"The joy of Scala" - Maxim Novak / Wix
Around eight years ago I started my journey as a developer. Since then, I've played around with many languages and thought that C# offers the best developer productivity. After joining Wix two years ago, I was exposed to the amazing world of Scala and Functional Programming and never looked back.
In Scala the code is much more concise, less ceremonious, immutable by default, combines functional with object oriented, seamlessly interoperates with Java, and many software engineering patterns are already baked into the language. Most importantly - Scala is FUN! By the end of the session you too will, hopefully, convert to Scala and never look back.
Recording of the lecture (Hebrew) - https://youtu.be/TcnYTwff2xU
This document provides an overview of functional programming concepts in Scala including:
- Scala supports both object-oriented and functional programming paradigms.
- Functional programming avoids side effects and uses immutable data and pure functions.
- Scala supports first-class functions, higher-order functions, and functional data structures like Options and Try.
- Functional patterns like mapping, filtering, and pattern matching help express programs concisely in a referentially transparent style.
Some notes about programming in Scala: it covers Scala syntax and semantics, programming techniques, idioms, patterns. Many Scala features are introduced, from basic to intermediate and advanced. These are not introductory notes, but they assume a working knowledge with some other programming language (Java, C#, C++), object-oriented programming (OOP) concepts, and functional programming (FP) concepts.
Intro to Functional Programming in ScalaShai Yallin
Scala is a functional and object-oriented programming language that runs on the Java Virtual Machine. It features type inference, immutable collections, pattern matching, and functions as first-class values. The document provides an overview of Scala's features such as its static typing, traits for multiple inheritance, and case classes for value objects. It also demonstrates Scala's collections API and use of functions and pattern matching.
Watch video (in Hebrew): http://parleys.com/play/53f7a9cce4b06208c7b7ca1e
Type classes are a fundamental feature of Scala, which allows you to layer new functionality on top of existing types externally, i.e. without modifying or recompiling existing code. When combined with implicits, this is a truly remarkable tool that enables many of the advanced features offered by the Scala library ecosystem. In this talk we'll go back to basics: how type classes are defined and encoded, and cover several prominent use cases.
A talk given at the Underscore meetup on 19 August, 2014.
This document provides an introduction to functional programming concepts and the Scala programming language. It begins with a brief history of object-oriented programming and why it became popular. It then discusses why functional programming has gained popularity in recent years due to its ability to handle parallel, reactive and distributed systems through immutable data and avoidance of mutable state. The document introduces functional programming concepts like immutable data, first-class functions and purity. It provides an example of functional-style programming in Scala. Finally, it outlines topics that will be covered about practical functional programming in Scala, including collections, functions, pattern matching and lazy evaluation.
Slides from 12/13/12 presentation to SF Scala. Video: https://marakana.com/s/post/1354/learning_functional_programming_scala_video
This document provides an overview of Scala fundamentals including:
- Scala is a programming language for the JVM that supports both object-oriented and functional paradigms.
- It defines variables, values, lazy values, functions, types, classes, objects, traits, and higher-order functions.
- Classes can extend other classes and traits, allowing for multiple inheritance. Objects are used as singletons.
- Functional concepts like immutability, anonymous functions, and higher-order functions are supported.
Scala Intro training @ Lohika, Odessa, UA.
This is a basic Scala Programming Language overview intended to evangelize the language among any-language programmers.
Category theory concepts such as objects, arrows, and composition directly map to concepts in Scala. Objects represent types, arrows represent functions between types, and composition represents function composition. Scala examples demonstrate how category theory diagrams commute, with projection functions mapping to tuple accessors. Thinking in terms of interfaces and duality enriches both category theory and programming language concepts. Learning category theory provides a uniform way to reason about programming language structures and properties of data types.
Scala is becoming the language of choice for many development teams. This talk highlights how Scala excels in the world of multi-core processing and explores how it compares to Java 8.
