Miracle-dz/UniFAR-FTUs huggingface dataset:
Long term conductivity of narrow fractures filled with a proppant monolayer in shale gas reservoirs

The primary goal of hydraulic fracturing is to create a high conductive pathway. Gas shale is mainly fractured by slick-water. A complex network of narrow secondary fractures is created in slick water fracturing. These narrow fractures without proppants maintain low conductivity. A partial monolayer of proppant can be used to enhance the conductivity of these fractures and then improve the production. Due to the interaction between proppants and fracture surface under confining stress, the proppants will embed into the formations, which results in a decrease in fracture width and conductivity. Researches available in literature have addressed the problem. However, the shale reveals varying amounts of creep deformation in response to applied stress, which will continuously enhance the proppant embedment and reduce fracture width. The influence of this time dependent effect on the long term conductivity of partial monolayer proppant is not well understood. Study the characteristics and controlling factors of the long term change in conductivity can benefit to the production analysis and hydraulic fracturing optimization. Therefore, models combined numerical and analytical methods are developed in this paper. A finite element model is developed to simulate the long term change in fracture width. Then a simplified model based on Carman-Kozeny equation is used to calculate the long term conductivity. Simulation results show that after considering long term creep effects, there is still an optimal proppant concentration, which remains the maximum residual conductivity after proppant embedment. And the calculated optimal proppant concentration becomes larger than that without considering the long term effect. The simulation results also indicate that the optimal concentration depends on stress, rock mechanical properties, proppant mechanical properties and time.
2D shape manipulation via topology‐aware rigid grid

This paper presents a new method which allows user to manipulate a two‐dimensional shape in an intuitive and flexible way. The shape is discretized as a regular grid. User places handles on the grid and manipulates the shape by moving the handles to the desired positions. To meet the constraints of the user's manipulation, the grid is then deformed in an as‐rigid‐as‐possible way. However, this straightforward approach tends to produce unnatural deformations when the grid resolution is not high enough to capture the topological structure of the shape. In the proposed method, the regular grid is trimmed and only the cells that are inside the fatty regions of the shape are preserved, namely “interior grid. ” When user manipulates the shape, the interior grid and the shape boundary curve are deformed with minimum distortions. To make the deformations of the interior grid and the boundary curve consistent, a junction energy is introduced. In this way, the unnatural deformation effects could be effectively removed and the physically plausible results can be obtained. Meanwhile, the proposed approach provides user an intuitive and simple way to adjust the shape global and local stiffnesses. The deformation is formulated as an energy minimization problem. The energy function is non‐quadratic and could be efficiently solved using an iterative solver with the fast summation technique that exploits the interior grid and boundary curve regularities. In addition, the method could be easily extended to manipulate curves and stick figures. Experimental results demonstrate the capability and flexibility of the new method. Copyright © 2009 John Wiley & Sons, Ltd.
Statistical noise model in GRAPPA-reconstructed images

Noise in Parallel Imaging Parallel MRI (pMRI) techniques extended the applicability of multiple-coil systems by increasing the acquisition rate via subsampled acquisitions of the k-space data. Many reconstruction methods have been proposed in order to suppress the aliasing and underlying artifacts created by the subsampling. Dominant among these are SENSE and GRAPPA. One of the effects in accelerated pMRI acquisitions is a significant change in the noise model, which depends on reconstruction scheme. From a statistical point of view, it is known that, if no subsampling of the k-space is done, the composite magnitude signal from several coils can be modeled as a non-central Chi distribution [Const97]. When k-space is subsampled, the noise power in-fact varies across the reconstructed image, and yet (maybe? ) different for each receiving coil. Depending on the way the information from each coil is combined (i.e, depending on the reconstruction method), the statistics of the image may no longer follow the non-central Chi statistics. In SENSE, for instance, noise in the x-space is known to be Rician but non-stationary, i.e. the noise power varies from point to point [Thumb07]. Some authors suggest that GRAPPA-reconstructed images may follow a non-central Chi distribution [Thumb07], but no thorough study has been done. The great importance of the characterization of noise statistics in MRI has been extensively reported in literature. Many filtering, noise estimation or diffusion tensor estimation methods for conventional (non-parallel) MRI rely on the Rician noise model. However, during the last years some techniques have been developed assuming a non-central Chi distribution of noise. As an example, in [TV09] authors prove that the Weighted Least Squares method used in DTI to estimate the diffusion tensor is also valid under the non-central Chi assumption. In [Breuer09] authors present a novel g-factor derivation for GRAPPA, and the variance of noise in each coil calculated as
ON THE CRITICAL THERMAL INCREMENT FOR THE LOCOMOTION OF A DIPLOPOD

