bursty or heavy-tailed distributions can slow spreading compared to memory-less processes. The document also presents examples of how neutralizing temporal structures like inter-event times or beginning/end times can impact spreading simulations. Finally, it discusses how different temporal network datasets exhibit diverse temporal structures.
Temporal Networks of Human InteractionPetter Holme
Temporal networks provide a framework for modeling systems of interactions that occur between nodes over time. These networks capture both the topological structure of connections as well as the timing of interactions. Three key aspects of temporal networks discussed in the document are:
1) Temporal networks can be represented using contact sequences that capture when interactions occur between nodes, unlike static networks which only represent connections.
2) The temporal structure of interactions, such as patterns in the timing of contacts, can impact dynamical processes unfolding on the network like information or disease spreading.
3) Randomizing the timing of contacts in empirical temporal network data can alter dynamical processes, highlighting the importance of temporal structure beyond just topology.
This document describes an adaptive model for the spread of infection on networks. It begins by introducing network analysis and percolation models. It then presents the basic SIS (Susceptible-Infected-Susceptible) model for modeling infection spread and derives the steady-state infection rates. It introduces the concept of network adaptation and reviews models of financial contagion that lack adaptation. It then presents a new popularity-based network model and develops an adaptive SIS model that incorporates network changes in response to infection spread. Computational analysis shows the existence of a phase boundary for this adaptive model.
Complex Networks Analysis @ Universita Roma TreMatteo Moci
This document discusses complex networks and their analysis. It provides a brief history of network analysis starting in the 18th century with Euler's work on the Seven Bridges of Königsberg problem. It then covers key topics like different types of networks, graph modeling approaches, measures to analyze networks, and applications of network analysis to domains like the web, social networks, and disease spreading. The document emphasizes that understanding network structure and interactions is important for studying complex systems and influences within networks.
Optimizing sentinel surveillance in static and temporal networksPetter Holme
The document discusses objective measures for comparing different approaches to sentinel surveillance in networks. It runs simulations of disease spread using the SIR model on empirical temporal and static networks. It finds that nodes identified as important by different objective measures like time to detection, time to detection or extinction, and frequency of detection do not always coincide. The correlations between measures depend on network structure, with stronger correlations in temporal networks. Centrality metrics are also correlated with objective measures, but not perfectly. The best objective measure depends on the aspect of sentinel surveillance that is most important.
Some key models of social network generation are discussed, including random graph models, Watts-Strogatz models, and scale-free networks. Scale-free networks can generate networks with few components, small diameters, and heavy-tailed degree distributions, but do not capture high clustering. Biological networks like metabolic and protein interaction networks also tend to be scale-free.
- Temporal networks are dynamic networks that change over time. They are commonly represented through temporal contact sequences or time-varying adjacency matrices.
- Key properties of temporal networks include distributions of contact durations and inter-contact times, measures of burstiness, and persistence/correlation of network structures over time.
- Analyzing temporal paths, centrality measures, motifs, and comparing empirical networks to temporal null models can provide insights into the structure and dynamics of temporal networks not evident from static representations.
This document summarizes a presentation on machine learning of epidemic processes in networks. It discusses using machine learning to predict epidemic spreading from network structure. Specifically, it covers using features like degree, clustering, and centrality measures as inputs to algorithms like random forests and neural networks to predict the fraction of infected nodes. The best approach uses a combination of network measures, not a single measure. This allows machine learning to help identify influential spreaders and understand how network structure influences epidemic dynamics.
How the information content of your contact pattern representation affects pr...Petter Holme
This document summarizes a presentation about how the structure of temporal networks, which model patterns of human contact over time, can affect the predictability of disease outbreaks. The presentation discusses how different levels of information contained in representations of contact patterns, from fully mixed to temporal network models, influence the size and uncertainty of epidemics simulated using an SIR compartmental model. It analyzes several real-world temporal network datasets and examines how metrics that characterize the network structure correlate with the shape of the relationship between outbreak size and the basic reproduction number R0.
