Miracle-dz/UniFAR-FTUs huggingface dataset:
Literasi Numerasi di SD Muhammadiyah

Abstrak: Tujuan penelitian ini untuk mendeskripsikan (1) rancangan program literasi numerasi, (2) pelaksanaan program literasi numerasi, (3) upaya dalam mengatasi kendala pelaksanaan program literasi numerasi, (4) faktor pendukung dan (5) faktor penghambat pelaksaan program literasi numerasi. Jenis penelitian ini adalah deskripsif kualitatif. Hasil penelitian yaitu (1) rancangan literasi numerasi di SD Muhammadiyah 1 Kota Malang secara umum belum terprogram dan secara khusus menyesuaikan materi literasi numerasi pada pembelajaran matematika dan tema pada pembelajaran tematik kurikulum 2013. (2) Pelaksanaan program literasi numerasi telah sesuai dengan tiga tahapan program literasi sekolah yakni tahap pembiasaan, pengembangan dan pembelajaran, serta sesuai dengan lima indikator literasi numerasi. (3) Upaya yang dilakukan dalam mengatasi kendala program literasi numerasi telah beracuan pada tujuan umum literasi di sekolah. (4) Faktor pendukung terdiri dari pemerintah dan sasaran program literasi numerasi. (5) Faktor penghambat beracuan pada tiga sasaran literasi numerasi. Sasaran literasi numerasi terdiri dari basis kelas, budaya sekolah, dan masyarakat.Kata Kunci: Program, Literasi, Numerasi, Sekolah DasarAbstract: The purpose of this research is to describe : (1) plan of the numeracy literacy program in elementary school Muhammadiyah 1 Malang, (2) realize the numeracy literacy program in elementary school Muhammadiyah 1 Malang, (3) effort to solve problems in realizing the numeracy literacy program in elementary school Muhammadiyah 1 Malang, (4) proponent factor in realizing the numeracy literacy program in elementary school Muhammadiyah 1 Malang, (5) inhibiting factor in realizing the numeracy literacy program in elementary school Muhammadiyah 1 Malang. This research used a qualitative research approach by using descriptive type. The result of this research showed that (1) plan of the numeracy literacy program in elementary school Muhammadiyah 1 Malang is not in program list yet and in especially case, it still makes an adjustment with the numeracy literacy matter in learning mathematics and lesson topic 2013 curriculum. (2) The realization of this numeracy literacy program in elementary school Muhammadiyah 1 Malang is matched with the three steps of school literacy program, they are habitual, development and learning step it also conforms to five indicators of numeracy literacy. (3) The effort to solve problems in realizing the numeracy literacy program based on the general purpose of the literacy in school. (4) Government and numeracy literacy program’s object is included in proponent factor. (5) The inhibiting factor is aimed at three of numeracy literacy program’s objects. Numeracy literacy targets consist of the class base, school culture base, and community base.Keywords: Program, Lliteracy, Numeracy, Elementary School
Developing a spiking neural model of Long Short-Term Memory architectures

Current advances in Deep Learning have shown significant improvements in common Machine Learning applications such as image, speech and text recognition. Specifically, in order to process time series, deep Neural Networks (NNs) with Long Short-Term Memory (LSTM) units are widely used in sequence recognition problems to store recent information and to use it for future predictions. The efficiency in data analysis, especially when big-sized data sets are involved, can be greatly improved thanks to the advancement of the ongoing research on Neural Networks (NNs) and Machine Learning for many applications in Physics. However, whenever acquisition and processing of data at different time resolutions is required, a synchronization problem for which the same piece of information is processed multiple times arises, and the advantageous efficiency of NNs, which lack the natural notion of time, ceases to exist. Spiking Neural Networks (SNNs) are the next generation of NNs that allow efficient information coding and processing by means of spikes, i.e. binary pulses propagating between neurons. In this way, information can be encoded in time, and the communication of information is activated only when the input to the neurons change, thus giving higher efficiency. In the present work, analog neurons are used for training and then they are substituted with spiking neurons in order to perform tasks. The aim for this project is to find a transfer function which allows a simple and accurate switching between analog and spiking neurons, and then to prove that the obtained network performs well in different tasks. At first, an analytical transfer function for more biologically plausible values for some neuronal parameters is derived and tested. Subsequently, the stochastic nature of the biological neurons is implemented in the neuronal model used. A new transfer function is then approximated by studying the stochastic behavior of artificial neurons, allowing to implement a simplified description for the gates and the input cell in the LSTM units. The stochastic LSTM networks are then tested on Sequence Prediction and T-Maze, i.e. typical memory-involving Machine Learning tasks, showing that almost all the resulting spiking networks correctly compute the original tasks. The main conclusion drawn from this project is that by means of a neuronal model comprising of a stochastic description of the neuron it is possible to obtain an accurate mapping from analog to spiking memory networks, which gives good results on Machine Learning tasks. (Less)
Knowledge and Attitude of Indian Rural Community towards Leprosy after Post Elimination Phase: A Survey from Karad Block, India -

