gebhart/patrepeval huggingface dataset:
Monitoring Acid Stimulation Treatments in Naturally Fractured Reservoirs with Slickline Distributed Temperature Sensing (2015)

Abstract Production from naturally fractured reservoirs can be greatly enhanced by stimulation with acid. When pumping the treatment from the surface (a bullhead treatment), the acid tends to enter the reservoir at the most permeable interval (conductive natural fractures). Without any other injectivity control, the acid will not likely divert from this path. Mechanical diversion techniques using swab cups or inflatable packers on jointed pipe or coiled tubing (CT) can ensure injection into the various intervals. With these methods, surface pressure can be monitored to assess fluid placement effectiveness for each zone treated, but uncertainty of the friction pressure in the pipe while pumping can result in an incorrect interpretation of fluid entry. These mechanical isolation techniques are typically more costly and time consuming than bullhead jobs. Diversion techniques for bullhead stimulation treatments using chemical or foam diverters are more efficient, but lack the confirmation that all the zones received acid because of uncertainty of interpreting downhole behavior from surface pressure. A new technique using fiber-optic distributed temperature sensing (DTS) measurements offers a solution when bullheading by providing an indication of where the acid has been injected into the fractured reservoir. A system has been developed consisting of a fiber-optic element encased in an acid-resistant slickline (commonly referred to as SL-DTS) that can be deployed in wells to monitor acid treatments in real time. The entire length of the fiber-optic strand functions as a temperature sensor with a vertical resolution of approximately 3.5 ft. The SL-DTS is gravity deployed or can be pumped into highly deviated or horizontal wells prior to the stimulation treatment. Once in place, the fiber is interrogated with pulsed laser energy to record temperature profiles versus time over the entire fiber length. These time-based data allow easy observation of thermal events due to fluid injections as well as any exothermic reaction of the acid system with the formation. Surface-temperature fluid pumped into perforations provides a cooling effect along the flow path. Interaction of the acid system with carbonate-containing minerals in the formation generates heat. Temperature profiles observed during and after pumping can be used to detect and quantify the distribution of the fluid system into the various intervals. This method was used to successfully delineate acid placement in several deviated wells during bullhead stimulations. The data illustrate zones successfully treated as well as zones that may be targeted for remedial treatment. SL-DTS offers a unique opportunity to optimize stimulation placement in fractured reservoirs or any reservoir with variable injectivity.
PipeCNN: An OpenCL-based open-source FPGA accelerator for convolution neural networks (2017)

Convolutional neural networks (CNNs) have been employed in many applications, such as image classification, video analysis and speech recognition. Being compute-intensive, CNNs are widely accelerated by GPUs with high power dissipations. Recently, studies were carried out exploiting FPGA as CNN accelerator because of its reconfigurability and advantage on energy efficiency over GPU, especially when OpenCL-based high-level synthesis tools are now available providing fast verification and implementation flows. In this paper, we demonstrate PipeCNN - an efficient FPGA accelerator that can be implemented on a variety of FPGA platforms with reconfigurable performance and cost. The PipeCNN project is openly accessible, and thus can be used either by researchers as a generic framework to explore new hardware architectures or by teachers as a off-the-self design example for any academic courses related to FPGAs.
Multichannel blind identification: from subspace to maximum likelihood methods (1998)

A review of blind channel estimation algorithms is presented. From the (second-order) moment-based methods to the maximum likelihood approaches, under both statistical and deterministic signal models. We outline basic ideas behind several new developments, the assumptions and identifiability conditions required by these approaches, and the algorithm characteristics and their performance. This review serves as an introductory reference for this currently active research area.
Recycling Test Cases to Detect Security Vulnerabilities (2012)

The design of new protocols and features, e.g., in the context of organizations such as the IETF, produces a flow of novel standards and amendments that lead to ever changing implementations. These implementations can be difficult to test for security vulnerabilities because existing tools often lag behind. In the paper, we propose a new methodology that addresses this issue by recycling test cases from several sources, even if aimed at distinct protocols. It resorts to protocol reverse engineering techniques to build parsers that are capable of extracting the relevant payloads from the test cases, and then applies them to new test cases tailored to the particular features that need to be checked. An evaluation with 10 commercial and open-source testing tools and a large set of FTP vulnerabilities shows that our approach is able to get better or equal vulnerability coverage than the original tools. In a more detailed experiment with two fuzzers, our solution showed an improvement of 19% on vulnerability coverage when compared with the two combined fuzzers, being capable of finding 25 additional vulnerabilities.
New and convenient synthesis of 1-aryl-1,2-alkanediones (1978)

