Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF POWER SYSTEM LOW FREQUENCY OSCILLATION WITH EMPIRICAL MODE DECOMPOSITION

The low frequency oscillation (LFO) in power systems often consists of composite responses of various oscillation modes. Thus a direct analysis of LFO for revealing system dynamic characteristics is not straightforward. The primary objective of this study is to utilize the method of empirical mode decomposition (EMD) to expand the LFO response under study into multiple intrinsic mode functions (IMFs) for detailed investigation of power system dynamics. Then time-frequency power information is explored using wavelet transform to obtain time-frequency power graph which is further resolved into a time-frequencypower diagram. Finally, information from the time-frequencypower diagram is further examined to determine those generators that demonstrate higher degree of participation in the LFO of power system. The comparative analysis in frequency domain shows that, at specific frequencies in LFO modes, higher oscillation participation power also shows a larger participation factor in its frequency domain analysis.
Status of Gender equality work in Norwegian Kindergartens – New kindergartens in old tracks

Introduction In 2008 the Ministry of Education and Research adopted the Action Plan for Gender Equality in kindergartens. The Action Plan is now at its end and a study of how kindergartens follow up their responsibility for gender equality has been carried out. The Center for Equality, in cooperation with the Eastern Norway Research Institute, was engaged by the Ministry to accomplish the study. The main objective in this session is to present the main findings from the analysis.
Maximum Power Point Tracking Method Based on Perturb and Observe Coupled with a Neural Network for Photovoltaic Systems Operating Under Fast Changing Environments

The output power of Photovoltaic (PV) arrays presents a nonlinear behavior. Its maximum power point varies with the cell’s temperature and solar radiation. It is due to this situation that Maximum Power Point Tracking (MPPT) methods have been proposed and used in order to maximize the PV array output power. This paper presents an artificial neural network (ANN) combined with the classic Perturbation and Observation (P&O) algorithm to accelerate the search of such Maximum Power Point. Simulations generated using Matlab/Simulink show the improvement compared to the P&O alone and the hardware implementation, using a 16-bit microcontroller corroborates these findings.
THE QUANTIFICATION OF THE THRESHOLD LEVEL METHOD ON LOW FLOWS STUDIES

Streamflow drought properties have been extensively analysed for the design of hydrotechnical projects and water resources planning and management during the last two decades. Information on the magnitude and frequency of low flows is very important for streamflow drought analysis at operational level in public water supply systems. The objective, of this study, is to investigate the sensitivity of the threshold level method in the derivation of low flow severity-duration-frequency (SDF) curves. Low flow severity is defined as the total water deficit volume to the target threshold for a given drought duration. Four (4) threshold level methods (fixed, seasonal, monthly and daily) were employed and compared to assess the subjectivity of the threshold level method (fixed or variable) in the estimation of derived streamflow deficits and durations at Yermasoyia watershed, Cyprus using a thirty year daily discharge dataset. The 50 and 70 percentile values of the flow duration curve are selected as the threshold choices for all study methods which are suitable for semiarid catchments where zero runoff occurs during summer months. Then, the four threshold methods are applied and three pooling procedures are applied to derive independent sequences of low-flow events. Application of the three pooling algorithms showed that the inter-event time and volume criterion is the most unbiased pooling method and this method was selected to estimate the duration and the deficit volume or severity of the identified drought events. Finally, the SDF curves are developed based on the annual maximum severities for fixed durations at 30, 60, 90, 180 and 360 days. Based on individual probabilistic analysis, the best theoretical probability distribution is selected for each threshold method and then the SDF curves for the four thresholds were developed to quantify the relationship among the severities, durations, and frequencies or return periods. These curves also integrate the return period–duration curve to quantify the extent of the threshold method. Hence, based on typical drought characteristics (deficit volume and duration) and threshold levels, this study developed quantitative relationships among drought parameters using fixed, seasonal, monthly and daily threshold levels.
An integral approach to modeling PBL transports and clouds: EDMF @ ECMWF

The importance of low clouds in terms of climate is illustrated by the fact that an additional 4% of low cloud cover would offset the radiative forcing resulting from CO2 doubling (Randall et al., 1984). But land stratus, particularly during high pressure winter situations, can also provide a challenge to atmospheric models ( Köhler, 2005). The improvement of stratocumulus and shallow convection in forecast and climate models has been the subject of a range of model developments by different groups (Lock et al., 2000; Lappen and Randall, 2001; Grenier and Bretherton, 2001; Bretherton et al., 2004). Here we summarise the path chosen at ECMWF to improve and unify the treatment of boundary layer clouds (Köhler 2005 or Tompkins et al. 2004 for more details).
Metal Particle Melting and Solidification for Powder Based Additive Manufacturing using the Optimal Transportation Meshfree Method

