nils-herrmann/clean_scirepeval huggingface dataset:
Association Between HEDIS Performance and Primary Care Physician Age, Group Affiliation, Training, and Participation in ACA Exchanges
Venue: Journal of General Internal Medicine
Year: 2020

There are a limited number of studies investigating the relationship between primary care physician (PCP) characteristics and the quality of care they deliver.  To examine the association between PCP performance and physician age, solo versus group affiliation, training, and participation in California's Affordable Care Act (ACA) exchange.  Observational study of 2013 2014 data from Healthcare Effectiveness Data and Information Set (HEDIS) measures and select physician characteristics. Participants PCPs in California HMO and PPO practices n 5053) with part of their patient panel covered by a large commercial health insurance company.  Hemoglobin A1c testing; medical attention nephropathy; appropriate treatment hypertension (ACE/ARB) breast cancer screening; proportion days covered by statins; monitoring ACE/ARBs; monitoring diuretics. A composite performance measure also was constructed.  For the average 35 versus 75 year old PCP, regression adjusted mean composite relative performance scores were at the 60th versus 47th percentile (89% vs. 86% composite absolute HEDIS scores; p .001) For group versus solo PCPs, scores were at the 55th versus 50th percentiles (88% vs. 87% composite absolute HEDIS scores; p .001) The effect of age on performance was greater for group versus solo PCPs. There was no association between scores and participation in ACA exchanges.  The associations between population based care performance measures and PCP age, solo versus group affiliation, training, and participation in ACA exchanges, while statistically significant in some cases, were small. Understanding how to help older PCPs excel equally well in group practice compared with younger PCPs may be a fruitful avenue of future research.
The Effects of Cognitive Stimulation of Instagram on Anxiety, Fear of Missing Out, Memory, and Self Esteem
Year: 2019

With the increasing use of social media in the daily lives of undergraduate college students, it is important to investigate the effects of social media on psychological well being and cognitive function. Lillard and Peterson (2011) found that overstimulating children's televisions shows, such as SpongeBob, negatively impacted children's impulsive behaviors and attention. The current research focused on emerging adults and considered the effect of overstimulation of social media, specifically Instagram, on anxiety level, self esteem, fear of missing out (FoMo) and memory. Participants were randomly assigned into either the control (coloring on an iPad) or experimental (Instagram stimulation) group and after a set of pre test questions regarding self esteem and anxiety, participants were exposed to cognitive stimulation for 10 minutes. Then participants completed a post test questionnaire of self esteem, anxiety, memory recall, and fear of missing out (FoMo) Researchers hypothesized that the Instagram stimulation group would have lower levels of self esteem, higher anxiety, higher levels of FoMo, and lower memory recall than those in the coloring group. Effects of exposure to Instagram has not been thoroughly researched, therefore it is an important avenue to study as emerging adults have an increasing habit of looking at Instagram on a regular basis. An independent samples ttest and a mixed model ANOVA found that those exposed to Instagram had heightened levels of FoMo. In addition, participants, regardless of condition, experienced a significant increase in self esteem from preto post test. Future research may consider examining the effects of stimulation of Instagram regarding short term memory and self esteem.
Warm Molecular Hydrogen in the Spitzer SINGS Galaxy Sample
Year: 2007

Results on the properties of warm molecular hydrogen in 57 normal galaxies are derived from measurements of H_2 rotational transitions, obtained as part of SINGS. This study extends previous extragalactic surveys of emission lines of H_2 to fainter and more common systems (L_(FIR) 10^(7) 6 x 10^(10) L_) The 17 mm S(1) transition is securely detected in the nuclear regions of 86% of galaxies with stellar masses above 10^(9.5) M_. The derived column densities of warm H_2 (T 100 K) although averaged over kiloparsec scale areas, are commensurate with values observed in resolved photodissociation regions. They amount to between 1% and >30% of the total H_2. The power emitted in the three lowest energy transitions is on average 30% of the power of the bright [Si II] cooling line (34.8 mm) and about 4 x 10^ 4) of the total infrared power for star forming galaxies, which is consistent with excitation in PDRs. The fact that the H_2 line intensities scale tightly with the aromatic band emission, even though the average radiation field intensity varies by a factor of 10, can also be understood if both tracers originate predominantly in PDRs, either dense or diffuse. Many of the 25 LINER/Seyfert targets strongly depart from the rest of the sample, in having warmer excited H_2 and excess H_2 rotational power with respect to the dust emission. We propose a threshold in H_2 to aromatic band power ratios, allowing the identification of low luminosity AGNs by an excess H_2 excitation. A dominant contribution from shock heating is favored in these objects. Finally, we detect in nearly half the star forming targets nonequilibrium ortho to para ratios, consistent with the effects of FUV pumping combined with incomplete ortho para thermalization, or possibly nonequilibrium photodissociation fronts.
Visual Navigation With Multiple Goals Based on Deep Reinforcement Learning.
Venue: IEEE transactions on neural networks and learning systems
Year: 2021