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTNew and convenient synthesis of 1-aryl-1,2-alkanedionesNorbert De Kimpe, Roland Verhe, Laurent De Buyck, and Niceas SchampCite this: J. Org. Chem. 1978, 43, 14, 2933–2935Publication Date (Print):July 1, 1978Publication History Published online1 May 2002Published inissue 1 July 1978https://pubs.acs.org/doi/10.1021/jo00408a047https://doi.org/10.1021/jo00408a047research-articleACS PublicationsRequest reuse permissionsArticle Views223Altmetric-Citations20LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts
Review on Data Mining Techniques for Prediction of Water Quality (2017)

Data mining is the exploration and scrutiny of large quantity of information that is able to discover meaningful and significant patterns. This paper studies various data mining techniques for prediction of water quality. This paper reviews the models and various evaluation methods that describe and distinguish the classes of water quality. Various data mining techniques like Artificial neural networks, Naive bayes, Back propogation algorithm, KNN etc has been explored in this paper.
Circuit optimization via adjoint Lagrangians (1997)

The circuit tuning problem is best approached by means of gradient-based nonlinear optimization algorithms. For large circuits, gradient computation can be the bottleneck in the optimization procedure. Traditionally, when the number of measurements is large relative to the number of tunable parameters, the direct method is used to repeatedly solve the associated sensitivity circuit to obtain all the necessary gradients. Likewise, when the parameters outnumber the measurements, the adjoint method is employed to solve the adjoint circuit repeatedly for each measurement to compute the sensitivities. In this paper, we propose the adjoint Lagrangian method, which computes all the gradients necessary for augmented-Lagrangian-based optimization in a single adjoint analysis. After the nominal simulation of the circuit has been carried out, the gradients of the merit function are expressed as the gradients of a weighted sum of circuit measurements. The weights are dependent on the nominal solution and on optimizer quantities such as Lagrange multipliers. By suitably choosing the excitations of the adjoint circuit, the gradients of the merit function are computed via a single adjoint analysis, irrespective of the number of measurements and the number of parameters of the optimization. This procedure requires close integration between the nonlinear optimization software and the circuit simulation program. The adjoint Lagrangian formulation has been implemented in the JiffyTune tool which optimizes delay, area, slew (transition time) and power measurements by adjusting transistor widths and wire sizes. Speedups of over 35x have been realized in the gradient computation procedure by using the adjoint Lagrangian formulation, leading to speedups of up to 2.5x in the overall optimization procedure. Perhaps more importantly, these speedups have rendered feasible the tuning of large circuits. A circuit with 6,900 transistors was optimized in under two hours of CPU time.
Cytokine-induced release of plasminogen activator inhibitor-1 by human mesothelial cells. (1995)

Inflammatory cytokines mediate the release of PAI-1 by mesothelial cells, and so may affect the deposition of fibrin within the peritoneal cavity.
The new electric shielding scheme with comb-shaped wire and its applications in SMPS (2011)

Common-mode conduction EMI is a key consideration in switch-mode power supplies. Copper foil electric field shielding is commonly used in the SMPS to shield the transformers or inductors in order to reduce the CM conduction interference. However, the copper foil in the magnetic components not only shield the electric field, but also affect the magnetic field distributions due to high-frequency eddy current effects, resulting in extra eddy current losses in the foil and the change of magnetic parameter of the magnetic components. This paper analyzed these behaviors with copper foil shielding and proposed the new shielding scheme which has good electric field shielding effect, but with no effect on the magnetic field. The new shielding media used comb-shaped wire array to form the shielding sheet, instead of traditional complete copper foil. With the new shielding sheet, the eddy current effect due to AC magnetic field can be avoided. The electric field shielding behaviors with the new shielding scheme was deeply analyzed with different wire densities and compared with traditional copper foil shielding. Finally, the application example of the electric shielding for LLC transformer was demonstrated.
Efficient Inverse Modeling of Barotropic Ocean Tides (2002)

Abstract A computationally efficient relocatable system for generalized inverse (GI) modeling of barotropic ocean tides is described. The GI penalty functional is minimized using a representer method, which requires repeated solution of the forward and adjoint linearized shallow water equations (SWEs). To make representer computations efficient, the SWEs are solved in the frequency domain by factoring the coefficient matrix for a finite-difference discretization of the second-order wave equation in elevation. Once this matrix is factored representers can be calculated rapidly. By retaining the first-order SWE system (defined in terms of both elevations and currents) in the definition of the discretized GI penalty functional, complete generality in the choice of dynamical error covariances is retained. This allows rational assumptions about errors in the SWE, with soft momentum balance constraints (e.g., to account for inaccurate parameterization of dissipation), but holds mass conservation constraints. Wh...
Unsupervised learning with mixed numeric and nominal data (2002)

