It's the presentation slides I prepared for my college workshop. This demonstrates how you can talk with PostgreSql db using python scripting.For queries, mail at [email protected]
This document provides a summary of the history and capabilities of SciPy. It discusses how SciPy was founded in 2001 by Travis Oliphant with packages for optimization, sparse matrices, interpolation, integration, special functions, and more. It highlights key contributors to the SciPy community and ecosystem. It describes why Python is well-suited for technical computing due to its syntax, built-in array support, and ability to support multiple programming styles. It outlines NumPy's array-oriented approach and benefits for technical problems. Finally, it discusses new projects like Blaze and Numba that aim to further improve the SciPy software stack.
Tkinter is a standard GUI library for Python that provides a powerful object-oriented interface to the Tk GUI toolkit. It allows for the creation of GUI applications through widgets like buttons, labels, text boxes, and more. Tkinter applications start with importing the library and creating a main window with Tk(), then entering the main event loop with mainloop() to wait for and process events. Widgets can be organized and placed within the main window using geometry managers like pack(), grid(), and place(). Events can also be handled through binding Python functions to different widget events.
1. Python can be used to automate repetitive tasks like data entry, file processing, report generation etc. This saves time and reduces human errors.
2. Python has many libraries for machine learning, data analysis and visualization which can be used to analyze patent data, identify trends, cluster similar technologies etc.
3. Web scraping and web development frameworks like Django can be used to build internal tools and dashboards to manage workflows more efficiently.
4. Python scripts can be written to extract and process data from various sources, perform calculations, format reports in a standardized way reducing manual efforts.
This presentation educates you about Python - GUI Programming(Tkinter), Tkinter Programming with syntaxe example, Tkinter Widgets with Operator & Description, Standard attributes.
For more topics stay tuned with learnbay.
Introduction about Python by JanBask Training, we are offering Online Pyton Training. You should visit: http://www.janbasktraining.com/python/ for Pyton Training.
Este documento describe los conceptos básicos de algoritmos y solución de problemas computacionales. Explica que un algoritmo es un conjunto de pasos lógicos para resolver un problema, y describe métodos como seudocódigo, diagramas de flujo y diagramas Nassi-Schniderman para escribir algoritmos. También detalla las etapas básicas para la resolución de problemas, incluyendo la definición del problema, análisis, narrativa de solución, esquema de entrada-proceso-salida y desarrollo del algoritmo.
SE_Lec 05_System Modelling and Context ModelAmr E. Mohamed
System modeling is the process of developing abstract models of a system using graphical notations like the Unified Modeling Language (UML) to represent different views of a system. Models help analysts understand system functionality and communicate with customers. Models of existing and new systems are used during requirements engineering to clarify current systems, discuss strengths/weaknesses, and explain proposed requirements.
Bca i-fundamental of computer-u-2- application and system softwareRai University
This document provides information on different types of software and software development models. It discusses:
1) The differences between system software (e.g. operating systems, compilers) which manage hardware, and application software (e.g. word processors, spreadsheets) which perform tasks for users.
2) Common software development models including the waterfall model, V-shaped model, and evolutionary prototyping model. The waterfall model follows sequential phases of requirements, design, implementation, and testing. The V-shaped model adds parallel testing phases. Prototyping allows iterative user feedback.
3) Descriptions of various system software including operating systems, compilers, linkers, loaders, and interpreters and
This document discusses various problems that can be solved using backtracking, including graph coloring, the Hamiltonian cycle problem, the subset sum problem, the n-queen problem, and map coloring. It provides examples of how backtracking works by constructing partial solutions and evaluating them to find valid solutions or determine dead ends. Key terms like state-space trees and promising vs non-promising states are introduced. Specific examples are given for problems like placing 4 queens on a chessboard and coloring a map of Australia.
The document describes using a branch and bound algorithm to solve the Travelling Salesman Problem (TSP). It starts from node 1 and explores the solution space by calculating costs of paths through different nodes. It maintains costs and paths of explored "live nodes" and explores the node with the lowest cost at each step. After node 10 with cost 28 is explored, node 11 with the same cost of 28 is explored by extending the path through node 3.
Lecture 3 Computer Science Research SEM1 22_23 (1).pptxNabilaHassan13
The document discusses research in computer science. It defines research as a systematic process of investigating problems to find valid answers supported by evidence. Computer science research derives from mathematics and philosophy. It involves studying computational phenomena, developing models, and investigating properties of abstract objects through formal methods. Research can be theoretical, focusing on creation and analysis of abstract models, or empirical, involving observation and experimentation. The document outlines the basic steps in theoretical and empirical computer science research processes. It also categorizes the scope of computer science research.
