nils-herrmann/clean_scirepeval huggingface dataset:
Environmental impacts of household consumption in Europe: Comparing process based LCA and environmentally extended input output analysis
Venue: Journal of cleaner production
Year: 2019

The environmental impacts generated by household consumption are generally calculated through footprints, allocating the supply chain impacts to the final consumers. This study compares the result of the Consumer Footprint indicator, aimed at assessing the impacts of household consumption in Europe, calculated with the two standard approaches usually implemented for footprint calculations: (i) a bottom up approach, based on process Life cycle assessment of a set of products and services representing household consumption, and (ii) a top down approach, based on environmentally extended input output tables (EXIOBASE 3) Environmental impacts are calculated considering 14 environmental impact categories out of the 16 included in the EF2017 impact assessment method. Both footprints show similar total values regarding climate change, freshwater eutrophication and fossil resource use, but in the meantime very large differences (more than a factor 2) regarding particulate matter, photochemical ozone formation, land use and mineral resource use. The exclusion of services in the bottom up approach can explain only to some extent these differences. However, the two approaches converge in identifying food as the main driver of impact in most of the impact categories considered (with a generally lower contribution in top down compared to bottom up) Housing and mobility are relevant as well for some impact categories (e.g. particulate matter and fossil resource depletion) Some substances are identified as hotspot by both approaches, e.g. the emission of NH3 to air (for acidification and terrestrial eutrophication) of NOx to air (for acidification, marine and terrestrial eutrophication, and, to some extent, photochemical ozone formation) of P to water and to soil (for freshwater eutrophication) and of fossil CO2 to air (for climate change) Significant differences at the inventory side are key drivers for the differences in total impacts. These include: (i) differences in the intensity of emissions, (ii) differences in the coverage of elementary flows, (iii) differences in the level of detail relative to elementary flows. Overall, the key converging results from both approaches (in particular regarding most contributing areas of consumption and substances) can be considered as a robust basis to support the definition of policies aimed at reducing the environmental footprint of household consumption in Europe.
Altruistic Behavior among Twins
Venue: Human nature
Year: 2018

According to kin selection theory, indirect reproductive advantages may induce individuals to care for others with whom they share genes by common descent, and the amount of care, including self sacrifice, will increase with the proportion of genes shared. Twins represent a natural situation in which this hypothesis can be tested. Twin pairs experience the same early environment because they were born and raised at the same time and in the same family but their genetic relatedness differs depending on zygosity. We compared the degree of willingness to fight and sacrifice for the co twin among monozygotic (MZ) and dizygotic (DZ) pairs in a sample of 1443 same sex and opposite sex twins. We also analyzed the effect of the subject's gender and that of the co twin on those altruistic behaviors. Results partly supported the postulated explanation. MZ twins (who share nearly their entire genome) were significantly more likely than DZ twins (who on average share half of their segregating genes) to self sacrifice for their co twins, but zygosity did not affect willingness to fight for him/her. The genders of the subject and of the co twin, not genetic relatedness, were the best predictors of aggressive altruistic intentions.
Religious Affiliation Child Marriages Nexus in Zimbabwe A Case of
Year: 2019

The study seeks to investigate the dynamics of religious affiliation and child marriages in Zimbabwe. A qualitative research approach was employed. The study was carried out in the Marange area, the home of one of the first African Initiated Churches (AICs) in Zimbabwe; the Johane Marange. Amongst other findings, the study revealed that in the Marange area, most child marriages are attributed to religious affiliation. The study recommends a review of laws related to child protection in Zimbabwe and a total condemnation of child marriages at all levels, amongst other policy prescriptions.
Towards fast and scalable trapped ion quantum logic with integrated photonics
Venue: OPTO
Year: 2019

Trapped ion qubits promise certain fundamental advantages for quantum information processing (QIP) owing to their indistinguishability and relatively high isolation from noisy environments. Though these qualities have allowed demonstrations of the necessary primitives for quantum computation, the complexity of the optical apparatus required is a major impediment to implementation at scales where quantum systems offer a clear advantage over classical computers. Here, we build on previous work with trap integrated waveguide optics, describing designs and simulations for commercial foundry fabricated ion trap chips with integrated Si3N4 waveguides and grating couplers to implement multi qubit operations. We detail a design intended to address and implement quantum logic gates between 5 ions in a single register, and a configuration which utilizes the stable on chip path lengths of waveguide devices to enact a novel fast entangling two qubit gate. The devices and approaches presented here could form elements of a scalable architecture for trapped ion QIP.
Epidemiologic characteristic of infectious diarrhea in Jiangmen during 2009 2011
Year: 2012

