nils-herrmann/clean_scirepeval huggingface dataset:
The impact of indiscriminate media coverage of a celebrity suicide on a society with a high suicide rate: epidemiological findings on copycat suicides from South Korea.
Venue: Journal of affective disorders
Year: 2014

This study examines the extent to which the indiscriminate media coverage of the famous young actress Lee Eun ju's suicide in 2005 affected suicides overall and in specific subgroups (by age, gender, and suicide method) in a suicide prone society, South Korea.  South Korea's 2003 2005 suicide data (n=34,237) were obtained from death certificate records of the National Statistical Office (NSO) Data was analyzed with Poisson time series auto regression models.  After adjusting for confounding factors (such as seasonal variation, calendar year, temperature, humidity, and unemployment rate) there was a significant increase in suicide (RR=1.40, 95% CI=1.30 1.51, no. of excess mortalities=331; 95% CI=267 391) during the 4 weeks after Lee's suicide. This increase was more prominent in subgroups with similar characteristics to the celebrity. In particular, the relative risk of suicide during this period was the largest (5.24; 95% CI=3.31 8.29) in young women who used the same suicide method as the celebrity. Moreover, the incidence of these copycat suicides during the same time significantly increased in both genders and in all age subgroups among those who committed suicide using the same method as the celebrity (hanging) LIMITATIONS It is difficult to prove conclusively that the real motivation of the suicides was Lee's death.  The findings from this study imply that, if the media indiscreetly reports the suicide of a celebrity in a suicide prone society, the copycat effect can be far reaching and very strong, particularly for vulnerable people.
River profile controls on channel morphology, debris flow disturbance, and the spatial extent of salmonids in steep mountain streams
Year: 2012

Received 29 December 2011; revised 5 September 2012; accepted 17 September 2012;   In the geologically and topographically diverse mountain ranges of the Pacific Northwest, a broad scale means of prioritizing salmonid habitat conservation areas based on geomorphic process domains is examined. We propose that steepness and concavity indices derived from the relation between drainage area and channel slope provide a means of identifying basins that express different reach scale morphologies, fish habitat capacity, and risk of episodic disturbance. Strongly concave river profiles that develop in mountainous terrain indicate that almost all of the relief in the drainage network occurs in headwater streams. In these basins a large proportion of the channel network has low gradient morphologies, which provide favorable habitat for many salmonid species. The severity of pulse disturbances is also reduced because low gradient main stem channels inhibit debris flow conveyance, and in these networks the distribution of fish can expand into tributaries, allowing for a spatial spreading of risk. In contrast, rivers with poorly concave or steeper profiles have a greater abundance of high gradient reaches that limit the distribution of fish to a small portion of the channel network and facilitate debris flow passage. The combined influence of a limited spatial distribution of fish and an increased risk of debris flows may cause populations in these basins to be less resilient to pulse disturbances. A case example from the Klamath Mountains, an area with broad variation in the steepness and concavity of river profiles, was used to develop this approach and aid conservation planning for imperiled populations of anadromous salmonids.
Pharmacogenomics in type II diabetes mellitus management: Steps toward personalized medicine
Venue: Pharmacogenomics and personalized medicine
Year: 2009

Advances in genotype technology in the last decade have put the pharmacogenomics revolution at the forefront of future medicine in clinical practice. Discovery of novel gene variations in drug transporters, drug targets, effector proteins and metabolizing enzymes in the form of single nucleotide polymorphisms (SNPs) continue to provide insight into the biological phenomena that govern drug efficacy and toxicity. To date, novel gene discoveries extracted from genome wide association scans and candidate gene studies in at least four antidiabetic drug classes have helped illuminate possible causes of interindividual variability in response. Inadequate protocol guidelines for pharmacogenomics studies often leads to poorly designed studies, making it hard to formulate a definitive conclusion regarding the clinical relevance of the information at hand. These issues, along with the ethical, social, political, legislative, technological, and economic challenges associated with pharmacogenomics have only delayed its entry to mainstream clinical practice. On the other hand, these issues are being actively pursued and rapid progress is being made in each area which assures the possibility of gaining widespread acceptance in clinical practice.
Modeling the Causes of Inflation in Romania
Year: 2017