Learning to adapt to a series of different goals in visual navigation is challenging. In this work, we present a model embedded actor critic architecture for the multigoal visual navigation task. To enhance the task cooperation in multigoal learning, we introduce two new designs to the reinforcement learning scheme: inverse dynamics model (InvDM) and multigoal colearning (MgCl) Specifically, InvDM is proposed to capture the navigation relevant association between state and goal and provide additional training signals to relieve the sparse reward issue. MgCl aims at improving the sample efficiency and supports the agent to learn from unintentional positive experiences. Besides, to further improve the scene generalization capability of the agent, we present an enhanced navigation model that consists of two self supervised auxiliary task modules. The first module, which is named path closed loop detection, helps to understand whether the state has been experienced. The second one, namely the state target matching module, tries to figure out the difference between state and goal. Extensive results on the interactive platform AI2 THOR demonstrate that the agent trained with the proposed method converges faster than state of the art methods while owning good generalization capability. The video demonstration is available at https:/vsislab.github.io/mgvn.
Security Services Using Blockchains: A State of the Art Survey
Venue: IEEE Communications Surveys Tutorials
Year: 2019

This paper surveys blockchain based approaches for several security services. These services include authentication, confidentiality, privacy and access control list, data and resource provenance, and integrity assurance. All these services are critical for the current distributed applications, especially due to the large amount of data being processed over the networks and the use of cloud computing. Authentication ensures that the user is who he/she claims to be. Confidentiality guarantees that data cannot be read by unauthorized users. Privacy provides the users the ability to control who can access their data. Provenance allows an efficient tracking of the data and resources along with their ownership and utilization over the network. Integrity helps in verifying that the data has not been modified or altered. These services are currently managed by centralized controllers, for example, a certificate authority. Therefore, the services are prone to attacks on the centralized controller. On the other hand, blockchain is a secured and distributed ledger that can help resolve many of the problems with centralization. The objectives of this paper are to give insights on the use of security services for current applications, to highlight the state of the art techniques that are currently used to provide these services, to describe their challenges, and to discuss how the blockchain technology can resolve these challenges. Further, several blockchain based approaches providing such security services are compared thoroughly. Challenges associated with using blockchain based security services are also discussed to spur further research in this area.
Pilots' mental workload prediction based on timeline analysis
Venue: Technology and health care official journal of the European Society for Engineering and Medicine
Year: 2020

The aircraft cockpit is a highly intensive human computer interaction system, and its design directly affects flight safety.  To optimize the display interface design in complex flight tasks, the present study aimed to propose a dynamic conceptual framework and a timeline task analysis method for the quantization of the dynamic time effect of mental workload and the influencing factors of task types in the mental workload prediction model.  The multi factor mental workload prediction model based on attention resource allocation was integrated to establish the dynamic prediction model of mental workload. The ergonomics simulation experiment was carried out by recording the data on the performance of embedded subtasks, National Aeronautics and Space Administration Task Load Index (NASA TLX) subjective evaluation, and eye tracking.  The results indicated that the prediction model had a good prediction accuracy and effectiveness under different simulated interfaces and complex tasks, and the real time monitoring of pilots' mental workload state was realized.  In conclusion, the prediction model and the experimental method could be applied to avoid the overload of the pilot throughout the flight phase by optimizing the display interface and adjusting the flight task.
Application of Finite Element Software in Fatigue Analysis of the Welded Joints
Year: 2011

Based on the fatigue behaviors of welded structure and the distinction between static fatigue and vibration fatigue and sound fatigue, the general fatigue analysis methods of static fatigue and vibration fatigue are introduced in this paper, such as nominal stress method, structure stress method, hot stress method and power spectral density method. Especially application status and common software of finite element analysis are expounded and analyzed in fatigue analysis of the welded joint. In recent years, finite element technology is applied widely on analysis all kinds of welded joints, the main problems include two points, built suitable model and generated reasonable mesh. Nowadays, finite element software has many kinds of usage and different software has different functions. According to the stress analysis process of static fatigue and vibration fatigue, three large scale general finite element software ANSYS, MSC.NASTRAN/PANTRAN and ABAQUS, some comparative analyses have been done in building model, partitioning mesh and applying method At last some important conclusions are given. Different finite element software has equivalent development background and reliable analysis performance. Different finite element software have different application platform. In order to get better analysis result, each other's merits are drew from different software and matched with the application.
Data Colonialism: Rethinking Big Data's Relation to the Contemporary Subject
Year: 2019