El documento describe dos ejercicios para analizar el flujo de materiales en procesos industriales. El primer ejercicio analiza una procesadora de pescado y pide trazar un diagrama de flujo del proceso. El segundo ejercicio, llamado "Haciendo café", pide a los participantes analizar el proceso de hacer café y establecer un balance de materiales. Se proveen datos e instrucciones para que los grupos completen los ejercicios y presenten sus resultados.
This document provides an overview of Windows Presentation Foundation (WPF). WPF introduces a new display engine based on DirectX, allowing for resolution independence, vector graphics, and leveraging of modern GPU hardware. It also integrates multimedia and provides a new .NET-based development model separating UI from logic using XAML. Key topics covered include XAML, layout panels, controls, styles/templates, data binding, and commands. WPF allows richer user interfaces, collaboration between designers and developers, and interoperability with existing code.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The document provides an introduction to the Unified Modeling Language (UML). It discusses the key principles of modeling and an overview of UML. The UML is a standard language for writing software blueprints and can be used to visualize, specify, construct, and document software systems. The UML has building blocks like classes, objects, use cases and relationships. It consists of diagrams like class diagrams, sequence diagrams, and state machine diagrams. The UML addresses multiple views of a system including use case, design, interaction and implementation views.
SOFTWARE PROJECT PLANNING on Software Engineeringwork90665
Project planning involves applying knowledge, skills, tools, and techniques to project activities to meet requirements. It includes project initiation, planning, execution, monitoring, and closure. Objectives are to define roles and responsibilities, ensure alignment with business objectives, check feasibility of schedules and requirements. Principles include planning before starting, risk analysis, tracking the project plan, and ensuring quality. Issues include time and budget management. Activities include estimating time, effort, resources, and risks, identifying critical success factors, preparing a project charter and plan, and commencing the project.
This document discusses various topics related to computer networking including protocols, sockets, IP addresses, URLs, reading web page source code, downloading files from the internet, TCP/IP and UDP servers and clients, file servers and clients, two-way communication between servers and clients, and sending simple emails. It provides information on networking hardware and software, protocols like TCP/IP and UDP, socket programming, parsing URLs, creating servers and clients, and sending/receiving data over networks.
This document provides an introduction to the Python programming language. It discusses what Python is, its key features such as being multi-purpose, object oriented, and interpreted. It describes Python's releases and popularity compared to other languages. The document also covers how to run and write Python programs, popular IDEs and code editors, installing packages with pip, categories of public Python packages, and package popularity. It discusses Python modularity with Anaconda and conda versus pip for installation.
Jupyter Notebooks allow users to write and run code interactively in the browser by combining code and rich text in a single document. They can be run locally on localhost:8888 after installing Anaconda, a Python distribution containing popular scientific libraries, or Jupyter, which is launched by typing $ jupyter notebook in a terminal. Jupyter Notebooks provide code, text, and some terminal functionality in an interactive browser-based environment for data science and scientific computing.
Introduction to Python IDLE | IDLE Tutorial | EdurekaEdureka!
The document discusses Python IDLE, an integrated development environment that comes bundled with Python. It provides features like syntax highlighting, multi-window text editing, breakpoints, and call stack visibility. IDLE can be customized using options in the Format, Edit, and Options menus. Examples are also provided on how to check for palindromes and print pyramid patterns in Python using IDLE.
The document discusses regular expressions and languages. It covers topics like regular expression operators including union, concatenation, and closure. It provides examples of regular expressions and their corresponding regular languages. It also describes methods to convert finite automata to regular expressions, including the Rij formula method, state elimination method, and Arden's method. Examples are provided to illustrate finite automata to regular expression conversions.
The document discusses Python programming language. Some key points:
- Python was invented by Guido Van Rossum and is a combination of various programming languages.
- It is a general purpose, high-level programming language that is interpreted, interactive, object-oriented and extensible.
- Python can be used for web applications, desktop applications, data analysis, artificial intelligence, and more. Popular companies that use Python include Google, YouTube, and NASA.
- Python code is portable and can run on various platforms. It has a simple syntax and is easy to learn.
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Tutorial covers all the basic knowledge of statistics and probability for Python.
