nils-herrmann/clean_scirepeval huggingface dataset:
Influencia de la granulometria y las adiciones del cemento en las lecturas de contenido de Cr VI
Year: 2006

Con motivo de la entrada en vigor en Enero de 2005 de la Normativa Europea por la que se limitaba el contenido en Cromo hexavalente soluble en agua en los cementos, y dentro de su linea de accion en el sector cementero, Sika S.A. puso en marcha un programa de desarrollo de reductores liquidos, como complemento a su oferta de aditivos de molienda. Este articulo esta basado en diversos estudios realizados como parte de este programa. Para evaluar la eficacia de distintas sustancias reductoras incorporadas al cemento, se opto por efectuar moliendas de laboratorio, empleando un molino de bolas de 0.5 m de longitud por 0.7 m de diametro. Las sustancias a evaluar fueron agregadas a los materiales de partida (clinker yeso adiciones) con el fin de no sustraerlas a una posible degradacion (oxidacion) facilitada por el contacto intimo con la atmosfera del interior del molino. Con ello se intento tener en cuenta en cierta medida la merma de eficacia que despues pudiera tener lugar en la molienda industrial. Como paso previo, se efectuaron una serie de moliendas reproduciendo diferentes tipos de cemento, para contar con cementos ?patron? frente a los que evaluar el efecto reductor de las sustancias objeto de ensayo. Las moliendas de cementos Tipo I (clinker y yeso) efectuadas a diferentes finuras, permitieron encontrar diferencias de lectura del contenido en Cromo hexavalente para cementos de una misma composicion, y fabricados con un mismo clinker, pudiendo establecerse una relacion con la finura de molido (valores mas bajos para los clinkeres menos molidos)
CTCModel: a Keras Model for Connectionist Temporal Classification
Venue: ArXiv
Year: 2019

We report an extension of a Keras Model, called CTCModel, to perform the Connection ist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference method for dealing with unsegmented input sequences, i.e. with data that are a couple of observation and label sequences where each label is related to a subset of observation frames. CTCModel makes use of the CTC implementation in the Tensorflow backend for training and predicting sequences of labels using Keras. It consists of three branches made of Keras models: one for training, computing the CTC loss function; one for predicting, providing sequences of labels; and one for evaluating that returns standard metrics for analyzing sequences of predictions.
The impact of Sukuk on the performance of conventional and Islamic banks
Venue: Pacific Basin Finance Journal
Year: 2019

This paper examines the impact of Sukuk market development on banks' profitability using a dataset of 71 Islamic banks (IBs) and 146 conventional banks (CBs) spanning 13 countries over the 2003 2014 period. Using a dynamic panel model, we find that the overall bank profitability is negatively impacted by Sukuk market development. However, when we control for whether the bank is Islamic or conventional, important findings emerge. The results suggest that Sukuk development reduces IBs' profitability but has no impact on CBs' performance. In addition, the evidence shows that these adverse effects on IBs' profitability are substantially lower after the 2008 global financial crisis. Accordingly, our findings suggest that IBs were able to overcome Sukuk competition after the crisis.
A Demonstration of the Solid Platform for Social Web Applications
Venue: WWW
Year: 2016

Solid is a decentralized platform for social Web applications. In the Solid platform, users' data is managed independently of the applications that create and consume this data. Each user stores their data in a Web accessible personal online datastore (or pod) Each user can have one or more pods from different pod providers, and can easily switch between providers. Applications access data in users' pods using well defined protocols, and a decentralized authentication and access control mechanism guarantees the privacy of the data. In this decentralized architecture, applications can operate on users' data wherever it is stored. Users control access to their data, and have the option to switch between applications at any time. We will demonstrate the utility of Solid and how it is experienced from the point of view of end users and application developers. For this, we will use a set of Solid servers and multiple Web applications that use these servers. We believe that experience with a concrete platform such as Solid is highly valuable in truly appreciating the power of a decentralized social Web.
Auditory feedback blocks memory benefits of cueing during sleep
Venue: Nature communications
Year: 2015