A good deal of attention has been paid to the temperature coefficients of vital processes. The data available up to 1914-15 are for the most part collected in Kanitz' (1915) book. The chief interest of this work has centered about the "R. G. T. " (van't Hoff) rule. In common with many chemical reactions, at ordinary temperatures the velocities of a variety of protoplasmic activities are found to be about doubled or trebled by a 10 ° rise of temperature. The temperature coefficient, Q,0, giving the ratio of velocities for an interval of 10 °, is a quite imperfect means of characterizing a process. I t is in any case not a constant quantity, but depends upon the particular temperature. Moreover, it is known that in the neighborhood of certain temperatures, varying with the process considered, abrupt changes occur in the temperature relations of many biological phenomena. A means is required of dealing with these "critical points. " Such complexity of dynamical balance as must be postulated for living matter leads one to expect that by considerable change of temperature a different fundamental reaction might be brought into control of the velocity of a given phenomena. For certain vital activities which may be taken as typical,, it is found that the underlying or controlling processes may be treated as systems of "irreversible" first order reactions (Osterhout, 1914, 1922; Hecht, 1918-19, 1918-19, a; Osterhout and Haas, 1918-19).
Logic-Based Distributed Routing for NoCs

The design of scalable and reliable interconnection networks for multicore chips (NoCs) introduces new design constraints like power consumption, area, and ultra low latencies. Although 2D meshes are usually proposed for NoCs, heterogeneous cores, manufacturing defects, hard failures, and chip virtualization may lead to irregular topologies. In this context, efficient routing becomes a challenge. Although switches can be easily configured to support most routing algorithms and topologies by using routing tables, this solution does not scale in terms of latency and area. We propose a new circuit that removes the need for using routing tables. The new mechanism, referred to as logic-based distributed routing (LBDR), enables the implementation in NoCs of many routing algorithms for most of the practical topologies we might find in the near future in a multicore chip. From an initial topology and routing algorithm, a set of three bits per switch output port is computed. By using a small logic block, LHDR mimics (demonstrated by evaluation) the behavior of routing algorithms implemented with routing tables. This result is achieved both in regular and irregular topologies. Therefore, LBDR removes the need for using routing tables for distributed routing, thus enabling flexible, fast and power-efficient routing in NoCs.
Deviance in Classrooms

Preface. 1. A Critical Introduction to Labelling Theory. 2. Deviance and Education. 3. Rules in School. 4. Rules in Context. 5. The Imputation of Deviance. 6. A Theory of Typing. 7.The Typing of Deviant Pupils. 8. Reactions to Deviance. 9. Some Implications. Notes. Bibliographical Index. Subject Index.
Classification of Linked Data Sources Using Semantic Scoring

Linked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs:comment and rdfs:label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results. key words: linked data, semantic classification, wordnet
Customer Purchasing Behavior using Sequential Pattern Mining Technique

In this competitive world, wherever each organization or company must improve yourself for collaborating within the market. For this improvement, we'd like to grasp the client purchasing behavior [2]. Presently data processing provides several techniques to enhance it. Aim of this paper is to shortcoming of the “Frequent Pattern Mining Technique” and use the “Sequential Pattern Mining Technique” to enhance the client purchasing Behavior. Because Apriori generate millions of candidate sets [19] and scan the group action information repeatedly and FP-Growth generate the Large No. of Projected database. As we all know that one amongst the foremost fashionable data processing approaches is "Clustering Analysis", that is truly helpful for divide the information attribute into similar variety of teams that take the high intra similarity and low lay to rest similarity and “Sequential Pattern” technique to seek out the co-relations between attributes of a relation and have applications in promoting, monetary and retail sector and it's unremarkably applied to investigate market baskets to assist organizers to work out that things are consecutive purchased along by customers. This paper proposes an efficient technique to extract information from transactions records that is extremely helpful for increasing the client Satisfaction. Client details are divided victimization K-means [1] and sequential Pattern[4] Mining Technique “Prefix” algorithm is applied to spot client behavior over Banking knowledge. Firstly, we divide into the database into a n number of partition with the help of Clustering technique Then Sequential algorithm provide the sequential patterns over banking data of Indian bank. According to the result of the both technique, the processing time of mining is decreased and the efficiency of algorithm has improved.
A General Definition of the Heritable Variation That Determines the Potential of a Population to Respond to Selection

