Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

介绍了Kirk—Othmer Encyclopedia of Chemical Technology（化工技术百科全书）（第五版）电子图书网络版数据库，并对该数据库使用方法和检索途径作出了说明，且结合实例简单地介绍了该数据库的检索方法。

Kirk-Othmer Encyclopedia of Chemical Technology数据库介绍及实例

An overview of sustainability assessment methodologies

「脆弱性(Vulnerability)」とは何か

An oil well's productivity is generally considered the standard measure of the well's performance. However, productivity depends on several factors, including fluid characteristics, formation damage, the reservoir's formation, and the kind of completion the well undergoes. How a partial completion can affect a well's performance will be investigated in detail in this study, as nearly every vertical well is only partially completed as a result of gas cap or water coning issues. Partially penetrated wells typically experience a larger pressure drop of fluid flow caused by restricted regions, thus increasing the skin factor. A major challenge for engineers when developing completion designs or optimizing skin factor variables is devising and testing suitable partial penetration skin and comparing completion options. Several researchers have studied and calculated a partial penetration skin factor, but some of their results tend to be inaccurate and cause excessive errors. The present work proposes experimental work and a numerical simulation model for accurate estimation of the pseudo-skin factor for partially penetrated wells. The work developed a simple correlation for predicting the partial penetration skin factor for perforated vertical wells. The work also compared the results from available models that are widely accepted by the industry as a basis for gauging the accuracy of the new correlation in estimating the skin factor. Compared to other approaches, the novel correlation performs well by providing estimates for the partial penetration skin factor that are relatively close to those obtained by the tested models. This work's main contribution is the presentation of a novel correlation that simplifies the estimation of the partial penetration skin factor in partially completed vertical wells.

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Quantifying the partial penetration skin factor for evaluating the completion efficiency of vertical oil wells

Continuous monitoring of subsea pipelines is the best way to detect leaks quickly. A number of systems and technologies exist for this purpose. The present work describes an analytical approach to making decisions related to best technology selection. The proposed approach uses a risk-based methodology for identifying the best technology. The key focus of the present work is to develop a method to quantify uncertainties involved with leak detection technologies on subsea arctic pipelines and use the quantified uncertainty in decision-making. The proposed methodology considers a broad range of factors. It gives an overall risk, as well as, revealing the strengths and weaknesses of the leak detection technologies in different subcategories. The proposed methodology is demonstrated using a case study.Copyright © 2015 by ASME

Risk-Based Evaluation of Subsea Pipeline Leak Detection Technologies

Process safety assists in improving the operational, economic, and environmental performance of processing facilities. This study addresses the critical process safety challenges at the process design and operation stage through an effective risk minimization strategy. The process risk index (PRI), a heuristic-based approach, is used to map risk and explore the effect of process deviations on process safety. The PRI method is explained using the blue hydrogen process - steam methane reforming (SMR) process. A risk map of the entire facility uses traffic-light colors as risk indicators is developed. The synergistic effects and parameters are analyzed using process simulation tools and converted into risk values. The risk map identifies the reforming, the water-gas shift, and the separator units as the highest risk units for given deviations in the operational parameters. We have benchmarked the PRI with the fire and explosion damage index (FEDI) and observed that the PRI is more sensitive to finding process deviations and hazards. This method can detect root causes of process safety issues at a unit-specific level better than the FEDI. Additionally, the PRI provides updated risk maps once risk minimization strategies are implemented. The PRI serves as a tool for process safety analysis of hazardous operations such as hydrogen production processes with better sensitivity and flexibility in scaling. 

Process Risk Index (PRI) – a methodology to analyze the design and operational hazards in the processing facility

This paper strives to identify the significant and unprecedented challenges in loss prevention and process safety facing industry and its senior leaders due to the social / political / economical changes and unrest of the past few years and the threats to world stability. The pandemic with its travel constraints, price hikes, and economic troubles, reinforced by changing energy markets due to the war in Ukraine is threatening global economic stability. This overall situation has introduced a dramatic step change to the conduct of operations for many manufacturers. On top of that comes the threat of climate change and the ongoing energy and digitalization adjustments that industry will have to make to improve socially responsible and to remain competitive in the face of changing expectations. These Industry 4.0 adjustments introduce a great challenge for the management of multinational companies given the greater range of expectations in its operational jurisdictions. Leadership needs to take stock, re-evaluate their business processes and engage resources not tapped in the past to establish a new foundation on which to remain competitive in the future. Within a rather turbulent world, retirement of experienced staff, the multiplicity of several generations working together with their different attitudes and life styles, posed an additional challenge: the need for skilled and motivated staff to overcome the hurdles. This paper frames these current and future challenges and provides guidance to industry leadership and management to assist in their struggle to meet demands and maintain competitive operations. 

Faisal Khan

Papers

Quantifying the partial penetration skin factor for evaluating the completion efficiency of vertical oil wells

Risk-Based Evaluation of Subsea Pipeline Leak Detection Technologies

Process Risk Index (PRI) – a methodology to analyze the design and operational hazards in the processing facility

Leadership 4.0: The Changing Landscape of Industry Management in the Smart Digital Era

Arctic Pipeline Integrity Management using Real-Time Condition Monitoring