This document discusses real-time data and real-time systems. It defines real-time systems as systems where the correctness depends on both the logical result and the time the result is produced. Real-time systems must respond to events in a fast and predictable way. The document discusses soft, firm, and hard deadlines and gives examples of hard real-time systems like nuclear reactor control. It also discusses challenges in validating that a real-time system can meet all its timing constraints and potential solutions like real-time operating systems and distributed systems.
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017Big Data Spain
All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
A Study Review of Common Big Data Architecture for Small-Medium EnterpriseRidwan Fadjar
This slide was created to present the result of my paper about "A Study Review of Common Big Data Architecture for Small-Medium Enterprise" at MSCEIS FPMIPA Universitas Pendidikan Indonesia 2019.
In cooperate with: https://www.linkedin.com/in/faijinali and https://www.linkedin.com/in/fajriabdillah
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
High Performance Computing and Big Data Geoffrey Fox
We propose a hybrid software stack with Large scale data systems for both research and commercial applications running on the commodity (Apache) Big Data Stack (ABDS) using High Performance Computing (HPC) enhancements typically to improve performance. We give several examples taken from bio and financial informatics.
We look in detail at parallel and distributed run-times including MPI from HPC and Apache Storm, Heron, Spark and Flink from ABDS stressing that one needs to distinguish the different needs of parallel (tightly coupled) and distributed (loosely coupled) systems.
We also study "Java Grande" or the principles to use to allow Java codes to perform as fast as those written in more traditional HPC languages. We also note the differences between capacity (individual jobs using many nodes) and capability (lots of independent jobs) computing.
We discuss how this HPC-ABDS concept allows one to discuss convergence of Big Data, Big Simulation, Cloud and HPC Systems. See http://hpc-abds.org/kaleidoscope/
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017Big Data Spain
All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
A Study Review of Common Big Data Architecture for Small-Medium EnterpriseRidwan Fadjar
This slide was created to present the result of my paper about "A Study Review of Common Big Data Architecture for Small-Medium Enterprise" at MSCEIS FPMIPA Universitas Pendidikan Indonesia 2019.
In cooperate with: https://www.linkedin.com/in/faijinali and https://www.linkedin.com/in/fajriabdillah
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
High Performance Computing and Big Data Geoffrey Fox
We propose a hybrid software stack with Large scale data systems for both research and commercial applications running on the commodity (Apache) Big Data Stack (ABDS) using High Performance Computing (HPC) enhancements typically to improve performance. We give several examples taken from bio and financial informatics.
We look in detail at parallel and distributed run-times including MPI from HPC and Apache Storm, Heron, Spark and Flink from ABDS stressing that one needs to distinguish the different needs of parallel (tightly coupled) and distributed (loosely coupled) systems.
We also study "Java Grande" or the principles to use to allow Java codes to perform as fast as those written in more traditional HPC languages. We also note the differences between capacity (individual jobs using many nodes) and capability (lots of independent jobs) computing.
We discuss how this HPC-ABDS concept allows one to discuss convergence of Big Data, Big Simulation, Cloud and HPC Systems. See http://hpc-abds.org/kaleidoscope/
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
View the Big Data Technology Stack in a nutshell. This Big Data Technology Stack deck covers the different layers of the Big Data world and summarizes the major technologies in vogue today.
Enabling Next Gen Analytics with Azure Data Lake and StreamSetsStreamsets Inc.
Big data and the cloud are perfect partners for companies who want to unlock maximum value from all of their unstructured, semi-structured, and structured data. The challenge has been how to create and manage a reliable end-to-end solution that spans data ingestion, storage and analysis in the face of the volume, velocity and variety of big data sources.
In this webinar, we will show you how to achieve big data bliss by combining StreamSets Data Collector, which specializes in creating and running complex any-to-any dataflows, with Microsoft's Azure Data Lake and Azure analytic solutions.
We will walk through an example of how a major bank is using StreamSets to transport their on-premise data to the Azure Cloud Computing Platform and Azure Data Lake to take advantage of analytics tools with unprecedented scale and performance.
FIS: Accelerating Digital Intelligence in FinTech: Spark Summit East talk by...Spark Summit
In 2017, 60% of the US population will be a digital banking user. The challenges to meet the demands of a more engaged customer is increasing as the banking experience becomes less formal and easier to deliver. Building a better customer experience starts with how you handle your data to leverage accountable, actionable analytics. To take this on, FIS had to overcome the challenge posed by increasing data volumes and data velocity, enterprise level operational complexity and requirements, and work with antiquated traditional techniques. Within a year we transformed to Apache Spark and Databricks, providing thousands of financial institutions the ability to build better relationships with their customers by understanding the behaviors and interactions in digital banking.