Video Presentation: http://youtu.be/8vxTowBXJSg
Scala - where objects and functions meetMario Fusco
The document provides an overview of a two-day training course on Scala that covers topics like object orientation, functional programming, pattern matching, generics, traits, case classes, tuples, collections, concurrency, options and monads. The course aims to show how Scala combines object-oriented and functional programming approaches and provides examples of commonly used Scala features like classes, traits, pattern matching, generics and collections.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
Quark: A Purely-Functional Scala DSL for Data Processing & AnalyticsJohn De Goes
Quark is a new Scala DSL for data processing and analytics that runs on top of the Quasar Analytics compiler. Quark is adept at processing semi-structured data and compiles query plans to operations that run entirely inside a target data source. In this presentation, John A. De Goes provides an overview of the open source library, showing several use cases in data processing and analytics. John also demonstrates a powerful technique that every developer can use to create their own purely-functional, type-safe DSLs in the Scala programming language.
Scala is a multi-paradigm language that runs on the JVM and interoperates with Java code and libraries. It combines object-oriented and functional programming by allowing functions to be treated as objects and supports features like traits, pattern matching, and immutable data structures. The Scala compiler infers types and generates boilerplate code like getters/setters, making development more productive compared to Java. While Scala has a learning curve, it allows a more concise and scalable language for building applications.
This document discusses the history and evolution of functional programming in Java, including lambda expressions and streams. It describes how lambda expressions allow passing behaviors as arguments to methods like normal data. This improves API design, opportunities for optimization, and code readability. Streams encourage a lazy, pipelined style and can execute operations in parallel. Functional idioms like immutability and pure functions help enforce correctness and isolation of side effects.
Map(), flatmap() and reduce() are your new best friends: simpler collections,...Chris Richardson
This document summarizes a presentation about functional programming and how functions like map(), flatMap(), and reduce() can simplify collection processing, concurrency, and big data problems. The presentation introduces functional programming concepts and how languages like Java 8 have adopted these with features like lambda expressions and streams. It provides examples of how to use streams to map, filter, and reduce collections in a more declarative way compared to imperative for loops. It also discusses how functions and futures can help simplify concurrent operations by allowing asynchronous work to be expressed more clearly.
Introduction to Functional Programming in JavaScripttmont
A presentation I did for work on functional programming. It's meant as an introduction to functional programming, and I implemented the fundamentals of functional programming (Church Numerals, Y-Combinator, etc.) in JavaScript.
This document provides an introduction to the Scala programming language. It discusses what Scala is, how to get started, basic concepts like mutability and functions, and Scala features like classes, traits, pattern matching, and collections. Scala combines object-oriented and functional programming. It runs on the Java Virtual Machine and is compatible with Java. The document provides code examples to demonstrate Scala concepts and features.
The document discusses the introduction and advantages of lambda expressions and functional programming in Java 8. Some key points include:
- Lambda expressions allow passing behaviors as arguments to methods, simplifying code by removing bulky anonymous class syntax. This enables more powerful and expressive APIs.
- Streams provide a way to process collections of data in a declarative way, leveraging laziness to improve efficiency. Operations can be pipelined for fluent processing.
- Functional programming with immutable data and avoidance of side effects makes code more modular and easier to reason about, enabling optimizations like parallelism. While not natively supported, Java 8 features like lambda expressions facilitate a more functional approach.
This document provides an overview of functional programming in Scala. It begins with an introduction to functional programming basics like purity and referential transparency. It then covers functional data structures in Scala, including immutable lists. The document outlines topics on handling errors without exceptions, strict vs non-strict functions, purely functional state, and common FP structures like monoids and monads. Exercises are provided at the end to implement functions like tail, dropWhile, and foldLeft/foldRight on immutable lists.
Martin Odersky discusses the past, present, and future of Scala over the past 5 years and next 5 years. Key points include:
- Scala has grown significantly in usage and community over the past 6 years since its first release.
- Scala 2.8 will include improvements like new collections, package objects, named/default parameters, and better tool support.
- Over the next 5 years, Scala will focus on improving concurrency and parallelism through better abstractions, analyses, and static typing support.
