Tabla de Contenidos: “…13.3.3 Deletion -- 13.3.4 Drawbacks of m-Way Search Trees -- 13.4 B Trees -- 13.5 Operations on B Trees -- 13.5.1 Searching -- 13.5.2 Insertion -- 13.5.3 Deletion -- 13.6 Height of B Trees -- 13.7 Variations of B Tree -- 13.7.1 B* Tree -- 13.7.2 B+ Tree -- 13.8 Applications -- 13.8.1 Databases -- Summary -- Exercises -- Chapter 14 Red-Black Trees and Splay Trees -- 14.1 Introduction -- 14.2 Representation of a Red-Black Tree -- 14.3 Operations -- 14.3.1 Searching -- 14.3.2 Insertion -- 14.3.3 Deletion -- 14.4 Splay Trees -- 14.4.1 Splay Rotations -- 14.4.2 Amortized Analysis -- 14.5 Applications -- Summary -- Exercises -- Chapter 15 Pattern Matching and Tries -- 15.1 Introduction -- 15.2 Terminology -- 15.3 Pattern Matching Algorithms -- 15.3.1 Fixed Pattern Matching Algorithms -- 15.3.2 Regular Expression Pattern Matching -- 15.4 Fixed Pattern Matching Algorithms -- 15.4.1 Brute Force Pattern Matching Algorithm -- 15.4.2 Th e Boyer-Moore Algorithm -- 15.4.3 Knuth-Morris-Pratt Algorithm (KMP) -- 15.5 Applications of Pattern Matching Algorithms -- 15.6 Tries -- 15.6.1 Standard Tries -- 15.6.2 Compressed Tries -- 15.6.3 Suffix Tries -- 15.7 Applications of Tries -- Summary -- Exercises -- Chapter 16 Sorting and Searching -- 16.1 Sorting -- 16.1.1 Bubble Sort -- 16.1.2 Insertion Sort -- 16.1.3 Selection Sort -- 16.1.4 Quick Sort -- 16.1.5 Merge Sort -- 16.1.6 Shell Sort -- 16.1.7 Radix Sort -- 16.1.8 Heap Sort -- 16.2 Searching -- 16.2.1 Linear Search or Sequential Search -- 16.2.2 Binary Search -- 16.2.3 Fibonacci Search -- Summary -- Exercises -- Index…”

Tabla de Contenidos: “…3.3.1.7 Different statistical method -- 3.3.1.7.1 Central tendency -- Mean -- Why do not use the mean -- Median -- Mode -- Variance and standard deviation -- Z-score -- Quartiles -- Percentile -- 3.4 Measurement and scaling concepts -- 3.4.1 Comparative scales -- 3.4.1.1 Paired comparison scale -- 3.4.1.2 Rank order scale -- 3.4.1.3 Constant sum scale -- 3.4.1.4 Q-sort scale -- 3.4.2 Non-comparative scales -- 3.4.2.1 Continuous rating scale -- 3.4.2.2 Itemized rating scale -- 3.4.2.2.1 Likert scale -- 3.4.2.2.2 Stapel scale -- 3.4.2.2.3 Semantic differential scale -- 3.5 Various types of scale -- 3.5.1 Nominal -- 3.5.2 Ordinal -- 3.5.3 Interval -- 3.5.4 Ratio -- 3.6 Primary data analysis with Python -- 3.7 Conclusion -- 3.8 Case study -- 3.8.1 Case study: taxonomy of data in a healthcare organization -- 3.8.2 Case study: taxonomy of data in the automobile industry -- 3.8.3 Case study on the data theory -- 3.9 Exercise -- 3.9.1 Objective type question -- 3.9.2 Descriptive type question -- Further reading -- 4 Multivariate data analytics and cognitive analytics -- Abbreviations -- 4.1 Introduction -- 4.2 Factor analytics -- 4.3 Principal component analytics -- 4.4 Cluster analytics -- 4.4.1 K-means -- 4.4.1.1 Algorithms -- 4.4.1.2 K-means clustering -- 4.1.2.1 Steps of the K-means clustering algorithm -- 4.1.2.2 Practice problems based on K-means clustering algorithm -- 4.4.2 Cluster analysis of driverless car dataset -- 4.4.2.1 Problem -- 4.5 Linear regression analysis -- 4.5.1 Mathematical expression for regression analysis -- 4.5.2 Solved example of linear regression analysis of driverless car -- 4.5.2.1 Problem -- 4.5.2.2 Solution -- 4.6 Logistic regression analysis -- 4.7 Application of analytics across value chain -- 4.8 Multivariate data analytics with Python -- 4.9 Conclusion -- 4.10 Case study…”