Selective Laser Melting (SLM) is an additive manufacturing (AM) process, where a powder bed is partially melted. Layer by layer, complex 3-dimensional geometries can be produced including overhangs where the non-melted powder acts as a support structure. Even today the multiple interacting physical phenomena of the SLM process are not yet fully understood. Still, the material and process development mainly relies on experimental studies which are costly and time intensive. Computational simulation tools offer the potential to gain a deeper understanding of the interaction between the process, the structure and the final properties. This can help to find optimal process parameters and opens the possibility to individualize additively manufactured products. The melting of powder particles leads to extremely large deformations. Using conventional mesh based simulation tools, like the FEM, ill-shaped elements and hence degenerated computations can occur. Galerkin meshfree methods eliminate the mesh dependency by employing a more flexible relation between integration points and nodes. The Optimal Transportation Meshfree (OTM) method [1] is a novel Galerkin meshfree approach which simplifies the computational modelling of the melting process of many particles towards a single product. Based on the OTM approximation scheme an approach to account for the phase transition and the fusion of particles will be presented. Furthermore, the solidification process [2] is modelled in order to show induced residual stresses. These stresses can yield to undesired deformations which can destroy additively manufactured parts. Some examples demonstrate the possibilities of the developed approach to understand the mechanism of SLM processes.
Architecture matters: How well neural networks explain IT representation does not depend on depth and performance alone

Humans are able to classify complex visual objects with extremely high accuracy. Recently, deep convolutional neural network (DCNN) models have reached and even surpassed human performance at this task. Among recent networks, the deeper the architecture, the better the performance. Although loosely inspired by biological brains, it remains unclear whether models reaching human-level accuracy also perform computations similar to those in the human brain. In earlier studies using shallower architectures with poorer object classification accuracy, greater depth and higher task performance were associated with improved explanation of inferior temporal cortex (IT) (Khaligh-Razavi & Kriegeskorte, 2014; Yamins et al., 2014). Our results show that this is not the case for state-of-the-art deep architectures that near or surpass human performance; the deepest, best-performing models are not best at explaining representations in human IT. In particular, deep residual networks (ResNets) are a relatively poor match to the brain, despite their very high classification performance. These findings open the door to detailed explorations of the architectures that best account for the representational transformations, and thus computations, performed in the ventral visual stream.
Short-range wind speed predictions in subtropical region using Artificial Intelligence

Short-range wind speed predictions for subtropical region is performed by applying Artificial Neural Network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations to Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), a deep learning algorithm based method, are applied for each site and height. A quantitative analysis is conducted and the statistical results are evaluated to select the configuration that best predicts the real data. These methods have lower computational costs than other techniques, such as numerical modelling. The proposed method is an important scientific contribution for reliable large-scale wind power forecasting and integration into existing grid systems in Uruguay. The best results of the shortterm wind speed forecasting was for MLP, which performed the forecasts using a hybrid method based on recursive inference, followed by LSTM, at all the anemometer heights tested, suggesting that this method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.
Dynamics of Engagement in Citizen Science: Results from the “Yes, I do!”-Project

Citizen science projects need to attract citizens and motivate them to dedicate their energy and time to science. Recruiting enough participants and keeping them engaged throughout the project is often a big challenge for the scientists involved. In this paper, recruiting and engagement strategies are evaluated for a successful midsize online citizen science project in the field of humanities. Quantitative measures are applied to track the quantity and quality of citizens’ contributions over time, allowing understanding of the dynamics of engagement in citizen science. The study shows that monitoring the level of activity and the quality of contributions provides useful insights about a project’s dynamics. We found that a small core group of volunteers was responsible for most of the input to the project, that their transcriptions were very accurate from the start, and that prompt feedback on their performance was important to keep activity levels high.
The Short-Run Effects of Product Differentiation on Trade

Gravity models of trade have long found that estimates of trade determinants differ across both time and products. In this paper, we demonstrate that there is a systematic relationship between these two influences. Past research has found that trade factors such as distance, common language, and preferential trade agreements matter more for differentiated products than homogeneous products. We find, however, that these differences are only a short-run phenomenon. As goods trade for longer periods of time, differentiated and homogeneous goods exhibit increasingly similar trade patterns. We propose a search model of trade that explains this behavior and find strong empirical support using gravity models that account for product type and trade duration. Additionally, we identify several other relationships between trade determinants and time. Determinants that affect recurrent trade costs, such as distance and preferential treatment, become more important over time. Determinants that impact fixed costs, such as language and colonial ties, become less important. These findings indicate that accurate modeling of trade through time and across different products ought to consider the systematic relationships across both dimensions.
Combining Classification-centered and Relation-based Argument Mining Methods