Java 7, 8 & 9 - Moving the language forwardMario Fusco
The document summarizes new features in Java 7-8 including lambda expressions, switch on strings, try-with-resources, and the fork/join framework. Java 8 will focus on lambda expressions to provide functional programming capabilities and default methods to allow interfaces to have default implementations without breaking existing implementations. Java 9 may include additional modularization support.
Practical Functional Programming Presentation by Bogdan Hodorog3Pillar Global
Bogdan Hodorog's presentation on Practical Functional Programming at the Functional Angle Meetup help at 3Pillar's office in Timisoara. Bogdan Hodorog is a Software Engineer who is passionate about building, trying, and playing with software...of all sorts. He currently specializes in Python but is interested in programming languages ad operating systems of all kinds.
Introduction to parallel and distributed computation with sparkAngelo Leto
Lecture about Apache Spark at the Master in High Performance Computing organized by SISSA and ICTP
Covered topics: Apache Spark, functional programming, Scala, implementation of simple information retrieval programs using TFIDF and the Vector Model
This document provides an overview of the Scala programming language. Some key points:
- Scala is a statically typed language that runs on the JVM and supports both object-oriented and functional programming.
- Scala code examples demonstrate features like traits, implicit conversions, pattern matching, immutable data structures, and functional-style programming with maps, options, and pattern matching.
- Scala is used to build scalable and concurrent applications with libraries like Akka for actors and STM for software transactional memory. Popular web frameworks built on Scala include Play, Lift and Scalatra.
(How) can we benefit from adopting scala?Tomasz Wrobel
Scala offers benefits from adopting it such as increased productivity through concise and expressive code, static typing with type inference, support for both object-oriented and functional programming paradigms, and interoperability with Java. Switching from Java to Scala involves some changes like using val for immutable variables and var for mutable, but overall the syntax is quite similar which eases the transition.
This document provides an overview of the Scala programming language. It discusses Scala's JVM compatibility, basic data types like Int, Double, String, collections like List and Map, control flow structures like if/else and for loops, functions using def and =>, pattern matching, and common functions on collections like sum, length, and mean. It also shows examples of using Scala from the command line and with SBT.
Scala Intro training @ Lohika, Odessa, UA.
This is a basic Scala Programming Language overview intended to evangelize the language among any-language programmers.
Category theory concepts such as objects, arrows, and composition directly map to concepts in Scala. Objects represent types, arrows represent functions between types, and composition represents function composition. Scala examples demonstrate how category theory diagrams commute, with projection functions mapping to tuple accessors. Thinking in terms of interfaces and duality enriches both category theory and programming language concepts. Learning category theory provides a uniform way to reason about programming language structures and properties of data types.
Scala is becoming the language of choice for many development teams. This talk highlights how Scala excels in the world of multi-core processing and explores how it compares to Java 8.
Video Presentation: http://youtu.be/8vxTowBXJSg
Scala - where objects and functions meetMario Fusco
The document provides an overview of a two-day training course on Scala that covers topics like object orientation, functional programming, pattern matching, generics, traits, case classes, tuples, collections, concurrency, options and monads. The course aims to show how Scala combines object-oriented and functional programming approaches and provides examples of commonly used Scala features like classes, traits, pattern matching, generics and collections.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
Quark: A Purely-Functional Scala DSL for Data Processing & AnalyticsJohn De Goes
Quark is a new Scala DSL for data processing and analytics that runs on top of the Quasar Analytics compiler. Quark is adept at processing semi-structured data and compiles query plans to operations that run entirely inside a target data source. In this presentation, John A. De Goes provides an overview of the open source library, showing several use cases in data processing and analytics. John also demonstrates a powerful technique that every developer can use to create their own purely-functional, type-safe DSLs in the Scala programming language.
Scala is a multi-paradigm language that runs on the JVM and interoperates with Java code and libraries. It combines object-oriented and functional programming by allowing functions to be treated as objects and supports features like traits, pattern matching, and immutable data structures. The Scala compiler infers types and generates boilerplate code like getters/setters, making development more productive compared to Java. While Scala has a learning curve, it allows a more concise and scalable language for building applications.
