New Titles

Head first design patterns : Building extensible & maintainable object oriented software

Freeman, Eric.

Included Index. Auf dem Cover: Building extensible and maintainable object-oriented software. - Previous edition: Sebastopol, California: O'Reilly, 2004 Includes index. You know you don't want to reinvent the wheel, so you look to Design Patterns: the lessons learned by those who've faced the same software design problems. With Design Patterns, you get to take advantage of the best practices and experience of others so you can spend your time on something more challenging. Something more fun. This book shows you the patterns that matter, when to use them and why, how to apply them to your own designs, and the object-oriented design principles on which they're based. Join hundreds of thousands of developers who've improved their object-oriented design skills through Head First Design Patterns. What's so special about this book? If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. With Head First Design Patterns, 2E you'll learn design principles and patterns in a way that won't put you to sleep, so you can get out there to solve software design problems and speak the language of patterns with others on your team.

Date Published:2021

Data visualization with python and javascript : Scrape, clean, explore, and transform your data

Dale, Kyran.

Includes bibliographical references and index. Cover Copyright Table of Contents Preface The Second Edition Conventions Used in This Book Using Code Examples O'Reilly Online Learning How to Contact Us Acknowledgments Second Edition Introduction Who This Book Is For Minimal Requirements to Use This Book Why Python and JavaScript? Why Not Python in the Browser? Why Python for Data Processing Python's Getting Better All the Time What You'll Learn The Choice of Libraries Preliminaries The Dataviz Toolchain 1. Scraping Data with Scrapy 2. Cleaning Data with pandas 3. Exploring Data with pandas and Matplotlib 4. Delivering Your Data with Flask 5. Transforming Data into Interactive Visualizations with Plotly and D3 Smaller Libraries Using the Book A Little Bit of Context Summary Recommended Books Part I. Basic Toolkit Chapter 1. Development Setup The Accompanying Code Python Anaconda Installing Extra Libraries Virtual Environments JavaScript Content Delivery Networks Installing Libraries Locally Databases Getting MongoDB Up and Running Easy MongoDB with Docker Integrated Development Environments Summary Chapter 2. A Language-Learning Bridge Between Python and JavaScript Similarities and Differences Interacting with the Code Python JavaScript Basic Bridge Work Style Guidelines, PEP 8, and use strict CamelCase Versus Underscore Importing Modules, Including Scripts JavaScript Modules Keeping Your Namespaces Clean Outputting "Hello World!" Simple Data Processing String Construction Significant Whitespace Versus Curly Brackets Comments and Doc-Strings Declaring Variables Using let or var Strings and Numbers Booleans Data Containers: dicts, objects, lists, Arrays Functions. Iterating: for Loops and Functional Alternatives Conditionals: if, else, elif, switch File Input and Output Classes and Prototypes Differences in Practice Method Chaining Enumerating a List Tuple Unpacking Collections Underscore Functional Array Methods and List Comprehensions Map, Reduce, and Filter with Python's Lambdas JavaScript Closures and the Module Pattern A Cheat Sheet Summary Chapter 3. Reading and Writing Data with Python Easy Does It Passing Data Around Working with System Files CSV, TSV, and Row-Column Data Formats JSON Dealing with Dates and Times SQL Creating the Database Engine Defining the Database Tables Adding Instances with a Session Querying the Database Easier SQL with Dataset MongoDB Dealing with Dates, Times, and Complex Data Summary Chapter 4. Webdev 101 The Big Picture Single-Page Apps Tooling Up The Myth of IDEs, Frameworks, and Tools A Text-Editing Workhorse Browser with Development Tools Terminal or Command Prompt Building a Web Page Serving Pages with HTTP The DOM The HTML Skeleton Marking Up Content CSS JavaScript Data Chrome DevTools The Elements Tab The Sources Tab Other Tools A Basic Page with Placeholders Positioning and Sizing Containers with Flex Filling the Placeholders with Content Scalable Vector Graphics The < g> Element Circles Applying CSS Styles Lines, Rectangles, and Polygons Text Paths Scaling and Rotating Working with Groups Layering and Transparency JavaScripted SVG Summary Part II. Getting Your Data Chapter 5. Getting Data Off the Web with Python Getting Web Data with the Requests Library Getting Data Files with Requests Using Python to Consume Data from a Web API. Consuming a RESTful Web API with Requests Getting Country Data for the Nobel Dataviz Using Libraries to Access Web APIs Using Google Spreadsheets Using the Twitter API with Tweepy Scraping Data Why We Need to Scrape Beautiful Soup and lxml A First Scraping Foray Getting the Soup Selecting Tags Crafting Selection Patterns Caching the Web Pages Scraping the Winners' Nationalities Summary Chapter 6. Heavyweight Scraping with Scrapy Setting Up Scrapy Establishing the Targets Targeting HTML with Xpaths Testing Xpaths with the Scrapy Shell Selecting with Relative Xpaths A First Scrapy Spider Scraping the Individual Biography Pages Chaining Requests and Yielding Data Caching Pages Yielding Requests Scrapy Pipelines Scraping Text and Images with a Pipeline Specifying Pipelines with Multiple Spiders Summary Part III. Cleaning and Exploring Data with pandas Chapter 7. Introduction to NumPy The NumPy Array Creating Arrays Array Indexing and Slicing A Few Basic Operations Creating Array Functions Calculating a Moving Average Summary Chapter 8. Introduction to pandas Why pandas Is Tailor-Made for Dataviz Why pandas Was Developed Categorizing Data and Measurements The DataFrame Indices Rows and Columns Selecting Groups Creating and Saving DataFrames JSON CSV Excel Files SQL MongoDB Series into DataFrames Summary Chapter 9. Cleaning Data with pandas Coming Clean About Dirty Data Inspecting the Data Indices and pandas Data Selection Selecting Multiple Rows Cleaning the Data Finding Mixed Types Replacing Strings Removing Rows Finding Duplicates Sorting Data Removing Duplicates Dealing with Missing Fields Dealing with Times and Dates The Full clean_data Function. Adding the born_in column Merging DataFrames Saving the Cleaned Datasets Summary Chapter 10. Visualizing Data with Matplotlib pyplot and Object-Oriented Matplotlib Starting an Interactive Session Interactive Plotting with pyplot's Global State Configuring Matplotlib Setting the Figure's Size Points, Not Pixels Labels and Legends Titles and Axes Labels Saving Your Charts Figures and Object-Oriented Matplotlib Axes and Subplots Plot Types Bar Charts Scatter Plots seaborn FacetGrids PairGrids Summary Chapter 11. Exploring Data with pandas Starting to Explore Plotting with pandas Gender Disparities Unstacking Groups Historical Trends National Trends Prize Winners Per Capita Prizes by Category Historical Trends in Prize Distribution Age and Life Expectancy of Winners Age at Time of Award Life Expectancy of Winners Increasing Life Expectancies over Time The Nobel Diaspora Summary Part IV. Delivering the Data Chapter 12. Delivering the Data Serving the Data Organizing Your Flask Files Serving Data with Flask Delivering Data Files Dynamic Data with Flask APIs A Simple Data API with Flask Using Static or Dynamic Delivery Summary Chapter 13. RESTful Data with Flask The Tools for a RESTful Job Creating the Database A Flask RESTful Data Server Serializing with marshmallow Adding our RESTful API Routes Posting Data to the API Extending the API with MethodViews Paginating the Data Returns Deploying the API Remotely with Heroku CORS Consuming the API Using JavaScript Summary Part V. Visualizing Your Data with D3 and Plotly Chapter 14. Bringing Your Charts to the Web with Matplotlib and Plotly Static Charts with Matplotlib Adapting to Screen Sizes. Using Remote Images or Assets Charting with Plotly Basic Charts Plotly Express Plotly Graph-Objects Mapping with Plotly Adding Custom Controls with Plotly From Notebook to Web with Plotly Native JavaScript Charts with Plotly Fetching JSON Files User-Driven Plotly with JavaScript and HTML Summary Chapter 15. Imagining a Nobel Visualization Who Is It For? Choosing Visual Elements Menu Bar Prizes by Year A Map Showing Selected Nobel Countries A Bar Chart Showing Number of Winners by Country A List of the Selected Winners A Mini-Biography Box with Picture The Complete Visualization Summary Chapter 16. Building a Visualization Preliminaries Core Components Organizing Your Files Serving the Data The HTML Skeleton CSS Styling The JavaScript Engine Importing the Scripts Modular JS with Imports Basic Data Flow The Core Code Initializing the Nobel Prize Visualization Ready to Go Data-Driven Updates Filtering Data with Crossfilter Running the Nobel Prize Visualization App Summary Chapter 17. Introducing D3-The Story of a Bar Chart Framing the Problem Working with Selections Adding DOM Elements Leveraging D3 Measuring Up with D3's Scales Quantitative Scales Ordinal Scales Unleashing the Power of D3 with Data Binding/Joining Updating the DOM with Data Putting the Bar Chart Together Axes and Labels Transitions Updating the Bar Chart Summary Chapter 18. Visualizing Individual Prizes Building the Framework Scales Axes Category Labels Nesting the Data Adding the Winners with a Nested Data-Join A Little Transitional Sparkle Updating the Bar Chart Summary Chapter 19. Mapping with D3 Available Maps D3's Mapping Data Formats GeoJSON TopoJSON. Chapter 2. A Language-Learning Bridge Between Python and JavaScript -- Similarities and Differences -- Interacting with the Code -- Python -- JavaScript -- Basic Bridge Work -- Style Guidelines, PEP 8, and use strict -- CamelCase Versus Underscore -- Importing Modules, Including Scripts -- JavaScript Modules -- Keeping Your Namespaces Clean -- Outputting "Hello World!" -- Simple Data Processing -- String Construction -- Significant Whitespace Versus Curly Brackets -- Comments and Doc-Strings -- Declaring. How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries Read less.

