nils-herrmann/clean_scirepeval huggingface dataset:
INFORMATsIINI TEKhNOLOGIYi ROZVITKU KOMPETENTsII MENEDZhERIV Z UPRAVLINNIa PROEKTAMI NA OSNOVI GLOBAL'NIKh TRENDIV
Year: 2018

Global processes and their trends taking place in various fields of knowledge with a significant acceleration affect the development of information and communication technologies and project management competences, programs and project portfolios. The purpose of the article is to develop and investigate the mechanisms for assessing the project managers' competences for their development based on global trends and Agile's flexible methodology in project management in creating modern information and communication technologies in the field of project management. Global trends that create challenges in developing project management competences and programs are related to global acceleration, digitalization of society, development of block chains, cloud and fog technologies, active introduction of intelligent systems, transition from "rational economy" to "behavioural economy" The convergence of these trends creates a new platform for the effectiveness of applying existing methodologies, knowledge systems and methods for evaluating the competence of project managers. On the basis of convergence, integration and harmonization of methodologies for project and program management, a step by step approach to research has been identified. The formation of an updated system of competences based on global trends lies in the replacement of paradigms from traditional life cycle project models to flexible models such as Agile. Decision making in the conditions of uncertainty on the basis of datamining become more justified. The article deals with modern information models of projects and programs, changing system of project managers' competences, bringing together decision making models and methods. At the same time, the change of competences, as actions or functions that project managers must perform, affects the model of competency and the assessment system, including key indicators of competency. The informative model of diagnostics application of competency models and methods of project management in digital and "behavioural economics" is based on the use of flexible methodologies. Some key indicators of competency are defined that are applied in the assessment of the project manager competency based on his competences in the project team. Patterns of project managers' behaviour when designing product and project management have been studied. The patterns allowed authors to identify bottlenecks in applying Agile project management methodologies in the convergence of trend information models and technologies. An example of information technology for managing the competency assessment of project managers by the IPMA ICB4 model is given.
Comprehensive prediction of drug protein interactions and side effects for the human proteome
Venue: Scientific reports
Year: 2015

Identifying unexpected drug protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug protein interaction prediction, FINDSITEcomb, whose average precision is ~30% and recall ~27% is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24% Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38) while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http:/cssb.biology.gatech.edu/dr.prodis/ DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.
Prediciton of high pressure vapor liquid equilibrium of six binary systems, carbon dioxide with six esters, using an artificial neural network model
Year: 2008

Artificial neural networks are applied to high pressure vapor liquid equilibrium (VLE) related literature data to develop and validate a model capable of predicting VLE of six CO2 ester binaries (CO2 ethyl caprate, CO2 ethyl caproate, CO2 ethyl caprylate, CO2 diethyl carbonate, CO2 ethyl butyrate and CO2 isopropyl acetate) A feed forward, back propagation network is used with one hidden layer. The model has five inputs (two intensive state variables and three pure ester properties) and two outputs (two intensive state variables).The network is systematically trained with 112 data points in the temperature and pressure ranges (308.2 328.2 K) (1.665 9.218 MPa) respectively and is validated with 56 data points in the temperature range (308.2 328.2 K) Different combinations of network architecture and training algorithms are studied. The training and validation strategy is focused on the use of a validation agreement vector, determined from linear regression analysis of the plots of the predicted versus experimental outputs, as an indication of the predictive ability of the neural network model. Statistical analyses of the predictability of the optimised neural network model show excellent agreement with experimental data (a coefficient of correlation equal to 0.9995 and 0.9886, and a root mean square error equal to 0.0595 and 0.00032 for the predicted equilibrium pressure and CO2 vapor phase composition respectively) Furthermore, the comparison in terms of average absolute relative deviation between the predicted results for each binary for the whole temperature range and literature results predicted by some cubic equation of state with various mixing rules and excess Gibbs energy models shows that the artificial neural network model gives far better results.
Effects of Taping and Orthoses on Foot Biomechanics in Adults with Flat Arched Feet.
Venue: Medicine and science in sports and exercise
Year: 2016

