nils-herrmann/clean_scirepeval huggingface dataset:
Spatio temporal Variation of PM2.5 Pollution and its Relationship with Meteorology among Five Megacities in China
Year: 2018

Fine particles are a crucial air pollutant in terms of their impact on the ambient environment, citizens' health and traffic visibility. In this study, temporal and spatial variations in PM2.5 were analyzed in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang in China from June 2013 till May 2017 using hourly data collected from the U.S. Embassy and Consulate monitoring system. The distributions of the annual, seasonal, monthly and diurnal concentration were illustrated by the attainment rate and the severity rate, as well as the length of time. After that, the coefficient of divergence (COD) was adopted to study the spatial heterogeneity among five typical megacities. Additionally, the relationship between PM2.5 and meteorology was calculated by Pearson's correlation and stepwise multiple linear regression. The results show that annual PM2.5 concentrations were overall downward trends in all areas. Clear seasonal variations were identified, with the least pollution in summer and the most in winter. The hourly distribution was dramatically different, while the average concentration during the daytime was higher than at night except in Shanghai, and the weekends had higher pollution than the weekdays except in Chengdu. Also, the monthly attainment rate displayed an inverted U distribution; oppositely, the severity rate revealed a U shaped distribution. With the increasing pollution levels, the duration of the PM2.5 pollution was observed to be continuously declining in Guangzhou, Shanghai and Beijing but fluctuating in the other two cities. Furthermore, COD values indicated that there was an obvious spatial heterogeneity between Beijing Chengdu Shenyang and Shanghai Guangzhou. As for meteorology, the pressure had a significantly positive impact on the PM2.5 concentration, while the temperature and rainfall had a negative influence. These results show the pollution levels of PM2.5 in different cities at distinct times and confirm the important role of meteorological conditions in air quality. The findings also provide forecast models of PM2.5 for the five cities.
Musculoskeletal Pain and Teleworking in Times of the COVID 19: Analysis of the Impact on the Workers at Two Spanish Universities
Venue: International journal of environmental research and public health
Year: 2020

The special situation brought about by the coronavirus pandemic and the confinement imposed by the Government, has given rise to numerous changes in working habits. The workers at the universities have had to start a period of teleworking that could give rise to consequences for the musculoskeletal system. The objective of this article is to analyze the impact of the confinement on the musculoskeletal health of the staff of two Spanish universities. A cross sectional, observational study was carried out on the workers. Data was taken in April May 2020 and included: The Standardized Kuorinka Modified Nordic Questionnaire, the Perceived Stress Scale and another one on sociodemographic data. This study comprised 472 people. The areas of pain noted during the confinement period concluded that it was less in all cases (p 0.001) The frequency of physical activity carried out increased significantly during the period of confinement (p 0.04) especially in women. The type of physical activity done was also seen to modify during this period (p 0.001) with a preference for strength training and stretching exercises. In conclusion, the confinement gave rise to changes in the lifestyle and in the musculoskeletal pain of the workers at the universities. All of this must be taken into account by health institutions and those responsible for the Prevention of Occupational Risks at Spanish universities.
How Breath Control Can Change Your Life: A Systematic Review on Psycho Physiological Correlates of Slow Breathing
Venue: Front. Hum. Neurosci.
Year: 2018

The psycho physiological changes in brain body interaction observed in most of meditative and relaxing practices rely on voluntary slowing down of breath frequency. However, the identification of mechanisms linking breath control to its psychophysiological effects is still under debate. This systematic review is aimed at unveiling psychophysiological mechanisms underlying slow breathing techniques <10 breaths/minute) and their effects on healthy subjects.  A systematic search of MEDLINE and SCOPUS databases, using keywords related to both breathing techniques and to their psychophysiological outcomes, focusing on cardio respiratory and central nervous system, has been conducted. From a pool of 2,461 abstracts only 15 articles met eligibility criteria and were included in the review. The present systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines.  The main effects of slow breathing techniques cover autonomic and central nervous systems activities as well as the psychological status. Slow breathing techniques promote autonomic changes increasing Heart Rate Variability and Respiratory Sinus Arrhythmia paralleled by Central Nervous System (CNS) activity modifications. EEG studies show an increase in alpha and a decrease in theta power. Anatomically, the only available fMRI study highlights increased activity in cortical (e.g. prefrontal, motor, and parietal cortices) and subcortical (e.g. pons, thalamus, sub parabrachial nucleus, periaqueductal gray, and hypothalamus) structures. Psychological/behavioral outputs related to the abovementioned changes are increased comfort, relaxation, pleasantness, vigor and alertness, and reduced symptoms of arousal, anxiety, depression, anger, and confusion.  Slow breathing techniques act enhancing autonomic, cerebral and psychological flexibility in a scenario of mutual interactions: we found evidence of links between parasympathetic activity (increased HRV and LF power) CNS activities (increased EEG alpha power and decreased EEG theta power) related to emotional control and psychological well being in healthy subjects. Our hypothesis considers two different mechanisms for explaining psychophysiological changes induced by voluntary control of slow breathing: one is related to a voluntary regulation of internal bodily states (enteroception) the other is associated to the role of mechanoceptors within the nasal vault in translating slow breathing in a modulation of olfactory bulb activity, which in turn tunes the activity of the entire cortical mantle.
ANALISIS KREATIVITAS MATEMATIKA SISWA KELAS VII DALAM PEMBELAJARAN SINEKTIK
Year: 2016

