nils-herrmann/clean_scirepeval huggingface dataset:
Genetic risk for major depressive disorder and loneliness in sex specific associations with coronary artery disease
Venue: Molecular Psychiatry
Year: 2019

Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD) but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome wide association study in 18,385 European ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04 1.18; P 8.43 x 10 4) and 1.13 (95% CI, 1.07 1.20; P 4.51 x 10 6) per 1 SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03 1.15; P 0.002) while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95 1.06; P 0.97) Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European ancestry participants (1598 incident CAD cases) In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99 1.14; P 0.07) and 1.07 (1.01 1.15; P 0.03) respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.
Factors Influencing Consumer s Intention to Use E payment System An Empirical Study
Year: 2018

UNNAYAN International Bulletin of Management and Economics Volume IX July 2018 (Special Issue) 14 In the recent years, electronic commerce (e commerce) has growing rapidly. E commerce became an opportunity for companies to increase their selling. E commerce provides the capability of buying and selling products, information and services on the Internet and on other online environments. In an ecommerce surrounding, payments made in an electronic form, and are therefore called Electronic Payment. Electronic payment is a division of an e commerce transaction to include e payment for buying and selling of goods or services accessible through the internet. With the rapid growth of technology, the process to make electronic transactions are continuously increasing while the percentage of cash and cheques transactions continuously decreasing. Thus, the main objective of this paper is to explore the factors which influence consumer's intention to use e payment system for various purposes. Using principal component method of exploratory factor analysis, four factors viz. Perceived Usefulness, Perceived Social Esteem, Perceived Security and Perceived Reliability are identified that are affecting consumer's intention. The research is mainly focused on primary data which were collected from e commerce users through well structured questionnaire. The outcomes of this study are helpful to the users of e payment system, researchers who will be working in this field and companies planning to adopt or to improve their electronic payment system.  E Payment System, Usefulness, Social Esteem, Security and Reliability
Improved cooperative particle swarm optimizer for design of fuzzy neural network control system
Year: 2008

An improved cooperative PSO(ICPSO) is proposed for the cooperative PSO' incapable of converging at the local or global optimum.It is proved that the algorithm can converge at the global optimization solution with probability one.ICPSO is applied to the neural network modeling of a nonlinear plant,and also employed to the offline and online training of the fuzzy neural network adaptive controller in the system.Simulation results show that ICPSO has advantages of increasing the precision and enhancing the generalization capability of the model,and the fuzzy neural network(FNN) controller trained by ICPSO is effective for the system control.
Application of Multiport Flow Selector (MPFS) Onshore Gathering Facilities (OGF) for Garraf Oilfield in Iraq
Year: 2015

In the past, the conventional manifold is a popular approach by many Operators as gathering station for crude and gas production from wellheads in the oil gas field. This approach is slowly being substitute by smaller footprint with automated system so called compact manifold, especially for offshore field. However, for the first time in PETRONAS Carigali Operation worldwide, PCIHBV has implemented a new concept for the production gathering using a technology named as Multiport Flow Selector valve, MPFS. The concept was applied in the recent wellpads development at Garraf Oil Field in Iraq. The function is to gather seven (7) crude production wells and it is also equipped with fully automation system for the provision of well testing of each well whilst the rest of production is directed to production header. The entire well testing activities can be performed remotely via Distributed Control System, DCS in Control Room. The technology involves less human intervention which allowed smaller Operators manning at the wellpads and resulted more efficient operation. The advantages support the operation nature in Garraf Oil Field as the security and safety of personnel is at high risk.
Toward a reliable anomaly based intrusion detection in real world environments
Venue: Comput. Networks
Year: 2017

A popular approach for detecting network intrusion attempts is to monitor the network traffic for anomalies. Extensive research effort has been invested in anomaly based network intrusion detection using machine learning techniques; however, in general these techniques remain a research topic, rarely being used in real world environments. In general, the approaches proposed in the literature lack representative datasets and reliable evaluation methods that consider real world network properties during the system evaluation. In general, the approaches adopt a set of assumptions about the training data, as well as about the validation methods, rendering the created system unreliable for open world usage. This paper presents a new method for creating intrusion databases. The objective is that the databases should be easy to update and reproduce with real and valid traffic, representative, and publicly available. Using our proposed method, we propose a new evaluation scheme specific to the machine learning intrusion detection field. Sixteen intrusion databases were created, and each of the assumptions frequently adopted in studies in the intrusion detection literature regarding network traffic behavior was validated. To make machine learning detection schemes feasible, we propose a new multi objective feature selection method that considers real world network properties. The results show that most of the assumptions frequently applied in studies in the literature do not hold when using a machine learning detection scheme for network based intrusion detection. However, the proposed multi objective feature selection method allows the system accuracy to be improved by considering real world network properties during the model creation process.
Antiresorptive agent related osteonecrosis of the jaw: Position Paper 2017 of the Japanese Allied Committee on Osteonecrosis of the Jaw
Venue: Journal of Bone and Mineral Metabolism
Year: 2016