There is a paucity of evidence on the biomechanical effects of foot taping and foot orthoses in realistic conditions. This study aimed to determine the immediate effect and relationships between changes in multisegment foot biomechanics with foot taping and customized foot orthoses in adults with flat arched feet.  Multisegment foot biomechanics were measured in 18 adults with flat arched feet (age 25.1 2.8 yr; height 1.73 .13 m, body mass 70.3 15.7 kg) during walking in four conditions in random order: neutral athletic shoe, neutral shoe with tape (low Dye method and modified method) and neutral shoe with customized foot orthoses. In shoe foot biomechanics were compared between conditions using a purpose developed foot model with three dimensional kinematic analysis and inverse dynamics.  Foot orthoses significantly delayed peak eversion compared to the neutral shoe (44% stance vs 39% P 0.002) Deformation across the midfoot and medial longitudinal arch was reduced with both the low Dye taping (2.4deg, P 0.001) and modified taping technique (5.5deg, P 0.001) All interventions increased peak dorsiflexion of the first metatarsophalangeal joint (1.4deg 3.2deg, P 0.001 0.023) Biomechanical responses to taping significantly predicted corresponding changes to foot orthoses (R2 0.08 0.52, P 0.006 to <0.001) CONCLUSIONS Foot orthoses more effectively altered timing of hindfoot motion whereas taping was superior in supporting the midfoot and medial longitudinal arch. The biomechanical response to taping was significantly related to the subsequent change observed with the use of foot orthoses.
Beam Acquisition and Training in Millimeter Wave Networks With Narrowband Pilots
Venue: IEEE Journal on Selected Areas in Communications
Year: 2019

This paper studies initial beam acquisition in a millimeter wave network consisting of multiple access points (APs) and mobile devices. A training protocol for joint estimation of transmit and receive beams is presented with a general frame structure consisting of an initial access sub frame followed by data transmission sub frames. During the initial subframe, APs and mobiles sweep through a set of beams and determine the best transmit and receive beams via a handshake. All pilot signals are narrowband (tones) and the mobiles are distinguished by their assigned pilot frequencies. Both non coherent and coherent beam estimation methods based on, respectively, power detection and maximum likelihood (ML) are presented. To avoid exchanging information about beamforming vectors between APs and mobiles, a local maximum likelihood (LML) algorithm is also presented. An efficient fast Fourier transform implementation is proposed for ML and LML to achieve high resolution. A system level optimization is performed in which the frame length, training time, and training bandwidth are selected to maximize a rate objective taking into account blockage and mobility. Simulation results based on a realistic network topology are presented to compare the performance of different estimation methods and training codebooks, and demonstrate the effectiveness of the proposed protocol.
Prediction of the Phase Behavior and Properties of Hydrocarbons with a One Parameter PC SAFT Approach Assisted by a Group Contribution Method
Year: 2017

Modeling thermodynamic properties can be challenging when the data availability for parameters identification is limited. Fully predictive group contribution (GC) methods have been developed as an alternative to overcome data scarcity. However, in order to provide a higher degree of accuracy, most recent GC approaches require detailed information on the molecule's structure, which is not acquirable for systems with unspecified components. This work intends to establish the foundation to overcome this limitation. The proposed PC SAFT approach assisted by a homosegmented group contribution scheme permits parameters calculation with accuracy due to one adjustable parameter without requiring meticulous information regarding the molecular structure, for instance, the relative position between carbon centered groups. This semipredictive approach is especially suitable for cases in which some data are available but are not taken into consideration by current GC models. A genetic algorithm based routine was devel.
The Document Components Ontology (DoCO)
Venue: Semantic Web
Year: 2016

The availability in machine readable form of descriptions of the structure of documents, as well as of the document discourse (e.g. the scientific discourse within scholarly articles) is crucial for facilitating semantic publishing and the overall comprehension of documents by both users and machines. In this paper we introduce DoCO, the Document Components Ontology, an OWL 2 DL ontology that provides a general purpose structured vocabulary of document elements to describe both structural and rhetorical document components in RDF. In addition to describing the formal description of the ontology, this paper showcases its utility in practice in a variety of our own applications and other activities of the Semantic Publishing community that rely on DoCO to annotate and retrieve document components of scholarly articles.
Speeding up Convolutional Neural Networks Using Fine tuned CP Decomposition
Venue: ICLR
Year: 2015