To analyze epidemiologic characteristics of infectious diarrhea in Jiang men and explore the limits of current monitoring system. Descriptive epidemiological method was applied to analyze characteristics of reported infectious diarrhea cases derived from routine infectious disease monitoring system during 2009 2011.Results There were totally reported 53 624 infectious diarrhea cases with ascending trend and annual incidence from 378 /100 000 to 428 /100 000 during 2009 2011.40.64% was below 5 years old.The peak seasons of infectious diarrhea were autumn and winter.The top 3 areas of high incidence in Jingmen were Heshan,Pengjiang and Xinhui.There occurred 3 outbreaks of infectious diarrhea during 2009 2011,all of them was caused by norovirus. Infectious diarrhea plays an important role in the notifiable infectious disease reported in Jiangmen.We should evaluate and improve the infectious diarrhea reporting system,and strengthen the surveillance and control work for infectious diarrhea.
Ground state solutions for a Choquard equation with lower critical exponent and local nonlinear perturbation
Year: 2020

This paper deals with the following Choquard equation with a local nonlinear perturbation: D u u I a u a N 1 u a N 1 u f u x R N u H 1 R N where I a R N R is the Riesz potential, N 3 a 0 N the exponent a N 1 is critical with respect to the Hardy Littlewood Sobolev inequality, and the nonlinear perturbation f is only required to satisfy some weak assumptions near 0 and Our results improve the previous related ones in the literature.
Experimental Cyclic Behavior of Precast Hybrid Beam Column Connections with Welded Components
Year: 2017

Post earthquake observations revealed that seismic performance of beam column connections in precast concrete structures affect the overall response extensively. Seismic design of precast reinforced concrete structures requires improved beam column connections to transfer reversed load effects between structural elements. In Turkey, hybrid beam column connections with welded components have been applied extensively in precast concrete industry for decades. Beam bottom longitudinal rebars are welded to beam end plates while top longitudinal rebars are placed to designated gaps in joint panels before casting of topping concrete in this type of connections. The paper presents the major findings of an experimental test programme including one monolithic and five precast hybrid half scale specimens representing interior beam column connections of a moment frame of high ductility level. The required welding area between beam bottom longitudinal rebars and beam end plates were calculated based on welding coefficients considered as a test parameter. It is observed that the maximum strain developed in the beam bottom flexural reinforcement plays an important role in the overall behavior of the connections. Two additional specimens which include unbonded lengths on the longitudinal rebars to reduce that strain demands were also tested. Strength, stiffness and energy dissipation characteristics of test specimens were investigated with respect to test variables. Seismic performances of test specimens were evaluated by obtaining damage indices.
Transcriptional Regulation of Macrophages Polarization by MicroRNAs
Venue: Front. Immunol.
Year: 2018

Diversity and plasticity are the hallmarks of cells from the monocyte macrophage lineage. Macrophages undergo classical M1 or alternative M2 activation in response to the microenvironment signals. Several transcription factors, such as peroxisome proliferator activated receptors, signal transducers and activators of transcription, CCAAT enhancer binding proteins, interferon regulatory factors, Kruppel like factors, GATA binding protein 3, nuclear transcription factor kB, and c MYC, were found to promote the expression of specific genes, which dictate the functional polarization of macrophages. Importantly, these transcription factors can be regulated by microRNAs (miRNAs) a group of small non coding RNAs, which regulate gene expression through translation repression or mRNA degradation. Recent studies have also revealed that miRNAs control macrophage polarization by regulating transcription factors in response to the microenvironment signals. This review will summarize recent progress of miRNAs in the transcriptional regulation of macrophage polarization and provide the insights into the development of macrophage centered diagnostic and therapeutic strategies.
Safety in Wireless Sensor Network: Types of Attacks and Solutions
Year: 2016

Wireless Sensor networks (WSNs) have become the most interesting regions of exploring in the 21st century. WSNs include a large number of sensor nodes, actuator nodes, gateways, and clients. Recent progress in wireless technologies has facilitated the vast variety of uses of WSNs in the military, traffic observation, target tracking, environment monitoring, healthcare monitoring, and so on. The designers of WSNs have been faced many new challenges, like sensed quantities, the size of nodes, and nodes' autonomy. As a result, improved solutions to these confront and perfections in the modern technologies are required. The prospect expansions in sensor nodes must construct very influential and expenditure effective devices so that they may be used in applications. This paper also describes the modern research challenges for WSNs. And finally it reviews a number of security methods presently planned or implemented in WSNs.  wireless sensor networks, Future trends, recent advances, research challenges etc.
A Neural Network Based Method for Judging the Rationality of Litigation Request
Venue: ICIRA
Year: 2019