This document discusses rekeying load in group key distributions that use cover-free families. It provides bounds on the number of messages needed to rekey a distribution after one or two users have been ejected simultaneously. For certain distributions based on symmetric combinatorial designs, the bounds shown are tight. In general, determining the minimal number of messages needed for rekeying a system based on a cover-free family is an NP-hard problem.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Inferring networks from multiple samples with consensus LASSOtuxette
This document provides a short overview of network inference using graphical Gaussian models (GGMs). It discusses inferring networks from multiple samples, with the motivation being to identify genes that are linked independently or depending on different conditions. A naive approach of performing independent estimations on each sample is described. Joint network inference using the consensus LASSO method is then introduced to better identify common and condition-specific network structures across multiple related samples.
Inferring networks from multiple samples with consensus LASSOtuxette
This document provides an overview of biological concepts and network inference methods. It discusses DNA, transcription, gene expression, and how transcriptomic data is obtained. Gene networks can be inferred from expression data using correlations or partial correlations between genes. Network inference focuses on direct relationships between genes and can identify interactions for previously unannotated genes.
Disintegration of the small world property with increasing diversity of chemi...N. Sukumar
Authors: Ganesh Prabhu, Sudeepto Bhattacharya,, Michael Krein, N. Sukumar (ORCID: 0000-0002-2724-9944). Full paper in J. Math. Chem. 54(10), 1916-1941 (2016).
The Majority Rule is applied to a topology that consists of two coupled
random networks, thereby mimicking the modular structure observed in social
networks. We calculate analytically the asymptotic behaviour of the model and derive a
phase diagram that depends on the frequency of random opinion flips and on the inter-
connectivity between the two communities. It is shown that three regimes may take
place: a disordered regime, where no collective phenomena takes place; a symmetric
regime, where the nodes in both communities reach the same average opinion; an
asymmetric regime, where the nodes in each community reach an opposite average
opinion. The transition from the asymmetric regime to the symmetric regime is shown
to be discontinuous.
The document defines distributed systems as a set of autonomous computational resources that communicate through a network to achieve a common goal. It discusses key concepts like cooperation, autonomy, and communication in distributed systems. It also covers advantages like reliability, resource sharing, and scalability, as well as disadvantages like security and complexity. The document then discusses the evolution of distributed systems and various architectural models like master-worker, client-server, and peer-to-peer. Finally, it outlines several fields of application for distributed systems like computer science, science/engineering, business, and public administration.
This document discusses functional brain networks and network science approaches to studying the brain. It begins by defining complex systems and network science. It then outlines the main types of brain networks - anatomical and functional networks. Functional brain networks are constructed from time series data measuring brain activity and can be analyzed using network measures to study properties like segregation, integration and resilience.
The document discusses a machine learning-based technique for detecting wormhole attacks in wireless sensor networks. It proposes using a multipoint relay-based Watchdog monitoring and prevention protocol. The technique will use a dynamic threshold to detect wormhole attacker nodes. Then, clustering and Watchdog-based optimistic path selection will be used to communicate packets and reduce packet dropping, improving the network's performance. The approach aims to address limitations of existing Watchdog techniques, such as not being able to distinguish collisions from attacks. It incorporates a cooperative cross-layer monitoring framework to handle falsely reported attacks.
Exploiting friendship relations for efficient routing in mobileramya1591
The document presents a proposed algorithm for routing messages in delay tolerant networks (DTNs) like mobile social networks. It evaluates a new social pressures metric (SPM) to detect the quality of friendships between nodes for determining the best routing paths. Prior work on multicopy, single-copy, and erasure-coding routing approaches in DTNs is reviewed. The proposed algorithm routes messages along paths containing nodes in the destination's friendship communities. Simulations show it performs better than three previous benchmark algorithms in terms of delivery ratio, cost, and efficiency.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
Defending against collaborative attacks byranjith kumar
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
Maps of sparse memory networks reveal overlapping communities in network flowsUmeå University
1. The document discusses using sparse memory networks and higher-order flow modeling to map flows through complex systems and reveal overlapping communities.
2. It addresses three challenges: the detectability limit of conventional approaches, the problem of many different network representations, and selecting an appropriate scale and model.
3. The solutions proposed are using higher-order network representations like memory and multilayer networks, representing these with a single sparse memory network framework, and choosing an appropriate sparse network using state lumping and cross-validation.