Date Published:2023

Brand follower personality congruence and follower engagement

Habib, Fatima,

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Management to the Department of FAST School of Management. Chapter 1: Introduction- Chapter 1.1: Research Background- Chapter 1.2: Research Context- Chapter 1.3: Research Focus- Chapter 1.4: Research Objectives- Chapter 1.5: Research Questions- Chapter 1.6: Significance of the Research- Chapter 1.7: Thesis Structure- Chapter 2: Literature Review- Chapter 2.1: Introduction- Chapter 2.2: Study Work- Chapter 2.3: Chapter Summary- Chapter 3: Methodology- Chapter 4: Results and Analysis- Chapter 5: Discussions- Chapter 6: Conclusion- Chapter 6.1: Introduction- Chapter 6.2: Collective Conclusion- Chapter 6.3: Limitations of the Mixed Methods Study- Chapter 6.4: Managerial Implications Of the Mixed Methods study- Chapter 6.5: Future Research Directions. In the dynamic digital landscape of business today, brands strive to enhance follower engagement for sustainable brand presence. Understanding follower behavior is critical, as brand-follower personality congruence (BFPC) can significantly influence follower behavior. Furthermore, the recent evolution of big data technologies enables brands to assess and predict follower personality traits at an unprecedented scale. Catering to these needs requires a comprehensive approach that supplements traditional research with computationally advanced techniques.This research addresses this challenge by employing a mixed-methods approach to offer a multi-faceted yet holistic understanding of BFPC and its impact, grounded in the Big Five Personality Model. In the first study, this research analyzes a moderated mediation model that examines the influence of BFPC on the behavioral aspect of follower engagement in the mobile applications industry. The research considers the moderating role of brand knowledge and the mediating role of perceived usefulness. Building upon the theory of self-congruity and the Technology Acceptance Model, this study offers a theoretical foundation for understanding how personality alignment and perceived utility influence meaningful digital engagement. Data collected from 405 mobile application users and analyzed using a PLS-SEM revealed that higher congruence significantly enhances the perceived usefulness of the application, leading to increased behavioral engagement. Furthermore, followers with higher levels of brand knowledge exhibit a stronger perception of perceived usefulness. Study 2 complements this research by investigating how advanced computational models can predict human personality traits. Pre-trained Large Language Models (LLMs) are effective but require substantial computational resources, escalating their carbon and water footprint. This compares a small model ALBERT (l 1.8M parameters) with a larger model, RoBERTa (125M parameters) in predicting Big Five Personality traits using the PANDORA dataset. Results are evaluated on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), and computational resource consumption. 'While ALBERT consumed lower levels of system memory with lower heat emission, it took higher computation time compared to RoBERTa. Notably, the study produced comparable levels of MSE, RMSE, and training loss reduction. This highlights the influence of training data quality on the model's performance, outweighing the significance of model size. By framing this concern through the lens of Green Al, the study positions sustainable digital innovation and ethical AI management as central contributions of this research. In doing so, the study not only advances computational modeling for personality assessment but also offers pathways for researchers and marketing managers to design data• driven strategies that minimize environmental impact.This dissertation offers actionable insights for marketers, brand managers, and data scientists by combining quantitative analysis with computational modeling. The findings contribute to both marketing theory and technological innovation, offering a roadmap for future interdisciplinary research and practical yet sustainable applications in the digital economy.