Presents a similarity-based agglomerative clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure proposed by D.W. Goodall (1966) for biological taxonomy, that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions about the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram, and a simple distinctness heuristic is used to extract a partition of the data. The performance of the SBAC algorithm has been studied on real and artificially-generated data sets. The results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach.
Thrombosis of the Cerebral Veins and Sinuses (2005)

Thrombosis of the cerebral veins and sinuses affects mainly young adults and children. This potentially disabling or fatal condition is usually not diagnosed until a week after the onset of symptoms. This review article describes the clinical presentations of the disorder such as those that occur after a head injury or lumbar puncture. Therapeutic options include anticoagulation, thrombolysis, and measures to control intracranial hypertension.
Evaluation of carrier gases for use in high-field asymmetric waveform ion mobility spectrometry (2000)

Effects of carrier gas type (N2, O2, CO2, N2O, and SF6) on changes in the ratio of high- to low-field ion mobility, Kh/K, of cesium, gramicidin S, tetrahexylammonium, heptadecanoic acid, and aspartic acid in fields of up to 67 Td are presented. The theory of the mobility of ions at high E/N in different gases is discussed. Plots of Kh/K as a function of the ionic energy parameter, E/N, for the five ions in each of the gases were derived from experimental data collected using a high-field asymmetric waveform ion mobility spectrometer. The change in the ratio of high- to low-field ion mobility of cesium in carrier gases of O2 and N2 showed excellent agreement with literature values. The behavior of cesium in O2 and N2 is used to illustrate that the ratio Kh/K as a function of effective temperature is invariant with gas type as long as the well depth of the interaction potential significantly exceeds thermal energy. From these results, it appears that the well depth of the interaction potential of the heavier ions studied here, including gramicidin S, tetrahexylammonium, and heptadecanoic acid, with bath gases such as N2 and O2, is shallow relative to thermal energy.
Flower-like PEGylated MoS2 nanoflakes for near-infrared photothermal cancer therapy (2015)

Photothermal cancer therapy has attracted considerable interest for cancer treatment in recent years, but the effective photothermal agents remain to be explored before this strategy can be applied clinically. In this study, we therefore develop flower-like molybdenum disulfide (MoS2) nanoflakes and investigate their potential for photothermal ablation of cancer cells. MoS2 nanoflakes are synthesized via a facile hydrothermal method and then modified with lipoic acid-terminated polyethylene glycol (LA-PEG), endowing the obtained nanoflakes with high colloidal stability and very low cytotoxicity. Upon irradiation with near infrared (NIR) laser at 808 nm, the nanoflakes showed powerful ability of inducing higher temperature, good photothermal stability and high photothermal conversion efficiency. The in vitro photothermal effects of MoS2-PEG nanoflakes with different concentrations were also evaluated under various power densities of NIR 808-nm laser irradiation, and the results indicated that an effective photothermal killing of cancer cells could be achieved by a low concentration of nanoflakes under a low power NIR 808-nm laser irradiation. Furthermore, cancer cell in vivo could be efficiently destroyed via the photothermal effect of MoS2-PEG nanoflakes under the irradiation. These results thus suggest that the MoS2-PEG nanoflakes would be as promising photothermal agents for future photothermal cancer therapy.
Uplink power control with MMSE receiver in multi-cell MU-massive-MIMO systems (2014)

In the current literature considering multi-cell multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems, equal uplink power allocation among users is typically assumed, which does not exploit the potential of peruser power control. By contrast, in this paper we apply multi-cell uplink power control, assuming the minimum mean-square-error receiver based on the pilot contaminated channel estimation and a very large but finite number of antennas at the base station. We derive the lower bound on the average post-processing uplink signal to interference-plus-noise ratio (SINR) with individual power assignment between pilot and data transmissions for each user, which facilitates a joint iterative uplink pilot and data power control strategy that minimizes the sum transmit power of all users subject to the per-user SINR and per-user power constraints. The convergence of the proposed algorithm to a unique fixed point optimal solution is discussed for both single- and multi-user scenarios. Numerical results indicate the significance of uplink power control which further improves the energy efficiency in MU-Massive-MIMO systems.
A Survey on Ensemble Learning for Data Stream Classification (2017)

Ensemble-based methods are among the most widely used techniques for data stream classification. Their popularity is attributable to their good performance in comparison to strong single learners while being relatively easy to deploy in real-world applications. Ensemble algorithms are especially useful for data stream learning as they can be integrated with drift detection algorithms and incorporate dynamic updates, such as selective removal or addition of classifiers. This work proposes a taxonomy for data stream ensemble learning as derived from reviewing over 60 algorithms. Important aspects such as combination, diversity, and dynamic updates, are thoroughly discussed. Additional contributions include a listing of popular open-source tools and a discussion about current data stream research challenges and how they relate to ensemble learning (big data streams, concept evolution, feature drifts, temporal dependencies, and others).
Leveraging decoded HEVC motion for fast, high quality optical flow estimation (2017)