It is now widely accepted that re exposure to memory cues during sleep reactivates memories and can improve later recall. However, the underlying mechanisms are still unknown. As reactivation during wakefulness renders memories sensitive to updating, it remains an intriguing question whether reactivated memories during sleep also become susceptible to incorporating further information after the cue. Here we show that the memory benefits of cueing Dutch vocabulary during sleep are in fact completely blocked when memory cues are directly followed by either correct or conflicting auditory feedback, or a pure tone. In addition, immediate (but not delayed) auditory stimulation abolishes the characteristic increases in oscillatory theta and spindle activity typically associated with successful reactivation during sleep as revealed by high density electroencephalography. We conclude that plastic processes associated with theta and spindle oscillations occurring during a sensitive period immediately after the cue are necessary for stabilizing reactivated memory traces during sleep.
Global crop yield response to extreme heat stress under multiple climate change futures
Year: 2014

Extreme heat stress during the crop reproductive period can be critical for crop productivity. Projected changes in the frequency and severity of extreme climatic events are expected to negatively impact crop yields and global food production. This study applies the global crop model PEGASUS to quantify, for the first time at the global scale, impacts of extreme heat stress on maize, spring wheat and soybean yields resulting from 72 climate change scenarios for the 21st century. Our results project maize to face progressively worse impacts under a range of RCPs but spring wheat and soybean to improve globally through to the 2080s due to CO2 fertilization effects, even though parts of the tropic and sub tropic regions could face substantial yield declines. We find extreme heat stress at anthesis (HSA) by the 2080s (relative to the 1980s) under RCP 8.5, taking into account CO2 fertilization effects, could double global losses of maize yield (DY 12.8 6.7% versus 7.0 5.3% without HSA) reduce projected gains in spring wheat yield by half (DY 34.3 13.5% versus 72.0 10.9% without HSA) and in soybean yield by a quarter (DY 15.3 26.5% versus 20.4 22.1% without HSA) The range reflects uncertainty due to differences between climate model scenarios; soybean exhibits both positive and negative impacts, maize is generally negative and spring wheat generally positive. Furthermore, when assuming CO2 fertilization effects to be negligible, we observe drastic climate mitigation policy as in RCP 2.6 could avoid more than 80% of the global average yield losses otherwise expected by the 2080s under RCP 8.5. We show large disparities in climate impacts across regions and find extreme heat stress adversely affects major producing regions and lower income countries.
InTime: A Machine Learning Approach for Efficient Selection of FPGA CAD Tool Parameters
Venue: FPGA
Year: 2015

FPGA CAD tool parameters controlling synthesis optimizations, place and route effort, mapping criteria along with user supplied physical constraints can affect timing results of the circuit by as much as 70% without any change in original source code. A correct selection of these parameters across a diverse set of benchmarks with varying characteristics and design goals is challenging. The sheer number of parameters and option values that can be selected is large (thousands of combinations for modern CAD tools) with often conflicting interactions. In this paper, we present InTime, a machine learning approach supported by a cloud based (or cluster based) compilation infrastructure for automating the selection of these parameters effectively to minimize timing costs. InTime builds a database of results from a series of preliminary runs based on canned configurations of CAD options. It then learns from these runs to predict the next series of CAD tool options to improve timing results. Towards the end, we rely on a limited degree of statistical sampling of certain options like placer and synthesis seeds to further tighten results. Using our approach, we show 70% reduction in final timing results across industrial benchmark problems for the Altera CAD flow. This is 30% better than vendor supplied design space exploration tools that attempts a similar optimization using canned heuristics.
A Review on Pedestrian Detection Techniques
Year: 2017