Date Published:2025

The development and validation of social media monitoring by workplace contacts scale and testing its impact on employees online self-disclosure and outcomes

Hamnah Rahat

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Management to the Department of FAST School of Management. Chapter 1: Introduction- Chapter 1.2: Chapter Introduction- Chapter 1.2: Research Background- Chapter 1.3: Problem Statement and Research Gap- Chapter 1.4: Purpose and Scope of the Study- Chapter 1.5: Research Question and Objectives- Chapter 1.6: Research Context- Chapter 1.7: Research Design- Chapter 1.8: Thesis Outline- Chapter 1.9: Chapter Summary- Chapter 2: Literature Review- Chapter 2.1: Chapter Introduction- Chapter 2.2: Social Media and The Workplace- Chapter 2.3: Impact of Smmwc on Outcomes- Chapter 2.4: Chapter Summary- Chapter 3- Research Methodology- Chapter 3.1: Chapter Introduction- Chapter 3.2: Research Philosophy- Chapter 3.3: Resesrch Design- Chapter 4: Results- Chapter 5: Discussion and Conclusion. The monitoring of employees' private social media accounts b) employers and colleagues is a growing phenomenon. However, empirical research in this area remains limited. To better understand this phenomenon and its impact on employee outcomes, this thesis developed and validated a scale for measuring Social Media Monitoring by Workplace Contacts (Sl\U\I\VC). It examined how Sl\Il\IvVC influenced employees• online dis• closure and assessed whether SfvIT'd\VC affected employee" outcomes, in• cluding invasion of privacy• job satisfaction, emotional exhaustion, self• concept. clarity; and social capital. It further determined whether em• ployees online •disclosure mediated these relationships.Two field studies were conducted. In the first study a 15-item Sl\Il\IvVC scale was developed and validated using data from 334 employees, follow• ing Hinkin's approach of scaling (1998). The items were categorized into four dimensions based on Foucault and Bot an 's concept of the panoptic effect. _The results supported three types of validity: factorial validity: discriminant validity, in relation to electronic: performance monitoring and users' perceptions of social media monitoring bv multiple audiences: and criterion-related validitv, with respect lo employees • invasion of pri• vacy and online disclosure. In the second study, time-lagged data were collected from 302 employee to replicate the factor structure of the scale. This study also assessed the impact of Si\l f\i\TC on online disclosure dimensions. includinc PO'• itive valence, breadth. depth, and authenticity. It examined the impact of Sl\Ii\J\VC on the outcomes of invasion of privacy. job satisfaction. emotional exhaustion. self-concept claritv. and social capital. and exam• ined the mediating role of 011li11c: disclosure in the relationship between S1Il\I\VC and these employee outcomes. The findings indicated that S1I I\\ C wa positively associated with the ouline disclo me cli111e11- sions of breadth. depth. and authenticity.

Date Published:2025

Predictive maintenance and fault diagnosis of induction motor using system modeling and deep learning

Ishaq, Muhammad Irfan,

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Electrical Engineering to the Department of Electrical Engineering. Chapter 1: Introduction- Chapter 1.2: Problem Statement- Chapter 1.2: Research Questions- Chapter 1.3: Proposed Solution- Chapter 1.4: Research Contributions- Chapter 1.5: Thesis Organization- Chapter 2: Literature Review- Chapter 2.1: Classical Methods- Chapter 2.2: Signal Processing Based Approaches- Chapter 2.3: Machine Learning and Deep Learning Approaches- Chapter 2.4: Limitations of the Current Fault Diagnosis and Identification Methods- Chapter 3: Methodology- Chapter 3.1: Proposed Methodology- Chapter 3.2: Data Collection- Chapter 3.3: Model Construction- Chapter 4: Result and Discussions- Chapter 5: Conclusion Limitations and Future Work- Chapter 5.1: Conclusion- Chapter 5.2: Limitations- Chapter 5.3: Future Research Directions. Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. Conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns for both electrical and mechanical faults at different speeds, particularly under variable speed, abrupt fault occurrence and changing load conditions. This study aims to address these challenges by proposing efficient hybrid deep learning models to improve both accuracy and computational efficiency in IM fault diagnosis. To resolve these issues, this thesis presents novel approach that include hybrid Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) along with the Transfer Leaming mechanism for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. The proposed methods can identify broader range of most encountered faults such as rotor imbalances, misalignment, stator winding issues, voltage imbalances, broken rotor bars, Bowed Rotor and broken bearings positively.A key feature of the proposed approach is the incorporation of transfer learning, which leverages pre-trained models to extract generalized feature representations. This significantly reduces data requirements and computational costs by minimizing the need for extensive labeled datasets and by accelerating the model training process.Consequently, the approach becomes more suitable for real-world industrial applications, where data availability and computational resources are often limited Experiments performed using the University of Ottawa Electric Motor Dataset - Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) dataset reveals that all three accelerometers are 99.96% accurate at constant speed and 99.96% at variable speed under both loaded and unloaded conditions. In terms of fault diagnostic accuracy in an IM operating at different speeds and load conditions, these methodologies outperform cutting-edge methodologies in the literature. This approach also addresses the abrupt fault occurrence at different operating conditions and reduces the computational cost as compared to the existing methods. Moreover, using the publicly available Case Western Reserve University (CWRU) dataset, this study validates the robustness of the proposed methods in terms of operational issues in an IM. Finally, the proposed methods achieve incredible results at varying speeds under loaded conditions, stressing the need to improve industrial equipment reliability and maintenance methods.This work tackles important research gaps in the present literature by improving fault accuracy for both mechanical and electrical faults under different operating conditions of motors. The Transfer learning approach reduces the data requirements and computational costs, that make it suitable for practical implementation to real world industrial motors operations.