This chapter provides a historical introduction to mathematical modeling of epidemics and rumors. It discusses early empirical modeling from the 1700s, the first deterministic SIR model from the early 1900s, the development of homogeneous mixing models in the mid-1900s, and early stochastic models. The chapter outlines different modeling approaches and terminology. It concludes that modeling has progressed from curve-fitting empirical data to developing deterministic and stochastic models in both continuous and discrete time to better understand disease transmission dynamics.
Underspecified Scientific Claims in NanopublicationsTobias Kuhn
(CC Attribution License does not apply to included third-party material on slides 3 and 4; see the paper for the references: http://www.tkuhn.ch/pub/kuhn2012wole.pdf )
The document discusses a project called the NASA Blue Beam Project which involves using technology to manipulate humans on a global scale. It describes methods like using satellites to broadcast electromagnetic waves that can interact with people's thoughts and induce artificial thoughts. The goal is said to be total social control by using fear, mind control, and advanced technology to convince people that a new world order and global religion are necessary through faked supernatural events like alien invasions. It warns that these methods could cause hysteria and chaos on a global scale.
Some key models of social network generation are discussed, including random graph models, Watts-Strogatz models, and scale-free networks. Scale-free networks can generate networks with few components, small diameters, and heavy-tailed degree distributions, but do not capture high clustering. Biological networks like metabolic and protein interaction networks also tend to be scale-free.
- Temporal networks are dynamic networks that change over time. They are commonly represented through temporal contact sequences or time-varying adjacency matrices.
- Key properties of temporal networks include distributions of contact durations and inter-contact times, measures of burstiness, and persistence/correlation of network structures over time.
- Analyzing temporal paths, centrality measures, motifs, and comparing empirical networks to temporal null models can provide insights into the structure and dynamics of temporal networks not evident from static representations.
This document summarizes a presentation on machine learning of epidemic processes in networks. It discusses using machine learning to predict epidemic spreading from network structure. Specifically, it covers using features like degree, clustering, and centrality measures as inputs to algorithms like random forests and neural networks to predict the fraction of infected nodes. The best approach uses a combination of network measures, not a single measure. This allows machine learning to help identify influential spreaders and understand how network structure influences epidemic dynamics.
How the information content of your contact pattern representation affects pr...Petter Holme
This document summarizes a presentation about how the structure of temporal networks, which model patterns of human contact over time, can affect the predictability of disease outbreaks. The presentation discusses how different levels of information contained in representations of contact patterns, from fully mixed to temporal network models, influence the size and uncertainty of epidemics simulated using an SIR compartmental model. It analyzes several real-world temporal network datasets and examines how metrics that characterize the network structure correlate with the shape of the relationship between outbreak size and the basic reproduction number R0.
This document discusses rekeying load in group key distributions that use cover-free families. It provides bounds on the number of messages needed to rekey a distribution after one or two users have been ejected simultaneously. For certain distributions based on symmetric combinatorial designs, the bounds shown are tight. In general, determining the minimal number of messages needed for rekeying a system based on a cover-free family is an NP-hard problem.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Inferring networks from multiple samples with consensus LASSOtuxette
This document provides a short overview of network inference using graphical Gaussian models (GGMs). It discusses inferring networks from multiple samples, with the motivation being to identify genes that are linked independently or depending on different conditions. A naive approach of performing independent estimations on each sample is described. Joint network inference using the consensus LASSO method is then introduced to better identify common and condition-specific network structures across multiple related samples.
Inferring networks from multiple samples with consensus LASSOtuxette
This document provides an overview of biological concepts and network inference methods. It discusses DNA, transcription, gene expression, and how transcriptomic data is obtained. Gene networks can be inferred from expression data using correlations or partial correlations between genes. Network inference focuses on direct relationships between genes and can identify interactions for previously unannotated genes.
Disintegration of the small world property with increasing diversity of chemi...N. Sukumar
Authors: Ganesh Prabhu, Sudeepto Bhattacharya,, Michael Krein, N. Sukumar (ORCID: 0000-0002-2724-9944). Full paper in J. Math. Chem. 54(10), 1916-1941 (2016).