Pedestrian detection is a process in which number of person are detected from the image or the live video. This information send to user in the forms of sound or video alarm. It helps user in taking necessary preventive decision. Pedestrian detection process comes under security and safety aspect. As, security and safety is prime concern in today's digital world, it has gaining more concentration. It is being a very popular topic for research. This paper introduces various pedestrian detection techniques based on image processing and its various problems in detail. With variation in illumination, postures and clothing of pedestrian, detection process is a really difficult and challenging task. However, as demand of accuracy and speed of detection in real time is increasing, researchers are more focusing to tackle these problems. Efficient pedestrian detection with highest accuracy and speed of detection is
AVALIACAO DA PRODUCAO DE LIPASES E BIOSSURFACTANTE POR MICRORGANISMOS ISOLADOS DE SEDIMENTOS DO MANGUEZAL DA BAIA BABITONGA EM SANTA CATARINA, BRASIL
Year: 2020

Fungos e bacterias presentes nos manguezais sao de extrema importancia para a reciclagem de nutrientes para o ambiente e podem representar reservatorios geneticos para pesquisa e desenvolvimento de produtos biotecnologicos. A Baia Babitonga, em Santa Catarina, abriga a maior parte das florestas de manguezal da regiao Sul do Brasil e pouco se sabe sobre a sua comunidade microbiana. Este trabalho teve como objetivo bioprospectar bacterias isoladas do manguezal, com potencial de producao de lipases e biossurfactantes. Amostras do sedimento foram inoculadas em meio contendo oleo de soja como fonte de carbono para a ativacao dos microrganismos. O isolamento foi realizado em meios de cultivo seletivos para deteccao de lipase (Rodamina B) e biossurfactante (Agar Sangue) Os isolados positivos para ambos os testes foram identificados (biologia molecular) e cultivados para avaliacao das cineticas de crescimento celular, producao de lipase e de biossurfactante (determinacao da tensao superficial e o indice de emulsificacao) Tres isolados capazes de produzir ambos os metabolitos foram identificados: Psychrobacter marincola, Entereccocus casseliflavus e Bacillus pumilus que apresentaram concentracao celular maxima de 3,95 0,08 g/L (72 h) 9,06 0,99 g/L (72 h) e 3,44 0,20 g/L (24 h) respectivamente. Apenas Psychrobacter marincola foi capaz de produzir lipase em cultivo submerso, em 96 h, ainda que de forma bastante incipiente (0,0045 0,0001 U/mL) Nenhum dos microrganismos foi capaz de reduzir a tensao superficial do meio de cultivo. Somente B. pumilus proporcionou um aumento de 24,26 no indice de emulsificacao, em 72 h de cultivo.
Wideband SIW Cavity Backed Slot Array Antenna with Flat Gain Characteristics for 79 GHz Automotive Radar
Venue: 2019 13th European Conference on Antennas and Propagation (EuCAP)
Year: 2019

A wideband millimeter wave SIW cavity backed slot array antenna is proposed for 79 GHz automotive radar applications. The antenna element is composed of a slot fed substrate integrated waveguide (SIW) cavity which operates in TE210 mode, 2 pairs of rotationally symmetrical L shaped radiating slots and 2 matching metallic blind holes. An 8 way SIW parallel power divider together with the antenna element is then utilized to feed a 2 x 4 array. By arranging the positions of metallic blind holes and metallic via holes, the electromagnetic energy leaking from bonding layer can be suppressed so that it causes no disturbance on radiation pattern while the SIW structure remains unchanged. The measured results show that the array antenna has flat gain characteristics in the frequency band of 75 82 GHz with gain variation less than 1 dB. Simulation and measurement results agree very well.
Online Consumer Purchase Intention for Digital Newspapers in Sri Lanka
Year: 2021