Date Published:2025

Design of robust tolerant controller for unmanned aerial vehicle

Abdullah, Muhammad,

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Electrical Engineering to the Department of Electrical Engineering. Chapter 1: Introduction- Chapter 1.2: Problem Statement- Chapter 1.3: Research Methodology- Chapter 1.4: Dissertation Contributions- Chapter 1.5: Organization of the Dissertation- Chapter 2: Literature Review- Chapter 2.1: Introduction to Unmanned Aerial Vehicles Control System- Chapter 2.2: Fault Tolerant control Strategies- Chapter 2.3: Faults Detestation and Diagnosis in Vehicles- Chapter 3: Quad copter Dynamics and Faults Modeling- Chapter 3.1: Introductions- Chapter 3.2: Dynamics of the Quadcopter- Chapter 3.3: Fault Modeling and Rotor Dynamics- Chapter 4: Methodology- Chapter 4.1: Introduction- Chapter 4.2: Uav Mathematical Model Development- Chapter 4.3:Fault Simulation- Chapter 5: Results and Discussion- Chapter 6: Conclusion and Future Work. The advent of Unmanned Aerial Vehicles (UAVs) has revolutionized var• ious sectors, but the increasing use of autonomous technologies has high• lighted the importance of reliability and safety. A critical vulnerability is motor failure during flight, particularly in quadcopters, where faults can disrupt aircraft dynamics, leading to loss of control, trajectory devi• ations, and safety risks. Conventional control systems often lack the nec• essary resilience. The traditional control systems often do not feature the resilience needed to handle unpredictable hardware malfunctions, thus restricting the viable extent of safe UAV operation. This thesis addressed the problem of motor failures in the quadcopter through modeling and proposing a complete fault-tolerant control (FTC) system. The combi• nation of an Integral Back-Stepping Controller (IBSC), which makes the translational motion much more precise, and a robust Non-Linear Dis• turbance Observer-based Sliding Mode Control (NL-DO-SMC) to deal with rotational motion is proposed, analyzed, and deployed. The in• herent robustness of Sliding Mode Control to cope with parameter vari• ability and disturbance rejection is built. While the NL-DO in SMC allows real-time estimation of unknown disturbances and predictions of the instantaneous motor faults' impacts on quadcopter dynamics. This predictive ability allows the system to predict the failure of actuators, hence honing fault tolerance. The FTC model has been verified to be effective, using a range of MATLAB simulations and, more importantly, using Hardware-in-the-Loop (HIL) experimentation analysis on platform of FPGA basis. HIL provides high fidelity simulation level, simulating the actual complexity of flight. The results showed that the proposed FTC system mitigated detected actuator failures and ensured complete control of functional trajectories, kept the desired rotation angles, and tracked them accurately when serious motor faults occurred up to 50% on any single motor. Our proposed model extended the fault tolerance from 40% to 50% com pared to conventional methods through simula• tions as well as FPGA-based HIL experiments. This work will upport UAV safety, dependability, and resilience in operation and enable th m to be integrated much more confidently and widely, into applicati ns 111 critical areas.

Date Published:2025

Analyzing rainfall distribution in pakistan and its relationship to climate change through probabilistic analysis

Haseeb, Fahad [21l-7811],

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Civil Engineering to the Department of Civil Engineering. Chapter 1: Introduction- Chapter 1.2: Research Gap and Novelty of Study- Chapter 1.3: Scope of the Dissertation- Chapter 1.4: Definition of key Terms Used in the Dissertation- Chapter 1.5: Structure of the Dissertation- Chapter 1.6: Chapter Summary- Chapter 2: Literature Review- Chapter 2.1: Introduction- Chapter 2.2: Probabilistic Approach in Rainfall data Modeling- Chapter 2.3 : Dissertation Overview- Chapter 2.4: Chapter Summary- Chapter 3: Research Methodology- Chapter 3.1: Introduction- Chapter 3.2: Research Philosophy- Chapter 3.3 Research Strategy- Chapter 3.4: Data and Study Area- Chapter 3.5: Chapter Summary- Chapter 4: Result and Discussion- Chapter 5: Conclusion- Chapter 5.1: Introduction- Chapter 5.2: Summary of the Key Finding- Chapter 5.3: Theoretical Implantation- Chapter 5.4: Practical Implication- Chapter 5.5: Recommendation and Future Work- Chapter 5.6: Conclusion- Chapter 5.7: Chapter Summary. This dissertation comprehensively analyzes rainfall distribution in Pakistan and its relationship to climate change through probabilistic analysis. The objective of this study was to employs a comprehensive methodological approach that integrates statistical evaluation of long-term observed rainfall records, projections from climate models, and advanced probabilistic methods to explore how climate change is affecting rainfall behavior, water availability, agricultural systems, and disaster risk in Pakistan.Predicting extreme rainfall in regions with variable and dry climates remains a considerable challenge due to difficulties in selecting the most appropriate probability distribution models. This research presents a framework to assess suitable rainfall distributions throughout Pakistan and develops a probabilistic zoning map to guide flood risk management and optimize water resource planning. A 42-year precipitation dataset from the Pakistan Meteorological Department was cross-validated against CHIRPS satellite-derived data. Among nine distributions evaluated, five were found most applicable. In Punjab, particularly its central and southern areas, the Log-Normal distribution was predominant, while Gamma was more common in coastal Sindh. Balochistan showed a complex pattern with Log-Pearson, Log-Normal, and Weibull distributed across the region. Gilgit-Baltistan aligned exclusively with the Generalized Extreme Value model, and Khyber Pakhtunkhwa showed dominance of both Log Pearson and Generalized Extreme Value. Generalized Extreme Value was typical of cold, elevated zones; Log Pearson appeared frequently in mountainous terrains; and Weibull was more aligned with areas adjacent to the Indus River prone to flooding. The study offers a regionalized approach to extreme rainfall modeling, supporting climate resilience and sustainable infrastructure planning across Pakistan's diverse ecological zones.The second component of this research focuses on creating future IDF curve projections for major cities/districts, using climate projections adjusted with statistical downscaling to improve local accuracy. Historical rainfall data from meteorological stations was used to build IDF curves for the baseline period. Future rainfall scenarios were evaluated using a range of climate models, with the most suitable model selected for projecting extreme rainfall. The projections were statistically corrected to ensure realistic representation of local rainfall patterns. These downscaled projections were used in the rainfall intensity analysis to assess future changes, under the critical climate pathways. Results indicate a notable rise in rainfall intensity across different storm durations and return intervals, with more pronounced increases observed under the higher emissions scenario. Results indicate a 20-60% increase in short-duration rainfall intensities and up to 110% in long-return-period events, particularly in central and northern regions. At the city level, Bahawalpur exhibits the largest increase, with a 111.4% rise, followed by Multan, where l 00-year rainfall nearly doubles. Sargodha, Umarkot, and Badin also show substantial intensification, with Badin's I Ou-year rainfall expected to rise by 88.5% and Umarkot's by 73.3%. Statistical downscaling improves climate model outputs, enhancing local projections and increasing projected intensities by 2% to 12%, which is important for accurate urban flood risk assessment. For instance, Lahore's I 00-year rainfall intensity increased by 10.4%, and Sargodha's by 19.8% for the 5-year event.Regional trend analysis indicates that northern and central plains (Lahore, Sialkot, Sargodha, Peshawar) are most vulnerable to intensified extreme rainfall, while semi-arid and coastal areas of Sindh-traditionally low-risk-are projected to experience new flood threats.