The Majority Rule is applied to a topology that consists of two coupled
random networks, thereby mimicking the modular structure observed in social
networks. We calculate analytically the asymptotic behaviour of the model and derive a
phase diagram that depends on the frequency of random opinion flips and on the inter-
connectivity between the two communities. It is shown that three regimes may take
place: a disordered regime, where no collective phenomena takes place; a symmetric
regime, where the nodes in both communities reach the same average opinion; an
asymmetric regime, where the nodes in each community reach an opposite average
opinion. The transition from the asymmetric regime to the symmetric regime is shown
to be discontinuous.
The document defines distributed systems as a set of autonomous computational resources that communicate through a network to achieve a common goal. It discusses key concepts like cooperation, autonomy, and communication in distributed systems. It also covers advantages like reliability, resource sharing, and scalability, as well as disadvantages like security and complexity. The document then discusses the evolution of distributed systems and various architectural models like master-worker, client-server, and peer-to-peer. Finally, it outlines several fields of application for distributed systems like computer science, science/engineering, business, and public administration.
This document discusses functional brain networks and network science approaches to studying the brain. It begins by defining complex systems and network science. It then outlines the main types of brain networks - anatomical and functional networks. Functional brain networks are constructed from time series data measuring brain activity and can be analyzed using network measures to study properties like segregation, integration and resilience.
The document discusses a machine learning-based technique for detecting wormhole attacks in wireless sensor networks. It proposes using a multipoint relay-based Watchdog monitoring and prevention protocol. The technique will use a dynamic threshold to detect wormhole attacker nodes. Then, clustering and Watchdog-based optimistic path selection will be used to communicate packets and reduce packet dropping, improving the network's performance. The approach aims to address limitations of existing Watchdog techniques, such as not being able to distinguish collisions from attacks. It incorporates a cooperative cross-layer monitoring framework to handle falsely reported attacks.
Exploiting friendship relations for efficient routing in mobileramya1591
The document presents a proposed algorithm for routing messages in delay tolerant networks (DTNs) like mobile social networks. It evaluates a new social pressures metric (SPM) to detect the quality of friendships between nodes for determining the best routing paths. Prior work on multicopy, single-copy, and erasure-coding routing approaches in DTNs is reviewed. The proposed algorithm routes messages along paths containing nodes in the destination's friendship communities. Simulations show it performs better than three previous benchmark algorithms in terms of delivery ratio, cost, and efficiency.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
Defending against collaborative attacks byranjith kumar
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
Maps of sparse memory networks reveal overlapping communities in network flowsUmeå University
1. The document discusses using sparse memory networks and higher-order flow modeling to map flows through complex systems and reveal overlapping communities.
2. It addresses three challenges: the detectability limit of conventional approaches, the problem of many different network representations, and selecting an appropriate scale and model.
3. The solutions proposed are using higher-order network representations like memory and multilayer networks, representing these with a single sparse memory network framework, and choosing an appropriate sparse network using state lumping and cross-validation.
This chapter provides a historical introduction to mathematical modeling of epidemics and rumors. It discusses early empirical modeling from the 1700s, the first deterministic SIR model from the early 1900s, the development of homogeneous mixing models in the mid-1900s, and early stochastic models. The chapter outlines different modeling approaches and terminology. It concludes that modeling has progressed from curve-fitting empirical data to developing deterministic and stochastic models in both continuous and discrete time to better understand disease transmission dynamics.
Underspecified Scientific Claims in NanopublicationsTobias Kuhn
(CC Attribution License does not apply to included third-party material on slides 3 and 4; see the paper for the references: http://www.tkuhn.ch/pub/kuhn2012wole.pdf )
The document discusses a project called the NASA Blue Beam Project which involves using technology to manipulate humans on a global scale. It describes methods like using satellites to broadcast electromagnetic waves that can interact with people's thoughts and induce artificial thoughts. The goal is said to be total social control by using fear, mind control, and advanced technology to convince people that a new world order and global religion are necessary through faked supernatural events like alien invasions. It warns that these methods could cause hysteria and chaos on a global scale.
A Review Of MRI Findings In SchizophreniaScott Faria
This document summarizes a review of 193 MRI studies of schizophrenia from 1988 to 2000. It finds evidence of brain abnormalities in schizophrenia, including ventricular enlargement in 80% of studies, third ventricle enlargement in 73% of studies, and preferential involvement of medial temporal lobe structures like the hippocampus and amygdala in 74% of studies. It also finds moderate evidence of frontal and parietal lobe abnormalities. Future studies could help clarify the timing of abnormalities and examine brain connectivity using new techniques.
