Tabla de Contenidos: “…Classification des entités publiques par ministère -- Références -- Suède -- Cadre général -- L'administration publique et les agences en Suède -- Les commissions d'enquête et le processus de prise de décision -- Cadre juridique et organisationnel -- Restrictions aux pouvoirs des ministres à l'égard des agences -- Textes de base -- Types d'agence -- Les structures de gouvernance -- Conseil d'administration -- La nomination des directeurs généraux -- Le recrutement et le statut des fonctionnaires -- Les ressources et la procédure budgétaire -- Budgétisation par programmes -- La transparence de la procédure budgétaire -- Cohérence des politiques, mécanismes de surveillance et de responsabilité -- Cohérence -- Information comptable et rapport d'activité -- Évaluations et audits -- Contrôle parlementaire -- Transparence -- Recours -- Le médiateur (Ombudsman) -- Évaluation, enseignements à tirer et évolutions récentes -- Les agences sur la scène internationale -- Enseignements d'ordre général -- Royaume-Uni -- Introduction générale -- Partie 1 : Les agences exécutives -- Cadre général -- Instauration des agences au Royaume-Uni -- Le rôle et la taille des agences -- Cadre juridique et organisationnel -- Les structures de gouvernance -- Direction et personnel de l'agence -- Rémunération -- Avis stratégique de source indépendante -- Les ressources et la procédure budgétaire -- Modalités financières -- Établissements publics à caractère commercial -- Cohérence des politiques, mécanismes de surveillance et de responsabilité -- Mécanismes de reddition de comptes -- Cohérence des politiques -- Améliorations récentes des mécanismes de reddition de comptes et de cohérence des politiques -- Contrôles internes et initiatives en matière de qualité…”

Tabla de Contenidos: “…Technical impact of major design changes -- 5.1 VisiBroker to IBM ORB change -- 5.1.1 IBM ORB setup -- 5.1.2 Using Server Access Interface with the IBM ORB -- 5.1.3 WebSphere MQ triggering replacing Object Activation Daemon -- 5.1.4 Setting up WebSphere MQ triggering -- 5.2 Tooling changes to WebSphere InterChange Server -- 5.2.1 WebSphere InterChange Server development model -- 5.2.2 Configuration files updated to XML -- 5.2.3 Eclipse platform -- 5.2.4 System Manager -- 5.2.5 System Monitor -- 5.2.6 Business Object Probes -- 5.2.7 Web-based flow and failed-flow management -- 5.2.8 Integrated Test Environment -- 5.2.9 Collaboration debugger -- 5.2.10 Activity Editor -- Chapter 6. …”

Tabla de Contenidos: “…3.3.1 Centralized System and Its Physiognomies -- 3.3.2 Advantages -- 3.3.3 Disadvantages of Centralized System -- 3.4 Distributed System Architecture -- 3.4.1 Advantages of Distributed System Architecture -- 3.4.2 Drawbacks of Distributed System Architecture -- 3.5 Data Communication Taking Place in Distributed IoT Environment -- 3.5.1 Internet of Things (IoT) Protocol -- 3.5.2 Constrained Application Protocol (CoAP) -- 3.5.3 Message Queuing Telemetry Transport (MQTT) -- 3.5.4 Wi-Fi -- 3.5.5 Zigbee -- 3.5.6 Extensible Messaging and Presence Protocol (XMPP) -- 3.5.7 Data Distribution Service (DDS) -- 3.5.8 Advanced Message Queuing Protocol (AMQP) -- 3.5.9 Smart Home and IoT Applications: An Example -- 3.5.10 IoT Services, Machines and Applications -- 3.5.11 Sensor-Based IoT Services -- 3.5.12 Application in IoT Environment -- 3.5.13 Future of IoT in a COVID-19 Pandemic -- 3.6 Conclusion -- References -- Chapter 4 Contribution of Cloud-Based Services in Post-Pandemic Technology Sustainability and Challenges: A Future Direction -- 4.1 Introduction -- 4.2 Cloud-Based Solutions -- 4.2.1 Information and Communications Technology -- 4.2.2 Artificial Intelligence and Machine Learning -- 4.2.3 Data Analytics and Business Intelligence -- 4.3 Impact of Industry 4.0 in the Cloud Computing Industry -- 4.3.1 Agriculture and Forestry -- 4.3.2 Entertainment, Media, and Hospitality -- 4.3.3 Robotics and Automation, Manufacturing and Maintenance -- 4.4 Significance and Impact of Cloud in the Pandemic Outbreak -- 4.5 Conclusion and Future Directions -- References -- Chapter 5 Network Security in Evolving Networking Technologies: Developments and Future Directions -- 5.1 Introduction -- 5.2 Background on Attacks, Security Services and Challenges -- 5.2.1 Types of Attacks Possible on Network -- 5.2.2 Security Services -- 5.2.3 Challenges…”