With the advancement of the construction of the intelligence court, artificial intelligence technology has become more and more significant in the litigation risk analysis. The reasonable prediction of the litigation request can guide the parties to form a reasonable litigation expectation and guide the case diversion; it can also provide intelligent risk warning to assist judges. The current litigation request reasonable prediction method has a low accuracy rate, and lacks a method for rationalizing the expectation of heterogeneous and diverse cases; at the same time, based on machine learning methods, some research focus on the rationality judgment of litigation requests. However, the rationality of litigation request feature design, selection and model are still to be explored. In this paper, we propose a rationality prediction framework for litigation requests based on multi party evidence correlation model and neural network. Firstly, based on the judicial knowledge of litigation request and the multi party evidence association model, the judicial characteristics of the reasonableness of litigation request is designed. Then we train and predict the judicial features based on the deep neural network model. Finally, the prediction and evaluation of the reasonableness of litigation is realized.
TAHAP KOMPETENSI PEMIMPIN SEKOLAH DI SEKOLAH JENIS KEBANGSAAN TAMIL DALAM DAERAH TERPILIH DI SELANGOR
Year: 2018

This study was conducted to assess the level of competence of school leaders in Tamil national type schools in selected districts in Selangor. This study was also conducted to obtain the views of the level of competence by Tamil school leaders. This study also was conducted to identify the challenges faced by school leaders as well as proposals to address this challenge in the form of reviews. This study used survey involved quantitative and qualitative methods. Data collected by distributing questionnaires to 150 school leaders in 61 Tamil schools in selected districts in Selangor. Quantitative data were analyzed using the computer program Statistical Package for Social Science (SPSS) for Windows version 20.0. The method used is descriptive statistics such as mean and standard deviation. Qualitative data were analyzed using thematic analysis. The findings show that the level of competence is in high level. The study found a significant relationship between demographics and the level of competence of school leaders in Selangor. The findings also show there is a significant difference between the levels of competence based on the location of the school principal. The findings show that there are a lot of the challenges faced by school leaders as well as suggestions for overcoming challenges that appear .Implication of the study that importance of competence does affect school performance.
No Effect of Sleep on the Generalization of Fear Learning
Year: 2014

Sleep has been shown to be involved both in emotion regulation and in the active processing of information. We combined these two concepts and tested if sleep affected the generalization of fear learning. In a fear conditioning paradigm, participants were shown images of a small and a big circle where one of them was paired with an aversive sound, making it the CS+ Fear was measured with skin conductance responses. Participants were then randomly divided into a sleep or a wake group. The sleep group took a 2 h nap while the wake group rested for 2 h. Participants were then exposed to the two circles seen before, combined with 8 novel circles that gradually varied in size from the small one to the big one. We looked at how many circle sizes away from the CS+ that participants still exhibited a fear response, and if this differed between the sleep and the wake group. We found no effect of sleep on the slope of the generalization across the different circles. There was a main effect of circle size, F(1,25) 10.42, P 0.01, but no main effect of sleep/wake, F (1,25) 0.40, P 0.54, and no interaction between sleep/wake X circle size, F(1,25) 0.62, P 0.44.Conclusions: The fear conditioning manipulation worked, with a gradual increase of fear depending on the stimuli's similarity to the CS+ However, there was no effect of sleep or wake, which could possibly be explained by that just a 2 h nap not being a sufficient sleep manipulation to detect any differences.
Handbook of mathematical methods in imaging
Year: 2011

Linear Inverse Problems. Large Scale Inverse Problems in Imaging.  Compressive Sensing. Duality and Convex Programming. EM Algorithms.     Synthetic Aperture Radar Imaging. Tomography. Optical Imaging. Photoacoustic and Thermoacoustic Tomography: Image Formation Principles. Mathematics of Photoacoustic and Thermoacoustic Tomography. Wave Phenomena.  Total Variation in Imaging. Numerical Methods and Applications in Total Variation Image Restoration. Mumford and Shah Model and its Applications in Total Variation Image Restoration. Local Smoothing Neighbourhood Filters. Neighbourhood Filters and the Recovery of 3D Information. Splines and Multiresolution Analysis. Gabor Analysis for Imaging. Shaper Spaces. Variational Methods in Shape Analysis. Manifold Intrinsic Similarity. Image Segmentation with Shape Priors: Explicit Versus Implicit Representations. Starlet Transform in Astronomical Data Processing. Differential Methods for Multi Dimensional Visual Data Analysis. Wave fronts in Imaging, Quinto. Ultrasound Tomography, Natterer. Optical Flow, Schnoerr. Morphology, Petros. Maragos. PDEs, Weickert. Registration, Modersitzki. Discrete Geometry in Imaging, Bobenko, Pottmann. Visualization, Hege. Fast Marching and Level Sets, Osher. Couple Physics Imaging, Arridge. Imaging in Random Media, Borcea. Conformal Methods, Gu. Texture, Peyre. Graph Cuts, Darbon. Imaging in Physics with Fourier Transform (i.e. Phase Retrieval e.g Dark field imaging)  (this is also FFT based) Mark E. Brezinski. Spect, PET, Faukas, Louis.
Learning from failure: designing for complex sociotechnical systems
Venue: UbiComp/ISWC Adjunct
Year: 2017

