nils-herrmann/clean_scirepeval huggingface dataset:
Taking ethanol quality beyond fuel grade: A review

Ethanol production in the United States approached 15 billion gal/year in 2015. Only about 2.5% of this was food grade alcohol, but this represents a higher value product than fuels or other uses. The ethanol production process includes corn milling, cooking, saccharification, fermentation, and separation by distillation. Volatile byproducts are produced during the fermentation of starch. These include other alcohols, aldehydes, ketones, fatty acids and esters. Food grade ethanol is generally produced by wet milling, where starch and sugars are separated from the other corn components, resulting in much smaller concentrations of the impurities than are obtained from fermentation of dry milled corn, where cyclic and heterocyclic compounds are produced from lignin in the corn hull. Some of these volatile byproducts are likely to show up in the distillate and these fermentation byproducts in ethanol could cause unpleasant flavours and affect human health if used for human consumption. There is some interest in improving ethanol quality, since human consumption represents a higher value. Advanced purification techniques, such as ozone oxidation, currently used for drinking water and municipal wastewater treatment, offer possibilities for adaptation in ethanol quality improvement. The development of analytical techniques has enabled the detection of low concentration compounds and simple quality assurance of food grade alcohol. This review includes the most recent ethanol production methods, potential ethanol purification techniques and analytical techniques. Application of such techniques would aid in the development of simplified alcohol production.
Insights into Facebook Pages: an early adolescent health research study page targeted at parents
Venue: International journal of adolescent medicine and health

Facebook has been used in health research, but there is a lack of literature regarding how Facebook may be used to recruit younger adolescents. A Facebook Page was created for an adolescent cohort study on the effects of puberty hormones on well being and behaviour in early adolescence. Used as a communication tool with existing participants, it also aimed to alert potential participants to the study. The purpose of this paper is to provide a detailed description of the development of the study Facebook Page and present the fan response to the types of posts made on the Page using the Facebook generated Insights data. Two types of posts were made on the study Facebook Page. The first type was study related update posts and events. The second was relevant adolescent and family research and current news posts. Observations on the use of and response to the Page were made over 1 year across three phases (phase 1, very low Facebook use; phase 2, high Facebook use; phase 3, low Facebook use) Most Page fans were female (88.6% with the largest group of fans aged between 35 and 44 years. Study related update posts with photographs were the most popular. This paper provides a model on which other researchers could base Facebook communication and potential recruitment in the absence of established guidelines.
Optimization and Prediction of Motorcycle Injection System Performance with Feed Forward Back Propagation Method Artificial Neural Network (ANN)

This research studied the use of Artificial Neural Network (ANN) using feed forward back propagation model to optimize and predict the performance of a motorcycle fuel injection systems of gasoline. The parameters such as speed, throttle position, ignition timing and injection timing is used as the input parameters. While the parameters of fuel consumption and engine torque is used as the output layer. Lavenberg Marquardt model type with train function tanh sigmoid and 25 neurons number is used to generate the target value and the desired output. Variation of ignition timing as optimization variable in a wide range of speed and throttle position is used in experimental tests. ANN is used to investigate the prediction of performance motorcycle engines and compared with the test results. Results showed that the operation of ANN in predicting engine performance is very good. From the test results obtained a smooth contour MAP compared to the initial state. The prediction result and performance test show a good correlation in small error value of training and test that is regression with range 0.98 0.99, mean relative error with range 0.1315 0.4281% and the root mean square error with range 0.2422 0.9754% This study shows that the feed forward back propagation on ANN model can be used to predict accurately the performance of a motorcycle engine injection system.
Modeling tensile crack propagation in concrete gravity dams via crack path field and strain injection techniques

This work investigates tensile crack propagation in concrete gravity dams by using some recently developed numerical techniques (crack path field and strain injection techniques)    The work carefully addresses aspects related to mesh independence, robustness and computational cost, which are the main issues in fracture modeling. The novel technique consists of a procedure to insert, in the selected domain areas, specific strain fields for enhancing the performance of the underlying finite elements in modeling fracture. Representative numerical simulations of concrete dams show the accuracy and robustness of the methodology. The code used for the simulations is open source and available at http:/www.cimne.com/compdesmat/
Heavy Fermion Superconductivity in Non magnetic Cage Compound PrV2Al20