The document discusses various scientific concepts including theories, hypotheses, laws, and the scientific method. It provides examples of scientific theories such as the theory of gravity, cell theory, and the theory of evolution. It also discusses the importance of scientific literacy in evaluating information and making decisions. Pseudoscience is defined as a set of beliefs that may use science but are based on subjective reasoning. The last part discusses why it's important for people to understand scientific principles and think scientifically so they don't fall for scams and can make better informed decisions.
Cell Culture Techniques (Michael Aschner, Lucio Costa) (Z-Library).pdfsymbssglmr
This document provides prefaces and contributor information for the book "Cell Culture Techniques, Second Edition". The prefaces discuss the Neuromethods series focusing on tools and techniques for investigating the nervous system. The book aims to provide technical protocols as well as theoretical background to help readers understand the origins and potential developments of the techniques. The contributors section lists researchers who contributed chapters applying specific neuroscience methods and models.
This document summarizes a book review of "Viruses and Interferon: Current Research". The review provides the following key points:
- The book covers the fundamentals of the biological and mechanistic complexities of the interferon system and how interferons are induced and signal to induce antiviral proteins.
- Each topic is discussed by experts in 10 chapters, though there are some redundancies between chapters.
- The individual chapters are high quality and self-sufficient, but an introductory chapter providing an overview would have been helpful for uninitiated readers.
- The first chapter introduces double-stranded RNA as an important regulator of immunity. The next three chapters thoroughly address how the
Proceedings _ Part II _ Collection of Abstracts - 09-30-2015Jordan Zimmerman
This document summarizes research presented at a conference on health effects related to hand-arm vibration. It includes an abstract for each of 10 presentations given at the conference. The presentations covered topics like systematic reviews of the relationship between vibration exposure and health risks, characteristics of vibration-induced white finger among Chinese workers, and the effects of power tool vibration duration on peripheral nerve endings based on animal research. The document provides context about the conference and brief summaries of the research presented in each abstract to give the reader an overview of the work discussed at the event.
Wiener and human augmentation may 2012Greg_Adamson
Norbert Wiener and human augmentation
This document discusses the work of Norbert Wiener, the founder of cybernetics. It summarizes Wiener's views on the increasing relationship between humans and machines. Wiener believed humans must modify themselves to exist in our technology-modified environment. He took a multi-disciplinary approach and collaborated across fields. Wiener explored prosthetics and saw potential for enhancing humans. While recognizing limits, he believed humans could in theory be sent over telegraph lines, placing no theoretical limits on human-machine relationships.
UNCLASSIFIED: A Mind/Brain/Matter Model Consistent with Quantum Physics and ...swilsonmc
Thomas E. Bearden
Prepared for the 1979 MUFON Annual Symposium
CSC (Computer Sciences Corporation)
The author introduces a speculative model of mind and matter and their
interaction that is consistent with the experimental basis of physics, and
which offers mechanisms for paranormal phenomena of all types, including
UFO phenomena. Certain conclusions are reached by a new fourth law of logic,
which is briefly described and summarized. A new photon interaction model.
of quantized observable changc is also presented.
A solution to the problem of the nature of mind is generated, using the
author's fourth law of logic, and a seven-dimensional hyperspatial physical
model of a living biosystem is developed. Using this basic model, an infinitedimensional
cotemporal hyperspatial model of the physical universe complete
with all its life forms is constructed. Levels of unconsciousness--
including the collective human species unconscious--emerge naturally as
types of crosstalk between hyperframes.
A natural set of relations betwee.<thoughtforms,>s formula, the psychokinetic power of a mind level
increases exponentially as the number of biosvstem stages involved. At
the level of the collective human species unconscious, the psychokinesis
is sufficient to materialize symbolic tulpoids (thought forms), given a
sufficient stress stimulus in large groups. Using the cold war as the
major stress stimulus on mankind since World War I, the author shows that
most major UFO waves in the literature precisely fit the model...-.