Results: Among the total 570 community members, max 35.96 % were belonged to 30 to 40 yrs and 30.87% agriculturist by occupation. Max, 78.94% and 68.59% respondents had good quality of knowledge and positive attitude towards leprosy respectively. Age, education and occupation of respondents were significantly associated with good knowledge and positive attitude towards leprosy whereas, gender in addition played significant role with positive attitude. A positive correlation was observed between knowledge and attitude of community members towards leprosy.
Classification of connected unimodular Lie groups with discrete series

© Annales de l’institut Fourier, 1980, tous droits réservés. L’accès aux archives de la revue « Annales de l’institut Fourier » (http://annalif.ujf-grenoble.fr/) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/legal.php). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
Self-esteem and the quality of romantic relationships

Abstract. The relation between self-esteem and romantic relationships has been the focus of many research studies. In this article, we review theoretical perspectives and available evidence on (a) the effect of people’s self-esteem on the quality of their romantic relationships, (b) the effect of self-esteem similarity between partners on relationship quality, and (c) the psychological mechanisms that might account for the link between self-esteem and relationship quality. Overall, the evidence suggests that high self-esteem is beneficial in romantic relationships. Furthermore, research using data from both partners of couples suggests that high self-esteem has a positive effect also on the partner’s happiness with the relationship. However, research indicates that the degree of self-esteem similarity between partners does not influence the couple’s relationship satisfaction. Although evidence suggests that perceived regard and secure attachment between the partners explains why self-esteem is beneficial .
Testing the Waters: Behavior Across Participant Pools

We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire undergraduate university student population, a representative sample of the US population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments with student participants. Behavior in the student population offers bounds on behaviors in other populations, and correlations between behaviors are similar across samples. Furthermore, non-student samples exhibit higher levels of noise. Adding historical lab participation data, we find a small set of attributes over which lab participants differ from non-lab participants. An additional set of lab experiments shows no evidence of observer effects. (JEL C83, D90, D91)
Trainee Teachers’ Perceptions on Cyberbullying: Guidelines for Educators and Families

The present work analyzes university students' perceptions on cyberbullying. More specifically, the main objective was to understand the level of concern displayed by young students in teacher training programs regarding cyberbullying; our analysis includes their degree of self-confidence and their commitment when faced with this problem, their self-reported training on the subject, and the measures they consider adequate to approach it in the educational context. Using a survey research design, 408 students from Early Childhood Education, Primary Education Degrees, and the Master’s Degree in Secondary Education, and Vocational Training from national public universities participated. The results shed light upon the coexistence of three differentiated profiles of teachers in training when faced with cyberbullying. However, although there is clear evidence of the high level of concern regarding this problem in every case, they placed more importance on raising awareness of the issue with families, prevention, management and training as basic pillars for re-directing the worrisome reality experienced in educational centers. The need to include these contents in the initial training of future education professionals, for overcoming cyberbullying, and the importance of collaborative work between parents and educators are the conclusions of this research study that are in agreement with other studies.
Botulinum Toxin in Pain Management