This document discusses the history and evolution of functional programming in Java, including lambda expressions and streams. It describes how lambda expressions allow passing behaviors as arguments to methods like normal data. This improves API design, opportunities for optimization, and code readability. Streams encourage a lazy, pipelined style and can execute operations in parallel. Functional idioms like immutability and pure functions help enforce correctness and isolation of side effects.
Map(), flatmap() and reduce() are your new best friends: simpler collections,...Chris Richardson
This document summarizes a presentation about functional programming and how functions like map(), flatMap(), and reduce() can simplify collection processing, concurrency, and big data problems. The presentation introduces functional programming concepts and how languages like Java 8 have adopted these with features like lambda expressions and streams. It provides examples of how to use streams to map, filter, and reduce collections in a more declarative way compared to imperative for loops. It also discusses how functions and futures can help simplify concurrent operations by allowing asynchronous work to be expressed more clearly.
Introduction to Functional Programming in JavaScripttmont
A presentation I did for work on functional programming. It's meant as an introduction to functional programming, and I implemented the fundamentals of functional programming (Church Numerals, Y-Combinator, etc.) in JavaScript.
This document provides an introduction to the Scala programming language. It discusses what Scala is, how to get started, basic concepts like mutability and functions, and Scala features like classes, traits, pattern matching, and collections. Scala combines object-oriented and functional programming. It runs on the Java Virtual Machine and is compatible with Java. The document provides code examples to demonstrate Scala concepts and features.
The document discusses the introduction and advantages of lambda expressions and functional programming in Java 8. Some key points include:
- Lambda expressions allow passing behaviors as arguments to methods, simplifying code by removing bulky anonymous class syntax. This enables more powerful and expressive APIs.
- Streams provide a way to process collections of data in a declarative way, leveraging laziness to improve efficiency. Operations can be pipelined for fluent processing.
- Functional programming with immutable data and avoidance of side effects makes code more modular and easier to reason about, enabling optimizations like parallelism. While not natively supported, Java 8 features like lambda expressions facilitate a more functional approach.
This document provides an overview of functional programming in Scala. It begins with an introduction to functional programming basics like purity and referential transparency. It then covers functional data structures in Scala, including immutable lists. The document outlines topics on handling errors without exceptions, strict vs non-strict functions, purely functional state, and common FP structures like monoids and monads. Exercises are provided at the end to implement functions like tail, dropWhile, and foldLeft/foldRight on immutable lists.
Martin Odersky discusses the past, present, and future of Scala over the past 5 years and next 5 years. Key points include:
- Scala has grown significantly in usage and community over the past 6 years since its first release.
- Scala 2.8 will include improvements like new collections, package objects, named/default parameters, and better tool support.
- Over the next 5 years, Scala will focus on improving concurrency and parallelism through better abstractions, analyses, and static typing support.
Java 7, 8 & 9 - Moving the language forwardMario Fusco
The document summarizes new features in Java 7-8 including lambda expressions, switch on strings, try-with-resources, and the fork/join framework. Java 8 will focus on lambda expressions to provide functional programming capabilities and default methods to allow interfaces to have default implementations without breaking existing implementations. Java 9 may include additional modularization support.
Practical Functional Programming Presentation by Bogdan Hodorog3Pillar Global
Bogdan Hodorog's presentation on Practical Functional Programming at the Functional Angle Meetup help at 3Pillar's office in Timisoara. Bogdan Hodorog is a Software Engineer who is passionate about building, trying, and playing with software...of all sorts. He currently specializes in Python but is interested in programming languages ad operating systems of all kinds.
Introduction to parallel and distributed computation with sparkAngelo Leto
Lecture about Apache Spark at the Master in High Performance Computing organized by SISSA and ICTP
Covered topics: Apache Spark, functional programming, Scala, implementation of simple information retrieval programs using TFIDF and the Vector Model
This document provides an overview of the Scala programming language. Some key points:
- Scala is a statically typed language that runs on the JVM and supports both object-oriented and functional programming.