Tabla de Contenidos: “…10.2.11 Precision Medicine -- 10.3 AI and ML-Based Manufacturing -- 10.3.1 Continuous Manufacturing -- 10.3.2 Process Improvement and Fault Detection -- 10.3.3 Predictive Maintenance (PdM) -- 10.3.4 Quality Control and Yield -- 10.3.5 Troubleshooting -- 10.3.6 Supply Chain Management -- 10.3.7 Warehouse Management -- 10.3.8 Predicting Remaining Useful Life -- 10.3.9 Challenges -- 10.4 AI and ML-Based Drug Product Marketing -- 10.4.1 Product Launch -- 10.4.2 Real-Time Personalization and Consumer Behavior -- 10.4.3 Better Customer Relationships -- 10.4.4 Enhanced Marketing Measurement -- 10.4.5 Predictive Marketing Analytics -- 10.4.6 Price Dynamics -- 10.4.7 Market Segmentation -- 10.4.8 Challenges -- 10.5 Future Prospects and Way Forward -- 10.6 Conclusion -- References -- Chapter 11 Artificial Intelligence and Machine Learning Applications in Vaccine Development -- 11.1 Introduction -- 11.2 Prioritizing Proteins as Vaccine Candidates -- 11.3 Predicting Binding Scores of Candidate Proteins -- 11.4 Predicting Potential Epitopes -- 11.5 Design of Multi-Epitope Vaccine -- 11.6 Tracking the RNA Mutations of a Virus -- Conclusion -- References -- Chapter 12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products -- Abbreviations -- 12.1 Introduction -- 12.2 AI and ML for Pandemic -- 12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development -- 12.3.1 Spectroscopic Techniques -- 12.3.2 Chromatographic Techniques -- 12.3.3 Electrochemical Techniques -- 12.3.4 Electrophoretic Techniques -- 12.3.5 Hyphenated Techniques -- 12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products -- 12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development -- 12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products…”

Tabla de Contenidos: “…Vijayan and Kimberly A. Gray Article: Probing Water and CO2 Interactions at the Surface of Collapsed Titania Nanotubes Using IR Spectroscopy Reprinted from: Molecules 2015, 20(9), 15469-15487 http://www.mdpi.com/1420-3049/20/9/15469 -- Chapter 2: UV and Visible-Light Sensitive Photocatalysts: Efficiency Effects of Nature, Composition, Preparation, Structure and Texture Peter J. …”

