This document provides an overview of formal relational query languages, including relational algebra, tuple relational calculus, and domain relational calculus. It discusses the basic operators of relational algebra like select, project, union, and difference. It also provides examples of queries expressed in both tuple relational calculus and domain relational calculus, and covers concepts like safety of expressions. The document is from the 6th edition of the textbook "Database System Concepts" and is intended to teach formal query languages for relational databases.
This document discusses formal relational query languages, including relational algebra, tuple relational calculus, and domain relational calculus. Relational algebra is a procedural query language that uses operators like select, project, join, and set difference. Tuple relational calculus and domain relational calculus are nonprocedural query languages that use predicates and quantifiers to specify queries. Examples of queries written in each language are provided to illustrate their syntax and capabilities.
Relational Algebra and relational queries .pptShahidSultan24
This document describes chapter 6 of the textbook "Database System Concepts, 6th Ed." which covers formal relational query languages. It introduces relational algebra as a procedural query language with basic operators like select, project, union, set difference, cartesian product, and rename. It also covers tuple and domain relational calculus. Examples of relational algebra queries are provided to find the largest salary or names of instructors and courses taught. Additional relational algebra concepts like composition of operations, set intersection, natural join, assignment, and outer join are also summarized.
The document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts". It introduces the relational model and relational algebra. The relational model uses relations (tables) to store data and relational algebra contains operations like select, project, join etc. to manipulate these relations. Some key points covered are the structure of relational databases, database schema, keys, relational query languages like SQL, and the core operators of relational algebra.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. It also covers modifying the database using INSERT, DELETE, ALTER and DROP statements.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It discusses the history and standards of SQL, the data definition language for creating tables with attributes and constraints, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries, string operations and more. Examples are provided throughout to illustrate SQL concepts and syntax.
This document discusses formal relational query languages, including relational algebra, tuple relational calculus, and domain relational calculus. Relational algebra is a procedural query language that uses operators like select, project, join, and set difference. Tuple relational calculus and domain relational calculus are nonprocedural query languages that use predicates and quantifiers to specify queries. Examples of queries written in each language are provided to illustrate their syntax and capabilities.
Relational Algebra and relational queries .pptShahidSultan24
This document describes chapter 6 of the textbook "Database System Concepts, 6th Ed." which covers formal relational query languages. It introduces relational algebra as a procedural query language with basic operators like select, project, union, set difference, cartesian product, and rename. It also covers tuple and domain relational calculus. Examples of relational algebra queries are provided to find the largest salary or names of instructors and courses taught. Additional relational algebra concepts like composition of operations, set intersection, natural join, assignment, and outer join are also summarized.
The document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts". It introduces the relational model and relational algebra. The relational model uses relations (tables) to store data and relational algebra contains operations like select, project, join etc. to manipulate these relations. Some key points covered are the structure of relational databases, database schema, keys, relational query languages like SQL, and the core operators of relational algebra.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. It also covers modifying the database using INSERT, DELETE, ALTER and DROP statements.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It discusses the history and standards of SQL, the data definition language for creating tables with attributes and constraints, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries, string operations and more. Examples are provided throughout to illustrate SQL concepts and syntax.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. The document is made up of multiple slides that cover these SQL topics at a high-level.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It introduces SQL, covering its history, data definition language, data types, CREATE TABLE statement, integrity constraints, updating tables, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It introduces SQL, covering its history, data definition language, data types, CREATE TABLE statement, integrity constraints, updating tables, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
The document discusses relational algebra and tuple relational calculus. It defines the basic operators of relational algebra including selection, projection, union, difference, Cartesian product, and rename. It provides examples of how to write queries using each operator. It also discusses tuple relational calculus, defining domains, predicates, quantifiers, and how to write safe queries using this calculus.