PrT2Al20 (T Ti, V) are ideal systems to study the quadrupole Kondo effect and quantum criticality arising from orbital degrees of freedom. Both systems have the nonmagnetic cubic G3 crystal electric field ground doublet with the well separated excited state. In particular, PrV2Al20 exhibits anomalous metallic behavior above and below the multipolar ordering temperatures, reflecting the even stronger hybridization between f and conduction electrons possibly due to a proximity to a orbital quantum critical point. Here, we discuss the heavy fermion superconductivity (SC) of PrV2Al20 in detail. The SC appears at Tc 0.05 K with the highly enhanced effective mass (m* /m0 140) estimated using the temperature dependence of the upper critical field. In addition, large electronic specific heat coefficient of g 0.9 J/mol K2 above Tc and the large specific heat jump at Tc of DC/Tc 0.3 J/mol K2 provide direct evidences of the heavy fermion SC. This observation indicates the first realization of the novel SC arising from the orbital fluctuation of the f electrons at ambient pressure.
Strong orbital fluctuations in multipolar ordered states of PrV2Al20

Pr T 2 Al 20 T =Ti, V) are ideal systems to study the quadrupole Kondo effect and quantum criticality arising from orbital degrees of freedom. The both systems have the nonmagnetic cubic G 3 crystal electric field ground doublet with the well separated excited state. In particular, PrV 2 Al 20 exhibits anomalous metallic behavior above and below the multipolar ordering temperatures, reflecting the even stronger hybridization between f and conduction electrons possibly due to a proximity to an orbital quantum critical point. Here we report the anomalous metallic behaviors found in a pure single crystal of PrV 2 Al 20 Our detailed analyses revealed that the resistivity indicates power law temperature dependence proportional to T 3 Furthermore, we pointed out that the 4 f electron contribution to the specific heat also exhibits power law behavior proportional to T 4 Both observations are in a sharp contrast to the gapped behavior found in PrTi 2 Al 20 and indicate the strong c f hybridization and strong orbital fluctuations in PrV 2 Al 20 In addition, the 4 f electron contribution to the entropy in PrV 2 Al 20 reaches only 50% of R ln 2 at an orbital ordering at T =0.75 K, suggesting another 50% of R ln 2 expected for G 3 doublet is already released at higher temperature possibly due to quadrupole Kondo effect.
Stability and Degradation of Perovskite Electrocatalysts for Oxygen Evolution Reaction

Pr 0.2 Ba 0.8 CoO 3 d (PBCO) and Ba 0.5 Sr 0.5 Co 0.5 Fe 0.5 O 3 d (BSCF) perovskite thin film electrodes were used as model systems for testing degradation and stability during oxygen evolution reaction (OER) in alkaline water electrolysis. The catalyst films were prepared by chemical solution deposition (CSD) and are binder free. Stepwise aging experiments illustrate a systematical approach to different degrees of degradation of the perovskites after defined testing cycles of cyclic voltammetry in application near conditions. XPS and EDX characterization at each aging step enables a monitoring of the change in chemical composition during degradation. XPS analysis points to a change in the defect chemistry of PBCO during degradation. The influence of all monitored parameters on the overall electrode service lifetime is shown in a novel end of service life test (ESLT) at thin film perovskite electrodes of 100 nm thickness.
The Ontology of Interactive Kinds

This paper defends the notions of an interactive kind and a looping effect as features of social and human scientific classifications and aims to give a realist interpretation of them. I argue that interactive kinds can best be modeled as a special case of changing causal property cluster kinds. In order to do so, I develop a typology of looping effects according to the sort of entities that are affected, the main types of which are individual looping, category looping, and kind looping. Based on this distinction, I identify interactive kinds as those causal property cluster kinds that are subjected to kind looping.
The Age of Methods: William Whewell, Charles Peirce, and Scientific Kinds
Venue: Isis

For William Whewell and, later, Charles Peirce, the methods of science merited scientific examination themselves. Looking to history to build an inductive account of the scientific process, both men transformed scientific methods into scientific evidence. What resulted was a peculiar instance of what Ian Hacking calls "the looping effects of human kinds," in which classifying human behavior changes that behavior. In the cases of Whewell and Peirce, the behavior in question was their own: namely, scientific study. This essay brings Hacking's formulation to bear on the status of science in nineteenth century intellectual history, revealing the continued entanglement of science and philosophy even as they were being prized apart. Focusing on how Whewell and Peirce turned themselves into "scientific kinds," the essay reveals the slippage between "what" and "how" across what Peirce once called "the age of methods."
Benefits of partial myopia in a durable product supply chain considering pricing and advertising
Venue: J. Oper. Res. Soc.