Genetic selection is a major force shaping life on earth. In classical genetic theory, response to selection is the product of the strength of selection and the additive genetic variance in a trait. The additive genetic variance reflects a population’s intrinsic potential to respond to selection. The ordinary additive genetic variance, however, ignores the social organization of life. With social interactions among individuals, individual trait values may depend on genes in others, a phenomenon known as indirect genetic effects. Models accounting for indirect genetic effects, however, lack a general definition of heritable variation. Here I propose a general definition of the heritable variation that determines the potential of a population to respond to selection. This generalizes the concept of heritable variance to any inheritance model and level of organization. The result shows that heritable variance determining potential response to selection is the variance among individuals in the heritable quantity that determines the population mean trait value, rather than the usual additive genetic component of phenotypic variance. It follows, therefore, that heritable variance may exceed phenotypic variance among individuals, which is impossible in classical theory. This work also provides a measure of the utilization of heritable variation for response to selection and integrates two well-known models of maternal genetic effects. The result shows that relatedness between the focal individual and the individuals affecting its fitness is a key determinant of the utilization of heritable variance for response to selection.
Host-parasite interaction between cultivated mushroom, Agaricus bisporus hybrid strain sylvan A15, and the mycoparasite Verticillium fungicola, a causal agent of dry bubble disease

Verticillium fungicola is one of the most economically significant pathogens of the cultivated mushroom Agaricus bisporus throughout the world. A. bisporus basidiomes are reported to be more susceptible to infection by V. Fungicola than the vegetative mycelium. V. Fungicola spores were also reported to be dependent on A. bisporus mycelium. Transmission and scanning electron microscopy were used to investigate the host-pathogen interactions at the mycelial and sporophore stages of A. bisporus. The transmission electron micrographs showed that the V. Fungicola mycelium grows very close to the A. bisporus mycelium. There were no specialised penetration structures or any evidence of direct penetration of the pathogen in the host tissue. The scanning electron micrograph of the necrotic tissue of the diseased mushroom shows clusters of phialoconidia and hyphae of the pathogen in large numbers.
A Central Control System for the Environmental Laboratory

For a practical environmental laboratory, many environmental test chambers are needed, and any central control system designed for them would face the problems listed as follow: 1. Different protocol types. 2. Different connecting interfaces. 3. Different operating method. 4. Large distributed area. To address these problems, a laboratory central control system (LCCS) architecture is proposed in this paper, and the application software is then developed to monitor and control 15 chambers in one of the environmental laboratories in Chengdu City. This application demonstrates great value in increasing the efficiency of the environment test in that laboratory.
Isolation and Identification of Mushroom Pathogens from Agrocybe aegerita

Agrocybe aegerita is an important mushroom cultivated in Korea, with good feel and a peculiar fragrance. A. aegerita can be cultivated throughout the year using culture bottles but is more susceptible to contamination than other mushrooms. Twenty-two pathogens were isolated from the fruiting bodies and compost of A. aegerita, and seven isolates were isolated from Pleurotus ostreatus to compare with the A. aegerita isolates, collected from Gimje, Iksan, Gunsan of Chonbuk, and Chilgok of Gyeongbuk Province in 2009. These isolates were identified based on morphological and molecular characteristics. Of the 29 isolates, 26 were identified as Trichoderma spp. and the remaining three were Aspergillus spp. , Mucor spp. , and Penicillium spp. A phylogenetic analysis revealed that the 26 isolates of Trichoderma were divided into four taxa, namely T. harzianum, T. pleuroticola, T. longibrachiatum, and T. atroviride. Among the Trichoderma spp. , 16 isolates (55.2%) were identified as T. harzianum, six as T. pleuroticola (20.7%), two as T. longibrachiatum, and the remaining two were T. atroviride.
Modern Linear and Nonlinear Econometrics

Acknowledgements. - Part I. Linear and Nonlinear Econometric Inference: Estimation and Testing. Estimation in Linear and Nonlinear Models. Generalized Methods of Moments. Testing in Linear and Nonlinear Models. - Part II. Time Series Analysis. A Typology of Dynamic Models. Univariate ARIMA Models. Cointegration and Transfer Functions. Multivariate Time Series. Varying Parameters Models. - Part III. Categorical and Limited Dependent Variables. Discrete Choice Models. Limited responses, duration and count data. - Part IV. Panel Data Analysis. Linear Panel Data Models. Nonlinear Panel Data Models. - A. Nonlinear Optimization and Estimation. - B. Mathematical Formulation of GMM. - C. Stability Criteria for AR(p) Models. - D. MLE of the RSM with Endogenous Prices. - E. Volatility Modeling.
Catastrophe Insurance: Consumer Demand, Markets and Regulation