Dr Vagg trained at Monash University. Following graduation in 1994, he worked as a Medical Officer for the RAAF, seeing active service with InterFET in East Timor in 1999-2000. After transferring to the Reserve in 2000, he has completed Fellowship training for the Australasian Faculty of Rehabilitation Medicine and is currently the Pain Management Fellow at Barwon Health in Geelong, Victoria, completing Fellowship training for the Faculty of Pain Medicine, ANZCA.
Regional anesthesia in pediatrics : marching forward opyright

Purpose of review This review highlights new data and current trends of well tolerated and effective paediatric regional anesthesia. Historically, the practice of paediatric regional anesthesia was based largely on information from adult studies, but recent literature contains information on paediatric specific risks and benefits of old and new techniques as well as the impact of new ideas and technologies.
Odour sampling on passive area sources: Principles and methods

Despite a certain simplicity and a well-defined methodology for the assessment of odour emissions from point sources and active area sources, odour sampling on passive area sources is still a rather debated task, meaning that, up to now, there is not a codified sampling procedure allowing to give reproducible results among different laboratories. Three different kinds of area sources can be distinguished, i.e. liquid passive area sources, solid passive area sources, and landfill surfaces. These three different types of sources are characterized by different volatilization and/or emission phenomena, and shall thus be treated differently while sampling in order to assess a representative odour emission rate. This paper aims to give an up-to-date overview of the principles and methods for odour sampling on passive (or semi-passive) area sources as well as for the estimation of the odour emission rates relevant to those sources, most of which still require further development and validation. The considerations here proposed highlight how the understanding of the volatilization phenomena that constitute the odour emission are fundamental for the evaluation and quantification of odour emissions.
EVALUATION AND APPLICATION OF THE INVASIVE WEED MIKANIA MICRANTHA AS AN ALTERNATIVE REINFORCEMENT IN RECYCLED HIGH DENSITY POLYETHYLENE

In this study Mikania micrantha particle (MP) and fiber (MF) were added to recycled high density polyethylene (rHDPE) for producing natural fiber (or particle) reinforced plastic composites (NFRPC) by the flat-platen pressing process. The results showed that the flexural strength and stiffness of NFRPC were significantly improved through incorporating M. micrantha particle and fiber. Higher aspect ratio of reinforcement displayed stronger mechanical properties. The vertical density profile in composites significantly influenced the mechanical properties of NFRPC. A conventional V-shaped profile and a uniform vertical density profile (homo-profile) were observed in MP and MF based NFRPC, respectively. Additionally, with increasing lignocellulose content, a more uniform vertical density profile and higher wood screw holding strength were observed. These results indicate M. micrantha particle and fiber are excellent reinforcements for NFRPC applications.
On the Effects of Surface Roughness on Boundary Layer Transition

Surface roughness can influence laminar-turbulent transition in many different ways. This paper outlines selected analyses performed at the NASA Langley Research Center, ranging in speed from subsonic to hypersonic Mach numbers and highlighting the beneficial as well as adverse roles of the surface roughness in technological applications. The first theme pertains to boundary-layer tripping on the forebody of a hypersonic airbreathing configuration via a spanwise periodic array of trip elements, with the goal of understanding the physical mechanisms underlying roughness-induced transition in a high-speed boundary layer. The effect of an isolated, finite amplitude roughness element on a supersonic boundary layer is considered next. The other set of flow configurations examined herein corresponds to roughness based laminar flow control in subsonic and supersonic swept wing boundary layers. A common theme to all of the above configurations is the need to apply higher fidelity, physics based techniques to develop reliable predictions of roughness effects on laminar-turbulent transition.
Impacts of temperature extremes on European vegetation during the growing season

Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.
Relationship between minimum gap and success probability in adiabatic quantum computing