Antiresorptive agent related osteonecrosis of the jaw (ARONJ) is an intractable, though rare, complication in cancer patients with bone metastases and patients with osteoporosis who are treated with antiresorptive agents, including bisphosphonates and denosumab. Despite the more than 10 years that have passed since the first cases of bisphosphonate related osteonecrosis of the jaw (BRONJ) were reported, our understanding of the epidemiology and pathophysiology of ARONJ remains limited, and data supported by evidence based medicine are still sparse. However, the diagnosis and staging of ARONJ, identification of risk factors, and development of preventive and therapeutic approaches have advanced significantly over the past decade. The Position Paper 2017 is an updated version of the Position Paper 2010 of the Japanese Allied Committee on Osteonecrosis of the Jaw, which now comprises six Japanese academic societies. The Position Paper 2017 describes a new diagnostic definition for ARONJ, as proposed by the American Association of Oral and Maxillofacial Surgeons (AAOMS) summarizes our current understanding of the pathophysiology of ARONJ based on a literature search, and suggests methods for physicians and dentists/oral surgeons to manage the disease. In addition, the appropriateness of discontinuing antiresorptive medications (drug holiday) before, during, and after invasive dental treatments is discussed extensively. More importantly, the manuscript also proposes, for the first time, the importance of interactive communication and cooperation between physicians and dentists/oral surgeons for the successful treatment of ARONJ. The Position Paper 2017 is intended to serve as a guide for improving the management of ARONJ patients in Japan.
CM3: Cooperative Multi goal Multi stage Multi agent Reinforcement Learning
Venue: ICLR
Year: 2020

A variety of cooperative multi agent control problems require agents to achieve individual goals while contributing to collective success. This multi goal multi agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others' success, and credit assignment for interactions between actions and goals of different agents. To address both challenges, we restructure the problem into a novel two stage curriculum, in which single agent goal attainment is learned prior to learning multi agent cooperation, and we derive a new multi goal multi agent policy gradient with a credit function for localized credit assignment. We use a function augmentation scheme to bridge value and policy functions across the curriculum. The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi goal multi agent problems: cooperative navigation in difficult formations, negotiating multi vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.
A novel data glove using inertial and magnetic sensors for motion capture and robotic arm hand teleoperation
Venue: Ind. Robot
Year: 2017

The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm hand.  The data glove comprises 18 low cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm hand teleoperation scheme.  Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm hand system.  Integrated with 18 low cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm hand.
Generative Adversarial Networks Based Data Augmentation for Brain Computer Interface
Venue: IEEE Transactions on Neural Networks and Learning Systems
Year: 2021

The performance of a classifier in a brain computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time and cost efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end to end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave one subject out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention $p 0.01$ and 5.45% for focused attention $p 0.01$ In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% $p 0.02$ This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real life applications, whereby users' attention may be diverted.
Strength and durability of concrete containing recycled concrete aggregates
Year: 2018

Recycled concrete aggregates (RCA) sourced from waste concrete are a sustainable alternative to natural crushed stone aggregates. The strength and durability properties of concrete containing RCA were evaluated by a comprehensive experimental investigation involving nine control mixes. The variables considered in the experimental study are water cement ratio, cement content in concrete and percentage replacement of coarse aggregate. The strength properties such as compressive strength, modulus of elasticity, splitting tensile strength and flexural strength are studied. Durability properties such as water absorption, sorptivity, acid attack resistance and chloride permeability are also determined. The test results showed that up to 25% of natural crushed stone aggregates in concrete may be replaced with RCA, without significantly affecting the strength of concrete and that the partial replacement of natural aggregates with RCA can be recommended in areas of moderate exposure conditions. Mathematical models developed in the study can be used for the a priori prediction of the strength parameters of RCA concrete. A mix design methodology using the developed models is proposed to aid practicing engineers to determine the mix proportions of RCA concrete.
Soil microbial loop and nutrient uptake by plants: a test using a coupled C:N model of plant microbial interactions
Venue: Plant and Soil
Year: 2006