Tabla de Contenidos: “…Machine generated contents note: Preface List of Abbreviations 1 Introduction 1.1 Forward Problem 1.2 Inverse Problem 1.3 Issues in Inverse Problem Solving 1.4 Linear, Nonlinear and Linearized Problems 2 Signal and System as Vectors 2.1 Vector Space 2.1.1 Vector Space and Subspace 2.1.2 Basis, Norm and Inner Product 2.1.3 Hilbert Space 2.2 Vector Calculus 2.2.1 Gradient 2.2.2 Divergence 2.2.3 Curl 2.2.4 Curve 2.2.5 Curvature 2.3 Taylor's Expansion 2.4 Linear System of Equations 2.4.1 Linear System and Transform 2.4.2 Vector Space of Matrix 2.4.3 Least Square Solution 2.4.4 Singular Value Decomposition (SVD) 2.4.5 Pseudo-inverse 2.5 Fourier Transform 2.5.1 Series Expansion 2.5.2 Fourier Transform 2.5.3 Discrete Fourier Transform (DFT) 2.5.4 Fast Fourier Transform (FFT) 2.5.5 Two-dimensional Fourier Transform References 3 Basics for Forward Problem 3.1 Understanding PDE using Images as Examples 3.2 Heat Equation 3.2.1 Formulation of Heat Equation 3.2.2 One-dimensional Heat Equation 3.2.3 Two-dimensional Heat Equation and Isotropic Diffusion 3.2.4 Boundary Conditions 3.3 Wave Equation 3.4 Laplace and Poisson Equations 3.4.1 Boundary Value Problem 3.4.2 Laplace Equation in a Circle 3.4.3 Laplace Equation in Three-dimensional Domain 3.4.4 Representation Formula for Poisson Equation References 4 Analysis for Inverse Problem 4.1 Examples of Inverse Problems in Medical Imaging 4.1.1 Electrical Property Imaging 4.1.2 Mechanical Property Imaging 4.1.3 Image Restoration 4.2 Basic Analysis 4.2.1 Sobolev Space 4.2.2 Some Important Estimates 4.2.3 Helmholtz Decomposition 4.3 Variational Problems 4.3.1 Lax-Milgram Theorem 4.3.2 Ritz Approach 4.3.3 Euler-Lagrange Equations 4.3.4 Regularity Theory and Asymptotic Analysis 4.4 Tikhonov Regularization and Spectral Analysis 4.4.1 Overview of Tikhonov Regularization 4.4.2 Bounded Linear Operators in Banach Space 4.4.3 Regularization in Hilbert Space or Banach Space 4.5 Basics of Real Analysis 4.5.1 Riemann Integrable 4.5.2 Measure Space 4.5.3 Lebesgue Measurable Function 4.5.4 Pointwise, Uniform, Norm Convergence and Convergence in Measure 4.5.5 Differentiation Theory References 5 Numerical Methods 5.1 Iterative Method for Nonlinear Problem 5.2 Numerical Computation of One-dimensional Heat equation 5.2.1 Explicit Scheme 5.2.2 Implicit Scheme 5.2.3 Crank-Nicolson Method 5.3 Numerical Solution of Linear System of Equations 5.3.1 Direct Method using LU Factorization 5.3.2 Iterative Method using Matrix Splitting 5.3.3 Iterative Method using Steepest Descent Minimization 5.3.4 Conjugate Gradient (CG) Method 5.4 Finite Difference Method (FDM) 5.4.1 Poisson Equation 5.4.2 Elliptic Equation 5.5 Finite Element Method (FEM) 5.5.1 One-dimensional Model 5.5.2 Two-dimensional Model 5.5.3 Numerical Examples References 6 CT, MRI and Image Processing Problems 6.1 X-ray CT 6.1.1 Inverse Problem 6.1.2 Basic Principle and Nonlinear Effects 6.1.3 Inverse Radon Transform 6.1.4 Artifacts in CT 6.2 MRI 6.2.1 Basic Principle 6.2.2 K-space Data 6.2.3 Image Reconstruction 6.3 Image Restoration 6.3.1 Role of p in (6.35) 6.3.2 Total Variation Restoration 6.3.3 Anisotropic Edge-preserving Diffusion 6.3.4 Sparse Sensing 6.4 Segmentation 6.4.1 Active Contour Method 6.4.2 Level Set Method 6.4.3 Motion Tracking for Echocardiography References 7 Electrical Impedance Tomography 7.1 Introduction 7.2 Measurement Method and Data 7.2.1 Conductivity and Resistance 7.2.2 Permittivity and Capacitance 7.2.3 Phasor and Impedance 7.2.4 Admittivity and Trans-impedance 7.2.5 Electrode Contact Impedance 7.2.6 EIT System 7.2.7 Data Collection Protocol and Data Set 7.2.8 Linearity between