Penelitian ini bertujuan untuk mengetahui kemampuan berpikir kreatif siswa kelas VII pada pembelajaran matematika dengan model pembelajaran sinektik; serta membandingkannya dengan pembelajaran ekspositori. Model pembelajaran sinektik yang dimaksud dalam artikel ini adalah pembelajaran matematika yang dalam langkah langkah pembelajarannya melibatkan penggunaan penalaran analogi untuk merangsang dan mendukung proses berpikir siswa untuk memperoleh solusi terhadap suatu masalah yang dihadapinya. Materi pembelajaran dalam penelitian ini adalah geometri, untuk sub materi sifat sifat sudut yang terbentuk jika dua garis berpotongan atau dua garis sejajar berpotongan dengan garis lain. Penelitian dilakukan di kelas VII SMP Negeri 2 Jatibarang, Brebes, Jawa Tengah, Indonesia. Data kemampuan berpikir kreatif siswa diperoleh melalui tes tertulis dan metode analisis data menggunakan uji statistik. Berdasar hasil analisis data disimpulkan bahwa proporsi siswa yang diajar dengan model sinektik yang mencapai kriteria ketuntasan minimal lebih dari 75% peningkatan kemampuan berpikir kreatif matematika siswa sebelum dan setelah mengikuti pembelajaran sinektik dalam kategori sedang; dan rata rata kemampuan berpikir kreatif siswa yang mengikuti pembelajaran sinektik lebih baik dari pembelajaran ekspositori.
Invariant Models for Causal Transfer Learning
Venue: J. Mach. Learn. Res.
Year: 2018

Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the usual covariate shift assumption and assume that it holds true for a subset of predictor variables: the conditional distribution of the target variable given this subset of predictors is invariant over all tasks. We show how this assumption can be motivated from ideas in the field of causality. We focus on the problem of Domain Generalization, in which no examples from the test task are observed. We prove that in an adversarial setting using this subset for prediction is optimal in Domain Generalization; we further provide examples, in which the tasks are sufficiently diverse and the estimator therefore outperforms pooling the data, even on average. If examples from the test task are available, we also provide a method to transfer knowledge from the training tasks and exploit all available features for prediction. However, we provide no guarantees for this method. We introduce a practical method which allows for automatic inference of the above subset and provide corresponding code. We present results on synthetic data sets and a gene deletion data set.
Embedding engagement in an Australian 'sandstone' university: from community service to university engagement
Year: 2011

There has been much recent interest and debate in Australia around the topics of university engagement, knowledge trans/ er, and engaged scholarship. Diverse responses relating to teaching and learning, research, and community service are evident in many institutions. However, there is a paucity of empirical research describing institutional responses to engagement. This paper presents one university's journey in developing an understanding of its current engagement status and the challenges in moving toward a structured institutional mission and operations that both direct and support engagement activities appropriate to that university.
The Third Wave Of Virtual Work.
Year: 2013

In three major waves of change over the past 30 years, employers and workers have converged on new arrangements for getting knowledge work done. First, home computers and e mail spawned an army of freelancers, offering both workers and employers new flexibility. Next, mobile technology and global teamwork gave the same kind of work anywhere, work anytime flexibility to full time employees, without asking them to forsake career progress and development within their companies. Now, in a third wave, new ways of providing community and shared space are curing a side effect of virtualization worker isolation and driving increased collaboration. The authors write that to make the most of this third wave of change, employers should rethink the compact they forge with workers. Five fundamental aspects of knowledge work require fresh thinking: the value of the relationship with a larger enterprise; the settings in which work is done; the organization of workflows and how individual contributors add value; the technologies used to support higher achievement; and the degree to which employment arrangements are tailored to individuals. The three waves of transformation surge forward at differing velocities across sectors and geographies and mix together in societies. Understanding how your business participates in each wave will help you make wise decisions about technology, work models, talent sources, and people practices.
3d Transition Metals for C H Activation.
Venue: Chemical reviews
Year: 2019