On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products
which use real time data streaming by providing modifications to Hadoop.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
View the Big Data Technology Stack in a nutshell. This Big Data Technology Stack deck covers the different layers of the Big Data world and summarizes the major technologies in vogue today.
Enabling Next Gen Analytics with Azure Data Lake and StreamSetsStreamsets Inc.
Big data and the cloud are perfect partners for companies who want to unlock maximum value from all of their unstructured, semi-structured, and structured data. The challenge has been how to create and manage a reliable end-to-end solution that spans data ingestion, storage and analysis in the face of the volume, velocity and variety of big data sources.
In this webinar, we will show you how to achieve big data bliss by combining StreamSets Data Collector, which specializes in creating and running complex any-to-any dataflows, with Microsoft's Azure Data Lake and Azure analytic solutions.
We will walk through an example of how a major bank is using StreamSets to transport their on-premise data to the Azure Cloud Computing Platform and Azure Data Lake to take advantage of analytics tools with unprecedented scale and performance.
FIS: Accelerating Digital Intelligence in FinTech: Spark Summit East talk by...Spark Summit
In 2017, 60% of the US population will be a digital banking user. The challenges to meet the demands of a more engaged customer is increasing as the banking experience becomes less formal and easier to deliver. Building a better customer experience starts with how you handle your data to leverage accountable, actionable analytics. To take this on, FIS had to overcome the challenge posed by increasing data volumes and data velocity, enterprise level operational complexity and requirements, and work with antiquated traditional techniques. Within a year we transformed to Apache Spark and Databricks, providing thousands of financial institutions the ability to build better relationships with their customers by understanding the behaviors and interactions in digital banking.
On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products
which use real time data streaming by providing modifications to Hadoop.
Join this video course on Udemy. Click the below link
https://www.udemy.com/mastering-rtos-hands-on-with-freertos-arduino-and-stm32fx/?couponCode=SLIDESHARE
>> The Complete FreeRTOS Course with Programming and Debugging <<
"The Biggest objective of this course is to demystifying RTOS practically using FreeRTOS and STM32 MCUs"
STEP-by-STEP guide to port/run FreeRTOS using development setup which includes,
1) Eclipse + STM32F4xx + FreeRTOS + SEGGER SystemView
2) FreeRTOS+Simulator (For windows)
Demystifying the complete Architecture (ARM Cortex M) related code of FreeRTOS which will massively help you to put this kernel on any target hardware of your choice.
There are many operating systemsReal-Time Operating SystemReal-t.pdfankitmobileshop235
There are many operating systems
Real-Time Operating System
Real-time applications usually are executed on top of a Real-time Operating System (RTOS).
Specific scheduling algorithms can be designed. When possible, static cyclic schedules are
calculated off-line.
Real-time systems are those systems in which the correctness of the system depends not only on
the logical result of computation, but also on the time at which the results are produced.
RTOS is therefore an operating system that supports real-time applications by providing
logically correct result within the deadline required. Basic Structure is similar to regular OS but,
in addition, it provides mechanisms to allow real time scheduling of tasks.
Though real-time operating systems may or may not increase the speed of execution, they can
provide much more precise and predictable timing characteristics than general-purpose OS.
A real-time system is defined as a data processing system in which the time interval required to
process and respond to inputs is so small that it controls the environment. The time taken by the
system to respond to an input and display of required updated information is termed as the
response time. So in this method, the response time is very less as compared to online
processing.
Real-time systems are used when there are rigid time requirements on the operation of a
processor or the flow of data and real-time systems can be used as a control device in a dedicated
application. A real-time operating system must have well-defined, fixed time constraints,
otherwise the system will fail. For example, Scientific experiments, medical imaging systems,
industrial control systems, weapon systems, robots, air traffic control systems, etc.
Design considerations
Designing a proper RTOS architecture needs some delicate decisions. The basic services like
process management, inter-process communication, interrupt handling, or process
synchronization have to be provided in an efficient manner making use of a very restricted
resource budget.
Multi-core architectures need special techniques for process management, memory management,
and synchronization. The upcoming Wireless Sensor Networks (WSN) generate special demands
for RTOS support leading to dedicated solutions. Another special area is given by multimedia
applications. Very high data rates have to be supported under (soft) RT constraints.