Why Python for Statistics?
What is Probability?
Data and Distribution
Revisiting the Normal
Poker Prediction Use-Case
Check out our Python Tutorial Playlist: https://goo.gl/WsBpKe
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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The document provides an introduction to MATLAB and Simulink. It describes MATLAB as a numerical computing environment and matrix laboratory that is used for data analysis, algorithm development, modeling, and more across many disciplines. Simulink is introduced as a block diagram environment for multi-domain simulation and model-based design. Key features and uses of MATLAB and Simulink are outlined, including acquiring and analyzing data, developing functions and algorithms, modeling and simulation.
Cascading provides a simpler way to write MapReduce programs through data flows. It uses a pipe and tap metaphor where data flows through pipes and is read from or written to taps. This allows assembling MapReduce jobs as data flow graphs in a more logical way compared to the traditional MapReduce API.
Bca i-fundamental of computer-u-2- application and system softwareRai University
This document provides information on different types of software and software development models. It discusses:
1) The differences between system software (e.g. operating systems, compilers) which manage hardware, and application software (e.g. word processors, spreadsheets) which perform tasks for users.
2) Common software development models including the waterfall model, V-shaped model, and evolutionary prototyping model. The waterfall model follows sequential phases of requirements, design, implementation, and testing. The V-shaped model adds parallel testing phases. Prototyping allows iterative user feedback.
3) Descriptions of various system software including operating systems, compilers, linkers, loaders, and interpreters and
This document discusses various problems that can be solved using backtracking, including graph coloring, the Hamiltonian cycle problem, the subset sum problem, the n-queen problem, and map coloring. It provides examples of how backtracking works by constructing partial solutions and evaluating them to find valid solutions or determine dead ends. Key terms like state-space trees and promising vs non-promising states are introduced. Specific examples are given for problems like placing 4 queens on a chessboard and coloring a map of Australia.
The document describes using a branch and bound algorithm to solve the Travelling Salesman Problem (TSP). It starts from node 1 and explores the solution space by calculating costs of paths through different nodes. It maintains costs and paths of explored "live nodes" and explores the node with the lowest cost at each step. After node 10 with cost 28 is explored, node 11 with the same cost of 28 is explored by extending the path through node 3.
Lecture 3 Computer Science Research SEM1 22_23 (1).pptxNabilaHassan13
The document discusses research in computer science. It defines research as a systematic process of investigating problems to find valid answers supported by evidence. Computer science research derives from mathematics and philosophy. It involves studying computational phenomena, developing models, and investigating properties of abstract objects through formal methods. Research can be theoretical, focusing on creation and analysis of abstract models, or empirical, involving observation and experimentation. The document outlines the basic steps in theoretical and empirical computer science research processes. It also categorizes the scope of computer science research.
El documento describe dos ejercicios para analizar el flujo de materiales en procesos industriales. El primer ejercicio analiza una procesadora de pescado y pide trazar un diagrama de flujo del proceso. El segundo ejercicio, llamado "Haciendo café", pide a los participantes analizar el proceso de hacer café y establecer un balance de materiales. Se proveen datos e instrucciones para que los grupos completen los ejercicios y presenten sus resultados.
This document provides an overview of Windows Presentation Foundation (WPF). WPF introduces a new display engine based on DirectX, allowing for resolution independence, vector graphics, and leveraging of modern GPU hardware. It also integrates multimedia and provides a new .NET-based development model separating UI from logic using XAML. Key topics covered include XAML, layout panels, controls, styles/templates, data binding, and commands. WPF allows richer user interfaces, collaboration between designers and developers, and interoperability with existing code.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The document provides an introduction to the Unified Modeling Language (UML). It discusses the key principles of modeling and an overview of UML. The UML is a standard language for writing software blueprints and can be used to visualize, specify, construct, and document software systems. The UML has building blocks like classes, objects, use cases and relationships. It consists of diagrams like class diagrams, sequence diagrams, and state machine diagrams. The UML addresses multiple views of a system including use case, design, interaction and implementation views.
SOFTWARE PROJECT PLANNING on Software Engineeringwork90665
Project planning involves applying knowledge, skills, tools, and techniques to project activities to meet requirements. It includes project initiation, planning, execution, monitoring, and closure. Objectives are to define roles and responsibilities, ensure alignment with business objectives, check feasibility of schedules and requirements. Principles include planning before starting, risk analysis, tracking the project plan, and ensuring quality. Issues include time and budget management. Activities include estimating time, effort, resources, and risks, identifying critical success factors, preparing a project charter and plan, and commencing the project.