- Scala code examples demonstrate features like traits, implicit conversions, pattern matching, immutable data structures, and functional-style programming with maps, options, and pattern matching.
- Scala is used to build scalable and concurrent applications with libraries like Akka for actors and STM for software transactional memory. Popular web frameworks built on Scala include Play, Lift and Scalatra.
(How) can we benefit from adopting scala?Tomasz Wrobel
Scala offers benefits from adopting it such as increased productivity through concise and expressive code, static typing with type inference, support for both object-oriented and functional programming paradigms, and interoperability with Java. Switching from Java to Scala involves some changes like using val for immutable variables and var for mutable, but overall the syntax is quite similar which eases the transition.
This document provides an overview of the Scala programming language. It discusses Scala's JVM compatibility, basic data types like Int, Double, String, collections like List and Map, control flow structures like if/else and for loops, functions using def and =>, pattern matching, and common functions on collections like sum, length, and mean. It also shows examples of using Scala from the command line and with SBT.
Introduction à Scala - Michel Schinz - January 2010JUG Lausanne
Scala is a programming language that combines object-oriented and functional programming. It runs on the JVM and is interoperable with Java. Scala is statically typed and concise.
Scala allows modeling of concepts like rational numbers and mutable cells. Classes can implement traits to mix in functionality like logging. Pattern matching makes deconstructing data structures like lists and optional values easy. The Scala library includes collections, functions, and other functional programming constructs.
This document compares the functional programming languages Haskell and Scala. It outlines their similarities such as being high-level, statically typed, and supporting functions as first-class values. It describes some of their conceptual differences like Haskell emphasizing purity and lazy evaluation while Scala allows side effects. It then provides examples to illustrate extra features of each language, such as Haskell's pointfree style and compiler extensions, and Scala's support for object-oriented and imperative programming. Finally, it discusses some practical considerations for using each language.
Scala is a multi-paradigm programming language that runs on the JVM. It combines object-oriented and functional programming concepts. SBT is the build tool used for Scala projects and allows for incremental compilation. Scala has a uniform approach to collections that emphasizes immutability and uses higher-order
Scala is an object-oriented and functional programming language that runs on the Java Virtual Machine. It was created in 2001 by Martin Odersky and aims to integrate features of object-oriented and functional languages. Scala code is compiled to JVM bytecode and supports interoperability with Java libraries and frameworks. Some key features of Scala include support for immutable data structures, pattern matching, traits for mixing composition, and functional programming constructs like functions as first-class values. Scala has gained popularity in industry at companies like LinkedIn, Twitter, and The Guardian.
This document discusses refactoring Scala code to improve quality and maintainability. It provides examples of issues like stringly typed code, abuse of collections, and poor scoping. The presentation recommends addressing these by using case classes instead of tuples, parsing values instead of passing raw strings, giving intermediate steps meaningful names, and separating logic and data through traits or type classes. Well-scoped code with a small public API and private implementation details is emphasized.
This document provides an agenda and overview for a Spark workshop covering Spark basics and streaming. The agenda includes sections on Scala, Spark, Spark SQL, and Spark Streaming. It discusses Scala concepts like vals, vars, defs, classes, objects, and pattern matching. It also covers Spark RDDs, transformations, actions, sources, and the spark-shell. Finally, it briefly introduces Spark concepts like broadcast variables, accumulators, and spark-submit.
This document summarizes Scala concepts covered in previous sessions including object-oriented programming, functional programming, self types, type members, implicits, type classes, logic programming with types, and the Shapeless library. It provides examples and explanations of these concepts.
The document discusses real-time big data management and Apache Flink. It provides an overview of Apache Flink, including its architecture, components, and APIs for batch and streaming data processing. It also provides examples of word count programs in Java, Scala, and Java 8 that demonstrate how to write Flink programs for batch and streaming data.