Tabla de Contenidos: “…-- No single point of failure -- Tunable consistency -- Data center awareness -- Linear scalability -- Built on the JVM -- Appropriate use cases for Cassandra -- Overview of the internals -- Data modeling in Cassandra -- Partition keys -- Clustering keys -- Putting it all together -- Optimal use cases…”

Tabla de Contenidos: “…Setting the margin with plt.tight_layout() -- Aligning subplots of different dimensions with plt.subplot2grid() -- Drawing inset plots with fig.add_axes() -- Adjusting subplot dimensions post hoc with plt.subplots_adjust -- Adjusting axes and ticks -- Customizing tick spacing with locators -- Removing ticks with NullLocator -- Locating ticks in multiples with MultipleLocator -- Locators to display date and time -- Customizing tick formats with formatters -- Using a non-linear axis scale -- More on Pandas-Matplotlib integration -- Showing distribution with the KDE plot -- Showing the density of bivariate data with hexbin plots -- Expanding plot types with Seaborn -- Visualizing multivariate data with a heatmap -- Showing hierarchy in multivariate data with clustermap -- Image plotting -- Financial plotting -- 3D plots with Axes3D -- Geographical plotting -- Basemap -- GeoPandas -- Summary -- Chapter 5: Embedding Matplotlib in GTK+3 -- Installing and setting up GTK+3 -- A brief introduction to GTK+3 -- Introduction to the GTK+3 signal system -- Installing Glade -- Designing the GUI using Glade -- Summary -- Chapter 6: Embedding Matplotlib in Qt 5 -- A brief introduction to Qt 5 and PyQt 5 -- Differences between Qt 4 and PyQt 4 -- Introducing QT Creator / QT Designer -- Summary -- Chapter 7: Embedding Matplotlib in wxWidgets Using wxPython -- A brief introduction to wxWidgets and wxPython -- Embedding Matplotlib in a GUI from wxGlade -- Summary -- Chapter 8: Integrating Matplotlib with Web Applications -- Installing Docker -- Docker for Windows users -- Docker for Mac users -- More about Django -- Django development in Docker containers -- Starting a new Django site -- Installation of Django dependencies -- Django environment setup -- Running the development server -- Showing Bitcoin prices using Django and Matplotlib -- Creating a Django app…”

Tabla de Contenidos: “…RMSE calculation with MLlib -- RMSE calculation with R -- Explanations of the results -- Biggest influencers -- Visualizing trends -- The rules of sending out alerts -- Scores to rank city zones -- Summary -- Chapter 10: Learning Telco Data on Spark -- Spark for using Telco Data -- The use case -- Spark computing -- Methods for learning from Telco Data -- Descriptive statistics and visualization -- Linear and logistic regression models -- Decision tree and random forest -- Data and feature development -- Data reorganizing -- Feature development and selection -- Model estimation -- SPSS on Spark - SPSS Analytics Server -- Model evaluation -- RMSE calculations with MLlib -- RMSE calculations with R -- Confusion matrix and error ratios with MLlib and R -- Results explanation -- Descriptive statistics and visualizations -- Biggest influencers -- Special insights -- Visualizing trends -- Model deployment -- Rules to send out alerts -- Scores subscribers for churn and for Call Center calls -- Scores subscribers for purchase propensity -- Summary -- Chapter 11: Modeling Open Data on Spark -- Spark for learning from open data -- The use case -- Spark computing -- Methods for scoring and ranking -- Cluster analysis -- Principal component analysis -- Regression models -- Score resembling -- Data and feature preparation -- Data cleaning -- Data merging -- Feature development -- Feature selection -- Model estimation -- SPSS on Spark - SPSS Analytics Server -- Model evaluation -- RMSE calculations with MLlib -- RMSE calculations with R -- Results explanation -- Comparing ranks -- Biggest influencers -- Deployment -- Rules for sending out alerts -- Scores for ranking school districts -- Summary -- Index…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: The Era of Big Data -- Big Data - The monster re-defined -- Big Data toolbox - dealing with the giant -- Hadoop - the elephant in the room -- Databases -- Hadoop Spark-ed up -- R - The unsung Big Data hero -- Summary -- Chapter 2: Introduction to R Programming Language and Statistical Environment -- Learning R -- Revisiting R basics -- Getting R and RStudio ready -- Setting the URLs to R repositories -- R data structures -- Vectors -- Scalars -- Matrices -- Arrays -- Data frames -- Lists -- Exporting R data objects -- Applied data science with R -- Importing data from different formats -- Exploratory Data Analysis -- Data aggregations and contingency tables -- Hypothesis testing and statistical inference -- Tests of differences -- Independent t-test example (with power and effect size estimates) -- ANOVA example -- Tests of relationships -- An example of Pearson's r correlations -- Multiple regression example -- Data visualization packages -- Summary -- Chapter 3: Unleashing the Power of R from Within -- Traditional limitations of R -- Out-of-memory data -- Processing speed -- To the memory limits and beyond -- Data transformations and aggregations with the ff and ffbase packages -- Generalized linear models with the ff and ffbase packages -- Logistic regression example with ffbase and biglm -- Expanding memory with the bigmemory package -- Parallel R -- From bigmemory to faster computations -- An apply() example with the big.matrix object -- A for() loop example with the ffdf object -- Using apply() and for() loop examples on a data.frame -- A parallel package example -- A foreach package example -- The future of parallel processing in R -- Utilizing Graphics Processing Units with R.…”