This document discusses various topics related to computer networking including protocols, sockets, IP addresses, URLs, reading web page source code, downloading files from the internet, TCP/IP and UDP servers and clients, file servers and clients, two-way communication between servers and clients, and sending simple emails. It provides information on networking hardware and software, protocols like TCP/IP and UDP, socket programming, parsing URLs, creating servers and clients, and sending/receiving data over networks.
This document provides an introduction to the Python programming language. It discusses what Python is, its key features such as being multi-purpose, object oriented, and interpreted. It describes Python's releases and popularity compared to other languages. The document also covers how to run and write Python programs, popular IDEs and code editors, installing packages with pip, categories of public Python packages, and package popularity. It discusses Python modularity with Anaconda and conda versus pip for installation.
Jupyter Notebooks allow users to write and run code interactively in the browser by combining code and rich text in a single document. They can be run locally on localhost:8888 after installing Anaconda, a Python distribution containing popular scientific libraries, or Jupyter, which is launched by typing $ jupyter notebook in a terminal. Jupyter Notebooks provide code, text, and some terminal functionality in an interactive browser-based environment for data science and scientific computing.
Introduction to Python IDLE | IDLE Tutorial | EdurekaEdureka!
The document discusses Python IDLE, an integrated development environment that comes bundled with Python. It provides features like syntax highlighting, multi-window text editing, breakpoints, and call stack visibility. IDLE can be customized using options in the Format, Edit, and Options menus. Examples are also provided on how to check for palindromes and print pyramid patterns in Python using IDLE.
The document discusses regular expressions and languages. It covers topics like regular expression operators including union, concatenation, and closure. It provides examples of regular expressions and their corresponding regular languages. It also describes methods to convert finite automata to regular expressions, including the Rij formula method, state elimination method, and Arden's method. Examples are provided to illustrate finite automata to regular expression conversions.
The document discusses Python programming language. Some key points:
- Python was invented by Guido Van Rossum and is a combination of various programming languages.
- It is a general purpose, high-level programming language that is interpreted, interactive, object-oriented and extensible.
- Python can be used for web applications, desktop applications, data analysis, artificial intelligence, and more. Popular companies that use Python include Google, YouTube, and NASA.
- Python code is portable and can run on various platforms. It has a simple syntax and is easy to learn.
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Tutorial covers all the basic knowledge of statistics and probability for Python.
Why Python for Statistics?
What is Probability?
Data and Distribution
Revisiting the Normal
Poker Prediction Use-Case
Check out our Python Tutorial Playlist: https://goo.gl/WsBpKe
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The document provides an introduction to MATLAB and Simulink. It describes MATLAB as a numerical computing environment and matrix laboratory that is used for data analysis, algorithm development, modeling, and more across many disciplines. Simulink is introduced as a block diagram environment for multi-domain simulation and model-based design. Key features and uses of MATLAB and Simulink are outlined, including acquiring and analyzing data, developing functions and algorithms, modeling and simulation.
Cascading provides a simpler way to write MapReduce programs through data flows. It uses a pipe and tap metaphor where data flows through pipes and is read from or written to taps. This allows assembling MapReduce jobs as data flow graphs in a more logical way compared to the traditional MapReduce API.
Connect python application with postgreSQL database using psycopg2.
Perform DDL & DML operations
Create table , Insert/update/delete and select records
Understand connection & cursor class
This document provides an overview of basic usage of the Apache Spark framework for data analysis. It describes what Spark is, how to install it, and how to use it from Scala, Python, and R. It also explains the key concepts of RDDs (Resilient Distributed Datasets), transformations, and actions. Transformations like filter, map, join, and reduce return new RDDs, while actions like collect, count, and first return results to the driver program. The document provides examples of common transformations and actions in Spark.
This is an quick introduction to Scalding and Monoids. Scalding is a Scala library that makes writing MapReduce jobs very easy. Monoids on the other hand promise parallelism and quality and they make some more challenging algorithms look very easy.
The talk was held at the Helsinki Data Science meetup on January 9th 2014.
This document discusses using Python to connect to and interact with a PostgreSQL database. It covers:
- Popular Python database drivers for PostgreSQL, including Psycopg which is the most full-featured.