As ubiquitous computing solutions are becoming part of everyday life, their design has to account for the challenges of complex sociotechnical systems. Unexpected behavior and interactions between multiple systems, users and stakeholders challenge design methods and raise the risk of failure. This workshop provides an opportunity for the UbiComp community to reflect on these failures and juxtapose them with human centered design methods. Through short participant authored failure reports, hands on design sessions in groups and all group discussion, we will share challenges and reflect on lessons learned for the design of complex sociotechnical systems.
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
Venue: ArXiv
Year: 2020

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this paper, we consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features in order to reduce the effects of the confounding variables. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features. We investigate multiple types of fake news methods (classifiers) such as logistic regression, random forests, and support vector machines. We have consistent observations of performance improvement.
Motivational factors in self directed informal learning from online learning resources
Year: 2016

Learning is becoming more self directed and informal with the support of emerging technologies. A variety of online resources have promoted informal learning by allowing people to learn on demand and just when needed. It is significant to understand self directed informal learners' motivational aspects, their learning goals, obstacles, and successes when using online resources. However, scant knowledge exists about self directed learning factors, such as informal learners' behaviors and motivations, which would suggest guidelines for improving their learning performance. The goal of this study was to contribute to the understanding of learners' motivational factors and self directed learning aspects of informal learning when using online learning resources. In this study, a survey of learners from various informal learning websites was conducted. Quantitative analysis including descriptive and frequency analysis regarding informal learning experiences was conducted. The results show that the main flow of self directed informal learning through online resources: freedom and choice; control; and interest and engagement.
Uncertainty quantification of channel reservoirs assisted by cluster analysis and deep convolutional generative adversarial networks
Year: 2020

Reservoir characterization is to find reservoir properties of interest by combining available geological information. In channel reservoirs, flow responses are very sensitive depending on the characteristics of channels. Therefore, it is crucial to characterize the distribution and connectivity of channels to estimate future production performances. In this paper, we propose a method of uncertainty quantification on channel reservoir models. This novel method is based on a sequential combination of 3 machine learning methods: principal component analysis (PCA) K means clustering, and deep convolutional generative adversarial networks (DCGAN) PCA and K means clustering are used for classification of reservoir models and DCGAN replicates a group of new models based on the selected data from the PCA and K means clustering. The cluster analysis by PCA and K means clustering is to select some of realizations, which have similar production performance with the true response. After feature extraction by the two largest components from PCA, K means clustering makes several groups among which we use silhouette method to determine the number of clusters. Then, we can find a group of models showing similar production performances with the true in an efficient way by using a representative model from each cluster. Based on the selected models, we train DCGAN to generate new realizations that do not exist among the initial models. After the cluster analysis on the regenerated models to filter out again, we can get calibrated models without using any conventional inverse algorithm. We apply the proposed method in 2 two dimensional channel reservoir cases. In the both case, the proposed method reduces prediction uncertainty as well as characterizes channels' pattern and connectivity reliably.
COVID 19 in the UK and Occupational Health and Safety: Predictable not Inevitable Failures by Government, and Trade Union and Nongovernmental Organization Responses
Venue: New solutions a journal of environmental and occupational health policy NS
Year: 2020

This commentary examines the occupational health and safety issues faced by the UK workers in the COVID 19 pandemic, against the background of government cuts in health care and in occupational health and safety budgets, and a deregulatory climate. The UK government has been obsessed, blinkered, and distracted by the desire to leave the European Union (Brexit) The state of knowledge about the virus, especially from international agencies that identified pandemic threats and strategies to combat it, is outlined. UK politicians, government bodies, medical and scientific advisors, and employers periodically ignored or abused that knowledge. Regulatory and ministerial inaction and errors on the workplace virus risks emerged. In contrast, several trade unions, health professional bodies, and nongovernmental organizations identified COVID 19 threats from poor personal protection equipment, working practices, and knowledge gaps and offered solutions for health care workers, social care workers, production workers, and service workers in "essential" occupations.