Current and Voltage 7.3 Representation of Physical Phenomena 7.3.1 Derivation of Elliptic PDE 7.3.2 Elliptic PDE for Four-electrode Method 7.3.3 Elliptic PDE for Two-electrode Method 7.3.4 Min-max Property of Complex Potential 7.4 Forward Problem and Model 7.4.1 Continuous Neumann-to-Dirichlet Data 7.4.2 Discrete Neumann-to-Dirichlet Data 7.4.3 Nonlinearity between Admittivity and Voltage 7.5 Uniqueness Theory and Direct Reconstruction Method 7.5.1 Calderon's Approach 7.5.2 Uniqueness and Three-dimensional Reconstruction: Infinite Measurements 7.5.3 Nachmann's D-bar Method in Two Dimension 7.6 Backprojection Algorithm 7.7 Sensitivity and Sensitivity Matrix 7.7.1 Perturbation and Sensitivity 7.7.2 Sensitivity Matrix 7.7.3 Linearization 7.7.4 Quality of Sensitivity Matrix 7.8 Inverse Problem of EIT 7.8.1 Inverse Problem of RC Circuit 7.8.2 Formulation of EIT Inverse Problem 7.8.3 Ill-posedness of EIT Inverse Problem 7.9 Static Imaging 7.9.1 Iterative Data Fitting Method 7.9.2 Static Imaging using 4-channel EIT System 7.9.3 Regularization 7.9.4 Technical Difficulty of Static Imaging 7.10 Time-difference Imaging 7.10.1 Data Sets for Time-difference Imaging 7.10.2 Equivalent Homogeneous Admittivity 7.10.3 Linear Time-difference Algorithm using Sensitivity Matrix 7.10.4 Interpretation of Time-difference Image 7.11 Frequency-difference Imaging 7.11.1 Data Sets for Frequency-difference Imaging 7.11.2 Simple Difference Ft,ω2− Ft,ω1 7.11.3 Weighted Difference Ft,ω2− [alpha] Ft,ω1 7.11.4 Linear Frequency-difference Algorithm using Sensitivity Matrix 7.11.5 Interpretation of Frequency-difference Image References 8 Anomaly Estimation and Layer Potential Techniques 8.1 Harmonic Analysis and Potential Theory 8.1.1 Layer Potentials and Boundary Value Problems for Laplace Equation 8.1.2 Regularity for Solution of Elliptic Equation along Boundary of Inhomogeneity 8.2 Anomaly Estimation using EIT 8.2.1 Size Estimation Method 8.2.2 Location Search Method 8.3 Anomaly Estimation using Planar Probe 8.3.1 Mathematical Formulation 8.3.2 Representation Formula References 9 Magnetic Resonance Electrical Impedance Tomography 9.1 Data Collection using MRI 9.1.1 Measurement of Bz 9.1.2 Noise in Measured Bz Data 9.1.3 Measurement of B = (Bx,By,Bz) 9.2 Forward Problem and Model Construction 9.2.1 Relation between J , Bz and σ 9.2.2 Three Key Observations 9.2.3 Data Bz Traces σ∇u © e z-directional Change of σ 9.2.4 Mathematical Analysis toward MREIT Model 9.3 Inverse Problem Formulation using B or J 9.4 Inverse Problem Formulation using Bz 9.4.1 Model with Two Linearly Independent Currents 9.4.2 Uniqueness 9.4.3 Defected Bz Data in a Local Region 9.5 Image Reconstruction Algorithm 9.5.1 J-substitution Algorithm 9.5.2 Harmonic Bz Algorithm 9.5.3 Gradient Bz Decomposition and Variational Bz Algorithm 9.5.4 Local Harmonic Bz Algorithm 9.5.5 Sensitivity Matrix Based Algorithm 9.5.6 Anisotropic Conductivity Reconstruction Algorithm 9.5.7 Other Algorithms 9.6 Validation and Interpretation 9.6.1 Image Reconstruction Procedure using Harmonic Bz Algorithm 9.6.2 Conductivity Phantom Imaging 9.6.3 Animal Imaging 9.6.4 Human Imaging 9.7 Applications References 10 Magnetic Resonance Elastography 10.1 Representation of Physical Phenomena 10.1.1 Overview of Hooke's Law 10.1.2 Strain Tensor in Lagrangian Coordinates 10.2 Forward Problem and Model 10.3 Inverse Problem in MRE 10.4 Reconstruction Algorithms 10.4.1 Reconstruction of [mu] with the Assumption of Local Homogeneity 10.4.2 Reconstruction of [mu] without the Assumption of Local Homogeneity 10.4.3 Anisotropic Elastic Moduli Reconstruction 10.5 Technical Issues in MRE References…”