Date Published:2025

Cross domain recommendations under cold start : Thematic mapping and aspect level sentiment modeling

Awais Azam

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Computer Science to the Department of Computer Science. Chapter 1: Introduction- 1.1:Background and Motivation- 1.2: Cold Start Problem Definition- 1.3: Existing Approaches- 1.4: Problem Statement- 1.5:Research Objectives- 1.6: Research Questions- 1.7: Summary Of Key Contributions- 1.8: Thesis Outline- Chapter 2: Background- Chapter 3: Literature Review- 3.1: Literature Discussion- 3.2: Research Trends- 3.3: Summary- Chapter 4: Research Methodology- Chapter 5: Results and Discussion- Chapter 6: Conclusion- 6.1: Overview- 6.2: What this Thesis Established- 6.3: Consolidated Contributions- 6.4: Design Lessons and Practical Guidance- 6.5: Limitations and Threats to Validity- 6.6: Future Work- 6.7: Closing Work. Personalized recommendation systems increasingly span multiple content domains-for example, suggesting books to users based on their movie preferences-but they often fail when the target domain has no historical activity from the user. This thesis investigates how to transfer preferences a.cross domains in such strict cold-start settings, without relying on overlapping users or items. It proposes two complementary, text-based frameworks that infer what users like from their reviews in a source domain and use this knowledge to recommend items in a target domain. The first framework, UTAM (Unified Thematic Alignment and Mapping), uses topic modeling and aspect-based sentiment analysis (ABSA) to build structured profiles that summarize which themes a user consistently discusses with positive or negative sentiment in movies. These theme-and-sentiment profiles are then aligned to books so that items can be ranked even when the user has never interacted with any book. UTAM exposes tunable settings (such as topic and cluster granularity, neighborhood size, and the number of dominant themes) that allow practitioners to trade off precision, recall, and catalog coverage in a controlled way. The second framework, LIPA (Language-Induced Profile Abstraction), treats transfer as a profile summarization and retrieval task. A large language model condenses a user's many reviews, and each item's reviews, into short neutral summaries. A sentence-embedding model then measures how well each user summary matches each book summary to generate recommendations through a single similarity search. LIPA requires no additional model training and preserves interpretability through human-consumable user and item profiles. Both frameworks are evaluated on a large Amazon Movies-e Books benchmark under a rigorous cold-start protocol. They consistently outperform strong single-domain and text-only baselines in terms of ranking quality and catalog coverage, while preserving the intorpretability of the underlying representations. The thesis also analyzes key design choices (such as neighborhood size, topic granularity, and summarization settings), discusses limitations related to review quality and offiine evaluation, and outlines future directions including emotion-aware modeling, dynamic updates with new data, cross-lingual deployment, privacy-preserving on-device summaries, and online A/B testing.

Date Published:2025

Engaging customers : Impact of social media marketing perceived innovativeness and CSR with the mediating role of customer satisfaction and the moderation of service convenience and environmental concerns of the consumer

Sarah Khan

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Management to the Department of FAST School of Management. Chapter 1: Introduction- Chapter 1.2: Chapter Introduction- Chapter 1.2: Introduction- Chapter 1.3: Gaps and Problems Statement- Chapter 1.4: Research Questions- Chapter 1.5: Research Objectives- Chapter 1.6: Chapter Summary- Chapter 2: Literature Review- Chapter 2.1: Chapter Introduction- Chapter 2.2: Review Of Literature- Chapter 2.3: Hypotheses Development- Chapter 2.4: Theoretical Framework- Chapter 2.5: Underpinning Theory- Chapter 2.6: Chapter Summary- Chapter 3: Research Methodology- Chapter 4: data Analysis and Result- Chapter 5: Discussion and Conclusion- Chapter 5.1: Chapter Introduction- Chapter 5.2: Main Finding of the Study.Chapter 5.3: Conclusion- Chapter 5.4: Chapter Summary. This study investigates key factors influencing customer engagement in the hospitality sector, with a particular focus on social media marketing (SMivI), perceived innovativeness (PI), corporate social responsibility (CSR), and the roles of customer satisfaction, service convenience, and environmental concerns. As customer en• gagement becomes a crucial determinant of business success, under• standing these relationships is essential for both academic research and managerial decision-making. The research begins by estab• lishing the context, significance, and objectives of the study, high• lighting the growing impact of digitalization and CSR initiatives in shaping customer behavior. Following this, it identifies gaps in existing literature, emphasizing the need for a comprehensive ap• proach to understanding engagement in the hospitality industry. The literature review provides an in-depth analysis of foundational theories, including the relationship S-0-R Model and engagement theory, to explain how businesses engage with customers through marketing, innovation, and responsibility initiatives. It critically evaluates past research on social media marketing, perceived inno• vativeness, CSR, and customer satisfaction, drawing connections between these variables and identifying inconsistencies in prior find• ings. The review also examines service convenience and environmental concerns as potential moderators, assessing their influence on customer engagement outcomes. The methodology section out• lines a quantitative technique using, time-lagged design, following a survey-based data collection from hospitality businesses in Pakistan. The study defines its target population, sampling strategy, and data collection process while ensuring the validity and relia• bility of measurement instruments. Statistical techniques such as VIF analysis, reliability, convergent validity, discriminant validity assessments (HTMT and Fornell & Larcker criterion), and SEM using Smart PLS were employed to evaluate direct, mediating, and moderating relationships among variables. The key findings indi• cate that social media marketing alone does not directly enhance customer engagement within the hospitality sector. However, a sig• nificant relationship exists between perceived innovativeness and customer engagement, suggesting that innovative hospitality services contribute to stronger customer involvement. .