Two key tasks in argument mining (AM) are classiﬁcation of argument components and identiﬁcation of relations between argument components. Approaches to solving the argument component classiﬁcation problem typically take a supervised learning approach, however a lack of suitable datasets makes this a challenge for identiﬁcation of ar-gument component relations. We propose a pipeline with a recurrent, branched structure that combines supervised learning of argument component classiﬁcations with NLP approaches to identiﬁcation of argument component relations, with the aim of improving both classiﬁcation of argument components (i
PROTEIN RELATIONSHIPS AMONG TITMICE (PAR Us)

ABSTRAC~. -AS a first step toward understanding the evolutionary and biogeographical relationships among species of Pam, we compared allozymes at 34 loci of nine North American and six Eurasian species representing six subgenera. The results of this electrophoretic survey provide the first broad summary of genetic relationships among species of the genus Parus. Distance Wagner and UPGMA analyses suggest hat: (1) the crested North American titmice (subgenus Baeolophus) are only distantly related to the other parids examined, (2) the Bridled Titmouse (P. wollweberi) is closest to Baeolophus titmice and convergent in appearance to the Crested Tit (P. cristatus); (3) the Marsh Tit (P. palustris) and Willow Tit (P. montanus) probably are sister taxa, but the Carolina Chickadee (P. carolinensis) and Black-capped Chickadee (P. atricapillus) may not be; (6) the Black-capped Chickadee is genetically closer to the Mountain Chickadee (P. gambeli) and Mexican Chickadee (P. s&ten] than to the Carolina Chickadee and (7) the Boreal Chickadee (P. hudsonicus) and Chestnut-backed Chickadee (P. rufescens) are sister taxa related in turn to the atricapillus species group.
Galaxy And Mass Assembly (GAMA): M-star-R-e relations of z=0 bulges, discs and spheroids

We perform automated bulge + disc decomposition on a sample of ∼ 7500 galaxies from the Galaxy And Mass Assembly (GAMA) survey in the redshift range of 0.002 < z < 0.06 using sigma , a wrapper around galfit 3. To achieve robust proﬁle measurements we use a novel approach of repeatedly ﬁtting the galaxies, varying the input parameters to sample a large fraction of the input parameter space. Using this method we reduce the catastrophic failure rate signiﬁcantly and verify the conﬁdence in the ﬁt indepen-dently of χ 2 . Additionally, using the median of the ﬁnal ﬁtting values and the 16 th and 84 th percentile produces more realistic error estimates than those provided by galfit , which are known to be underestimated. We use the results of our decompositions to analyse the stellar mass – half-light radius relations of bulges, discs and spheroids. We further investigate the association of components with a parent disc or elliptical relation to provide deﬁnite z = 0 disc and spheroid M (cid:63) − R e relations. We conclude by comparing our local disc and spheroid M (cid:63) − R e to simulated data from eagle and high redshift data from CANDELS-UDS. We show the potential of using the mass-size relation to study galaxy evolution in both cases but caution that for a fair comparison all data sets need to be processed and analysed in the same manner.
DETERMINATION OF THE ENERGY VELOCITY OF A LAMB WAVE BY MEANS OF FREQUENCY AND ANGULAR QUALITY FACTORS

The product frequency-frequency phase derivative of the reflection coefficient of an immersed elastic plate, calculated at a resonance frequency, is proportional to the frequency Qx factor. Denoting as y the incident angle sine, the product y-phase derivative with respect to y corresponds to an angular Qy factor. From the expressions of the different mean energies related to wave propagation and the definition of the energy velocity depending on the ratio of the average power flow through the plate thickness to the average total stored energy, we can express the energy velocity as a function of Qx, Qy and the phase velocity.
Domain modelling in Optimality Theory: Morphophonological cyclicity vs. stepwise prosodic parsing