This document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts" by Silberschatz, Korth and Sudarshan. It introduces the relational model, including the structure of relational databases, relational algebra operations, null values, and modification of databases. Key concepts covered include relations, tuples, relation schemas, keys, and the basic relational algebra operations of select, project, join, union, difference and rename. An example of a banking database with relations for branches, customers, accounts and loans is also provided.
This document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts" by Silberschatz, Korth and Sudarshan. It discusses the relational model, including the structure of relational databases, relational algebra operations, keys, relations and relation schemas. Examples are provided to illustrate relational algebra operations like select, project, join and set operations. The chapter also introduces an example banking database to demonstrate sample queries using relational algebra.
This document provides an overview of SQL and relational database concepts. It describes the history and standards of SQL, data definition and domain types in SQL, basic query structure including the SELECT, FROM, and WHERE clauses, and DML operations like INSERT, DELETE, and ALTER TABLE. Examples of table schemas and queries involving joins, aggregation, and renaming are provided to illustrate SQL syntax and capabilities.
This document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts" by Silberschatz, Korth and Sudarshan. It discusses the relational model, including the structure of relational databases, relational algebra operations, keys such as primary and foreign keys, and query languages. Example relations are provided to illustrate concepts like the select, project, union and cartesian product operations in relational algebra.
This document provides an overview of relational database concepts including:
- Relations are represented as tables with rows (tuples) and columns (attributes)
- Relations have a schema and can be queried using relational algebra which includes operations like select, project, join, and more
- Databases consist of multiple relations that store different parts of the information about an enterprise to avoid data repetition and null values
The document describes the relational model used in database systems. It defines key concepts like relations, relation schemas, tuples, attributes, domains, keys, and foreign keys. It also explains the basic relational algebra operations like select, project, join, union, difference etc. and provides examples of how to write queries using these operations on sample relations like customer, account, loan etc. to retrieve required information from the database.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 7th Ed." by Silberschatz, Korth and Sudarshan. It covers the history and components of SQL, data definition and manipulation languages, basic query structure, predicates, null values, and set operations in SQL. Key topics include the CREATE TABLE statement, data types, integrity constraints, SELECT statements, joins, ordering results, and aggregate functions.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
The document discusses query optimization in database systems. It covers topics like:
- Estimating the cost of different query evaluation plans using statistical information about relations.
- Transforming relational expressions using equivalence rules to generate logically equivalent expressions with different evaluation orders.
- Choosing the lowest cost plan based on cost estimates to optimize query evaluation.
This chapter discusses other relational query languages beyond SQL, including tuple relational calculus, domain relational calculus, and query-by-example (QBE). Tuple relational calculus allows queries to select tuples where a predicate is true. Domain relational calculus uses domain variables instead of tuple variables and is equivalent in power. Example queries are provided to illustrate how to express various conditions and relationships between relations in these languages. Safety restrictions ensure queries return finite relations.
The document discusses key concepts of the relational database model from Chapter 2 of the textbook "Database System Concepts, 6th Edition". It describes the structure of relations, which are tables made up of rows and columns. It defines entity types like attributes and tuples, and explains primary keys, foreign keys, and relationship types like one-to-one and one-to-many. It also introduces the algebraic operations of the relational algebra, which provides a declarative query language for relational databases including selection, projection, join, union and set differences.
This document discusses relational database design and functional dependencies. It covers topics like normal forms, decomposition using functional dependencies, closure of functional dependencies, and different types of functional dependencies like trivial, non-trivial, transitive, and multivalued dependencies. Examples are provided to illustrate concepts like lossy decomposition and closure of a set of functional dependencies.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. The document is made up of multiple slides that cover these SQL topics at a high-level.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It introduces SQL, covering its history, data definition language, data types, CREATE TABLE statement, integrity constraints, updating tables, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. It introduces SQL, covering its history, data definition language, data types, CREATE TABLE statement, integrity constraints, updating tables, basic query structure using SELECT, FROM, and WHERE clauses, and examples of joins, renaming, and self joins.