C H activation has surfaced as an increasingly powerful tool for molecular sciences, with notable applications to material sciences, crop protection, drug discovery, and pharmaceutical industries, among others. Despite major advances, the vast majority of these C H functionalizations required precious 4d or 5d transition metal catalysts. Given the cost effective and sustainable nature of earth abundant first row transition metals, the development of less toxic, inexpensive 3d metal catalysts for C H activation has gained considerable recent momentum as a significantly more environmentally benign and economically attractive alternative. Herein, we provide a comprehensive overview on first row transition metal catalysts for C H activation until summer 2018.
Emergency Message Dissemination Schemes Based on Congestion Avoidance in VANET and Vehicular FoG Computing
Venue: IEEE Access
Year: 2019

With the rapid growth in connected vehicles, FoG assisted vehicular ad hoc network (VANET) is an emerging and novel field of research. For information sharing, a number of messages are exchanged in various applications, including traffic monitoring and area specific live weather and social aspects monitoring. It is quite challenging where vehicles' speed, direction, and density of neighbors on the move are not consistent. In this scenario, congestion avoidance is also quite challenging to avoid communication loss during busy hours or in emergency cases. This paper presents emergency message dissemination schemes that are based on congestion avoidance scenario in VANET and vehicular FoG computing. In the similar vein, FoG assisted VANET architecture is explored that can efficiently manage the message congestion scenarios. We present a taxonomy of schemes that address message congestion avoidance. Next, we have included a discussion about comparison of congestion avoidance schemes to highlight the strengths and weaknesses. We have also identified that FoG servers help to reduce the accessibility delays and congestion as compared to directly approaching cloud for all requests in linkage with big data repositories. For the dependable applicability of FoG in VANET, we have identified a number of open research challenges.
CoAP based lightweight information exchange technique for demand response
Venue: 2017 International Conference on Information and Communication Technology Convergence (ICTC)
Year: 2017

Existing DR(Demand Response) protocols are designed with the most successful and popular application layer protocol, HTTP. However, due to the rapid development of the Internet of things (IoT) protocol, DR messaging technology based on the IoT(Internet of Things) protocol to be used for light energy devices is highly required. In this paper, we extend the existing DR protocol using CoAP which inherited the existing HTTP idea and style. Through the proposed CoAP based messaging method, the network overhead of SEP 2.0 and OpenADR 2.0 can be reduced by about 32% and 45% respectively, compared to using HTTP.
Trustworthy artificial intelligence (AI) in education
Year: 2020

This paper was written to support the G20 artificial intelligence (AI) dialogue. With the rise of artificial intelligence (AI) education faces two challenges: reaping the benefits of AI to improve education processes, both in the classroom and at the system level; and preparing students for new skillsets for increasingly automated economies and societies. AI applications are often still nascent, but there are many examples of promising uses that foreshadow how AI might transform education. With regard to the classroom, this paper highlights how AI can accelerate personalised learning, the support of students with special needs. At the system level, promising uses include predictive analysis to reduce dropout, and assessing new skillsets. A new demand for complex skills that are less easy to automate (e.g. higher cognitive skills like creativity and critical thinking) is also the consequence of AI and digitalisation. Reaching the full potential of AI requires that stakeholders trust not only the technology, but also its use by humans. This raises new policy challenges around "trustworthy AI" encompassing the privacy and security of data, but also possible wrongful uses of data leading to biases against individuals or groups.
Systemic onset juvenile idiopathic arthritis with macrophage activation syndrome misdiagnosed as Kawasaki disease: case report and literature review
Venue: Rheumatology International
Year: 2010

Patients with systemic onset juvenile idiopathic arthritis (SoJIA) are rarely known to develop coronary artery dilatation. The American heart association (AHA) statement on evaluation of suspected Kawasaki disease (KD) would lead some SoJIA patients (particularly in the early stages of the disease) to be inaccurately classified as KD. In addition to the institution of inappropriate therapy with IVIG, misdiagnosis as KD can delay definitive treatment for these SoJIA patients who probably have a worse predicted outcome. We present a 6 year old male patient with SoJIA who was initially classified as incomplete KD. The child developed life threatening macrophage activation syndrome (MAS) Previous literature regarding coronary dilatation in SoJIA is also reviewed.
Ringed seal search for global optimization via a sensitive search model Younes Saadi
Year: 2018