We propose a simple two step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine tuning. Given a layer, we use non linear least squares to compute a low rank CP decomposition of the 4D convolution kernel tensor into a sum of a small number of rank one tensors. At the second step, this decomposition is used to replace the original convolutional layer with a sequence of four convolutional layers with small kernels. After such replacement, the entire network is fine tuned on the training data using standard backpropagation process. We evaluate this approach on two CNNs and show that it is competitive with previous approaches, leading to higher obtained CPU speedups at the cost of lower accuracy drops for the smaller of the two networks. Thus, for the 36 class character classification CNN, our approach obtains a 8.5x CPU speedup of the whole network with only minor accuracy drop (1% from 91% to 90% For the standard ImageNet architecture (AlexNet) the approach speeds up the second convolution layer by a factor of 4x at the cost of $1\ increase of the overall top 5 classification error.
A Bayesian multivariate hierarchical spatial joint model for predicting crash counts by crash type at intersections and segments along corridors.
Venue: Accident; analysis and prevention
Year: 2018

The safety and operational improvements of corridors have been the focus of many studies since they carry most traffic on the road network. Estimating a crash prediction model for total crash counts identifies the crash risk factors that are associated with crash counts at a specific type of road entity. However, this may not reveal useful information to detect the road problems and implement effective countermeasures. Therefore, investigating the contributing factors for crash counts by different types is of great importance. This study aims to provide a good understanding of the contributing factors to crash counts by different types at intersections and roadway segments along corridors. Data from 255 signalized intersections and 220 roadway segments along 20 corridors have been used for this study. The investigated crash types include same direction, angle and turning, opposite direction, non motorized, single vehicle, and other multi vehicle crashes. Two models have been estimated, which are multivariate hierarchical Poisson lognormal (HPLN) spatial joint model and univariate HPLN spatial joint model. The significant variables include exposure measures and some geometric design variables at intersection, roadway segment, and corridor levels. The results revealed that the multivariate HPLN spatial joint model outperforms the univariate HPLN spatial joint model. Also, the correlations among crash counts of most types exist at individual road entity and between adjacent entities. Additionally, the significant explanatory variables are different across crash types, and the magnitude of the parameter estimates for the same independent variable is different across crash types. The results emphasize the need for estimating crash counts by type in a multivariate form to better detect the problems and provide appropriate countermeasures.
Exchange Rate Volatility, Currency Misalignment, and Risk of Recession in the Central and Eastern European Countries
Year: 2021

This study is aimed at estimation of the exchange rate volatility and its impact on the business cycle fluctuations in four central and eastern European countries (the Czech Republic, Hungary, Poland, and Romania) Exchange rate volatility is estimated with the EGARCH(1,1) model. It is found that exchange rate volatility is affected by the components of the Index of Economic Freedom from the Heritage Foundation, besides inflation and crisis developments. The empirical results using GMM estimation technique and comprehensive robustness checks suggest that exchange rate volatility reduces the risk of recession in the Czech Republic while the opposite effect is found for Hungary and Romania, with a neutrality for Poland. These findings continue to hold after controlling for the fiscal and monetary policy indicators. There is evidence that the RER undervaluation prevents sliding into a recession on a credible basis in Poland only, with a neutral stance for other countries. Except in Romania, higher levels of economic freedom is associated with worsening of the cyclical position of output. Among other results, stabilization policies in the recession imply fiscal tightening for the Czech Republic and Romania, higher money supply for the Czech Republic and Poland, and lower central bank reference rate for Hungary.
Analysis Of Japan'S Long Lasting Recession Since 1990s
Year: 2019

In the early 1990s, the burst of 'bubble' in real estate and stock markets makes Japan suffer from a long period of sluggish economy and recession. After that, a lot of research has been done to probe into the reasons for the economic stagnation. In this paper, we mainly focus on four reasons: multiple that bank insolvency risk and the stagnant production in corporate sector, the reduction of consumption and demand, the diminishing industrial competitiveness and the failure of industrial transformation, and the impact of demographic structure. In addition to the analysis of four reasons and their impact on the Japanese economy, policy analysis is also conducted to compare different policies implemented by the Japanese government and their effectiveness in reality in this paper.
Emotion affects prefrontal cortex activity during cognitive task performance: A change blindness functional near infrared spectroscopy study.
Year: 2014