Preface and Acknowledgements. 1: Introduction. 1. The Problem of Catastrophe Risk. 2. Overview of Study. 3. Summary of Findings. 2: An Overview of Catastrophe Insurance Markets. 1. Characteristics of Residential Property Insurance. 2. Challenges to Insuring Catastrophe Risk. 3. Market Structure. 4. Market Conduct and Performance. 3: Regulatory Institutions and Policies. 1. The Framework for Insurance Regulation. 2.Key Regulatory Policies Affecting Catastrophe Insurance. 3. Regulation in Florida. 4. Regulation in New York. 4: Supply, Demand and Regulation of Catastrophe Insurance. 1. The Supply of Catastrophe Insurance. 2. The Structure of Demand for Homeowners Insurance. 3. Summary. 5: Demand Estimation for Homeowners Insurance Policies. 1. Estimation of Quantity Demanded. 2. Florida Results. 3. New York. 4. Guaranty Funds. 5. Summary. 6: Summary and Conclusions. Appendix A: Structure-Conduct-Performance Framework. Appendix B: Introduction to Catastrophe Modeling. 1. AIR Catastrophe Modeling Technology. 2. Event Generation Module. 3. Local Intensity Module. 4. Damage Module. 5. Insured Loss Module. 6. Model Output. References. Index.
Detection of Clones in Sparse and Dense Data Sets Using Efficient Data Mining Techniques : A Comparative Study

Code clones are similar program structures recurring in variant forms in software system(s). Several techniques have been proposed to discover simple clones i.e., method level clones. But identifying structural clones has been a difficult task because this requires am iterative scan of the database. Structural clones show a bigger picture of simple clones. Hence identification of the structural level clones improves the performance of the system under development by enhancing the properties like reusability, maintainability and re-engineering. So to identify the structural clones a number of approaches have been developed but the efficiency of those algorithms are less. Hence in this paper we would like to propose two different techniques one for association mining and one for clustering to identify the structural clones. The proposed technique would not only scan sparse data but also dense data to identify the clones. We also try to detect exact and near miss clones. The techniques used would be mining frequent patterns using prefix trees and an efficient density based clustering algorithm. At last we make a comparison between the existing method and the one proposed in this paper. Keywords--structural Clones, FP-Tree, Re-use, Maintenance.
Strategies for successful organizational downsizing

This article reports the results of a study of 30 organizations that engaged in downsizing over a four-year period. Three questions were investigated: (1) What general strategies are used by organizations to downsize? (2) What are the effects of downsizing on organizational performance? (3) What are downsizing's “best practices? ” or, What are the differences between organizations that effectively downsize and those that do not? Three main strategies for downsizing are explained, the managerial actions to overcome the negative effects of downsizing are enumerated, and the best practices of successful downsizing firms are presented.
High-sensitivity dynamic coded aperture imaging

Coded aperture imaging aims at improving the sensitivity of standard imaging optics (pinholes and parallel-hole collimators) by using hundreds of pinholes. Since each pinhole projects a copy of the object onto the detector, copies overlap. The overlap can be undone by computer post-processing with a simple correlation if pinholes are arranged in particular patterns. These are designed so that, when the object is infinitely far from the detector, a perfect image is obtained. In applications such as nuclear medicine and small-animal imaging the object is kept close to the detector to maximize sensitivity and use of the same patterns was shown to result in noticeable artifacts. The existing technique to mitigate near-field artifacts requires taking two images of the object sequentially with two similar masks, but this makes the implicit assumption of a static object. In near-field imaging, however, it is possible to place two detectors on opposite sides of the object and acquire the two images simultaneously. This approach extends the technique to dynamic studies. It also has the benefit of doubling the overall count rate by better utilization of the heads of a clinical gamma camera. Experimental results show that coded aperture optics can follow the movement of a 370-kBq point source at 0.05 s per frame with 3.5-mm resolution over a 12/spl times/12 cm/sup 2/ field of view. Images of a disk source demonstrate that, as in static studies, near-field artifacts can be almost eliminated.