This thesis proposes a nature inspired metaheuristic algorithm for global optimization. The proposed algorithm, which is called Ringed Seal Search (RSS) is inspired from the movement of the animal ringed seal. The proposed algorithm is characterized by a search model namely the sensitive search model, where the exploitation exploration is adaptively balanced. The quality of the algorithm is comprehensively evaluated on various standard benchmark test functions using variety of quality metrics and using three baseline algorithms for comparison. The time consumption analysis shows that RSS consumes less time compared to its homologs. This result is compatible with the convergence analysis. The solution quality analysis demonstrates that the convergence speed of RSS obtained better solution quality, which can be interpreted as a mature search. The diversity evaluation shows that the proposed algorithm achieved an optimal diversity values in most of the benchmark test functions. The experimental results show that the proposed algorithm in this thesis improves the global optimization quality in uni objective and multi objective environments while the exploitation and exploration are adaptively balanced. Finally, the proposed algorithm is applied on a data clustering case study using seven benchmark datasets to validate and check its ability to solve real optimization problems. The obtained results show that the proposed algorithm can be used for data clustering.
A Simple and Robust Approach to Detecting Subject Verb Agreement Errors
Venue: NAACL
Year: 2019

While rule based detection of subject verb agreement (SVA) errors is sensitive to syntactic parsing errors and irregularities and exceptions to the main rules, neural sequential labelers have a tendency to overfit their training data. We observe that rule based error generation is less sensitive to syntactic parsing errors and irregularities than error detection and explore a simple, yet efficient approach to getting the best of both worlds: We train neural sequential labelers on the combination of large volumes of silver standard data, obtained through rule based error generation, and gold standard data. We show that our simple protocol leads to more robust detection of SVA errors on both in domain and out of domain data, as well as in the context of other errors and long distance dependencies; and across four standard benchmarks, the induced model on average achieves a new state of the art.
The dynamical signature of anhedonia in major depressive disorder: positive emotion dynamics, reactivity, and recovery
Venue: BMC Psychiatry
Year: 2019

BackgroundMajor Depressive Disorder (MDD) is the leading cause of disability worldwide. The cardinal features of MDD are depressed mood and anhedonia. Anhedonia is defined as a "markedly diminished interest or pleasure in all, or almost all, activities of the day" and has generally been investigated on group level using retrospective data (e.g. via questionnaire/interview) However, inferences based on group level findings not necessarily generalize to daily life experiences within individuals.MethodsWe repeatedly sampled pleasurable experiences within individuals' daily lives by means of Experience Sampling Methods, and compared how positive affect unfolded in the daily life of healthy controls versus patients diagnosed with MDD and anhedonia. We sampled Positive Affect (PA) and reward experiences on 10 semi random time points a day, for seven days in the daily lives of 47 MDD patients with anhedonia, and 40 controls.ResultsMultilevel models showed that anhedonia was associated with low PA, but not to differences in PA dynamics, nor reward frequency in daily life. In reaction to rewards, MDD patients with anhedonia showed no difference in their increase in PA (i.e. PA reactivity) and showed no signs of a faster return to baseline thereafter (i.e. PA recovery).ConclusionsOur results suggest that the dynamical signature of anhedonia in MDD can be described best as a lower average level of PA, and "normal" in terms of PA dynamics, daily reward reactivity and reward recovery.  https:/osf.io/gmfsc/register/565fb3678c5e4a66b5582f67.  https:/osf.io/cfkts
Automatic CEFR Level Prediction for Estonian Learner Text
Year: 2014

This paper reports on approaches for automatically predicting a learner's language proficiency in Estonian according to the European CEFR scale. We used the morphological and POS tag information extracted from the texts written by learners. We compared classification and regression modeling for this task. Our models achieve a classification accuracy of 79% and a correlation of 0.85 when modeled as regression. After a comparison between them, we concluded that classification is more effective than regression in terms of exact error and the direction of error. Apart from this, we investigated the most predictive features for both multiclass and binary classification between groups and also explored the nature of the correlations between highly predictive features. Our results show considerable improvement in classification accuracy over previously reported results and take us a step closer towards the automated assessment of Estonian learner text.
Strategic planning characteristics applied to project management
Year: 2017

This paper examines the application of strategic planning characteristics from prior strategic planning research to project management. Drawing from prior research in strategic planning, strategic information systems planning and strategic manufacturing planning, this research combines strategic planning characteristics derived from a rational approach with a second set of adaptive characteristics to create a comprehensive model. The resulting "rational adaptive" approach is then assessed empirically to evaluate its relevance to PM and whether it is associated with increased project success. In addition, the "rational adaptive" approach is mapped to established PM tools/techniques. Findings indicate that PM is captured by varying degrees of a rational adaptive approach, which is positively correlated with PM success and use of PM tools/techniques. These results suggest that strategic planning characteristics can be effectively incorporated into a generalized PM framework, yielding potentially useful insights regarding the relationship of PM behaviors to eventual project success.