Unparalleled development in information communication technology (ICT) has led people to live in the virtual world. Smartphone has made peoples' life more comfortable as they do purchase goods and services while they are on the move. People likely to buy the products online from candy to newspaper. Purchasing newspaper online is currently in a blunder stage, however, steady growth has been reported in the recent past on the purchase of e newspaper around the world. Therefore, this study aimed to investigate the factors that influence the purchase intention of consumers towards digital/e newspaper in Sri Lanka. Data were collected for this purpose via a structural questionnaire from those who involve online purchasing either in their home or office in Ampara districts of Sri Lanka. 305 sample response were collected and analysed further with Structural Equation Modelling (SEM) using AMOS. The results revealed that Website Quality (WEBQUAL) and Online Customer Reviews (OCR) post a significant positive influence on the Online Consumer Purchase Intention (OCPI) of the digital newspaper. Meanwhile, Paid Content (PC) was negatively associated with the purchase intention of the digital newspaper which indicates that consumer was not ready to pay the content if it is charged. On the other hand, Trust was not a significant factor as long as e newspaper purchase is concerned which is a noteworthy finding of this study unlike in the other context such as B2B market or B2C market. The findings of this provide some useful contribution to the literature of consumer behaviour and practitioners like newspaper companies to make a strategic decision on the future business model for digital/e newspaper in Sri Lanka.
4th IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2021, Victoria, BC, Canada, May 10 12, 2021
Venue: ICPS
Year: 2021

ICPS 2021 aims to provide a platform to exchange quality research and professional interactions for the development of sciences in regard to cyber physical systems. Industry experts, researchers and academics will share ideas and experiences surrounding frontier technologies, breakthroughs, innovative solutions, research results, as well as initiatives related to cyber physical systems and their applications. The conference will feature contributed and invited papers, as well as workshops and special sessions. Topics within the scope of the conference: ICPS Architectures: Industry Architectures, Industry Standards, RAMI 4.0, Industrial IoT ICPS Engineering: Engineering Methods and Tools, Lifecycle Management, Integration, HMI, Safety, Engineering Systems of ICPS ICPS Theory and Technologies: Core Technologies, Interoperability, Communication Networks, Connectivity OT/IT, Semantics, Control, Information Processing, Security, IoT/IoS, Machine Learning, Autonomous Systems, Cloud Fog Computing, Big/Smart Data, Security ICPS Applications: Smart Manufacturing, Robotics, Smart Cities, Energy Smart Grid, Smart Living, Smart Framing, Mobility, Water Management, Mining, Oil Gas, Intelligent Enterprise, Smart Transportation, Internet of Underwater Things, Smart Medical Systems ICPS Management and Ecosystems: Innovation Management, Innovation Ecosystems,  Markets, Entrepreneurship ICPS Education and Social Aspects: Digital Society, Education, Lifelong ICPS Learning, ICPS Curricula, Future of Work, Social Aspects, Sustainability, Machine Ethics
STEPANOV LIKE PSEUDO ALMOST AUTOMORPHIC FUNCTIONS IN LEBESGUE SPACES WITH VARIABLE EXPONENTS L
Year: 2013

In this article we introduce and study a new class of functions called Stepanov like pseudo almost automorphic functions with variable exponents, which generalizes in a natural way classical Stepanov like pseudo almost automorphic spaces. Basic properties of these new spaces are investigated. The existence of pseudo almost automorphic solutions to some first order differential equations with Sp,q(x) pseudo almost automorphic coefficients will also be studied.
Determinants of the Skilled Labour Demand and Supply in Building Construction Industry of Western Province in Sri Lanka
Year: 2019