We have developed a spatially explicit model of plant root and soil bacteria interactions in the rhizosphere in order to formalise and study the microbial loop hypothesis that postulates that plants can stimulate the release of mineral N from the soil organic matter by providing low molecular weight C molecules to C limited microorganisms able to liberate into the soil enzymes that degrade the organic matter. The model is based on a mechanistic description of diffusion of solutes in the soil, nutrient uptake by plants, bacterial activity and bacterial predation. Modelled soil bacterial populations grow, mediate transformations among several forms of nitrogen (mineral and organic) and compete for nitrogen with plants. Our objectives were to see if we could simulate the stimulation of turnover of the microbial loop by exudates and to study the effects of diffusion of C and N in the rhizosphere on these different processes. The model qualitatively mimics most of the characteristics of the microbial loop hypothesis. In particular, (1) plant exudates increase the growth of bacteria in the soil and (2) increase the degradation of soil organic matter and N mineralisation. (3) The increased bacterial biomass induces an increase in predator biomass and, as a result, (4) plant mineral N uptake is increased threefold compared with scenarios without exudation. However, the temporal dynamics simulated by the model are much slower than observed dynamics (the increase in uptake appears after a few months) Taking into consideration the diffusion of C and N containing molecules in soil has large effects on the spatial structure of the bacterial and predator biomass. However, the average biomass of bacteria and predators, N mineralisation and plant N uptake were not affected by these properties. The model provides a quantitative and mechanistic explanation of how plants could benefit from liberating low molecular organic matter and the subsequent stimulation of the microbial loop and increases N mineralisation.
PEBERS: Practical Ethereum Blockchain based Efficient Ride Hailing Service
Venue: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)
Year: 2020

Although the current ride hailing services have revolutionized the transportation industry in today's world, they are extremely centralized. Central authority has all the control and maintains data about the users. Such platforms raise concerns about the service policies and data reliability. In case the central server faces data tampering or ransomware attacks, all the records of data is either lost or compromised. Blockchain has gained exceptional recognition while addressing aforementioned issues because of its decentralized data auditability, anonymity and immutability. Research on blockchain technology explores the possibility of implementing decentralized application in ride hailing service. Blockchain based ride hailing architecture gives us the opportunity to deviate from centralized platforms to decentralized ones. In this paper we propose our system PEBERS: Practical Ethereum Blockchain based Efficient Ride Hailing Service, in which we demonstrate how decentralized system based on consortium blockchain can be developed to keep track of ride data. In this context we explore smart contracts to build and deploy functionalities such as create ride, auto deposit transfer, cancel and complete ride methods of our decentralized ride hailing application. Our experiments show the driver smart contracts proposed in our system consume less Gas and hence prove that its an efficient system than many other existing systems with respect to the expenses of passengers and profitability of drivers. This is an effort towards providing smarter transportation for a society moving towards smart city concept.
A prolonged investigation of an STEC O104 cluster in Hesse, Germany, 2011 and implications for outbreak management
Venue: Journal of Public Health
Year: 2013

AimDuring a large outbreak of Shiga toxin producing Escherichia coli (STEC) O104:H4 in Germany 2011 caused by Fenugreek sprouts, 56 cases of haemolytic uraemic syndrome (HUS) and 124 cases of STEC were notified in Hesse. Almost 50 of these cases were linked to six clusters. Timely identification of vehicles was warranted to halt the outbreak.Subject and methodsA retrospective cohort study was conducted. The description of this cluster investigation exemplarily addresses short comings and obstacles to the prompt identification of outbreak sources.ResultsThe only uncooked food item which could explain all cases was a salad containing radish sprouts. A second cluster probably related to the consumption of radish sprouts was discovered, raising the count to 4 HUS and 3 STEC cases for both clusters. The backtracking of the radish sprouts shortly after the source farm had been publicly mentioned, as probably implicated in the outbreak, provided an early support of this hypothesis.ConclusionIn contrast to the Fenugreek sprouts mentioned with other clusters, Radish sprouts were identified as the most probable source of the two clusters, indicating cross contamination at production. This investigation revealed several hitherto known but still not sufficiently addressed deficits such as a need for earlier mobilization of additional laboratory capacities, better documentation and sharing of information, better use of modern communication systems and data sources, clear responsibilities, early indication if support is needed, structured and intensified retrieval of possible common exposures, more resources to follow all traces at short notice.