Using his own collation and analysis of Soviet psychotronic weapon
development, the author is also able to fit into the model the several
thousand paranormal and bizarre cattle mutilations that have occurred in the
U.S. since 1973.
The presenration was propared for the 1979 MUFON Annual Symposium.
THOMAS E. BEARDEN, M. S., Nuclear Engineering
A nuclear engineer, wargames analyst, and military tactician, Lieutenant
Colonel (Retired) Thomas E. Bearden has over 24 years experience in air defense
systems, tactics, and operations; technical intelligence; nuclear weapons
employment; computerized wargames, and military systems requirements.
He is currently with the Alabama division of a large aerospace company
where he is Involved in developing and analyzing countermeasures to antiradiation
missiles for the Patriot, Improved Hawk, and Roland U.S. Army missile systems,
and in computerized air defense wargames and countermeasure studies.
Lieutenant Colonel Bearden obtained an M. S. in nuclear engineering from the
Georgia Institute of Technology...
Nervous system is the system designed for transmitting and processing necessary information for surviving in the world.
As one important step toward understanding of the process, we need to record electrical activities from as more neurons as possible, and need to reconstruct the comprehensive information flows (Informatic Connectome). This presentation will introduce one series of studies asking information processes among more than 500 neurons recorded from barrel cortex using our multi-electrode array system. The information network showed obviously unique features as follows:
For example, the strengths of information flow were log-normally distributed, and showed a long-tailed distribution. At the same time, the network organization hold hubs not only about the number of connections (as Non-Weighted networks) but also the amount of information flowing on the connections (as Weighted networks). These findings are important in relation with synaptic connections existing behind of the electrical signals. Furthermore, the hubs were surrounded by hierarchical or multi-scale organizations including Clusters and Communities. Within hubs, high out-degree hubs often received inputs from high informatics neurons, and hubs produced Rich Club organization by directly connecting each other. These architectures reflect mechanisms how Informatic Microconnectome can process information sparsely and effectively in our brain.
Highlighted notes while preparing for project on Computational Epidemics:
Computational Epidemiology (Review)
By Madhav Marathe, Anil Kumar S. Vullikanti
Communications of the ACM, July 2013, Vol. 56 No. 7, Pages 88-96
10.1145/2483852.2483871
An epidemic is said to arise in a community or region when cases of an illness or other health-related events occur in excess of normal expectancy. Epidemics are considered to have influenced significant historical events, including the plagues in Roman times and Middle Ages, the fall of the Han empire in the 3rd century in China, and the defeat of the Aztecs in the 1500s, due to a smallpox outbreak. The 1918 flu pandemic in the U.S. was responsible for more deaths than those due to World War I. The last 50 years have seen epidemics caused by HIV/AIDS, SARS, and influenza-like illnesses. Despite significant medical advances, according to the World Health organization (WHO), infectious diseases account for more than 13 million deaths a year.
This document provides an overview of computational neuroscience from modeling single neurons to neural circuits and behavior. It discusses:
- Models of single neurons from the Hodgkin-Huxley model to reduced models like FitzHugh-Nagumo and Izhikevich neurons.
- How neurons are organized into neural circuits using different connection types and how properties like synchronization emerge from circuit properties.
- Approaches to modeling larger brain areas as neural populations using techniques like neural fields to model mean firing rates over continuous space.
- Phenomena like neural coding, plasticity, learning and their role in computational models of behaviors and cognition. It provides examples of modeling visual attention, decision making and more
This document discusses common pitfalls in data science. It begins by noting that while the technical aspects of data science may seem straightforward, the real challenges lie elsewhere. Some of the key challenges discussed include: distinguishing correlation from causation, accounting for biases, properly evaluating models, deploying models responsibly, and understanding that data science requires more than just building predictive models. Specific examples are provided to illustrate issues like selection bias, overfitting, data leakage, and how proper evaluation and historical examples are important to avoid repeating mistakes. The document concludes by stating that while frameworks can automate some tasks, delivering actual value is the difficult part that requires understanding many factors beyond just the technical aspects of building models.