Consider a bilateral monopoly supply chain where a manufacturer distributes a durable product through a retailer to end consumers. This paper develops a differential game in which the manufacturer determines the advertising and the wholesale price while the retailer sets the retail price. Channel members are partially myopic when they only consider goodwill dynamics and disregard sales evolution. We derive and compare partially myopic and forward looking strategies with the following observations: First, under decentralized scenario, partially myopic channel members get higher profits than the forward looking ones, while the opposite result occurs in centralized scenario. Second, when channel members are partially myopic, the distribution channel can benefit from the strategic decentralization.
Assessing the Quality of Unstructured Data: An Initial Overview
Venue: LWDA

In contrast to structured data, unstructured data such as texts, speech, videos and pictures do not come with a data model that enables a computer to use them directly. Nowadays, computers can interpret the knowledge encoded in unstructured data using methods from text analytics, image recognition and speech recognition. Therefore, unstructured data are used increasingly in decision making processes. But although decisions are commonly based on unstructured data, data quality assessment methods for unstructured data are lacking. We consider data analysis pipelines built upon two types of data consumers, human consumers that usually come at the end of the pipeline and non human machine consumers (e.g. natural language processing modules such as part of speech tagger and named entity recognizer) that mainly work intermediate. We define data quality of unstructured data via (1) the similarity of the input data to the data expected by these consumers of unstructured data and via (2) the similarity of the input data to the data representing the real world. We deduce data quality dimensions from the elements in analytic pipelines for unstructured data and characterize them. Finally, we propose automatically measurable indicators for assessing the quality of unstructured text data and give hints towards an implementation.
Assessing the Corpus Size vs. Similarity Trade off for Word Embeddings in Clinical NLP
Venue: ClinicalNLP@COLING 2016

The proliferation of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data poor clinical NLP domain. In particular, large text corpora are necessary to build high quality word embeddings, yet often large corpora that are suitably representative of the target clinical data are unavailable. This forces a choice between building embeddings from small clinical corpora and less representative, larger corpora. This paper explores this trade off, as well as intermediate compromise solutions. Two standard clinical NLP tasks (the i2b2 2010 concept and assertion tasks) are evaluated with commonly used deep learning models (recurrent neural networks and convolutional neural networks) using a set of six corpora ranging from the target i2b2 data to large open domain datasets. While combinations of corpora are generally found to work best, the single best corpus is generally task dependent.
An integrated grammar based approach for mathematical expression recognition
Venue: Pattern Recognit.

Automatic recognition of mathematical expressions is a challenging pattern recognition problem since there are many ambiguities at different levels. On the one hand, the recognition of the symbols of the mathematical expression. On the other hand, the detection of the two dimensional structure that relates the symbols and represents the math expression. These problems are closely related since symbol recognition is influenced by the structure of the expression, while the structure strongly depends on the symbols that are recognized. For these reasons, we present an integrated approach that combines several stochastic sources of information and is able to globally determine the most likely expression. This way, symbol segmentation, symbol recognition and structural analysis are simultaneously optimized. In this paper we define the statistical framework of a model based on two dimensional grammars and its associated parsing algorithm. Since the search space is too large, restrictions are introduced for making the search feasible. We have developed a system that implements this approach and we report results on the large public dataset of the CROHME international competition. This approach significantly outperforms other proposals and was awarded best system using only the training dataset of the competition. HighlightsAn integrated mathematical expression recognition system is proposed.This system integrates several knowledge sources.The learning of the system is described.Experiments with public databases are reported.
Effect of soil foundation structure interaction and pier column non linearity on seismic response of bridges supported on shallow foundations

Traditionally, shallow spread footing type foundations are used for medium span bridges supported on hard soil and rock strata. Such bridges are modelled with fixed supports and no soil foundation structure interaction (SSI) is normally considered. The investigation presented herein utilized a sub structuring technique and finite element method (FEM) model for the analysis of a four span bridge designed for five different rock classes and subjected to an ensemble of actual ground motions. SSI was incorporated through Winkler springs while non linear behaviour of reinforced concrete pier column was modelled by an equivalent linear model. The results of the study were evaluated to delineate the effect of SSI and pier column non linearity on seismic response parameters of bridges founded on rock. It was found that the soil foundation structure interaction couldn't be neglected in all cases of rock classes and input ground motions. Furthermore, pier column non linearity influenced bridge displacement and base shear more significantly than SSI. Impact of foundation rocking was also examined and was found to be rather insignificant on bridge response parameters due to high rocking impedance of properly designed bridge foundations.
Energy efficient routing of WSN using particle swarm optimization and V LEACH protocol
Venue: 2016 International Conference on Communication and Signal Processing (ICCSP)