Tabla de Contenidos: “…Supervised Learning and Binary Classification -- 3.2.3. Generalized Linear Model -- 3.2.4. Adding Regularization With Lasso -- 3.2.5. …”

Tabla de Contenidos: “…-- 11.5.2 Die map-Funktion -- 11.5.3 Die reduce-Funktion -- 11.6 Bloom-Filter und HyperLogLog -- 11.6.1 Bloom-Filter -- 11.6.2 HyperLogLog -- 11.7 Die SHA-Algorithmen -- 11.7.1 Dateien vergleichen -- 11.7.2 Passwörter überprüfen -- 11.8 Locality-Sensitive Hashing -- 11.9 Diffie-Hellman-Schlüsselaustausch -- 11.10 Lineare Programmierung -- 11.11 Epilog -- Anhang: Lösungen zu den Übungen -- Kapitel 1 -- Kapitel 2 -- Kapitel 3 -- Kapitel 4 -- Kapitel 5 -- Kapitel 6 -- Kapitel 7 -- Kapitel 8 -- Kapitel 9 -- Kapitel 10 -- Stichwortverzeichnis…”

Tabla de Contenidos: “…-- Applications of POS tagging -- Training a POS tagger -- Training a sentiment classifier for movie reviews -- Training a bag-of-words classifier -- Summary -- Chapter 3: Deep Learning and TensorFlow -- Deep learning -- Perceptron -- Activation functions -- Sigmoid -- Hyperbolic tangent -- Rectified linear unit -- Neural network -- One-hot encoding -- Softmax -- Cross-entropy -- Training neural networks -- Backpropagation -- Gradient descent -- Stochastic gradient descent -- Regularization techniques -- Dropout -- Batch normalization -- L1 and L2 normalization -- Convolutional Neural Network -- Kernel -- Max pooling -- Recurrent neural network -- Long-Short Term Memory -- TensorFlow -- General Purpose - Graphics Processing Unit -- CUDA -- cuDNN -- Installation -- Hello world! …”

Tabla de Contenidos: “…-- Identifying opportunities for statistical regression -- Summarizing data -- Exploring relationships -- Testing significance of differences -- Project profitability -- R and statistical regression -- A working example -- Establishing the data profile -- The graphical analysis -- Predicting with our linear model -- Step 1: Chunking the data -- Step 2: Creating the model on the training data -- Step 3: Predicting the projected profit on test data -- Step 4: Reviewing the model…”