Tabla de Contenidos: “…Chapter 17 Structured Finance and the Financial Crisis of 2007-2009 -- 17.1 Structured Finance -- 17.2 Poor Risk Management Linked to the Structured Finance Market -- 17.2.1 Lack of Incentive Contracts in the Presence of Information Asymmetry -- 17.2.2 Poor Evaluation of Structured Products by Rating Agencies -- 17.2.3 Poor Pricing of Complex Financial Products -- 17.2.4 Poor Regulation of Structured Finance -- 17.3 Conclusion -- References -- Appendix: How to Create an AAA CDO Tranche from BBB Loans -- Chapter 18 Risk Management and Corporate Governance -- 18.1 Enron and Corporate Governance -- 18.2 Financial Crisis and Corporate Governance -- 18.3 New 2002 Governance Rules -- 18.4 Risk Management and Governance -- 18.5 Administrative Competence of Board Members -- 18.6 New Regulation for Financial Institutions -- 18.7 Economic Analysis of Governance Effect -- 18.7.1 Testable Hypothesis -- 18.7.2 Data and Variables -- 18.7.3 Model -- 18.7.4 Empirical Results -- 18.8 Conclusion -- References -- Appendix A: Governance of Canadian Federal Financial Institutions -- Appendix B: Details on the Construction of the Governance Indexes -- Measuring Governance Standards -- The Quality of the Audit Committee -- The Quality of the Board -- SOX Standards -- NYSE Standards -- SOX and NYSE Standards -- Appendix C: Variables -- Chapter 19 Risk Management and Industrial Organization -- 19.1 Entry, Production, and Hedging -- 19.2 Commitment to Hedging -- 19.3 Conclusion -- References -- Chapter 20 Real Implications of Corporate Risk Management -- 20.1 Real Implications of Corporate Risk Management: A Review -- 20.2 Methodology -- 20.2.1 Instrumental Variable -- 20.2.2 Essential Heterogeneity Model -- 20.3 US Oil Producers -- 20.3.1 Sample Construction -- 20.3.2 Descriptive Statistics -- 20.3.3 Oil Hedging Activity -- 20.3.4 Univariate Tests…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Applications of VLSI Design in Artificial Intelligence and Machine Learning -- 1.1 Introduction -- 1.2 Artificial Intelligence -- 1.3 Artificial Intelligence &amp -- VLSI (AI and VLSI) -- 1.4 Applications of AI -- 1.5 Machine Learning -- 1.6 Applications of ML -- 1.6.1 Role of ML in Manufacturing Process -- 1.6.2 Reducing Maintenance Costs and Improving Reliability -- 1.6.3 Enhancing New Design -- 1.7 Role of ML in Mask Synthesis -- 1.8 Applications in Physical Design -- 1.8.1 Lithography Hotspot Detection -- 1.8.2 Pattern Matching Approach -- 1.9 Improving Analysis Correlation -- 1.10 Role of ML in Data Path Placement -- 1.11 Role of ML on Route Ability Prediction -- 1.12 Conclusion -- References -- Chapter 2 Design of an Accelerated Squarer Architecture Based on Yavadunam Sutra for Machine Learning -- 2.1 Introduction -- 2.2 Methods and Methodology -- 2.2.1 Design of an n-Bit Squaring Circuit Based on (n-1)-Bit Squaring Circuit Architecture -- 2.2.1.1 Architecture for Case 1: A &lt -- B -- 2.2.1.2 Architecture for Case 2: A &gt -- B -- 2.2.1.3 Architecture for Case 3: A = B -- 2.3 Results and Discussion -- 2.4 Conclusion -- References -- Chapter 3 Machine Learning-Based VLSI Test and Verification -- 3.1 Introduction -- 3.2 The VLSI Testing Process -- 3.2.1 Off-Chip Testing -- 3.2.2 On-Chip Testing -- 3.2.3 Combinational Circuit Testing -- 3.2.3.1 Fault Model -- 3.2.3.2 Path Sensitizing -- 3.2.4 Sequential Circuit Testing -- 3.2.4.1 Scan Path Test -- 3.2.4.2 Built-In-Self Test (BIST) -- 3.2.4.3 Boundary Scan Test (BST) -- 3.2.5 The Advantages of VLSI Testing -- 3.3 Machine Learning's Advantages in VLSI Design -- 3.3.1 Ease in the Verification Process -- 3.3.2 Time-Saving -- 3.3.3 3Ps (Power, Performance, Price)…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Decision Making Using Fuzzy Logic Using Multicriteria -- 1.1 Introduction -- 1.2 Fuzzy Logic -- 1.3 Decision Making -- 1.4 Literature Review -- 1.5 Conclusion -- Acknowledgment -- References -- Chapter 2 Application of Fuzzy Logic in the Context of Risk Management -- 2.1 Introduction -- 2.2 Objectives of Risk Management -- 2.3 Improved Risk Estimation -- 2.3.1 Point-Wise Calculations on a Curve -- 2.3.2 Estimation of a Curve -- 2.3.3 Accuracy in Quantification is Raised -- 2.3.4 The Problems with the Basic Quantification Approach -- 2.4 Threat at Quantification Matrix -- 2.4.1 Qualitative Matrix -- 2.4.2 Errors in Scaling -- 2.4.3 Band Width at Various Scales -- 2.5 Fundamental Definitions -- 2.5.1 Positioning Statement -- 2.5.2 Risk Under the Level of Tolerance -- 2.5.3 Risk Elimination -- 2.6 Fuzzy Logic -- 2.7 Risk Related to Fuzzy Matrix -- 2.8 Conclusion -- Bibliography -- Chapter 3 Use of Fuzzy Logic for Controlling Greenhouse Environment: A Study Through the Lens of Web Monitoring -- 3.1 Introduction -- 3.2 Design (Hardware) -- 3.2.1 Sensor for Measuring Soil Moisture -- 3.2.2 Sensor for Measuring Humidity and Temperature -- 3.3 Programming Arduino Mega Board -- 3.3.1 Fuzzification -- 3.3.2 Fuzzy Inference -- 3.3.3 Communication via Remote Connections and a Web Server -- 3.4 Implementation of a Prototype -- 3.5 Results -- 3.6 Conclusion -- Bibliography -- Chapter 4 Fuzzy Logics and Marketing Decisions -- 4.1 Introduction -- 4.2 Literature -- 4.2.1 Fuzzy Logic (FL) -- 4.2.2 FL Application in Marketing -- 4.2.2.1 Communication and Advertising -- 4.2.2.2 Customer Service and Satisfaction -- 4.2.2.3 Customer Segmentation -- 4.2.2.4 CRM -- 4.2.2.5 Pricing -- 4.2.2.6 Evaluation of a Product -- 4.2.2.7 Uncertainty in the Development of New Products…”