In this paper, we analyze the main causes of inflation in Romania in the period 2007 2013. Determining the causes of inflation requires a thorough analysis to identify anti inflationary measures and to assess possible future effects on prices as well. The proposed econometric methods analyze on one hand the impact and the extent to which prices are influenced by monetary factors and production, and by salaries and the exchange rate, on the other hand. We also analyze several models containing the main macroeconomic variables which have a direct impact on inflation and also within these models we find patterns in various economic interdependences. From the six estimated models, only two of them meet the criteria of the VAR method used in our study and the main lessons drawn from data modeling are: the money supply, the index of producer prices and the inflation CORE2 are the main sources of variation in industrial prices; after their own shocks, the monetary factors have a significant impact on prices, primarily on the production prices and later on the industry prices, whereas the salaries and exchange rate have little impact and do not account for the inflation in our country during the investigated period.
Family violence in gambling help seeking populations
Year: 2018

Elevated rates of family violence among treatment seeking problem gamblers compared to general population estimates have been reported in Spain, Canada and Australia. This study examined the occurrence of family violence among 454 problem gambling help seeking clients (370 gamblers, 84 affected others) recruited through 3 national gambling treatment services in New Zealand. Measures used were the Problem Gambling Severity Index, and a modified version of the HITS Scale which assessed physical, emotional, verbal and sexual abuse. Past year family violence among gamblers in this sample was 46.8% for victimization, 41.2% for perpetration and 55.0% for any form of family violence. Among affected others the occurrence was 65.5% for victimization, 57.1% for perpetration and 71.4% for any form of violence. The most common type of violence was verbal intimate partner violence. Affected others and women gamblers reported higher rates of violence victimization and perpetration. These findings underscore the importance of screening gambling help seeking clients for family violence, and the development of prevention and treatment programmes to address violence in this population, with particular attention to affected others and women gamblers. Future research should assess coercive control and the gendered nature of family violence among problem gambling help seekers.
Application of artificial neural networks for optimized AHU discharge air temperature set point and minimized cooling energy in VAV system
Venue: Applied Thermal Engineering
Year: 2019

Chillers and boilers based air handling unit (AHU) system is one of the most widely used heating and cooling systems in office buildings in Korea. However, in most conventional forced air systems, the guidelines for the AHU discharge air temperature (DAT) are not fully established and thus AHU DAT are constantly fixed to a particular set point, regardless of dynamic changes of operating variables. In this circumstance, this study aimed at developing a control algorithm that can operate a conventional VAV system with optimal set points for the AHU DAT. Three story office building was modeled using co simulation technique between EnergyPlus and Matlab via BCVTB (Building Controls Virtual Test Bed) In addition, artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for the upcoming next time step, was embedded into the control algorithm using neural network toolbox within Matlab. By comparing the predicted energy for the different set points of the AHU DAT, the control algorithm can determine the most energy effective AHU DAT set point to minimize the cooling energy. The results showed that the prediction accuracy between simulated and predicted outcomes turned out to have a low coefficient of variation root mean square error (CvRMSE) value of approximately 24% In addition, the predictive control algorithm was able to significantly reduce cooling energy consumption by approximately 10% compared to a conventional control strategy of fixing AHU DAT to 14 degC. These findings suggest that the ANN model and the control algorithm showed energy saving potential for various types of forced air systems by taking dynamic operating conditions into account in each time step.
Graphene via sonication assisted liquid phase exfoliation.
Venue: Chemical Society reviews
Year: 2014

Graphene, the 2D form of carbon based material existing as a single layer of atoms arranged in a honeycomb lattice, has set the science and technology sectors alight with interest in the last decade in view of its astounding electrical and thermal properties, combined with its mechanical stiffness, strength and elasticity. Two distinct strategies have been undertaken for graphene production, i.e. the bottom up and the top down. The former relies on the generation of graphene from suitably designed molecular building blocks undergoing chemical reaction to form covalently linked 2D networks. The latter occurs via exfoliation of graphite into graphene. Bottom up techniques, based on the organic syntheses starting from small molecular modules, when performed in liquid media, are both size limited, because macromolecules become more and more insoluble with increasing size, and suffer from the occurrence of side reactions with increasing molecular weight. Because of these reasons such a synthesis has been performed more and more on a solid (ideally catalytically active) surface. Substrate based growth of single layers can be done also by chemical vapor deposition (CVD) or via reduction of silicon carbide, which unfortunately relies on the ability to follow a narrow thermodynamic path. Top down approaches can be accomplished under different environmental conditions. Alongside the mechanical cleavage based on the scotch tape approach, liquid phase exfoliation (LPE) methods are becoming more and more interesting because they are extremely versatile, potentially up scalable, and can be used to deposit graphene in a variety of environments and on different substrates not available using mechanical cleavage or growth methods. Interestingly, LPE can be applied to produce different layered systems exhibiting different compositions such as BN, MoS2, WS2, NbSe2, and TaS2, thereby enabling the tuning of numerous physico chemical properties of the material. Furthermore, LPE can be employed to produce graphene based composites or films, which are key components for many applications, such as thin film transistors, conductive transparent electrodes for indium tin oxide replacement, e.g. in light emitting diodes, or photovoltaics. In this review, we highlight the recent progress that has led to successful production of high quality graphene by means of LPE of graphite. In particular, we discuss the mechanisms of exfoliation and methods that are employed for graphene characterization. We then describe a variety of successful liquid phase exfoliation methods by categorizing them into two major classes, i.e. surfactant free and surfactant assisted LPE. Furthermore, exfoliation in aqueous and organic solutions is presented and discussed separately.
Selective synthesis of citrus flavonoids prunin and naringenin using heterogeneized biocatalyst on graphene oxide
Year: 2019