The key difference between general-computing operating systems and real-time operating
systems is the need for \" deterministic \" timing behavior in the real-time operating systems.
Formally, \"deterministic\" timing means that operating system services consume only known
and expected amounts of time. In theory, these service times could be expressed as mathematical
formulas. These formulas must be strictly algebraic and not include any random timing
components. Random elements in service times could cause random delays in application
software and could then make the application randomly .
Data Scienceis an interdisciplinary field about processes and systems to extractknowledgeor insights fromdata, which is a continuation of some of the data analysis fields such as statistics,data mining, andpredictive analytics, similar toKnowledge Discovery in Databases(KDD).
مهم است بدانید اولین قدم به سمت، تغییر داده و سیستمها به شکل دیجیتال آن است
ذخیره داده بر سرورها، ابر یا ابزارهاي
ذخیره محلی براي خلق یک سیستمی که کاربرد دیجیتالی آن داده - منابع ارزشمندي براي کسب وکار، توسعهدهندگان و کارآفرینان-
فراهم میکند حیاتی است....
مشتري بخش كاملي از زنجيره تأمين است.
شامل جابجايي كالا از تأمين كنندگان به سازندگان و توزيع كنندگان است. همچنين شامل جابجايي اطلاعات، پول و محصول در هر دو جهت است.
دقيق تر خواهد بود اگر از اصلاح ”شبكه تأمين“ يا ”وب تأمين“ استفاده شود.
يك زنجيره تأمين عموما شامل تأمين كنندگان، سازندگان، توزيع كنندگان، خرده فروشان و مشتريان است.
البته در برخي از موارد تمامي مراحل وجود ندارد.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
2. What is a Real-Time System?
Real-time systems have been defined as: "those systems in
which the correctness of the system depends not only on the
logical result of the computation, but also on the time at which
the results are produced";
J. Stankovic, "Misconceptions About Real-Time Computing," IEEE Computer, 21(10),
October 1988.
Real-time is the ability of the control system to respond to any
external or internal events in a fast and deterministic way.
We say that a system is deterministic if the response time is
predictable.
3. Some Definitions
Timing constraint: constraint imposed on timing behavior of a
job: hard, firm, or soft.
Release Time: Instant of time job becomes available for
execution.
Deadline: Instant of time a job's execution is required to be
completed.
Response time: Length of time from release time to instant job
completes.
4. Soft, Firm and Hard deadlines
The instant at which a result is needed is called a
deadline.
If the result has utility even after the deadline has passed,
the deadline is classified as soft, otherwise it is firm.
If a catastrophe could result if a firm deadline is missed, the
deadline is hard.
Examples?
5. Hard Real Time Systems
If it has a hard deadline for the completion of an action
meaning that the deadline must always be met, otherwise
the task has failed.
This types of systems deployed in embedded safety-critical
systems in which missed deadline can be catastrophic.
9. Soft Real Time Systems
Soft real time by default as “Not Hard Real Time.
Missing some deadlines by some amount under some circumstances may be
acceptable rather than failure.
In this systems there is usually a rising cost associated with lateness.
Soft real time means systems which have reduced constraints on “lateness”
but still must operate very quickly and repeatable.
Example:
Multimedia
Video Game Systems
Real Time Data Analytics systems
10. Validating a RTS is hard
Validation is simply the ability to be able to prove that you will meet your
constraints
Or for a non-hard time system, prove failure is rare.
This is a hard problem just with timing restrictions
How do you know that you will meet all deadlines?
And how do you know the worst-case for all these applications?
Sure you can measure a billion instances of the program running, but could
something make it worse?
Caches are a pain here.
11. Some Solutions
Embedded Systems
Real Time Operating Systems
Concurrent and Parallel Programming
Distributed Systems
12. What is Embedded Systems ?
An embedded system is a special-purpose computer system designed to perform
one or a few dedicated functions, often with real-time computing constraints.
Embedded systems contain a processor, software and Memory and The processor
may be 8051micro-controller or a Pentium-IV processor, Memory ROM and RAM
respectively
Processor
Memory
Input Output
13. What is Embedded Systems ?
Embedded systems also contain some type of inputs and outputs
Inputs to the system generally take the form of sensors and, communication
signals, or control knobs and buttons.
Outputs are generally displays, communication signals, or changes to the physical
world.
Real-time embedded systems is one major subclass of embedded systems and
time is most important part for this type of system
16. Real-Time Operating System
An RTOS is an OS for response time-controlled and event-controlled processes. It is very
essential for large scale embedded systems.