Tabla de Contenidos: “…-- #28 Success Is Uncomfortable -- #29 Bet on Hard Work, Not Luck -- #30 Enjoy the Journey -- Part III The Keys to Unlocking Your Team's Potential -- Month 7 The Art of Communication, Transparency, and Active Listening -- #1 Active Listening Is All-In Leadership -- #2 Be Transparent -- #3 Make Your Expectations Crystal Clear -- #4 Master the Art of Communication -- #5 Communicate Using Clarity, Purpose, and Trust -- #6 Become Obsessed with People and How They Think -- #7 Losing Top Talent Has a Price -- #8 People Want to Feel Seen and Understood -- #9 Listen to Your Employees' Problems -- #10 Make Sure Everyone Is Heard at Meetings -- #11 Don't Overshare -- #12 Don't Be a One-Upper: Support the Success and Wins of Your Team…”

“…Carbon pricing is a valuable instrument in the policy toolkit to promote clean energy transitions. …”

“…The full benefits of road pricing can be reaped by adjusting the prices to encourage more efficient economic and environmental outcomes. …”

“…Israeli house prices have risen by over 50% over the past three years. …”

“…He was awarded the IEEE Evolutionary Computation Pioneer Award, the Infosys Prize, the TWAS Prize in Engineering Sciences, the CajAstur Mamdani Prize, the Distinguished Alumni Award from IIT Kharagpur, the Edgeworth Pareto Award, the Bhatnagar Prize in Engineering Sciences, and the Bessel Research Award from Germany. …”

“…Key features of this approach, called product-led growth, or PLG, include free or very low-cost introductory pricing, with higher prices for more functionality (like what's offered by Zoom) and overall ease of use. …”

“…Main features of the boom -Demand and output -Employment and labour market -Prices and wages II. The BLEU balance of payments III. …”

“…Main features of the boom -Demand and output -Employment and labour market -Prices and wages II. The BLEU balance of payments III. …”

“…In France and Italy, legal reforms to implement their commitments to price transparency in the pharmaceutical market are consistent with European Union law. …”

“…For internationally traded agricultural products, it is not clear what constitutes “thin” markets and how this “thinness” contributes to price volatility. Nonetheless, in the current atmosphere of high food and agricultural prices, the sentiment that international prices are more volatile because agricultural markets are thin, is widely shared. …”

“…This annual publication provides statistical tables showing steel production, consumption and trade data, as well as other indicators of activity such as employment levels, annual investment expenditures by sector and by country, export prices, domestic prices and indices for certain iron and steel products…”