The document discusses Scala and why some developers think it could replace Java on the JVM. It provides quotes from several influential developers, including the creator of Java and Groovy, expressing their view that Scala is the best candidate to replace Java in the long run. Specifically, James Gosling says that if he had to pick another language on the JVM besides Java, it would be Scala. Charlie Nutter describes Scala as the current heir apparent to the Java throne and the momentum behind Scala is now unquestionable. James Strachan says that if he had seen the Programming in Scala book in 2003, he may have never created Groovy and that his tip for a long term replacement for Java is
This document provides an introduction to functional programming concepts in Scala including mutability, functions as first-class citizens, closures, pattern matching, recursion, lazy vs eager evaluation, type classes, ad-hoc polymorphism, concurrency, and functional data structures. It also briefly mentions additional Scala concepts that could be covered like existential types, self types, structural typing, compile-time metaprogramming, reactive programming with Akka, and more advanced functional programming topics.
C# 3.0 introduces many features common in functional programming languages like generics, first-class functions, lambda expressions, and type inference. However, C# retains its object-oriented roots, and some features like datatypes and laziness remain more fully realized in pure functional languages. While C# supports programming in a functional style, its performance characteristics and lack of optimizations mean it may not be a serious competitor to ML and Haskell for functional programming tasks.
Scala collections api expressivity and brevity upgrade from javaIndicThreads
Session presented at the 6th IndicThreads.com Conference on Java held in Pune, India on 2-3 Dec. 2011.
http://Java.IndicThreads.com
LISP is a programming language invented in 1958 that uses two simple data structures - atoms and lists. It heavily relies on recursion and functional programming. LISP defines all data as lists and represents programs as nested function calls, allowing for dynamic typing and easy abstraction. It introduced many concepts still used in modern languages, including conditionals, recursion, dynamic typing, garbage collection, and representing programs as mathematical expressions.
This document provides an overview of Scala for Java developers. It covers Scala basics like running on the JVM and supporting functional programming. It also summarizes key Scala syntax like type declarations, expressions, loops, functions, classes, traits, objects, and collections. The document compares Java and Scala concepts and provides resources to learn more about Scala.
This study will provide the audience with an understanding of the capabilities of soft tools such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Model Trees (MT), and Multi-Gene Genetic Programming (MGGP) as a statistical downscaling tool. Many projects are underway around the world to downscale the data from Global Climate Models (GCM). The majority of the statistical tools have a lengthy downscaling pipeline to follow. To improve its accuracy, the GCM data is re-gridded according to the grid points of the observed data, standardized, and, sometimes, bias-removal is required. The current work suggests that future precipitation can be predicted by using precipitation data from the nearest four grid points as input to soft tools and observed precipitation as output. This research aims to estimate precipitation trends in the near future (2021-2050), using 5 GCMs, for Pune, in the state of Maharashtra, India. The findings indicate that each one of the soft tools can model the precipitation with excellent accuracy as compared to the traditional method of Distribution Based Scaling (DBS). The results show that ANN models appear to give the best results, followed by MT, then MGGP, and finally SVR. This work is one of a kind in that it provides insights into the changing monsoon season in Pune. The anticipated average precipitation levels depict a rise of 300–500% in January, along with increases of 200-300% in February and March, and a 100-150% increase for April and December. In contrast, rainfall appears to be decreasing by 20-30% between June and September.
Third Review PPT that consists of the project d etails like abstract.Sowndarya6
CyberShieldX is an AI-driven cybersecurity SaaS web application designed to provide automated security analysis and proactive threat mitigation for business websites. As cyber threats continue to evolve, traditional security tools like OpenVAS and Nessus require manual configurations and lack real-time automation. CyberShieldX addresses these limitations by integrating AI-powered vulnerability assessment, intrusion detection, and security maintenance services. Users can analyze their websites by simply submitting a URL, after which CyberShieldX conducts an in-depth vulnerability scan using advanced security tools such as OpenVAS, Nessus, and Metasploit. The system then generates a detailed report highlighting security risks, potential exploits, and recommended fixes. Premium users receive continuous security monitoring, automatic patching, and expert assistance to fortify their digital infrastructure against emerging threats. Built on a robust cloud infrastructure using AWS, Docker, and Kubernetes, CyberShieldX ensures scalability, high availability, and efficient security enforcement. Its AI-driven approach enhances detection accuracy, minimizes false positives, and provides real-time security insights. This project will cover the system's architecture, implementation, and its advantages over existing security solutions, demonstrating how CyberShieldX revolutionizes cybersecurity by offering businesses a smarter, automated, and proactive defense mechanism against ever-evolving cyber threats.