Research is increasingly demonstrating that emotional processes interact with our cognitions and that the prefrontal cortex (PFC) is heavily involved during such tasks. Higher naturalistic negative mood is related to reduced PFC activity during working memory tasks (Aoki et al. 2011) In addition increases in PFC activity have been observed when attending to visual art of positive valence (Kreplin Fairclough, 2013) How emotion influences attentional processes and what the underlying role of the PFC is during attentional tasks warrants investigation. This study aimed to investigate if changes in PFC activity were evident during a change detection flicker task (e.g. Rensink, O'Regan, Clark, 1997) following induction of positive, negative as well as neutral affect. The Positive and Negative Affect Scale (Watson, Clark, Tellegan) was employed to collect self reported emotional affect. The study also sought to examine if there were any behavioural improvements in visual attention following induction of positive affect.
Study on Behaviour of Bolted Cold Formed Steel Angle Tension Members
Year: 2015

The main objective is to investigate the behaviour of cold formed steel single and double angles subjected to tension.  Sixteen single plain and lipped angle specimens and thirty two numbers of double angle specimens connected to the same side and opposite side of the gusset plate were tested in a Universal Testing Machine using ordinary black bolts of 10mm diameter. The experimental loads are compared with ultimate load calculated using BS 5950(Part V) 1998, and AS/NZS 4600:2005.  Most of the single and double angles failed by net section fracture failure and the outstanding leg are subjected to local bend due to shear lag effect.  Angles are used in a variety of structures such as trusses, transmission towers etc. as tension members. Angles may be used as single angles or double angles and the connection may be bolted or welded.
Effects of Climate Change on Rice Production at Khulna district, Bangladesh.
Year: 2019

In Bangladesh 164 million people depend on rice but due to climate change (CC) vulnerabilities the yield of rice is severely reducing. Therefore, this study aimed to explore the effects of CC on rice yield using recent trend analysis, Mann Kendall (MK) trend test and correlation. For this study, the required climate and selected rice production data were collected from Bangladesh Meteorological Department (BMD) and Department of Agriculture Extension (DAE) Khulna. In time series statistics both parametric and nonparametric methods were applied to detect a monotonic trend of climatic variability and Aman rice production. Correlation between climatic variability and rice production was also investigated. The study exposed that the temperature and rainfall had an increasing trend and statistically significant whereas the relative humidity found decreasing. Overall, the bright Sunshine had positive trend but week. According to nonparametric trend test of climatic variability and Aman yield only minimum temperature showed a positive trend which found statistically significant. In contrast, Mann Kendall trend test revealed that rainfall had positively significant while temperature had negative significant. The correlation revealed the Aman production is a strong response with minimum temperature whereas the Lona Coche showed a positive correlation with maximum temperature. The variation of Aman production exhibited a positive correlation with both Rainfall and humidity respectively and Lona Coche found a negative correlation. The sunshine had a week correlation against both yields. The findings suggest should give attention of temperature tolerant rice varieties to mitigate possible adverse effects of CC.
Optimization of the wireless power transfer system in an electric railway
Venue: 2014 IEEE Wireless Power Transfer Conference
Year: 2014

Wireless power transfer (WPT) can be an effective way to solve the energy supply problems in electric railways (ER) In order to develop the desired system, design optimization can be a solution to optimize the objective function (e.g. mass of a system, transfer efficiency, and air gap) while satisfying constraints such as electromagnetic field (EMF) magnetic saturation, and induced voltage. In this paper, the optimization framework for the railway specific WPT systems was developed by connecting between an optimization module and the electromagnetic commercial software. The framework was then applied to minimize the mass of a WPT system of the Wireless Low Floor Tram (WTRAM) under the multiple constraints. As a result of optimization, the mass of the system could be successfully reduced by 64.3% while the performance related constraints were being satisfied. The proposed framework would help us to reduce time and cost for the development of next generation ER systems.
Modeling of viscoelastic strain and creep for hardening structures based on cement under temperature gradients
Year: 2015

During the hardening period, materials based on cement experience deformation due to heat generation and moisture movement or internal consumption of water by cement. All these processes inuence the change of material volume. As a result, these factors can lead to water tightness problems, durability problems and damage due to frost, which means that numerical simulation of its properties is important problem. So, in order to predict and prevent these problems, a numerical model should be developed in a proper way. For example, accurate consideration of heat generation and heat loss should be incorporated to calculate the temperature distribution gradients. The rst goal of the research is to present numerical model of temperature calculation and then using this data to estimate thermal dilation and autogenous shrinkage during the contraction phase. As is well known, creep also inuences on stress state for hardening materials and is a relaxation factor during the cooling period. Finally, the aim is to model a creep eect.