The document discusses relational algebra and tuple relational calculus. It defines the basic operators of relational algebra including selection, projection, union, difference, Cartesian product, and rename. It provides examples of how to write queries using each operator. It also discusses tuple relational calculus, defining domains, predicates, quantifiers, and how to write safe queries using this calculus.
This document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts" by Silberschatz, Korth and Sudarshan. It introduces the relational model, including the structure of relational databases, relational algebra operations, null values, and modification of databases. Key concepts covered include relations, tuples, relation schemas, keys, and the basic relational algebra operations of select, project, join, union, difference and rename. An example of a banking database with relations for branches, customers, accounts and loans is also provided.
This document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts" by Silberschatz, Korth and Sudarshan. It discusses the relational model, including the structure of relational databases, relational algebra operations, keys, relations and relation schemas. Examples are provided to illustrate relational algebra operations like select, project, join and set operations. The chapter also introduces an example banking database to demonstrate sample queries using relational algebra.
This document provides an overview of SQL and relational database concepts. It describes the history and standards of SQL, data definition and domain types in SQL, basic query structure including the SELECT, FROM, and WHERE clauses, and DML operations like INSERT, DELETE, and ALTER TABLE. Examples of table schemas and queries involving joins, aggregation, and renaming are provided to illustrate SQL syntax and capabilities.
This document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts" by Silberschatz, Korth and Sudarshan. It discusses the relational model, including the structure of relational databases, relational algebra operations, keys such as primary and foreign keys, and query languages. Example relations are provided to illustrate concepts like the select, project, union and cartesian product operations in relational algebra.
This document provides an overview of relational database concepts including:
- Relations are represented as tables with rows (tuples) and columns (attributes)
- Relations have a schema and can be queried using relational algebra which includes operations like select, project, join, and more
- Databases consist of multiple relations that store different parts of the information about an enterprise to avoid data repetition and null values
The document describes the relational model used in database systems. It defines key concepts like relations, relation schemas, tuples, attributes, domains, keys, and foreign keys. It also explains the basic relational algebra operations like select, project, join, union, difference etc. and provides examples of how to write queries using these operations on sample relations like customer, account, loan etc. to retrieve required information from the database.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 7th Ed." by Silberschatz, Korth and Sudarshan. It covers the history and components of SQL, data definition and manipulation languages, basic query structure, predicates, null values, and set operations in SQL. Key topics include the CREATE TABLE statement, data types, integrity constraints, SELECT statements, joins, ordering results, and aggregate functions.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
The document discusses query optimization in database systems. It covers topics like:
- Estimating the cost of different query evaluation plans using statistical information about relations.
- Transforming relational expressions using equivalence rules to generate logically equivalent expressions with different evaluation orders.
- Choosing the lowest cost plan based on cost estimates to optimize query evaluation.
This chapter discusses other relational query languages beyond SQL, including tuple relational calculus, domain relational calculus, and query-by-example (QBE). Tuple relational calculus allows queries to select tuples where a predicate is true. Domain relational calculus uses domain variables instead of tuple variables and is equivalent in power. Example queries are provided to illustrate how to express various conditions and relationships between relations in these languages. Safety restrictions ensure queries return finite relations.
The document discusses key concepts of the relational database model from Chapter 2 of the textbook "Database System Concepts, 6th Edition". It describes the structure of relations, which are tables made up of rows and columns. It defines entity types like attributes and tuples, and explains primary keys, foreign keys, and relationship types like one-to-one and one-to-many. It also introduces the algebraic operations of the relational algebra, which provides a declarative query language for relational databases including selection, projection, join, union and set differences.
This document discusses relational database design and functional dependencies. It covers topics like normal forms, decomposition using functional dependencies, closure of functional dependencies, and different types of functional dependencies like trivial, non-trivial, transitive, and multivalued dependencies. Examples are provided to illustrate concepts like lossy decomposition and closure of a set of functional dependencies.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.