Call For Papers - International Journal on Natural Language Computing (IJNLC)kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a
set of theories and a set of technologies. This forum aims to bring together researchers who have
designed and build software that will analyze, understand, and generate languages that humans use
naturally to address computers.
Rearchitecturing a 9-year-old legacy Laravel application.pdfTakumi Amitani
An initiative to re-architect a Laravel legacy application that had been running for 9 years using the following approaches, with the goal of improving the system’s modifiability:
・Event Storming
・Use Case Driven Object Modeling
・Domain Driven Design
・Modular Monolith
・Clean Architecture
This slide was used in PHPxTKY June 2025.
https://phpxtky.connpass.com/event/352685/
Advanced Automation and Technology in Coal Handling PlantsInfopitaara
1. Overview of Coal Handling Plants
Basic functions: unloading, conveying, crushing, screening, storage, and feeding.
Traditional methods and their limitations.
Importance of coal handling to overall plant efficiency.
2. Key Challenges in Traditional Coal Handling
Dust pollution and health hazards.
Coal spillage and loss.
Equipment breakdown and maintenance costs.
Energy consumption and environmental concerns.
Labor-intensive operations and safety risks.
3. Emerging Technologies in Coal Handling
3.1 Automation and Control Systems
Programmable Logic Controllers (PLCs) and SCADA systems for real-time control.
Automated conveyor belt speed control and smart routing.
3.2 Internet of Things (IoT) Integration
Sensors for temperature, vibration, dust, and coal flow monitoring.
Data collection and remote operation capabilities.
3.3 Artificial Intelligence and Predictive Maintenance
AI algorithms analyzing sensor data to predict equipment failure.
Minimizing downtime and maintenance costs.
3.4 Advanced Dust Suppression Systems
Water misting, fogging systems with smart control.
Use of chemical suppressants and air filtration units.
3.5 Energy-Efficient Equipment
Variable Frequency Drives (VFDs) on motors and conveyors.
Regenerative braking and energy recovery systems.
3.6 Robotics and Remote Operation
Use of robotic arms for coal sampling, inspection, and minor repairs.
Remote-controlled equipment to improve safety.
4. Benefits of New Technologies
Improved operational efficiency and throughput.
Enhanced worker safety and reduced health risks.
Lower environmental footprint and compliance with emission standards.
Cost savings from reduced maintenance and energy consumption.
Data-driven decision making and better resource management.
5. Case Studies / Real-World Implementations
Examples of coal handling plants adopting new technologies.
Impact on performance metrics and ROI.
6. Implementation Challenges
Initial capital investment and return on investment timelines.
Training workforce for new technology adoption.
Integration with existing infrastructure.
Cybersecurity concerns in digital systems.
7. Future Trends in Coal Handling Plant Technology
Blockchain for supply chain transparency.
Augmented reality (AR) for maintenance and training.
Further AI and machine learning enhancements.
Sustainability-focused innovations like carbon capture integration.