Date Published:2025

A framework for efficient intrusion detection and classification for iot networks

Anjum Nazir

Submitted in partial fulfillment of the requirements for the degree of the Doctor of Philosophy in Computer Science to the Department of Computer Science. Chapter 1: Introduction- Chapter 2: Network Intrusion Detection- Chapter 3: Features Selection Using a Hybrid- Chapter 3.1: Introduction to Cellular Automata Tabu Search- Chapter 3.2: Framework and Working Model of the Cats Algorithm- Chapter 3.3: Cellular Automata Tabu Search Algorithm- Chapter 3.4: Testing Configuration- Chapter 3.5: Experiments and Results- Chapter 3.6: Chapter Summary- Chapter 4: Bert Based Privacy Preserving Threat Dectetion For Iot Networks- Chapter 4.1: Introduction to Transformer Based Deep learning Architecture- Chapter 4.2: An Introduction to Bidirectional Encoder Representations Froms Trans Former- Chapter 4.3: Related work- Chapter 4.4: Guardbert Architecture- Chapter 4.5: Chapter Summary- Chapter 5: Conclusion and Future Work - Chapter 51.: Summary- Chapter 5.2: Prospective Research Directions. . As network and Internet connectivity has improved significantly over last few years. This has spiked the growth and widespread deployment of Internet of Things (IoT) networks. For instance, healthcare, manufacturing, home automation. However, this exponential growth in loT networks sector has also introduced new set of security challenges to deal with them. Conventional intrusion detection systems (IDS) designed for legacy networks failed to address the unique requirements and security needs of loT networks. loT net• works have limited resources and often lacks the mechanism to handle confidentiality, integrity, and authenticity of the data or users. Also, due to the dynamic nature of devices (sensors, actuators, intermediary nodes) configured and deployed in loT networks also in• creases the complexity that render conventional IDS solutions inefficient and inadequate. Therefore, it is of the utmost importance to provide an effective, scalable, and special• ized framework for intrusion detection and classification that is designed specifically for loT networks. In this thesis, we introduced a comprehensive framework customized to address the security requirements of IoT-based networks. This framework has introduced a number of key innovations and utilized various state of the art practices to improve the performance. Firstly, we developed CAT-S, a hybrid feature selection algorithm. CAT-S combines powerful computational properties of Cellular Automata (CA) and the strategic search capabilities of the Tahu Search (TS) metaheuristic algorithm. The CAT-S meta• heuristic algorithm optimizes feature selection processes and significantly improves at• tack detection accuracy and efficiency of intrusion detection models. Secondly, we used domain knowledge expert to extract new features from TONJoT dataset. We extracted over 30 new features that enhanced the performance of our framework. Thirdly, we pro• posed GuardBERT algorithm. GuardBERT is an advanced transformer-based architecture meticulously designed for loT network environments. We also developed GuardCode state of the art privacy-preserving encoding technique. It is integrated with GuardBERT. uardCode transforms sensitive network data into textual formats optimized for process• ing by GuardBERT. Therefore, it ensures robust threat detection without compromising user privacy. The security effectiveness and erformance of CAT-S algorithm is evalu• ated on the state of the art TONJoT dataset. The TONJoT dataset is the most latest and comprehensive dataset available to the date of study. The study shows that the CAT• S algorithm demonstrated superior performance in comparison to other state of the art feature-selection algorithms. The CAT-S algorithm achieved higher attack detection ac• curacy upto 99.5% while simultaneously reducing the number of features to 13 only. The false positive rate for the CAT-S algorithm reaches 0.004, which is onsiderably good. By reducing the number of features to 13, CAT-S has succeeded in reducing the num• ber of features by over 72% while achieving higher accuracy and a better false positive rate. Furthermore, GuardBERT's a privacy preserving BERT based model showed ex• ceptional performance. It achieved an impressive 99.3% accuracy rate in detecting and classifying threats within IoT networks. Critically, GuardBERT maintained low infer• ence times-approximately 0.11 seconds per instance-thereby confi.nning its suitability for real-time threat detection in dynamic IoT security applications. .

Date Published:2025

Fortifying cloud container security : a strategy for securing containers in shared kernel architecture

Zehra, Sehar,

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in (Computer Science) at the National University of Computer and Emerging Sciences. Chapter 1: Introduction Chapter 2: Literature Review Chapter 3: Problem Analysis Chapter 4: Proposed Methodology: DeSFAM Framework Chapter 5: Implementation, Evaluation and Analysis Chapter 6: Results and Discussion Chapter 7: Conclusion and Future Work References. Containerization is fundamental to cloud-native computing, but its shared-kernel architecture exposes hosts to syscall-based attacks. Since all containers interact directly with the kernel, general-purpose images often grant unnecessarily broad syscall privileges, enabling container escape, privilege escalation, and other kernel-level exploits. Existing defenses, such as static Seccomp profiles and manually crafted syscall whitelists, lack adaptability and provide limited protection against behavioral anomalies or zero-day syscall patterns. To address these limitations, this thesis introduces DeSFAM (Dynamic eBPF-driven Syscall Filtering and Anomaly Mitigation), a real-time framework that enforces least-privilege execution and detects malicious syscall behavior. DeSFAM integrates: (1) hybrid syscall profiling through static analysis and dynamic eBPF tracing; (2) SyscallAD, an anomaly detection engine combining a Variational Autoencoder with an Isolation Forest; (3) risk scoring informed by MITRE ATT&CK techniques and CVE intelligence; and (4) adaptive enforcement using eBPF maps and Linux Security Module hooks to block, rate-limit, or log suspicious syscalls. Evaluation on the DongTing dataset and CVE-based attacks shows that DeSFAM achieves high detection performance (94% precision, 90% recall, 96% accuracy) with sub-millisecond latency and under 1% overhead, effectively mitigating syscall-level attacks without disrupting container execution. Overall, DeSFAM delivers a low-overhead and adaptive syscall defense for shared kernel container environments, significantly strengthening real-time security. .

Date Published:2025

Building the data warehouse

Inmon, William H.

The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media Discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects Covers advanced topics, including data monitoring and testing Although the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55.

Date Published:2005

Java in a nutshell : A desktop quick reference

Evans, Benjamin J.