We propose a method of improving the quality of decoded HEVC motion fields attached to B-frames, in order to make them more suitable for video analysis and enhancement tasks. We use decoded HEVC motion vectors as a sparse set of motion "seeds", which guide an edge-preserving affine interpolation of coded motion (HEVC-EPIC) in order to obtain a much more physical representation of the scene motion. We further propose HEVC-EPIC-BI, which adds a bidirectional motion completion step that leverages the fact that regions which are occluded in one direction are usually visible in the other. The use of decoded motion allows us to avoid the time-consuming estimation of "seeds". Experiments on a large variety of synthetic sequences show that compared to a state-of-the-art "seed-based" optical flow estimator, the computational complexity can be reduced by 80%, while incurring no increase at in average EPE at higher bit-rates, and a slight increase of 0.09 at low bit-rates.
Spin-Orbit-Torque-Based Spin-Dice: A True Random-Number Generator (2015)

New three-terminal spin dice, a true random number generator, are proposed. The design utilizes spin-orbit torque to hold a bi-stable perpendicular magnetic anisotropy nanomagnet in meta-stability and uses thermal noise as an entropy source to bias the magnet off its metastable state. Once the magnetization is tilted, the built-in anisotropy field of the magnet relaxes the magnetization to the stable state, producing a random bit whose value is determined solely by the thermal noise. Our proposed spin-dice are able to simultaneously achieve high throughput, low-energy, and ease of design even in the presence of process variations. Based on our simulations, our three-terminal spin-dice have the potential to outperform recently proposed spin-dice based on two-terminal magnetic tunnel junctions.
Coliquefaction of Waste Plastics with Coal (1994)

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTColiquefaction of Waste Plastics with CoalM. Mehdi Taghiei, Zhen Feng, Frank E. Huggins, and Gerald P. HuffmanCite this: Energy Fuels 1994, 8, 6, 1228–1232Publication Date (Print):November 1, 1994Publication History Published online1 May 2002Published inissue 1 November 1994https://pubs.acs.org/doi/10.1021/ef00048a010https://doi.org/10.1021/ef00048a010research-articleACS PublicationsRequest reuse permissionsArticle Views480Altmetric-Citations82LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts
Harm-Anton Klok教授作客应用化学系列讲座 (2012)

12月10日，瑞士洛桑理工大学材料工程学院高分子实验室Harm-Anton Klok教授来到中科院长春应化所进行学术访问，并做客应用化学系列讲座，为科研人员和研究生们做了题为Combating Diseases with Peptide - Synthetic Polymer Conjugates的学术报告。报告会上，Harm-Anton Klok教授受聘为长春应化所客座教授。多肽和蛋白质通常带有具有非常独特生物活性的自组装性质。多肽和蛋白在生物过程（例如，治疗过程）中有作为抑制剂或拮抗剂的可能性，而且也可以为靶向传递或定向细胞内的传输提供机会。将具有生物活性的多肽或蛋白与合成高分子进行特定的结合可能会克服多肽和蛋白质所具有的稳定性和血浆半衰期问题，以提高高分子-药物结合体的疗效和扩大基于多肽的治疗效果。Harm-Anton Klok教授在报告中介绍了三个部分：1. 非共价键的高分子-药物结合体，其中基于多肽的连接体不仅用于键合与释放药物，而且也参与指导细胞内运输。2. 高分子改性的HIV融合抑制剂比未改性的多肽相比显示出稳定性增加，而活性仍保持不变。3. 基于侧脸的多肽-高分子结合体的多价的HIV进入抑制剂可以增大多肽的活性。在这三种情况下，要成功的设计最终的结合体，精准的高分子合成都是至关重要的。这里着重介绍控制分子量、高分子-多肽结合体的位点以及高分子的构架对于多肽-高分子结合体最终性能和活性的重要性。Harm-Anton Klok 教授出生于1971年，1989年至1993年就读于荷兰屯特大学化学工程系。1997年在德国乌尔姆大学获得博士学位，随后在屯特大学和美国伊利诺伊大学香槟分校从事博士后研究。1999年年初加入德国马普学会高分子研究所课题负责人。2002年11月至今，在瑞士洛桑联邦理工学院任教授。当前研究兴趣为高分子精确合成、生物相关高分子、生物功能表面及其在光学、电子或医药等。