This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

This book constitutes the refereed post-conference proceedings of the 13th International Conference on Wireless and Satellite Services, WiSATS  2022, held in March 12-13, 2023. Due to COVID-19 pandemic the conference was held virtually. The 9 full papers were carefully reviewed and selected from 23 submissions. They were organized in topical sections as follows: Security and Privacy in Healthcare, Transportation, and Satellite Networks,  Advanced Technologies in Wireless Communication Systems, Network Efficiency and Reliability.

Databook of Surface Modification Additives, Second Edition contains data on ten groups of additives, including anti-scratch and mar-preventing additives, additives for surface tension reduction and wetting, hydrophobization additives, gloss enhancement and surface matting additives, additives for the formation of tack-free surface and tackifiers, and stain inhibiting additives. The information on each is divided into five sections, including General Information, Physical-Chemical Properties, Health and Safety, Ecological Properties, and Use and Performance. This data is provided for approximately 360 of the most important surface modification additives produced and used today. This databook will be an extremely useful resource for engineers, researchers and technicians interested in using additives to modify and improve the surface properties of materials.

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

This book constitutes the refereed proceedings of the 13th China National Conference on Computational Linguistics, CCL 2014, and of the First International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2014, held in Wuhan, China, in October 2014. The 27 papers presented were carefully reviewed and selected from 233 submissions. The papers are organized in topical sections on word segmentation; syntactic analysis and parsing the Web; semantics; discourse, coreference and pragmatics; textual entailment; language resources and annotation; sentiment analysis, opinion mining and text classification; large-scale knowledge acquisition and reasoning; text mining, open IE and machine reading of the Web; machine translation; multilinguality in NLP; underresourced languages processing; NLP applications.

Nonlinear diffusion equations, an important class of parabolic equations, come from a variety of diffusion phenomena which appear widely in nature. They are suggested as mathematical models of physical problems in many fields, such as filtration, phase transition, biochemistry and dynamics of biological groups. In many cases, the equations possess degeneracy or singularity. The appearance of degeneracy or singularity makes the study more involved and challenging. Many new ideas and methods have been developed to overcome the special difficulties caused by the degeneracy and singularity, which enrich the theory of partial differential equations.This book provides a comprehensive presentation of the basic problems, main results and typical methods for nonlinear diffusion equations with degeneracy. Some results for equations with singularity are touched upon.

This book constitutes the refereed proceedings of the Third China Conference on Knowledge Graph and Semantic Computing, CCKS 2018, held in Tianjin, China, in August 2018. The 27 revised full papers and 2 revised short papers presented were carefully reviewed and selected from 101 submissions. The papers cover wide research fields including the knowledge graph, information extraction, knowledge representation and reasoning, linked data.

This book contains the best selected papers of two Satellite Events held at the 20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016, in November 2016 in Bologna, Italy: The Second International Workshop on Educational Knowledge Management, EKM 2016, and the First Workshop: Detection, Representation and Management of Concept Drift in Linked Open Data, Drift-an-LOD 2016. The 6 revised full papers included in this volume were carefully reviewed and selected from the 13 full papers that were accepted for presentation at the conference from the initial 82 submissions. This volume also contains the 37 accepted contributions for the EKAW 2016 tutorials, demo and poster sessions, and the doctoral consortium. The special focus of this year's EKAW was "evolving knowledge", which concerns all aspects of the management and acquisition of knowledge representations of evolving, contextual, and local models. This includes change management, trend detection, model evolution, streaming data and stream reasoning, event processing, time-and space dependent models, contextual and local knowledge representations with a special emphasis on the evolvability and localization of knowledge and the correct usage of these limits.