The development of smart cities holds immense significance in shaping a nation's urban fabric and effectively addressing urban challenges that profoundly impact the economy. Among these challenges, road accidents pose a significant obstacle to urban progress, affecting lives, supply chain efficiency, and socioeconomic well-being. To address this issue effectively, accurate forecasting of road accidents is crucial for policy formulation and enhancing safety measures. Time series forecasting of road accidents provides invaluable insights for devising strategies, enabling swift actions in the short term to reduce accident rates, and informing well-informed road design and safety management policies for the long term, including the implementation of flyovers, and the enhancement of road quality to withstand all weather conditions. Deep Learning's exceptional pattern recognition capabilities have made it a favored approach for accident forecasting. The study comprehensively evaluates deep learning models, such as RNN, LSTM, CNN+LSTM, GRU, Transformer, and MLP, using a ten-year dataset from the esteemed Smart Road Accident Database in Hubballi-Dharwad. The findings unequivocally underscore LSTM's superiority, exhibiting lower errors in both yearly (RMSE: 0.291, MAE: 0.271, MAPE: 6.674%) and monthly (RMSE: 0.186, MAE: 0.176, MAPE: 5.850%) variations. Based on these compelling findings, the study provides strategic recommendations to urban development authorities, emphasizing comprehensive policy frameworks encompassing short-term and long-term measures to reduce accident rates alongside meticulous safety measures and infrastructure planning. By leveraging insights from deep learning models, urban development authorities can adeptly shape the urban landscape, fostering safer environments and contributing to global safety and prosperity.
Characterization of Polymeric Materials by Thermal Analysis, Spectroscopy an...1SI20ME092ShivayogiB
3d Printing Nano composites As the world of technology continually drives the scientific community and the
development of innovative instrumentation, it is important for the analytical chemist to
be certain to take advantage of the wide range of knowledge that can be gained by using
multiple modes of analysis. No single instrument is capable of entirely characterizing a
material; therefore, the knowledge gained from multiple modes of analysis must be
pieced together in order to provide the most accurate description of the sample. Using a
single method only provides one dimension, but with the use of additional methods the
analysis is multi-faceted. Instrument systems are designed to gather a distinct set of
data, with no single system providing complete analysis. By coupling traditional
thermal analysis techniques such as thermogravimetric (TGA), thermomechanical
(TMA), and dynamic scanning calorimetry (DSC) with spectroscopic techniques such
as Fourier Transform Infrared (FTIR), mass spectroscopy (MS), and X-ray diffraction
(XRD), all aspects surrounding the materials physical and chemical properties can be
determined almost entirely. Specifically the importance of evolved gas analysis (EGA),
thermal-IR, XRD, and micro-thermal analysis will be discussed.
This presentation highlights project development using software development life cycle (SDLC) with a major focus on incorporating research in the design phase to develop innovative solution. Some case-studies are also highlighted which makes the reader to understand the different phases with practical examples.
4. History
2003 - Released in EPFL
2008 - ‘Programming in Scala’
2010 - Version 2.8
2011 - Typesafe Inc. founded
2012 - First course on Coursera
2013 - Version 2.10
5. Recipe
● Take Java-like syntax
● Remove specifics and
unnecessary limitations
● Mix in functional features*
6. Complexity
● About 40 reserved words
● Only 2 namespaces vs. 4 in Java (fields, methods,
packages, types)
● Methods instead of operators
● No primitives (AnyVal)
23. Imperative example
def concat(list: List[Any]): String = {
val iter = list.iterator
var result = ""
while (iter.hasNext) {
result += iter.next
}
result
}
54. Try
def parse(js: Js): String
def store(name: String): Int
for {
name <- Try(parse(json))
id <- Try(store(name))
} yield id
scala.util
Failure[+T]
scala.util
Success[+T]
scala.util
Try[+T]
55. Future
def query(q: String): List[String]
val resF = Future { query(q) }
resF.onSuccess {
case list => …
}
resF.onFailure {
case ex => …
}
scala.concurrent
Future[+T]
56. Future
def query(q: String): List[String]
def store(l: List[String]): Int
queryF.onSuccess {
case list => {
val storeF = Future { store(list) }
storeF.onSuccess { case id => … }
}
}
scala.concurrent
Future[+T]
57. Future
val resultsF = Future{ query("WTF") }
for {
results <- resultsF
id <- Future{ store(results) }
} yield id
scala.concurrent
Future[+T]
58. Wait... there’s more!
● object-oriented programming
● arcane things
● actors
● ecosystem
● tools and frameworks
59. The book
● Suited for people with no prior
experience in Scala
● Includes functional programming tutorial
● Focuses on Play 2.5
● ScalikeJDBC, MacWire, Akka
● Webpack, Sass, React, EcmaScript 6