The main task of a RTOS is to manage the resources of the computer such that a particular
operation executes in precisely the same amount of time every time it occur.
Multitasking
Inter-Task communications
Deterministic response
Fast Response
Low Interrupt Latency
Synchronization
17. When RTOS is necessary?
RTOS is essential when…
A common and effective way of handling of the hardware source calls from the
interrupts
I/O management with devices, files, mailboxes becomes simple using an RTOS
Effectively scheduling and running and blocking of the tasks in cases of many
tasks and many more…..
In conclusion, an RTOS may not be necessary in a small-scaled embedded system.
An RTOS is necessary when scheduling of multiple processes and devices is
important.
21. Complexity
Relational Data (Tables/Transaction/Legacy Data)
Text Data (Web)
Semi-structured Data (XML)
Graph Data
Social Network, Semantic Web (RDF), …
Streaming Data
You can only scan the data once
Big Public Data (online, weather, finance, etc)
22. Speed
Data is begin generated fast and need to be processed fast
Online Data Analytics
Late decisions missing opportunities
Social media and networks
(all of us are generating data)
Mobile devices
(tracking all objects all the time)
Sensor technology and
networks
(measuring all kinds of data)
Lets look at an end to end architecture of putting together open source tools to do real time stream processing.
Lets start with the sources of data.
You want to write this data to a reliable high-throughput low latency messaging system, Kafka and Flume are popular choices, but there are many options out there, like ActiveMQ, RabbitMQ,etc.
Kafka is the system that is gaining the most popularity right now.
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With this architecture, the real-time processed data only gets leveraged when the next application query comes in. But often you want to take some action based on the real-time analysis of your data.
For proactive actions, write relevant events out to Kafka. Again, based on yoru stream processign engine you will find libraries that make this easy.
You can have an application that is continusouly listeing on your event queue, and can issues alerts, emails, etc
A stream processing system like Spark Streaming can then read your data streams from the messaging system.
Filter
Enrich or embellish your data with relevant metadata
Transform
Compute statistics based on moving windows of time
Feature Engineering + Predictive Analytics
… and much more
Almost always, you want to take your full fidelity raw data, and put it in HDFS, or an object store if your are running in the cloud.
The raw data can then be used in batch jobs where you may want to do deep complex processing that can not be done in a streaming fashion. Or you may have a team of data scientists who may want to explore the data and uncover new insights.
Why the dotted line: how you dump your data to HDFS depends on your messaging system. Almost all messaging systems will provide a way to transfer your data to HDFS
All this real-time processing is great, but not very useful if you can not serve the processed data to your application in real-time. Your need a system that can enable a lot of fast reads and writes. That is where NoSql stores come in. There are many choices here. Hbase, Cassnadra and MongoDb are popular choices.
All those end applications
Also, for most stream procsssing engine and NoSql store pairs, there are libraries available that make it easy to read from or write to your NoSql store from the stream processing engine: for example, the SparkOnHbase library makes it easy to write to Hbase from spark streamign jobs.
Another common scenario is indexing your data, in real-time, into a search system.
This is great if the data your are dealing with is textual data.
There are libararies that enable real-time indexing of your data in your stream proocessing engine, and writing it to a Search Engine.
Now the data is ready to be queried by your application.
This is a very common and popular architecture, and I am guessing this is in keeping with what most of you would have expected.
Again, write your processed output to HDFS. Again, why the dotter arrow. Weather or not you need to dump data to HDFS depends upon your serving system of choice. If you write it to Hbase, you may not need to duplicate it in HDFS. But if you are indexing the data in search or writing to a system like Redis, you may want to also write the processed otuptut to HDFS. Why? If nothing else, for auditing purposes. Errors will happen. And you may need to go back and audit what was done in your stream processing engine. Hence, put the data in hdfs and keep it there are some amount of time.
With this architecture, the real-time processed data only gets leveraged when the next application query comes in. But often you want to take some action based on the real-time analysis of your data.
For proactive actions, write relevant events out to Kafka. Again, based on yoru stream processign engine you will find libraries that make this easy.
You can have an application that is continusouly listeing on your event queue, and can issues alerts, emails, etc
By writing it to a message queue, you enable multiple downstream applications to consume the data as its produced, including enabling furthur processing of your data with a stream processing engine. Such multi-stage architectures, where you cosnume from say Kafka, process the data, produce a new stream in Kafka, and process