- The basics of connecting to a database, executing queries, and fetching results using the DB-API standard. This includes passing parameters, handling different data types, and error handling.
- Additional Psycopg features like server-side cursors, transaction handling, and custom connection factories to access columns by name rather than number.
In summary, it provides an overview of using Python with PostgreSQL for both basic and advanced database operations from the Python side.
Introduction to source{d} Engine and source{d} Lookout source{d}
Join us for a presentation and demo of source{d} Engine and source{d} Lookout. Combining code retrieval, language agnostic parsing, and git management tools with familiar APIs parsing, source{d} Engine simplifies code analysis. source{d} Lookout, a service for assisted code review that enables running custom code analyzers on GitHub pull requests.
Dr. Hsieh is teaching how to use the state-of-the-art libraries, Spark by Apache, to conduct data analysis on hadoop platform in ISSNIP 2015, Singapore. He started with teaching the basic operations like “map, reduce, flatten, and more,” followed by explaining the extension of Spark, including MLib, GraphX, and SparkSQL.
Connecting and using PostgreSQL database with psycopg2 [Python 2.7]Dinesh Neupane
This presentation covers the basic idea of connecting postgresql database with python and psycopg2 module.
Covered Topics:
1. Psycopg2 Installation
2. Connecting to PostgreSQL Database
3. Connection Parameters
4. Create and Drop Table
5. Adaptation of Python Values to SQL Types
6. SQL Transactions
7. DML
Advance Map reduce - Apache hadoop Bigdata training by Design PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers the Advance Map reduce concepts of Hadoop and Big Data.
For training queries you can contact us:
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This document discusses connecting to a PostgresSQL database using the Psycopg2 library in Python. It provides steps for installing Psycopg2, connecting to an existing database, executing commands like creating a table, querying the database, and retrieving data. Functions for connecting, creating cursors, committing transactions, and closing connections are also overviewed. An example Python code snippet demonstrates importing Psycopg2, connecting to a database, executing a query, fetching the results, and closing the connection.
This document provides information on storing and processing big data with Apache Hadoop and Cassandra. It discusses how to install and configure Cassandra and Hadoop, perform basic operations with their command line interfaces, and implement simple MapReduce jobs in Hadoop. Key points include how to deploy Cassandra and Hadoop clusters, store and retrieve data from Cassandra using Hector and CQL, and use high-level interfaces like Hive and Pig with Hadoop.
This document discusses refactoring Java code to Clojure using macros. It provides examples of refactoring Java code that uses method chaining to equivalent Clojure code using the threading macros (->> and -<>). It also discusses other Clojure features like type hints, the doto macro, and polyglot projects using Leiningen.
We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773
The growth of Internet and other web technologies requires the development of new
algorithms and architectures for parallel and distributed computing. International journal of
Distributed and parallel systems is a bimonthly open access peer-reviewed journal aims to
publish high quality scientific papers arising from original research and development from
the international community in the areas of parallel and distributed systems. IJDPS serves
as a platform for engineers and researchers to present new ideas and system technology,
with an interactive and friendly, but strongly professional atmosphere.
Sorting Order and Stability in Sorting.
Concept of Internal and External Sorting.
Bubble Sort,
Insertion Sort,
Selection Sort,
Quick Sort and
Merge Sort,
Radix Sort, and
Shell Sort,
External Sorting, Time complexity analysis of Sorting Algorithms.
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754
LiDAR-Based System for Autonomous Cars
Autonomous Driving with LiDAR Tech
LiDAR Integration in Self-Driving Cars
Self-Driving Vehicles Using LiDAR
LiDAR Mapping for Driverless Cars
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara
A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants.
🔧 Function of a Feed Water Heater:
It uses steam extracted from the turbine to preheat the feed water.
This reduces the fuel required to convert water into steam in the boiler.
It supports Regenerative Rankine Cycle, increasing plant efficiency.
🔍 Types of Feed Water Heaters:
Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
2. psycopg is a PostgreSQL database adapter for
the Python_ programming language. This is
version 2, a complete rewrite of the original
code to provide new-style classes for
connection and cursor objects and other sweet
candies.
Homepage:
http://initd.org/projects/psycopg2
3. PyGreSQL – PostgreSQL module for Python
PyGreSQL is an open-source Python module
that interfaces to a PostgreSQL database. It
embeds the PostgreSQL query library to allow
easy use of the powerful PostgreSQL features
from a Python script.
