Statistics for Spatial Data.

Problem-based approaches to learning have a long history of advocating experience-based education. Psychological research and theory suggests that by having students learn through the experience of solving problems, they can learn both content and thinking strategies. Problem-based learning (PBL) is an instructional method in which students learn through facilitated problem solving. In PBL, student learning centers on a complex problem that does not have a single correct answer. Students work in collaborative groups to identify what they need to learn in order to solve a problem. They engage in self-directed learning (SDL) and then apply their new knowledge to the problem and reflect on what they learned and the effectiveness of the strategies employed. The teacher acts to facilitate the learning process rather than to provide knowledge. The goals of PBL include helping students develop 1) flexible knowledge, 2) effective problem-solving skills, 3) SDL skills, 4) effective collaboration skills, and 5) intrinsic motivation. This article discusses the nature of learning in PBL and examines the empirical evidence supporting it. There is considerable research on the first 3 goals of PBL but little on the last 2. Moreover, minimal research has been conducted outside medical and gifted education. Understanding how these goals are achieved with less skilled learners is an important part of a research agenda for PBL. The evidence suggests that PBL is an instructional approach that offers the potential to help students develop flexible understanding and lifelong learning skills.

/pdf/problem-based-learning-what-and-how-do-students-learn-456c11ro51.pdf

Problem-Based Learning: What and How Do Students Learn?.

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

Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.

/pdf/carbon-capture-and-storage-ccs-the-way-forward-11rgsqyer0.pdf

Carbon capture and storage (CCS): the way forward

https://persianmof.weebly.com/uploads/4/8/8/3/48832931/chem._rev._2014,_114,_10575.pdf

Water stability and adsorption in metal-organic frameworks.

The enzymatic hydrolysis of cellulose encounters various limitations that are both substrate- and enzyme-related. Although the crystallinity of pure cellulosic Avicel plays a major role in determining the rate of hydrolysis by cellulases from Trichoderma reesei, we show that it stays constant during enzymatic conversion. The mode of action of cellulases was investigated by studying their kinetics on cellulose samples. A convenient method for reaching intermediate degrees of crystallinity with Avicel was therefore developed and the initial rate of the cellulase-catalyzed hydrolysis of cellulose was demonstrated to be linearly proportional to the crystallinity index of Avicel. Despite correlation with the adsorption capacity of cellulases onto cellulose, at a given enzyme loading, the initial enzymatic rate continued to increase with a decreasing crystallinity index, even though the bound enzyme concentration stayed constant. This finding supports the determinant role of crystallinity rather than adsorption on the enzymatic rate. Thus, the cellulase activity and initial rate data obtained from various samples may provide valuable information about the details of the mechanistic action of cellulase and the hydrolysable/reactive fractions of cellulose chains. X-ray diffraction provides insight into the mode of action of Cel7A from T. reesei. In the conversion of cellulose, the (021) face of the cellulose crystal was shown to be preferentially attacked by Cel7A from T. reesei.

https://febs.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1742-4658.2010.07585.x

Cellulose crystallinity--a key predictor of the enzymatic hydrolysis rate

Modeling cellulase kinetics on lignocellulosic substrates.

Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty

Machine learning: Overview of the recent progresses and implications for the process systems engineering field

This paper presents a general optimization model that enables the selection of fuel conversion technologies, capacities, biomass locations, and the logistics of transportation from the locations of forestry resources to the conversion sites and then to the final markets. A mixed integer linear programming (MILP) model has been formulated and implemented in a commercial software package (GAMS) using databases built in Excel. The MILP represents decisions regarding (1) the optimal number, locations, and sizes of various types of processing plants, (2) the amounts of biomass, intermediate products, and final products to be transported between the selected locations over a selected period, and maximizes the objective function of overall profit. The model has been tested based on an industry-representative data set that contains information on the existing wood resources, final product market locations and demands, and candidate locations and sizes for different types of processing plants, as well as the costs associated with the various processing units and transportation of materials, covering the Southeastern region of the United States. The model is applied to design both a distributed, and a more centralized, conversion system. The overall profits, values, cost, and supply network designs of both systems are analyzed using the optimization model. In particular, we investigate: 1) which parameters have major effect on the overall economics, and 2) the benefits of going to more distributed types of processing networks, in terms of the overall economics and the robustness to demand variations.

Matthew J. Realff

Papers

Cellulose crystallinity--a key predictor of the enzymatic hydrolysis rate

Modeling cellulase kinetics on lignocellulosic substrates.

Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty

Machine learning: Overview of the recent progresses and implications for the process systems engineering field

Design of biomass processing network for biofuel production using an MILP model