Causality in the sciences:
The conceptual toolbox for organisational diagnosis discusses causation from a scientific perspective. It provides concepts of causation that can be used as a conceptual toolbox to enhance research and establish links between disciplines. These concepts include: difference-making involving probabilities and counterfactuals; physical connections through processes and mechanisms; regularity; necessary and sufficient conditions; and capacities or dispositions. Adopting a causal approach allows for understanding, explaining, and intervening in phenomena of interest.
This document summarizes a case study of a soldier who suffered a primary blast brain injury from a large ordinance explosion. Neuroimaging showed abnormalities that later normalized on follow-up imaging. The case study is cited as reference #13 in the Wikipedia article on concussions.
Temporal network epidemiology: Subtleties and algorithmsPetter Holme
The SIR and SIS models are the canonical model of epidemics of infections that make people immune upon recovery. Many open questions in computational epidemiology concern the underlying contact structure’s impact on models like the SIR or SIS. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this talk, we discuss the detailed assumptions behind such simulations—how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also discuss fast algorithms for such simulations and the challenges in improving them.
Small networks are worth studying for several reasons: (1) Real networks are sometimes small; (2) We use small networks to reason about larger networks; (3) Small networks are the only ones that can be studied with slow exact algorithms. Understanding small networks poses challenges but allows asking different questions than usual, such as predicting node importance and studying epidemics as random walks. Studying all connected small graphs provides a reference model to understand structures imposed by connectivity.
The document discusses spin models on networks. It provides background on spin models in statistical physics, traditionally using lattices as the underlying graph. It explores reasons for studying spin models on networks and describes several specific models. The XY model is described, where spins are angles and the Hamiltonian favors alignment between connected spins. Results are presented for the XY model on Watts-Strogatz networks and a dynamic XY model. The YX model is introduced where spins are fixed but links can change. Analysis of the largest components and their behavior at the magnetic transition temperature is shown for the YX model. The free XY model is also analyzed, looking at magnetization, largest component size, and number of components for varying average degrees.
This is one segment of a talk where I presented the history of computational social science:
* The origins of computer simulations.
* The trouble to publish computational studies in the 1960s.
* The peak enthusiasm for computer simulations after "Limits of Growth"
* The precursors of social-media data science in the 1980's
This document summarizes a study of travel routes in 92 major cities to understand urban structure. The researchers introduced a new metric called "inness" to measure the inward bias of routes toward city centers. They found that on average, routes displayed a measurable drift toward centers, balanced by congestion forces varying between cities. The inness patterns were consistent with core-periphery structures and hierarchical routing in most cities. Individual city inness patterns reflected influences of urban development and geographical constraints. Overall, the results indicate intrinsic hierarchies in socioeconomic evolution shape urban structure.
Dynamics of Internet-mediated partnership formationPetter Holme
This document discusses the dynamics of internet-mediated partnerships and romantic relationships from the 1960s to present. It covers the early use of computers to analyze survey data and match individuals, the rise of internet communication technologies, and how they enabled new forms of online dating, romance, and sexual relationships. Key topics include the structure and evolution of online dating communities, information sharing in online prostitution networks, and how human dynamics and behaviors have scaled with new digital connections.
Modeling the evolution of the AS-level Internet: Integrating aspects of traff...Petter Holme
This document describes a model of the evolution of the Internet at the level of autonomous systems (AS). The model aims to integrate aspects of traffic, geography, and economics by including spatially explicit autonomous systems, realistic traffic flowing between them, and economic modeling of pricing. It discusses previous models that focused only on network topology without these real-world elements. The document then outlines the goals and components of the new model being proposed to more comprehensively capture the evolution of the AS-level Internet structure.
This document summarizes a research paper that used land use maps and daily activity patterns to infer human mobility within cities. The researchers analyzed data from over 25,000 people in Chicago that listed their daily trajectories and trip purposes. They also used a land use map of Chicago with 49 categories. Their model relates land use and mobility, with the flux between locations proportional to population, land use transition probabilities, and distance. They were able to calibrate distance dependencies and show the land use map improved predictions of population densities compared to a simple gravity model.
Why do metabolic networks look like they do?Petter Holme
Metabolic networks have certain common structural properties across organisms. They have broad degree distributions with a core-periphery structure and weak modular structure that sometimes corresponds to subcellular localization. Currency metabolites like ATP and NADH are highly connected and lower modularity. Null models are needed to compare network structures to random networks that obey mass balance constraints. The weak modular structure of metabolic networks may be related to their robustness against genetic and chemical perturbations.