Tabla de Contenidos: “…Creating a Data Explorer pool using the Azure CLI -- Summary -- Chapter 3: Exploring Azure Synapse Studio -- Technical requirements -- Exploring the user interface of Azure Synapse Studio -- Running your first query -- Creating a database -- Loading the data -- Verifying whether your data has loaded successfully -- Working with data in Azure Synapse notebooks -- Saving your work and configuring source control -- Managing and monitoring Data Explorer pools -- Scaling Data Explorer pools -- Pausing and resuming pools -- Monitoring Data Explorer pools -- Summary -- Chapter 4: Real-World Usage Scenarios -- Technical requirements -- Building a multi-purpose end-to-end analytics environment -- Sources -- Ingest -- Store -- Process -- Enrich -- Serve -- User -- Summary -- Managing IoT data -- Processing and analyzing geospatial data -- Enabling real-time analytics with big data -- Performing time series analytics -- Summary -- Part 2: Working with Data -- Chapter 5: Ingesting Data into Data Explorer Pools -- Technical requirements -- Understanding the data loading process -- Defining a retention policy -- Choosing a data load strategy -- Streaming ingestion -- Batching ingestion -- Performing data ingestion -- Using KQL control commands -- Building an Azure Synapse pipeline -- Implementing continuous ingestion -- Using other data ingestion mechanisms -- Summary -- Chapter 6: Data Analysis and Exploration with KQL and Python -- Technical requirements -- Analyzing data with KQL -- Selecting data -- Working with calculated columns -- Plotting charts -- Obtaining percentiles -- Creating a time series -- Detecting outliers -- Using linear regression -- Exploring Data Explorer pool data with Python -- Creating an Apache Spark pool -- Working with Azure Synapse notebooks -- Reading data from Data Explorer pools -- Plotting charts…”

Tabla de Contenidos: “…Sampling Distributions</p> <p>e. Linear Combinations</p> <p>f. Transformations</p> <p>g. …”

Tabla de Contenidos: “…Perfect magnetic media or HLI media - homogeneous, linear, isotropic (Figure 3.21) -- 3.8.1. Definition -- 3.9. …”

Tabla de Contenidos: “…-- Architecture of CNNs -- Types of layers in a CNN -- Building a perceptron-based linear regressor -- Building an image classifier using a single layer neural network -- Building an image classifier using a Convolutional Neural Network -- Summary -- Index…”

Tabla de Contenidos: “…Chapter 7: Uncertainty processing -- 7.1 Model-Domain Uncertainty -- 7.2 Feature-Domain Uncertainty -- 7.2.1 Observation Uncertainty -- Uncertainty propagation through multilayer perceptrons -- 7.3 Joint Uncertainty Decoding -- 7.3.1 Front-End JUD -- 7.3.2 Model JUD -- 7.4 Missing-Feature Approaches -- 7.5 Summary -- References -- Chapter 8: Joint model training -- 8.1 Speaker Adaptive and Source Normalization Training -- 8.2 Model Space Noise Adaptive Training -- 8.3 Joint Training for DNN -- 8.3.1 Joint Front-End and DNN Model Training -- 8.3.2 Joint Adaptive Training -- 8.4 Summary -- References -- Chapter 9: Reverberant speech recognition -- 9.1 Introduction -- 9.2 Acoustic Impulse Response -- 9.3 A Model of Reverberated Speech in Different Domains -- 9.4 The Effect of Reverberation on ASR Performance -- 9.5 Linear Filtering Approaches -- 9.6 Magnitude or Power Spectrum Enhancement -- 9.7 Feature Domain Approaches -- 9.7.1 Reverberation Robust Features -- 9.7.2 Feature Normalization -- 9.7.3 Model-Based Feature Enhancement -- 9.7.4 Data-Driven Enhancement -- 9.8 Acoustic Model Domain Approaches -- 9.9 The REVERB Challenge -- 9.10 To Probe Further -- 9.11 Summary -- References -- Chapter 10: Multi-channel processing -- 10.1 Introduction -- 10.2 The Acoustic Beamforming Problem -- 10.3 Fundamentals of Data-Dependent Beamforming -- 10.3.1 Signal Model and Objective Functions -- 10.3.2 Generalized Sidelobe Canceller -- 10.3.3 Relative Transfer Functions -- 10.4 Multi-Channel Speech Recognition -- 10.4.1 ASR on Beamformed Signals -- 10.4.2 Multi-Stream ASR -- 10.5 To Probe Further -- 10.6 Summary -- References -- Chapter 11: Summary and future directions -- 11.1 Robust Methods in the Era of GMM -- 11.2 Robust Methods in the Era of DNN -- 11.3 Multi-Channel Input and Robustness to Reverberation -- 11.4 Epilogue -- References -- Index -- Back Cover…”