In this paper, cluster head and vice cluster head selection is discussed and a method of energy efficient routing is presented based on both particle swarm optimisation technique and V LEACH protocol. Performance comparison with existing leach protocol shows proposed protocol provides better performance to minimize energy dissipation in the transmission and increases the life time of the wireless sensor networks, also other comparative performance metrics like End to End delay, data transmitted and total energy consumed shows proposed protocol provides better performance in comparison to existing leach protocol.
Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments
Venue: HCOMP

When collecting subjective human ratings of items, it can be difficult to measure and enforce data quality due to task subjectivity and lack of insight into how judges' arrive at each rating decision. To address this, we propose requiring judges to provide a specific type of rationale underlying each rating decision. We evaluate this approach in the domain of Information Retrieval, where human judges rate the relevance of Webpages to search queries. Cost benefit analysis over 10,000 judgments collected on Mechanical Turk suggests a win win: experienced crowd workers provide rationales with almost no increase in task completion time while providing a multitude of further benefits, including more reliable judgments and greater transparency for evaluating both human raters and their judgments. Further benefits include reduced need for expert gold, the opportunity for dual supervision from ratings and rationales, and added value from the rationales themselves.
Ritual human sacrifice promoted and sustained the evolution of stratified societies
Venue: Nature

Evidence for human sacrifice is found throughout the archaeological record of early civilizations, the ethnographic records of indigenous world cultures, and the texts of the most prolific contemporary religions. According to the social control hypothesis, human sacrifice legitimizes political authority and social class systems, functioning to stabilize such social stratification. Support for the social control hypothesis is largely limited to historical anecdotes of human sacrifice, where the causal claims have not been subject to rigorous quantitative cross cultural tests. Here we test the social control hypothesis by applying Bayesian phylogenetic methods to a geographically and socially diverse sample of 93 traditional Austronesian cultures. We find strong support for models in which human sacrifice stabilizes social stratification once stratification has arisen, and promotes a shift to strictly inherited class systems. Whilst evolutionary theories of religion have focused on the functionality of prosocial and moral beliefs, our results reveal a darker link between religion and the evolution of modern hierarchical societies.
Inferring Causal Direction from Relational Data
Venue: UAI

Inferring the direction of causal dependence from observational data is a fundamental problem in many scientific fields. Significant progress has been made in inferring causal direction from data that are independent and identically distributed (i.i.d. but little is understood about this problem in the more general relational setting with multiple types of interacting entities. This work examines the task of inferring the causal direction of peer dependence in relational data. We show that, in contrast to the i.i.d. setting, the direction of peer dependence can be inferred using simple procedures, regardless of the form of the underlying distribution, and we provide a theoretical characterization on the identifiability of direction. We then examine the conditions under which the presence of confounding can be detected. Finally, we demonstrate the efficacy of the proposed methods with synthetic experiments, and we provide an application on real world data.
Startup of air cooled condensers and dry cooling towers at low temperatures of the cooling air

The problems of startup and performance of air cooled condensers (ACC) and dry cooling towers (DCT) at low cooling air temperatures are considered. Effects of the startup of the ACC at sub zero temperatures are described. Different options of the ACC heating up are analyzed, and examples of existing technologies are presented (electric heating, heating up with hot air or steam, and internal and external heating) The use of additional heat exchanging sections, steam tracers, in the DCT design is described. The need for high power in cases of electric heating and heating up with hot air is noted. An experimental stand for research and testing of the ACC startup at low temperatures is described. The design of the three pass ACC unit is given, and its advantages over classical single pass design at low temperatures are listed. The formation of ice plugs inside the heat exchanging tubes during the start up of ACC and DCT at low cooling air temperatures is analyzed. Experimental data on the effect of the steam flow rate, steam nozzle distance from the heat exchange surface, and their orientation in space on the metal temperature were collected, and test results are analyzed. It is noted that the surface temperature at the end of the heat up is almost independent from its initial temperature. Recommendations for the safe start up of ACCs and DCTs are given. The heating flow necessary to sufficiently heat up heat exchange surfaces of ACCs and DCTs for the safe startup is estimated. The technology and the process of the heat up of the ACC with the heating steam external supply are described by the example of the startup of the full scale section of the ACC at sub zero temperatures of the cooling air, and the advantages of the proposed start up technology are confirmed.