Production of citrus flavonoids prunin and naringenin was performed selectively through the enzyme hydrolysis of naringin, a flavonoid glycoside abundant in grapefruit wastes. To produce the monoglycoside flavonoid, prunin, crude naringinase from Penicillium decumbens was purified by a single purification step resulting in an enzyme with high a rhamnosidase activity. Both crude and purified enzymes were covalently immobilized on graphene oxide. The activity of the immobilized enzymes at different pH levels and temperatures, and the thermal stability were determined and compared with those exhibited by the free naringinases using specific substrates: p nitrophenyl b D glucoside (Glc pNP) and p nitrophenyl alpha L rhamnopyranoside (Rha pNP) The crude and purified naringinase supported on GO were tested in the hydrolysis of naringin, giving naringenin and prunin, respectively, in excellent yields. The supported enzymes can be reused many times without loss of activity. The naringinase stabilized on GO has high potential to produce the valuable citrus flavonoids prunin and naringenin.
Pemanfaatan Herbal Dalam Penyembuhan Luka Perineum
Year: 2020

The puerperal period is the return of the reproductive organs as before pregnancy within six weeks after giving birth. The puerperal infection is a problem that often occurs during the puerperium, one of which is caused by a birth canal injury. Wound healing can be done conventionally or with complementary therapy. Complementary therapy is also known as traditional medicine or herbal medicine for health and effectiveness. This study is a discussion that attempts to complete the use of herbs in healing perineal wounds. The literary method in this article is to collect journals related to plants used to heal perineal wounds. The results of a review of 5 studies show that some plants such as aloe vera, cinnamon, red betel leaf, gotu kola leaf, and green tea leaves can be used as an alternative to healing perineal wounds.
CalliGAN: Unpaired Mutli chirography Chinese Calligraphy Image Translation
Venue: ACCV
Year: 2018

The style translation of Chinese calligraphy image is a challenging problem: changing the style and layout of strokes while retaining the content attributes like overall structure and combination of radicals. The existing calligraphy character generation methods, such as brush modeling, skeleton rendering, strokes extracting and assembling, etc. are ineffective and difficult to apply to multi chirography styles and numerous data. The recent neural style transfer methods for general image to image style translation tasks can not apply to our problem due to the different meanings of the "style" The GAN based methods demonstrating good results require image pairs for training, which is hard to collect in our task. Therefore, in this paper we propose a novel GAN based model, called CalliGAN, for the mutli chirography Chinese calligraphy image translation. In CalliGAN, We present a joint optimization method which only requires unpaired multiple chirography sets for training. In our experiment, we build a chirography style dataset called Chiro 4 and then compare our method with various general translation methods. The experiment results demonstrate the validity of our method on calligraphy style translation task.
A Systematic Approach to Light Load Calculation for DG Un Intentional Islanding
Venue: 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
Year: 2020

With the influx of distributed generation (DG) penetration, power utilities have adopted active anti islanding protection methods to prevent an unintentional islanding condition. One commonly used protection scheme is implemented through the use of a transfer trip from the upline connected substation to the DG facility and is required when the capacity of connected DGs exceed a certain threshold of local minimum load. The threshold established by standard IEEE 1547.2 2009 states that a 3:1 minimum load to generation ratio is acceptable to ensure the DG will not sustain an unintentional island. However, the term "minimum load" is not precisely defined in any standard, and in practice, a light load value is chosen by engineers through manual methods that can be inconsistent, not representative and change drastically based on various cases. To solve this problem, this paper proposes a new light load calculation method creating consistency and better accuracy; the method has been implemented in. NET C# application and is used within Dominion Energy.
Learning to Communicate with Multi agent Reinforcement Learning Using Value Decomposition Networks
Venue: 3PGCIC
Year: 2019