Tabla de Contenidos: “…-- Back to the Scientific Method -- Unit Cost-Centric Efficiency-A Deep and Universal Truth -- Chapter 6: Efficiency, Flow, and the Right Measures -- The "Right" Measures -- Defining Cost -- Chapter 7: Our Current Accounting Measuring Mess -- Reason 1: The Proliferation of ERP/MRP II -- Reason 2: The Growing Distance between the Front Office and Operations -- Reason 3: The Fading Away of Management Accounting -- Gap Explanation 1: An Increase in Complexity -- Gap Explanation 2: The Shift from Linear to Nonlinear Systems -- Silo Thinking -- Challenging a Deep Truth by Definition Shakes Up the Status Quo -- Chapter 8: The Evolution of Flow and ROI as Strategy -- The Birth of Product Costing -- The Birth of Decentralized Management -- An Accelerant-Andrew Carnegie -- The Rise of Wholesale Distribution and Large-Scale Retail -- The Birth of Conglomerates and Management Accounting -- Automation and the Death of the Craftsman -- Ford versus General Motors-A Lesson of Relevancy -- How Did GM Beat Ford? …”

Tabla de Contenidos: “…7.4.4 Real-Time Analysers -- 7.4.5 Remote Sensing -- 7.5 Data Reduction and Processing -- 7.5.1 Vibration Amplitude Versus Frequency Analysis -- 7.5.2 Spectrum Averaging -- 7.5.3 Amplitude Versus Frequency Versus Time Analysis -- 7.5.4 Amplitude/Phase Versus rpm Analysis -- 7.5.5 Time Waveform Analysis -- 7.5.6 Lissajous Pattern (Orbit) Analysis -- 7.5.7 Mode-Shape Analysis -- 7.6 Diagnosis and Corrective Actions -- 7.6.1 Steady-State Operating Regime -- 7.6.2 Detection of Perturbation Forces and Corrective Actions -- 7.7 Modal Analysis -- 7.8 Vibration Control -- Conclusion -- Exercises -- Chapter 8: Finite Element Method -- 8.1 Introduction -- 8.2 Important Conditions to be Satisfied -- 8.3 Modelling -- 8.4 Shape Functions -- 8.5 Bar Element -- 8.6 Boundary Conditions -- 8.7 Torsion Element -- 8.8 Beam Element -- Matlab-Tool for Computation -- Introduction -- (I) Display Windows -- (II) Arithmetic Operations -- (III) Built-in Functions -- (IV) Matrix -- (V) Polynomials: -- (VI) System of Linear Equations -- Conclusion -- Exercises -- Chapter 9: Fundamentals of Experimental Modal Analysis -- 9.1 Introduction -- 9.2 Frequency-Response Function -- 9.2.1 Frequency-Response Function-Basic Principles -- Exercises -- Chapter 10: Miscellaneous Topics in Vibration Analysis and Introduction to Noise Analysis -- 10.1 Flow-Induced Vibrations -- 10.2 Acoustics and Analysis of Noise -- 10.2.1 Basics of Sound -- 10.2.2 Amplitude, Frequency, Wavelength, and Velocity -- 10.2.3 Sound Field Definitions -- 10.3 Non-Stationary (Unsteady) Vibrations -- 10.4 Rotor Dynamics and Hydrodynamic Bearings -- Exercises -- Appendix - A -- Appendix - B -- Appendix - C -- Appendix - D -- Index…”

Tabla de Contenidos: “…Summary -- Chapter 5. Linear Systems with General Non-Viscous Damping -- 5.1. …”

Tabla de Contenidos: “…Anticipation of Potential Problems: P-Diagrams and DFMEA -- Target and Spec Limits -- Measurement System Analysis -- Capability Analysis -- Flow-Down or Decomposition -- Procedure for Critical Parameter Flow-Down or Decomposition -- Flow-Down Examples -- Initial Tolerance Allocation -- Summary -- Chapter 11 Software DFSS and Agile -- Measuring the Agile Design -- Data Collection Plan for ViewHome Prototype -- Summary -- Chapter 12 Software Architecture Decisions -- Software Architecture Decision-Making Process -- Using Design Heuristics to Make Decisions -- Common Design Heuristics and Principles -- Using Architecture Tactics to Make Decisions -- Using DFSS Design Trade-Off Analysis to Make Decisions -- Using Design Patterns, Simulation, Modeling, and Prototyping for Decisions -- Summary -- Chapter 13 Predictive Engineering: Continuous and Discrete Transfer Functions -- Discrete versus Continuous Critical Parameters -- Methods for Deriving a Transfer Function for a Discrete Critical Parameter -- Logistic Regression for Discrete Parameters -- Methods for Deriving a Transfer Function for a Continuous or Ordinal Critical Parameter -- Existing or Derived Equation (First Principles Modeling) -- Modeling within a Spreadsheet, Mathematical Modeling Software, or Simulation Software -- Empirical Modeling using Historical Data: Regression Analysis and General Linear Model -- Empirical Modeling using Design of Experiments -- Empirical Modeling using Response Surface Methods -- DOE with Simulators: Design and Analysis of Computer Experiments (DACE) -- Summary -- Chapter 14 Predictive Engineering: Optimization and Critical Parameter Flow-Up -- Critical Parameter Flow-Up: Monte Carlo Simulation -- Critical Parameter Flow-Up: Generation of System Moments (Root Sum of Squares) -- Critical Parameter Scorecard -- Selecting Critical Parameters for Optimization…”