5. Linux
Look for a package such as python-psycopg2
sudo apt-get install python-psycopg2 - to install
the package with all its dependencies.
Windows
http://www.stickpeople.com/projects/python/win-psycopg/
http://initd.org/psycopg/
6. Binary(...) Binary(buffer) -> new binary object
Build an object capable to hold a binary string value.
Date(...) - Date(year, month, day) -> new date
Build an object holding a date value.
Time(...) - Time(hour, minutes, seconds, tzinfo=None) –> new time
Build an object holding a time value.
Timestamp(...) -
Timestamp(year, month, day, hour, minutes, seconds, tzinfo=None) -
> new timestamp
Build an object holding a timestamp value
7. Create a new database connection and returns a new connection instance.
connect(dsn=None, database=None, user=None, password=None, host=No
ne, port=None, connection_factory=None, cursor_factory=None, async=Fal
se).
Parameters:
Using the *connection_factory* parameter a different class or connections
factory can be specified. It should be a callable object taking a dsn
argument.
Using the *cursor_factory* parameter, a new default cursor factory will be
used by cursor().
Using *async*=True an asynchronous connection will be created.
8. It allows to:
create new cursors using the cursor() method to execute
database commands and queries,
commit() - The changes are committed to the database.
rollback() In case of an error, we roll back any possible
changes to our database table.
9. The class cursor allows interaction with the database:
send commands to the database using methods such as execute() and
executemany(),
retrieve data from the database by iteration or using methods such as
fetchone(), fetchmany(), fetchall().
cur.description – to get metadata
copy-to – to copy db tables to a file
copy-from – to copy files to a db
scrollable()
mogrify()
10. import psycopg2
Connect to an existing database
conn=psycopg2.connect("dbname=test user=postgres")
- as a libpq connection string
conn=psycopg2.connect(database=“test” user=“postgres")
- as a set of keyword arguments
Open a cursor to perform database operations
cur = conn.cursor()
11. Psycopg cursor usually fetches all the record from database
during a query, usually which is a large dataset to be handled
in the client side, hence to do controlled transfer of data to the
client,we use server side cursors.
Psycopg wraps the database server side cursor in named
cursors. A named cursor is created using the cursor() method
specifying the name parameter.
To move in the dataset we use scroll() method and scrollable()
method to move backward as well
12. Make the changes to the database persistent
conn.commit()
Close communication with the database
cur.close()
conn.close()
15. cur.execute("DROP TABLE IF EXISTS tablename")
cur.execute("CREATE TABLE tablename (id serial
PRIMARY KEY, num integer, data varchar)“)
cur.executemany() ???????
Create and drop table
16. cur.execute("SELECT * FROM tablename")
cur.execute("INSERT INTO tablename
(num, data) VALUES (%s, %s)", ...
(100, “apple"))
insert
insertmany
17. Obtain data as Python objects
cur.fetchone(),cur.fetchall(),cur.fetchmany(5)
fetchone
fetchall
18. Always be a %s, even if a different placeholder (such as a %d for
integers or %f for floats) may look more appropriate:
cur.execute("INSERT INTO tablename VALUES (%d)", (42,)) # WRONG
cur.execute("INSERT INTO tablenameVALUES (%s)", (42,)) # correct
Named arguments are supported too using %(name)s placeholders.
cur.execute( """INSERT INTO tablename (an_int, a_date,
another_date, a_string) VALUES (%(int)s, %(date)s, %(date)s,
%(str)s);""", {'int': 10, 'str': "O'Reilly", 'date': datetime.date(2005,
11, 18)})
19. The Python string operator % is not used: the execute() method accepts a
tuple or dictionary of values as second parameter.
For positional variables binding, the second argument must always be a
sequence, even if it contains a single variable:
cur.execute("INSERT INTO foo VALUES (%s)", "bar") # WRONG
cur.execute("INSERT INTO foo VALUES (%s)", ("bar")) # WRONG
cur.execute("INSERT INTO foo VALUES (%s)", ("bar",)) # correct
cur.execute("INSERT INTO foo VALUES (%s)", ["bar"]) # correct
20. Python PostgreSQL
None NULL
Bool Bool
Float Real,double
Int,long Smallint,integer,bigint
Decimal Numeric
Str Varchar
Date Date
Datetime Timestamp
List array
Many standard Python types are adapted into SQL and returned as Python objects
when a query is executed.
Psycopg casts Python variables to SQL literals by type.