Intro Copyright Table of Contents Foreword Preface Changes in the Eighth Edition Contents of This Book Related Books Conventions Used in This Book Using Code Examples O'Reilly Online Learning How to Contact Us Acknowledgments Part I. Introducing Java Chapter 1. Introduction to the Java Environment The Language, the JVM, and the Ecosystem What Is the Java Language? What Is the JVM? What Is the Java Ecosystem? The Lifecycle of a Java Program Frequently Asked Questions Comparing Java to Other Languages Java Compared to JavaScript Java Compared to Python Java Compared to C Java Compared to C++ Answering Some Criticisms of Java Overly Verbose Slow to Change Performance Problems Insecure Too Corporate A Brief History of Java and the JVM Summary Chapter 2. Java Syntax from the Ground Up Java Programs from the Top Down Lexical Structure The Unicode Character Set Case Sensitivity and Whitespace Comments Reserved Words Identifiers Literals Punctuation Primitive Data Types The boolean Type The char Type Integer Types Floating-Point Types Primitive Type Conversions Expressions and Operators Operator Summary Arithmetic Operators String Concatenation Operator Increment and Decrement Operators Comparison Operators Boolean Operators Bitwise and Shift Operators Assignment Operators The Conditional Operator The instanceof Operator Special Operators Statements Expression Statements Compound Statements The Empty Statement Labeled Statements Local Variable Declaration Statements The if/else Statement The switch Statement The switch Expression The while Statement The do Statement The for Statement The foreach Statement The break Statement The continue Statement The return Statement The synchronized Statement The throw Statement The try/catch/finally Statement The try-with-resources Statement The assert Statement Methods Defining Methods Method Modifiers Checked and Unchecked Exceptions Variable-Length Argument Lists Introduction to Classes and Objects Defining a Class Creating an Object Using an Object Object Literals Lambda Expressions Arrays Array Types Creating and Initializing Arrays Using Arrays Multidimensional Arrays Reference Types Reference Versus Primitive Types Manipulating Objects and Reference Copies Comparing Objects Boxing and Unboxing Conversions Packages and the Java Namespace Package Declaration Globally Unique Package Names Importing Types Importing Static Members Java Source File Structure Defining and Running Java Programs Summary Chapter 3. Object-Oriented Programming in Java Overview of Classes and Records Basic OO Definitions Records Other Reference Types. This updated edition of the Nutshell guide not only helps experienced Java programmers get the most out of versions through Java 17, it also serves as a learning path for new developers. Chock-full of examples that demonstrate how to take complete advantage of modern Java APIs and development best practices, this thoroughly revised book includes new material on recent enhancements to the Java object model that every developer should know about. The first section provides a fast-paced, no-fluff introduction to the Java programming language and the core runtime aspects of the Java platform. The second section is a reference to core concepts and APIs that explains how to perform real programming work in the Java environment. Get up to speed on language details through Java 17 Learn object-oriented programming using basic Java syntax Explore generics, enumerations, annotations, and lambda expressions Understand techniques used in object-oriented design Examine how concurrency and memory are intertwined Work with Java collections and handle common data formats Delve into Java's latest I/O APIs including asynchronous channels Become familiar with development tools in OpenJDK. This updated edition of the Nutshell guide not only helps experienced Java programmers get the most out of versions through Java 17, it also serves as a learning path for new developers. Chock-full of examples that demonstrate how to take complete advantage of modern Java APIs and development best practices, this thoroughly revised book includes new material on recent enhancements to the Java object model that every developer should know about. The first section provides a fast-paced, no-fluff introduction to the Java programming language and the core runtime aspects of the Java platform. The second section is a reference to core concepts and APIs that explains how to perform real programming work in the Java environment. Get up to speed on language details through Java 17 Learn object-oriented programming using basic Java syntax Explore generics, enumerations, annotations, and lambda expressions Understand techniques used in object-oriented design Examine how concurrency and memory are intertwined Work with Java collections and handle common data formats Delve into Java's latest I/O APIs including asynchronous channels Become familiar with development tools in OpenJDK.

Date Published:2023

Learning Spark : Lighting fast data analytics

Damji, Jules S.

Introduction to Apache Spark: a unified analytics engine Downloading Apache Spark and getting started Apache Spark's structured APIs Spark SQL and DataFrames: introduction to built-in data sources Spark SQL and DataFrames: interacting with external data sources Spark SQL and Datasets Optimizing and tuning Spark applications Structured streaming Building reliable data lakes with Apache Spark Machine learning with MLib Managing, deploying, and scaling machine learning pipelines with Apache Spark Epilogue: Apache Spark 3.0. Data is bigger, arrives faster, and comes in a variety of formats-- and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Data is bigger, arrives faster, and comes in a variety of formatsâ and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, youâ ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow.

Date Published:2020

Designing Data Intensive Applications : The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Kleppmann, Martin

Chapter 1: Trade Offs in Data Systems Architecture 2: Defining Nonfunctional Requirements 3: Data Models and Query Languages. 4: Storage and Retrial 5: Encoding and Evolution 6: Replication 7: Sharding 8: Transactions 9: The Trouble with Distributed Systems 10: Consistency and Consensus 11: Batch Processing 12:Stream Processing 13: A Philosophy of Streaming Systems 14: Doing the Right Thing. Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of systems, including relational databases, NoSQL datastores, data warehouses, and data lakes. There are cloud services, on-premises services, and embedded databases. What are the right choices for your application? How do you make sense of all these buzzwords?In this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition,integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, learn how to choose the right tools for your needs, and understand the fundamentals of distributed systems.Peer under the hood of the systems you already use, and learn to use them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Learn how major cloud services are designed for scalability, fault tolerance, and consistency Understand the core principles upon which modern databases are built.

Date Published:2026

Database Internals : A Deep Dive into How Distributed Data Systems Work

Petrov, Alex

Part 1: Storage Engine 1: Introduction and Overview 2: B Tree Basics 3: File Formats 4: Implementing B Tress 5: Transaction Processing and Recovery 6: B Tess Variants 7: Log Structured Storage Part 2: Distributed System 8: Introduction and Overview 9: Failure Detection 10: Leader Election 11: Replication and Consistency 12: Anti Entropy and Dissemination 13: Distribution Transaction 14: Consensus Part 3: Conclusion. When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed. This book examines: Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each Storage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log Distributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns Database clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency.

Date Published:2019

Probability and statistics for engineers and scientists

Walpole, Ronald E.