This paper examines opaque examples of phrase-level phonology taken from Chilean Spanish under the framework of Stratal Optimality Theory (OT) (Rubach 1997; Bermúdez-Otero 2003, 2019) and Harmonic Serialism (HS) (McCarthy 2008a, b, 2016). The data show an interesting double repair of the coda /s/ taking place at word edges. It is argued that Stratal OT is superior in modelling phonological processes that take place at the interface between morphology and phonology because it embraces cyclicity. Under this model, prosodic structure is built serially, level by level, and in accordance with the morphological structure of the input string. In this way, opacity at constituent edges can be solved. Stratal OT also provides insight into word-internal morphological structure and the domain-specificity of phonological processes. It is demonstrated that a distinction in this model is necessary between the word and the phrase levels, and between the stem and the word levels. As illustrated by the behaviour of Spanish nouns, affixation and the resultant alternations inform us about the domains to which both morphological and phonological processes should be assigned. Against this background, Harmonic Serialism embraces an apparently simpler recursive mechanism in which stepwise prosodic parsing can be incorporated. What is more, it offers insight into the nature of operations in OT, as well as into such problematic issues as structure building and directionality. Nevertheless, despite the model’s ability to solve various cases of opacity, the need to distinguish between two competing repairs makes HS fail when confronted with the Chilean data under examination.
Investigating Locus of Control, Self Efficacy and Wellbeing-The relationships between all items across 3 instruments for a single item scale. Quality Improvement Fund Bid – Final report

 Exploratory quantitative analyses identified two distinct, yet related, subscales of locus of control (LOC): internal and external;  The scale for general sense of self-efficacy worked well and had good internal consistency;  Consistent with previous research, both internal LOC and self-efficacy were positively related to wellbeing after controlling for age, gender and marital status;  Contrary to expectations, there was no negative relation between external LOC after controlling for the other variables of interest;  Mediation analyses suggested that having higher levels of internal LOC may lead to more self-efficacy, which in turn, will predict higher levels of well-being.
Repdigits As Sums Of Two Lucas Numbers ∗

Let (Ln) be the Lucas sequence defined by Ln = Ln−1+Ln−2 for n ≥ 2 with initial conditions L0 = 2 and L1 = 1. A repdigit is a nonnegative integer whose digits are all equal. In this paper, we show that if Ln + Lm is a repdigit, then Ln + Lm = 2, 3, 4, 5, 6, 7, 8, 9, 11, 22, 33, 77, 333.
Modelling tipping-point phenomena of scientific coauthorship networks

In a range of scientific coauthorship networks, tipping points are detected in degree distributions, correlations between degrees and local clustering coefficients, etc. The existence of those tipping points could be treated as a result of the diversity of collaboration behaviours in scientific field. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two typical collaboration behaviours, namely the behaviour of leaders and that of other members in research teams. The model successfully predicts the tipping points, as well as many common features of coauthorship networks. For example, it realizes a process of deriving the complex scale-free property from the simple yes/no experiments. Moreover, it gives a reasonable explanation for the emergence of tipping points by the difference of collaboration behaviours between leaders and other members, which emerges in the evolution of research teams. The evolution synthetically addresses typical factors of generating collaborations, such as academic impacts, the homophily of authors, and the communications between research teams, etc.
Partial wedge resection of nail, nail bed and nail matrix in ingrown toenail treatment

Objectives: Ingrown toenail is a frequent problem which can be seen in all ages. There are many treatment methods both surgical and nonsurgical. In this study we evaluated the results of wedge resection of ingrown toe nail. Patients and Methods: A total of 80 toenails of 74 patients (55 female, 19 male) who complaint with discharge and deformity of their toenail underwent partial wedge resection of the nail and nail matrix. According to the Heifetz\'s staging system, 34 toenails were grade II, 46 toenails were grade III. There was active drainage due to an infectious process in all effected toe nails. All patients were operated after 10 day of oral antibiotic treatment. We evaluated the recurrence, postoperative infection, patient satisfaction and time to return to work. Results: The mean follow up period was 8 months (4-12 months). There was no recurrence in any patient. Two patients came with wound drainage in postoperative 14th day. Intravenous antibiotic treatment, debridement and wound dressing with rifampicine every other day were accomplished. All patients expressed their satisfaction with surgery. Conclusion: In treatment of ingrown toenail; wedge resection of nail, nail matrix and nail bed is a very effective treatment. Recurrence rate following this technique is negligible if it is done properly and it has high patient satisfaction.
LIKELIHOOD-BASED INFERENCE FOR MAT´ ERN TYPE III RE- PULSIVE POINT PROCESSES

In a repulsive point process, points act as if they are repelling one another, leading to underdispersed configurations when compared to a standard Poisson point process. Such models are useful when competition for resources exists, as in the locations of towns and trees. Bertil Matern introduced three approaches to modeling repulsive point processes, and used Types I and II. The third he regarded as intractable. In this paper an algorithm is developed that allows for arbitrarily accurate approximation of the likelihood for data modeled by the Matern Type III process. This method relies on a perfect simulation method that is shown to be fast in practice, generating samples in time that grows nearly linearly in the intensity parameter of the model, while the running times for more naive methods grow exponentially.