Original Research Article The construction industry is primarily labour intensive and heavily relies upon the skills of its workforce and the success of any construction output is directly impacted by the skilled labourers. With high turnover rates, maintaining a proper balance between skilled labour demand and supply is crucial for retaining desired labour requirement within the Sri Lankan construction industry. Consequently, considering the aforementioned requirement, this research aims to analyse the present situation of the skilled labourers in building construction industry in Sri Lanka. Such a study would benefit the construction industry by identifying the imbalances between demand and supply of specific construction skills to facilitates proper planning and implementing strategies to address the skill mismatch with the timely need to the present context of Sri Lankan construction industry which is experiencing a speedy growth after the ethnic war. Succeeding a comprehensive literature synthesis, a survey was carried out by taking the views from the project managers in construction industry. Views were obtained by structured questionnaire where Likert Scale was used to measure the influence of each factor and to reflect the main objectives of the study. Descriptive, significance and correlation analysis were conducted by the aid of SPSS tool for interpretation of results. The current skills and competency levels of labourers are not sufficient to fulfill the demand of the industry except for bricklaying and plastering. The future supply of the skills also indicated an unbalanced level in expected demand and expected supply of skills. The research findings can be used by all project managers of the construction industry in order to predict the supply and demand of labour requirement. The research represents the current supply and demand of labour requirement. The future supply and demand are predicted according to the project managers' opinions along with possible strategies to retain skilled labour. The findings of the research positively assist on addressing issues of current labour supply and demand in construction industry. Subsequently it contributes to enable pathways for future research and strategic policy making in a positive manner.
The effects of processing instruction on the acquisition of English progressive aspect
Year: 2016

This study was designed to study the effect of explicit grammar instruction on acquisition; specifically; the relationship between processing instruction (Van Patten 1996) and the acquisition of progressive aspect in English as a second language. An experimental study was carried out to determine any differences in the interpretation and production of grammatical structure before and after processing instruction; compared to traditional instruction. The results showed that the students who carried out the input processing activities were superior both in interpretation and production; especially concerning the aspectual contrast of the progressive form with the present simple.
Reducing Readmissions After Stroke With a Structured Nurse Practitioner/Registered Nurse Transitional Stroke Program
Venue: Stroke
Year: 2016

Our aim was to determine whether a standardized Transitional Stroke Clinic (TSC) led by nurse practitioners could reduce 30 day and 90 day readmissions for stroke or transient ischemic attack patients discharged home.  Phase I consisted of nurse practitioners calling only high risk patients discharged home within 7 days and performing an office visit within 2 to 4 weeks of discharge. Phase II consisted of all patients discharged home receiving both a 2 day follow up phone call by a registered nurse and a follow up visit with a nurse practitioner within 7 to 14 days. Differences in process metrics and readmissions across the 2 phases and overall were assessed. Increasing complexity with multiple chronic conditions (diabetes mellitus, coronary artery disease, and congestive heart failure) was represented in a continuous variable from 0 to 3. Multivariable logistic regression models for 30 day and 90 day readmissions were performed with adjustment for National Institutes of Health Stroke Scale (NIHSS) and previous hospitalizations.  From October 2012 through September 2015, 510 patients were enrolled. From phase I to II, a higher proportion of follow up calls were made and days from discharge to TSC decreased. Patients readmitted within 30 days were less likely to show for TSC visits (60.85% versus 76.3% P=0.021) Multivariable modeling showed that TSC visit was associated with a 48% reduction in 30 day readmission (odds ratio, 0.518; 95% confidence interval, 0.272 0.986) whereas multiple chronic conditions and previous stroke/transient ischemic attack increased the risk. TSC visit did not impact 90 day readmissions.  Evaluation in a nurse practitioner led structured clinic is a model that may reduce readmissions at 30 days for stroke patients discharged home.
Following Dirt Roads at Night Time: Sensors and Features for Lane Recognition and Tracking
Venue: IROS 2015
Year: 2015

The robust perception of roads is a major prerequisite in many Advanced Driver Assistant Systems such as Lane Departure Warning and Lane Keeping Assistant Systems. While road detection at day time is a well known topic in literature, few publications provide a detailed description about handling the lack of day light. In this paper we present multiple sensors and features for perceiving roads at day and night. The presented features are evaluated according to their quality for road detection. We generated a large number of labeled sample data and extracted the quality of the features from their probability distributions. The challenge of tracking an unmarked road under bad lighting conditions is demonstrated by comparing receiver operating characteristics (ROC) of the features at day and night time. Based on these results we present a road tracking system capable of tracking unmarked roads of lower order regardless of illumination conditions. Practical tests prove the robustness up to unmarked dirt roads under different weather conditions.