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

Date Published:2020

Introduction to Linear Algebra

Strang, Gilbert

Chapter 1: Vector and Matrices Chapter 2: Solving Linear Equations Chapter 3: The Four Fundamental Subspace Chapter 4: Orthogonality Chapter 5: Determinants Chapter 6: Eigenvalues and Eigenvectors Chapter 7: The Singular Value Decomposition Chapter 8: Linear Transformations Chapter 9: Linear Algebra in Optimization Chapter 10: Learning From Data . The sixth edition of Strang's phenomenally popular textbook features a more active start and new chapters on optimization and learning from data.Linear algebra now rivals or surpasses calculus in importance for people working in quantitative fields of all kinds: engineers, scientists, economists and business people. Gilbert Strang has taught linear algebra at MIT for more than 50 years and the course he developed has become a model for teaching around the world. His video lectures on MIT OpenCourseWare have been viewed over ten million times and his twelve textbooks are popular with readers worldwide. This sixth edition of Professor Strang's most popular book, Introduction to Linear Algebra, introduces the ideas of independent columns and the rank and column space of a matrix early on for a more active start. Then the book moves directly to the classical topics of linear equations, fundamental subspaces, least squares, eigenvalues and singular values – in each case expressing the key idea as a matrix factorization. The final chapters of this edition treat optimization and learning from data: the most active application of linear algebra today. Everything is explained thoroughly in Professor Strang's characteristic clear style. It is sure to delight and inspire the delight and inspire the next generation of learners.

Date Published:2023

Starting out with c : From control structures through objects

Gaddis, Tony.

Cover Inside Front Cover Title Page Copyright Page Contents at a Glance Contents Preface CHAPTER 1 Introduction to Computers and Programming 1.1 Why Program? 1.2 Computer Systems: Hardware and Software 1.3 Programs and Programming Languages 1.4 What Is a Program Made of? 1.5 Input, Processing, and Output 1.6 The Programming Process 1.7 Procedural and Object-Oriented Programming Review Questions and Exercises CHAPTER 2 Introduction to C++ 2.1 The Parts of a C++ Program 2.2 The cout Object 2.3 The #include Directive 2.4 Variables, Literals, and Assignment Statements 2.5 Identifiers 2.6 Integer Data Types 2.7 The char Data Type 2.8 The C++ string Class 2.9 Floating-Point Data Types 2.10 The bool Data Type 2.11 Determining the Size of a Data Type 2.12 More about Variable Assignments and Initialization 2.13 Scope 2.14 Arithmetic Operators 2.15 Comments 2.16 Named Constants 2.17 Programming Style Review Questions and Exercises Programming Challenges CHAPTER 3 Expressions and Interactivity 3.1 The cin Object 3.2 Mathematical Expressions 3.3 When You Mix Apples and Oranges: Type Conversion 3.4 Overflow and Underflow 3.5 Type Casting 3.6 Multiple Assignment and Combined Assignment 3.7 Formatting Output 3.8 Working with Characters and string Objects 3.9 More Mathematical Library Functions 3.10 Focus on Debugging: Hand Tracing a Program 3.11 Focus on Problem Solving: A Case Study Review Questions and Exercises Programming Challenges CHAPTER 4 Making Decisions 4.1 Relational Operators 4.2 The if Statement 4.3 Expanding the if Statement 4.4 The if/else Statement 4.5 Nested if Statements 4.6 The if/else if Statement 4.7 Flags 4.8 Logical Operators 4.9 Checking Numeric Ranges with Logical Operators 4.10 Menus 4.11 Focus on Software Engineering: Validating User Input 4.12 Comparing Characters and Strings 4.13 The Conditional Operator 4.14 The switch Statement 4.15 More about Blocks and Variable Scope Review Questions and Exercises Programming Challenges CHAPTER 5 Loops and Files 5.1 The Increment and Decrement Operators 5.2 Introduction to Loops: The while Loop 5.3 Using the while Loop for Input Validation 5.4 Counters 5.5 The do-while Loop 5.6 The for Loop 5.7 Keeping a Running Total 5.8 Sentinels 5.9 Focus on Software Engineering: Deciding Which Loop to Use 5.10 Nested Loops 5.11 Using Files for Data Storage 5.12 Optional Topics: Breaking and Continuing a Loop Review Questions and Exercises Programming Challenges CHAPTER 6 Functions 6.1 Focus on Software Engineering: Modular Programming 6.2 Defining and Calling Functions 6.3 Function Prototypes 6.4 Sending Data into a Function 6.5 Passing Data by Value. A clear and student-friendly way to teach the fundamentals of C++ Starting Out with C++: From Control Structures through Objects covers control structures, functions, arrays, and pointers before objects and classes in Tony Gaddis's hallmark accessible, step-by-step presentation. His books help beginning students understand the important details necessary to become skilled programmers at an introductory level. Gaddis motivates the study of both programming skills and the C++ programming language by presenting all the details needed to understand the ""how"" and the ""why""--But never losing si. A clear and student-friendly way to teach the fundamentals of C++ Starting Out with C++: From Control Structures through Objects covers control structures, functions, arrays, and pointers before objects and classes in Tony Gaddis’s hallmark accessible, step-by-step presentation. His books help beginning students understand the important details necessary to become skilled programmers at an introductory level. Gaddis motivates the study of both programming skills and the C++ programming language by presenting all the details needed to understand the “how” and the “why”–but never losing sight of the fact that most beginners struggle with this material. His approach is gradual and highly accessible, ensuring that students understand the logic behind developing high-quality programs. As with all Gaddis texts, clear and easy-to-read code listings, concise and practical real-world examples, and an abundance of exercises appear in every chapter. Updates to the 9th Edition include revised, improved problems throughout and a new chapter featuring completely rewritten and expanded material on the Standard Template Library (STL).

Date Published:2019

Understanding and using english grammar : With answer key

Azhar, Betty S.

Present And Past; Simple And Progressive Perfect And Perfect Progressive Tenses Future Time Review Of Verb Tenses Subject-Verb Agreement Nouns Articles Pronouns Modals, Part 1 Modals, Part 2 The Passive Noun Clauses Adjective Clauses Gerunds And Infinitives, Part 1 Gerunds And Infinitives, Part 2 Coordinating Conjunctions Adverb Clauses Reduction Of Adverb Clauses To Modifying Adverbial Phrases Connectives That Express Cause And Effect, Contrast, And Condition Conditional Sentences And Wishes. Comprehensive reference grammar book. Exercises include: fill-in-the-blank exercises, to pair work, dialogue creation and small group discussion.

Date Published:2017