Modeling the fat tails of size fluctuations in organizationsPetter Holme
Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527
From temporal to static networks, and backPetter Holme
Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard either―if we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an “exponential threshold” representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.
Exploring spatial networks with greedy navigatorsPetter Holme
The document discusses measuring and optimizing navigability in networks. It examines how to quantify how navigable a network is using metrics like the ratio of average path distances for greedy navigators versus random navigators. Networks can be optimized for navigability by adjusting edge weights or positions. Examples shown include spatial network models and infrastructure networks, finding that optimized networks have paths closer to shortest distances when using greedy navigation strategies.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
4. ‘40s
Timeline
1947 First programmable computer
1948 The first simulation study, the Monte Carlo project
‘50s
Timeline
1952 The first computational epidemiology study:
H Abbey. An examination of the Reed-Frost theory
of epidemics. Hum. Biol. 24: 201–233.
‘70s
Timeline
1978 The core group concept:
JA Yorke, HW Hethcote, A Nold. Dynamics and control of
the transmission of Gonorrhea. Sex. Transm. Dis. 5: 51–56.
5. Timeline
1984 Birth of network epidemiology:
DM Auerbach, WW Darrow, HW Jaffe, JW Curran, Cluster of
cases of the acquired immune deficiency syndrome:
Patients linked by sexual contact. Am. J. Med. 76: 487–492.
‘80s
6. ‘40s
Timeline
1947 First programmable computer
1948 The first simulation study, the Monte Carlo project
‘50s
Timeline
1952 The first computational epidemiology study:
H Abbey. An examination of the Reed-Frost theory
of epidemics. Hum. Biol. 24: 201–233.
‘70s
Timeline
1978 The core group concept:
JA Yorke, HW Hethcote, A Nold. Dynamics and control of
the transmission of Gonorrhea. Sex. Transm. Dis. 5: 51–56.
1984 Birth of network epidemiology:
DM Auerbach, WW Darrow, HW Jaffe, JW Curran, Cluster of
cases of the acquired immune deficiency syndrome:
Patients linked by sexual contact. Am. J. Med. 76: 487–492.
‘80s
Timeline
1995 Birth of computational network epidemiology:
M Kretzschmar. Deterministic and stochastic pair
formation models for the spread of sexually transmitted
diseases. J. Biol. Syst. 3: 789–801.
‘90s
7. Modeling
Step 1: Compartmental models
Susceptible
meets
Infectious
Infectious
With some probability or rate
Susceptible or
Recovered
With some rate or after some time
9. The core group idea
Core groups bring a
population over an
epidemic threshold,
even though it, on
average, wouldn’t be.
Being a member of a core
group = being important
for the disease. But any
individual in the core
group is insignificant for
the core group.
Theparadox
10. Which one depend on
outbreak scenarios and
intervention scenarios.
Many facets of importance
11. Which one depend on
outbreak scenarios and
intervention scenarios.
Many facets of importance
13. The hypotheses
Core groups can be captured by static network structure.
Structure & dynamics can be coupled by the SIS survival time.
For many vaccinees, the core group would be most important.
16. The hypotheses
Core groups can be captured by static network structure.
Structure & dynamics can be coupled by the SIS survival time.
Vaccination-impact very correlated with degree.
20. … of human interaction
J Saramäki & al., 2013. PNAS 111: 942–947.
LEC Rocha, F Liljeros, P Holme, 2010. PNAS 107: 5706–5711.
LEC Rocha, F Liljeros, P Holme, 2011. PLoS Comp. Biol. 7: e1001109.
M Karsai, J Saramäki & al. Phys. Rev. E 83: 025102.
P Holme, 2005. Phys. Rev. E 71: 046119.
21. Optimal static networks from
temporal network data
P Holme, 2013. PLoS. Comp. Biol. 9: e1003142.
Time-window networks good,
but be careful with the window size.
22. Simplified pictures of temporal
networks
P Holme, F Liljeros, 2014.
Sci. Rep. 4: 4999.
time
Beginning &
end of
relationships
more
important
than,
interevent
times for SIR
on empirical
data.