Tabla de Contenidos: “…Shope and Ji-Hyung Park Multiple Time-Scale Monitoring to Address Dynamic Seasonality and Storm Pulses of Stream Water Quality in Mountainous Watersheds Reprinted from: Water 2015, 7(11), 6117-6138 http://www.mdpi.com/2073-4441/7/11/6117180 -- Haoran Wang, Yongcan Chen, Zhaowei Liu and Dejun Zhu Effects of the "Run-of-River" Hydro Scheme on Macroinvertebrate Communities and Habitat Conditions in a Mountain River of Northeastern China Reprinted from: Water 2016, 8(1), 31 http://www.mdpi.com/2073-4441/8/1/31203 -- Dong-Hwan Kim, Tae-Soo Chon, Gyu-Suk Kwak, Sang-Bin Lee and Young-Seuk Park Effects of Land Use Types on Community Structure Patterns of Benthic Macroinvertebrates in Streams of Urban Areas in the South of the Korea Peninsula Reprinted from: Water 2016, 8(5), 187 http://www.mdpi.com/2073-4441/8/5/187225 -- Sun-Ah Hwang, Soon-Jin Hwang, Se-Rin Park and Sang-Woo Lee Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models Reprinted from: Water 2016, 8(4), 155 http://www.mdpi.com/2073-4441/8/4/155248 -- Young-Jin Yun and Kwang-Guk An Roles of N:P Ratios on Trophic Structures and Ecological Stream Health in Lotic Ecosystems Reprinted from: Water 2016, 8(1), 22 http://www.mdpi.com/2073-4441/8/1/22269 -- Kyoung-Jin An, Sang-Woo Lee, Soon-Jin Hwang, Se-Rin Park and Sun-Ah Hwang Exploring the Non-Stationary Effects of Forests and Developed Land within Watersheds on Biological Indicators of Streams Using Geographically-Weighted Regression Reprinted from: Water 2016, 8(4), 120 http://www.mdpi.com/2073-4441/8/4/120295 -- Ji Yoon Kim and Kwang-Guk An Integrated Ecological River Health Assessments, Based on Water Chemistry, Physical Habitat Quality and Biological Integrity Reprinted from: Water 2015, 7(11), 6378-6403 http://www.mdpi.com/2073-4441/7/11/6378324 -- Katarzyna Glińska-Lewczuk, Paweł Burandt, Roman Kujawa, Szymon Kobus, Krystian Obolewski, Julita Dunalska, Magdalena Grabowska, Sylwia Lew and Jarosław Chormański Environmental Factors Structuring Fish Communities in Floodplain Lakes of the Undisturbed System of the Biebrza River Reprinted from: Water 2016, 8(4), 146 http://www.mdpi.com/2073-4441/8/4/146353 -- Jeong-Hui Kim, Ju-Duk Yoon, Seung-Ho Baek, Sang-Hyeon Park, Jin-Woong Lee, Jae-An Lee and Min-Ho Jang An Efficiency Analysis of a Nature-Like Fishway for Freshwater Fish Ascending a Large Korean River Reprinted from: Water 2016, 8(1), 3 http://www.mdpi.com/2073-4441/8/1/3382 -- Darren Drapper and Andy Hornbuckle Field Evaluation of a Stormwater Treatment Train with Pit Baskets and Filter Media Cartridges in Southeast Queensland Reprinted from: Water 2015, 7(8), 4496-4510 http://www.mdpi.com/2073-4441/7/8/4496 404.…”