We are often told that data are the new oil. But unlike oil, data are not a substance found in nature. It must be appropriated. The capture and processing of social data unfolds through a process we call data relations, which ensures the "natural" conversion of daily life into a data stream. The result is nothing less than a new social order, based on continuous tracking, and offering unprecedented new opportunities for social discrimination and behavioral influence. We propose that this process is best understood through the history of colonialism. Thus, data relations enact a new form of data colonialism, normalizing the exploitation of human beings through data, just as historic colonialism appropriated territory and resources and ruled subjects for profit. Data colonialism paves the way for a new stage of capitalism whose outlines we only glimpse: the capitalization of life without limit.
A Mobile Application for Driver's Drowsiness Monitoring based on PERCLOS Estimation
Venue: IEEE Latin America Transactions
Year: 2019

Vehicular accidents caused by driver drowsiness involve about 7,000 people/year in Brazil, only on federal highways, and cause psychic damage and traumatic stresses for both the victims involved and their families. Drowsiness is characterized by reduced level of vigilance and concentration, which are essential during driving activity. Due to this adversity, many applications of drowsiness detection had been continuously developed through electrical body signals to alert the driver at the time when sleepiness is identified, such as heart rate variability (HRV) and electroencephalogram (EEG) Although these methods work, the use of electrodes in the driver's body is highly invasive. Therefore, we propose a drowsiness detection system based on driver's real time video capture, by estimating the percentage of eyelid closure over a period, without any contact device. Since the use of smartphones has been growing in the last decade, the system has been implemented in a mobile phone even with memory and processing limitations. Processing reduction procedures were developed to improve the application performance, such as the reduction of the region of interest and the limitation of the search window, which increased by 93.09% the number of frames per second and allowed the application to operate smoothly.
Fatigue failure analysis of roll steel pins from a chain assembly: Fracture mechanism and numerical modeling
Venue: Engineering Failure Analysis
Year: 2019

The current study is focused on the investigation of the failure case history pertaining to the in service fractured steel pins of a chain assembly installed in a continuous cold drawing machine. The findings of the present investigation showed that the pins failed due to multiple origin rotating bending fatigue under low applied load, due to the application of tensile and/or bending stresses, initiated from the pin circumference. The multiple crack initiation is consistent to severe stress concentration conditions, as imposed by the low fillet radius attributed to the original pin design. No microstructural abnormalities were detected, that could have been correlated to the principal cause of failure. In the context of the present evaluation, analytical methods such as visual inspection, optical microscopy, SEM fractography and microhardness testing were utilized. Moreover, a finite element analysis of the assembled chain was performed in order to investigate the stress distribution during the operation of the chain and to formulate a sound quantitative approach which will assist in better understanding of chain/pin loading conditions. The primarily insufficient component design resulted in high stress concentration factors interacted with the soft annealed material used for the component and caused fatigue under the presence of fluctuating stresses.
Augmenting Fixed Framework Agreements in Humanitarian Logistics with a Bonus Contract
Venue: Production and Operations Management
Year: 2019

Framework agreements (FAs) are commonly used by relief organizations to cooperate with suppliers to pre position relief items. Many Chinese local authorities with disaster responding experience choose a specific type of FA, the fixed framework agreement (FFA) which contains fixed terms for item quality, quantity, price, and delivery lead time. These FFAs, however, do not fully motivate suppliers to further improve the effectiveness of a humanitarian logistics (HL) operation. We suggest using a bonus contract as a supplemental agreement to FFAs to incentivize suppliers to reduce delivery lead times. We develop a performance measurement model using deprivation level functions (DLFs) to measure effectiveness, efficiency and cost effectiveness of an HL operation. With this model, we quantify the performance of an FFA with and without bonus contract and study the feasibility and optimality of the bonus contract. A case study on an FFA observed in practice is used to illustrate our methodology. The results show that the bonus contract can be both practical and capable of improving the performance of an HL operation.
Characterizing Twitter Data using Sentiment Analysis and Topic Modeling
Year: 2016