Recent research focuses on how agents can learn to communicate with each other. This communication between the agents allows them to share information and coordinate their behaviour. Recent efforts have proven successful in these cooperative problems. A major problem we face in multi agent reinforcement learning is the lazy agent problem, where some agents take advantage of the successful actions of other agents. This results in agents not being able to learn a functional policy. In this paper we will combine state of the art methods to design an architecture to address cooperative problems using communication while also eliminating the lazy agent problem. We propose two approaches for learning to communicate that use value decomposition to address the lazy agent problem. We find that the additive version of value decomposition gives us results which exceeds the results of the state of the art.
Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN
Venue: ArXiv
Year: 2019

Deep learning requires large datasets for training (convolutional) networks with millions of parameters. In neuroimaging, there are few open datasets with more than 100 subjects, which makes it difficult to, for example, train a classifier to discriminate controls from diseased persons. Generative adversarial networks (GANs) can be used to synthesize data, but virtually all research is focused on 2D images. In medical imaging, and especially in neuroimaging, most datasets are 3D or 4D. Here we therefore present preliminary results showing that a 3D progressive growing GAN can be used to synthesize MR brain volumes.
Recent advances targeting C C chemokine receptor type 2 for liver diseases in monocyte/macrophage
Venue: Liver international official journal of the International Association for the Study of the Liver
Year: 2020

Liver plays a critical role in metabolism, nutrient storage and detoxification. Emergency signals or appropriate immune response leads to pathological inflammation and breaks the steady state when liver dysfunction appears, which makes body more susceptible to chronic liver infection, autoimmune diseases and tumour. Compelling proof has illustrated the non redundant importance of C C chemokine receptor type 2 (CCR2) one of G protein coupled receptors, in different diseases. Selectively expressed on the surface of cells, CCR2 is involved in various signalling pathways and regulates the migration of cells. Especially, a peculiar role of CCR2 has been identified within decades in the onset and progression of hepatic diseases, which led to particular focusing on CCR2 as a new therapeutic and diagnostic target for non alcoholic fatty liver disease and hepatocellular carcinoma. In this review, we discuss the effect of CCR2 in monocytes/macrophages on liver diseases. The application and translation of the decades of discoveries into therapies promise novel approaches in the treatment of liver disease.
COVID 19 (SARS CoV 2) Ventilator Resource Management Using a Network Optimization Model and Predictive System Demand
Venue: medRxiv
Year: 2020

The COVID 19 (SARS CoV 2) pandemic is overwhelming global healthcare delivery systems due to the exponential spike in cases requiring specialty tests, facilities and equipment, including complex, precision devices like ventilators. In particular, the surge in critically ill patients has revealed a significant deficiency in regional availability of respiratory care ventilators. The authors offer a mathematical framework for ventilator distribution under scarcity conditions using an optimized network model and solver. The framework is interoperable with existing COVID 19 healthcare demand models and scales for different user defined system sizes, including hospital networks, city, state, regional and national scale prioritization. The authors' approach improves current capabilities for medical device resource management within the existing incident command system while accounting for availability of devices, ventilation treatment time periods, disinfection and cleaning between patients, as well as shipping logistics time. The authors present a proof of concept using a high fidelity COVID 19 data set from Colorado, discusses how to scale nationally, and emphasizes the importance of applying ethical human in the loop decision making when using this or similar approaches to managing medical device resources during epidemic emergencies.
Local Estimators for Spacecraft Formation Flying
Year: 2011

A formation estimation architecture for formation flying builds upon the local information exchange among multiple local estimators. Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter spacecraft communication, and control. Most existing formation flying estimation algorithms can only be supported via highly centralized, all to all, static relative sensing. New algorithms are needed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms should rely on a local information exchange network, relaxing the assumptions on existing algorithms. In this research, it was shown that only local observability is required to design a formation estimator and control law. The approach relies on breaking up the overall information exchange network into sequence of local subnetworks, and invoking an agreement type filter to reach consensus among local estimators within each local network. State estimates were obtained by a set of local measurements that were passed through a set of communicating Kalman filters to reach an overall state estimation for the formation. An optimization approach was also presented by means of which diffused estimates over the network can be incorporated in the local estimates obtained by each estimator via local measurements. This approach compares favorably with that obtained by a centralized Kalman filter, which requires complete knowledge of the raw measurement available to each estimator.