We explore the relationship between two figures of merit for an adiabatic quantum computation process: the success probability P and the minimum gap Δmin between the ground and first excited states, investigating to what extent the success probability for an ensemble of problem Hamiltonians can be fitted by a function of Δmin and the computation time T. We study a generic adiabatic algorithm and show that a rich structure exists in the distribution of P and Δmin. In the case of two qubits, P is to a good approximation a function of Δmin, of the stage in the evolution at which the minimum occurs and of T. This structure persists in examples of larger systems.
Neural Network Based Text to Phoneme Alignment and Mapping for Speech Technology

Many speech technology applications rely upon having a good mapping between representations of text and phonemes that generalizes well to novel inputs, and that in turn relies upon having a good underlying alignment of the text and phonemes. For large scale systems, any approaches other than machine learning will be impractical, so that is the approach normally adopted. The current state-of-the-art appears to be a symbolic rule-based learning approach, which seems surprising given the range of neural network systems for text to phoneme mapping that have been developed over the years. This paper reviews the neural network based approaches to this problem and identifies the key problems involved. It then goes on to solve those problems and demonstrate how it is possible for neural networks to simultaneously perform text to phoneme alignment and mapping with performance levels at least as good as the best existing systems.
T-dual solutions of the Hull–Strominger system on non-Kähler threefolds

Abstract We construct new examples of solutions of the Hull–Strominger system on non-Kähler torus bundles over K3 surfaces, with the property that the connection ∇ {\nabla} on the tangent bundle is Hermite–Yang–Mills. With this ansatz for the connection ∇ {\nabla} , we show that the existence of solutions reduces to known results about moduli spaces of slope-stable sheaves on a K3 surface, combined with elementary analytical methods. We apply our construction to find the first examples of T-dual solutions of the Hull–Strominger system on compact non-Kähler manifolds with different topology.
The eastern Australian, burrowing mud-shrimp Laomedia healyi (Crustacea, Macrura Reptantia, Laomediidae) with notes on larvae of the genus Laomedia

In volume 15 part 3 of this journal a preliminary description was published of a new thalassinid shrimp of the family Laomediidae from eastern Australian shallow waters (Yaldwyn & Wear, 1970). A preliminary account, with a systematically available new name, became necessary as a colour plate of the new species was due to appear in a general publication on Australian crustaceans (Healy & Yaldwyn, 1970) before the full description and figures presented here could be completed for the Australian. Zoologist. The species concerned is Laomedia healyi Yaldwyn and Wear, 1970, a relatively large, burrowing mud-shrimp known from mangrove swamps, and from subtidal channels draining such swamps, along the eastern Australian coastline between northern Queensland and central New South Wales.
Evaluation of Causal Structure Learning Methods on Mixed Data Types

Causal structure learning algorithms are very important in many fields, including biomedical sciences, because they can uncover the underlying causal network structure from observational data. Several such algorithms have been developed over the years, but they usually operate on datasets of a single data type: continuous or discrete variables only. More recently, we and others have proposed new causal structure learning algorithms for mixed data types. However, to-date there is no study that critically evaluates these methods' performance. In this paper, we provide the first extensive empirical evaluation of several popular causal structure learning methods on mixed data types and in a variety of parameter settings and sample sizes. Our results serve as a guide as to which method performs the best in a given context, and as such they are a first step towards a "method selection guide" for those running causal modeling methods on real life datasets.
Track-Pattern-Based Model for Seasonal Prediction of Tropical Cyclone Activity in the Western North Pacific

AbstractSkillful predictions of the seasonal tropical cyclone (TC) activity are important in mitigating the potential destruction from the TC approach/landfall in many coastal regions. In this study, a novel approach for the prediction of the seasonal TC activity over the western North Pacific is developed to provide useful probabilistic information on the seasonal characteristics of the TC tracks and vulnerable areas. The developed model, which is termed the “track-pattern-based model,” is characterized by two features: 1) a hybrid statistical–dynamical prediction of the seasonal activity of seven track patterns obtained by fuzzy c-means clustering of historical TC tracks and 2) a technique that enables researchers to construct a forecasting map of the spatial probability of the seasonal TC track density over the entire basin. The hybrid statistical–dynamical prediction for each pattern is based on the statistical relationship between the seasonal TC frequency of the pattern and the seasonal mean key pre.