As the global community becomes increasingly connected, it gets more and more common to express thoughts and opinions through social networking websites. Twitter, currently the largest microblog website in the world, is heavily used for this purpose. Well known politicians, comedians and trending persons use this medium to express their minds through 140 character messages. This makes Twitter one of the platforms being most influential on the global web communities' way of thinking. This thesis combines topic modeling and sentiment analysis in order to obtain information from tweets. While sentiment analysis seeks to find out what opinions people have, topic modeling tries to find out what they talk about. Convential topic modeling schemes, such as Latent Dirichlet Allocation, are known to perform inadequately when applied to tweets, due to the sparsity of short documents. To alleviate these disadvantages, we apply several pooling techniques, aggregating similar tweets into individual documents. We specifically study the aggregation of tweets sharing authors or hashtags. Our Twitter Sentiment Analysis system is comprised of seven di erent machine learning classifiers. These aim to predict whether a message's polarity is of neutral, negative or positive sentiment. Four machine learning algorithms, Maximum Entropy, Naive Bayes, Support Vector Machines and Stochastic Gradient Descent, have been proposed for performing sentiment classification in this thesis. The classifiers were trained through experiments of extensive grid searches on a parameter space and preprocessing methods in order to achieve optimal classification scores. To combine topic modeling with sentiment analysis, a visualization application called TweetMoods was built. TweetMoods simultaneously examines the topics contained in a Twitter corpus retrieved by a search query, and the sentiments expressed in these tweets. Our topic modeling results show that aggregating similar tweets into individual documents increases the topic coherence significantly. On performing message polarity classification on tweets, the Maximum Entropy classifier yielded results outperforming most earlier submitted work to the International Workshop on Semantic Evaluation of 2015. This proves the importance of our extensive grid searches on optimizing the parameter space of the classifiers.
Thresholds of mangrove survival under rapid sea level rise
Venue: Science
Year: 2020

Mangroves under sea level rise The rate of sea level rise has doubled from 1.8 millimeters per year over the 20th century to ~3.4 millimeters per year in recent years. Saintilan et al. investigated the likely effects of this increasing rate of rise on coastal mangrove forest, a tropical ecosystem of key importance for coastal protection (see the Perspective by Lovelock) They reviewed data on mangrove accretion 10,000 to 7000 years before present, when the rate of sea level rise was even higher than today as a result of glacial ice melt. Their analysis suggests an upper threshold of 7 millimeters per year as the maximum rate of sea level rise associated with mangrove vertical development, beyond which the ecosystem fails to keep up with the change. Under projected rates of sea level rise, they predict that a deficit between accretion and sea level rise is likely to commence in the next 30 years. Science, this issue p. 1118; see also p. 1050 Mangrove response to sea level rise in the final stages of deglaciation reveals survival thresholds that may be exceeded within 30 years. The response of mangroves to high rates of relative sea level rise (RSLR) is poorly understood. We explore the limits of mangrove vertical accretion to sustained periods of RSLR in the final stages of deglaciation. The timing of initiation and rate of mangrove vertical accretion were compared with independently modeled rates of RSLR for 78 locations. Mangrove forests expanded between 9800 and 7500 years ago, vertically accreting thick sequences of organic sediments at a rate principally driven by the rate of RSLR, representing an important carbon sink. We found it very likely >90% probability) that mangroves were unable to initiate sustained accretion when RSLR rates exceeded 6.1 millimeters per year. This threshold is likely to be surpassed on tropical coastlines within 30 years under high emissions scenarios.
First description of arginine catabolic mobile element (ACME) type VI harboring the kdp operon only in Staphylococcus epidermidis using short and long read whole genome sequencing: Further evidence of ACME diversity.
Venue: Infection, genetics and evolution journal of molecular epidemiology and evolutionary genetics in infectious diseases
Year: 2019

The arginine catabolic mobile element (ACME) was first described in methicillin resistant Staphylococcus aureus and is considered to enhance transmission, persistence and survival. Subsequently ACMEs were shown to be more prevalent in the coagulase negative Staphylococcus epidermidis. Previously, ACME types were distinguished by characteristic combinations of the arc and opp3 operons [I (arc+ opp3+ II (arc+ opp3 and III (arc opp3+ encoding an arginine deaminase pathway and oligopeptide permease transporter, respectively. Recently two novel ACME types harboring the potassium transporter encoding operon kdp were described in oral S. epidermidis isolates [IV (arc+ opp3 kdp+ and V (arc+ opp3+ kdp+ This study investigated two independent oral S. epidermidis isolates that yielded amplimers with kdp directed primers only when subjected to ACME typing PCRs. Hybrid assemblies based on Illumina MiSeq short read and Oxford Nanopore MinION long read whole genome sequences revealed that both isolates harbored a sixth, novel ACME type (VI) integrated into orfX. Both ACME VIs lacked the arc and opp3 operons, harbored the kdp operon adjacent to other commonly ACME associated genes including speG, hsd, sdr, and rep, but the structural organization of the adjacent regions were distinct. These ACMEs were flanked by different direct repeat sequences and the ACME VI positive isolates belonged to unrelated genetic clusters. Overall these findings are indicative of independent evolution. The identification of ACME type VI further illustrates the diversity of ACME elements in S. epidermidis. The presence of ACMEs harboring kdp may confer a selective advantage on oral S. epidermidis in a potassium rich environment such as found in dental plaque.